This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
##The following is written by Savanna van Mesdag, going through the relevant coding and analyses for CCAs and ANOSIMs for the plant and substrate data for the six study sites.
#setwd#
#To run the following code to carry out the analyses, the following packages must be installed#
install.packages("vegan", repos = "https://github.com/vegandevs/vegan")
## Installing package into 'C:/Users/Savanna/AppData/Local/R/win-library/4.4'
## (as 'lib' is unspecified)
## Warning: unable to access index for repository https://github.com/vegandevs/vegan/src/contrib:
## cannot open URL 'https://github.com/vegandevs/vegan/src/contrib/PACKAGES'
## Warning: package 'vegan' is not available for this version of R
##
## A version of this package for your version of R might be available elsewhere,
## see the ideas at
## https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
## Warning: unable to access index for repository https://github.com/vegandevs/vegan/bin/windows/contrib/4.4:
## cannot open URL 'https://github.com/vegandevs/vegan/bin/windows/contrib/4.4/PACKAGES'
install.packages("ggplot2", repos = "https://github.com/tidyverse/ggplot2")
## Installing package into 'C:/Users/Savanna/AppData/Local/R/win-library/4.4'
## (as 'lib' is unspecified)
## Warning: unable to access index for repository https://github.com/tidyverse/ggplot2/src/contrib:
## cannot open URL 'https://github.com/tidyverse/ggplot2/src/contrib/PACKAGES'
## Warning: package 'ggplot2' is not available for this version of R
##
## A version of this package for your version of R might be available elsewhere,
## see the ideas at
## https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages
## Warning: unable to access index for repository https://github.com/tidyverse/ggplot2/bin/windows/contrib/4.4:
## cannot open URL 'https://github.com/tidyverse/ggplot2/bin/windows/contrib/4.4/PACKAGES'
#Once installed, then need to load these r packages…
library(vegan)
## Warning: package 'vegan' was built under R version 4.4.1
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.6-8
library(ggplot2)
#Loading the data files…
urlfile1 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/Plant_Species_BD.csv'
PlantSpecies <-read.csv(urlfile1)
urlfile2 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/PLANT_CHEMISTRY_MG_KG.csv'
PlantChemistry <-read.csv(urlfile2)
#Need to do this to make sure the datasets are good to go.
col_sums <- apply(X = PlantSpecies, MARGIN = 2, FUN = sum);
rm <- which(is.na(col_sums) == TRUE);
PlantSpecies <- PlantSpecies[, -rm];
print(PlantSpecies)
## Agrostis.spp. Agrostis.canina Alchemilla.mollis Alopercus.pratensis
## 1 0 0 0 0
## 2 9 0 0 0
## 3 1 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 1 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 14 0 0 0
## 11 5 3 0 0
## 12 3 0 1 0
## 13 32 0 0 0
## 14 39 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 27
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Angelica.sylvestris Anthoxanthum.odoratum Anthyllis.vulneraria
## 1 0 81 0
## 2 0 18 0
## 3 0 8 0
## 4 0 82 0
## 5 0 55 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 1 0 0
## 13 1 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 4
## 25 0 0 0
## 26 0 0 127
## 27 0 0 0
## 28 0 0 32
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 70
## 37 0 0 189
## 38 0 7 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Aphanes.arvensis Arrhenatherum.elatius Atrichum.undulatum Avenula.pratensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 2 0 0
## 5 0 42 10 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 3 10 0
## 9 0 1 0 0
## 10 11 0 0 0
## 11 0 0 0 0
## 12 0 4 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 1 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 11
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 8 0 0
## 30 1 0 0 0
## 31 5 0 0 0
## 32 0 0 0 0
## 33 0 5 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Bellis.perennis Betula.pubescens Blackstonia.perfoliata
## 1 1 0 0
## 2 0 0 0
## 3 8 12 0
## 4 16 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 11 0 0
## 19 0 0 0
## 20 0 0 0
## 21 10 0 0
## 22 0 0 0
## 23 0 0 0
## 24 4 0 0
## 25 6 0 0
## 26 1 0 0
## 27 0 0 0
## 28 6 0 0
## 29 0 0 0
## 30 0 0 0
## 31 2 0 2
## 32 0 0 0
## 33 0 0 0
## 34 4 0 0
## 35 0 0 0
## 36 0 0 0
## 37 1 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Brachythecium.albicans Brachythecium.glareosum Brachythecium.mildeanum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 10 10
## 7 0 0 80
## 8 0 0 0
## 9 0 0 0
## 10 0 0 150
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 20
## 24 0 0 0
## 25 30 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Brachythecium.rutabulum Briza.media Bromus.hordeaceus
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 10 0 0
## 7 0 0 0
## 8 0 0 0
## 9 10 0 0
## 10 10 0 0
## 11 0 0 0
## 12 10 0 0
## 13 10 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 30 0 0
## 26 0 138 0
## 27 0 9 0
## 28 0 8 0
## 29 0 0 1
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Bryum.c.f..caespiticium Bryum.c.f..pallescens Bryum.capillare Bryum.spp.
## 1 0 0 0 0
## 2 0 0 10 0
## 3 10 10 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 10
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 60 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 20
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 50 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 60
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Calliergonella.cupsidata Campanula.rotundifolia Carex.distans Carex.flacca
## 1 0 0 0 0
## 2 0 0 0 0
## 3 10 0 0 0
## 4 10 0 0 0
## 5 30 0 0 0
## 6 30 0 0 0
## 7 120 0 0 0
## 8 150 0 0 1
## 9 90 0 0 0
## 10 0 0 0 9
## 11 0 0 0 0
## 12 50 0 0 0
## 13 110 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 23 0
## 19 120 0 0 24
## 20 0 0 0 0
## 21 10 0 0 0
## 22 0 0 0 0
## 23 0 0 52 0
## 24 0 0 0 0
## 25 30 0 0 0
## 26 20 0 0 0
## 27 110 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 10 0 0 120
## 31 0 0 0 40
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 42 0 0
## 39 0 0 0 3
## 40 0 0 0 0
## 41 0 0 0 2
## Carex.panicea Carlina.vulgaris Centaurea.nigra Centaurium.erythraea
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 2 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 11 0 0
## 17 0 5 0 0
## 18 0 4 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 3 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 14
## 32 0 2 0 1
## 33 0 0 0 0
## 34 0 5 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 1 0 0 0
## 38 0 3 0 0
## 39 0 2 1 0
## 40 0 1 0 0
## 41 0 7 0 0
## Centaurium.littorale Centaurium.pulchellum Cerastium.fontanum
## 1 0 0 0
## 2 0 0 6
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 1
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 8 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 1
## 27 0 0 2
## 28 0 0 1
## 29 0 0 0
## 30 0 0 1
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 4 0 0
## 37 0 0 0
## 38 4 0 1
## 39 5 0 0
## 40 0 0 0
## 41 1 0 0
## Chamaenerion.angustifolium Cirriphyllum.piliferum Cirsium.arvense
## 1 0 0 0
## 2 0 0 0
## 3 0 10 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 4 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 3
## 12 0 0 4
## 13 0 0 8
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Cirsium.palustre Crepis.capillaris Cynosurus.cristatus Dactylis.glomerata
## 1 0 4 0 1
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 1 0 0
## 5 0 0 0 0
## 6 4 0 0 0
## 7 0 0 0 0
## 8 3 0 0 0
## 9 0 0 0 0
## 10 0 0 16 12
## 11 0 0 0 1
## 12 0 0 0 0
## 13 0 0 0 4
## 14 0 0 0 54
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 54 0
## 31 0 0 15 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 10
## Danthonia.decumbens Daucus.carrota Dicranella.spp. Dicranum.scoparium
## 1 0 0 0 30
## 2 0 0 0 20
## 3 0 0 0 0
## 4 0 0 0 10
## 5 0 0 0 16
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 50
## 10 1 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 50 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 2 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 1 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Epilobium.montanum Equisetum.arvense Equisteum.variegatum Erigeron.acer
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 8 0 0 0
## 7 1 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 3 0 0 0
## 12 0 49 0 0
## 13 0 4 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 25
## 33 0 0 0 0
## 34 0 0 0 6
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 10 31 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Euphrasia.agg Festuca.ovina Festuca.rubra Festuca.rubra.agg.
## 1 3 0 0 0
## 2 0 0 0 5
## 3 6 0 0 0
## 4 0 0 0 0
## 5 1 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 16 0 0 0
## 17 0 0 0 0
## 18 0 17 0 0
## 19 0 55 0 0
## 20 0 27 0 0
## 21 0 5 0 0
## 22 0 0 0 0
## 23 0 25 0 0
## 24 0 66 0 0
## 25 0 0 19 0
## 26 14 0 1 0
## 27 0 0 39 0
## 28 0 2 2 0
## 29 0 0 54 0
## 30 0 0 42 0
## 31 7 0 19 0
## 32 2 0 22 0
## 33 1 0 41 0
## 34 4 0 44 0
## 35 0 0 10 0
## 36 0 0 0 0
## 37 0 0 4 0
## 38 0 0 2 0
## 39 36 0 44 0
## 40 0 0 0 0
## 41 31 0 33 0
## Filipendula.ulmaria Fissidens.adianthoides Fissidens.dubius Fissidens.exilis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 10 10 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 27 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 3 0 0 0
## 12 3 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 20 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 60
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Fragaria.vesca Galium.aparine Galium.arvense Galium.saxatile Galium.verum
## 1 67 0 0 0 0
## 2 38 0 0 0 0
## 3 65 0 0 0 0
## 4 117 0 0 0 0
## 5 68 0 0 0 0
## 6 0 0 0 0 0
## 7 0 0 0 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 1 0 0
## 12 0 0 0 0 0
## 13 0 0 0 0 0
## 14 11 0 0 0 0
## 15 0 47 0 0 0
## 16 0 0 0 0 0
## 17 0 0 0 0 0
## 18 0 0 0 0 0
## 19 0 0 0 0 0
## 20 0 0 0 1 0
## 21 0 0 0 0 0
## 22 0 0 0 0 0
## 23 0 0 0 0 0
## 24 0 0 0 0 0
## 25 0 0 0 0 87
## 26 0 0 0 0 88
## 27 0 0 0 0 0
## 28 0 0 0 0 1
## 29 0 0 0 0 0
## 30 0 0 0 0 0
## 31 0 0 0 0 0
## 32 0 0 0 0 0
## 33 0 0 0 0 2
## 34 0 0 0 0 0
## 35 0 0 0 0 0
## 36 0 0 0 0 0
## 37 0 0 0 0 0
## 38 0 0 0 0 0
## 39 0 0 0 0 0
## 40 0 0 0 0 0
## 41 0 0 0 0 0
## Geum.urbanum Glechoma.hederacea Helicotrichon.spp. Heracleum.sphondylium
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 8
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 1
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 48 5 0 0
## 15 0 0 0 1
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 1 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Hieracium.spp. Holcus.spp. Holcus.lanatus Holcus.mollis
## 1 0 0 30 0
## 2 0 0 80 0
## 3 0 0 0 0
## 4 0 0 28 0
## 5 0 0 36 0
## 6 0 5 0 0
## 7 0 14 0 0
## 8 0 36 0 0
## 9 0 0 0 143
## 10 0 0 0 1
## 11 0 0 0 56
## 12 0 0 0 17
## 13 0 0 0 4
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 42 0 0
## 17 0 0 0 0
## 18 0 40 8 0
## 19 0 58 3 0
## 20 0 70 0 0
## 21 0 23 0 0
## 22 0 0 0 0
## 23 0 13 0 0
## 24 0 0 0 0
## 25 0 55 5 0
## 26 0 0 1 0
## 27 0 50 25 0
## 28 1 3 2 0
## 29 0 169 0 0
## 30 0 10 0 0
## 31 0 30 0 0
## 32 0 0 0 0
## 33 0 82 5 10
## 34 0 4 0 0
## 35 0 47 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 18 0
## 39 0 23 0 0
## 40 0 78 0 0
## 41 0 10 0 0
## Homalothecium.lutescens Hylocomium.splendens Hypericum.perforatum
## 1 0 50 11
## 2 0 80 24
## 3 0 110 2
## 4 0 160 4
## 5 0 90 1
## 6 0 0 0
## 7 0 0 0
## 8 0 1 0
## 9 0 90 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 160 0 0
## 21 10 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 22
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Hypnum.cupressiforme Hypnum.imponens Hypnum.jutlandicum Hypochaeris.radicata
## 1 0 0 0 0
## 2 10 0 20 0
## 3 60 0 0 0
## 4 10 0 0 0
## 5 20 0 120 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 20 60 0 0
## 10 50 0 10 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 60 0 0 0
## 17 150 0 0 0
## 18 60 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 1
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 160 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Kindbergia.praelonga Lathyrus.pratensis Leontodon.hispidus
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 10 0 0
## 7 60 0 0
## 8 0 30 0
## 9 30 0 0
## 10 10 18 0
## 11 0 0 0
## 12 0 0 0
## 13 50 0 0
## 14 50 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 18
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 30
## 29 0 0 0
## 30 0 42 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 16
## 40 0 0 2
## 41 0 0 43
## Leontodon.saxatilis Leucanthemum.vulgare Linum.catharticum Lolium.perenne
## 1 0 20 0 0
## 2 0 15 0 0
## 3 0 0 0 0
## 4 0 7 0 0
## 5 0 1 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 5 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 1 0 0 0
## 25 0 12 0 0
## 26 0 0 0 0
## 27 0 1 0 0
## 28 0 3 0 0
## 29 0 2 0 85
## 30 0 0 0 2
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## 32 0 2 0 2
## 33 0 0 0 5
## 34 0 24 0 0
## 35 0 2 0 0
## 36 0 0 2 0
## 37 0 2 0 0
## 38 0 1 0 0
## 39 0 6 1 0
## 40 0 0 6 0
## 41 0 0 6 0
## Lophocolea.semiteres Lotus.corniculatus Luzula.multiflora
## 1 0 69 0
## 2 0 120 0
## 3 10 92 0
## 4 0 1 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 1
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 12 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 220 0
## 19 0 19 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 67 0
## 26 0 18 0
## 27 0 31 0
## 28 0 12 0
## 29 0 140 0
## 30 0 0 0
## 31 0 14 0
## 32 0 2 0
## 33 0 98 0
## 34 0 17 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 57 0
## 40 0 0 0
## 41 0 20 0
## Lysimachia.maritima Medicago.lupulina Myosotis.arvensis Ononis.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 2 0
## 9 0 0 0 0
## 10 0 0 0 2
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 3 0
## 16 0 0 0 0
## 17 0 0 1 0
## 18 0 5 0 0
## 19 0 0 0 0
## 20 0 0 6 2
## 21 0 0 4 63
## 22 0 0 0 0
## 23 80 0 0 0
## 24 0 3 0 0
## 25 0 3 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 153
## 32 0 3 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 2 0 0 0
## 36 0 0 0 5
## 37 0 0 0 1
## 38 0 91 0 111
## 39 0 0 0 2
## 40 0 0 0 0
## 41 0 0 0 0
## Oxalis.acetosella Pastinaca.sativa Pentaglottis.sempervirens
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 113 0 0
## 15 0 0 4
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 1 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Pillosella.officinarum Plantago.coronopus Plantago.lanceolata
## 1 0 0 13
## 2 0 0 0
## 3 0 0 0
## 4 0 0 22
## 5 0 0 36
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 8
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 33 0 1
## 17 286 0 0
## 18 2 0 0
## 19 0 1 0
## 20 54 19 0
## 21 0 0 1
## 22 1 0 0
## 23 0 114 0
## 24 22 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 4 0 3
## 29 0 0 0
## 30 0 0 1
## 31 0 0 3
## 32 3 0 0
## 33 0 0 0
## 34 14 0 3
## 35 2 0 0
## 36 0 0 0
## 37 0 0 0
## 38 91 0 5
## 39 39 50 20
## 40 0 0 0
## 41 0 0 29
## Pleurozium.schreberi Poa.annua Poa.spp. Polytrichum.commune
## 1 110 0 0 0
## 2 10 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 120 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 60 0 0 10
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 3 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 69 0 0
## 37 0 46 0 0
## 38 0 0 0 0
## 39 0 55 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Polytrichum.commune.agg. Polytrichum.formosum Potentilla.reptans
## 1 0 0 0
## 2 0 40 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 10 0 0
## 10 0 0 4
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 37
## 31 0 0 1
## 32 0 0 0
## 33 0 0 0
## 34 0 0 2
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Prunella.vulgaris Pseudoscleropidum.purum Pteridium.aquilinum
## 1 1 40 0
## 2 0 0 0
## 3 2 150 0
## 4 0 150 0
## 5 0 90 0
## 6 0 0 8
## 7 0 0 4
## 8 0 0 0
## 9 0 10 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 4
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 30 0
## 21 0 0 0
## 22 0 0 0
## 23 0 20 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 160 0
## 29 0 0 0
## 30 2 0 0
## 31 0 0 0
## 32 5 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 2 0 0
## Ranunculus.acris Ranunculus.repens Reseda.lutea Rhinanthus.minor
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 2 0 0 0
## 6 0 5 0 0
## 7 0 0 0 0
## 8 0 23 0 0
## 9 0 0 0 0
## 10 0 0 0 4
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 1 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 1 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Rhizomnium.punctatum Rhytidadelphus.squarrosus Rhytidadelphus.triquetris
## 1 0 160 20
## 2 0 0 0
## 3 0 30 0
## 4 0 150 20
## 5 0 30 0
## 6 0 130 0
## 7 0 160 0
## 8 4 110 0
## 9 0 110 0
## 10 0 0 0
## 11 0 0 0
## 12 0 140 0
## 13 0 20 0
## 14 0 0 0
## 15 0 0 0
## 16 0 60 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 140 0
## 21 0 20 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Rubus.fruticosus Sanguisorba.minor.spp.minor Saniona.uncinata Sedum.anglicum
## 1 0 0 0 0
## 2 0 0 0 0
## 3 1 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 3 0 0 0
## 7 0 0 0 0
## 8 0 0 10 0
## 9 0 0 10 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 34 0 0
## 26 0 10 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 6
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Senecio.jacobaea Senecio.vulgaris Sonchus.arvensis Stachys.sylvatica
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 3 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 1
## 12 1 0 0 1
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 4 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 1 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Stellaria.apetala Taraxacum.agg. Thuidium.tamariscinum Thymus.polytrichus
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 30 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 1 0 0
## 8 0 0 20 0
## 9 0 0 10 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 283
## 17 0 1 0 7
## 18 0 3 0 159
## 19 0 2 0 0
## 20 0 1 0 0
## 21 0 0 0 20
## 22 0 0 0 45
## 23 0 0 0 0
## 24 0 2 0 0
## 25 0 0 0 0
## 26 0 1 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 2 0 0 0
## 31 0 0 0 0
## 32 0 4 0 0
## 33 0 0 0 0
## 34 0 6 0 0
## 35 0 0 0 0
## 36 0 0 0 216
## 37 0 0 0 253
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 2 0 7
## 41 0 1 0 0
## Trichostomum.crispulum Trifolium.campestre Trifolium.dubium
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 60 0 162
## 17 0 0 118
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 110 0 0
## 23 0 11 0
## 24 0 8 0
## 25 0 64 0
## 26 0 22 0
## 27 0 8 0
## 28 0 2 0
## 29 0 0 0
## 30 0 2 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 150 0 0
## 36 30 0 0
## 37 60 0 0
## 38 0 0 0
## 39 70 0 0
## 40 0 0 0
## 41 0 0 0
## Trifolium.pratense Trifolium.repens Trisetum.flavescens Tussilago.farfara
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 5 35 1 0
## 9 0 0 0 0
## 10 1 0 0 0
## 11 0 0 0 0
## 12 0 0 0 15
## 13 0 0 0 4
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 2 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 2 0 0 0
## 26 0 4 0 0
## 27 0 17 0 0
## 28 0 0 3 0
## 29 0 0 0 0
## 30 0 2 0 0
## 31 0 0 123 0
## 32 0 0 1 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 3
## 40 0 0 0 0
## 41 21 0 0 0
## Urtica.diocia Veronica.officinalis Vicia.sativa Viola.riviniana
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 2 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 1 0 0 28
## 7 0 0 0 0
## 8 0 0 110 0
## 9 0 0 0 0
## 10 52 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 55 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 7 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Weissia.controversa Zygodon.stirtonii
## 1 0 0
## 2 0 0
## 3 20 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## 27 0 0
## 28 0 0
## 29 0 0
## 30 0 0
## 31 0 0
## 32 0 0
## 33 0 0
## 34 0 0
## 35 0 0
## 36 0 0
## 37 0 0
## 38 0 0
## 39 0 0
## 40 0 10
## 41 0 0
#Now to do some ANOSIM tests to assess any significant differences #between plant species in different chemical variables.
anoSiO2 <- anosim(PlantSpecies, PlantChemistry$SiO2, distance = "bray", permutations = 9999)
anoSiO2
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$SiO2, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7017
## Significance: 0.0445
##
## Permutation: free
## Number of permutations: 9999
#This initial test demonstrates significant differences between plants in different SiO2 #concentrations, with a high ANOSIM statistic, 0.7017, and a low p value of 0.0423.
anoAl2O3 <- anosim(PlantSpecies, PlantChemistry$Al2O3, distance = "bray", permutations = 9999)
anoAl2O3
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Al2O3, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1367
## Significance: 0.6525
##
## Permutation: free
## Number of permutations: 9999
#We do not see significant differences for the Al2O3 concentrations. Further significant #differences highlighted for each relevant test…
anoCaO <- anosim(PlantSpecies, PlantChemistry$CaO, distance = "bray", permutations = 9999)
anoCaO
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$CaO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.8535
## Significance: 0.0758
##
## Permutation: free
## Number of permutations: 9999
anoMgO <- anosim(PlantSpecies, PlantChemistry$MgO, distance = "bray", permutations = 9999)
anoMgO
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$MgO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1708
## Significance: 0.2269
##
## Permutation: free
## Number of permutations: 9999
anoNa2O <- anosim(PlantSpecies, PlantChemistry$Na2O, distance = "bray", permutations = 9999)
anoNa2O
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Na2O, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2471
## Significance: 0.194
##
## Permutation: free
## Number of permutations: 9999
anoK2O <- anosim(PlantSpecies, PlantChemistry$K2O, distance = "bray", permutations = 9999)
anoK2O
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$K2O, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.3112
## Significance: 0.0877
##
## Permutation: free
## Number of permutations: 9999
anoCr2O3 <- anosim(PlantSpecies, PlantChemistry$Cr2O3, distance = "bray", permutations = 9999)
anoCr2O3
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Cr2O3, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2463
## Significance: 0.0016
##
## Permutation: free
## Number of permutations: 9999
#ANOSIM statistic of 0.2463, with low p value of 0.0015. Significant differences between # plants on different Cr2O3 concentrations.
anoTiO2 <- anosim(PlantSpecies, PlantChemistry$TiO2, distance = "bray", permutations = 9999)
anoTiO2
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$TiO2, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1572
## Significance: 0.1873
##
## Permutation: free
## Number of permutations: 9999
anoMnO <- anosim(PlantSpecies, PlantChemistry$MnO, distance = "bray", permutations = 9999)
anoMnO
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$MnO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1542
## Significance: 0.0969
##
## Permutation: free
## Number of permutations: 9999
anoP2O5 <- anosim(PlantSpecies, PlantChemistry$P2O5, distance = "bray", permutations = 9999)
anoP2O5
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$P2O5, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2941
## Significance: 0.009
##
## Permutation: free
## Number of permutations: 9999
#ANOSIM statistic of 0.2941, with a low p value of 0.0078.There are significant differences #between plants in different P2O5 concentrations.
anoSrO <- anosim(PlantSpecies, PlantChemistry$SrO, distance = "bray", permutations = 9999)
anoSrO
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$SrO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1214
## Significance: 0.0402
##
## Permutation: free
## Number of permutations: 9999
anoBaO <- anosim(PlantSpecies, PlantChemistry$BaO, distance = "bray", permutations = 9999)
anoBaO
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$BaO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2961
## Significance: 0.0019
##
## Permutation: free
## Number of permutations: 9999
#ANOSIM statistic of 0.2961, with a low p value of 0.0031.There are significant differences #between plants in different BaO concentrations.
anoAs <- anosim(PlantSpecies, PlantChemistry$As, distance = "bray", permutations = 9999)
anoAs
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$As, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.01814
## Significance: 0.4119
##
## Permutation: free
## Number of permutations: 9999
anoAg <- anosim(PlantSpecies, PlantChemistry$Ag, distance = "bray", permutations = 9999)
anoAg
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Ag, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.006394
## Significance: 0.4446
##
## Permutation: free
## Number of permutations: 9999
anoB <- anosim(PlantSpecies, PlantChemistry$B, distance = "bray", permutations = 9999)
anoB
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$B, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.06893
## Significance: 0.2197
##
## Permutation: free
## Number of permutations: 9999
anoBe <- anosim(PlantSpecies, PlantChemistry$Be, distance = "bray", permutations = 9999)
anoBe
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Be, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1811
## Significance: 0.1193
##
## Permutation: free
## Number of permutations: 9999
#Skipping Bi as the values are all so similar and small.
anoCd <- anosim(PlantSpecies, PlantChemistry$Cd, distance = "bray", permutations = 9999)
anoCd
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Cd, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.04405
## Significance: 0.6631
##
## Permutation: free
## Number of permutations: 9999
anoCo <- anosim(PlantSpecies, PlantChemistry$Co, distance = "bray", permutations = 9999)
anoCo
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Co, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2712
## Significance: 0.0087
##
## Permutation: free
## Number of permutations: 9999
#ANOSIM statistic of 0.2712, with a low p value of 0.0087.There are significant differences #between plants in different Co concentrations.
anoCu <- anosim(PlantSpecies, PlantChemistry$Cu, distance = "bray", permutations = 9999)
anoCu
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Cu, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.132
## Significance: 0.2152
##
## Permutation: free
## Number of permutations: 9999
#Skipped Ga, Hg, La and Li due to similarity in values.
anoMo <- anosim(PlantSpecies, PlantChemistry$Mo, distance = "bray", permutations = 9999)
anoMo
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Mo, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.0447
## Significance: 0.2147
##
## Permutation: free
## Number of permutations: 9999
anoNi <- anosim(PlantSpecies, PlantChemistry$Ni, distance = "bray", permutations = 9999)
anoNi
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Ni, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2407
## Significance: 0.0337
##
## Permutation: free
## Number of permutations: 9999
#ANOSIM statistic of 0.2407, with a low p value of 0.0351. There are significant differences #between plants in different Ni concentrations.
anoPb <- anosim(PlantSpecies, PlantChemistry$Pb, distance = "bray", permutations = 9999)
anoPb
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Pb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.05781
## Significance: 0.5952
##
## Permutation: free
## Number of permutations: 9999
anoS <- anosim(PlantSpecies, PlantChemistry$S, distance = "bray", permutations = 9999)
anoS
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$S, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2388
## Significance: 0.0635
##
## Permutation: free
## Number of permutations: 9999
anoSb <- anosim(PlantSpecies, PlantChemistry$Sb, distance = "bray", permutations = 9999)
anoSb
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Sb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.009027
## Significance: 0.4473
##
## Permutation: free
## Number of permutations: 9999
anoSc <- anosim(PlantSpecies, PlantChemistry$Sc, distance = "bray", permutations = 9999)
anoSc
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Sc, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.0786
## Significance: 0.1702
##
## Permutation: free
## Number of permutations: 9999
#Skipping Th, Tl and U because of similarities in values
anoV <- anosim(PlantSpecies, PlantChemistry$V, distance = "bray", permutations = 9999)
anoV
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$V, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1315
## Significance: 0.1873
##
## Permutation: free
## Number of permutations: 9999
#Skipping W because of similarities in values
anoZn <- anosim(PlantSpecies, PlantChemistry$Zn, distance = "bray", permutations = 9999)
anoZn
##
## Call:
## anosim(x = PlantSpecies, grouping = PlantChemistry$Zn, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1196
## Significance: 0.6694
##
## Permutation: free
## Number of permutations: 9999
#6 out of 27 substrate variables show statistically significant differences #for plant species growing in those same sample locations. #Will do CCA for the whole dataset, but will then do analysis with those #6 substrate variables only.
#The initial CCA with the basic parameters and without specific substrate variables #selected. The dataset used includes too many variables for an anova P value to be #generated, as shown when attempting an anova below.
plant_cca <- cca(PlantSpecies, PlantChemistry, na.action = na.omit);
##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'Tl', 'W', 'Anhydrite',
## 'Aragonite', 'Augite', 'Biotite', 'Birnessite', 'Calcite', 'Clinochlore',
## 'Cuspidine', 'Diaspore', 'Dickite', 'Gehlenite', 'Goethite', 'Haematite',
## 'Illite', 'Kaolinite', 'Langite', 'Linnaeite', 'Magnesioferrite', 'Melilite',
## 'Merwinite', 'Microcline', 'Mullite', 'Muscovite', 'Nitratine', 'Orthoclase',
## 'Orthopyroxene', 'Periclase', 'Phengite', 'Pigeonite', 'Pseudowollastonite',
## 'Quartz', 'Spinel', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component
print(plant_cca);
## Call: cca(X = PlantSpecies, Y = PlantChemistry, na.action = na.omit)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 11.91 1.00
## Constrained 11.91 1.00 40
## Unconstrained 0.00 0.00 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## Some constraints or conditions were aliased because they were redundant.
## This can happen if terms are linearly dependent (collinear): 'Tl', 'W',
## 'Anhydrite', 'Aragonite', 'Augite', 'Biotite', 'Birnessite', 'Calcite',
## 'Clinochlore', 'Cuspidine', 'Diaspore', 'Dickite', 'Gehlenite', 'Goethite',
## 'Haematite', 'Illite', 'Kaolinite', 'Langite', 'Linnaeite',
## 'Magnesioferrite', 'Melilite', 'Merwinite', 'Microcline', 'Mullite',
## 'Muscovite', 'Nitratine', 'Orthoclase', 'Orthopyroxene', 'Periclase',
## 'Phengite', 'Pigeonite', 'Pseudowollastonite', 'Quartz', 'Spinel',
## 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10 CCA11
## 0.7967 0.7491 0.7151 0.6758 0.6347 0.6060 0.5689 0.5477 0.4941 0.4578 0.4288
## CCA12 CCA13 CCA14 CCA15 CCA16 CCA17 CCA18 CCA19 CCA20 CCA21 CCA22
## 0.4119 0.3872 0.3604 0.3428 0.3059 0.2836 0.2740 0.2664 0.2527 0.2289 0.2185
## CCA23 CCA24 CCA25 CCA26 CCA27 CCA28 CCA29 CCA30 CCA31 CCA32 CCA33
## 0.2083 0.2045 0.1893 0.1571 0.1504 0.1473 0.1268 0.1095 0.1065 0.0938 0.0869
## CCA34 CCA35 CCA36 CCA37 CCA38 CCA39 CCA40
## 0.0777 0.0661 0.0557 0.0472 0.0396 0.0286 0.0130
plot(plant_cca)
summary(plant_cca)
##
## Call:
## cca(X = PlantSpecies, Y = PlantChemistry, na.action = na.omit)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.92 1
## Constrained 11.92 1
## Unconstrained 0.00 0
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.79674 0.74908 0.71514 0.67576 0.63467 0.60596 0.56889
## Proportion Explained 0.06687 0.06287 0.06002 0.05671 0.05327 0.05086 0.04775
## Cumulative Proportion 0.06687 0.12974 0.18975 0.24647 0.29973 0.35059 0.39834
## CCA8 CCA9 CCA10 CCA11 CCA12 CCA13 CCA14
## Eigenvalue 0.54769 0.49408 0.45782 0.42883 0.41191 0.3872 0.36037
## Proportion Explained 0.04597 0.04147 0.03842 0.03599 0.03457 0.0325 0.03024
## Cumulative Proportion 0.44430 0.48577 0.52419 0.56018 0.59475 0.6273 0.65750
## CCA15 CCA16 CCA17 CCA18 CCA19 CCA20 CCA21
## Eigenvalue 0.34279 0.30587 0.28365 0.27397 0.26637 0.25273 0.22885
## Proportion Explained 0.02877 0.02567 0.02381 0.02299 0.02236 0.02121 0.01921
## Cumulative Proportion 0.68627 0.71194 0.73574 0.75874 0.78109 0.80230 0.82151
## CCA22 CCA23 CCA24 CCA25 CCA26 CCA27 CCA28
## Eigenvalue 0.21849 0.20831 0.20450 0.18934 0.15715 0.15042 0.14726
## Proportion Explained 0.01834 0.01748 0.01716 0.01589 0.01319 0.01262 0.01236
## Cumulative Proportion 0.83985 0.85733 0.87449 0.89038 0.90357 0.91620 0.92855
## CCA29 CCA30 CCA31 CCA32 CCA33 CCA34
## Eigenvalue 0.12681 0.109450 0.106459 0.093799 0.086888 0.077678
## Proportion Explained 0.01064 0.009186 0.008935 0.007872 0.007292 0.006519
## Cumulative Proportion 0.93920 0.948383 0.957318 0.965190 0.972482 0.979001
## CCA35 CCA36 CCA37 CCA38 CCA39 CCA40
## Eigenvalue 0.066101 0.055728 0.047200 0.039574 0.0286 0.013006
## Proportion Explained 0.005548 0.004677 0.003961 0.003321 0.0024 0.001092
## Cumulative Proportion 0.984549 0.989226 0.993187 0.996509 0.9989 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.79674 0.74908 0.71514 0.67576 0.63467 0.60596 0.56889
## Proportion Explained 0.06687 0.06287 0.06002 0.05671 0.05327 0.05086 0.04775
## Cumulative Proportion 0.06687 0.12974 0.18975 0.24647 0.29973 0.35059 0.39834
## CCA8 CCA9 CCA10 CCA11 CCA12 CCA13 CCA14
## Eigenvalue 0.54769 0.49408 0.45782 0.42883 0.41191 0.3872 0.36037
## Proportion Explained 0.04597 0.04147 0.03842 0.03599 0.03457 0.0325 0.03024
## Cumulative Proportion 0.44430 0.48577 0.52419 0.56018 0.59475 0.6273 0.65750
## CCA15 CCA16 CCA17 CCA18 CCA19 CCA20 CCA21
## Eigenvalue 0.34279 0.30587 0.28365 0.27397 0.26637 0.25273 0.22885
## Proportion Explained 0.02877 0.02567 0.02381 0.02299 0.02236 0.02121 0.01921
## Cumulative Proportion 0.68627 0.71194 0.73574 0.75874 0.78109 0.80230 0.82151
## CCA22 CCA23 CCA24 CCA25 CCA26 CCA27 CCA28
## Eigenvalue 0.21849 0.20831 0.20450 0.18934 0.15715 0.15042 0.14726
## Proportion Explained 0.01834 0.01748 0.01716 0.01589 0.01319 0.01262 0.01236
## Cumulative Proportion 0.83985 0.85733 0.87449 0.89038 0.90357 0.91620 0.92855
## CCA29 CCA30 CCA31 CCA32 CCA33 CCA34
## Eigenvalue 0.12681 0.109450 0.106459 0.093799 0.086888 0.077678
## Proportion Explained 0.01064 0.009186 0.008935 0.007872 0.007292 0.006519
## Cumulative Proportion 0.93920 0.948383 0.957318 0.965190 0.972482 0.979001
## CCA35 CCA36 CCA37 CCA38 CCA39 CCA40
## Eigenvalue 0.066101 0.055728 0.047200 0.039574 0.0286 0.013006
## Proportion Explained 0.005548 0.004677 0.003961 0.003321 0.0024 0.001092
## Cumulative Proportion 0.984549 0.989226 0.993187 0.996509 0.9989 1.000000
anova(plant_cca)
## No residual component
##
## Model: cca(X = PlantSpecies, Y = PlantChemistry, na.action = na.omit)
## Df ChiSquare F Pr(>F)
## Model 40 11.915
## Residual 0 0.000
#Now to do a CCA with the 7 statistically significant variables as #determined by the ANOSIM tests
Plant_CCA_Chem <- cca(PlantSpecies ~ SiO2 + Cr2O3 + P2O5 +
BaO + Co + Ni,
data = PlantChemistry)
plot(Plant_CCA_Chem)
summary(Plant_CCA_Chem)
##
## Call:
## cca(formula = PlantSpecies ~ SiO2 + Cr2O3 + P2O5 + BaO + Co + Ni, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.161 0.1814
## Unconstrained 9.754 0.8186
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.59649 0.44091 0.37329 0.30020 0.2692 0.18130 0.72033
## Proportion Explained 0.05006 0.03700 0.03133 0.02519 0.0226 0.01522 0.06046
## Cumulative Proportion 0.05006 0.08707 0.11839 0.14359 0.1662 0.18140 0.24186
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.68949 0.65378 0.59795 0.55559 0.54561 0.47844 0.44733
## Proportion Explained 0.05787 0.05487 0.05018 0.04663 0.04579 0.04015 0.03754
## Cumulative Proportion 0.29972 0.35459 0.40478 0.45141 0.49720 0.53735 0.57489
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.41372 0.39002 0.38418 0.36494 0.33578 0.28911 0.27257
## Proportion Explained 0.03472 0.03273 0.03224 0.03063 0.02818 0.02426 0.02288
## Cumulative Proportion 0.60962 0.64235 0.67459 0.70522 0.73340 0.75766 0.78054
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.2574 0.24237 0.21760 0.20842 0.20541 0.19132 0.18527
## Proportion Explained 0.0216 0.02034 0.01826 0.01749 0.01724 0.01606 0.01555
## Cumulative Proportion 0.8021 0.82248 0.84074 0.85824 0.87548 0.89153 0.90708
## CA23 CA24 CA25 CA26 CA27 CA28
## Eigenvalue 0.16350 0.14617 0.13304 0.12325 0.115441 0.102612
## Proportion Explained 0.01372 0.01227 0.01117 0.01034 0.009689 0.008612
## Cumulative Proportion 0.92080 0.93307 0.94424 0.95458 0.964269 0.972881
## CA29 CA30 CA31 CA32 CA33 CA34
## Eigenvalue 0.082128 0.069360 0.062230 0.050843 0.034782 0.023781
## Proportion Explained 0.006893 0.005821 0.005223 0.004267 0.002919 0.001996
## Cumulative Proportion 0.979774 0.985595 0.990818 0.995085 0.998004 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.5965 0.4409 0.3733 0.3002 0.2692 0.18130
## Proportion Explained 0.2760 0.2040 0.1727 0.1389 0.1246 0.08388
## Cumulative Proportion 0.2760 0.4800 0.6527 0.7916 0.9161 1.00000
anova(Plant_CCA_Chem)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ SiO2 + Cr2O3 + P2O5 + BaO + Co + Ni, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 6 2.1614 1.2557 0.01 **
## Residual 34 9.7537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Generating a CCA plot with the 6 significant varibles from the ANOSIMS produces a very #good-looking graph, except for the fact that Cr2O3 has a very short arrow. The F statistic #for the anova for this CCA is 1.2557 and the p value is 0.028. The p value indicates #statistical significance for this CCA, but the F statistic is fairly low. It will be #useful to carry out additional CCAs with different combinations of substrate variables to #see if there is one that is more statistically significant, with a higher F statistic.
#The following CCAs were carried out to determine which chemistry variables #most strongly influenced plant species presence, which included removing variables #with particularly short arrows, especially compared with variables with much longer arrows.
Plant_CCA_Chem1 <- cca(PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3, data =
PlantChemistry)
plot(Plant_CCA_Chem1)
summary(Plant_CCA_Chem1)
##
## Call:
## cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.000
## Constrained 1.691 0.142
## Unconstrained 10.224 0.858
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CA1 CA2 CA3
## Eigenvalue 0.66033 0.44210 0.36238 0.22657 0.73836 0.69904 0.66956
## Proportion Explained 0.05542 0.03710 0.03041 0.01902 0.06197 0.05867 0.05619
## Cumulative Proportion 0.05542 0.09252 0.12294 0.14195 0.20392 0.26259 0.31878
## CA4 CA5 CA6 CA7 CA8 CA9 CA10
## Eigenvalue 0.62275 0.560 0.52466 0.51159 0.45951 0.44148 0.41715
## Proportion Explained 0.05227 0.047 0.04403 0.04294 0.03856 0.03705 0.03501
## Cumulative Proportion 0.37105 0.418 0.46208 0.50501 0.54358 0.58063 0.61564
## CA11 CA12 CA13 CA14 CA15 CA16 CA17
## Eigenvalue 0.40486 0.34284 0.31916 0.31656 0.28850 0.27093 0.26671
## Proportion Explained 0.03398 0.02877 0.02679 0.02657 0.02421 0.02274 0.02238
## Cumulative Proportion 0.64962 0.67839 0.70518 0.73175 0.75596 0.77870 0.80108
## CA18 CA19 CA20 CA21 CA22 CA23 CA24
## Eigenvalue 0.24923 0.22491 0.21064 0.19291 0.18026 0.16528 0.15953
## Proportion Explained 0.02092 0.01888 0.01768 0.01619 0.01513 0.01387 0.01339
## Cumulative Proportion 0.82200 0.84088 0.85855 0.87474 0.88987 0.90374 0.91713
## CA25 CA26 CA27 CA28 CA29 CA30
## Eigenvalue 0.1489 0.14274 0.119108 0.107805 0.093070 0.087145
## Proportion Explained 0.0125 0.01198 0.009996 0.009048 0.007811 0.007314
## Cumulative Proportion 0.9296 0.94161 0.951608 0.960655 0.968467 0.975780
## CA31 CA32 CA33 CA34 CA35 CA36
## Eigenvalue 0.071814 0.057604 0.053815 0.049415 0.038050 0.017885
## Proportion Explained 0.006027 0.004835 0.004516 0.004147 0.003193 0.001501
## Cumulative Proportion 0.981807 0.986642 0.991158 0.995306 0.998499 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 0.6603 0.4421 0.3624 0.2266
## Proportion Explained 0.3904 0.2614 0.2143 0.1340
## Cumulative Proportion 0.3904 0.6518 0.8660 1.0000
anova(Plant_CCA_Chem1)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 4 1.6914 1.4889 0.001 ***
## Residual 36 10.2238
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#We now have an F statistic of 1.4889 and a p value of 0.001, which is our lowest p value #so far. It is worth testing further because 1.4889 is not an especially high F statistic #and the graph does not look ideal, especially as some of the variables have very short #arrows in comparison to, say, Al2O3.
Plant_CCA_Chem2.2 <-cca(PlantSpecies ~ Quartz + Gehlenite + Dickite +
Aluminium.oxide.hydroxide, data = PlantChemistry)
plot(Plant_CCA_Chem2.2)
summary(Plant_CCA_Chem2.2)
##
## Call:
## cca(formula = PlantSpecies ~ Quartz + Gehlenite + Dickite + Aluminium.oxide.hydroxide, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.9152 1.00000
## Constrained 0.9727 0.08163
## Unconstrained 10.9425 0.91837
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CA1 CA2 CA3
## Eigenvalue 0.35042 0.29305 0.24102 0.088199 0.73982 0.73340 0.68814
## Proportion Explained 0.02941 0.02459 0.02023 0.007402 0.06209 0.06155 0.05775
## Cumulative Proportion 0.02941 0.05400 0.07423 0.081634 0.14372 0.20528 0.26303
## CA4 CA5 CA6 CA7 CA8 CA9 CA10
## Eigenvalue 0.66867 0.61205 0.58169 0.56418 0.52152 0.45349 0.4420
## Proportion Explained 0.05612 0.05137 0.04882 0.04735 0.04377 0.03806 0.0371
## Cumulative Proportion 0.31915 0.37052 0.41934 0.46669 0.51045 0.54851 0.5856
## CA11 CA12 CA13 CA14 CA15 CA16 CA17
## Eigenvalue 0.41083 0.39639 0.38398 0.32309 0.29993 0.2823 0.27179
## Proportion Explained 0.03448 0.03327 0.03223 0.02712 0.02517 0.0237 0.02281
## Cumulative Proportion 0.62009 0.65336 0.68558 0.71270 0.73787 0.7616 0.78438
## CA18 CA19 CA20 CA21 CA22 CA23 CA24
## Eigenvalue 0.25217 0.2502 0.22497 0.21583 0.21018 0.18269 0.17026
## Proportion Explained 0.02116 0.0210 0.01888 0.01811 0.01764 0.01533 0.01429
## Cumulative Proportion 0.80554 0.8265 0.84542 0.86353 0.88117 0.89650 0.91079
## CA25 CA26 CA27 CA28 CA29 CA30
## Eigenvalue 0.15223 0.14701 0.12405 0.114539 0.107792 0.102907
## Proportion Explained 0.01278 0.01234 0.01041 0.009613 0.009047 0.008637
## Cumulative Proportion 0.92357 0.93591 0.94632 0.955932 0.964979 0.973616
## CA31 CA32 CA33 CA34 CA35 CA36
## Eigenvalue 0.084548 0.066974 0.060738 0.048160 0.030887 0.023067
## Proportion Explained 0.007096 0.005621 0.005098 0.004042 0.002592 0.001936
## Cumulative Proportion 0.980711 0.986332 0.991430 0.995472 0.998064 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 0.3504 0.2931 0.2410 0.08820
## Proportion Explained 0.3603 0.3013 0.2478 0.09068
## Cumulative Proportion 0.3603 0.6615 0.9093 1.00000
anova(Plant_CCA_Chem2.2)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ Quartz + Gehlenite + Dickite + Aluminium.oxide.hydroxide, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 4 0.9727 0.8 0.89
## Residual 36 10.9425
#F statistic of 0.8, p value of 0.862.
Plant_CCA_Chem3 <- cca(PlantSpecies ~ pH + SiO2 + Al2O3 +Fe2O3 + CaO,
data = PlantChemistry)
plot(Plant_CCA_Chem3)
summary(Plant_CCA_Chem3)
##
## Call:
## cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3 + CaO, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.101 0.1764
## Unconstrained 9.814 0.8236
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CA1 CA2
## Eigenvalue 0.66033 0.47885 0.3777 0.3598 0.22471 0.73380 0.6982
## Proportion Explained 0.05542 0.04019 0.0317 0.0302 0.01886 0.06159 0.0586
## Cumulative Proportion 0.05542 0.09561 0.1273 0.1575 0.17636 0.23795 0.2965
## CA3 CA4 CA5 CA6 CA7 CA8 CA9
## Eigenvalue 0.65383 0.59140 0.54642 0.51940 0.4671 0.4575 0.41717
## Proportion Explained 0.05487 0.04963 0.04586 0.04359 0.0392 0.0384 0.03501
## Cumulative Proportion 0.35142 0.40105 0.44691 0.49050 0.5297 0.5681 0.60312
## CA10 CA11 CA12 CA13 CA14 CA15 CA16
## Eigenvalue 0.40524 0.34859 0.32924 0.31657 0.30043 0.27994 0.26847
## Proportion Explained 0.03401 0.02926 0.02763 0.02657 0.02521 0.02349 0.02253
## Cumulative Proportion 0.63713 0.66638 0.69401 0.72058 0.74580 0.76929 0.79182
## CA17 CA18 CA19 CA20 CA21 CA22 CA23
## Eigenvalue 0.26142 0.23774 0.2120 0.21019 0.1918 0.17188 0.16405
## Proportion Explained 0.02194 0.01995 0.0178 0.01764 0.0161 0.01443 0.01377
## Cumulative Proportion 0.81376 0.83372 0.8515 0.86915 0.8852 0.89967 0.91344
## CA24 CA25 CA26 CA27 CA28 CA29
## Eigenvalue 0.15869 0.14713 0.12008 0.114805 0.093077 0.092361
## Proportion Explained 0.01332 0.01235 0.01008 0.009635 0.007812 0.007752
## Cumulative Proportion 0.92676 0.93911 0.94919 0.958820 0.966632 0.974384
## CA30 CA31 CA32 CA33 CA34 CA35
## Eigenvalue 0.071874 0.069234 0.055205 0.050173 0.040844 0.017894
## Proportion Explained 0.006032 0.005811 0.004633 0.004211 0.003428 0.001502
## Cumulative Proportion 0.980416 0.986226 0.990859 0.995070 0.998498 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5
## Eigenvalue 0.6603 0.4788 0.3777 0.3598 0.2247
## Proportion Explained 0.3142 0.2279 0.1797 0.1712 0.1069
## Cumulative Proportion 0.3142 0.5421 0.7219 0.8931 1.0000
anova(Plant_CCA_Chem3)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3 + CaO, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 5 2.1014 1.4989 0.001 ***
## Residual 35 9.8138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4989 and p value of 0.001, still worth doing more tests.
Plant_CCA_Chem4 <- cca(PlantSpecies ~ pH + SiO2 + Al2O3 +Fe2O3 + CaO + Cd,
data = PlantChemistry)
plot(Plant_CCA_Chem4)
summary(Plant_CCA_Chem4)
##
## Call:
## cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3 + CaO + Cd, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.341 0.1965
## Unconstrained 9.574 0.8035
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.66142 0.48385 0.39029 0.36331 0.22751 0.21478 0.7316
## Proportion Explained 0.05551 0.04061 0.03276 0.03049 0.01909 0.01803 0.0614
## Cumulative Proportion 0.05551 0.09612 0.12887 0.15937 0.17846 0.19649 0.2579
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.69807 0.65363 0.58832 0.54247 0.51701 0.46090 0.43394
## Proportion Explained 0.05859 0.05486 0.04938 0.04553 0.04339 0.03868 0.03642
## Cumulative Proportion 0.31648 0.37133 0.42071 0.46624 0.50963 0.54831 0.58473
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.41716 0.37974 0.34857 0.32923 0.31515 0.29477 0.27982
## Proportion Explained 0.03501 0.03187 0.02925 0.02763 0.02645 0.02474 0.02348
## Cumulative Proportion 0.61974 0.65161 0.68086 0.70850 0.73494 0.75968 0.78317
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.26847 0.25546 0.23062 0.21148 0.19998 0.17464 0.16692
## Proportion Explained 0.02253 0.02144 0.01936 0.01775 0.01678 0.01466 0.01401
## Cumulative Proportion 0.80570 0.82714 0.84650 0.86424 0.88103 0.89568 0.90969
## CA23 CA24 CA25 CA26 CA27 CA28
## Eigenvalue 0.16166 0.15865 0.14126 0.116481 0.098711 0.092362
## Proportion Explained 0.01357 0.01331 0.01186 0.009776 0.008284 0.007752
## Cumulative Proportion 0.92326 0.93658 0.94843 0.958207 0.966492 0.974243
## CA29 CA30 CA31 CA32 CA33 CA34
## Eigenvalue 0.072021 0.069779 0.055388 0.050176 0.041637 0.017895
## Proportion Explained 0.006044 0.005856 0.004649 0.004211 0.003494 0.001502
## Cumulative Proportion 0.980288 0.986144 0.990793 0.995004 0.998498 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.6614 0.4839 0.3903 0.3633 0.22751 0.21478
## Proportion Explained 0.2825 0.2067 0.1667 0.1552 0.09718 0.09174
## Cumulative Proportion 0.2825 0.4892 0.6559 0.8111 0.90826 1.00000
anova(Plant_CCA_Chem4)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3 + CaO + Cd, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 6 2.3412 1.3857 0.001 ***
## Residual 34 9.5740
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.3857, p value of 0.002. This one has a lower p value and a somewhat higher F statistic than some of the ones generated so far, but it is still worth doing further tests.
Plant_CCA_Chem5 <- cca(PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3 + CaO + Cr2O3,
data = PlantChemistry)
plot(Plant_CCA_Chem5)
summary(Plant_CCA_Chem5)
##
## Call:
## cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3 + CaO + Cr2O3, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.341 0.1964
## Unconstrained 9.575 0.8036
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.66121 0.47959 0.37899 0.36228 0.24681 0.21171 0.7327
## Proportion Explained 0.05549 0.04025 0.03181 0.03041 0.02071 0.01777 0.0615
## Cumulative Proportion 0.05549 0.09574 0.12755 0.15796 0.17867 0.19644 0.2579
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.69766 0.63669 0.58694 0.54118 0.51436 0.46632 0.44554
## Proportion Explained 0.05855 0.05343 0.04926 0.04542 0.04317 0.03914 0.03739
## Cumulative Proportion 0.31649 0.36992 0.41918 0.46460 0.50777 0.54691 0.58430
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.41170 0.40402 0.3456 0.3241 0.31633 0.29631 0.27925
## Proportion Explained 0.03455 0.03391 0.0290 0.0272 0.02655 0.02487 0.02344
## Cumulative Proportion 0.61885 0.65276 0.6818 0.7090 0.73551 0.76038 0.78382
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.26823 0.2514 0.23575 0.21107 0.2062 0.18555 0.16425
## Proportion Explained 0.02251 0.0211 0.01979 0.01771 0.0173 0.01557 0.01378
## Cumulative Proportion 0.80633 0.8274 0.84721 0.86493 0.8822 0.89780 0.91159
## CA23 CA24 CA25 CA26 CA27 CA28 CA29
## Eigenvalue 0.1608 0.1585 0.12161 0.115396 0.095511 0.092427 0.072529
## Proportion Explained 0.0135 0.0133 0.01021 0.009685 0.008016 0.007757 0.006087
## Cumulative Proportion 0.9251 0.9384 0.94859 0.958278 0.966294 0.974051 0.980139
## CA30 CA31 CA32 CA33 CA34
## Eigenvalue 0.07078 0.056031 0.050495 0.040972 0.018376
## Proportion Explained 0.00594 0.004702 0.004238 0.003439 0.001542
## Cumulative Proportion 0.98608 0.990781 0.995019 0.998458 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.6612 0.4796 0.3790 0.3623 0.2468 0.21171
## Proportion Explained 0.2825 0.2049 0.1619 0.1548 0.1054 0.09045
## Cumulative Proportion 0.2825 0.4874 0.6493 0.8041 0.9095 1.00000
anova(Plant_CCA_Chem5)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + Fe2O3 + CaO + Cr2O3, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 6 2.3406 1.3853 0.003 **
## Residual 34 9.5746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.3853, p value of 0.006
Plant_CCA_Chem6 <- cca(PlantSpecies ~ pH + SiO2 + Al2O3 + CaO + Cr2O3 + K2O,
data = PlantChemistry)
plot(Plant_CCA_Chem6)
summary(Plant_CCA_Chem6)
##
## Call:
## cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + CaO + Cr2O3 + K2O, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.489 0.2089
## Unconstrained 9.427 0.7911
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.66909 0.55955 0.38429 0.36058 0.30514 0.20993 0.73458
## Proportion Explained 0.05615 0.04696 0.03225 0.03026 0.02561 0.01762 0.06165
## Cumulative Proportion 0.05615 0.10312 0.13537 0.16563 0.19124 0.20886 0.27051
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.6863 0.63777 0.5707 0.52446 0.47890 0.46650 0.43833
## Proportion Explained 0.0576 0.05353 0.0479 0.04402 0.04019 0.03915 0.03679
## Cumulative Proportion 0.3281 0.38164 0.4295 0.47355 0.51374 0.55289 0.58968
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.40464 0.38949 0.3515 0.32679 0.31756 0.28584 0.26889
## Proportion Explained 0.03396 0.03269 0.0295 0.02743 0.02665 0.02399 0.02257
## Cumulative Proportion 0.62364 0.65633 0.6858 0.71325 0.73991 0.76390 0.78646
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.25475 0.24259 0.22336 0.20823 0.20036 0.18551 0.16488
## Proportion Explained 0.02138 0.02036 0.01875 0.01748 0.01682 0.01557 0.01384
## Cumulative Proportion 0.80784 0.82820 0.84695 0.86442 0.88124 0.89681 0.91065
## CA23 CA24 CA25 CA26 CA27 CA28
## Eigenvalue 0.16116 0.15793 0.12647 0.115798 0.095218 0.092042
## Proportion Explained 0.01353 0.01325 0.01061 0.009719 0.007991 0.007725
## Cumulative Proportion 0.92417 0.93743 0.94804 0.957760 0.965751 0.973476
## CA29 CA30 CA31 CA32 CA33 CA34
## Eigenvalue 0.074362 0.067986 0.05946 0.053478 0.041328 0.01942
## Proportion Explained 0.006241 0.005706 0.00499 0.004488 0.003469 0.00163
## Cumulative Proportion 0.979717 0.985423 0.99041 0.994901 0.998370 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.6691 0.5596 0.3843 0.3606 0.3051 0.20993
## Proportion Explained 0.2689 0.2248 0.1544 0.1449 0.1226 0.08436
## Cumulative Proportion 0.2689 0.4937 0.6481 0.7930 0.9156 1.00000
anova(Plant_CCA_Chem6)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + SiO2 + Al2O3 + CaO + Cr2O3 + K2O, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 6 2.4886 1.496 0.001 ***
## Residual 34 9.4266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.496, p value of 0.001
Plant_CCA_Chem6.1 <- cca(PlantSpecies ~ pH + Al2O3 + MgO + Cr2O3 + K2O,
data = PlantChemistry)
plot(Plant_CCA_Chem6.1)
summary(Plant_CCA_Chem6.1)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + MgO + Cr2O3 + K2O, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.046 0.1718
## Unconstrained 9.869 0.8282
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CA1 CA2
## Eigenvalue 0.67625 0.50981 0.35582 0.29587 0.20875 0.7423 0.67221
## Proportion Explained 0.05676 0.04279 0.02986 0.02483 0.01752 0.0623 0.05642
## Cumulative Proportion 0.05676 0.09954 0.12940 0.15424 0.17176 0.2341 0.29047
## CA3 CA4 CA5 CA6 CA7 CA8 CA9
## Eigenvalue 0.64761 0.59801 0.56416 0.51704 0.49243 0.44888 0.4206
## Proportion Explained 0.05435 0.05019 0.04735 0.04339 0.04133 0.03767 0.0353
## Cumulative Proportion 0.34482 0.39501 0.44236 0.48575 0.52708 0.56475 0.6000
## CA10 CA11 CA12 CA13 CA14 CA15 CA16
## Eigenvalue 0.4051 0.38374 0.34570 0.32892 0.29877 0.27562 0.26107
## Proportion Explained 0.0340 0.03221 0.02901 0.02761 0.02507 0.02313 0.02191
## Cumulative Proportion 0.6340 0.66625 0.69527 0.72287 0.74795 0.77108 0.79299
## CA17 CA18 CA19 CA20 CA21 CA22 CA23
## Eigenvalue 0.24919 0.23977 0.22152 0.20781 0.19052 0.16942 0.16629
## Proportion Explained 0.02091 0.02012 0.01859 0.01744 0.01599 0.01422 0.01396
## Cumulative Proportion 0.81390 0.83403 0.85262 0.87006 0.88605 0.90027 0.91422
## CA24 CA25 CA26 CA27 CA28 CA29
## Eigenvalue 0.15984 0.13778 0.12759 0.11248 0.095346 0.088723
## Proportion Explained 0.01341 0.01156 0.01071 0.00944 0.008002 0.007446
## Cumulative Proportion 0.92764 0.93920 0.94991 0.95935 0.967353 0.974800
## CA30 CA31 CA32 CA33 CA34 CA35
## Eigenvalue 0.076104 0.060542 0.05528 0.051254 0.035962 0.021123
## Proportion Explained 0.006387 0.005081 0.00464 0.004302 0.003018 0.001773
## Cumulative Proportion 0.981187 0.986268 0.99091 0.995209 0.998227 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5
## Eigenvalue 0.6762 0.5098 0.3558 0.2959 0.2088
## Proportion Explained 0.3304 0.2491 0.1739 0.1446 0.1020
## Cumulative Proportion 0.3304 0.5796 0.7534 0.8980 1.0000
anova(Plant_CCA_Chem6.1)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + MgO + Cr2O3 + K2O, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 5 2.0465 1.4516 0.001 ***
## Residual 35 9.8687
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4516, p value of 0.001
Plant_CCA_Chem6.2 <- cca(PlantSpecies ~ pH + Al2O3 + CaO + Cr2O3 + K2O + BaO,
data = PlantChemistry)
plot(Plant_CCA_Chem6.2)
summary(Plant_CCA_Chem6.2)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + CaO + Cr2O3 + K2O + BaO, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.363 0.1983
## Unconstrained 9.552 0.8017
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.67050 0.53091 0.37822 0.31297 0.28974 0.18037 0.73979
## Proportion Explained 0.05627 0.04456 0.03174 0.02627 0.02432 0.01514 0.06209
## Cumulative Proportion 0.05627 0.10083 0.13257 0.15884 0.18316 0.19829 0.26038
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.68382 0.64426 0.59908 0.5433 0.51670 0.46512 0.43660
## Proportion Explained 0.05739 0.05407 0.05028 0.0456 0.04336 0.03904 0.03664
## Cumulative Proportion 0.31777 0.37184 0.42212 0.4677 0.51109 0.55012 0.58676
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.41911 0.4016 0.37755 0.34539 0.31158 0.28410 0.27597
## Proportion Explained 0.03517 0.0337 0.03169 0.02899 0.02615 0.02384 0.02316
## Cumulative Proportion 0.62194 0.6556 0.68733 0.71631 0.74246 0.76631 0.78947
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.25578 0.2324 0.22008 0.20909 0.20603 0.16839 0.16257
## Proportion Explained 0.02147 0.0195 0.01847 0.01755 0.01729 0.01413 0.01364
## Cumulative Proportion 0.81094 0.8304 0.84891 0.86646 0.88375 0.89788 0.91152
## CA23 CA24 CA25 CA26 CA27 CA28
## Eigenvalue 0.15871 0.14570 0.13084 0.112995 0.102767 0.092826
## Proportion Explained 0.01332 0.01223 0.01098 0.009483 0.008625 0.007791
## Cumulative Proportion 0.92484 0.93707 0.94805 0.957538 0.966163 0.973953
## CA29 CA30 CA31 CA32 CA33 CA34
## Eigenvalue 0.075967 0.060043 0.05660 0.05338 0.041425 0.022937
## Proportion Explained 0.006376 0.005039 0.00475 0.00448 0.003477 0.001925
## Cumulative Proportion 0.980329 0.985368 0.99012 0.99460 0.998075 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.6705 0.5309 0.3782 0.3130 0.2897 0.18037
## Proportion Explained 0.2838 0.2247 0.1601 0.1325 0.1226 0.07634
## Cumulative Proportion 0.2838 0.5085 0.6686 0.8010 0.9237 1.00000
anova(Plant_CCA_Chem6.2)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + CaO + Cr2O3 + K2O + BaO, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 6 2.3627 1.4016 0.001 ***
## Residual 34 9.5525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4016, p value of 0.004
Plant_CCA_Chem6.3 <- cca(PlantSpecies ~ pH + Al2O3 + Fe2O3 + CaO + SrO + K2O,
data = PlantChemistry)
plot(Plant_CCA_Chem6.3)
summary(Plant_CCA_Chem6.3)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + Fe2O3 + CaO + SrO + K2O, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.376 0.1994
## Unconstrained 9.539 0.8006
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.67187 0.51988 0.38774 0.34342 0.25050 0.20285 0.74161
## Proportion Explained 0.05639 0.04363 0.03254 0.02882 0.02102 0.01702 0.06224
## Cumulative Proportion 0.05639 0.10002 0.13256 0.16138 0.18241 0.19943 0.26167
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.68475 0.65732 0.56467 0.53273 0.5243 0.46221 0.45089
## Proportion Explained 0.05747 0.05517 0.04739 0.04471 0.0440 0.03879 0.03784
## Cumulative Proportion 0.31914 0.37431 0.42170 0.46641 0.5104 0.54920 0.58704
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.41523 0.40304 0.36964 0.34403 0.29863 0.28856 0.27151
## Proportion Explained 0.03485 0.03383 0.03102 0.02887 0.02506 0.02422 0.02279
## Cumulative Proportion 0.62189 0.65572 0.68674 0.71561 0.74068 0.76489 0.78768
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.25202 0.24479 0.22774 0.20422 0.1919 0.17030 0.16365
## Proportion Explained 0.02115 0.02054 0.01911 0.01714 0.0161 0.01429 0.01373
## Cumulative Proportion 0.80883 0.82938 0.84849 0.86563 0.8817 0.89603 0.90976
## CA23 CA24 CA25 CA26 CA27 CA28
## Eigenvalue 0.15261 0.14625 0.12703 0.112969 0.098321 0.09377
## Proportion Explained 0.01281 0.01227 0.01066 0.009481 0.008252 0.00787
## Cumulative Proportion 0.92257 0.93484 0.94550 0.954985 0.963236 0.97111
## CA29 CA30 CA31 CA32 CA33 CA34
## Eigenvalue 0.080200 0.07519 0.057766 0.053407 0.051623 0.026087
## Proportion Explained 0.006731 0.00631 0.004848 0.004482 0.004333 0.002189
## Cumulative Proportion 0.977837 0.98415 0.988996 0.993478 0.997811 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.6719 0.5199 0.3877 0.3434 0.2505 0.20285
## Proportion Explained 0.2827 0.2188 0.1632 0.1445 0.1054 0.08536
## Cumulative Proportion 0.2827 0.5015 0.6647 0.8092 0.9146 1.00000
anova(Plant_CCA_Chem6.3)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + Fe2O3 + CaO + SrO + K2O, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 6 2.3763 1.4116 0.001 ***
## Residual 34 9.5389
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4116, p value of 0.001
Plant_CCA_Chem6.4 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + SrO + K2O,
data = PlantChemistry)
plot(Plant_CCA_Chem6.4)
summary(Plant_CCA_Chem6.4)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + SrO + K2O, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.421 0.2031
## Unconstrained 9.495 0.7969
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.67211 0.53559 0.39184 0.32871 0.2872 0.20510 0.74179
## Proportion Explained 0.05641 0.04495 0.03289 0.02759 0.0241 0.01721 0.06226
## Cumulative Proportion 0.05641 0.10136 0.13424 0.16183 0.1859 0.20315 0.26540
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.68259 0.65814 0.55451 0.53529 0.51313 0.45212 0.4206
## Proportion Explained 0.05729 0.05524 0.04654 0.04492 0.04307 0.03795 0.0353
## Cumulative Proportion 0.32269 0.37792 0.42446 0.46939 0.51245 0.55040 0.5857
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.41881 0.40348 0.37645 0.34577 0.30011 0.28993 0.27108
## Proportion Explained 0.03515 0.03386 0.03159 0.02902 0.02519 0.02433 0.02275
## Cumulative Proportion 0.62085 0.65471 0.68630 0.71532 0.74051 0.76484 0.78759
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.25185 0.24650 0.22919 0.20553 0.19160 0.17068 0.16331
## Proportion Explained 0.02114 0.02069 0.01924 0.01725 0.01608 0.01432 0.01371
## Cumulative Proportion 0.80873 0.82942 0.84865 0.86590 0.88198 0.89631 0.91001
## CA23 CA24 CA25 CA26 CA27 CA28
## Eigenvalue 0.15207 0.1489 0.12723 0.112333 0.098435 0.096695
## Proportion Explained 0.01276 0.0125 0.01068 0.009428 0.008261 0.008115
## Cumulative Proportion 0.92278 0.9353 0.94595 0.955377 0.963639 0.971754
## CA29 CA30 CA31 CA32 CA33 CA34
## Eigenvalue 0.076198 0.074787 0.056641 0.053319 0.049622 0.025988
## Proportion Explained 0.006395 0.006277 0.004754 0.004475 0.004165 0.002181
## Cumulative Proportion 0.978149 0.984426 0.989179 0.993654 0.997819 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.6721 0.5356 0.3918 0.3287 0.2872 0.20510
## Proportion Explained 0.2777 0.2213 0.1619 0.1358 0.1186 0.08473
## Cumulative Proportion 0.2777 0.4989 0.6608 0.7966 0.9153 1.00000
anova(Plant_CCA_Chem6.4)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + SrO + K2O, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 6 2.4205 1.4446 0.001 ***
## Residual 34 9.4947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4446, p value of 0.001
Plant_CCA_Chem6.5 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + S + K2O,
data = PlantChemistry)
plot(Plant_CCA_Chem6.5)
summary(Plant_CCA_Chem6.5)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + S + K2O, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.426 0.2036
## Unconstrained 9.489 0.7964
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.66957 0.53290 0.39979 0.34537 0.31078 0.16751 0.74120
## Proportion Explained 0.05619 0.04472 0.03355 0.02899 0.02608 0.01406 0.06221
## Cumulative Proportion 0.05619 0.10092 0.13447 0.16346 0.18954 0.20360 0.26581
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.68218 0.65665 0.5851 0.53674 0.50016 0.44234 0.42009
## Proportion Explained 0.05725 0.05511 0.0491 0.04505 0.04198 0.03712 0.03526
## Cumulative Proportion 0.32306 0.37817 0.4273 0.47232 0.51430 0.55142 0.58668
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.40666 0.39156 0.37745 0.33056 0.3074 0.28818 0.27210
## Proportion Explained 0.03413 0.03286 0.03168 0.02774 0.0258 0.02419 0.02284
## Cumulative Proportion 0.62081 0.65367 0.68535 0.71309 0.7389 0.76308 0.78591
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.26705 0.23929 0.22535 0.20478 0.1942 0.18487 0.16245
## Proportion Explained 0.02241 0.02008 0.01891 0.01719 0.0163 0.01552 0.01363
## Cumulative Proportion 0.80832 0.82841 0.84732 0.86451 0.8808 0.89632 0.90996
## CA23 CA24 CA25 CA26 CA27 CA28 CA29
## Eigenvalue 0.15857 0.14961 0.13268 0.12098 0.10568 0.093755 0.074928
## Proportion Explained 0.01331 0.01256 0.01114 0.01015 0.00887 0.007869 0.006288
## Cumulative Proportion 0.92327 0.93582 0.94696 0.95711 0.96598 0.973848 0.980136
## CA30 CA31 CA32 CA33 CA34
## Eigenvalue 0.064833 0.059530 0.050787 0.042218 0.019313
## Proportion Explained 0.005441 0.004996 0.004262 0.003543 0.001621
## Cumulative Proportion 0.985577 0.990574 0.994836 0.998379 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.6696 0.5329 0.3998 0.3454 0.3108 0.16751
## Proportion Explained 0.2760 0.2197 0.1648 0.1424 0.1281 0.06905
## Cumulative Proportion 0.2760 0.4957 0.6605 0.8028 0.9310 1.00000
anova(Plant_CCA_Chem6.5)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + S + K2O, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 6 2.4259 1.4487 0.001 ***
## Residual 34 9.4893
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4487, p value of 0.001
Plant_CCA_Chem6.5 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O, data =
PlantChemistry)
plot(Plant_CCA_Chem6.5)
summary(Plant_CCA_Chem6.5)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0
## Constrained 2.383 0.2
## Unconstrained 9.532 0.8
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.67370 0.53683 0.39668 0.32544 0.25798 0.19251 0.74167
## Proportion Explained 0.05654 0.04505 0.03329 0.02731 0.02165 0.01616 0.06225
## Cumulative Proportion 0.05654 0.10160 0.13489 0.16220 0.18385 0.20001 0.26225
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.68466 0.64723 0.57808 0.53692 0.49032 0.45570 0.4218
## Proportion Explained 0.05746 0.05432 0.04852 0.04506 0.04115 0.03825 0.0354
## Cumulative Proportion 0.31972 0.37403 0.42255 0.46761 0.50876 0.54701 0.5824
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.41161 0.40405 0.37934 0.32658 0.31469 0.2872 0.28156
## Proportion Explained 0.03455 0.03391 0.03184 0.02741 0.02641 0.0241 0.02363
## Cumulative Proportion 0.61695 0.65086 0.68270 0.71011 0.73652 0.7606 0.78425
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.25324 0.24731 0.21728 0.20788 0.19452 0.1871 0.1633
## Proportion Explained 0.02125 0.02076 0.01824 0.01745 0.01633 0.0157 0.0137
## Cumulative Proportion 0.80551 0.82626 0.84450 0.86195 0.87827 0.8940 0.9077
## CA23 CA24 CA25 CA26 CA27 CA28
## Eigenvalue 0.16217 0.15203 0.12842 0.117823 0.111618 0.096592
## Proportion Explained 0.01361 0.01276 0.01078 0.009888 0.009368 0.008107
## Cumulative Proportion 0.92129 0.93405 0.94482 0.954713 0.964081 0.972188
## CA29 CA30 CA31 CA32 CA33 CA34
## Eigenvalue 0.082024 0.074693 0.056649 0.053778 0.044759 0.019486
## Proportion Explained 0.006884 0.006269 0.004754 0.004513 0.003757 0.001635
## Cumulative Proportion 0.979072 0.985340 0.990095 0.994608 0.998365 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.6737 0.5368 0.3967 0.3254 0.2580 0.19251
## Proportion Explained 0.2827 0.2253 0.1665 0.1366 0.1083 0.08078
## Cumulative Proportion 0.2827 0.5080 0.6744 0.8110 0.9192 1.00000
anova(Plant_CCA_Chem6.5)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 6 2.3831 1.4167 0.001 ***
## Residual 34 9.5320
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4167, p value of 0.001
Plant_CCA_Chem7 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O,
data = PlantChemistry)
plot(Plant_CCA_Chem7)
summary(Plant_CCA_Chem7)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.784 0.2336
## Unconstrained 9.131 0.7664
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.67597 0.55216 0.41151 0.38813 0.31736 0.2490 0.18967
## Proportion Explained 0.05673 0.04634 0.03454 0.03257 0.02663 0.0209 0.01592
## Cumulative Proportion 0.05673 0.10307 0.13761 0.17018 0.19682 0.2177 0.23364
## CA1 CA2 CA3 CA4 CA5 CA6 CA7
## Eigenvalue 0.74146 0.66736 0.63538 0.56760 0.50076 0.47533 0.45320
## Proportion Explained 0.06223 0.05601 0.05333 0.04764 0.04203 0.03989 0.03804
## Cumulative Proportion 0.29587 0.35187 0.40520 0.45284 0.49486 0.53476 0.57279
## CA8 CA9 CA10 CA11 CA12 CA13 CA14
## Eigenvalue 0.41176 0.40656 0.38256 0.33833 0.32023 0.30939 0.28709
## Proportion Explained 0.03456 0.03412 0.03211 0.02839 0.02688 0.02597 0.02409
## Cumulative Proportion 0.60735 0.64147 0.67358 0.70197 0.72885 0.75481 0.77891
## CA15 CA16 CA17 CA18 CA19 CA20 CA21
## Eigenvalue 0.26443 0.25186 0.21769 0.20869 0.20463 0.19158 0.16671
## Proportion Explained 0.02219 0.02114 0.01827 0.01751 0.01717 0.01608 0.01399
## Cumulative Proportion 0.80110 0.82224 0.84051 0.85802 0.87520 0.89128 0.90527
## CA22 CA23 CA24 CA25 CA26 CA27 CA28
## Eigenvalue 0.16306 0.15391 0.13113 0.118251 0.1156 0.099443 0.084853
## Proportion Explained 0.01369 0.01292 0.01101 0.009924 0.0097 0.008346 0.007121
## Cumulative Proportion 0.91895 0.93187 0.94287 0.952799 0.9625 0.970845 0.977967
## CA29 CA30 CA31 CA32 CA33
## Eigenvalue 0.081960 0.057774 0.054370 0.048567 0.019858
## Proportion Explained 0.006879 0.004849 0.004563 0.004076 0.001667
## Cumulative Proportion 0.984845 0.989694 0.994257 0.998333 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.6760 0.5522 0.4115 0.3881 0.3174 0.24903 0.18967
## Proportion Explained 0.2428 0.1983 0.1478 0.1394 0.1140 0.08946 0.06813
## Cumulative Proportion 0.2428 0.4412 0.5890 0.7284 0.8424 0.93187 1.00000
anova(Plant_CCA_Chem7)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 7 2.7838 1.4372 0.001 ***
## Residual 33 9.1313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4372, p value of 0.001
Plant_CCA_Chem8 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
SiO2, data = PlantChemistry)
plot(Plant_CCA_Chem8)
summary(Plant_CCA_Chem8)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + SiO2, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 3.130 0.2627
## Unconstrained 8.785 0.7373
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.67598 0.58272 0.4134 0.38864 0.3456 0.31717 0.22803
## Proportion Explained 0.05673 0.04891 0.0347 0.03262 0.0290 0.02662 0.01914
## Cumulative Proportion 0.05673 0.10564 0.1403 0.17295 0.2020 0.22858 0.24771
## CCA8 CA1 CA2 CA3 CA4 CA5 CA6
## Eigenvalue 0.17821 0.7268 0.66115 0.6339 0.5672 0.47986 0.46065
## Proportion Explained 0.01496 0.0610 0.05549 0.0532 0.0476 0.04027 0.03866
## Cumulative Proportion 0.26267 0.3237 0.37915 0.4324 0.4800 0.52023 0.55889
## CA7 CA8 CA9 CA10 CA11 CA12 CA13
## Eigenvalue 0.45319 0.40669 0.38513 0.35903 0.32200 0.31503 0.3003
## Proportion Explained 0.03803 0.03413 0.03232 0.03013 0.02702 0.02644 0.0252
## Cumulative Proportion 0.59693 0.63106 0.66338 0.69351 0.72054 0.74698 0.7722
## CA14 CA15 CA16 CA17 CA18 CA19 CA20
## Eigenvalue 0.27381 0.25916 0.24012 0.21084 0.20789 0.19166 0.17352
## Proportion Explained 0.02298 0.02175 0.02015 0.01769 0.01745 0.01608 0.01456
## Cumulative Proportion 0.79516 0.81691 0.83706 0.85476 0.87220 0.88829 0.90285
## CA21 CA22 CA23 CA24 CA25 CA26 CA27
## Eigenvalue 0.16434 0.15952 0.13117 0.12916 0.11629 0.1013 0.08603
## Proportion Explained 0.01379 0.01339 0.01101 0.01084 0.00976 0.0085 0.00722
## Cumulative Proportion 0.91664 0.93003 0.94104 0.95188 0.96164 0.9701 0.97736
## CA28 CA29 CA30 CA31 CA32
## Eigenvalue 0.082493 0.061051 0.054681 0.049216 0.022314
## Proportion Explained 0.006923 0.005124 0.004589 0.004131 0.001873
## Cumulative Proportion 0.984284 0.989407 0.993997 0.998127 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8
## Eigenvalue 0.676 0.5827 0.4134 0.3886 0.3456 0.3172 0.22803 0.17821
## Proportion Explained 0.216 0.1862 0.1321 0.1242 0.1104 0.1013 0.07286 0.05694
## Cumulative Proportion 0.216 0.4022 0.5343 0.6584 0.7689 0.8702 0.94306 1.00000
anova(Plant_CCA_Chem8)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + SiO2, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 8 3.1298 1.425 0.001 ***
## Residual 32 8.7854
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.425, p value of 0.001
Plant_CCA_Chem9 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
BaO, data = PlantChemistry)
plot(Plant_CCA_Chem9)
summary(Plant_CCA_Chem9)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + BaO, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.000
## Constrained 3.062 0.257
## Unconstrained 8.853 0.743
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.67599 0.57025 0.41514 0.3896 0.33699 0.27554 0.21490
## Proportion Explained 0.05673 0.04786 0.03484 0.0327 0.02828 0.02312 0.01804
## Cumulative Proportion 0.05673 0.10459 0.13943 0.1721 0.20042 0.22354 0.24158
## CCA8 CA1 CA2 CA3 CA4 CA5 CA6
## Eigenvalue 0.18404 0.73947 0.6637 0.63353 0.56589 0.49146 0.47522
## Proportion Explained 0.01545 0.06206 0.0557 0.05317 0.04749 0.04125 0.03988
## Cumulative Proportion 0.25702 0.31909 0.3748 0.42796 0.47545 0.51670 0.55658
## CA7 CA8 CA9 CA10 CA11 CA12 CA13
## Eigenvalue 0.44848 0.41165 0.40604 0.36930 0.33459 0.31031 0.29162
## Proportion Explained 0.03764 0.03455 0.03408 0.03099 0.02808 0.02604 0.02448
## Cumulative Proportion 0.59422 0.62877 0.66284 0.69384 0.72192 0.74796 0.77244
## CA14 CA15 CA16 CA17 CA18 CA19 CA20
## Eigenvalue 0.27273 0.25661 0.2240 0.21541 0.20655 0.20176 0.16691
## Proportion Explained 0.02289 0.02154 0.0188 0.01808 0.01734 0.01693 0.01401
## Cumulative Proportion 0.79533 0.81686 0.8357 0.85374 0.87107 0.88801 0.90201
## CA21 CA22 CA23 CA24 CA25 CA26 CA27
## Eigenvalue 0.16377 0.16251 0.13373 0.12294 0.116095 0.10521 0.092032
## Proportion Explained 0.01375 0.01364 0.01122 0.01032 0.009743 0.00883 0.007724
## Cumulative Proportion 0.91576 0.92940 0.94062 0.95094 0.960683 0.96951 0.977237
## CA28 CA29 CA30 CA31 CA32
## Eigenvalue 0.08198 0.05779 0.055535 0.049133 0.026791
## Proportion Explained 0.00688 0.00485 0.004661 0.004124 0.002248
## Cumulative Proportion 0.98412 0.98897 0.993628 0.997752 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.6760 0.5703 0.4151 0.3896 0.3370 0.27554 0.21490
## Proportion Explained 0.2207 0.1862 0.1356 0.1272 0.1100 0.08997 0.07017
## Cumulative Proportion 0.2207 0.4069 0.5425 0.6697 0.7798 0.86974 0.93991
## CCA8
## Eigenvalue 0.18404
## Proportion Explained 0.06009
## Cumulative Proportion 1.00000
anova(Plant_CCA_Chem9)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + BaO, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 8 3.0625 1.3838 0.001 ***
## Residual 32 8.8527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.3838, p value of 0.001
Plant_CCA_Chem10 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
Cu, data = PlantChemistry)
plot(Plant_CCA_Chem10)
summary(Plant_CCA_Chem10)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 3.136 0.2632
## Unconstrained 8.780 0.7368
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.67789 0.56069 0.45214 0.41072 0.35892 0.31135 0.19242
## Proportion Explained 0.05689 0.04706 0.03795 0.03447 0.03012 0.02613 0.01615
## Cumulative Proportion 0.05689 0.10395 0.14190 0.17637 0.20649 0.23262 0.24877
## CCA8 CA1 CA2 CA3 CA4 CA5 CA6
## Eigenvalue 0.1715 0.72511 0.66691 0.63538 0.54023 0.47599 0.46291
## Proportion Explained 0.0144 0.06086 0.05597 0.05333 0.04534 0.03995 0.03885
## Cumulative Proportion 0.2632 0.32402 0.37999 0.43332 0.47866 0.51861 0.55746
## CA7 CA8 CA9 CA10 CA11 CA12 CA13
## Eigenvalue 0.42037 0.40656 0.3837 0.37944 0.33691 0.31583 0.30651
## Proportion Explained 0.03528 0.03412 0.0322 0.03185 0.02828 0.02651 0.02572
## Cumulative Proportion 0.59274 0.62686 0.6591 0.69090 0.71918 0.74569 0.77141
## CA14 CA15 CA16 CA17 CA18 CA19 CA20
## Eigenvalue 0.27632 0.25186 0.23485 0.21499 0.2085 0.20303 0.18423
## Proportion Explained 0.02319 0.02114 0.01971 0.01804 0.0175 0.01704 0.01546
## Cumulative Proportion 0.79460 0.81574 0.83545 0.85349 0.8710 0.88803 0.90349
## CA21 CA22 CA23 CA24 CA25 CA26
## Eigenvalue 0.16367 0.15684 0.13571 0.12000 0.115608 0.102677
## Proportion Explained 0.01374 0.01316 0.01139 0.01007 0.009703 0.008617
## Cumulative Proportion 0.91723 0.93039 0.94178 0.95185 0.961551 0.970169
## CA27 CA28 CA29 CA30 CA31 CA32
## Eigenvalue 0.085126 0.082489 0.063095 0.05565 0.048947 0.02014
## Proportion Explained 0.007144 0.006923 0.005295 0.00467 0.004108 0.00169
## Cumulative Proportion 0.977313 0.984236 0.989531 0.99420 0.998310 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8
## Eigenvalue 0.6779 0.5607 0.4521 0.4107 0.3589 0.31135 0.19242 0.1715
## Proportion Explained 0.2162 0.1788 0.1442 0.1310 0.1145 0.09929 0.06137 0.0547
## Cumulative Proportion 0.2162 0.3950 0.5392 0.6702 0.7846 0.88393 0.94530 1.0000
anova(Plant_CCA_Chem10)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 8 3.1357 1.4286 0.001 ***
## Residual 32 8.7795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4286, p value of 0.001
Plant_CCA_Chem11 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
Cu + Calcite, data = PlantChemistry)
plot(Plant_CCA_Chem11)
summary(Plant_CCA_Chem11)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + Calcite, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 3.442 0.2889
## Unconstrained 8.473 0.7111
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.67988 0.56085 0.48487 0.41268 0.36950 0.31674 0.25647
## Proportion Explained 0.05706 0.04707 0.04069 0.03463 0.03101 0.02658 0.02152
## Cumulative Proportion 0.05706 0.10413 0.14482 0.17946 0.21047 0.23705 0.25858
## CCA8 CCA9 CA1 CA2 CA3 CA4 CA5
## Eigenvalue 0.18996 0.1715 0.71405 0.66689 0.63247 0.53605 0.47385
## Proportion Explained 0.01594 0.0144 0.05993 0.05597 0.05308 0.04499 0.03977
## Cumulative Proportion 0.27452 0.2889 0.34884 0.40481 0.45789 0.50288 0.54265
## CA6 CA7 CA8 CA9 CA10 CA11 CA12
## Eigenvalue 0.42403 0.40659 0.40371 0.38122 0.35663 0.33331 0.31296
## Proportion Explained 0.03559 0.03412 0.03388 0.03199 0.02993 0.02797 0.02627
## Cumulative Proportion 0.57824 0.61236 0.64624 0.67824 0.70817 0.73614 0.76241
## CA13 CA14 CA15 CA16 CA17 CA18 CA19
## Eigenvalue 0.27798 0.2538 0.24456 0.21517 0.20961 0.20303 0.18928
## Proportion Explained 0.02333 0.0213 0.02053 0.01806 0.01759 0.01704 0.01589
## Cumulative Proportion 0.78574 0.8070 0.82757 0.84563 0.86322 0.88026 0.89614
## CA20 CA21 CA22 CA23 CA24 CA25
## Eigenvalue 0.18025 0.16332 0.14845 0.12164 0.116862 0.113372
## Proportion Explained 0.01513 0.01371 0.01246 0.01021 0.009808 0.009515
## Cumulative Proportion 0.91127 0.92498 0.93744 0.94765 0.957453 0.966968
## CA26 CA27 CA28 CA29 CA30 CA31
## Eigenvalue 0.102186 0.082606 0.063206 0.055656 0.050502 0.039426
## Proportion Explained 0.008576 0.006933 0.005305 0.004671 0.004238 0.003309
## Cumulative Proportion 0.975544 0.982477 0.987782 0.992453 0.996691 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8
## Eigenvalue 0.6799 0.5608 0.4849 0.4127 0.3695 0.31674 0.2565 0.18996
## Proportion Explained 0.1975 0.1629 0.1408 0.1199 0.1073 0.09201 0.0745 0.05518
## Cumulative Proportion 0.1975 0.3604 0.5013 0.6211 0.7285 0.82049 0.8950 0.95017
## CCA9
## Eigenvalue 0.17153
## Proportion Explained 0.04983
## Cumulative Proportion 1.00000
anova(Plant_CCA_Chem11)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + Calcite, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 9 3.4425 1.3995 0.001 ***
## Residual 31 8.4727
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.3995, p value of 0.001
Plant_CCA_Chem12 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
Cu + TiO2, data = PlantChemistry)
plot(Plant_CCA_Chem12)
summary(Plant_CCA_Chem12)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 3.468 0.2911
## Unconstrained 8.447 0.7089
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.68143 0.59994 0.4802 0.41497 0.37513 0.32983 0.25097
## Proportion Explained 0.05719 0.05035 0.0403 0.03483 0.03148 0.02768 0.02106
## Cumulative Proportion 0.05719 0.10754 0.1478 0.18267 0.21415 0.24183 0.26290
## CCA8 CCA9 CA1 CA2 CA3 CA4 CA5
## Eigenvalue 0.17410 0.16194 0.72436 0.6613 0.6256 0.53815 0.46291
## Proportion Explained 0.01461 0.01359 0.06079 0.0555 0.0525 0.04516 0.03885
## Cumulative Proportion 0.27751 0.29110 0.35189 0.4074 0.4599 0.50506 0.54391
## CA6 CA7 CA8 CA9 CA10 CA11 CA12
## Eigenvalue 0.44391 0.42021 0.40603 0.37991 0.36371 0.32553 0.30947
## Proportion Explained 0.03726 0.03527 0.03408 0.03188 0.03053 0.02732 0.02597
## Cumulative Proportion 0.58116 0.61643 0.65051 0.68239 0.71292 0.74024 0.76621
## CA13 CA14 CA15 CA16 CA17 CA18 CA19
## Eigenvalue 0.28081 0.26265 0.24457 0.2192 0.20913 0.20368 0.18925
## Proportion Explained 0.02357 0.02204 0.02053 0.0184 0.01755 0.01709 0.01588
## Cumulative Proportion 0.78978 0.81182 0.83235 0.8507 0.86830 0.88539 0.90127
## CA20 CA21 CA22 CA23 CA24 CA25 CA26
## Eigenvalue 0.16384 0.15708 0.13575 0.12338 0.11979 0.107430 0.085624
## Proportion Explained 0.01375 0.01318 0.01139 0.01036 0.01005 0.009016 0.007186
## Cumulative Proportion 0.91502 0.92821 0.93960 0.94995 0.96001 0.969024 0.976210
## CA27 CA28 CA29 CA30 CA31
## Eigenvalue 0.083023 0.064795 0.062993 0.049031 0.023616
## Proportion Explained 0.006968 0.005438 0.005287 0.004115 0.001982
## Cumulative Proportion 0.983178 0.988616 0.993903 0.998018 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8
## Eigenvalue 0.6814 0.5999 0.4802 0.4150 0.3751 0.32983 0.25097 0.1741
## Proportion Explained 0.1965 0.1730 0.1384 0.1196 0.1082 0.09509 0.07236 0.0502
## Cumulative Proportion 0.1965 0.3694 0.5079 0.6275 0.7357 0.83076 0.90311 0.9533
## CCA9
## Eigenvalue 0.16194
## Proportion Explained 0.04669
## Cumulative Proportion 1.00000
anova(Plant_CCA_Chem12)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 9 3.4685 1.4144 0.001 ***
## Residual 31 8.4467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4144, p value of 0.001
Plant_CCA_Chem13 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
Cu + TiO2 + P2O5, data = PlantChemistry)
plot(Plant_CCA_Chem13)
summary(Plant_CCA_Chem13)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 3.803 0.3192
## Unconstrained 8.112 0.6808
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.68669 0.61521 0.51182 0.44195 0.38047 0.34212 0.27119
## Proportion Explained 0.05763 0.05163 0.04296 0.03709 0.03193 0.02871 0.02276
## Cumulative Proportion 0.05763 0.10926 0.15222 0.18931 0.22124 0.24996 0.27272
## CCA8 CCA9 CCA10 CA1 CA2 CA3 CA4
## Eigenvalue 0.22265 0.17403 0.15685 0.71187 0.65912 0.61690 0.52127
## Proportion Explained 0.01869 0.01461 0.01316 0.05974 0.05532 0.05177 0.04375
## Cumulative Proportion 0.29140 0.30601 0.31917 0.37892 0.43423 0.48601 0.52976
## CA5 CA6 CA7 CA8 CA9 CA10 CA11
## Eigenvalue 0.45865 0.44243 0.41654 0.40291 0.36557 0.33104 0.32385
## Proportion Explained 0.03849 0.03713 0.03496 0.03381 0.03068 0.02778 0.02718
## Cumulative Proportion 0.56825 0.60538 0.64034 0.67415 0.70483 0.73262 0.75980
## CA12 CA13 CA14 CA15 CA16 CA17 CA18
## Eigenvalue 0.28191 0.26436 0.25575 0.22694 0.21060 0.20656 0.19021
## Proportion Explained 0.02366 0.02219 0.02146 0.01905 0.01767 0.01734 0.01596
## Cumulative Proportion 0.78346 0.80564 0.82711 0.84615 0.86383 0.88117 0.89713
## CA19 CA20 CA21 CA22 CA23 CA24 CA25
## Eigenvalue 0.18272 0.16382 0.13811 0.12381 0.12069 0.108122 0.094558
## Proportion Explained 0.01534 0.01375 0.01159 0.01039 0.01013 0.009074 0.007936
## Cumulative Proportion 0.91246 0.92621 0.93780 0.94820 0.95832 0.967399 0.975335
## CA26 CA27 CA28 CA29 CA30
## Eigenvalue 0.083182 0.07125 0.064610 0.05063 0.024215
## Proportion Explained 0.006981 0.00598 0.005423 0.00425 0.002032
## Cumulative Proportion 0.982316 0.98830 0.993718 0.99797 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.6867 0.6152 0.5118 0.4420 0.3805 0.34212 0.27119
## Proportion Explained 0.1806 0.1618 0.1346 0.1162 0.1000 0.08996 0.07131
## Cumulative Proportion 0.1806 0.3423 0.4769 0.5931 0.6932 0.78314 0.85445
## CCA8 CCA9 CCA10
## Eigenvalue 0.22265 0.17403 0.15685
## Proportion Explained 0.05855 0.04576 0.04124
## Cumulative Proportion 0.91300 0.95876 1.00000
anova(Plant_CCA_Chem13)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 10 3.8030 1.4064 0.001 ***
## Residual 30 8.1122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4064, p value of 0.001
Plant_CCA_Chem14 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
Cu + TiO2 + P2O5 + Mo, data = PlantChemistry)
plot(Plant_CCA_Chem14)
summary(Plant_CCA_Chem14)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5 + Mo, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 4.249 0.3566
## Unconstrained 7.666 0.6434
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.70550 0.62013 0.54603 0.46523 0.43933 0.36368 0.32570
## Proportion Explained 0.05921 0.05205 0.04583 0.03905 0.03687 0.03052 0.02734
## Cumulative Proportion 0.05921 0.11126 0.15708 0.19613 0.23300 0.26352 0.29086
## CCA8 CCA9 CCA10 CCA11 CA1 CA2 CA3
## Eigenvalue 0.27087 0.19735 0.16264 0.15299 0.66934 0.61738 0.58965
## Proportion Explained 0.02273 0.01656 0.01365 0.01284 0.05618 0.05181 0.04949
## Cumulative Proportion 0.31359 0.33015 0.34380 0.35664 0.41282 0.46463 0.51412
## CA4 CA5 CA6 CA7 CA8 CA9 CA10
## Eigenvalue 0.51142 0.45610 0.42517 0.40772 0.36571 0.36014 0.3241
## Proportion Explained 0.04292 0.03828 0.03568 0.03422 0.03069 0.03023 0.0272
## Cumulative Proportion 0.55704 0.59532 0.63100 0.66522 0.69591 0.72614 0.7533
## CA11 CA12 CA13 CA14 CA15 CA16 CA17
## Eigenvalue 0.28533 0.26889 0.2586 0.23679 0.22685 0.21039 0.19412
## Proportion Explained 0.02395 0.02257 0.0217 0.01987 0.01904 0.01766 0.01629
## Cumulative Proportion 0.77729 0.79985 0.8216 0.84143 0.86047 0.87813 0.89442
## CA18 CA19 CA20 CA21 CA22 CA23 CA24
## Eigenvalue 0.18672 0.16485 0.13879 0.13273 0.12190 0.110381 0.094619
## Proportion Explained 0.01567 0.01384 0.01165 0.01114 0.01023 0.009264 0.007941
## Cumulative Proportion 0.91009 0.92392 0.93557 0.94671 0.95694 0.966205 0.974147
## CA25 CA26 CA27 CA28 CA29
## Eigenvalue 0.083437 0.077854 0.067524 0.053670 0.025563
## Proportion Explained 0.007003 0.006534 0.005667 0.004504 0.002145
## Cumulative Proportion 0.981149 0.987683 0.993350 0.997855 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7055 0.6201 0.5460 0.4652 0.4393 0.36368 0.32570
## Proportion Explained 0.1660 0.1459 0.1285 0.1095 0.1034 0.08558 0.07665
## Cumulative Proportion 0.1660 0.3120 0.4404 0.5499 0.6533 0.73890 0.81554
## CCA8 CCA9 CCA10 CCA11
## Eigenvalue 0.27087 0.19735 0.16264 0.153
## Proportion Explained 0.06374 0.04644 0.03827 0.036
## Cumulative Proportion 0.87929 0.92573 0.96400 1.000
anova(Plant_CCA_Chem14)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5 + Mo, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 11 4.2494 1.4614 0.001 ***
## Residual 29 7.6657
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4614, p value of 0.001
Plant_CCA_Chem15 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
Cu + TiO2 + P2O5 + Mo + Ni, data = PlantChemistry)
plot(Plant_CCA_Chem15)
summary(Plant_CCA_Chem15)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5 + Mo + Ni, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 4.588 0.3851
## Unconstrained 7.327 0.6149
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.71183 0.64202 0.55363 0.47675 0.43955 0.40567 0.34900
## Proportion Explained 0.05974 0.05388 0.04646 0.04001 0.03689 0.03405 0.02929
## Cumulative Proportion 0.05974 0.11362 0.16009 0.20010 0.23699 0.27104 0.30033
## CCA8 CCA9 CCA10 CCA11 CCA12 CA1 CA2
## Eigenvalue 0.29763 0.24213 0.17098 0.15781 0.14146 0.66303 0.61714
## Proportion Explained 0.02498 0.02032 0.01435 0.01324 0.01187 0.05565 0.05179
## Cumulative Proportion 0.32531 0.34563 0.35998 0.37322 0.38509 0.44074 0.49254
## CA3 CA4 CA5 CA6 CA7 CA8 CA9
## Eigenvalue 0.51295 0.49344 0.44763 0.40784 0.40176 0.36303 0.34883
## Proportion Explained 0.04305 0.04141 0.03757 0.03423 0.03372 0.03047 0.02928
## Cumulative Proportion 0.53559 0.57700 0.61457 0.64880 0.68251 0.71298 0.74226
## CA10 CA11 CA12 CA13 CA14 CA15 CA16
## Eigenvalue 0.30969 0.26900 0.25913 0.23977 0.22776 0.21310 0.20171
## Proportion Explained 0.02599 0.02258 0.02175 0.02012 0.01911 0.01788 0.01693
## Cumulative Proportion 0.76825 0.79083 0.81257 0.83270 0.85181 0.86970 0.88663
## CA17 CA18 CA19 CA20 CA21 CA22 CA23
## Eigenvalue 0.19252 0.18672 0.16414 0.13843 0.12492 0.118184 0.109612
## Proportion Explained 0.01616 0.01567 0.01378 0.01162 0.01048 0.009919 0.009199
## Cumulative Proportion 0.90278 0.91845 0.93223 0.94385 0.95433 0.964252 0.973451
## CA24 CA25 CA26 CA27 CA28
## Eigenvalue 0.084553 0.082986 0.068923 0.053673 0.026200
## Proportion Explained 0.007096 0.006965 0.005784 0.004505 0.002199
## Cumulative Proportion 0.980547 0.987512 0.993296 0.997801 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7118 0.6420 0.5536 0.4768 0.4396 0.40567 0.34900
## Proportion Explained 0.1551 0.1399 0.1207 0.1039 0.0958 0.08841 0.07606
## Cumulative Proportion 0.1551 0.2951 0.4157 0.5196 0.6154 0.70382 0.77988
## CCA8 CCA9 CCA10 CCA11 CCA12
## Eigenvalue 0.29763 0.24213 0.17098 0.15781 0.14146
## Proportion Explained 0.06486 0.05277 0.03726 0.03439 0.03083
## Cumulative Proportion 0.84474 0.89751 0.93478 0.96917 1.00000
anova(Plant_CCA_Chem15)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5 + Mo + Ni, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 12 4.5885 1.4613 0.001 ***
## Residual 28 7.3267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4613, p value of 0.001
Plant_CCA_Chem16 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
Cu + TiO2 + P2O5 + Mo + Ni + Pb, data = PlantChemistry)
plot(Plant_CCA_Chem16)
summary(Plant_CCA_Chem16)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5 + Mo + Ni + Pb, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 4.942 0.4147
## Unconstrained 6.973 0.5853
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.71191 0.64376 0.55547 0.50120 0.43960 0.4063 0.36058
## Proportion Explained 0.05975 0.05403 0.04662 0.04206 0.03689 0.0341 0.03026
## Cumulative Proportion 0.05975 0.11378 0.16040 0.20246 0.23935 0.2735 0.30372
## CCA8 CCA9 CCA10 CCA11 CCA12 CCA13 CA1
## Eigenvalue 0.34700 0.27796 0.23251 0.16913 0.15780 0.13855 0.64607
## Proportion Explained 0.02912 0.02333 0.01951 0.01419 0.01324 0.01163 0.05422
## Cumulative Proportion 0.33284 0.35617 0.37568 0.38988 0.40312 0.41475 0.46897
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.5839 0.51274 0.49224 0.44729 0.40226 0.38189 0.35106
## Proportion Explained 0.0490 0.04303 0.04131 0.03754 0.03376 0.03205 0.02946
## Cumulative Proportion 0.5180 0.56101 0.60232 0.63986 0.67362 0.70567 0.73513
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.31622 0.2872 0.26227 0.24085 0.22804 0.21311 0.20184
## Proportion Explained 0.02654 0.0241 0.02201 0.02021 0.01914 0.01789 0.01694
## Cumulative Proportion 0.76167 0.7858 0.80779 0.82800 0.84714 0.86502 0.88196
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.19710 0.1871 0.16910 0.14846 0.1370 0.1240 0.112020
## Proportion Explained 0.01654 0.0157 0.01419 0.01246 0.0115 0.0104 0.009401
## Cumulative Proportion 0.89851 0.9142 0.92840 0.94086 0.9524 0.9628 0.972166
## CA23 CA24 CA25 CA26 CA27
## Eigenvalue 0.092140 0.08341 0.073482 0.053835 0.028783
## Proportion Explained 0.007733 0.00700 0.006167 0.004518 0.002416
## Cumulative Proportion 0.979899 0.98690 0.993066 0.997584 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7119 0.6438 0.5555 0.5012 0.43960 0.40629 0.36058
## Proportion Explained 0.1441 0.1303 0.1124 0.1014 0.08896 0.08222 0.07297
## Cumulative Proportion 0.1441 0.2743 0.3867 0.4882 0.57711 0.65933 0.73229
## CCA8 CCA9 CCA10 CCA11 CCA12 CCA13
## Eigenvalue 0.34700 0.27796 0.23251 0.16913 0.15780 0.13855
## Proportion Explained 0.07022 0.05625 0.04705 0.03423 0.03193 0.02804
## Cumulative Proportion 0.80251 0.85876 0.90581 0.94003 0.97196 1.00000
anova(Plant_CCA_Chem16)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5 + Mo + Ni + Pb, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 13 4.9418 1.4718 0.001 ***
## Residual 27 6.9734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4718, p value of 0.001
Plant_CCA_Chem17 <- cca(PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O +
Cu + TiO2 + P2O5 + Mo + Ni + Sc, data = PlantChemistry)
plot(Plant_CCA_Chem17)
summary(Plant_CCA_Chem17)
##
## Call:
## cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5 + Mo + Ni + Sc, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 4.832 0.4055
## Unconstrained 7.083 0.5945
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.72629 0.64265 0.5648 0.50155 0.44979 0.42552 0.3646
## Proportion Explained 0.06096 0.05394 0.0474 0.04209 0.03775 0.03571 0.0306
## Cumulative Proportion 0.06096 0.11489 0.1623 0.20438 0.24213 0.27785 0.3084
## CCA8 CCA9 CCA10 CCA11 CCA12 CCA13 CA1
## Eigenvalue 0.29879 0.25525 0.18369 0.16258 0.15477 0.101453 0.65116
## Proportion Explained 0.02508 0.02142 0.01542 0.01365 0.01299 0.008515 0.05465
## Cumulative Proportion 0.33353 0.35495 0.37036 0.38401 0.39700 0.405513 0.46016
## CA2 CA3 CA4 CA5 CA6 CA7 CA8
## Eigenvalue 0.61699 0.51059 0.46813 0.44695 0.40567 0.39524 0.3622
## Proportion Explained 0.05178 0.04285 0.03929 0.03751 0.03405 0.03317 0.0304
## Cumulative Proportion 0.51194 0.55480 0.59408 0.63160 0.66564 0.69881 0.7292
## CA9 CA10 CA11 CA12 CA13 CA14 CA15
## Eigenvalue 0.34689 0.29950 0.26731 0.25875 0.23912 0.22540 0.21099
## Proportion Explained 0.02911 0.02514 0.02243 0.02172 0.02007 0.01892 0.01771
## Cumulative Proportion 0.75833 0.78346 0.80590 0.82761 0.84768 0.86660 0.88431
## CA16 CA17 CA18 CA19 CA20 CA21 CA22
## Eigenvalue 0.19256 0.18814 0.1644 0.14277 0.12618 0.118604 0.110352
## Proportion Explained 0.01616 0.01579 0.0138 0.01198 0.01059 0.009954 0.009261
## Cumulative Proportion 0.90047 0.91626 0.9301 0.94204 0.95263 0.962581 0.971843
## CA23 CA24 CA25 CA26 CA27
## Eigenvalue 0.098136 0.083573 0.070802 0.054399 0.028590
## Proportion Explained 0.008236 0.007014 0.005942 0.004566 0.002399
## Cumulative Proportion 0.980079 0.987093 0.993035 0.997601 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7263 0.6426 0.5648 0.5016 0.44979 0.42552 0.36463
## Proportion Explained 0.1503 0.1330 0.1169 0.1038 0.09309 0.08807 0.07547
## Cumulative Proportion 0.1503 0.2833 0.4002 0.5040 0.59710 0.68517 0.76064
## CCA8 CCA9 CCA10 CCA11 CCA12 CCA13
## Eigenvalue 0.29879 0.25525 0.18369 0.16258 0.15477 0.1015
## Proportion Explained 0.06184 0.05283 0.03802 0.03365 0.03203 0.0210
## Cumulative Proportion 0.82248 0.87531 0.91332 0.94697 0.97900 1.0000
anova(Plant_CCA_Chem17)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + Al2O3 + V + CaO + Co + K2O + Na2O + Cu + TiO2 + P2O5 + Mo + Ni + Sc, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 13 4.8318 1.4167 0.001 ***
## Residual 27 7.0834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4167, p value of 0.001
Plant_CCA_Chem17.1 <- cca(PlantSpecies ~ pH + P2O5 +SiO2 + Al2O3, data = PlantChemistry)
plot(Plant_CCA_Chem17.1)
summary(Plant_CCA_Chem17.1)
##
## Call:
## cca(formula = PlantSpecies ~ pH + P2O5 + SiO2 + Al2O3, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.92 1.0000
## Constrained 1.88 0.1578
## Unconstrained 10.04 0.8422
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CA1 CA2 CA3
## Eigenvalue 0.65252 0.48498 0.39111 0.3515 0.72539 0.7018 0.67461
## Proportion Explained 0.05476 0.04070 0.03282 0.0295 0.06088 0.0589 0.05662
## Cumulative Proportion 0.05476 0.09547 0.12829 0.1578 0.21867 0.2776 0.33418
## CA4 CA5 CA6 CA7 CA8 CA9 CA10
## Eigenvalue 0.60542 0.56604 0.53181 0.46156 0.45286 0.4194 0.40772
## Proportion Explained 0.05081 0.04751 0.04463 0.03874 0.03801 0.0352 0.03422
## Cumulative Proportion 0.38499 0.43250 0.47713 0.51587 0.55388 0.5891 0.62330
## CA11 CA12 CA13 CA14 CA15 CA16 CA17
## Eigenvalue 0.36319 0.33783 0.31977 0.30047 0.28576 0.27222 0.25484
## Proportion Explained 0.03048 0.02835 0.02684 0.02522 0.02398 0.02285 0.02139
## Cumulative Proportion 0.65378 0.68213 0.70897 0.73419 0.75817 0.78102 0.80240
## CA18 CA19 CA20 CA21 CA22 CA23 CA24
## Eigenvalue 0.23298 0.21587 0.2073 0.19815 0.1918 0.17379 0.16156
## Proportion Explained 0.01955 0.01812 0.0174 0.01663 0.0161 0.01459 0.01356
## Cumulative Proportion 0.82196 0.84007 0.8575 0.87410 0.8902 0.90478 0.91834
## CA25 CA26 CA27 CA28 CA29 CA30
## Eigenvalue 0.15308 0.12438 0.118993 0.113709 0.091497 0.084993
## Proportion Explained 0.01285 0.01044 0.009987 0.009543 0.007679 0.007133
## Cumulative Proportion 0.93119 0.94163 0.951615 0.961158 0.968837 0.975971
## CA31 CA32 CA33 CA34 CA35 CA36
## Eigenvalue 0.070946 0.061773 0.056059 0.049024 0.030681 0.017831
## Proportion Explained 0.005954 0.005184 0.004705 0.004114 0.002575 0.001497
## Cumulative Proportion 0.981925 0.987109 0.991814 0.995929 0.998503 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 0.6525 0.485 0.3911 0.3515
## Proportion Explained 0.3471 0.258 0.2080 0.1869
## Cumulative Proportion 0.3471 0.605 0.8131 1.0000
anova(Plant_CCA_Chem17.1)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + P2O5 + SiO2 + Al2O3, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 4 1.8801 1.6861 0.001 ***
## Residual 36 10.0351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.6861, p value of 0.001
Plant_CCA_Chem17.2 <- cca(PlantSpecies ~ pH + P2O5 +SiO2 + Al2O3 +
V, data = PlantChemistry)
plot(Plant_CCA_Chem17.2)
summary(Plant_CCA_Chem17.2)
##
## Call:
## cca(formula = PlantSpecies ~ pH + P2O5 + SiO2 + Al2O3 + V, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.142 0.1798
## Unconstrained 9.773 0.8202
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CA1 CA2
## Eigenvalue 0.6637 0.48605 0.39118 0.35188 0.2490 0.71987 0.70099
## Proportion Explained 0.0557 0.04079 0.03283 0.02953 0.0209 0.06042 0.05883
## Cumulative Proportion 0.0557 0.09650 0.12933 0.15886 0.1798 0.24017 0.29901
## CA3 CA4 CA5 CA6 CA7 CA8 CA9
## Eigenvalue 0.65646 0.57632 0.55174 0.52203 0.45888 0.44818 0.4194
## Proportion Explained 0.05509 0.04837 0.04631 0.04381 0.03851 0.03761 0.0352
## Cumulative Proportion 0.35410 0.40247 0.44877 0.49259 0.53110 0.56871 0.6039
## CA10 CA11 CA12 CA13 CA14 CA15 CA16
## Eigenvalue 0.40459 0.36137 0.3372 0.31843 0.29449 0.27827 0.27205
## Proportion Explained 0.03396 0.03033 0.0283 0.02672 0.02472 0.02335 0.02283
## Cumulative Proportion 0.63787 0.66819 0.6965 0.72322 0.74794 0.77129 0.79412
## CA17 CA18 CA19 CA20 CA21 CA22 CA23
## Eigenvalue 0.25189 0.2264 0.21232 0.20485 0.1918 0.17866 0.16642
## Proportion Explained 0.02114 0.0190 0.01782 0.01719 0.0161 0.01499 0.01397
## Cumulative Proportion 0.81526 0.8343 0.85209 0.86928 0.8854 0.90037 0.91434
## CA24 CA25 CA26 CA27 CA28 CA29 CA30
## Eigenvalue 0.15362 0.1466 0.12238 0.117509 0.09484 0.089906 0.071314
## Proportion Explained 0.01289 0.0123 0.01027 0.009862 0.00796 0.007546 0.005985
## Cumulative Proportion 0.92723 0.9395 0.94980 0.959663 0.96762 0.975168 0.981154
## CA31 CA32 CA33 CA34 CA35
## Eigenvalue 0.061799 0.056081 0.049759 0.03908 0.017835
## Proportion Explained 0.005187 0.004707 0.004176 0.00328 0.001497
## Cumulative Proportion 0.986340 0.991047 0.995223 0.99850 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5
## Eigenvalue 0.6637 0.4860 0.3912 0.3519 0.2490
## Proportion Explained 0.3099 0.2269 0.1826 0.1643 0.1163
## Cumulative Proportion 0.3099 0.5368 0.7194 0.8837 1.0000
anova(Plant_CCA_Chem17.2)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + P2O5 + SiO2 + Al2O3 + V, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 5 2.1418 1.5341 0.001 ***
## Residual 35 9.7733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.5341, p value of 0.001
Plant_CCA_Chem17.3 <- cca(PlantSpecies ~ pH + P2O5 +SiO2 + Al2O3 +
Co, data = PlantChemistry)
plot(Plant_CCA_Chem17.3)
summary(Plant_CCA_Chem17.3)
##
## Call:
## cca(formula = PlantSpecies ~ pH + P2O5 + SiO2 + Al2O3 + Co, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 2.132 0.1789
## Unconstrained 9.783 0.8211
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CA1 CA2
## Eigenvalue 0.65751 0.48527 0.42155 0.3515 0.21640 0.72388 0.70172
## Proportion Explained 0.05518 0.04073 0.03538 0.0295 0.01816 0.06075 0.05889
## Cumulative Proportion 0.05518 0.09591 0.13129 0.1608 0.17895 0.23970 0.29860
## CA3 CA4 CA5 CA6 CA7 CA8 CA9
## Eigenvalue 0.66269 0.57993 0.56532 0.51101 0.46122 0.44630 0.41725
## Proportion Explained 0.05562 0.04867 0.04745 0.04289 0.03871 0.03746 0.03502
## Cumulative Proportion 0.35421 0.40288 0.45033 0.49322 0.53193 0.56938 0.60440
## CA10 CA11 CA12 CA13 CA14 CA15 CA16
## Eigenvalue 0.4076 0.36314 0.32041 0.31519 0.29653 0.27957 0.2634
## Proportion Explained 0.0342 0.03048 0.02689 0.02645 0.02489 0.02346 0.0221
## Cumulative Proportion 0.6386 0.66908 0.69597 0.72243 0.74731 0.77078 0.7929
## CA17 CA18 CA19 CA20 CA21 CA22 CA23
## Eigenvalue 0.25482 0.21596 0.2121 0.19815 0.1978 0.18700 0.17375
## Proportion Explained 0.02139 0.01812 0.0178 0.01663 0.0166 0.01569 0.01458
## Cumulative Proportion 0.81426 0.83239 0.8502 0.86682 0.8834 0.89912 0.91370
## CA24 CA25 CA26 CA27 CA28 CA29
## Eigenvalue 0.15382 0.13600 0.12104 0.115419 0.106435 0.086197
## Proportion Explained 0.01291 0.01141 0.01016 0.009687 0.008933 0.007234
## Cumulative Proportion 0.92661 0.93802 0.94818 0.957869 0.966802 0.974036
## CA30 CA31 CA32 CA33 CA34 CA35
## Eigenvalue 0.080520 0.068821 0.056060 0.052084 0.032675 0.019202
## Proportion Explained 0.006758 0.005776 0.004705 0.004371 0.002742 0.001612
## Cumulative Proportion 0.980794 0.986570 0.991275 0.995646 0.998388 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5
## Eigenvalue 0.6575 0.4853 0.4216 0.3515 0.2164
## Proportion Explained 0.3084 0.2276 0.1977 0.1648 0.1015
## Cumulative Proportion 0.3084 0.5360 0.7337 0.8985 1.0000
anova(Plant_CCA_Chem17.3)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + P2O5 + SiO2 + Al2O3 + Co, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 5 2.1322 1.5257 0.001 ***
## Residual 35 9.7830
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.5257, p value of 0.001
Plant_CCA_Chem17.4 <- cca(PlantSpecies ~ pH + P2O5 + Al2O3 +
Co + V, data = PlantChemistry)
plot(Plant_CCA_Chem17.4)
summary(Plant_CCA_Chem17.4)
##
## Call:
## cca(formula = PlantSpecies ~ pH + P2O5 + Al2O3 + Co + V, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 1.973 0.1656
## Unconstrained 9.942 0.8344
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CA1 CA2
## Eigenvalue 0.64175 0.48592 0.3849 0.26070 0.2002 0.71571 0.70160
## Proportion Explained 0.05386 0.04078 0.0323 0.02188 0.0168 0.06007 0.05888
## Cumulative Proportion 0.05386 0.09464 0.1269 0.14882 0.1656 0.22569 0.28457
## CA3 CA4 CA5 CA6 CA7 CA8 CA9
## Eigenvalue 0.65684 0.57067 0.55604 0.5159 0.50650 0.4575 0.41689
## Proportion Explained 0.05513 0.04789 0.04667 0.0433 0.04251 0.0384 0.03499
## Cumulative Proportion 0.33970 0.38760 0.43426 0.4776 0.52007 0.5585 0.59346
## CA10 CA11 CA12 CA13 CA14 CA15 CA16
## Eigenvalue 0.40610 0.38445 0.36126 0.31604 0.29933 0.27681 0.26161
## Proportion Explained 0.03408 0.03227 0.03032 0.02652 0.02512 0.02323 0.02196
## Cumulative Proportion 0.62754 0.65981 0.69013 0.71665 0.74178 0.76501 0.78696
## CA17 CA18 CA19 CA20 CA21 CA22 CA23
## Eigenvalue 0.25637 0.24353 0.21221 0.20741 0.19673 0.18715 0.17804
## Proportion Explained 0.02152 0.02044 0.01781 0.01741 0.01651 0.01571 0.01494
## Cumulative Proportion 0.80848 0.82892 0.84673 0.86413 0.88065 0.89635 0.91129
## CA24 CA25 CA26 CA27 CA28 CA29
## Eigenvalue 0.15415 0.13994 0.13285 0.114326 0.110433 0.090925
## Proportion Explained 0.01294 0.01174 0.01115 0.009595 0.009268 0.007631
## Cumulative Proportion 0.92423 0.93598 0.94713 0.956721 0.965989 0.973621
## CA30 CA31 CA32 CA33 CA34 CA35
## Eigenvalue 0.081669 0.068771 0.055804 0.050092 0.039348 0.018632
## Proportion Explained 0.006854 0.005772 0.004683 0.004204 0.003302 0.001564
## Cumulative Proportion 0.980475 0.986246 0.990930 0.995134 0.998436 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5
## Eigenvalue 0.6417 0.4859 0.3849 0.2607 0.2002
## Proportion Explained 0.3252 0.2462 0.1950 0.1321 0.1014
## Cumulative Proportion 0.3252 0.5714 0.7665 0.8986 1.0000
anova(Plant_CCA_Chem17.4)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + P2O5 + Al2O3 + Co + V, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 5 1.9734 1.3895 0.001 ***
## Residual 35 9.9417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.3895, p value of 0.003
Plant_CCA_Chem17.5 <- cca(PlantSpecies ~ pH + P2O5 + Al2O3 +
Co + V + CaO + K2O + Mo, data = PlantChemistry)
plot(Plant_CCA_Chem17.5)
summary(Plant_CCA_Chem17.5)
##
## Call:
## cca(formula = PlantSpecies ~ pH + P2O5 + Al2O3 + Co + V + CaO + K2O + Mo, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.000
## Constrained 3.241 0.272
## Unconstrained 8.674 0.728
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.69544 0.57931 0.48814 0.45499 0.35940 0.29098 0.21104
## Proportion Explained 0.05837 0.04862 0.04097 0.03819 0.03016 0.02442 0.01771
## Cumulative Proportion 0.05837 0.10699 0.14795 0.18614 0.21630 0.24072 0.25844
## CCA8 CA1 CA2 CA3 CA4 CA5 CA6
## Eigenvalue 0.16157 0.68459 0.67236 0.62800 0.5350 0.47006 0.46719
## Proportion Explained 0.01356 0.05746 0.05643 0.05271 0.0449 0.03945 0.03921
## Cumulative Proportion 0.27200 0.32945 0.38588 0.43859 0.4835 0.52293 0.56214
## CA7 CA8 CA9 CA10 CA11 CA12 CA13
## Eigenvalue 0.42912 0.40485 0.38308 0.36621 0.32985 0.32237 0.30240
## Proportion Explained 0.03601 0.03398 0.03215 0.03073 0.02768 0.02706 0.02538
## Cumulative Proportion 0.59816 0.63214 0.66429 0.69502 0.72271 0.74976 0.77514
## CA14 CA15 CA16 CA17 CA18 CA19 CA20
## Eigenvalue 0.26251 0.2526 0.22326 0.21264 0.19856 0.19107 0.18689
## Proportion Explained 0.02203 0.0212 0.01874 0.01785 0.01666 0.01604 0.01568
## Cumulative Proportion 0.79717 0.8184 0.83711 0.85496 0.87162 0.88766 0.90334
## CA21 CA22 CA23 CA24 CA25 CA26
## Eigenvalue 0.16465 0.15954 0.13505 0.11945 0.118224 0.097235
## Proportion Explained 0.01382 0.01339 0.01133 0.01002 0.009922 0.008161
## Cumulative Proportion 0.91716 0.93055 0.94189 0.95191 0.961833 0.969994
## CA27 CA28 CA29 CA30 CA31 CA32
## Eigenvalue 0.085243 0.075691 0.072575 0.056439 0.047289 0.020293
## Proportion Explained 0.007154 0.006352 0.006091 0.004737 0.003969 0.001703
## Cumulative Proportion 0.977148 0.983500 0.989591 0.994328 0.998297 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.6954 0.5793 0.4881 0.4550 0.3594 0.29098 0.21104
## Proportion Explained 0.2146 0.1788 0.1506 0.1404 0.1109 0.08978 0.06512
## Cumulative Proportion 0.2146 0.3933 0.5440 0.6843 0.7952 0.88503 0.95015
## CCA8
## Eigenvalue 0.16157
## Proportion Explained 0.04985
## Cumulative Proportion 1.00000
anova(Plant_CCA_Chem17.5)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + P2O5 + Al2O3 + Co + V + CaO + K2O + Mo, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 8 3.2409 1.4945 0.001 ***
## Residual 32 8.6743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4945, p value of 0.001
Plant_CCA_Chem17.6 <- cca(PlantSpecies ~ pH + P2O5 + Al2O3 +
Co + V + CaO + K2O + Mo + Ni, data = PlantChemistry)
plot(Plant_CCA_Chem17.6)
summary(Plant_CCA_Chem17.6)
##
## Call:
## cca(formula = PlantSpecies ~ pH + P2O5 + Al2O3 + Co + V + CaO + K2O + Mo + Ni, data = PlantChemistry)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 11.915 1.0000
## Constrained 3.582 0.3007
## Unconstrained 8.333 0.6993
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.70612 0.60199 0.50334 0.45538 0.39747 0.31494 0.23676
## Proportion Explained 0.05926 0.05052 0.04224 0.03822 0.03336 0.02643 0.01987
## Cumulative Proportion 0.05926 0.10979 0.15203 0.19025 0.22361 0.25004 0.26991
## CCA8 CCA9 CA1 CA2 CA3 CA4 CA5
## Eigenvalue 0.2109 0.15555 0.68332 0.64598 0.60008 0.51222 0.46926
## Proportion Explained 0.0177 0.01306 0.05735 0.05422 0.05036 0.04299 0.03938
## Cumulative Proportion 0.2876 0.30067 0.35801 0.41223 0.46259 0.50558 0.54496
## CA6 CA7 CA8 CA9 CA10 CA11 CA12
## Eigenvalue 0.45784 0.42703 0.39541 0.38274 0.36617 0.32328 0.30513
## Proportion Explained 0.03843 0.03584 0.03319 0.03212 0.03073 0.02713 0.02561
## Cumulative Proportion 0.58339 0.61923 0.65241 0.68454 0.71527 0.74240 0.76801
## CA13 CA14 CA15 CA16 CA17 CA18 CA19
## Eigenvalue 0.26272 0.25444 0.22374 0.21756 0.20596 0.19631 0.19021
## Proportion Explained 0.02205 0.02135 0.01878 0.01826 0.01729 0.01648 0.01596
## Cumulative Proportion 0.79006 0.81141 0.83019 0.84845 0.86573 0.88221 0.89817
## CA20 CA21 CA22 CA23 CA24 CA25 CA26
## Eigenvalue 0.16638 0.16138 0.1430 0.12437 0.118829 0.115489 0.095605
## Proportion Explained 0.01396 0.01354 0.0120 0.01044 0.009973 0.009693 0.008024
## Cumulative Proportion 0.91213 0.92568 0.9377 0.94812 0.958091 0.967784 0.975808
## CA27 CA28 CA29 CA30 CA31
## Eigenvalue 0.082864 0.075441 0.060130 0.04933 0.020485
## Proportion Explained 0.006954 0.006331 0.005047 0.00414 0.001719
## Cumulative Proportion 0.982762 0.989094 0.994140 0.99828 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7061 0.6020 0.5033 0.4554 0.3975 0.31494 0.23676
## Proportion Explained 0.1971 0.1680 0.1405 0.1271 0.1109 0.08791 0.06609
## Cumulative Proportion 0.1971 0.3651 0.5056 0.6328 0.7437 0.83161 0.89770
## CCA8 CCA9
## Eigenvalue 0.21092 0.15555
## Proportion Explained 0.05888 0.04342
## Cumulative Proportion 0.95658 1.00000
anova(Plant_CCA_Chem17.6)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = PlantSpecies ~ pH + P2O5 + Al2O3 + Co + V + CaO + K2O + Mo + Ni, data = PlantChemistry)
## Df ChiSquare F Pr(>F)
## Model 9 3.5825 1.4809 0.001 ***
## Residual 31 8.3327
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.4809, p value of 0.001
#Out of multiple CCAs with different combinations of different substrate variables, #the one with the highest F statistic is Plant_CCA_Chem17.1, with an F statistic of 1.6861. #However, for this CCA, the arrow for SiO2 is very short in comparison with that #of the other variables in the CCA. A CCA that may better represent the variety of #variables that significantly influence the plant species’ presence is Plant_CCA_Chem17.2, #which has a lower F statistic of 1.5341. (but the same p value of 0.001). From both a #visual standpoint as well as a statistical standpoint, Plant_CCA_Chem17.2 is the most #appropriate CCA for the substrate and plant data for all of the six study sites.
#Now to construct a more visually appropriate graph for Plant_CCA_Chem17.2.
options(max.print=1000000)
Plant_CCA_Chem17.2$CCA$v
## CCA1 CCA2 CCA3 CCA4
## Agrostis.spp. -0.833080122 0.77476402 0.400787415 -1.98063629
## Agrostis.canina 0.623736881 -0.46438794 -1.298282562 -3.15483736
## Alchemilla.mollis 0.178176796 -0.72191610 -1.028183836 -0.02297314
## Alopercus.pratensis 1.279951178 0.99613927 2.971890285 -0.16351709
## Angelica.sylvestris 0.031878731 0.13617742 -0.868565767 -0.86810661
## Anthoxanthum.odoratum -1.640852883 -1.52703922 0.296935753 -0.63729365
## Anthyllis.vulneraria 0.783926509 0.48491353 -1.530503304 -1.12099453
## Aphanes.arvensis -0.307099932 0.43874962 -1.545838694 0.33295947
## Arrhenatherum.elatius -1.013895210 -0.75849547 0.053288382 -0.28876454
## Atrichum.undulatum -1.388352612 0.67719956 -0.065136690 0.89702211
## Avenula.pratensis 1.279951178 0.99613927 2.971890285 -0.16351709
## Bellis.perennis 0.105177487 -0.67710212 0.429256459 0.40444994
## Betula.pubescens -1.185831979 -2.53219279 -1.381739465 -0.16674097
## Blackstonia.perfoliata 1.769466831 -0.86940805 -2.242662836 0.69770099
## Brachythecium.albicans 0.634139460 0.07018656 -0.166147894 0.05204666
## Brachythecium.glareosum -2.189892874 4.10955505 4.019324833 0.40997601
## Brachythecium.mildeanum -1.160522998 1.57717917 -0.793189431 0.16805722
## Brachythecium.rutabulum -0.350145061 0.96203288 -0.238898299 0.08385736
## Briza.media 0.055797345 0.18123720 -3.958888181 1.61026416
## Bromus.hordeaceus 0.521438450 0.61384462 -0.609349053 -1.83188227
## Bryum.c.f..caespiticium -1.185831979 -2.53219279 -1.381739465 -0.16674097
## Bryum.c.f..pallescens 0.764982845 -0.86393569 1.289728482 1.11391075
## Bryum.capillare -1.531024245 -1.70040531 0.096179833 -1.78248657
## Bryum.spp. 0.935104193 0.84117179 0.157205499 -1.54421157
## Calliergonella.cupsidata -0.400059507 1.26420703 -0.145743890 0.36198794
## Campanula.rotundifolia 1.169154914 0.32549492 -1.718322281 -0.98642858
## Carex.distans 1.242383549 0.74328727 0.002730858 -3.31939345
## Carex.flacca 1.400937262 -0.32904152 -1.474981116 0.22371309
## Carex.panicea 1.364728010 0.68839514 -0.505567398 -2.70701024
## Carlina.vulgaris 0.942571342 -0.42551407 1.406583994 1.28411027
## Centaurea.nigra -0.288081831 1.32285850 -0.551787759 1.31912711
## Centaurium.erythraea 1.713598834 -0.78198126 -1.819484239 0.82181019
## Centaurium.littorale 1.125986364 0.22223000 -0.260824928 -0.77839848
## Centaurium.pulchellum 1.199355956 1.33989911 -0.213426394 -5.55733648
## Cerastium.fontanum -0.320967668 -0.67316444 -0.385280919 -0.88151912
## Chamaenerion.angustifolium -1.099102177 2.81210535 -0.466857190 1.97350650
## Cirriphyllum.piliferum -1.185831979 -2.53219279 -1.381739465 -0.16674097
## Cirsium.arvense 0.111237543 0.24488928 -0.911944307 -1.55082169
## Cirsium.palustre -1.722411147 3.55350518 2.096675395 1.08006050
## Crepis.capillaris -1.975068305 -1.70529859 0.747900661 -0.82972107
## Cynosurus.cristatus 1.109363682 -0.20823030 -1.975595110 0.17623736
## Dactylis.glomerata -1.036251975 1.05550070 1.554987482 -2.01205703
## Danthonia.decumbens -1.431535800 1.11102212 -1.175216805 0.19509147
## Daucus.carrota 0.223408027 -0.11790409 0.019768529 0.07333142
## Dicranella.spp. 0.625261891 -1.22915209 1.505466433 1.63257330
## Dicranum.scoparium -1.527751078 -0.19806281 -0.718763163 0.09218063
## Epilobium.montanum -1.396800404 2.80643944 2.286363152 -0.38885228
## Equisetum.arvense 0.316897378 -0.44669613 -1.117460659 -0.28322111
## Equisteum.variegatum 1.169154914 0.32549492 -1.718322281 -0.98642858
## Erigeron.acer 0.958365521 0.23632282 3.751097218 2.47355761
## Euphrasia.agg 0.672042371 -0.37879503 0.283717102 1.05209292
## Festuca.ovina 0.818642114 -0.02297193 1.523853909 0.34059699
## Festuca.rubra 0.956935273 0.02390331 0.349686707 0.16212060
## Festuca.rubra.agg. -1.531024245 -1.70040531 0.096179833 -1.78248657
## Filipendula.ulmaria -1.718829290 3.25451740 3.077041554 0.04654305
## Fissidens.adianthoides -1.185831979 -2.53219279 -1.381739465 -0.16674097
## Fissidens.dubius 0.046946624 -0.63470762 1.177790534 0.06796993
## Fissidens.exilis 0.625261891 -1.22915209 1.505466433 1.63257330
## Fragaria.vesca -1.573825413 -1.61080505 0.072527183 -0.64634697
## Galium.aparine -0.494671959 4.39546143 1.282530813 1.63868223
## Galium.arvense 0.623736881 -0.46438794 -1.298282562 -3.15483736
## Galium.saxatile 0.735901683 -0.34324138 1.652174409 1.80856910
## Galium.verum 0.337942726 0.11415871 -2.297078854 0.94745631
## Geum.urbanum -1.267581655 1.31842184 2.228051622 -3.13465442
## Glechoma.hederacea -1.267581655 1.31842184 2.228051622 -3.13465442
## Helicotrichon.spp. 0.521438450 0.61384462 -0.609349053 -1.83188227
## Heracleum.sphondylium -1.083558775 2.30584570 -0.647983117 1.39815864
## Hieracium.spp. -0.148281678 -0.34123906 -0.038334084 0.55013364
## Holcus.spp. 0.694655496 0.18637740 0.559062003 0.10260998
## Holcus.lanatus -0.963422386 -1.04204723 0.164124055 -0.96376439
## Holcus.mollis -0.511376028 1.10417216 -1.961705909 0.18543989
## Homalothecium.lutescens 0.767904594 -0.26445428 1.729804754 1.69256403
## Hylocomium.splendens -1.460154411 -1.14584364 -0.500126392 -0.24160739
## Hypericum.perforatum -0.722445056 -1.34517078 0.941873795 -0.13543294
## Hypnum.cupressiforme 0.399480782 -0.50804386 -0.398806987 0.42600312
## Hypnum.imponens -1.145907655 1.99277136 -2.519719205 1.64586469
## Hypnum.jutlandicum -1.641654725 -1.31881755 0.203743238 -0.36822861
## Hypochaeris.radicata 0.535466880 1.00305737 0.619525780 -1.56410663
## Kindbergia.praelonga -0.983390075 1.71077787 -0.098089945 -0.45647388
## Lathyrus.pratensis 0.130793822 0.96569181 -1.388656511 0.70889377
## Leontodon.hispidus 0.385148562 -0.26113515 1.249934727 1.11788058
## Leontodon.saxatilis 0.697853140 -0.60652740 1.554900782 1.77483196
## Leucanthemum.vulgare -0.268621258 -0.89206886 0.813737509 0.07539455
## Linum.catharticum 0.685726919 -0.27580862 1.770890757 0.82684832
## Lolium.perenne 0.578476817 0.57312437 -0.501934958 -1.64830936
## Lophocolea.semiteres -1.185831979 -2.53219279 -1.381739465 -0.16674097
## Lotus.corniculatus 0.218125783 -0.56521043 0.013607226 -0.14511039
## Luzula.multiflora -1.099102177 2.81210535 -0.466857190 1.97350650
## Lysimachia.maritima 1.197641504 1.27796640 -0.185530640 -5.37535804
## Medicago.lupulina 1.141730783 0.25056309 -1.308846185 -0.64671266
## Myosotis.arvensis 0.419584709 1.21775750 1.585051016 1.29470125
## Ononis.repens 1.442056047 -0.08231158 -1.018238804 -0.07657379
## Oxalis.acetosella -1.267581655 1.31842184 2.228051622 -3.13465442
## Pastinaca.sativa 0.931446881 0.44199374 4.105016115 2.55933891
## Pentaglottis.sempervirens -0.494671959 4.39546143 1.282530813 1.63868223
## Pillosella.officinarum 0.949684496 -0.72491251 0.456036323 1.15078433
## Plantago.coronopus 1.193081641 0.68244594 0.049398974 -3.00525442
## Plantago.lanceolata -0.542313380 -0.73938235 0.551982366 0.24469166
## Pleurozium.schreberi -1.721781072 -0.90127637 -0.025758130 -0.14408076
## Poa.annua 1.217446783 0.44379435 -0.056941844 -1.73780802
## Poa.spp. 0.521438450 0.61384462 -0.609349053 -1.83188227
## Polytrichum.commune -1.145907655 1.99277136 -2.519719205 1.64586469
## Polytrichum.commune.agg. -1.145907655 1.99277136 -2.519719205 1.64586469
## Polytrichum.formosum -1.531024245 -1.70040531 0.096179833 -1.78248657
## Potentilla.reptans 1.370505144 -0.29633174 -1.852666677 0.15147582
## Prunella.vulgaris 0.338330990 -0.45193506 1.609435819 1.17986570
## Pseudoscleropidum.purum -0.957661091 -1.23086522 -0.187505047 -0.14377789
## Pteridium.aquilinum -1.690259976 2.93288216 2.360827035 -0.19905645
## Ranunculus.acris -1.677603048 -1.45770623 0.336583809 -0.17946229
## Ranunculus.repens -1.230805088 2.97342261 0.344083951 1.58194562
## Reseda.lutea 0.931446881 0.44199374 4.105016115 2.55933891
## Rhinanthus.minor -1.431535800 1.11102212 -1.175216805 0.19509147
## Rhizomnium.punctatum -1.099102177 2.81210535 -0.466857190 1.97350650
## Rhytidadelphus.squarrosus -0.895813673 0.54049376 0.324865348 0.58597339
## Rhytidadelphus.triquetris -1.785526388 -1.56296838 0.477426317 -0.67499674
## Rubus.fruticosus -1.938877651 2.44911809 2.669058758 0.26579677
## Sanguisorba.minor.spp.minor 0.498277132 0.09012796 -1.147650394 0.46720541
## Saniona.uncinata -1.122504916 2.40243835 -1.493288197 1.80968559
## Sedum.anglicum 0.931446881 0.44199374 4.105016115 2.55933891
## Senecio.jacobaea 0.349807623 -0.05403574 -0.818766444 -0.92742770
## Senecio.vulgaris 0.697853140 -0.60652740 1.554900782 1.77483196
## Sonchus.arvensis -1.113672498 2.19399669 -0.823393147 1.51847659
## Stachys.sylvatica 0.400956839 -0.59315202 -1.163233199 -1.58890525
## Stellaria.apetala 1.678860802 -0.41545942 -2.138558758 0.02579996
## Taraxacum.agg. 0.774485306 -0.22946489 1.799960996 1.81257379
## Thuidium.tamariscinum -1.292163598 0.60662132 -0.562254392 0.72358486
## Thymus.polytrichus 1.191308621 0.15810838 0.586529511 -0.76269914
## Trichostomum.crispulum 1.079014407 -0.60623429 0.778397912 0.67614722
## Trifolium.campestre 0.576967129 0.20824021 -0.842771149 -0.11649275
## Trifolium.dubium 1.060657569 -0.69034756 1.172171557 1.21528484
## Trifolium.pratense -0.007538585 0.55055435 1.641853586 1.28342778
## Trifolium.repens -0.388377240 1.95447895 -0.368007265 0.82870769
## Trisetum.flavescens 1.695561874 -0.81802194 -2.127534158 0.71875366
## Tussilago.farfara 0.286557869 -0.36863234 -0.831103604 -0.20164877
## Urtica.diocia -0.858133917 2.61939473 0.217499709 0.98558468
## Veronica.officinalis -1.185831979 -2.53219279 -1.381739465 -0.16674097
## Vicia.sativa -1.099102177 2.81210535 -0.466857190 1.97350650
## Viola.riviniana -2.189892874 4.10955505 4.019324833 0.40997601
## Weissia.controversa -1.185831979 -2.53219279 -1.381739465 -0.16674097
## Zygodon.stirtonii 0.459671361 -0.96891938 2.090277964 1.63877389
## CCA5
## Agrostis.spp. 0.99520306
## Agrostis.canina -0.26905251
## Alchemilla.mollis 1.73629747
## Alopercus.pratensis 6.22636864
## Angelica.sylvestris 1.10283630
## Anthoxanthum.odoratum -0.05871122
## Anthyllis.vulneraria -0.46190020
## Aphanes.arvensis 0.53458147
## Arrhenatherum.elatius -0.23392101
## Atrichum.undulatum -0.20861279
## Avenula.pratensis 6.22636864
## Bellis.perennis 0.45071929
## Betula.pubescens 0.77969182
## Blackstonia.perfoliata -0.19041803
## Brachythecium.albicans -0.33570227
## Brachythecium.glareosum -0.89039348
## Brachythecium.mildeanum 0.53676567
## Brachythecium.rutabulum 0.18774468
## Briza.media 0.22599456
## Bromus.hordeaceus -0.50905023
## Bryum.c.f..caespiticium 0.77969182
## Bryum.c.f..pallescens 2.97769377
## Bryum.capillare -0.16242496
## Bryum.spp. -0.86737598
## Calliergonella.cupsidata -0.06555227
## Campanula.rotundifolia 0.29676532
## Carex.distans -1.52928809
## Carex.flacca -0.16523909
## Carex.panicea -0.69966332
## Carlina.vulgaris 0.55496733
## Centaurea.nigra -0.23691457
## Centaurium.erythraea -0.37302350
## Centaurium.littorale -1.00081643
## Centaurium.pulchellum -1.57790389
## Cerastium.fontanum 0.25045868
## Chamaenerion.angustifolium 0.15220789
## Cirriphyllum.piliferum 0.77969182
## Cirsium.arvense 0.65953556
## Cirsium.palustre -0.44356432
## Crepis.capillaris 0.01554101
## Cynosurus.cristatus 0.25075307
## Dactylis.glomerata 1.11036577
## Danthonia.decumbens 0.89617366
## Daucus.carrota -1.16323337
## Dicranella.spp. -1.38551017
## Dicranum.scoparium 0.02772461
## Epilobium.montanum -0.61336395
## Equisetum.arvense 1.42736079
## Equisteum.variegatum 0.29676532
## Erigeron.acer -2.78136591
## Euphrasia.agg -0.27760197
## Festuca.ovina -1.31487672
## Festuca.rubra -1.14306259
## Festuca.rubra.agg. -0.16242496
## Filipendula.ulmaria -0.59511785
## Fissidens.adianthoides 0.77969182
## Fissidens.dubius -0.99363999
## Fissidens.exilis -1.38551017
## Fragaria.vesca 0.11877895
## Galium.aparine -3.49900962
## Galium.arvense -0.26905251
## Galium.saxatile -2.33307568
## Galium.verum 0.02497579
## Geum.urbanum 1.88829689
## Glechoma.hederacea 1.88829689
## Helicotrichon.spp. -0.50905023
## Heracleum.sphondylium 0.19566678
## Hieracium.spp. -1.50073660
## Holcus.spp. -0.47045517
## Holcus.lanatus -0.31041386
## Holcus.mollis 0.23766216
## Homalothecium.lutescens -1.82957895
## Hylocomium.splendens 0.12462091
## Hypericum.perforatum -0.78298360
## Hypnum.cupressiforme 0.50753234
## Hypnum.imponens 0.29761145
## Hypnum.jutlandicum -0.41745852
## Hypochaeris.radicata -1.06908413
## Kindbergia.praelonga 0.76698107
## Lathyrus.pratensis 0.31493430
## Leontodon.hispidus -1.78000228
## Leontodon.saxatilis -0.86627665
## Leucanthemum.vulgare -0.87801413
## Linum.catharticum 0.29825790
## Lolium.perenne -0.54473696
## Lophocolea.semiteres 0.77969182
## Lotus.corniculatus -0.66798206
## Luzula.multiflora 0.15220789
## Lysimachia.maritima -1.57743237
## Medicago.lupulina 0.07156511
## Myosotis.arvensis -0.02313681
## Ononis.repens 1.13353806
## Oxalis.acetosella 1.88829689
## Pastinaca.sativa -2.92950015
## Pentaglottis.sempervirens -3.49900962
## Pillosella.officinarum -0.58529408
## Plantago.coronopus -1.73163462
## Plantago.lanceolata -0.76772706
## Pleurozium.schreberi -0.18081937
## Poa.annua -1.15530174
## Poa.spp. -0.50905023
## Polytrichum.commune 0.29761145
## Polytrichum.commune.agg. 0.29761145
## Polytrichum.formosum -0.16242496
## Potentilla.reptans 0.13187266
## Prunella.vulgaris -1.44964255
## Pseudoscleropidum.purum -0.38523892
## Pteridium.aquilinum 0.16936197
## Ranunculus.acris -0.56943347
## Ranunculus.repens -0.06966448
## Reseda.lutea -2.92950015
## Rhinanthus.minor 0.89617366
## Rhizomnium.punctatum 0.15220789
## Rhytidadelphus.squarrosus 0.41320259
## Rhytidadelphus.triquetris 0.06811933
## Rubus.fruticosus -0.47287216
## Sanguisorba.minor.spp.minor -0.16609799
## Saniona.uncinata 0.22490967
## Sedum.anglicum -2.92950015
## Senecio.jacobaea 0.61362362
## Senecio.vulgaris -0.86627665
## Sonchus.arvensis 0.56993797
## Stachys.sylvatica 0.73362248
## Stellaria.apetala 0.18206487
## Taraxacum.agg. -1.79125457
## Thuidium.tamariscinum 0.17821281
## Thymus.polytrichus 1.43292393
## Trichostomum.crispulum -0.34595385
## Trifolium.campestre -0.40965704
## Trifolium.dubium 3.58136646
## Trifolium.pratense -1.65042817
## Trifolium.repens 0.02686566
## Trisetum.flavescens -0.23985093
## Tussilago.farfara 1.01682289
## Urtica.diocia -1.41796889
## Veronica.officinalis 0.77969182
## Vicia.sativa 0.15220789
## Viola.riviniana -0.89039348
## Weissia.controversa 0.77969182
## Zygodon.stirtonii -1.88634940
#The “v values” show the positions of individual species on the graph, with the CCA1 value #representing the x axis and the CCA2 value representing the y axis, in the case of the #graph created in R.
#The "v values" show the positions of individual species on the graph, with the CCA1 value
#representing the x axis and the CCA2 value representing the y axis, in the case of the
#graph created in R.
plot(Plant_CCA_Chem17.2, choices = c(1,2), display = c("wa", "bp"), xlim = c(-6.5, 2.5), ylim = c(-4.5, 6.5))
#Increasing the xlim and ylim to give more room for writing on graph
points(x = 0.09178966, y = -1.264581673, pch = 15, col = "black")
#This point represents Agrostis canina
text('A. canina', x = 3.5, y = -1.75, cex = 0.88, pos = 2, col = "black")
#Adding text to this point.
points(x = -0.494671959, y = 4.39546143, pch = 15, col = "black")
#This point represents Galium aparine.
text('G. aparine', x = 3.7, y = 4.39546143, cex = 0.88, pos = 2, col = "black")
#Adding text to the point for Galium aparine.
points(x = -1.036251975, y = 1.05550070, pch = 15, col = "black")
#This point represents Dactylis glomerata.
text('D. glomerata', x = -1.036251975, y = 0.35, cex = 0.88, pos = 2, col = "black")
#Adding text to this point.
points(x = 0.634139460, y = 0.07018656, pch = 15, col = "black")
#This point represents Brachythecium albicans
text('B. albicans', x = 8, y = -1, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = -1.267581655, y = 1.31842184, pch = 15, col = "black")
#This point represents Geum urbanum.
text('G. urbanum', x = -1.267581655, y = 1.31842184, cex = 0.88, pos = 2, col = "black")
#Adding text to this point.
points(x = -0.858133917, y = 2.61939473, pch = 15, col = "black")
#This point represents Urtica dioica.
text('U. dioica', x = -3, y = 3, cex = 0.88, pos = 2, col = "black")
#Adding text to this point.
####Barrow Data####
urlfile3 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/BarrowPlantSpecies.csv'
BarrowPS <-read.csv(urlfile3)
urlfile4 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/BARROW_PLANT_CHEMISTRY_MG_KG.csv'
BarrowPC <-read.csv(urlfile4)
col_sums_Barrow <- apply(X = BarrowPS, MARGIN = 2, FUN = sum);
rm_Barrow <- which(is.na(col_sums) == TRUE);
BarrowPS <- BarrowPS[, -rm_Barrow];
print(PlantSpecies)
## Agrostis.spp. Agrostis.canina Alchemilla.mollis Alopercus.pratensis
## 1 0 0 0 0
## 2 9 0 0 0
## 3 1 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 1 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 14 0 0 0
## 11 5 3 0 0
## 12 3 0 1 0
## 13 32 0 0 0
## 14 39 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 27
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Angelica.sylvestris Anthoxanthum.odoratum Anthyllis.vulneraria
## 1 0 81 0
## 2 0 18 0
## 3 0 8 0
## 4 0 82 0
## 5 0 55 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 1 0 0
## 13 1 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 4
## 25 0 0 0
## 26 0 0 127
## 27 0 0 0
## 28 0 0 32
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 70
## 37 0 0 189
## 38 0 7 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Aphanes.arvensis Arrhenatherum.elatius Atrichum.undulatum Avenula.pratensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 2 0 0
## 5 0 42 10 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 3 10 0
## 9 0 1 0 0
## 10 11 0 0 0
## 11 0 0 0 0
## 12 0 4 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 1 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 11
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 8 0 0
## 30 1 0 0 0
## 31 5 0 0 0
## 32 0 0 0 0
## 33 0 5 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Bellis.perennis Betula.pubescens Blackstonia.perfoliata
## 1 1 0 0
## 2 0 0 0
## 3 8 12 0
## 4 16 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 11 0 0
## 19 0 0 0
## 20 0 0 0
## 21 10 0 0
## 22 0 0 0
## 23 0 0 0
## 24 4 0 0
## 25 6 0 0
## 26 1 0 0
## 27 0 0 0
## 28 6 0 0
## 29 0 0 0
## 30 0 0 0
## 31 2 0 2
## 32 0 0 0
## 33 0 0 0
## 34 4 0 0
## 35 0 0 0
## 36 0 0 0
## 37 1 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Brachythecium.albicans Brachythecium.glareosum Brachythecium.mildeanum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 10 10
## 7 0 0 80
## 8 0 0 0
## 9 0 0 0
## 10 0 0 150
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 20
## 24 0 0 0
## 25 30 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Brachythecium.rutabulum Briza.media Bromus.hordeaceus
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 10 0 0
## 7 0 0 0
## 8 0 0 0
## 9 10 0 0
## 10 10 0 0
## 11 0 0 0
## 12 10 0 0
## 13 10 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 30 0 0
## 26 0 138 0
## 27 0 9 0
## 28 0 8 0
## 29 0 0 1
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Bryum.c.f..caespiticium Bryum.c.f..pallescens Bryum.capillare Bryum.spp.
## 1 0 0 0 0
## 2 0 0 10 0
## 3 10 10 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 10
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 60 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 20
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 50 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 60
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Calliergonella.cupsidata Campanula.rotundifolia Carex.distans Carex.flacca
## 1 0 0 0 0
## 2 0 0 0 0
## 3 10 0 0 0
## 4 10 0 0 0
## 5 30 0 0 0
## 6 30 0 0 0
## 7 120 0 0 0
## 8 150 0 0 1
## 9 90 0 0 0
## 10 0 0 0 9
## 11 0 0 0 0
## 12 50 0 0 0
## 13 110 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 23 0
## 19 120 0 0 24
## 20 0 0 0 0
## 21 10 0 0 0
## 22 0 0 0 0
## 23 0 0 52 0
## 24 0 0 0 0
## 25 30 0 0 0
## 26 20 0 0 0
## 27 110 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 10 0 0 120
## 31 0 0 0 40
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 42 0 0
## 39 0 0 0 3
## 40 0 0 0 0
## 41 0 0 0 2
## Carex.panicea Carlina.vulgaris Centaurea.nigra Centaurium.erythraea
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 2 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 11 0 0
## 17 0 5 0 0
## 18 0 4 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 3 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 14
## 32 0 2 0 1
## 33 0 0 0 0
## 34 0 5 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 1 0 0 0
## 38 0 3 0 0
## 39 0 2 1 0
## 40 0 1 0 0
## 41 0 7 0 0
## Centaurium.littorale Centaurium.pulchellum Cerastium.fontanum
## 1 0 0 0
## 2 0 0 6
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 1
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 8 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 1
## 27 0 0 2
## 28 0 0 1
## 29 0 0 0
## 30 0 0 1
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 4 0 0
## 37 0 0 0
## 38 4 0 1
## 39 5 0 0
## 40 0 0 0
## 41 1 0 0
## Chamaenerion.angustifolium Cirriphyllum.piliferum Cirsium.arvense
## 1 0 0 0
## 2 0 0 0
## 3 0 10 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 4 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 3
## 12 0 0 4
## 13 0 0 8
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Cirsium.palustre Crepis.capillaris Cynosurus.cristatus Dactylis.glomerata
## 1 0 4 0 1
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 1 0 0
## 5 0 0 0 0
## 6 4 0 0 0
## 7 0 0 0 0
## 8 3 0 0 0
## 9 0 0 0 0
## 10 0 0 16 12
## 11 0 0 0 1
## 12 0 0 0 0
## 13 0 0 0 4
## 14 0 0 0 54
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 54 0
## 31 0 0 15 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 10
## Danthonia.decumbens Daucus.carrota Dicranella.spp. Dicranum.scoparium
## 1 0 0 0 30
## 2 0 0 0 20
## 3 0 0 0 0
## 4 0 0 0 10
## 5 0 0 0 16
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 50
## 10 1 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 50 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 2 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 1 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Epilobium.montanum Equisetum.arvense Equisteum.variegatum Erigeron.acer
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 8 0 0 0
## 7 1 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 3 0 0 0
## 12 0 49 0 0
## 13 0 4 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 25
## 33 0 0 0 0
## 34 0 0 0 6
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 10 31 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Euphrasia.agg Festuca.ovina Festuca.rubra Festuca.rubra.agg.
## 1 3 0 0 0
## 2 0 0 0 5
## 3 6 0 0 0
## 4 0 0 0 0
## 5 1 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 16 0 0 0
## 17 0 0 0 0
## 18 0 17 0 0
## 19 0 55 0 0
## 20 0 27 0 0
## 21 0 5 0 0
## 22 0 0 0 0
## 23 0 25 0 0
## 24 0 66 0 0
## 25 0 0 19 0
## 26 14 0 1 0
## 27 0 0 39 0
## 28 0 2 2 0
## 29 0 0 54 0
## 30 0 0 42 0
## 31 7 0 19 0
## 32 2 0 22 0
## 33 1 0 41 0
## 34 4 0 44 0
## 35 0 0 10 0
## 36 0 0 0 0
## 37 0 0 4 0
## 38 0 0 2 0
## 39 36 0 44 0
## 40 0 0 0 0
## 41 31 0 33 0
## Filipendula.ulmaria Fissidens.adianthoides Fissidens.dubius Fissidens.exilis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 10 10 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 27 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 3 0 0 0
## 12 3 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 20 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 60
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Fragaria.vesca Galium.aparine Galium.arvense Galium.saxatile Galium.verum
## 1 67 0 0 0 0
## 2 38 0 0 0 0
## 3 65 0 0 0 0
## 4 117 0 0 0 0
## 5 68 0 0 0 0
## 6 0 0 0 0 0
## 7 0 0 0 0 0
## 8 0 0 0 0 0
## 9 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 1 0 0
## 12 0 0 0 0 0
## 13 0 0 0 0 0
## 14 11 0 0 0 0
## 15 0 47 0 0 0
## 16 0 0 0 0 0
## 17 0 0 0 0 0
## 18 0 0 0 0 0
## 19 0 0 0 0 0
## 20 0 0 0 1 0
## 21 0 0 0 0 0
## 22 0 0 0 0 0
## 23 0 0 0 0 0
## 24 0 0 0 0 0
## 25 0 0 0 0 87
## 26 0 0 0 0 88
## 27 0 0 0 0 0
## 28 0 0 0 0 1
## 29 0 0 0 0 0
## 30 0 0 0 0 0
## 31 0 0 0 0 0
## 32 0 0 0 0 0
## 33 0 0 0 0 2
## 34 0 0 0 0 0
## 35 0 0 0 0 0
## 36 0 0 0 0 0
## 37 0 0 0 0 0
## 38 0 0 0 0 0
## 39 0 0 0 0 0
## 40 0 0 0 0 0
## 41 0 0 0 0 0
## Geum.urbanum Glechoma.hederacea Helicotrichon.spp. Heracleum.sphondylium
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 8
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 1
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 48 5 0 0
## 15 0 0 0 1
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 1 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Hieracium.spp. Holcus.spp. Holcus.lanatus Holcus.mollis
## 1 0 0 30 0
## 2 0 0 80 0
## 3 0 0 0 0
## 4 0 0 28 0
## 5 0 0 36 0
## 6 0 5 0 0
## 7 0 14 0 0
## 8 0 36 0 0
## 9 0 0 0 143
## 10 0 0 0 1
## 11 0 0 0 56
## 12 0 0 0 17
## 13 0 0 0 4
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 42 0 0
## 17 0 0 0 0
## 18 0 40 8 0
## 19 0 58 3 0
## 20 0 70 0 0
## 21 0 23 0 0
## 22 0 0 0 0
## 23 0 13 0 0
## 24 0 0 0 0
## 25 0 55 5 0
## 26 0 0 1 0
## 27 0 50 25 0
## 28 1 3 2 0
## 29 0 169 0 0
## 30 0 10 0 0
## 31 0 30 0 0
## 32 0 0 0 0
## 33 0 82 5 10
## 34 0 4 0 0
## 35 0 47 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 18 0
## 39 0 23 0 0
## 40 0 78 0 0
## 41 0 10 0 0
## Homalothecium.lutescens Hylocomium.splendens Hypericum.perforatum
## 1 0 50 11
## 2 0 80 24
## 3 0 110 2
## 4 0 160 4
## 5 0 90 1
## 6 0 0 0
## 7 0 0 0
## 8 0 1 0
## 9 0 90 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 160 0 0
## 21 10 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 22
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Hypnum.cupressiforme Hypnum.imponens Hypnum.jutlandicum Hypochaeris.radicata
## 1 0 0 0 0
## 2 10 0 20 0
## 3 60 0 0 0
## 4 10 0 0 0
## 5 20 0 120 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 20 60 0 0
## 10 50 0 10 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 60 0 0 0
## 17 150 0 0 0
## 18 60 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 1
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 160 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Kindbergia.praelonga Lathyrus.pratensis Leontodon.hispidus
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 10 0 0
## 7 60 0 0
## 8 0 30 0
## 9 30 0 0
## 10 10 18 0
## 11 0 0 0
## 12 0 0 0
## 13 50 0 0
## 14 50 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 18
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 30
## 29 0 0 0
## 30 0 42 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 16
## 40 0 0 2
## 41 0 0 43
## Leontodon.saxatilis Leucanthemum.vulgare Linum.catharticum Lolium.perenne
## 1 0 20 0 0
## 2 0 15 0 0
## 3 0 0 0 0
## 4 0 7 0 0
## 5 0 1 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 5 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 1 0 0 0
## 25 0 12 0 0
## 26 0 0 0 0
## 27 0 1 0 0
## 28 0 3 0 0
## 29 0 2 0 85
## 30 0 0 0 2
## 31 0 0 0 0
## 32 0 2 0 2
## 33 0 0 0 5
## 34 0 24 0 0
## 35 0 2 0 0
## 36 0 0 2 0
## 37 0 2 0 0
## 38 0 1 0 0
## 39 0 6 1 0
## 40 0 0 6 0
## 41 0 0 6 0
## Lophocolea.semiteres Lotus.corniculatus Luzula.multiflora
## 1 0 69 0
## 2 0 120 0
## 3 10 92 0
## 4 0 1 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 1
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 12 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 220 0
## 19 0 19 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 67 0
## 26 0 18 0
## 27 0 31 0
## 28 0 12 0
## 29 0 140 0
## 30 0 0 0
## 31 0 14 0
## 32 0 2 0
## 33 0 98 0
## 34 0 17 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 57 0
## 40 0 0 0
## 41 0 20 0
## Lysimachia.maritima Medicago.lupulina Myosotis.arvensis Ononis.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 2 0
## 9 0 0 0 0
## 10 0 0 0 2
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 3 0
## 16 0 0 0 0
## 17 0 0 1 0
## 18 0 5 0 0
## 19 0 0 0 0
## 20 0 0 6 2
## 21 0 0 4 63
## 22 0 0 0 0
## 23 80 0 0 0
## 24 0 3 0 0
## 25 0 3 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 153
## 32 0 3 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 2 0 0 0
## 36 0 0 0 5
## 37 0 0 0 1
## 38 0 91 0 111
## 39 0 0 0 2
## 40 0 0 0 0
## 41 0 0 0 0
## Oxalis.acetosella Pastinaca.sativa Pentaglottis.sempervirens
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 113 0 0
## 15 0 0 4
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 1 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Pillosella.officinarum Plantago.coronopus Plantago.lanceolata
## 1 0 0 13
## 2 0 0 0
## 3 0 0 0
## 4 0 0 22
## 5 0 0 36
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 8
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 33 0 1
## 17 286 0 0
## 18 2 0 0
## 19 0 1 0
## 20 54 19 0
## 21 0 0 1
## 22 1 0 0
## 23 0 114 0
## 24 22 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 4 0 3
## 29 0 0 0
## 30 0 0 1
## 31 0 0 3
## 32 3 0 0
## 33 0 0 0
## 34 14 0 3
## 35 2 0 0
## 36 0 0 0
## 37 0 0 0
## 38 91 0 5
## 39 39 50 20
## 40 0 0 0
## 41 0 0 29
## Pleurozium.schreberi Poa.annua Poa.spp. Polytrichum.commune
## 1 110 0 0 0
## 2 10 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 120 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 60 0 0 10
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 3 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 69 0 0
## 37 0 46 0 0
## 38 0 0 0 0
## 39 0 55 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Polytrichum.commune.agg. Polytrichum.formosum Potentilla.reptans
## 1 0 0 0
## 2 0 40 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 10 0 0
## 10 0 0 4
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 37
## 31 0 0 1
## 32 0 0 0
## 33 0 0 0
## 34 0 0 2
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Prunella.vulgaris Pseudoscleropidum.purum Pteridium.aquilinum
## 1 1 40 0
## 2 0 0 0
## 3 2 150 0
## 4 0 150 0
## 5 0 90 0
## 6 0 0 8
## 7 0 0 4
## 8 0 0 0
## 9 0 10 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 4
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 30 0
## 21 0 0 0
## 22 0 0 0
## 23 0 20 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 160 0
## 29 0 0 0
## 30 2 0 0
## 31 0 0 0
## 32 5 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 2 0 0
## Ranunculus.acris Ranunculus.repens Reseda.lutea Rhinanthus.minor
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 2 0 0 0
## 6 0 5 0 0
## 7 0 0 0 0
## 8 0 23 0 0
## 9 0 0 0 0
## 10 0 0 0 4
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 1 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 1 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Rhizomnium.punctatum Rhytidadelphus.squarrosus Rhytidadelphus.triquetris
## 1 0 160 20
## 2 0 0 0
## 3 0 30 0
## 4 0 150 20
## 5 0 30 0
## 6 0 130 0
## 7 0 160 0
## 8 4 110 0
## 9 0 110 0
## 10 0 0 0
## 11 0 0 0
## 12 0 140 0
## 13 0 20 0
## 14 0 0 0
## 15 0 0 0
## 16 0 60 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 140 0
## 21 0 20 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## Rubus.fruticosus Sanguisorba.minor.spp.minor Saniona.uncinata Sedum.anglicum
## 1 0 0 0 0
## 2 0 0 0 0
## 3 1 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 3 0 0 0
## 7 0 0 0 0
## 8 0 0 10 0
## 9 0 0 10 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 34 0 0
## 26 0 10 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 6
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Senecio.jacobaea Senecio.vulgaris Sonchus.arvensis Stachys.sylvatica
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 3 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 1
## 12 1 0 0 1
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 4 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 1 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Stellaria.apetala Taraxacum.agg. Thuidium.tamariscinum Thymus.polytrichus
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 30 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 1 0 0
## 8 0 0 20 0
## 9 0 0 10 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 283
## 17 0 1 0 7
## 18 0 3 0 159
## 19 0 2 0 0
## 20 0 1 0 0
## 21 0 0 0 20
## 22 0 0 0 45
## 23 0 0 0 0
## 24 0 2 0 0
## 25 0 0 0 0
## 26 0 1 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 2 0 0 0
## 31 0 0 0 0
## 32 0 4 0 0
## 33 0 0 0 0
## 34 0 6 0 0
## 35 0 0 0 0
## 36 0 0 0 216
## 37 0 0 0 253
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 2 0 7
## 41 0 1 0 0
## Trichostomum.crispulum Trifolium.campestre Trifolium.dubium
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 60 0 162
## 17 0 0 118
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 110 0 0
## 23 0 11 0
## 24 0 8 0
## 25 0 64 0
## 26 0 22 0
## 27 0 8 0
## 28 0 2 0
## 29 0 0 0
## 30 0 2 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 0 0
## 35 150 0 0
## 36 30 0 0
## 37 60 0 0
## 38 0 0 0
## 39 70 0 0
## 40 0 0 0
## 41 0 0 0
## Trifolium.pratense Trifolium.repens Trisetum.flavescens Tussilago.farfara
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 5 35 1 0
## 9 0 0 0 0
## 10 1 0 0 0
## 11 0 0 0 0
## 12 0 0 0 15
## 13 0 0 0 4
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 2 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 2 0 0 0
## 26 0 4 0 0
## 27 0 17 0 0
## 28 0 0 3 0
## 29 0 0 0 0
## 30 0 2 0 0
## 31 0 0 123 0
## 32 0 0 1 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 3
## 40 0 0 0 0
## 41 21 0 0 0
## Urtica.diocia Veronica.officinalis Vicia.sativa Viola.riviniana
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 2 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 1 0 0 28
## 7 0 0 0 0
## 8 0 0 110 0
## 9 0 0 0 0
## 10 52 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 55 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 7 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## Weissia.controversa Zygodon.stirtonii
## 1 0 0
## 2 0 0
## 3 20 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 0 0
## 27 0 0
## 28 0 0
## 29 0 0
## 30 0 0
## 31 0 0
## 32 0 0
## 33 0 0
## 34 0 0
## 35 0 0
## 36 0 0
## 37 0 0
## 38 0 0
## 39 0 0
## 40 0 10
## 41 0 0
#Now to do some ANOSIM tests to assess any significant differences between plant species in #different chemical variables.
#anoBSiO2 <- anosim(BarrowPS, BarrowPC$SiO2, distance = “bray”, permutations = 9999)
#When this ANOSIM is run, the error message: “there should be replicates within groups” #comes up. This error message comes up for another substrate variables for the Barrow #data. From now on, only variables that were tested successfully will be included.
anoBK2O <- anosim(BarrowPS, BarrowPC$K2O, distance = "bray", permutations = 9999)
anoBK2O
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$K2O, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.3704
## Significance: 0.3281
##
## Permutation: free
## Number of permutations: 9999
#Not statistically significant, will mention statistically significant results when #they are generated.
anoBCr2O3 <- anosim(BarrowPS, BarrowPC$Cr2O3, distance = "bray", permutations = 9999)
anoBCr2O3
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Cr2O3, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.009804
## Significance: 0.5134
##
## Permutation: free
## Number of permutations: 9999
anoBTiO2 <- anosim(BarrowPS, BarrowPC$TiO2, distance = "bray", permutations = 9999)
anoBTiO2
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$TiO2, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.01701
## Significance: 0.5226
##
## Permutation: free
## Number of permutations: 9999
anoBMnO <- anosim(BarrowPS, BarrowPC$MnO, distance = "bray", permutations = 9999)
anoBMnO
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$MnO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2051
## Significance: 0.3197
##
## Permutation: free
## Number of permutations: 9999
anoBP2O5 <- anosim(BarrowPS, BarrowPC$P2O5, distance = "bray", permutations = 9999)
anoBP2O5
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$P2O5, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.6981
## Significance: 0.0429
##
## Permutation: free
## Number of permutations: 9999
#R statistic of 0.6981 and p value of 0.0456 for P2O5.
anoBSrO <- anosim(BarrowPS, BarrowPC$SrO, distance = "bray", permutations = 9999)
anoBSrO
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$SrO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1506
## Significance: 0.2044
##
## Permutation: free
## Number of permutations: 9999
anoBBaO <- anosim(BarrowPS, BarrowPC$BaO, distance = "bray", permutations = 9999)
anoBBaO
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$BaO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.3333
## Significance: 0.3464
##
## Permutation: free
## Number of permutations: 9999
anoBAg <- anosim(BarrowPS, BarrowPC$Ag, distance = "bray", permutations = 9999)
anoBAg
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Ag, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2128
## Significance: 0.1985
##
## Permutation: free
## Number of permutations: 9999
anoBAs <- anosim(BarrowPS, BarrowPC$As, distance = "bray", permutations = 9999)
anoBAs
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$As, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2564
## Significance: 0.2192
##
## Permutation: free
## Number of permutations: 9999
anoBB <- anosim(BarrowPS, BarrowPC$B, distance = "bray", permutations = 9999)
anoBB
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$B, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.09158
## Significance: 0.3156
##
## Permutation: free
## Number of permutations: 9999
anoBCd <- anosim(BarrowPS, BarrowPC$Cd, distance = "bray", permutations = 9999)
anoBCd
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Cd, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.01333
## Significance: 0.4498
##
## Permutation: free
## Number of permutations: 9999
anoBCo <- anosim(BarrowPS, BarrowPC$Co, distance = "bray", permutations = 9999)
anoBCo
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Co, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.5
## Significance: 0.0575
##
## Permutation: free
## Number of permutations: 9999
#R statistic of 0.5 and a close-to-significant p value of 0.0586 for Co.
anoBCu <- anosim(BarrowPS, BarrowPC$Cu, distance = "bray", permutations = 9999)
anoBCu
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Cu, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.4444
## Significance: 0.7241
##
## Permutation: free
## Number of permutations: 9999
anoBNi <- anosim(BarrowPS, BarrowPC$Ni, distance = "bray", permutations = 9999)
anoBNi
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Ni, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.434
## Significance: 0.1667
##
## Permutation: free
## Number of permutations: 9999
anoBS <- anosim(BarrowPS, BarrowPC$S, distance = "bray", permutations = 9999)
anoBS
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$S, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.4444
## Significance: 0.7254
##
## Permutation: free
## Number of permutations: 9999
anoBSb <- anosim(BarrowPS, BarrowPC$Sb, distance = "bray", permutations = 9999)
anoBSb
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Sb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1795
## Significance: 0.8429
##
## Permutation: free
## Number of permutations: 9999
anoBSc <- anosim(BarrowPS, BarrowPC$Sc, distance = "bray", permutations = 9999)
anoBSc
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Sc, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.07979
## Significance: 0.3496
##
## Permutation: free
## Number of permutations: 9999
anoBV <- anosim(BarrowPS, BarrowPC$V, distance = "bray", permutations = 9999)
anoBV
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$V, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 1
## Significance: 0.0182
##
## Permutation: free
## Number of permutations: 9999
#A high R statistic of 1, with a p value of 0.0186, for V.
#With two of the variables showing significant differences in different Barrow #samples, CCAs will be appropriate to demonstrate what is going on in the data.
BarrowCCA <-cca(BarrowPS, BarrowPC)
##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'MnO', 'P2O5', 'SrO',
## 'BaO', 'LOI', 'Ag', 'As', 'B', 'Be', 'Bi', 'Cd', 'Co', 'Cu', 'Ga', 'Hg', 'La',
## 'Li', 'Mo', 'Ni', 'Pb', 'S', 'Sb', 'Sc', 'Th', 'Tl', 'U', 'V', 'W', 'Zn',
## 'Akermanite', 'Albite', 'Aluminium.oxide.hydroxide', 'Anhydrite', 'Aragonite',
## 'Augite', 'Biotite', 'Birnessite', 'Calcite', 'Clinochlore', 'Cuspidine',
## 'Diaspore', 'Dickite', 'Gehlenite', 'Goethite', 'Haematite', 'Illite',
## 'Kaolinite', 'Langite', 'Linnaeite', 'Magnesioferrite', 'Melilite',
## 'Merwinite', 'Microcline', 'Mullite', 'Muscovite', 'Nitratine', 'Orthoclase',
## 'Orthopyroxene', 'Periclase', 'Pigeonite', 'Phengite', 'Pseudowollastonite',
## 'Quartz', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component
print(BarrowCCA)
## Call: cca(X = BarrowPS, Y = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.784 1.000
## Constrained 4.784 1.000 10
## Unconstrained 0.000 0.000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## Some constraints or conditions were aliased because they were redundant.
## This can happen if terms are linearly dependent (collinear): 'MnO', 'P2O5',
## 'SrO', 'BaO', 'LOI', 'Ag', 'As', 'B', 'Be', 'Bi', 'Cd', 'Co', 'Cu', 'Ga',
## 'Hg', 'La', 'Li', 'Mo', 'Ni', 'Pb', 'S', 'Sb', 'Sc', 'Th', 'Tl', 'U', 'V',
## 'W', 'Zn', 'Akermanite', 'Albite', 'Aluminium.oxide.hydroxide',
## 'Anhydrite', 'Aragonite', 'Augite', 'Biotite', 'Birnessite', 'Calcite',
## 'Clinochlore', 'Cuspidine', 'Diaspore', 'Dickite', 'Gehlenite', 'Goethite',
## 'Haematite', 'Illite', 'Kaolinite', 'Langite', 'Linnaeite',
## 'Magnesioferrite', 'Melilite', 'Merwinite', 'Microcline', 'Mullite',
## 'Muscovite', 'Nitratine', 'Orthoclase', 'Orthopyroxene', 'Periclase',
## 'Pigeonite', 'Phengite', 'Pseudowollastonite', 'Quartz', 'Staurolite',
## 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component.
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10
## 0.8172 0.7282 0.6488 0.5718 0.5651 0.4823 0.3271 0.2994 0.2659 0.0785
summary(BarrowCCA)
##
## Call:
## cca(X = BarrowPS, Y = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.784 1
## Constrained 4.784 1
## Unconstrained 0.000 0
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8
## Eigenvalue 0.8172 0.7282 0.6488 0.5718 0.5651 0.4823 0.32715 0.29937
## Proportion Explained 0.1708 0.1522 0.1356 0.1195 0.1181 0.1008 0.06838 0.06257
## Cumulative Proportion 0.1708 0.3230 0.4586 0.5781 0.6962 0.7970 0.86542 0.92800
## CCA9 CCA10
## Eigenvalue 0.26594 0.07853
## Proportion Explained 0.05559 0.01641
## Cumulative Proportion 0.98359 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8
## Eigenvalue 0.8172 0.7282 0.6488 0.5718 0.5651 0.4823 0.32715 0.29937
## Proportion Explained 0.1708 0.1522 0.1356 0.1195 0.1181 0.1008 0.06838 0.06257
## Cumulative Proportion 0.1708 0.3230 0.4586 0.5781 0.6962 0.7970 0.86542 0.92800
## CCA9 CCA10
## Eigenvalue 0.26594 0.07853
## Proportion Explained 0.05559 0.01641
## Cumulative Proportion 0.98359 1.00000
plot(BarrowCCA)
#This CCA uses all of the plant data and substrate data. As before, substrate samples will #be selected for CCAs that can be further examined using anova tests.
BarrowCCA1.1 <-cca(BarrowPS ~ pH_level + CaO + MgO + K2O + MgO + Al2O3 + Cr2O3,
data = BarrowPC)
print(BarrowCCA1.1)
## Call: cca(formula = BarrowPS ~ pH_level + CaO + MgO + K2O + MgO + Al2O3 +
## Cr2O3, data = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.7843 1.0000
## Constrained 3.3955 0.7097 6
## Unconstrained 1.3888 0.2903 4
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8007 0.6694 0.6292 0.5458 0.4629 0.2875
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3 CA4
## 0.5421 0.3504 0.2938 0.2025
summary(BarrowCCA1.1)
##
## Call:
## cca(formula = BarrowPS ~ pH_level + CaO + MgO + K2O + MgO + Al2O3 + Cr2O3, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.784 1.0000
## Constrained 3.395 0.7097
## Unconstrained 1.389 0.2903
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1 CA2
## Eigenvalue 0.8007 0.6694 0.6292 0.5458 0.46287 0.2875 0.5421 0.35042
## Proportion Explained 0.1674 0.1399 0.1315 0.1141 0.09675 0.0601 0.1133 0.07324
## Cumulative Proportion 0.1674 0.3073 0.4388 0.5529 0.64961 0.7097 0.8230 0.89626
## CA3 CA4
## Eigenvalue 0.29381 0.20250
## Proportion Explained 0.06141 0.04233
## Cumulative Proportion 0.95767 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.8007 0.6694 0.6292 0.5458 0.4629 0.28751
## Proportion Explained 0.2358 0.1971 0.1853 0.1607 0.1363 0.08468
## Cumulative Proportion 0.2358 0.4330 0.6183 0.7790 0.9153 1.00000
plot(BarrowCCA1.1)
anova(BarrowCCA1.1)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = BarrowPS ~ pH_level + CaO + MgO + K2O + MgO + Al2O3 + Cr2O3, data = BarrowPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.3955 1.6299 0.001 ***
## Residual 4 1.3888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.6299 and p value of 0.001.
BarrowCCA1.2 <-cca(BarrowPS ~ pH_level + CaO + K2O + Al2O3 + Pb + BaO + Calcite,
data = BarrowPC)
print(BarrowCCA1.2)
## Call: cca(formula = BarrowPS ~ pH_level + CaO + K2O + Al2O3 + Pb + BaO +
## Calcite, data = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.7843 1.0000
## Constrained 3.8216 0.7988 7
## Unconstrained 0.9627 0.2012 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.7990 0.7092 0.6201 0.5655 0.4759 0.3491 0.3027
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4930 0.3464 0.1232
summary(BarrowCCA1.2)
##
## Call:
## cca(formula = BarrowPS ~ pH_level + CaO + K2O + Al2O3 + Pb + BaO + Calcite, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.7843 1.0000
## Constrained 3.8216 0.7988
## Unconstrained 0.9627 0.2012
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CA1
## Eigenvalue 0.799 0.7092 0.6201 0.5655 0.47594 0.34914 0.30269 0.4930
## Proportion Explained 0.167 0.1482 0.1296 0.1182 0.09948 0.07298 0.06327 0.1031
## Cumulative Proportion 0.167 0.3152 0.4449 0.5630 0.66253 0.73551 0.79877 0.9018
## CA2 CA3
## Eigenvalue 0.34644 0.12325
## Proportion Explained 0.07241 0.02576
## Cumulative Proportion 0.97424 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7990 0.7092 0.6201 0.5655 0.4759 0.34914 0.30269
## Proportion Explained 0.2091 0.1856 0.1623 0.1480 0.1245 0.09136 0.07921
## Cumulative Proportion 0.2091 0.3947 0.5569 0.7049 0.8294 0.92079 1.00000
anova(BarrowCCA1.2)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = BarrowPS ~ pH_level + CaO + K2O + Al2O3 + Pb + BaO + Calcite, data = BarrowPC)
## Df ChiSquare F Pr(>F)
## Model 7 3.8216 1.7012 0.002 **
## Residual 3 0.9627
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(BarrowCCA1.2)
#F statistic of 1.7012 and p value of 0.001
BarrowCCA1.3 <-cca(BarrowPS ~ pH_level + Cd + K2O + Al2O3 + Pb + BaO + Calcite,
data = BarrowPC)
print(BarrowCCA1.3)
## Call: cca(formula = BarrowPS ~ pH_level + Cd + K2O + Al2O3 + Pb + BaO +
## Calcite, data = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.7843 1.0000
## Constrained 3.7693 0.7878 7
## Unconstrained 1.0150 0.2122 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.8041 0.6865 0.6151 0.5653 0.4426 0.3398 0.3159
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4643 0.4117 0.1391
summary(BarrowCCA1.3)
##
## Call:
## cca(formula = BarrowPS ~ pH_level + Cd + K2O + Al2O3 + Pb + BaO + Calcite, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.784 1.0000
## Constrained 3.769 0.7878
## Unconstrained 1.015 0.2122
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8041 0.6865 0.6151 0.5653 0.44263 0.33975 0.31589
## Proportion Explained 0.1681 0.1435 0.1286 0.1182 0.09252 0.07101 0.06603
## Cumulative Proportion 0.1681 0.3116 0.4401 0.5583 0.65081 0.72182 0.78785
## CA1 CA2 CA3
## Eigenvalue 0.46428 0.41168 0.13905
## Proportion Explained 0.09704 0.08605 0.02906
## Cumulative Proportion 0.88489 0.97094 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8041 0.6865 0.6151 0.5653 0.4426 0.33975 0.31589
## Proportion Explained 0.2133 0.1821 0.1632 0.1500 0.1174 0.09014 0.08381
## Cumulative Proportion 0.2133 0.3955 0.5587 0.7086 0.8261 0.91619 1.00000
anova(BarrowCCA1.3)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = BarrowPS ~ pH_level + Cd + K2O + Al2O3 + Pb + BaO + Calcite, data = BarrowPC)
## Df ChiSquare F Pr(>F)
## Model 7 3.7693 1.5915 0.001 ***
## Residual 3 1.0150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(BarrowCCA1.3)
#F statistic of 1.5915 and p value of 0.002
BarrowCCA1.4 <-cca(BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Calcite,
data = BarrowPC)
print(BarrowCCA1.4)
## Call: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Calcite, data
## = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.7843 1.0000
## Constrained 2.7935 0.5839 5
## Unconstrained 1.9908 0.4161 5
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.7867 0.6796 0.6052 0.3846 0.3373
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3 CA4 CA5
## 0.6168 0.5222 0.4209 0.3203 0.1106
summary(BarrowCCA1.4)
##
## Call:
## cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Calcite, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.784 1.0000
## Constrained 2.793 0.5839
## Unconstrained 1.991 0.4161
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CA1 CA2 CA3
## Eigenvalue 0.7867 0.6796 0.6052 0.3846 0.33727 0.6168 0.5222 0.42092
## Proportion Explained 0.1644 0.1421 0.1265 0.0804 0.07049 0.1289 0.1092 0.08798
## Cumulative Proportion 0.1644 0.3065 0.4330 0.5134 0.58388 0.7128 0.8220 0.90994
## CA4 CA5
## Eigenvalue 0.32029 0.11059
## Proportion Explained 0.06695 0.02311
## Cumulative Proportion 0.97689 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5
## Eigenvalue 0.7867 0.6796 0.6052 0.3846 0.3373
## Proportion Explained 0.2816 0.2433 0.2166 0.1377 0.1207
## Cumulative Proportion 0.2816 0.5249 0.7416 0.8793 1.0000
anova(BarrowCCA1.4)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Calcite, data = BarrowPC)
## Df ChiSquare F Pr(>F)
## Model 5 2.7935 1.4032 0.003 **
## Residual 5 1.9908
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(BarrowCCA1.4)
#F statistic of 1.4032 and p value of 0.002
BarrowCCA1.5 <-cca(BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + Pb + Calcite,
data = BarrowPC)
print(BarrowCCA1.5)
## Call: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + Pb +
## Calcite, data = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.7843 1.0000
## Constrained 3.8385 0.8023 7
## Unconstrained 0.9458 0.1977 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.7987 0.7205 0.6123 0.5664 0.5006 0.3367 0.3033
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4613 0.3326 0.1519
summary(BarrowCCA1.5)
##
## Call:
## cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + Pb + Calcite, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.7843 1.0000
## Constrained 3.8385 0.8023
## Unconstrained 0.9458 0.1977
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CA1
## Eigenvalue 0.7987 0.7205 0.6123 0.5664 0.5006 0.33667 0.3033 0.46130
## Proportion Explained 0.1669 0.1506 0.1280 0.1184 0.1046 0.07037 0.0634 0.09642
## Cumulative Proportion 0.1669 0.3175 0.4455 0.5639 0.6685 0.73892 0.8023 0.89874
## CA2 CA3
## Eigenvalue 0.33262 0.15186
## Proportion Explained 0.06952 0.03174
## Cumulative Proportion 0.96826 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7987 0.7205 0.6123 0.5664 0.5006 0.33667 0.30333
## Proportion Explained 0.2081 0.1877 0.1595 0.1476 0.1304 0.08771 0.07902
## Cumulative Proportion 0.2081 0.3958 0.5553 0.7028 0.8333 0.92098 1.00000
anova(BarrowCCA1.5)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + Pb + Calcite, data = BarrowPC)
## Df ChiSquare F Pr(>F)
## Model 7 3.8385 1.7394 0.001 ***
## Residual 3 0.9458
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(BarrowCCA1.5)
#F statistic of 1.7394 and p value of 0.001
BarrowCCA1.6 <-cca(BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + Cd + Calcite,
data = BarrowPC)
print(BarrowCCA1.6)
## Call: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + Cd +
## Calcite, data = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.7843 1.0000
## Constrained 3.8575 0.8063 7
## Unconstrained 0.9267 0.1937 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.8032 0.7098 0.6100 0.5608 0.5214 0.3372 0.3151
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4590 0.3245 0.1432
summary(BarrowCCA1.6)
##
## Call:
## cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + Cd + Calcite, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.7843 1.0000
## Constrained 3.8575 0.8063
## Unconstrained 0.9267 0.1937
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8032 0.7098 0.6100 0.5608 0.5214 0.33717 0.31510
## Proportion Explained 0.1679 0.1484 0.1275 0.1172 0.1090 0.07047 0.06586
## Cumulative Proportion 0.1679 0.3163 0.4438 0.5610 0.6700 0.74043 0.80629
## CA1 CA2 CA3
## Eigenvalue 0.45903 0.32449 0.14324
## Proportion Explained 0.09594 0.06782 0.02994
## Cumulative Proportion 0.90224 0.97006 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8032 0.7098 0.6100 0.5608 0.5214 0.3372 0.31510
## Proportion Explained 0.2082 0.1840 0.1581 0.1454 0.1352 0.0874 0.08168
## Cumulative Proportion 0.2082 0.3922 0.5504 0.6957 0.8309 0.9183 1.00000
anova(BarrowCCA1.6)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + Cd + Calcite, data = BarrowPC)
## Df ChiSquare F Pr(>F)
## Model 7 3.8575 1.7839 0.002 **
## Residual 3 0.9267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(BarrowCCA1.6)
#F statistic of 1.7839 and p value of 0.001
BarrowCCA1.7 <-cca(BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + As + Calcite,
data = BarrowPC)
print(BarrowCCA1.7)
## Call: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + As +
## Calcite, data = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.7843 1.0000
## Constrained 3.7660 0.7872 7
## Unconstrained 1.0183 0.2128 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.8130 0.7057 0.6142 0.5347 0.4617 0.3366 0.3001
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4719 0.3936 0.1527
summary(BarrowCCA1.7)
##
## Call:
## cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + As + Calcite, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.784 1.0000
## Constrained 3.766 0.7872
## Unconstrained 1.018 0.2128
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8130 0.7057 0.6142 0.5347 0.4617 0.33662 0.30013
## Proportion Explained 0.1699 0.1475 0.1284 0.1118 0.0965 0.07036 0.06273
## Cumulative Proportion 0.1699 0.3174 0.4458 0.5576 0.6541 0.72443 0.78716
## CA1 CA2 CA3
## Eigenvalue 0.47193 0.39363 0.15273
## Proportion Explained 0.09864 0.08228 0.03192
## Cumulative Proportion 0.88580 0.96808 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8130 0.7057 0.6142 0.5347 0.4617 0.33662 0.30013
## Proportion Explained 0.2159 0.1874 0.1631 0.1420 0.1226 0.08938 0.07969
## Cumulative Proportion 0.2159 0.4033 0.5663 0.7083 0.8309 0.92031 1.00000
anova(BarrowCCA1.7)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Ni + As + Calcite, data = BarrowPC)
## Df ChiSquare F Pr(>F)
## Model 7 3.7660 1.585 0.002 **
## Residual 3 1.0183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(BarrowCCA1.7)
#F statistic of 1.585 and p value of 0.001
BarrowCCA1.8 <-cca(BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Pb + As + Calcite,
data = BarrowPC)
print(BarrowCCA1.8)
## Call: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Pb + As +
## Calcite, data = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.7843 1.0000
## Constrained 3.8046 0.7952 7
## Unconstrained 0.9797 0.2048 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.7980 0.6957 0.6118 0.5705 0.4903 0.3365 0.3017
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4735 0.3520 0.1542
summary(BarrowCCA1.8)
##
## Call:
## cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Pb + As + Calcite, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.7843 1.0000
## Constrained 3.8046 0.7952
## Unconstrained 0.9797 0.2048
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7980 0.6957 0.6118 0.5705 0.4903 0.33654 0.30172
## Proportion Explained 0.1668 0.1454 0.1279 0.1193 0.1025 0.07034 0.06307
## Cumulative Proportion 0.1668 0.3122 0.4401 0.5593 0.6618 0.73217 0.79523
## CA1 CA2 CA3
## Eigenvalue 0.47352 0.35195 0.15420
## Proportion Explained 0.09897 0.07356 0.03223
## Cumulative Proportion 0.89421 0.96777 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7980 0.6957 0.6118 0.5705 0.4903 0.33654 0.3017
## Proportion Explained 0.2098 0.1829 0.1608 0.1500 0.1289 0.08846 0.0793
## Cumulative Proportion 0.2098 0.3926 0.5534 0.7034 0.8322 0.92070 1.0000
anova(BarrowCCA1.8)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Pb + As + Calcite, data = BarrowPC)
## Df ChiSquare F Pr(>F)
## Model 7 3.8046 1.6644 0.002 **
## Residual 3 0.9797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot(BarrowCCA1.8)
#F statistic of 1.6644 and p value of 0.001
BarrowCCA2 <- cca(BarrowPS ~ Na2O + pH_level + MgO + CaO + Al2O3 + Cr2O3, data = BarrowPC)
print(BarrowCCA2)
## Call: cca(formula = BarrowPS ~ Na2O + pH_level + MgO + CaO + Al2O3 + Cr2O3,
## data = BarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 4.7843 1.0000
## Constrained 3.3915 0.7089 6
## Unconstrained 1.3928 0.2911 4
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 86 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8022 0.6645 0.6288 0.5407 0.4594 0.2958
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3 CA4
## 0.5675 0.3797 0.3068 0.1388
summary(BarrowCCA2)
##
## Call:
## cca(formula = BarrowPS ~ Na2O + pH_level + MgO + CaO + Al2O3 + Cr2O3, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.784 1.0000
## Constrained 3.392 0.7089
## Unconstrained 1.393 0.2911
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1
## Eigenvalue 0.8022 0.6645 0.6288 0.5407 0.45942 0.29580 0.5675
## Proportion Explained 0.1677 0.1389 0.1314 0.1130 0.09603 0.06183 0.1186
## Cumulative Proportion 0.1677 0.3066 0.4380 0.5510 0.64706 0.70889 0.8275
## CA2 CA3 CA4
## Eigenvalue 0.37966 0.30684 0.13880
## Proportion Explained 0.07935 0.06413 0.02901
## Cumulative Proportion 0.90685 0.97099 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.8022 0.6645 0.6288 0.5407 0.4594 0.29580
## Proportion Explained 0.2365 0.1959 0.1854 0.1594 0.1355 0.08722
## Cumulative Proportion 0.2365 0.4325 0.6179 0.7773 0.9128 1.00000
plot(BarrowCCA2)
anova(BarrowCCA2)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = BarrowPS ~ Na2O + pH_level + MgO + CaO + Al2O3 + Cr2O3, data = BarrowPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.3915 1.6234 0.002 **
## Residual 4 1.3928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.6234 and a p value of 0.001
#After carrying out multiple CCAs with different substrate variables, the one with the #highest F statistic and lowest p value is BarrowCCA1.6, with an F statistic of 1.7839 and #a p value of 0.001. The plot for this CCA is also satisfying from a visual point of view.
#To better visualise the BarrowCCA1.6 graph…
BarrowCCA1.6$CCA$v
## CCA1 CCA2 CCA3 CCA4
## Anthyllis.vulneraria -1.31783160 0.35344535 1.72526151 0.10831579
## Aphanes.arvensis 1.47725932 -1.45761344 0.89570041 -1.02027936
## Arrhenatherum.elatius 0.41847103 0.92233498 -1.21985277 -0.58026244
## Bellis.perennis -0.82748643 -0.77869917 -0.29202006 -0.10794327
## Blackstonia.perfoliata 1.50629701 -1.54369063 1.04515424 -1.85832964
## Brachythecium.albicans 0.10596200 0.52186038 0.38591936 0.29236224
## Brachythecium.rutabulum 0.10596200 0.52186038 0.38591936 0.29236224
## Briza.media -1.08445495 0.84019621 2.03456891 0.26570300
## Bromus.hordeaceus 0.43181039 1.01456410 -0.94551197 -0.92298231
## Calliergonella.cupsidata -0.13149294 1.08372274 0.04481003 0.43849112
## Carex.flacca 1.37562743 -1.15634329 0.37261199 1.91289664
## Carlina.vulgaris -0.07277018 0.11542055 -1.64881538 -0.35202766
## Centaurium.erythraea 1.39223263 -1.43816970 0.79375160 -1.77550801
## Cerastium.fontanum -0.44087789 0.23554575 0.25979022 0.60450856
## Cynosurus.cristatus 1.36994614 -1.13950210 0.34337102 2.07686300
## Daucus.carrota -1.29452700 -0.84029216 -0.70822708 -0.66584380
## Erigeron.acer -0.16892842 0.05979744 -2.43403419 -0.54447573
## Euphrasia.agg -0.16861263 0.13758349 0.99812102 -0.37531258
## Festuca.ovina -2.16781758 -2.37668105 -1.74960213 1.43259817
## Festuca.rubra 0.38636736 0.30260809 -0.76250886 0.12681728
## Galium.verum -0.49157541 0.72140172 1.32036880 0.30472551
## Helicotrichon.spp. 0.43181039 1.01456410 -0.94551197 -0.92298231
## Hieracium.spp. -2.14035453 -1.64782243 -0.23294158 -0.98281037
## Holcus.spp. 0.38602690 0.68352800 -0.67238629 -0.39231100
## Holcus.lanatus -0.17771215 1.06544204 -0.42965627 0.15467772
## Holcus.mollis 0.39712806 0.77476838 -1.65879807 -0.03191065
## Hypericum.perforatum -0.02001079 0.14593942 -1.21798736 -0.24643668
## Hypochaeris.radicata -0.15616075 1.45419652 -0.47304882 0.24807544
## Lathyrus.pratensis 1.33207090 -1.02722751 0.14843124 3.16997207
## Leontodon.hispidus -2.15096525 -1.92942689 -0.81892407 -0.04958434
## Leontodon.saxatilis -2.16864979 -2.39876768 -1.79556155 1.50579237
## Leucanthemum.vulgare -0.12117400 0.19052215 -0.75262119 -0.18601055
## Lolium.perenne 0.43557796 0.93761258 -0.99805762 -0.78196931
## Lotus.corniculatus 0.19242291 0.69218956 -0.64193091 -0.35637040
## Medicago.lupulina -0.75578548 -0.61259464 -1.37850920 0.39404984
## Ononis.repens 1.50629701 -1.54369063 1.04515424 -1.85832964
## Pastinaca.sativa -0.20466866 0.03912337 -2.72588543 -0.61600510
## Pillosella.officinarum -1.32943885 -1.33031760 -1.52706046 0.55576888
## Plantago.lanceolata -0.06301341 -1.01639484 -0.10688928 -0.60927580
## Poa.spp. 0.43181039 1.01456410 -0.94551197 -0.92298231
## Potentilla.reptans 1.26882247 -0.98148074 0.10252839 2.87344409
## Prunella.vulgaris 0.23439979 -0.26554831 -1.90465210 0.46570267
## Pseudoscleropidum.purum -2.14035453 -1.64782243 -0.23294158 -0.98281037
## Ranunculus.repens -0.15616075 1.45419652 -0.47304882 0.24807544
## Reseda.lutea -0.20466866 0.03912337 -2.72588543 -0.61600510
## Sanguisorba.minor.spp.minor -0.16443489 0.61788885 0.82765566 0.30301405
## Sedum.anglicum -0.20466866 0.03912337 -2.72588543 -0.61600510
## Senecio.jacobaea 0.43181039 1.01456410 -0.94551197 -0.92298231
## Senecio.vulgaris -2.16864979 -2.39876768 -1.79556155 1.50579237
## Stellaria.apetala 1.33207090 -1.02722751 0.14843124 3.16997207
## Taraxacum.agg. -0.48921717 -0.21700151 -1.49792538 -0.04552504
## Trifolium.campestre -0.35166734 0.38932905 0.54369090 0.42056145
## Trifolium.pratense 0.10596200 0.52186038 0.38591936 0.29236224
## Trifolium.repens -0.18807514 1.14975776 0.06840299 0.51800641
## Trisetum.flavescens 1.40668346 -1.53368733 0.98526977 -1.82786600
## CCA5 CCA6 CCA7
## Anthyllis.vulneraria -0.15909328 -0.01637360 -1.27877544
## Aphanes.arvensis -0.37431626 0.16909897 0.21072447
## Arrhenatherum.elatius 0.82904525 0.99413159 -1.72571133
## Bellis.perennis -0.15793714 -0.86917411 0.45913227
## Blackstonia.perfoliata -0.66163215 0.23899293 0.35042576
## Brachythecium.albicans -0.47222875 0.05360410 2.10858157
## Brachythecium.rutabulum -0.47222875 0.05360410 2.10858157
## Briza.media -0.52367916 0.03357637 -1.32815327
## Bromus.hordeaceus 1.37679534 1.50471714 -1.31947802
## Calliergonella.cupsidata -0.03004727 0.03917917 2.65329483
## Carex.flacca 0.63128935 -0.07552989 -0.27823004
## Carlina.vulgaris -1.99353884 -4.78080560 -0.92758623
## Centaurium.erythraea -0.94561252 0.26269594 0.27963569
## Cerastium.fontanum 0.59815172 -0.15055271 1.21378877
## Cynosurus.cristatus 0.68750332 -0.08920479 -0.30556290
## Daucus.carrota 1.60133814 -0.43866171 -0.42644434
## Erigeron.acer -4.12799862 -0.86200667 -0.76999796
## Euphrasia.agg -1.00442047 -0.84266537 -1.08163765
## Festuca.ovina -2.35603223 2.72840836 -0.04398466
## Festuca.rubra -0.14203246 -0.72408045 -0.18213617
## Galium.verum -0.58188337 0.06248474 0.12825326
## Helicotrichon.spp. 1.37679534 1.50471714 -1.31947802
## Hieracium.spp. 2.42568466 -0.74658992 0.54817580
## Holcus.spp. 0.50513517 0.61932151 -0.25794795
## Holcus.lanatus 0.11867588 0.02481545 2.50811105
## Holcus.mollis -0.04735490 0.17719472 -2.37568462
## Hypericum.perforatum -0.82241932 -6.93094307 -1.01405059
## Hypochaeris.radicata 0.11971398 0.04817286 3.89299276
## Lathyrus.pratensis 1.06226318 -0.18037082 -0.48778197
## Leontodon.hispidus 0.57820313 0.59602305 0.31938653
## Leontodon.saxatilis -2.50093275 2.83371134 -0.06192892
## Leucanthemum.vulgare -0.57038921 -3.72028343 0.05548814
## Lolium.perenne 1.16034766 1.37888579 -1.34502627
## Lotus.corniculatus 0.35825771 0.28135716 -0.48811467
## Medicago.lupulina -2.63149972 1.16061783 0.44507577
## Ononis.repens -0.66163215 0.23899293 0.35042576
## Pastinaca.sativa -4.92133766 0.59453807 -0.71142533
## Pillosella.officinarum -1.66501547 -0.83475114 -0.36048180
## Plantago.lanceolata 0.38871628 -2.24959910 -0.08341291
## Poa.spp. 1.37679534 1.50471714 -1.31947802
## Potentilla.reptans 0.92493167 -0.50741534 -0.49314020
## Prunella.vulgaris -3.21173742 0.37313553 -0.64752723
## Pseudoscleropidum.purum 2.42568466 -0.74658992 0.54817580
## Ranunculus.repens 0.11971398 0.04817286 3.89299276
## Reseda.lutea -4.92133766 0.59453807 -0.71142533
## Sanguisorba.minor.spp.minor -0.53231703 0.05911486 1.22539587
## Sedum.anglicum -4.92133766 0.59453807 -0.71142533
## Senecio.jacobaea 1.37679534 1.50471714 -1.31947802
## Senecio.vulgaris -2.50093275 2.83371134 -0.06192892
## Stellaria.apetala 1.06226318 -0.18037082 -0.48778197
## Taraxacum.agg. -2.33525763 -2.57401785 -0.83317678
## Trifolium.campestre -0.55190635 0.24853346 1.19448055
## Trifolium.pratense -0.47222875 0.05360410 2.10858157
## Trifolium.repens 0.05274762 0.03346099 2.52589439
## Trisetum.flavescens -0.62224440 0.21851101 0.34673599
## attr(,"na.action")
## Agrostis.spp. Agrostis.canina
## 1 2
## Alchemilla.mollis Alopercus.pratensis
## 3 4
## Angelica.sylvestris Anthoxanthum.odoratum
## 5 6
## Atrichum.undulatum Avenula.pratensis
## 10 11
## Betula.pubescens Brachythecium.glareosum
## 13 16
## Brachythecium.mildeanum Bryum.c.f..caespiticium
## 17 21
## Bryum.c.f..pallescens Bryum.capillare
## 22 23
## Bryum.spp. Campanula.rotundifolia
## 24 26
## Carex.distans Carex.panicea
## 27 29
## Centaurea.nigra Centaurium.littorale
## 31 33
## Centaurium.pulchellum Chamaenerion.angustifolium
## 34 36
## Cirriphyllum.piliferum Cirsium.arvense
## 37 38
## Cirsium.palustre Crepis.capillaris
## 39 40
## Dactylis.glomerata Danthonia.decumbens
## 42 43
## Dicranella.spp. Dicranum.scoparium
## 45 46
## Epilobium.montanum Equisetum.arvense
## 47 48
## Equisteum.variegatum Festuca.rubra.agg.
## 49 54
## Filipendula.ulmaria Fissidens.adianthoides
## 55 56
## Fissidens.dubius Fissidens.exilis
## 57 58
## Fragaria.vesca Galium.aparine
## 59 60
## Galium.arvense Galium.saxatile
## 61 62
## Geum.urbanum Glechoma.hederacea
## 64 65
## Heracleum.sphondylium Homalothecium.lutescens
## 67 72
## Hylocomium.splendens Hypnum.cupressiforme
## 73 75
## Hypnum.imponens Hypnum.jutlandicum
## 76 77
## Kindbergia.praelonga Linum.catharticum
## 79 84
## Lophocolea.semiteres Luzula.multiflora
## 86 88
## Lysimachia.maritima Myosotis.arvensis
## 89 91
## Oxalis.acetosella Pentaglottis.sempervirens
## 93 95
## Plantago.coronopus Pleurozium.schreberi
## 97 99
## Poa.annua Polytrichum.commune
## 100 102
## Polytrichum.commune.agg. Polytrichum.formosum
## 103 104
## Pteridium.aquilinum Ranunculus.acris
## 108 109
## Ranunculus.parviflorus Rhinanthus.minor
## 110 113
## Rhizomnium.punctatum Rhytidadelphus.squarrosus
## 114 115
## Rhytidadelphus.triquetris Rubus.fruticosus
## 116 117
## Saniona.uncinata Sonchus.arvensis
## 119 123
## Stachys.sylvatica Thuidium.tamariscinum
## 124 127
## Thymus.polytrichus Trichostomum.crispulum
## 128 129
## Trifolium.dubium Tussilago.farfara
## 131 135
## Urtica.diocia Veronica.officinalis
## 136 137
## Vicia.sativa Viola.riviniana
## 138 139
## Weissia.controversa Zygodon.stirtonii
## 140 141
## attr(,"class")
## [1] "exclude"
#The “v values” show the positions of individual species on the graph, with the CCA1 value #representing the x axis and the CCA2 value representing the y axis, in the case of the #graph created in R.
plot(BarrowCCA1.6, choices = c(1,2), display = c("wa", "bp"), xlim = c(-4, 3),
ylim = c(-3, 2))
#Increasing the xlim and ylim to give more room for writing on graph
points(x = -2.14035453, y = -1.64782243, pch = 15, col = "black")
#This point represents Pseudoscleropodium purum
text('P. purum', x = -2.14035453, y = -1.64782243, cex = 0.88, pos = 2, col = "black")
#Adding text for Pseudoscleropodium purum
points(x = -1.08445495, y = 0.84019621, pch = 15, col = "black")
#This point represents Briza media
text('Briza media', x = -1.08445495, y = 0.84019621, cex = 0.88, pos = 2, col = "black")
#Adding text for Briza media
points(x = -0.06301341, y = -1.01639484, pch = 15, col = "black")
#This point represents Plantago lanceolata
text('P. lanceolata', x = 1.1, y = -1.3, cex = 0.88, pos = 2, col = "black")
#Adding text for Plantago lanceolata
points(x = -2.16781758, y = -2.37668105, pch = 15, col = "black")
#This point represents Festuca ovina
text('F. ovina', x = -2.16781758, y = -2.37668105, cex = 0.88, pos = 2, col = "black")
#Adding text for Festuca ovina
points(x = 1.50629701, y = -1.54369063, pch = 15, col = "black")
#This point represents Blackstonia perfoliata
text('B. perfoliata', x = 3.5, y = -1.54369063, cex = 0.88, pos = 2, col = "black")
#Adding text for Blackstonia perfoliata
points(x = 0.43181039, y = 1.01456410, pch = 15, col = "black")
#This point represents Bromus hordeaceus
text('B. hordeaceus', x = 1.64, y = 1.01456410, pch = 15, col = "black")
#Adding text for Bromus hordeaceus
#Fallin Data#
urlfile5 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-for-six-study-sites-for-PhD/PhD-files/FallinPlantSpecies.csv'
FallinPS <- read.csv(urlfile5, header = TRUE, colClasses = c("numeric"))
urlfile6 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/FALLIN_PLANT_CHEMISTRY_MG_KG.csv'
FallinPC <-read.csv(urlfile6, header = TRUE, colClasses = c("numeric"))
head(FallinPS)
## Agrostis.spp. Agrostis.canina Alchemilla.mollis Alopercus.pratensis
## 1 0 0 0 0
## 2 9 0 0 0
## 3 1 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Angelica.sylvestris Anthoxanthum.odoratum Anthyllis.vulneraria
## 1 0 81 0
## 2 0 18 0
## 3 0 8 0
## 4 0 82 0
## 5 0 55 0
## Aphanes.arvensis Arrhenatherum.elatius Atrichum.undulatum Avenula.pratensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 2 0 0
## 5 0 42 10 0
## Bellis.perennis Betula.pubescens Blackstonia.perfoliata
## 1 1 0 0
## 2 0 0 0
## 3 8 12 0
## 4 16 0 0
## 5 0 0 0
## Brachythecium.albicans Brachythecium.glareosum Brachythecium.mildeanum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Brachythecium.rutabulum Briza.media Bromus.hordeaceus Bryum.c.f..caespiticium
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 10
## 4 0 0 0 0
## 5 0 0 0 0
## Bryum.c.f..pallescens Bryum.capillare Bryum.spp. Calliergonella.cupsidata
## 1 0 0 0 0
## 2 0 10 0 0
## 3 10 0 0 10
## 4 0 0 0 10
## 5 0 0 0 30
## Calypogeia.arguta Campanula.rotundifolia Carex.distans Carex.flacca
## 1 0 0 0 0
## 2 20 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 30 0 0 0
## Carex.panicea Carlina.vulgaris Centaurea.nigra Centaurium.erythraea
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Centaurium.littorale Centaurium.pulchellum Cerastium.fontanum
## 1 0 0 0
## 2 0 0 6
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Chamaenerion.angustifolium Cirriphyllum.piliferum Cirsium.arvense
## 1 0 0 0
## 2 0 0 0
## 3 0 10 0
## 4 0 0 0
## 5 0 0 0
## Cirsium.palustre Crepis.capillaris Cynosurus.cristatus Dactylis.glomerata
## 1 0 4 0 1
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 1 0 0
## 5 0 0 0 0
## Dactylorhiza.fuchsii Danthonia.decumbens Daucus.carrota Dicranella.spp.
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Dicranum.scoparium Epilobium.montanum Equisetum.arvense Equisteum.variegatum
## 1 30 0 0 0
## 2 20 0 0 0
## 3 0 0 0 0
## 4 10 0 0 0
## 5 16 0 0 0
## Erigeron.acer Euphrasia.agg Festuca.ovina Festuca.rubra Festuca.rubra.agg.
## 1 0 3 0 0 0
## 2 0 0 0 0 5
## 3 0 6 0 0 0
## 4 0 0 0 0 0
## 5 0 1 0 0 0
## Filipendula.ulmaria Fissidens.adianthoides Fissidens.dubius Fissidens.exilis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 10 10 0
## 4 0 0 0 0
## 5 0 0 0 0
## Fragaria.vesca Galium.aparine Galium.arvense Galium.saxatile Galium.verum
## 1 67 0 0 0 0
## 2 38 0 0 0 0
## 3 65 0 0 0 0
## 4 117 0 0 0 0
## 5 68 0 0 0 0
## Geum.urbanum Glechoma.hederacea Helicotrichon.spp. Heracleum.sphondylium
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Hieracium.spp. Holcus.spp. Holcus.lanatus Holcus.mollis
## 1 0 0 30 0
## 2 0 0 80 0
## 3 0 0 0 0
## 4 0 0 28 0
## 5 0 0 36 0
## Homalothecium.lutescens Hylocomium.splendens Hypericum.perforatum
## 1 0 50 11
## 2 0 80 24
## 3 0 110 2
## 4 0 160 4
## 5 0 90 1
## Hypnum.cupressiforme Hypnum.imponens Hypnum.jutlandicum Hypochaeris.radicata
## 1 0 0 0 0
## 2 10 0 20 0
## 3 60 0 0 0
## 4 10 0 0 0
## 5 20 0 120 0
## Kindbergia.praelonga Lathyrus.pratensis Leontodon.hispidus
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Leontodon.saxatilis Leucanthemum.vulgare Linum.catharticum Lolium.perenne
## 1 0 20 0 0
## 2 0 15 0 0
## 3 0 0 0 0
## 4 0 7 0 0
## 5 0 1 0 0
## Lophocolea.semiteres Lotus.corniculatus Luzula.multiflora Lysimachia.maritima
## 1 0 69 0 0
## 2 0 120 0 0
## 3 10 92 0 0
## 4 0 1 0 0
## 5 0 0 0 0
## Medicago.lupulina Myosotis.arvensis Ononis.repens Oxalis.acetosella
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Pastinaca.sativa Pentaglottis.sempervirens Pillosella.officinarum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Plantago.coronopus Plantago.lanceolata Pleurozium.schreberi Poa.annua
## 1 0 13 110 0
## 2 0 0 10 0
## 3 0 0 0 0
## 4 0 22 0 0
## 5 0 36 120 0
## Poa.spp. Polytrichum.commune Polytrichum.commune.agg. Polytrichum.formosum
## 1 0 0 0 0
## 2 0 0 0 40
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Potentilla.reptans Prunella.vulgaris Pseudoscleropidum.purum
## 1 0 1 40
## 2 0 0 0
## 3 0 2 150
## 4 0 0 150
## 5 0 0 90
## Pteridium.aquilinum Ranunculus.acris Ranunculus.parviflorus Ranunculus.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 2 0 0
## Reseda.lutea Rhinanthus.minor Rhizomnium.punctatum Rhytidadelphus.squarrosus
## 1 0 0 0 160
## 2 0 0 0 0
## 3 0 0 0 30
## 4 0 0 0 150
## 5 0 0 0 30
## Rhytidadelphus.triquetris Rubus.fruticosus Sanguisorba.minor.spp.minor
## 1 20 0 0
## 2 0 0 0
## 3 0 1 0
## 4 20 0 0
## 5 0 0 0
## Saniona.uncinata Sedum.anglicum Senecio.jacobaea Senecio.vulgaris
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Sonchus.arvensis Stachys.sylvatica Stellaria.apetala Taraxacum.agg.
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Thuidium.tamariscinum Thymus.polytrichus Trichostomum.crispulum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 30 0 0
## 5 0 0 0
## Trifolium.campestre Trifolium.dubium Trifolium.pratense Trifolium.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Trisetum.flavescens Tussilago.farfara Urtica.diocia Veronica.officinalis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 2
## 4 0 0 0 0
## 5 0 0 0 0
## Vicia.sativa Viola.riviniana Weissia.controversa Zygodon.stirtonii
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 20 0
## 4 0 0 0 0
## 5 0 0 0 0
head(FallinPC)
## pH_level SiO2 Al2O3 Fe2O3 CaO MgO Na2O K2O
## 1 6.070 179027.6 88649.43 18534.78 4431.083 4522.769 667.6712 15274.72
## 2 6.403 226705.9 83621.55 34901.33 2429.949 3377.001 519.2998 18097.22
## 3 7.073 201464.5 93941.94 28116.91 4002.268 4703.680 741.8569 16685.97
## 4 6.620 189311.1 84680.05 38468.40 3716.392 4341.858 593.4855 15191.70
## 5 6.633 173418.4 85738.56 18115.12 4145.206 4281.555 667.6712 15772.81
## Cr2O3 TiO2 MnO P2O5 SrO BaO LOI Ag As B Be
## 1 82.10429 4015.597 387.2287 1099.8580 169.11891 358.2605 37.9 0.15 8 10 1.4
## 2 75.26227 5454.020 1006.7947 999.8709 84.55946 447.8257 24.5 0.15 5 5 1.1
## 3 88.94632 4614.940 929.3490 1199.8451 84.55946 358.2605 30.4 0.15 3 5 1.4
## 4 82.10429 4375.203 697.0117 1499.8063 84.55946 358.2605 32.4 0.15 8 10 1.4
## 5 75.26227 3955.663 387.2287 1399.8192 169.11891 358.2605 39.5 0.20 5 10 1.6
## Bi Cd Co Cu Ga Hg La Li Mo Ni Pb S Sb Sc Th Tl U V W Zn Akermanite
## 1 1.5 0.25 20 51 5 0.5 10 10 1.0 29 42 800 1.5 2 10 5 5 11 5 57 0
## 2 1.5 0.25 28 44 5 0.5 20 5 1.0 31 50 800 1.5 3 10 5 5 8 5 53 0
## 3 1.5 0.25 23 48 5 0.5 10 10 0.5 32 51 600 1.5 3 10 5 5 15 5 44 0
## 4 1.5 0.25 24 44 5 0.5 10 10 1.0 35 45 900 1.5 4 10 5 5 18 5 61 0
## 5 1.5 0.25 31 65 5 0.5 10 10 1.0 35 69 800 1.5 2 10 5 5 10 5 54 0
## Albite Aluminium.oxide.hydroxide Anhydrite Aragonite Biotite Birnessite
## 1 0 0 0 0 1 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 1
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## Calcite Clinochlore Cuspidine Diaspore Dickite Gehlenite Goethite Haematite
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 1 0 0 1 0 0 1
## Illite Kaolinite Langite Linnaeite Magnesioferrite Melilite Merwinite
## 1 0 0 0 0 0 0 0
## 2 0 1 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 1 0 0 0 0
## 5 0 0 0 0 0 0 0
## Microcline Mullite Muscovite Orthoclase Orthopyroxene Periclase Phengite
## 1 0 0 1 0 0 0 0
## 2 0 0 0 0 0 0 1
## 3 0 0 1 0 0 0 0
## 4 0 0 1 0 0 0 0
## 5 0 0 1 0 0 0 0
## Pseudowollastonite Quartz Staurolite Valentinite
## 1 0 1 0 0
## 2 0 1 0 0
## 3 0 1 0 0
## 4 0 1 0 0
## 5 0 1 0 0
sapply(FallinPS, class)
## Agrostis.spp. Agrostis.canina
## "numeric" "numeric"
## Alchemilla.mollis Alopercus.pratensis
## "numeric" "numeric"
## Angelica.sylvestris Anthoxanthum.odoratum
## "numeric" "numeric"
## Anthyllis.vulneraria Aphanes.arvensis
## "numeric" "numeric"
## Arrhenatherum.elatius Atrichum.undulatum
## "numeric" "numeric"
## Avenula.pratensis Bellis.perennis
## "numeric" "numeric"
## Betula.pubescens Blackstonia.perfoliata
## "numeric" "numeric"
## Brachythecium.albicans Brachythecium.glareosum
## "numeric" "numeric"
## Brachythecium.mildeanum Brachythecium.rutabulum
## "numeric" "numeric"
## Briza.media Bromus.hordeaceus
## "numeric" "numeric"
## Bryum.c.f..caespiticium Bryum.c.f..pallescens
## "numeric" "numeric"
## Bryum.capillare Bryum.spp.
## "numeric" "numeric"
## Calliergonella.cupsidata Calypogeia.arguta
## "numeric" "numeric"
## Campanula.rotundifolia Carex.distans
## "numeric" "numeric"
## Carex.flacca Carex.panicea
## "numeric" "numeric"
## Carlina.vulgaris Centaurea.nigra
## "numeric" "numeric"
## Centaurium.erythraea Centaurium.littorale
## "numeric" "numeric"
## Centaurium.pulchellum Cerastium.fontanum
## "numeric" "numeric"
## Chamaenerion.angustifolium Cirriphyllum.piliferum
## "numeric" "numeric"
## Cirsium.arvense Cirsium.palustre
## "numeric" "numeric"
## Crepis.capillaris Cynosurus.cristatus
## "numeric" "numeric"
## Dactylis.glomerata Dactylorhiza.fuchsii
## "numeric" "numeric"
## Danthonia.decumbens Daucus.carrota
## "numeric" "numeric"
## Dicranella.spp. Dicranum.scoparium
## "numeric" "numeric"
## Epilobium.montanum Equisetum.arvense
## "numeric" "numeric"
## Equisteum.variegatum Erigeron.acer
## "numeric" "numeric"
## Euphrasia.agg Festuca.ovina
## "numeric" "numeric"
## Festuca.rubra Festuca.rubra.agg.
## "numeric" "numeric"
## Filipendula.ulmaria Fissidens.adianthoides
## "numeric" "numeric"
## Fissidens.dubius Fissidens.exilis
## "numeric" "numeric"
## Fragaria.vesca Galium.aparine
## "numeric" "numeric"
## Galium.arvense Galium.saxatile
## "numeric" "numeric"
## Galium.verum Geum.urbanum
## "numeric" "numeric"
## Glechoma.hederacea Helicotrichon.spp.
## "numeric" "numeric"
## Heracleum.sphondylium Hieracium.spp.
## "numeric" "numeric"
## Holcus.spp. Holcus.lanatus
## "numeric" "numeric"
## Holcus.mollis Homalothecium.lutescens
## "numeric" "numeric"
## Hylocomium.splendens Hypericum.perforatum
## "numeric" "numeric"
## Hypnum.cupressiforme Hypnum.imponens
## "numeric" "numeric"
## Hypnum.jutlandicum Hypochaeris.radicata
## "numeric" "numeric"
## Kindbergia.praelonga Lathyrus.pratensis
## "numeric" "numeric"
## Leontodon.hispidus Leontodon.saxatilis
## "numeric" "numeric"
## Leucanthemum.vulgare Linum.catharticum
## "numeric" "numeric"
## Lolium.perenne Lophocolea.semiteres
## "numeric" "numeric"
## Lotus.corniculatus Luzula.multiflora
## "numeric" "numeric"
## Lysimachia.maritima Medicago.lupulina
## "numeric" "numeric"
## Myosotis.arvensis Ononis.repens
## "numeric" "numeric"
## Oxalis.acetosella Pastinaca.sativa
## "numeric" "numeric"
## Pentaglottis.sempervirens Pillosella.officinarum
## "numeric" "numeric"
## Plantago.coronopus Plantago.lanceolata
## "numeric" "numeric"
## Pleurozium.schreberi Poa.annua
## "numeric" "numeric"
## Poa.spp. Polytrichum.commune
## "numeric" "numeric"
## Polytrichum.commune.agg. Polytrichum.formosum
## "numeric" "numeric"
## Potentilla.reptans Prunella.vulgaris
## "numeric" "numeric"
## Pseudoscleropidum.purum Pteridium.aquilinum
## "numeric" "numeric"
## Ranunculus.acris Ranunculus.parviflorus
## "numeric" "numeric"
## Ranunculus.repens Reseda.lutea
## "numeric" "numeric"
## Rhinanthus.minor Rhizomnium.punctatum
## "numeric" "numeric"
## Rhytidadelphus.squarrosus Rhytidadelphus.triquetris
## "numeric" "numeric"
## Rubus.fruticosus Sanguisorba.minor.spp.minor
## "numeric" "numeric"
## Saniona.uncinata Sedum.anglicum
## "numeric" "numeric"
## Senecio.jacobaea Senecio.vulgaris
## "numeric" "numeric"
## Sonchus.arvensis Stachys.sylvatica
## "numeric" "numeric"
## Stellaria.apetala Taraxacum.agg.
## "numeric" "numeric"
## Thuidium.tamariscinum Thymus.polytrichus
## "numeric" "numeric"
## Trichostomum.crispulum Trifolium.campestre
## "numeric" "numeric"
## Trifolium.dubium Trifolium.pratense
## "numeric" "numeric"
## Trifolium.repens Trisetum.flavescens
## "numeric" "numeric"
## Tussilago.farfara Urtica.diocia
## "numeric" "numeric"
## Veronica.officinalis Vicia.sativa
## "numeric" "numeric"
## Viola.riviniana Weissia.controversa
## "numeric" "numeric"
## Zygodon.stirtonii
## "numeric"
sapply(FallinPC, class)
## pH_level SiO2 Al2O3
## "numeric" "numeric" "numeric"
## Fe2O3 CaO MgO
## "numeric" "numeric" "numeric"
## Na2O K2O Cr2O3
## "numeric" "numeric" "numeric"
## TiO2 MnO P2O5
## "numeric" "numeric" "numeric"
## SrO BaO LOI
## "numeric" "numeric" "numeric"
## Ag As B
## "numeric" "numeric" "numeric"
## Be Bi Cd
## "numeric" "numeric" "numeric"
## Co Cu Ga
## "numeric" "numeric" "numeric"
## Hg La Li
## "numeric" "numeric" "numeric"
## Mo Ni Pb
## "numeric" "numeric" "numeric"
## S Sb Sc
## "numeric" "numeric" "numeric"
## Th Tl U
## "numeric" "numeric" "numeric"
## V W Zn
## "numeric" "numeric" "numeric"
## Akermanite Albite Aluminium.oxide.hydroxide
## "numeric" "numeric" "numeric"
## Anhydrite Aragonite Biotite
## "numeric" "numeric" "numeric"
## Birnessite Calcite Clinochlore
## "numeric" "numeric" "numeric"
## Cuspidine Diaspore Dickite
## "numeric" "numeric" "numeric"
## Gehlenite Goethite Haematite
## "numeric" "numeric" "numeric"
## Illite Kaolinite Langite
## "numeric" "numeric" "numeric"
## Linnaeite Magnesioferrite Melilite
## "numeric" "numeric" "numeric"
## Merwinite Microcline Mullite
## "numeric" "numeric" "numeric"
## Muscovite Orthoclase Orthopyroxene
## "numeric" "numeric" "numeric"
## Periclase Phengite Pseudowollastonite
## "numeric" "numeric" "numeric"
## Quartz Staurolite Valentinite
## "numeric" "numeric" "numeric"
rowSums(FallinPS)
## [1] 711 525 639 820 798
#Time to do the ANOSIM tests for the Fallin data
#anoFSiO2 <- anosim(FallinPS, FallinPC$SiO2, distance = “bray”, permutations = 9999)
#When this ANOSIM is run, the error message: “there should be replicates within groups” #comes up. This error message came up for other substrate variables for the #data. From now on, only variables that were tested successfully will be included.
anoFNa2O <- anosim(FallinPS, FallinPC$Na2O, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFNa2O
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$Na2O, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7778
## Significance: 0.2
##
## Permutation: free
## Number of permutations: 119
anoFCr2O3 <- anosim(FallinPS, FallinPC$Cr2O3, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFCr2O3
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$Cr2O3, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.125
## Significance: 0.46667
##
## Permutation: free
## Number of permutations: 119
anoFSrO <- anosim(FallinPS, FallinPC$SrO, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFSrO
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$SrO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.08333
## Significance: 0.7
##
## Permutation: free
## Number of permutations: 119
anoFBaO <- anosim(FallinPS, FallinPC$BaO, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFBaO
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$BaO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.9167
## Significance: 0.2
##
## Permutation: free
## Number of permutations: 119
anoFAs <- anosim(FallinPS, FallinPC$As, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFAs
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$As, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.125
## Significance: 0.46667
##
## Permutation: free
## Number of permutations: 119
anoFBe <- anosim(FallinPS, FallinPC$Be, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFBe
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$Be, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.5238
## Significance: 0.2
##
## Permutation: free
## Number of permutations: 119
anoFCu <- anosim(FallinPS, FallinPC$Cu, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFCu
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$Cu, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -1
## Significance: 1
##
## Permutation: free
## Number of permutations: 119
anoFNi <- anosim(FallinPS, FallinPC$Ni, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFNi
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$Ni, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.3333
## Significance: 0.4
##
## Permutation: free
## Number of permutations: 119
anoFS <- anosim(FallinPS, FallinPC$S, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFS
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$S, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.04762
## Significance: 0.6
##
## Permutation: free
## Number of permutations: 119
anoFSc <- anosim(FallinPS, FallinPC$Sc, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoFSc
##
## Call:
## anosim(x = FallinPS, grouping = FallinPC$Sc, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.125
## Significance: 0.46667
##
## Permutation: free
## Number of permutations: 119
#None of the ANOSIM tests led to stastistically significant results. Because of this, #CCAs might not be entirely appropriate to represent the data visually. #Both CCA and NMDS analyses will be utilised to visualise the plant data.
FallinNMDS <- metaMDS(FallinPS, noshare = TRUE, autotransform = FALSE, trymax = 500)
## Run 0 stress 0.08152132
## Run 1 stress 0.0839104
## Run 2 stress 0.02952015
## ... New best solution
## ... Procrustes: rmse 0.2122265 max resid 0.3062251
## Run 3 stress 0.0839104
## Run 4 stress 0.0927171
## Run 5 stress 0.02952015
## ... New best solution
## ... Procrustes: rmse 5.981661e-07 max resid 1.00664e-06
## ... Similar to previous best
## Run 6 stress 0.05541858
## Run 7 stress 0.0554184
## Run 8 stress 0.0839104
## Run 9 stress 0.09271713
## Run 10 stress 0.05541844
## Run 11 stress 0.02952015
## ... Procrustes: rmse 6.57376e-07 max resid 1.103654e-06
## ... Similar to previous best
## Run 12 stress 0.09271721
## Run 13 stress 0.09271712
## Run 14 stress 0.02952015
## ... Procrustes: rmse 5.085642e-07 max resid 7.735351e-07
## ... Similar to previous best
## Run 15 stress 0.09271712
## Run 16 stress 0.02952015
## ... Procrustes: rmse 3.73838e-07 max resid 5.479893e-07
## ... Similar to previous best
## Run 17 stress 0.09271715
## Run 18 stress 0.05541853
## Run 19 stress 0.09271712
## Run 20 stress 0.09271717
## *** Best solution repeated 4 times
plot(FallinNMDS)
FallinNMDS
##
## Call:
## metaMDS(comm = FallinPS, trymax = 500, autotransform = FALSE, noshare = TRUE)
##
## global Multidimensional Scaling using monoMDS
##
## Data: FallinPS
## Distance: bray
##
## Dimensions: 2
## Stress: 0.02952015
## Stress type 1, weak ties
## Best solution was repeated 4 times in 20 tries
## The best solution was from try 5 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'FallinPS'
#This simple NMDS shows communities represented as circles and species represented as
#red crosses.
plot(FallinNMDS, "sites") # Produces distance
plot(FallinNMDS, "species", xlim = c(-0.5, 0.5), ylim = c(-1.5, 1.0))
orditorp(FallinNMDS, "species")
#We are only seeing some of the Fallin species in the graph produced by the function
#"orditorp", but this still gives an idea of how different species are differently and/or
#similarly distributed in different communities on Fallin Bing.
#CCA analyses for Fallin data... It might be easier to visualise what is going on
#using an appropriate CCA, especially with substrate variables being taken into account
#in the CCA.
#CCA analyses for Fallin data…#
FallinCCA <- cca(FallinPS ~ pH_level + SiO2 + Al2O3 + Fe2O3 + CaO + MgO + MgO + Na2O +
K2O + Cr2O3 + TiO2 + MnO + P2O5 + SrO + BaO + Ag + As + B + Be + Bi + Cd + Co + Cu +
Ga + Hg + La + Li + Mo + Ni + Pb + S + Sb + Sc + Th + Tl + U + V + W + Zn + Akermanite +
Albite + Aluminium.oxide.hydroxide + Anhydrite + Aragonite + Biotite + Birnessite +
Calcite + Clinochlore + Cuspidine + Diaspore + Dickite + Gehlenite + Goethite + Haematite +
Illite + Kaolinite + Langite + Linnaeite + Magnesioferrite + Melilite + Merwinite +
Microcline + Mullite + Muscovite + Orthoclase + Orthopyroxene + Periclase + Phengite +
Pseudowollastonite + Quartz + Staurolite + Valentinite, data = FallinPC)
##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'CaO', 'MgO', 'Na2O',
## 'K2O', 'Cr2O3', 'TiO2', 'MnO', 'P2O5', 'SrO', 'BaO', 'Ag', 'As', 'B', 'Be',
## 'Bi', 'Cd', 'Co', 'Cu', 'Ga', 'Hg', 'La', 'Li', 'Mo', 'Ni', 'Pb', 'S', 'Sb',
## 'Sc', 'Th', 'Tl', 'U', 'V', 'W', 'Zn', 'Akermanite', 'Albite',
## 'Aluminium.oxide.hydroxide', 'Anhydrite', 'Aragonite', 'Biotite', 'Birnessite',
## 'Calcite', 'Clinochlore', 'Cuspidine', 'Diaspore', 'Dickite', 'Gehlenite',
## 'Goethite', 'Haematite', 'Illite', 'Kaolinite', 'Langite', 'Linnaeite',
## 'Magnesioferrite', 'Melilite', 'Merwinite', 'Microcline', 'Mullite',
## 'Muscovite', 'Orthoclase', 'Orthopyroxene', 'Periclase', 'Phengite',
## 'Pseudowollastonite', 'Quartz', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component
print(FallinCCA)
## Call: cca(formula = FallinPS ~ pH_level + SiO2 + Al2O3 + Fe2O3 + CaO + MgO
## + MgO + Na2O + K2O + Cr2O3 + TiO2 + MnO + P2O5 + SrO + BaO + Ag + As + B +
## Be + Bi + Cd + Co + Cu + Ga + Hg + La + Li + Mo + Ni + Pb + S + Sb + Sc +
## Th + Tl + U + V + W + Zn + Akermanite + Albite + Aluminium.oxide.hydroxide
## + Anhydrite + Aragonite + Biotite + Birnessite + Calcite + Clinochlore +
## Cuspidine + Diaspore + Dickite + Gehlenite + Goethite + Haematite + Illite
## + Kaolinite + Langite + Linnaeite + Magnesioferrite + Melilite + Merwinite
## + Microcline + Mullite + Muscovite + Orthoclase + Orthopyroxene + Periclase
## + Phengite + Pseudowollastonite + Quartz + Staurolite + Valentinite, data =
## FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.9405 1.0000 4
## Unconstrained 0.0000 0.0000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## Some constraints or conditions were aliased because they were redundant.
## This can happen if terms are linearly dependent (collinear): 'CaO', 'MgO',
## 'Na2O', 'K2O', 'Cr2O3', 'TiO2', 'MnO', 'P2O5', 'SrO', 'BaO', 'Ag', 'As',
## 'B', 'Be', 'Bi', 'Cd', 'Co', 'Cu', 'Ga', 'Hg', 'La', 'Li', 'Mo', 'Ni',
## 'Pb', 'S', 'Sb', 'Sc', 'Th', 'Tl', 'U', 'V', 'W', 'Zn', 'Akermanite',
## 'Albite', 'Aluminium.oxide.hydroxide', 'Anhydrite', 'Aragonite', 'Biotite',
## 'Birnessite', 'Calcite', 'Clinochlore', 'Cuspidine', 'Diaspore', 'Dickite',
## 'Gehlenite', 'Goethite', 'Haematite', 'Illite', 'Kaolinite', 'Langite',
## 'Linnaeite', 'Magnesioferrite', 'Melilite', 'Merwinite', 'Microcline',
## 'Mullite', 'Muscovite', 'Orthoclase', 'Orthopyroxene', 'Periclase',
## 'Phengite', 'Pseudowollastonite', 'Quartz', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component.
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.31034 0.30762 0.22427 0.09823
plot(FallinCCA)
FallinCCA2 <- cca(FallinPS ~ pH_level + SiO2 + Al2O3 +
Fe2O3,
data= FallinPC)
##
## The model is overfitted with no unconstrained (residual) component
print(FallinCCA2)
## Call: cca(formula = FallinPS ~ pH_level + SiO2 + Al2O3 + Fe2O3, data =
## FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.9405 1.0000 4
## Unconstrained 0.0000 0.0000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## The model is overfitted with no unconstrained (residual) component.
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.31034 0.30762 0.22427 0.09823
plot(FallinCCA2)
FallinCCA3 <- cca(FallinPS ~ pH_level + SiO2 + Al2O3,
data= FallinPC)
print(FallinCCA3)
## Call: cca(formula = FallinPS ~ pH_level + SiO2 + Al2O3, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.6991 0.7434 3
## Unconstrained 0.2413 0.2566 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30817 0.27501 0.11594
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.24134
plot(FallinCCA3)
anova(FallinCCA3)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ pH_level + SiO2 + Al2O3, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.69912 0.9656 0.475
## Residual 1 0.24134
#F statistic of 0.9656, p value of 0.475
FallinCCA4 <- cca(FallinPS ~ pH_level + SiO2 + CaO, data= FallinPC)
print(FallinCCA4)
## Call: cca(formula = FallinPS ~ pH_level + SiO2 + CaO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7223 0.7680 3
## Unconstrained 0.2182 0.2320 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30843 0.28080 0.13306
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.21817
plot(FallinCCA4)
anova(FallinCCA4)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ pH_level + SiO2 + CaO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.72228 1.1035 0.325
## Residual 1 0.21817
#F statistic of 1.1035, p value of 0.3333
FallinCCA5 <- cca(FallinPS ~ pH_level + SiO2 + MgO, data= FallinPC)
print(FallinCCA5)
## Call: cca(formula = FallinPS ~ pH_level + SiO2 + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7721 0.8210 3
## Unconstrained 0.1684 0.1790 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30942 0.29229 0.17036
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.16838
plot(FallinCCA5)
anova(FallinCCA5)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ pH_level + SiO2 + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.77208 1.5285 0.1833
## Residual 1 0.16838
#F statistic of 1.5285, p value of 0.2
FallinCCA6 <- cca(FallinPS ~ Al2O3 + SiO2 + MgO, data= FallinPC)
print(FallinCCA6)
## Call: cca(formula = FallinPS ~ Al2O3 + SiO2 + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7483 0.7957 3
## Unconstrained 0.1922 0.2043 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30947 0.30456 0.13426
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.19216
plot(FallinCCA6)
anova(FallinCCA6)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ Al2O3 + SiO2 + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.74830 1.298 0.2083
## Residual 1 0.19216
#F statistic of 1.298, p value of 0.2
FallinCCA7 <- cca(FallinPS ~ Na2O + Al2O3 + MgO, data= FallinPC)
print(FallinCCA7)
## Call: cca(formula = FallinPS ~ Na2O + Al2O3 + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7680 0.8166 3
## Unconstrained 0.1724 0.1834 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30764 0.28205 0.17832
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.17244
plot(FallinCCA7)
anova(FallinCCA7)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ Na2O + Al2O3 + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.76801 1.4846 0.175
## Residual 1 0.17244
#F statistic of 1.4846, p value of 0.1583
FallinCCA8 <- cca(FallinPS ~ K2O + Pb + MgO,
data= FallinPC)
print(FallinCCA8)
## Call: cca(formula = FallinPS ~ K2O + Pb + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7869 0.8367 3
## Unconstrained 0.1536 0.1633 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30772 0.25876 0.22042
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.15356
plot(FallinCCA8)
anova(FallinCCA8)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ K2O + Pb + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.78690 1.7081 0.08333 .
## Residual 1 0.15356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.7081, p value of 0.08333
FallinCCA9 <- cca(FallinPS ~ K2O + Cr2O3 + MgO, data= FallinPC)
print(FallinCCA9)
## Call: cca(formula = FallinPS ~ K2O + Cr2O3 + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7896 0.8396 3
## Unconstrained 0.1508 0.1604 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30880 0.29062 0.19022
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.15082
plot(FallinCCA9)
anova(FallinCCA9)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ K2O + Cr2O3 + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.78964 1.7452 0.075 .
## Residual 1 0.15082
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.7452, p value of 0.09167
FallinCCA10 <- cca(FallinPS ~ K2O + Cu + MgO, data= FallinPC)
print(FallinCCA10)
## Call: cca(formula = FallinPS ~ K2O + Cu + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7908 0.8408 3
## Unconstrained 0.1497 0.1592 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30866 0.28930 0.19280
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.14969
plot(FallinCCA10)
anova(FallinCCA10)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ K2O + Cu + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.79076 1.7608 0.06667 .
## Residual 1 0.14969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.7608, p value of 0.075
FallinCCA10.1 <- cca(FallinPS ~ K2O + Cu + Zn,
data= FallinPC)
print(FallinCCA10.1)
## Call: cca(formula = FallinPS ~ K2O + Cu + Zn, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7953 0.8456 3
## Unconstrained 0.1452 0.1544 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30877 0.29116 0.19532
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.1452
plot(FallinCCA10.1)
anova(FallinCCA10.1)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ K2O + Cu + Zn, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.79525 1.8256 0.06667 .
## Residual 1 0.14520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.8256, p value of 0.06667
FallinCCA11 <- cca(FallinPS ~ K2O + Pb + MgO, data= FallinPC)
print(FallinCCA11)
## Call: cca(formula = FallinPS ~ K2O + Pb + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7869 0.8367 3
## Unconstrained 0.1536 0.1633 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30772 0.25876 0.22042
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.15356
plot(FallinCCA11)
anova(FallinCCA11)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ K2O + Pb + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.78690 1.7081 0.08333 .
## Residual 1 0.15356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.7081, p value of 0.08333
FallinCCA12 <- cca(FallinPS ~ K2O + SrO + MgO, data= FallinPC)
print(FallinCCA12)
## Call: cca(formula = FallinPS ~ K2O + SrO + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7402 0.7871 3
## Unconstrained 0.2003 0.2129 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.31012 0.29597 0.13411
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.20026
plot(FallinCCA12)
anova(FallinCCA12)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ K2O + SrO + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.74019 1.232 0.1917
## Residual 1 0.20026
#F statistic of 1.232, p value of 0.2167
FallinCCA13 <- cca(FallinPS ~ K2O + Zn + MgO, data= FallinPC)
print(FallinCCA13)
## Call: cca(formula = FallinPS ~ K2O + Zn + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7931 0.8433 3
## Unconstrained 0.1474 0.1567 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30828 0.28394 0.20084
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.14739
plot(FallinCCA13)
anova(FallinCCA13)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ K2O + Zn + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.79306 1.7935 0.08333 .
## Residual 1 0.14739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.7935, p value of 0.08333
FallinCCA14 <- cca(FallinPS ~ K2O + Dickite + MgO, data= FallinPC)
print(FallinCCA14)
## Call: cca(formula = FallinPS ~ K2O + Dickite + MgO, data = FallinPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 0.9405 1.0000
## Constrained 0.7921 0.8423 3
## Unconstrained 0.1483 0.1577 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.30789 0.27171 0.21250
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.14835
plot(FallinCCA14)
anova(FallinCCA14)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = FallinPS ~ K2O + Dickite + MgO, data = FallinPC)
## Df ChiSquare F Pr(>F)
## Model 3 0.79211 1.7798 0.09167 .
## Residual 1 0.14835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.7798, p value of 0.08333
#Due to the fact that, though several combinations of data were tried, none of the CCAs #generated for the Fallin data generated significant values in the relevant ANOVA tests. #This would indicate that the NMDS generated previously better represents the data and the #differences between both communities and species.
###Addiewell###
urlfile7 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/AddiewellPlantSpecies.csv'
AddiewellPS <- read.csv(urlfile7, header = TRUE, colClasses = c("numeric"))
urlfile8 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/ADDIEWELL_PLANT_CHEMISTRY_MG_KG.csv'
AddiewellPC <-read.csv(urlfile8, header = TRUE,
colClasses = c("numeric"))
head(AddiewellPS)
## Agrostis.spp. Agrostis.canina Alchemilla.mollis Alopercus.pratensis
## 1 1 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 14 0 0 0
## Angelica.sylvestris Anthoxanthum.odoratum Anthyllis.vulneraria
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Aphanes.arvensis Arrhenatherum.elatius Atrichum.undulatum Avenula.pratensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 3 10 0
## 4 0 1 0 0
## 5 11 0 0 0
## Bellis.perennis Betula.pubescens Blackstonia.perfoliata
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Brachythecium.albicans Brachythecium.glareosum Brachythecium.mildeanum
## 1 0 10 10
## 2 0 0 80
## 3 0 0 0
## 4 0 0 0
## 5 0 0 150
## Brachythecium.rutabulum Briza.media Bromus.hordeaceus Bryum.c.f..caespiticium
## 1 10 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 10 0 0 0
## 5 10 0 0 0
## Bryum.c.f..pallescens Bryum.capillare Bryum.spp. Calliergonella.cupsidata
## 1 0 0 0 30
## 2 0 0 0 120
## 3 0 0 10 150
## 4 0 0 0 90
## 5 0 0 0 0
## Calypogeia.arguta Campanula.rotundifolia Carex.distans Carex.flacca
## 1 130 0 0 0
## 2 80 0 0 0
## 3 30 0 0 1
## 4 110 0 0 0
## 5 10 0 0 9
## Carex.panicea Carlina.vulgaris Centaurea.nigra Centaurium.erythraea
## 1 0 0 0 0
## 2 0 0 2 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Centaurium.littorale Centaurium.pulchellum Cerastium.fontanum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Chamaenerion.angustifolium Cirriphyllum.piliferum Cirsium.arvense
## 1 0 0 0
## 2 0 0 0
## 3 4 0 0
## 4 0 0 0
## 5 0 0 0
## Cirsium.palustre Crepis.capillaris Cynosurus.cristatus Dactylis.glomerata
## 1 4 0 0 0
## 2 0 0 0 0
## 3 3 0 0 0
## 4 0 0 0 0
## 5 0 0 16 12
## Dactylorhiza.fuchsii Danthonia.decumbens Daucus.carrota Dicranella.spp.
## 1 0 0 0 0
## 2 0 0 0 0
## 3 22 0 0 0
## 4 0 0 0 0
## 5 1 1 0 0
## Dicranum.scoparium Epilobium.montanum Equisetum.arvense Equisteum.variegatum
## 1 0 8 0 0
## 2 0 1 0 0
## 3 0 0 0 0
## 4 50 0 0 0
## 5 0 0 0 0
## Erigeron.acer Euphrasia.agg Festuca.ovina Festuca.rubra Festuca.rubra.agg.
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## Filipendula.ulmaria Fissidens.adianthoides Fissidens.dubius Fissidens.exilis
## 1 27 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Fragaria.vesca Galium.aparine Galium.arvense Galium.saxatile Galium.verum
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## Geum.urbanum Glechoma.hederacea Helicotrichon.spp. Heracleum.sphondylium
## 1 0 0 0 0
## 2 0 0 0 8
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 1
## Hieracium.spp. Holcus.spp. Holcus.lanatus Holcus.mollis
## 1 0 5 0 0
## 2 0 14 0 0
## 3 0 36 0 0
## 4 0 0 0 143
## 5 0 0 0 1
## Homalothecium.lutescens Hylocomium.splendens Hypericum.perforatum
## 1 0 0 0
## 2 0 0 0
## 3 0 1 0
## 4 0 90 0
## 5 0 0 0
## Hypnum.cupressiforme Hypnum.imponens Hypnum.jutlandicum Hypochaeris.radicata
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 20 60 0 0
## 5 50 0 10 0
## Kindbergia.praelonga Lathyrus.pratensis Leontodon.hispidus
## 1 10 0 0
## 2 60 0 0
## 3 0 30 0
## 4 30 0 0
## 5 10 18 0
## Leontodon.saxatilis Leucanthemum.vulgare Linum.catharticum Lolium.perenne
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Lophocolea.semiteres Lotus.corniculatus Luzula.multiflora Lysimachia.maritima
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 1 0
## 4 0 0 0 0
## 5 0 0 0 0
## Medicago.lupulina Myosotis.arvensis Ononis.repens Oxalis.acetosella
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 2 0 0
## 4 0 0 0 0
## 5 0 0 2 0
## Pastinaca.sativa Pentaglottis.sempervirens Pillosella.officinarum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Plantago.coronopus Plantago.lanceolata Pleurozium.schreberi Poa.annua
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 60 0
## 5 0 8 0 0
## Poa.spp. Polytrichum.commune Polytrichum.commune.agg. Polytrichum.formosum
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 10 10 0
## 5 0 0 0 0
## Potentilla.reptans Prunella.vulgaris Pseudoscleropidum.purum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 10
## 5 4 0 0
## Pteridium.aquilinum Ranunculus.acris Ranunculus.parviflorus Ranunculus.repens
## 1 8 0 0 5
## 2 4 0 0 0
## 3 0 0 0 23
## 4 0 0 0 0
## 5 0 0 0 0
## Reseda.lutea Rhinanthus.minor Rhizomnium.punctatum Rhytidadelphus.squarrosus
## 1 0 0 0 130
## 2 0 0 0 160
## 3 0 0 4 110
## 4 0 0 0 110
## 5 0 4 0 0
## Rhytidadelphus.triquetris Rubus.fruticosus Sanguisorba.minor.spp.minor
## 1 0 3 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Saniona.uncinata Sedum.anglicum Senecio.jacobaea Senecio.vulgaris
## 1 0 0 0 0
## 2 0 0 0 0
## 3 10 0 0 0
## 4 10 0 0 0
## 5 0 0 0 0
## Sonchus.arvensis Stachys.sylvatica Stellaria.apetala Taraxacum.agg.
## 1 0 0 0 0
## 2 3 0 0 1
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Thuidium.tamariscinum Thymus.polytrichus Trichostomum.crispulum
## 1 0 0 0
## 2 0 0 0
## 3 20 0 0
## 4 10 0 0
## 5 0 0 0
## Trifolium.campestre Trifolium.dubium Trifolium.pratense Trifolium.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 5 35
## 4 0 0 0 0
## 5 0 0 1 0
## Trisetum.flavescens Tussilago.farfara Urtica.diocia Veronica.officinalis
## 1 0 0 1 0
## 2 0 0 0 0
## 3 1 0 0 0
## 4 0 0 0 0
## 5 0 0 52 0
## Vicia.sativa Viola.riviniana Weissia.controversa Zygodon.stirtonii
## 1 0 28 0 0
## 2 0 0 0 0
## 3 110 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
head(AddiewellPC)
## pH_level SiO2 Al2O3 Fe2O3 CaO MgO Na2O K2O
## 1 5.046 118728.5 53983.53 39237.77 5503.119 3859.430 1557.899 6724.197
## 2 6.175 164069.7 76476.67 49799.10 8790.696 6512.788 2151.385 11290.010
## 3 6.227 148176.9 72507.30 50778.29 8218.944 5970.055 2151.385 10210.818
## 4 5.989 182299.6 86797.06 51407.77 7861.598 7357.038 3115.799 13282.364
## 5 5.717 198659.8 84415.43 54555.19 6146.341 5427.323 2967.428 11622.069
## Cr2O3 TiO2 MnO P2O5 SrO BaO LOI Ag As B Be
## 1 82.10429 3236.452 1084.240 3899.496 84.55946 268.6954 54.8 0.20 26 10 1.5
## 2 95.78834 4495.072 1006.795 4199.458 84.55946 358.2605 37.7 0.30 24 10 1.7
## 3 88.94632 4375.203 1084.240 4499.419 84.55946 358.2605 41.5 0.20 24 10 1.8
## 4 95.78834 5274.217 1161.686 4699.393 169.11891 358.2605 31.9 0.15 60 10 1.5
## 5 102.63036 6353.035 619.566 3599.535 169.11891 447.8257 28.6 0.15 20 10 1.3
## Bi Cd Co Cr Cu Ga Hg La Li Mo Ni Pb S Sb Sc Th Ti Tl U V W
## 1 1.5 0.90 20 87 10 0.5 20 10 4 52 148 1600 3.0 3 10 5 5 55 5 221 0.001
## 2 1.5 0.70 22 56 10 0.5 30 20 3 63 92 1500 1.5 6 10 5 5 57 5 154 0.001
## 3 1.5 0.80 23 59 10 0.5 30 20 4 62 103 1700 1.5 5 10 5 5 59 5 180 0.001
## 4 1.5 0.80 22 54 10 1.0 30 40 3 61 36 1400 1.5 7 10 5 5 53 5 98 0.001
## 5 1.5 0.25 14 41 10 0.5 20 20 2 43 65 3500 1.5 5 10 5 5 48 5 84 0.001
## Zn Akermanite Albite Aluminium.oxide.hydroxide Anhydrite Aragonite
## 1 0.0221 0 0 0 0 0
## 2 0.0154 0 0 0 0 0
## 3 0.0180 0 0 0 0 0
## 4 0.0098 0 0 0 0 0
## 5 0.0084 0 0 0 0 0
## Biotite Birnessite Calcite Clinochlore Cuspidine Diaspore Dickite Gehlenite
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 1 0
## Goethite Haematite Illite Kaolinite Langite Linnaeite Magnesioferrite
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 1
## 3 0 0 0 0 0 0 0
## 4 0 0 1 0 0 0 0
## 5 0 1 0 0 0 0 0
## Melilite Merwinite Microcline Mullite Muscovite Orthoclase Orthopyroxene
## 1 0 0 0 0 1 0 0
## 2 0 0 0 0 1 0 0
## 3 0 0 0 0 1 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 1 0 0
## Periclase Phengite Pseudowollastonite Quartz Staurolite Valentinite
## 1 0 0 0 1 0 0
## 2 0 0 0 1 0 0
## 3 0 0 0 1 0 0
## 4 0 0 0 1 0 0
## 5 0 0 0 0 0 0
#Now to run some ANOSIMS…
#anoASiO2 <- anosim(AddiewellPS, AddiewellPC$SiO2, distance = “bray”, permutations = 9999)
#When this ANOSIM is run, the error message: “there should be replicates within groups” #comes up. This error message came up for other substrate variables for the #data. From now on, only variables that were tested successfully will be included.
anoANa2O <- anosim(AddiewellPS, AddiewellPC$Na2O, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoANa2O
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$Na2O, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7778
## Significance: 0.2
##
## Permutation: free
## Number of permutations: 119
#High R statistic, but with a non-significant p value of 0.2
anoACr2O3 <- anosim(AddiewellPS, AddiewellPC$Cr2O3, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoACr2O3
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$Cr2O3, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.5556
## Significance: 0.3
##
## Permutation: free
## Number of permutations: 119
anoAMnO <- anosim(AddiewellPS, AddiewellPC$MnO, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoAMnO
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$MnO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1111
## Significance: 0.5
##
## Permutation: free
## Number of permutations: 119
anoASrO <- anosim(AddiewellPS, AddiewellPC$SrO, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoASrO
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$SrO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.4167
## Significance: 0.1
##
## Permutation: free
## Number of permutations: 119
anoABaO <- anosim(AddiewellPS, AddiewellPC$BaO, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoABaO
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$BaO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.5238
## Significance: 0.3
##
## Permutation: free
## Number of permutations: 119
anoAAg <- anosim(AddiewellPS, AddiewellPC$Ag, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoAAg
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$Ag, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.375
## Significance: 1
##
## Permutation: free
## Number of permutations: 119
anoAAs <- anosim(AddiewellPS, AddiewellPC$As, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoAAs
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$As, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7778
## Significance: 0.2
##
## Permutation: free
## Number of permutations: 119
anoABe <- anosim(AddiewellPS, AddiewellPC$Be, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoABe
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$Be, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.3333
## Significance: 0.4
##
## Permutation: free
## Number of permutations: 119
anoACd <- anosim(AddiewellPS, AddiewellPC$Cd, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoACd
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$Cd, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1111
## Significance: 0.6
##
## Permutation: free
## Number of permutations: 119
anoACo <- anosim(AddiewellPS, AddiewellPC$Co, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoACo
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$Co, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.5556
## Significance: 0.3
##
## Permutation: free
## Number of permutations: 119
anoASb <- anosim(AddiewellPS, AddiewellPC$Sb, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoASb
##
## Call:
## anosim(x = AddiewellPS, grouping = AddiewellPC$Sb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -1
## Significance: 1
##
## Permutation: free
## Number of permutations: 119
#None of the ANOSIM tests led to stastistically significant results. Because of this, #CCAs might not be entirely appropriate to represent the data visually. #NMDS analyses will be utilised instead to visualise the plant data.
AddiewellNMDS <- metaMDS(AddiewellPS, noshare = TRUE, autotransform = FALSE, trymax = 500)
## Run 0 stress 4.555731e-05
## Run 1 stress 0
## ... New best solution
## ... Procrustes: rmse 0.1394386 max resid 0.199908
## Run 2 stress 5.334061e-05
## ... Procrustes: rmse 0.02781573 max resid 0.03736889
## Run 3 stress 0
## ... Procrustes: rmse 0.04312613 max resid 0.06217065
## Run 4 stress 9.628981e-05
## ... Procrustes: rmse 0.2605213 max resid 0.4116508
## Run 5 stress 6.607449e-05
## ... Procrustes: rmse 0.1559432 max resid 0.239096
## Run 6 stress 8.004008e-05
## ... Procrustes: rmse 0.2101967 max resid 0.3296899
## Run 7 stress 4.756845e-05
## ... Procrustes: rmse 0.1958024 max resid 0.3024243
## Run 8 stress 9.927506e-05
## ... Procrustes: rmse 0.2967313 max resid 0.4221668
## Run 9 stress 5.733367e-05
## ... Procrustes: rmse 0.2906466 max resid 0.4051213
## Run 10 stress 0.000124645
## ... Procrustes: rmse 0.2940408 max resid 0.4214675
## Run 11 stress 5.512373e-05
## ... Procrustes: rmse 0.1825714 max resid 0.2816075
## Run 12 stress 9.789625e-05
## ... Procrustes: rmse 0.09300779 max resid 0.1378204
## Run 13 stress 3.864466e-05
## ... Procrustes: rmse 0.1534289 max resid 0.2375754
## Run 14 stress 8.745128e-05
## ... Procrustes: rmse 0.1625155 max resid 0.2342181
## Run 15 stress 8.064009e-05
## ... Procrustes: rmse 0.1579679 max resid 0.2466169
## Run 16 stress 5.418653e-05
## ... Procrustes: rmse 0.2443975 max resid 0.3972858
## Run 17 stress 9.876665e-05
## ... Procrustes: rmse 0.09985639 max resid 0.1617047
## Run 18 stress 0
## ... Procrustes: rmse 0.03220173 max resid 0.05169976
## Run 19 stress 5.4069e-05
## ... Procrustes: rmse 0.071993 max resid 0.1164186
## Run 20 stress 7.830082e-05
## ... Procrustes: rmse 0.1491511 max resid 0.2014685
## Run 21 stress 8.71607e-05
## ... Procrustes: rmse 0.2158104 max resid 0.3236803
## Run 22 stress 9.508762e-05
## ... Procrustes: rmse 0.2658905 max resid 0.4200486
## Run 23 stress 9.586218e-05
## ... Procrustes: rmse 0.2237021 max resid 0.2843755
## Run 24 stress 9.103485e-05
## ... Procrustes: rmse 0.2559357 max resid 0.4206671
## Run 25 stress 5.734731e-05
## ... Procrustes: rmse 0.08941837 max resid 0.1293258
## Run 26 stress 0.0001934982
## ... Procrustes: rmse 0.126113 max resid 0.190014
## Run 27 stress 9.427861e-05
## ... Procrustes: rmse 0.215611 max resid 0.3046192
## Run 28 stress 0
## ... Procrustes: rmse 0.03576356 max resid 0.05520864
## Run 29 stress 9.439427e-05
## ... Procrustes: rmse 0.2384413 max resid 0.3885948
## Run 30 stress 9.735806e-05
## ... Procrustes: rmse 0.1686201 max resid 0.2577146
## Run 31 stress 9.744262e-05
## ... Procrustes: rmse 0.2644396 max resid 0.4327375
## Run 32 stress 9.633098e-05
## ... Procrustes: rmse 0.2314866 max resid 0.2818013
## Run 33 stress 9.727373e-05
## ... Procrustes: rmse 0.2134852 max resid 0.2977612
## Run 34 stress 4.412856e-05
## ... Procrustes: rmse 0.2273248 max resid 0.3568848
## Run 35 stress 9.809342e-05
## ... Procrustes: rmse 0.2027214 max resid 0.267987
## Run 36 stress 7.944163e-05
## ... Procrustes: rmse 0.1283586 max resid 0.1841102
## Run 37 stress 9.901716e-05
## ... Procrustes: rmse 0.2267937 max resid 0.2856523
## Run 38 stress 9.595322e-05
## ... Procrustes: rmse 0.2679173 max resid 0.39266
## Run 39 stress 4.921681e-05
## ... Procrustes: rmse 0.1130304 max resid 0.1610114
## Run 40 stress 9.877517e-05
## ... Procrustes: rmse 0.08107369 max resid 0.1168006
## Run 41 stress 6.06864e-05
## ... Procrustes: rmse 0.1638209 max resid 0.2049504
## Run 42 stress 8.624905e-05
## ... Procrustes: rmse 0.2705442 max resid 0.4358683
## Run 43 stress 9.928364e-05
## ... Procrustes: rmse 0.2084866 max resid 0.320789
## Run 44 stress 9.994884e-05
## ... Procrustes: rmse 0.0566009 max resid 0.08906317
## Run 45 stress 8.971732e-05
## ... Procrustes: rmse 0.2078997 max resid 0.3324217
## Run 46 stress 5.983457e-05
## ... Procrustes: rmse 0.01697042 max resid 0.02855343
## Run 47 stress 9.795553e-05
## ... Procrustes: rmse 0.2258254 max resid 0.3624661
## Run 48 stress 9.077041e-05
## ... Procrustes: rmse 0.2326288 max resid 0.2817835
## Run 49 stress 1.780997e-06
## ... Procrustes: rmse 0.03455924 max resid 0.03951522
## Run 50 stress 0.0005588758
## Run 51 stress 9.743025e-05
## ... Procrustes: rmse 0.2264446 max resid 0.2909201
## Run 52 stress 8.984606e-05
## ... Procrustes: rmse 0.2154693 max resid 0.3195796
## Run 53 stress 0
## ... Procrustes: rmse 0.03001268 max resid 0.04955042
## Run 54 stress 8.846187e-05
## ... Procrustes: rmse 0.2264834 max resid 0.2863919
## Run 55 stress 5.686017e-05
## ... Procrustes: rmse 0.2350458 max resid 0.3027396
## Run 56 stress 7.96967e-05
## ... Procrustes: rmse 0.2246847 max resid 0.345365
## Run 57 stress 9.415498e-05
## ... Procrustes: rmse 0.03078587 max resid 0.04816126
## Run 58 stress 7.103488e-05
## ... Procrustes: rmse 0.1161369 max resid 0.155063
## Run 59 stress 9.926696e-05
## ... Procrustes: rmse 0.1503476 max resid 0.2378199
## Run 60 stress 8.939536e-05
## ... Procrustes: rmse 0.2087667 max resid 0.2660027
## Run 61 stress 9.92562e-05
## ... Procrustes: rmse 0.2238046 max resid 0.2892603
## Run 62 stress 9.477649e-05
## ... Procrustes: rmse 0.1514453 max resid 0.2479281
## Run 63 stress 8.586595e-05
## ... Procrustes: rmse 0.2478378 max resid 0.4151296
## Run 64 stress 9.982107e-05
## ... Procrustes: rmse 0.2756214 max resid 0.4354381
## Run 65 stress 3.969653e-05
## ... Procrustes: rmse 0.03531637 max resid 0.05149841
## Run 66 stress 8.336222e-05
## ... Procrustes: rmse 0.2213674 max resid 0.2918831
## Run 67 stress 8.244809e-05
## ... Procrustes: rmse 0.08956448 max resid 0.1226475
## Run 68 stress 3.776605e-05
## ... Procrustes: rmse 0.205024 max resid 0.2685254
## Run 69 stress 9.813386e-05
## ... Procrustes: rmse 0.2670257 max resid 0.4438814
## Run 70 stress 9.325933e-05
## ... Procrustes: rmse 0.06159678 max resid 0.08251229
## Run 71 stress 8.241086e-05
## ... Procrustes: rmse 0.2430191 max resid 0.3833474
## Run 72 stress 8.13581e-05
## ... Procrustes: rmse 0.2269246 max resid 0.2827465
## Run 73 stress 7.201479e-05
## ... Procrustes: rmse 0.2410271 max resid 0.2864472
## Run 74 stress 9.866711e-05
## ... Procrustes: rmse 0.2189354 max resid 0.2967141
## Run 75 stress 8.080119e-05
## ... Procrustes: rmse 0.1979584 max resid 0.3282312
## Run 76 stress 8.540973e-05
## ... Procrustes: rmse 0.2147006 max resid 0.3086361
## Run 77 stress 7.576413e-05
## ... Procrustes: rmse 0.1894706 max resid 0.2937232
## Run 78 stress 2.676033e-05
## ... Procrustes: rmse 0.2222876 max resid 0.3568479
## Run 79 stress 9.820088e-05
## ... Procrustes: rmse 0.2756163 max resid 0.4354511
## Run 80 stress 3.737498e-05
## ... Procrustes: rmse 0.2350954 max resid 0.3338334
## Run 81 stress 5.170606e-05
## ... Procrustes: rmse 0.03348428 max resid 0.05290226
## Run 82 stress 0.0001615429
## ... Procrustes: rmse 0.1172044 max resid 0.1755751
## Run 83 stress 9.629054e-05
## ... Procrustes: rmse 0.227002 max resid 0.2856541
## Run 84 stress 5.749932e-06
## ... Procrustes: rmse 0.01607455 max resid 0.02001607
## Run 85 stress 0
## ... Procrustes: rmse 0.1161329 max resid 0.1733693
## Run 86 stress 8.757929e-05
## ... Procrustes: rmse 0.2230758 max resid 0.2922253
## Run 87 stress 9.889862e-05
## ... Procrustes: rmse 0.04534924 max resid 0.06498945
## Run 88 stress 9.911963e-05
## ... Procrustes: rmse 0.2574684 max resid 0.3733342
## Run 89 stress 0.0002175778
## ... Procrustes: rmse 0.09485009 max resid 0.1413596
## Run 90 stress 9.63271e-05
## ... Procrustes: rmse 0.2226154 max resid 0.2899366
## Run 91 stress 9.589235e-05
## ... Procrustes: rmse 0.2326569 max resid 0.2985666
## Run 92 stress 9.851625e-05
## ... Procrustes: rmse 0.2234867 max resid 0.3562357
## Run 93 stress 9.673675e-05
## ... Procrustes: rmse 0.2338646 max resid 0.2790294
## Run 94 stress 6.004884e-05
## ... Procrustes: rmse 0.2493266 max resid 0.4028386
## Run 95 stress 9.987296e-05
## ... Procrustes: rmse 0.07106277 max resid 0.1133562
## Run 96 stress 6.78939e-05
## ... Procrustes: rmse 0.02605334 max resid 0.04261392
## Run 97 stress 9.995533e-05
## ... Procrustes: rmse 0.220866 max resid 0.2685383
## Run 98 stress 9.438229e-05
## ... Procrustes: rmse 0.2204371 max resid 0.3366578
## Run 99 stress 6.421406e-05
## ... Procrustes: rmse 0.270726 max resid 0.4310821
## Run 100 stress 2.696124e-05
## ... Procrustes: rmse 0.01978326 max resid 0.02632141
## Run 101 stress 9.227456e-05
## ... Procrustes: rmse 0.2450924 max resid 0.379314
## Run 102 stress 7.940942e-05
## ... Procrustes: rmse 0.2149571 max resid 0.3092425
## Run 103 stress 9.793945e-05
## ... Procrustes: rmse 0.2335197 max resid 0.2812273
## Run 104 stress 9.912932e-05
## ... Procrustes: rmse 0.2911421 max resid 0.4217707
## Run 105 stress 0
## ... Procrustes: rmse 0.01593162 max resid 0.02528953
## Run 106 stress 9.491467e-05
## ... Procrustes: rmse 0.2023571 max resid 0.2677698
## Run 107 stress 9.64597e-05
## ... Procrustes: rmse 0.1095906 max resid 0.1617171
## Run 108 stress 9.860447e-05
## ... Procrustes: rmse 0.2067299 max resid 0.3165291
## Run 109 stress 7.262788e-05
## ... Procrustes: rmse 0.2196147 max resid 0.3513147
## Run 110 stress 9.388294e-05
## ... Procrustes: rmse 0.2678891 max resid 0.4331526
## Run 111 stress 2.990623e-05
## ... Procrustes: rmse 0.2381909 max resid 0.3748571
## Run 112 stress 5.576394e-05
## ... Procrustes: rmse 0.1678861 max resid 0.2518949
## Run 113 stress 7.738752e-05
## ... Procrustes: rmse 0.061483 max resid 0.08766965
## Run 114 stress 9.755985e-05
## ... Procrustes: rmse 0.2684587 max resid 0.4011294
## Run 115 stress 8.465723e-05
## ... Procrustes: rmse 0.1883264 max resid 0.2836363
## Run 116 stress 2.015106e-05
## ... Procrustes: rmse 0.03172841 max resid 0.05339025
## Run 117 stress 9.880862e-05
## ... Procrustes: rmse 0.2183645 max resid 0.2963482
## Run 118 stress 9.466567e-05
## ... Procrustes: rmse 0.1163633 max resid 0.1875544
## Run 119 stress 9.212682e-05
## ... Procrustes: rmse 0.07853849 max resid 0.1127383
## Run 120 stress 9.526069e-05
## ... Procrustes: rmse 0.2162678 max resid 0.2771077
## Run 121 stress 5.073535e-05
## ... Procrustes: rmse 0.3219801 max resid 0.4370961
## Run 122 stress 5.543104e-05
## ... Procrustes: rmse 0.1655598 max resid 0.2557364
## Run 123 stress 5.541805e-05
## ... Procrustes: rmse 0.2651334 max resid 0.4364372
## Run 124 stress 8.917724e-05
## ... Procrustes: rmse 0.2206187 max resid 0.293042
## Run 125 stress 0
## ... Procrustes: rmse 0.03103839 max resid 0.0517568
## Run 126 stress 9.868087e-05
## ... Procrustes: rmse 0.2703791 max resid 0.4052905
## Run 127 stress 5.151571e-05
## ... Procrustes: rmse 0.1887136 max resid 0.2840723
## Run 128 stress 8.627828e-05
## ... Procrustes: rmse 0.1431563 max resid 0.2168914
## Run 129 stress 0.0002353894
## ... Procrustes: rmse 0.2944349 max resid 0.4295659
## Run 130 stress 8.382393e-05
## ... Procrustes: rmse 0.08444356 max resid 0.1199069
## Run 131 stress 8.487414e-05
## ... Procrustes: rmse 0.179345 max resid 0.2665164
## Run 132 stress 3.585677e-05
## ... Procrustes: rmse 0.05596548 max resid 0.08449081
## Run 133 stress 9.237112e-05
## ... Procrustes: rmse 0.2624736 max resid 0.4458163
## Run 134 stress 0.0001379646
## ... Procrustes: rmse 0.3057661 max resid 0.4220396
## Run 135 stress 9.818747e-05
## ... Procrustes: rmse 0.2197128 max resid 0.2827065
## Run 136 stress 9.728388e-05
## ... Procrustes: rmse 0.2192869 max resid 0.3025434
## Run 137 stress 9.990504e-05
## ... Procrustes: rmse 0.2284228 max resid 0.3121845
## Run 138 stress 8.692552e-05
## ... Procrustes: rmse 0.1677465 max resid 0.2654495
## Run 139 stress 9.447367e-05
## ... Procrustes: rmse 0.2398571 max resid 0.3775603
## Run 140 stress 0.0007236415
## Run 141 stress 5.352662e-05
## ... Procrustes: rmse 0.1590046 max resid 0.2094758
## Run 142 stress 9.315055e-05
## ... Procrustes: rmse 0.2179084 max resid 0.3322182
## Run 143 stress 8.729033e-05
## ... Procrustes: rmse 0.2323969 max resid 0.2800636
## Run 144 stress 0
## ... Procrustes: rmse 0.03920591 max resid 0.05540056
## Run 145 stress 9.84576e-05
## ... Procrustes: rmse 0.06416471 max resid 0.09333506
## Run 146 stress 0.0003089019
## ... Procrustes: rmse 0.03591237 max resid 0.05331922
## Run 147 stress 7.372445e-05
## ... Procrustes: rmse 0.2015201 max resid 0.3255738
## Run 148 stress 9.167227e-05
## ... Procrustes: rmse 0.2213787 max resid 0.2911152
## Run 149 stress 0.0001172193
## ... Procrustes: rmse 0.1904607 max resid 0.2965131
## Run 150 stress 2.092111e-05
## ... Procrustes: rmse 0.156601 max resid 0.2213505
## Run 151 stress 2.627147e-05
## ... Procrustes: rmse 0.05892797 max resid 0.09179957
## Run 152 stress 0
## ... Procrustes: rmse 0.02953372 max resid 0.03743263
## Run 153 stress 7.444393e-05
## ... Procrustes: rmse 0.2154414 max resid 0.3218797
## Run 154 stress 9.958915e-05
## ... Procrustes: rmse 0.09608842 max resid 0.1408996
## Run 155 stress 6.737471e-05
## ... Procrustes: rmse 0.1827998 max resid 0.2925356
## Run 156 stress 0
## ... Procrustes: rmse 0.02089313 max resid 0.03376175
## Run 157 stress 9.818532e-05
## ... Procrustes: rmse 0.03711718 max resid 0.06151738
## Run 158 stress 8.107503e-05
## ... Procrustes: rmse 0.2079819 max resid 0.2522333
## Run 159 stress 4.978986e-05
## ... Procrustes: rmse 0.1860952 max resid 0.2802297
## Run 160 stress 9.400989e-05
## ... Procrustes: rmse 0.2148671 max resid 0.3139049
## Run 161 stress 8.800288e-05
## ... Procrustes: rmse 0.2512408 max resid 0.3960858
## Run 162 stress 9.935937e-05
## ... Procrustes: rmse 0.2718659 max resid 0.4057625
## Run 163 stress 9.343577e-05
## ... Procrustes: rmse 0.2304909 max resid 0.2823695
## Run 164 stress 9.586156e-05
## ... Procrustes: rmse 0.2328287 max resid 0.2801917
## Run 165 stress 8.374148e-05
## ... Procrustes: rmse 0.07612721 max resid 0.1045267
## Run 166 stress 0
## ... Procrustes: rmse 0.03836524 max resid 0.05510684
## Run 167 stress 9.395692e-05
## ... Procrustes: rmse 0.2227071 max resid 0.3586932
## Run 168 stress 8.837876e-05
## ... Procrustes: rmse 0.119388 max resid 0.1808288
## Run 169 stress 8.496701e-05
## ... Procrustes: rmse 0.2262469 max resid 0.3505862
## Run 170 stress 3.788856e-05
## ... Procrustes: rmse 0.236963 max resid 0.2784925
## Run 171 stress 5.755605e-05
## ... Procrustes: rmse 0.1472538 max resid 0.2227193
## Run 172 stress 9.215724e-05
## ... Procrustes: rmse 0.2387749 max resid 0.2821092
## Run 173 stress 6.67605e-05
## ... Procrustes: rmse 0.02693745 max resid 0.04535003
## Run 174 stress 9.607199e-05
## ... Procrustes: rmse 0.2570506 max resid 0.3913349
## Run 175 stress 8.499008e-05
## ... Procrustes: rmse 0.1861608 max resid 0.305338
## Run 176 stress 9.665384e-05
## ... Procrustes: rmse 0.2156676 max resid 0.3061237
## Run 177 stress 9.799095e-05
## ... Procrustes: rmse 0.2283665 max resid 0.3765548
## Run 178 stress 9.307685e-05
## ... Procrustes: rmse 0.2126754 max resid 0.2945302
## Run 179 stress 7.569902e-05
## ... Procrustes: rmse 0.2267289 max resid 0.2857681
## Run 180 stress 9.366532e-05
## ... Procrustes: rmse 0.2211097 max resid 0.3024885
## Run 181 stress 3.479911e-05
## ... Procrustes: rmse 0.0729068 max resid 0.1033163
## Run 182 stress 8.897008e-05
## ... Procrustes: rmse 0.0928177 max resid 0.1463303
## Run 183 stress 9.447818e-05
## ... Procrustes: rmse 0.2308205 max resid 0.2853456
## Run 184 stress 5.813384e-05
## ... Procrustes: rmse 0.130485 max resid 0.1615704
## Run 185 stress 1.574195e-05
## ... Procrustes: rmse 0.1756863 max resid 0.2883974
## Run 186 stress 6.113626e-05
## ... Procrustes: rmse 0.150811 max resid 0.1997707
## Run 187 stress 0.0001718735
## ... Procrustes: rmse 0.3115801 max resid 0.4331315
## Run 188 stress 9.89834e-05
## ... Procrustes: rmse 0.07655785 max resid 0.124899
## Run 189 stress 9.536423e-05
## ... Procrustes: rmse 0.2673298 max resid 0.383448
## Run 190 stress 9.465914e-05
## ... Procrustes: rmse 0.2579418 max resid 0.4118988
## Run 191 stress 0
## ... Procrustes: rmse 0.01519549 max resid 0.02333838
## Run 192 stress 8.642068e-05
## ... Procrustes: rmse 0.03423584 max resid 0.04668039
## Run 193 stress 8.861975e-05
## ... Procrustes: rmse 0.09015589 max resid 0.1381508
## Run 194 stress 0.0009539386
## Run 195 stress 8.514157e-05
## ... Procrustes: rmse 0.2298627 max resid 0.2830141
## Run 196 stress 9.226761e-05
## ... Procrustes: rmse 0.2610752 max resid 0.3793442
## Run 197 stress 9.702963e-05
## ... Procrustes: rmse 0.2288476 max resid 0.3590011
## Run 198 stress 9.18886e-05
## ... Procrustes: rmse 0.2077449 max resid 0.3234064
## Run 199 stress 0
## ... Procrustes: rmse 0.01764292 max resid 0.02866165
## Run 200 stress 9.868496e-05
## ... Procrustes: rmse 0.03171357 max resid 0.04360209
## Run 201 stress 8.648616e-05
## ... Procrustes: rmse 0.2209003 max resid 0.2893964
## Run 202 stress 0
## ... Procrustes: rmse 0.1129993 max resid 0.1663926
## Run 203 stress 9.015246e-05
## ... Procrustes: rmse 0.21441 max resid 0.3113304
## Run 204 stress 0.0007402198
## Run 205 stress 9.907486e-05
## ... Procrustes: rmse 0.2553563 max resid 0.4357233
## Run 206 stress 9.878225e-05
## ... Procrustes: rmse 0.1310512 max resid 0.2131081
## Run 207 stress 9.562383e-05
## ... Procrustes: rmse 0.2170472 max resid 0.3284098
## Run 208 stress 8.491573e-05
## ... Procrustes: rmse 0.2043086 max resid 0.3091006
## Run 209 stress 9.986556e-05
## ... Procrustes: rmse 0.06985616 max resid 0.09226173
## Run 210 stress 0
## ... Procrustes: rmse 0.02954655 max resid 0.03905473
## Run 211 stress 9.887097e-05
## ... Procrustes: rmse 0.2145926 max resid 0.3118193
## Run 212 stress 8.672763e-05
## ... Procrustes: rmse 0.07883148 max resid 0.1106458
## Run 213 stress 9.552328e-05
## ... Procrustes: rmse 0.2756395 max resid 0.435466
## Run 214 stress 8.030272e-05
## ... Procrustes: rmse 0.008093424 max resid 0.01167093
## Run 215 stress 8.377006e-05
## ... Procrustes: rmse 0.2460264 max resid 0.3749677
## Run 216 stress 6.315374e-05
## ... Procrustes: rmse 0.2139316 max resid 0.3338638
## Run 217 stress 9.600009e-05
## ... Procrustes: rmse 0.2383862 max resid 0.2822971
## Run 218 stress 1.681224e-05
## ... Procrustes: rmse 0.2150344 max resid 0.3167319
## Run 219 stress 9.624566e-05
## ... Procrustes: rmse 0.09208638 max resid 0.1458466
## Run 220 stress 4.528991e-05
## ... Procrustes: rmse 0.2280467 max resid 0.3695835
## Run 221 stress 7.210223e-05
## ... Procrustes: rmse 0.2795019 max resid 0.4216335
## Run 222 stress 8.995761e-05
## ... Procrustes: rmse 0.26992 max resid 0.4360469
## Run 223 stress 9.801996e-05
## ... Procrustes: rmse 0.2217833 max resid 0.2948313
## Run 224 stress 9.332327e-05
## ... Procrustes: rmse 0.2055231 max resid 0.3159187
## Run 225 stress 8.811138e-05
## ... Procrustes: rmse 0.2181188 max resid 0.2992833
## Run 226 stress 7.815537e-05
## ... Procrustes: rmse 0.1998291 max resid 0.3154951
## Run 227 stress 9.459647e-05
## ... Procrustes: rmse 0.2258328 max resid 0.3511319
## Run 228 stress 9.443383e-05
## ... Procrustes: rmse 0.2146524 max resid 0.3114568
## Run 229 stress 9.605316e-05
## ... Procrustes: rmse 0.2539661 max resid 0.4368433
## Run 230 stress 9.073898e-05
## ... Procrustes: rmse 0.1282267 max resid 0.2016957
## Run 231 stress 8.718319e-05
## ... Procrustes: rmse 0.2013003 max resid 0.2476598
## Run 232 stress 0
## ... Procrustes: rmse 0.02913345 max resid 0.03777831
## Run 233 stress 9.5537e-05
## ... Procrustes: rmse 0.1161463 max resid 0.188922
## Run 234 stress 9.164859e-05
## ... Procrustes: rmse 0.2147602 max resid 0.3095165
## Run 235 stress 8.835543e-05
## ... Procrustes: rmse 0.2387392 max resid 0.2808208
## Run 236 stress 9.062919e-05
## ... Procrustes: rmse 0.220133 max resid 0.2932983
## Run 237 stress 8.327114e-05
## ... Procrustes: rmse 0.2067937 max resid 0.3003896
## Run 238 stress 0
## ... Procrustes: rmse 0.02977867 max resid 0.05024357
## Run 239 stress 2.465723e-05
## ... Procrustes: rmse 0.2100273 max resid 0.3477689
## Run 240 stress 5.615589e-05
## ... Procrustes: rmse 0.2275514 max resid 0.3513519
## Run 241 stress 9.892598e-05
## ... Procrustes: rmse 0.2259336 max resid 0.2863412
## Run 242 stress 3.108023e-05
## ... Procrustes: rmse 0.2147561 max resid 0.3344392
## Run 243 stress 9.992826e-05
## ... Procrustes: rmse 0.3107591 max resid 0.4349551
## Run 244 stress 4.012506e-05
## ... Procrustes: rmse 0.2244659 max resid 0.3492232
## Run 245 stress 9.985868e-05
## ... Procrustes: rmse 0.2595346 max resid 0.4006705
## Run 246 stress 0.2597159
## Run 247 stress 9.426285e-05
## ... Procrustes: rmse 0.2170601 max resid 0.3003397
## Run 248 stress 9.58549e-05
## ... Procrustes: rmse 0.2357231 max resid 0.3819207
## Run 249 stress 2.507703e-06
## ... Procrustes: rmse 0.03425143 max resid 0.05588093
## Run 250 stress 8.449197e-05
## ... Procrustes: rmse 0.2145592 max resid 0.3107716
## Run 251 stress 0
## ... Procrustes: rmse 0.03264172 max resid 0.05445739
## Run 252 stress 4.354489e-05
## ... Procrustes: rmse 0.1944124 max resid 0.3020225
## Run 253 stress 9.127963e-05
## ... Procrustes: rmse 0.2318188 max resid 0.2810083
## Run 254 stress 9.9896e-05
## ... Procrustes: rmse 0.0364434 max resid 0.05962512
## Run 255 stress 9.869726e-05
## ... Procrustes: rmse 0.2548659 max resid 0.4399031
## Run 256 stress 9.267591e-05
## ... Procrustes: rmse 0.2411954 max resid 0.2814186
## Run 257 stress 7.669612e-05
## ... Procrustes: rmse 0.2501118 max resid 0.4092715
## Run 258 stress 9.624677e-05
## ... Procrustes: rmse 0.1568631 max resid 0.2471394
## Run 259 stress 5.311043e-05
## ... Procrustes: rmse 0.2234535 max resid 0.349556
## Run 260 stress 7.2058e-05
## ... Procrustes: rmse 0.2397339 max resid 0.4090438
## Run 261 stress 9.485484e-05
## ... Procrustes: rmse 0.2183755 max resid 0.2789266
## Run 262 stress 9.529087e-05
## ... Procrustes: rmse 0.2624594 max resid 0.3822431
## Run 263 stress 7.756567e-05
## ... Procrustes: rmse 0.1993385 max resid 0.2690513
## Run 264 stress 9.04262e-05
## ... Procrustes: rmse 0.2343446 max resid 0.2796963
## Run 265 stress 9.72807e-05
## ... Procrustes: rmse 0.2495066 max resid 0.3939783
## Run 266 stress 9.59705e-05
## ... Procrustes: rmse 0.2255459 max resid 0.3436493
## Run 267 stress 9.890344e-05
## ... Procrustes: rmse 0.273031 max resid 0.430011
## Run 268 stress 4.406207e-05
## ... Procrustes: rmse 0.16554 max resid 0.2166854
## Run 269 stress 7.878926e-05
## ... Procrustes: rmse 0.1179613 max resid 0.1744615
## Run 270 stress 6.46707e-05
## ... Procrustes: rmse 0.1917776 max resid 0.3062559
## Run 271 stress 9.278496e-05
## ... Procrustes: rmse 0.2441458 max resid 0.29423
## Run 272 stress 6.392639e-05
## ... Procrustes: rmse 0.04321804 max resid 0.06346816
## Run 273 stress 9.458601e-05
## ... Procrustes: rmse 0.2369582 max resid 0.3719893
## Run 274 stress 7.067913e-05
## ... Procrustes: rmse 0.2377487 max resid 0.3542771
## Run 275 stress 9.144662e-05
## ... Procrustes: rmse 0.2563462 max resid 0.4011915
## Run 276 stress 8.19607e-05
## ... Procrustes: rmse 0.2191761 max resid 0.2939813
## Run 277 stress 9.056787e-05
## ... Procrustes: rmse 0.2335803 max resid 0.2803886
## Run 278 stress 9.142638e-05
## ... Procrustes: rmse 0.01940319 max resid 0.03284806
## Run 279 stress 7.101178e-05
## ... Procrustes: rmse 0.2240965 max resid 0.3075409
## Run 280 stress 3.901716e-05
## ... Procrustes: rmse 0.2027703 max resid 0.3159066
## Run 281 stress 9.483752e-05
## ... Procrustes: rmse 0.2409162 max resid 0.3986245
## Run 282 stress 5.062513e-05
## ... Procrustes: rmse 0.06294738 max resid 0.0922721
## Run 283 stress 0
## ... Procrustes: rmse 0.02687407 max resid 0.04230708
## Run 284 stress 9.353839e-05
## ... Procrustes: rmse 0.2757957 max resid 0.4358492
## Run 285 stress 7.116343e-05
## ... Procrustes: rmse 0.2537459 max resid 0.379897
## Run 286 stress 5.803889e-05
## ... Procrustes: rmse 0.1900861 max resid 0.2882796
## Run 287 stress 0
## ... Procrustes: rmse 0.03523595 max resid 0.05809847
## Run 288 stress 8.03632e-05
## ... Procrustes: rmse 0.05767118 max resid 0.07948652
## Run 289 stress 9.808804e-05
## ... Procrustes: rmse 0.2208076 max resid 0.3057976
## Run 290 stress 0.0002240968
## ... Procrustes: rmse 0.2916229 max resid 0.4471729
## Run 291 stress 0.0005309804
## Run 292 stress 2.939947e-05
## ... Procrustes: rmse 0.07217383 max resid 0.1051911
## Run 293 stress 9.597306e-05
## ... Procrustes: rmse 0.2006537 max resid 0.3203286
## Run 294 stress 7.863315e-05
## ... Procrustes: rmse 0.2534168 max resid 0.4003256
## Run 295 stress 0
## ... Procrustes: rmse 0.02625304 max resid 0.04274233
## Run 296 stress 8.353433e-05
## ... Procrustes: rmse 0.232615 max resid 0.2800702
## Run 297 stress 9.71354e-05
## ... Procrustes: rmse 0.0932321 max resid 0.1367016
## Run 298 stress 8.879714e-05
## ... Procrustes: rmse 0.2218843 max resid 0.372732
## Run 299 stress 9.917554e-05
## ... Procrustes: rmse 0.07285773 max resid 0.1169259
## Run 300 stress 2.026094e-05
## ... Procrustes: rmse 0.04788283 max resid 0.06284705
## Run 301 stress 9.762272e-05
## ... Procrustes: rmse 0.2358589 max resid 0.3704093
## Run 302 stress 6.754942e-05
## ... Procrustes: rmse 0.2691008 max resid 0.4264226
## Run 303 stress 9.653956e-05
## ... Procrustes: rmse 0.1980838 max resid 0.2975387
## Run 304 stress 8.406496e-05
## ... Procrustes: rmse 0.2317357 max resid 0.2816406
## Run 305 stress 2.255962e-05
## ... Procrustes: rmse 0.2192249 max resid 0.3317525
## Run 306 stress 9.912545e-05
## ... Procrustes: rmse 0.2475658 max resid 0.4052789
## Run 307 stress 8.565184e-05
## ... Procrustes: rmse 0.2389092 max resid 0.2795607
## Run 308 stress 9.879975e-05
## ... Procrustes: rmse 0.223019 max resid 0.290045
## Run 309 stress 9.414603e-05
## ... Procrustes: rmse 0.1912378 max resid 0.2583599
## Run 310 stress 9.682373e-05
## ... Procrustes: rmse 0.05943224 max resid 0.0837104
## Run 311 stress 8.784742e-05
## ... Procrustes: rmse 0.2138837 max resid 0.3104114
## Run 312 stress 9.796021e-05
## ... Procrustes: rmse 0.2692764 max resid 0.4245752
## Run 313 stress 8.023633e-05
## ... Procrustes: rmse 0.1286175 max resid 0.1921336
## Run 314 stress 8.736614e-05
## ... Procrustes: rmse 0.2544823 max resid 0.4372355
## Run 315 stress 0
## ... Procrustes: rmse 0.03114795 max resid 0.04722776
## Run 316 stress 9.517923e-05
## ... Procrustes: rmse 0.2808585 max resid 0.4373482
## Run 317 stress 3.502927e-05
## ... Procrustes: rmse 0.1329272 max resid 0.2072012
## Run 318 stress 0.0003428202
## ... Procrustes: rmse 0.2919088 max resid 0.4473864
## Run 319 stress 7.850613e-05
## ... Procrustes: rmse 0.130198 max resid 0.2120276
## Run 320 stress 9.005412e-05
## ... Procrustes: rmse 0.2392467 max resid 0.2799363
## Run 321 stress 6.268496e-05
## ... Procrustes: rmse 0.1908126 max resid 0.2801866
## Run 322 stress 9.314761e-05
## ... Procrustes: rmse 0.1607159 max resid 0.2465428
## Run 323 stress 9.406998e-05
## ... Procrustes: rmse 0.2245435 max resid 0.2879981
## Run 324 stress 9.728599e-05
## ... Procrustes: rmse 0.248691 max resid 0.3926081
## Run 325 stress 8.376094e-05
## ... Procrustes: rmse 0.2372829 max resid 0.278674
## Run 326 stress 8.596343e-05
## ... Procrustes: rmse 0.2173887 max resid 0.3205483
## Run 327 stress 5.083762e-05
## ... Procrustes: rmse 0.2206111 max resid 0.3306668
## Run 328 stress 9.316241e-05
## ... Procrustes: rmse 0.2229965 max resid 0.2902135
## Run 329 stress 9.156412e-05
## ... Procrustes: rmse 0.2350294 max resid 0.2865447
## Run 330 stress 6.251641e-05
## ... Procrustes: rmse 0.1900446 max resid 0.3106813
## Run 331 stress 7.735517e-05
## ... Procrustes: rmse 0.2319572 max resid 0.3641021
## Run 332 stress 9.566366e-05
## ... Procrustes: rmse 0.225624 max resid 0.3804905
## Run 333 stress 4.456765e-05
## ... Procrustes: rmse 0.1465506 max resid 0.2044227
## Run 334 stress 0.000325465
## ... Procrustes: rmse 0.305776 max resid 0.4396932
## Run 335 stress 9.710654e-05
## ... Procrustes: rmse 0.2664702 max resid 0.4025564
## Run 336 stress 7.025001e-05
## ... Procrustes: rmse 0.2596004 max resid 0.4501322
## Run 337 stress 9.569037e-05
## ... Procrustes: rmse 0.078918 max resid 0.1131525
## Run 338 stress 0.0001422824
## ... Procrustes: rmse 0.07562601 max resid 0.1125873
## Run 339 stress 9.45158e-05
## ... Procrustes: rmse 0.2246356 max resid 0.3208068
## Run 340 stress 0.0001127112
## ... Procrustes: rmse 0.3193583 max resid 0.4363333
## Run 341 stress 7.279207e-05
## ... Procrustes: rmse 0.2322836 max resid 0.39452
## Run 342 stress 9.934792e-05
## ... Procrustes: rmse 0.1955152 max resid 0.3175611
## Run 343 stress 4.47022e-05
## ... Procrustes: rmse 0.2029181 max resid 0.311753
## Run 344 stress 9.290252e-05
## ... Procrustes: rmse 0.2331482 max resid 0.284153
## Run 345 stress 9.221173e-05
## ... Procrustes: rmse 0.07694299 max resid 0.1196494
## Run 346 stress 9.636383e-05
## ... Procrustes: rmse 0.2277065 max resid 0.2841338
## Run 347 stress 7.800077e-05
## ... Procrustes: rmse 0.2111202 max resid 0.3190936
## Run 348 stress 0.0007152809
## Run 349 stress 3.797463e-05
## ... Procrustes: rmse 0.2561769 max resid 0.4041569
## Run 350 stress 9.80986e-05
## ... Procrustes: rmse 0.2756077 max resid 0.4354373
## Run 351 stress 8.181405e-05
## ... Procrustes: rmse 0.01112962 max resid 0.01859293
## Run 352 stress 9.752746e-05
## ... Procrustes: rmse 0.2756333 max resid 0.4354625
## Run 353 stress 3.550077e-05
## ... Procrustes: rmse 0.02987142 max resid 0.04313905
## Run 354 stress 7.737677e-05
## ... Procrustes: rmse 0.2679133 max resid 0.4304921
## Run 355 stress 8.058331e-05
## ... Procrustes: rmse 0.2090418 max resid 0.2527612
## Run 356 stress 0
## ... Procrustes: rmse 0.02854658 max resid 0.04861127
## Run 357 stress 9.598882e-05
## ... Procrustes: rmse 0.2288705 max resid 0.3078335
## Run 358 stress 9.194062e-05
## ... Procrustes: rmse 0.2362482 max resid 0.3840288
## Run 359 stress 9.399578e-05
## ... Procrustes: rmse 0.2768731 max resid 0.4544541
## Run 360 stress 9.245807e-05
## ... Procrustes: rmse 0.07268131 max resid 0.1045204
## Run 361 stress 9.902559e-05
## ... Procrustes: rmse 0.06215039 max resid 0.07416722
## Run 362 stress 9.94354e-05
## ... Procrustes: rmse 0.1567125 max resid 0.239554
## Run 363 stress 8.038687e-05
## ... Procrustes: rmse 0.01996674 max resid 0.02876073
## Run 364 stress 6.327407e-05
## ... Procrustes: rmse 0.2265531 max resid 0.3627596
## Run 365 stress 9.860285e-05
## ... Procrustes: rmse 0.0634059 max resid 0.08285764
## Run 366 stress 8.156436e-05
## ... Procrustes: rmse 0.1974699 max resid 0.3128646
## Run 367 stress 9.019817e-05
## ... Procrustes: rmse 0.227693 max resid 0.2845695
## Run 368 stress 0
## ... Procrustes: rmse 0.03100595 max resid 0.05125389
## Run 369 stress 0.0007471337
## Run 370 stress 8.624724e-05
## ... Procrustes: rmse 0.2630572 max resid 0.4107576
## Run 371 stress 9.695127e-05
## ... Procrustes: rmse 0.256469 max resid 0.437598
## Run 372 stress 0
## ... Procrustes: rmse 0.008701701 max resid 0.0125888
## Run 373 stress 9.829594e-05
## ... Procrustes: rmse 0.2067778 max resid 0.3214226
## Run 374 stress 9.8081e-05
## ... Procrustes: rmse 0.234915 max resid 0.2813255
## Run 375 stress 7.425583e-05
## ... Procrustes: rmse 0.2090835 max resid 0.3178968
## Run 376 stress 0
## ... Procrustes: rmse 0.07141801 max resid 0.102436
## Run 377 stress 9.30378e-05
## ... Procrustes: rmse 0.1836637 max resid 0.247503
## Run 378 stress 0.0001999938
## ... Procrustes: rmse 0.2889778 max resid 0.4415621
## Run 379 stress 8.840409e-05
## ... Procrustes: rmse 0.08981497 max resid 0.1111339
## Run 380 stress 7.603139e-05
## ... Procrustes: rmse 0.03348609 max resid 0.05302925
## Run 381 stress 9.568671e-05
## ... Procrustes: rmse 0.2865129 max resid 0.4038414
## Run 382 stress 9.890269e-05
## ... Procrustes: rmse 0.2431348 max resid 0.285062
## Run 383 stress 9.592975e-05
## ... Procrustes: rmse 0.1684165 max resid 0.2638142
## Run 384 stress 5.295168e-05
## ... Procrustes: rmse 0.2254008 max resid 0.287906
## Run 385 stress 0
## ... Procrustes: rmse 0.09737922 max resid 0.1433448
## Run 386 stress 0
## ... Procrustes: rmse 0.02782086 max resid 0.04485353
## Run 387 stress 9.854801e-05
## ... Procrustes: rmse 0.2944811 max resid 0.4332908
## Run 388 stress 9.698294e-05
## ... Procrustes: rmse 0.2243561 max resid 0.2746936
## Run 389 stress 9.533769e-05
## ... Procrustes: rmse 0.2403142 max resid 0.2808052
## Run 390 stress 9.929638e-05
## ... Procrustes: rmse 0.2349624 max resid 0.2828917
## Run 391 stress 9.185399e-05
## ... Procrustes: rmse 0.255397 max resid 0.4400584
## Run 392 stress 8.663467e-05
## ... Procrustes: rmse 0.2257871 max resid 0.2952578
## Run 393 stress 0
## ... Procrustes: rmse 0.03187479 max resid 0.0539442
## Run 394 stress 0
## ... Procrustes: rmse 0.04729701 max resid 0.06339186
## Run 395 stress 9.962047e-05
## ... Procrustes: rmse 0.2561598 max resid 0.4407241
## Run 396 stress 9.313751e-05
## ... Procrustes: rmse 0.2280222 max resid 0.3797631
## Run 397 stress 9.880259e-05
## ... Procrustes: rmse 0.09980475 max resid 0.1455098
## Run 398 stress 9.118694e-05
## ... Procrustes: rmse 0.275667 max resid 0.435527
## Run 399 stress 9.590134e-05
## ... Procrustes: rmse 0.23504 max resid 0.2786147
## Run 400 stress 7.677479e-05
## ... Procrustes: rmse 0.2268099 max resid 0.3528259
## Run 401 stress 9.687396e-05
## ... Procrustes: rmse 0.2147496 max resid 0.3145069
## Run 402 stress 9.185347e-05
## ... Procrustes: rmse 0.2448013 max resid 0.3987138
## Run 403 stress 8.809892e-05
## ... Procrustes: rmse 0.1023722 max resid 0.1556129
## Run 404 stress 6.79617e-05
## ... Procrustes: rmse 0.2554619 max resid 0.4117373
## Run 405 stress 9.297547e-05
## ... Procrustes: rmse 0.2500383 max resid 0.3716491
## Run 406 stress 7.272019e-05
## ... Procrustes: rmse 0.2013647 max resid 0.3245387
## Run 407 stress 6.812951e-05
## ... Procrustes: rmse 0.07810092 max resid 0.1266781
## Run 408 stress 0.0003703443
## ... Procrustes: rmse 0.07982707 max resid 0.1196938
## Run 409 stress 8.978245e-05
## ... Procrustes: rmse 0.1466155 max resid 0.2250636
## Run 410 stress 9.574283e-05
## ... Procrustes: rmse 0.2596262 max resid 0.4088865
## Run 411 stress 0.0002769717
## ... Procrustes: rmse 0.3120436 max resid 0.4330489
## Run 412 stress 8.974988e-05
## ... Procrustes: rmse 0.2174129 max resid 0.2772742
## Run 413 stress 4.462418e-05
## ... Procrustes: rmse 0.1476912 max resid 0.2208791
## Run 414 stress 8.422944e-05
## ... Procrustes: rmse 0.2392492 max resid 0.3297825
## Run 415 stress 8.154914e-05
## ... Procrustes: rmse 0.2380415 max resid 0.3894336
## Run 416 stress 5.25995e-05
## ... Procrustes: rmse 0.2323588 max resid 0.3195136
## Run 417 stress 8.816897e-05
## ... Procrustes: rmse 0.2216945 max resid 0.2930355
## Run 418 stress 9.497403e-05
## ... Procrustes: rmse 0.2188269 max resid 0.3327576
## Run 419 stress 9.046932e-05
## ... Procrustes: rmse 0.1078563 max resid 0.1741128
## Run 420 stress 8.632903e-05
## ... Procrustes: rmse 0.2208885 max resid 0.3261937
## Run 421 stress 9.950429e-05
## ... Procrustes: rmse 0.1995912 max resid 0.3042185
## Run 422 stress 6.122063e-05
## ... Procrustes: rmse 0.2528445 max resid 0.4354527
## Run 423 stress 9.455573e-05
## ... Procrustes: rmse 0.2213979 max resid 0.3212053
## Run 424 stress 9.792788e-05
## ... Procrustes: rmse 0.2571578 max resid 0.432684
## Run 425 stress 0
## ... Procrustes: rmse 0.04548339 max resid 0.06628296
## Run 426 stress 9.854064e-05
## ... Procrustes: rmse 0.2554386 max resid 0.4247143
## Run 427 stress 0
## ... Procrustes: rmse 0.08494806 max resid 0.1216343
## Run 428 stress 9.623568e-05
## ... Procrustes: rmse 0.2702595 max resid 0.4392824
## Run 429 stress 9.376665e-05
## ... Procrustes: rmse 0.245473 max resid 0.373825
## Run 430 stress 8.206008e-05
## ... Procrustes: rmse 0.2252953 max resid 0.2872246
## Run 431 stress 9.735089e-05
## ... Procrustes: rmse 0.14694 max resid 0.23872
## Run 432 stress 4.99489e-05
## ... Procrustes: rmse 0.1565731 max resid 0.2130734
## Run 433 stress 7.928159e-05
## ... Procrustes: rmse 0.1641939 max resid 0.2265678
## Run 434 stress 8.092843e-05
## ... Procrustes: rmse 0.244528 max resid 0.3855202
## Run 435 stress 9.662347e-05
## ... Procrustes: rmse 0.217878 max resid 0.2973681
## Run 436 stress 9.549408e-05
## ... Procrustes: rmse 0.2561546 max resid 0.4326797
## Run 437 stress 9.759213e-05
## ... Procrustes: rmse 0.2339005 max resid 0.2799411
## Run 438 stress 6.48888e-05
## ... Procrustes: rmse 0.1468361 max resid 0.2199348
## Run 439 stress 6.59974e-05
## ... Procrustes: rmse 0.1462041 max resid 0.2032058
## Run 440 stress 9.498861e-05
## ... Procrustes: rmse 0.04138316 max resid 0.05472961
## Run 441 stress 0
## ... Procrustes: rmse 0.06942347 max resid 0.09792683
## Run 442 stress 9.837632e-05
## ... Procrustes: rmse 0.2533589 max resid 0.3817529
## Run 443 stress 5.085659e-05
## ... Procrustes: rmse 0.02867107 max resid 0.04585516
## Run 444 stress 9.933902e-05
## ... Procrustes: rmse 0.2137123 max resid 0.3005852
## Run 445 stress 5.104407e-05
## ... Procrustes: rmse 0.1580489 max resid 0.2145353
## Run 446 stress 9.648709e-05
## ... Procrustes: rmse 0.0742925 max resid 0.1025462
## Run 447 stress 0.0002590302
## ... Procrustes: rmse 0.2959958 max resid 0.4262425
## Run 448 stress 9.02819e-05
## ... Procrustes: rmse 0.209556 max resid 0.3392181
## Run 449 stress 9.462175e-05
## ... Procrustes: rmse 0.2238921 max resid 0.2892609
## Run 450 stress 7.729072e-05
## ... Procrustes: rmse 0.2225207 max resid 0.3216539
## Run 451 stress 9.056759e-05
## ... Procrustes: rmse 0.08121007 max resid 0.1162538
## Run 452 stress 9.726661e-05
## ... Procrustes: rmse 0.09480156 max resid 0.1474081
## Run 453 stress 9.975859e-05
## ... Procrustes: rmse 0.2790455 max resid 0.4377186
## Run 454 stress 8.399496e-05
## ... Procrustes: rmse 0.2516333 max resid 0.3856527
## Run 455 stress 4.515116e-05
## ... Procrustes: rmse 0.2284668 max resid 0.33025
## Run 456 stress 8.857325e-05
## ... Procrustes: rmse 0.2134521 max resid 0.2759809
## Run 457 stress 9.590961e-05
## ... Procrustes: rmse 0.2332462 max resid 0.2801812
## Run 458 stress 3.674049e-05
## ... Procrustes: rmse 0.1936236 max resid 0.2879405
## Run 459 stress 9.909255e-05
## ... Procrustes: rmse 0.2249898 max resid 0.2790781
## Run 460 stress 8.60942e-05
## ... Procrustes: rmse 0.04451893 max resid 0.07172618
## Run 461 stress 9.746941e-05
## ... Procrustes: rmse 0.2255474 max resid 0.339368
## Run 462 stress 9.31125e-05
## ... Procrustes: rmse 0.2238536 max resid 0.3013185
## Run 463 stress 8.519307e-05
## ... Procrustes: rmse 0.108933 max resid 0.1638492
## Run 464 stress 8.660003e-05
## ... Procrustes: rmse 0.2205404 max resid 0.3453623
## Run 465 stress 8.991698e-05
## ... Procrustes: rmse 0.2521634 max resid 0.3659159
## Run 466 stress 2.675409e-05
## ... Procrustes: rmse 0.04872326 max resid 0.06920561
## Run 467 stress 5.36961e-05
## ... Procrustes: rmse 0.06088975 max resid 0.08629013
## Run 468 stress 9.664431e-05
## ... Procrustes: rmse 0.2211369 max resid 0.3355072
## Run 469 stress 2.853497e-05
## ... Procrustes: rmse 0.2107122 max resid 0.3492797
## Run 470 stress 0.0001896754
## ... Procrustes: rmse 0.1423791 max resid 0.2166194
## Run 471 stress 8.64418e-05
## ... Procrustes: rmse 0.2503737 max resid 0.3987527
## Run 472 stress 0.0001244962
## ... Procrustes: rmse 0.310245 max resid 0.4368616
## Run 473 stress 9.298615e-05
## ... Procrustes: rmse 0.2756289 max resid 0.4354629
## Run 474 stress 9.361769e-05
## ... Procrustes: rmse 0.2415814 max resid 0.4125999
## Run 475 stress 9.687431e-05
## ... Procrustes: rmse 0.1162057 max resid 0.1753214
## Run 476 stress 9.863084e-05
## ... Procrustes: rmse 0.2222163 max resid 0.3068564
## Run 477 stress 8.159918e-05
## ... Procrustes: rmse 0.2033148 max resid 0.3094495
## Run 478 stress 0
## ... Procrustes: rmse 0.02300545 max resid 0.03135208
## Run 479 stress 9.358367e-05
## ... Procrustes: rmse 0.221202 max resid 0.2799728
## Run 480 stress 9.442935e-05
## ... Procrustes: rmse 0.2149894 max resid 0.2705135
## Run 481 stress 7.864605e-05
## ... Procrustes: rmse 0.1395414 max resid 0.2014377
## Run 482 stress 7.093423e-05
## ... Procrustes: rmse 0.2236884 max resid 0.2801549
## Run 483 stress 9.021826e-05
## ... Procrustes: rmse 0.2255416 max resid 0.3540517
## Run 484 stress 8.712607e-05
## ... Procrustes: rmse 0.2313265 max resid 0.2813314
## Run 485 stress 9.701111e-05
## ... Procrustes: rmse 0.2560269 max resid 0.395143
## Run 486 stress 9.633498e-05
## ... Procrustes: rmse 0.2339795 max resid 0.2795778
## Run 487 stress 7.678059e-05
## ... Procrustes: rmse 0.1286553 max resid 0.2029963
## Run 488 stress 9.114384e-05
## ... Procrustes: rmse 0.2573281 max resid 0.4441394
## Run 489 stress 9.542448e-05
## ... Procrustes: rmse 0.2756342 max resid 0.4354664
## Run 490 stress 9.277839e-05
## ... Procrustes: rmse 0.2533689 max resid 0.392537
## Run 491 stress 0.0001379902
## ... Procrustes: rmse 0.1977211 max resid 0.3084087
## Run 492 stress 8.257375e-05
## ... Procrustes: rmse 0.275668 max resid 0.4355326
## Run 493 stress 8.863e-05
## ... Procrustes: rmse 0.09655526 max resid 0.1399787
## Run 494 stress 9.847084e-05
## ... Procrustes: rmse 0.2982617 max resid 0.4178466
## Run 495 stress 9.04915e-05
## ... Procrustes: rmse 0.2015843 max resid 0.2986058
## Run 496 stress 9.873228e-05
## ... Procrustes: rmse 0.2253865 max resid 0.3540535
## Run 497 stress 9.902728e-05
## ... Procrustes: rmse 0.2560512 max resid 0.3733946
## Run 498 stress 0
## ... Procrustes: rmse 0.1197349 max resid 0.1784656
## Run 499 stress 8.842792e-05
## ... Procrustes: rmse 0.2176721 max resid 0.3159418
## Run 500 stress 4.204977e-05
## ... Procrustes: rmse 0.1280962 max resid 0.183641
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 28: no. of iterations >= maxit
## 471: stress < smin
## 1: stress ratio > sratmax
## Warning in metaMDS(AddiewellPS, noshare = TRUE, autotransform = FALSE, trymax =
## 500): stress is (nearly) zero: you may have insufficient data
## Warning in postMDS(out$points, dis, plot = max(0, plot - 1), ...): skipping
## half-change scaling: too few points below threshold
plot(AddiewellNMDS)
#This is a simple NMDS plot, with the different plant communities as circles and the #red crosses as species.
AddiewellNMDS
##
## Call:
## metaMDS(comm = AddiewellPS, trymax = 500, autotransform = FALSE, noshare = TRUE)
##
## global Multidimensional Scaling using monoMDS
##
## Data: AddiewellPS
## Distance: bray
##
## Dimensions: 2
## Stress: 0
## Stress type 1, weak ties
## Best solution was not repeated after 500 tries
## The best solution was from try 1 (random start)
## Scaling: centring, PC rotation
## Species: expanded scores based on 'AddiewellPS'
plot(AddiewellNMDS, "sites") # Produces distance
plot(AddiewellNMDS, "species", xlim = c(-0.5, 0.5), ylim = c(-1.5, 1.0))
orditorp(AddiewellNMDS, "species")
#The orditorp function provides some of the plant species on the site on the graph. #The species are grouped fairly close together, apart from Pleurozium schreberi. It will #be interesting to look at CCAs of the plant and substrate data at Addiewell Bing, to #better visualise the relationship between plants and substrate.
#Now for some CCAS…
AddiewellCCA <- cca(AddiewellPS, AddiewellPC)
##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'CaO', 'MgO', 'Na2O',
## 'K2O', 'Cr2O3', 'TiO2', 'MnO', 'P2O5', 'SrO', 'BaO', 'LOI', 'Ag', 'As', 'B',
## 'Be', 'Bi', 'Cd', 'Co', 'Cr', 'Cu', 'Ga', 'Hg', 'La', 'Li', 'Mo', 'Ni', 'Pb',
## 'S', 'Sb', 'Sc', 'Th', 'Ti', 'Tl', 'U', 'V', 'W', 'Zn', 'Akermanite', 'Albite',
## 'Aluminium.oxide.hydroxide', 'Anhydrite', 'Aragonite', 'Biotite', 'Birnessite',
## 'Calcite', 'Clinochlore', 'Cuspidine', 'Diaspore', 'Dickite', 'Gehlenite',
## 'Goethite', 'Haematite', 'Illite', 'Kaolinite', 'Langite', 'Linnaeite',
## 'Magnesioferrite', 'Melilite', 'Merwinite', 'Microcline', 'Mullite',
## 'Muscovite', 'Orthoclase', 'Orthopyroxene', 'Periclase', 'Phengite',
## 'Pseudowollastonite', 'Quartz', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component
print(AddiewellCCA)
## Call: cca(X = AddiewellPS, Y = AddiewellPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 1.706 1.000
## Constrained 1.706 1.000 4
## Unconstrained 0.000 0.000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## Some constraints or conditions were aliased because they were redundant.
## This can happen if terms are linearly dependent (collinear): 'CaO', 'MgO',
## 'Na2O', 'K2O', 'Cr2O3', 'TiO2', 'MnO', 'P2O5', 'SrO', 'BaO', 'LOI', 'Ag',
## 'As', 'B', 'Be', 'Bi', 'Cd', 'Co', 'Cr', 'Cu', 'Ga', 'Hg', 'La', 'Li',
## 'Mo', 'Ni', 'Pb', 'S', 'Sb', 'Sc', 'Th', 'Ti', 'Tl', 'U', 'V', 'W', 'Zn',
## 'Akermanite', 'Albite', 'Aluminium.oxide.hydroxide', 'Anhydrite',
## 'Aragonite', 'Biotite', 'Birnessite', 'Calcite', 'Clinochlore',
## 'Cuspidine', 'Diaspore', 'Dickite', 'Gehlenite', 'Goethite', 'Haematite',
## 'Illite', 'Kaolinite', 'Langite', 'Linnaeite', 'Magnesioferrite',
## 'Melilite', 'Merwinite', 'Microcline', 'Mullite', 'Muscovite',
## 'Orthoclase', 'Orthopyroxene', 'Periclase', 'Phengite',
## 'Pseudowollastonite', 'Quartz', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component.
##
## 88 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.6828 0.5091 0.3583 0.1556
plot(AddiewellCCA)
summary(AddiewellCCA)
##
## Call:
## cca(X = AddiewellPS, Y = AddiewellPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.706 1
## Constrained 1.706 1
## Unconstrained 0.000 0
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 0.6828 0.5091 0.3583 0.15559
## Proportion Explained 0.4003 0.2984 0.2101 0.09121
## Cumulative Proportion 0.4003 0.6987 0.9088 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 0.6828 0.5091 0.3583 0.15559
## Proportion Explained 0.4003 0.2984 0.2101 0.09121
## Cumulative Proportion 0.4003 0.6987 0.9088 1.00000
AddiewellCCA2 <- cca(AddiewellPS ~ Co + Pb + S + As, data = AddiewellPC)
##
## The model is overfitted with no unconstrained (residual) component
print(AddiewellCCA2)
## Call: cca(formula = AddiewellPS ~ Co + Pb + S + As, data = AddiewellPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 1.706 1.000
## Constrained 1.706 1.000 4
## Unconstrained 0.000 0.000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## The model is overfitted with no unconstrained (residual) component.
##
## 88 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.6828 0.5091 0.3583 0.1556
plot(AddiewellCCA2)
summary(AddiewellCCA2)
##
## Call:
## cca(formula = AddiewellPS ~ Co + Pb + S + As, data = AddiewellPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.706 1
## Constrained 1.706 1
## Unconstrained 0.000 0
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 0.6828 0.5091 0.3583 0.15559
## Proportion Explained 0.4003 0.2984 0.2101 0.09121
## Cumulative Proportion 0.4003 0.6987 0.9088 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 0.6828 0.5091 0.3583 0.15559
## Proportion Explained 0.4003 0.2984 0.2101 0.09121
## Cumulative Proportion 0.4003 0.6987 0.9088 1.00000
AddiewellCCA3 <- cca(AddiewellPS ~ V + Pb + S + As, data = AddiewellPC)
##
## The model is overfitted with no unconstrained (residual) component
print(AddiewellCCA3)
## Call: cca(formula = AddiewellPS ~ V + Pb + S + As, data = AddiewellPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 1.706 1.000
## Constrained 1.706 1.000 4
## Unconstrained 0.000 0.000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## The model is overfitted with no unconstrained (residual) component.
##
## 88 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.6828 0.5091 0.3583 0.1556
plot(AddiewellCCA3)
summary(AddiewellCCA3)
##
## Call:
## cca(formula = AddiewellPS ~ V + Pb + S + As, data = AddiewellPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.706 1
## Constrained 1.706 1
## Unconstrained 0.000 0
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 0.6828 0.5091 0.3583 0.15559
## Proportion Explained 0.4003 0.2984 0.2101 0.09121
## Cumulative Proportion 0.4003 0.6987 0.9088 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 0.6828 0.5091 0.3583 0.15559
## Proportion Explained 0.4003 0.2984 0.2101 0.09121
## Cumulative Proportion 0.4003 0.6987 0.9088 1.00000
AddiewellCCA4 <- cca(AddiewellPS ~ Pb + S + As, data = AddiewellPC)
print(AddiewellCCA4)
## Call: cca(formula = AddiewellPS ~ Pb + S + As, data = AddiewellPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 1.7058 1.0000
## Constrained 1.3838 0.8112 3
## Unconstrained 0.3220 0.1888 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 88 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.6603 0.4565 0.2669
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.322
plot(AddiewellCCA4)
summary(AddiewellCCA4)
##
## Call:
## cca(formula = AddiewellPS ~ Pb + S + As, data = AddiewellPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.706 1.0000
## Constrained 1.384 0.8112
## Unconstrained 0.322 0.1888
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.6603 0.4565 0.2669 0.3220
## Proportion Explained 0.3871 0.2676 0.1565 0.1888
## Cumulative Proportion 0.3871 0.6547 0.8112 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.6603 0.4565 0.2669
## Proportion Explained 0.4772 0.3299 0.1929
## Cumulative Proportion 0.4772 0.8071 1.0000
anova(AddiewellCCA4)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = AddiewellPS ~ Pb + S + As, data = AddiewellPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.38377 1.4323 0.2417
## Residual 1 0.32203
#F statistic of 1.4323 and p value of 0.1583
AddiewellCCA5 <- cca(AddiewellPS ~ pH_level + Al2O3 + Fe2O3, data = AddiewellPC)
print(AddiewellCCA5)
## Call: cca(formula = AddiewellPS ~ pH_level + Al2O3 + Fe2O3, data =
## AddiewellPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 1.7058 1.0000
## Constrained 1.4006 0.8211 3
## Unconstrained 0.3052 0.1789 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 88 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.6092 0.5078 0.2836
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.30519
plot(AddiewellCCA5)
summary(AddiewellCCA5)
##
## Call:
## cca(formula = AddiewellPS ~ pH_level + Al2O3 + Fe2O3, data = AddiewellPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.7058 1.0000
## Constrained 1.4006 0.8211
## Unconstrained 0.3052 0.1789
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.6092 0.5078 0.2836 0.3052
## Proportion Explained 0.3572 0.2977 0.1662 0.1789
## Cumulative Proportion 0.3572 0.6549 0.8211 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.6092 0.5078 0.2836
## Proportion Explained 0.4350 0.3626 0.2024
## Cumulative Proportion 0.4350 0.7976 1.0000
anova(AddiewellCCA5)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = AddiewellPS ~ pH_level + Al2O3 + Fe2O3, data = AddiewellPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.40062 1.5298 0.2
## Residual 1 0.30519
#F statstic of 1.5298 and p value of 0.15.
AddiewellCCA6 <- cca(AddiewellPS ~ Pb + Al2O3 + Fe2O3, data = AddiewellPC)
print(AddiewellCCA6)
## Call: cca(formula = AddiewellPS ~ Pb + Al2O3 + Fe2O3, data = AddiewellPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 1.7058 1.0000
## Constrained 1.4727 0.8633 3
## Unconstrained 0.2331 0.1367 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 88 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.6577 0.5091 0.3059
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.23311
plot(AddiewellCCA6)
summary(AddiewellCCA6)
##
## Call:
## cca(formula = AddiewellPS ~ Pb + Al2O3 + Fe2O3, data = AddiewellPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.7058 1.0000
## Constrained 1.4727 0.8633
## Unconstrained 0.2331 0.1367
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.6577 0.5091 0.3059 0.2331
## Proportion Explained 0.3856 0.2984 0.1794 0.1367
## Cumulative Proportion 0.3856 0.6840 0.8633 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.6577 0.5091 0.3059
## Proportion Explained 0.4466 0.3457 0.2077
## Cumulative Proportion 0.4466 0.7923 1.0000
anova(AddiewellCCA6)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = AddiewellPS ~ Pb + Al2O3 + Fe2O3, data = AddiewellPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.47269 2.1058 0.1083
## Residual 1 0.23311
#F statistic of 2.1058 and p value of 0.09167.
AddiewellCCA7 <- cca(AddiewellPS ~ V + S + As, data = AddiewellPC)
print(AddiewellCCA7)
## Call: cca(formula = AddiewellPS ~ V + S + As, data = AddiewellPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 1.7058 1.0000
## Constrained 1.4584 0.8550 3
## Unconstrained 0.2474 0.1450 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 88 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.6812 0.4874 0.2898
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.24739
plot(AddiewellCCA7)
summary(AddiewellCCA7)
##
## Call:
## cca(formula = AddiewellPS ~ V + S + As, data = AddiewellPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.7058 1.000
## Constrained 1.4584 0.855
## Unconstrained 0.2474 0.145
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.6812 0.4874 0.2898 0.2474
## Proportion Explained 0.3993 0.2858 0.1699 0.1450
## Cumulative Proportion 0.3993 0.6851 0.8550 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.6812 0.4874 0.2898
## Proportion Explained 0.4671 0.3342 0.1987
## Cumulative Proportion 0.4671 0.8013 1.0000
anova(AddiewellCCA7)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = AddiewellPS ~ V + S + As, data = AddiewellPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.45842 1.9651 0.1417
## Residual 1 0.24739
#F statistic of 1.9651 and p value of 0.08333.
AddiewellCCA8 <- cca(AddiewellPS ~ V + S + Co, data = AddiewellPC)
print(AddiewellCCA8)
## Call: cca(formula = AddiewellPS ~ V + S + Co, data = AddiewellPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 1.7058 1.0000
## Constrained 1.4646 0.8586 3
## Unconstrained 0.2412 0.1414 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 88 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.6784 0.4986 0.2876
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.24122
plot(AddiewellCCA8)
summary(AddiewellCCA8)
##
## Call:
## cca(formula = AddiewellPS ~ V + S + Co, data = AddiewellPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.7058 1.0000
## Constrained 1.4646 0.8586
## Unconstrained 0.2412 0.1414
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.6784 0.4986 0.2876 0.2412
## Proportion Explained 0.3977 0.2923 0.1686 0.1414
## Cumulative Proportion 0.3977 0.6900 0.8586 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.6784 0.4986 0.2876
## Proportion Explained 0.4632 0.3405 0.1964
## Cumulative Proportion 0.4632 0.8036 1.0000
anova(AddiewellCCA8)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = AddiewellPS ~ V + S + Co, data = AddiewellPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.46459 2.0239 0.15
## Residual 1 0.24122
#F statistic of 2.0239 and p value of 0.09167.
AddiewellCCA9 <- cca(AddiewellPS ~ V + As + Co, data = AddiewellPC)
print(AddiewellCCA9)
## Call: cca(formula = AddiewellPS ~ V + As + Co, data = AddiewellPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 1.7058 1.0000
## Constrained 1.4096 0.8264 3
## Unconstrained 0.2962 0.1736 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 88 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.6826 0.4787 0.2483
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.29619
plot(AddiewellCCA9)
summary(AddiewellCCA9)
##
## Call:
## cca(formula = AddiewellPS ~ V + As + Co, data = AddiewellPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 1.7058 1.0000
## Constrained 1.4096 0.8264
## Unconstrained 0.2962 0.1736
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.6826 0.4787 0.2483 0.2962
## Proportion Explained 0.4001 0.2807 0.1456 0.1736
## Cumulative Proportion 0.4001 0.6808 0.8264 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.6826 0.4787 0.2483
## Proportion Explained 0.4842 0.3396 0.1762
## Cumulative Proportion 0.4842 0.8238 1.0000
anova(AddiewellCCA9)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = AddiewellPS ~ V + As + Co, data = AddiewellPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.40962 1.5864 0.1417
## Residual 1 0.29619
#F statistic of 1.5864 and p value of 0.1083.
#After doing several CCAs and anovas for the data, using different combinations of varibles, #I have not been able to find any statistically significant CCAs for the Addiewell data. #It can be determined that the NMDS analyses are more appropriate for representation #of the plant data for this field site.
#Penicuik Data#
urlfile9 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/PenicuikPlantSpecies.csv'
PenicuikPS <- read.csv(urlfile9, header = TRUE, colClasses = c("numeric"))
urlfile10 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/PENICUIK_PLANT_CHEMISTRY_MG_KG.csv'
PenicuikPC <-read.csv(urlfile10, header = TRUE, colClasses = c("numeric"))
head(PenicuikPS)
## Agrostis.spp. Agrostis.canina Alchemilla.mollis Alopercus.pratensis
## 1 5 3 0 0
## 2 3 0 1 0
## 3 32 0 0 0
## 4 39 0 0 0
## 5 0 0 0 0
## Angelica.sylvestris Anthoxanthum.odoratum Anthyllis.vulneraria
## 1 0 0 0
## 2 1 0 0
## 3 1 0 0
## 4 0 0 0
## 5 0 0 0
## Aphanes.arvensis Arrhenatherum.elatius Atrichum.undulatum Avenula.pratensis
## 1 0 0 0 0
## 2 0 4 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Bellis.perennis Betula.pubescens Blackstonia.perfoliata
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Brachythecium.albicans Brachythecium.glareosum Brachythecium.mildeanum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Brachythecium.rutabulum Briza.media Bromus.hordeaceus Bryum.c.f..caespiticium
## 1 0 0 0 0
## 2 10 0 0 0
## 3 10 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Bryum.c.f..pallescens Bryum.capillare Bryum.spp. Calliergonella.cupsidata
## 1 0 0 0 0
## 2 0 0 0 50
## 3 0 0 0 110
## 4 0 0 0 0
## 5 0 0 0 0
## Calypogeia.arguta Campanula.rotundifolia Carex.distans Carex.flacca
## 1 0 0 0 0
## 2 0 0 0 0
## 3 60 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Carex.panicea Carlina.vulgaris Centaurea.nigra Centaurium.erythraea
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Centaurium.littorale Centaurium.pulchellum Cerastium.fontanum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Chamaenerion.angustifolium Cirriphyllum.piliferum Cirsium.arvense
## 1 0 0 3
## 2 0 0 4
## 3 0 0 8
## 4 0 0 0
## 5 0 0 0
## Cirsium.palustre Crepis.capillaris Cynosurus.cristatus Dactylis.glomerata
## 1 0 0 0 1
## 2 0 0 0 0
## 3 0 0 0 4
## 4 0 0 0 54
## 5 0 0 0 0
## Dactylorhiza.fuchsii Danthonia.decumbens Daucus.carrota Dicranella.spp.
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Dicranum.scoparium Epilobium.montanum Equisetum.arvense Equisteum.variegatum
## 1 0 3 0 0
## 2 0 0 49 0
## 3 0 0 4 0
## 4 0 0 0 0
## 5 0 0 0 0
## Erigeron.acer Euphrasia.agg Festuca.ovina Festuca.rubra Festuca.rubra.agg.
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## Filipendula.ulmaria Fissidens.adianthoides Fissidens.dubius Fissidens.exilis
## 1 3 0 0 0
## 2 3 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Fragaria.vesca Galium.aparine Galium.arvense Galium.saxatile Galium.verum
## 1 0 0 1 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 11 0 0 0 0
## 5 0 47 0 0 0
## Geum.urbanum Glechoma.hederacea Helicotrichon.spp. Heracleum.sphondylium
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 48 5 0 0
## 5 0 0 0 1
## Hieracium.spp. Holcus.spp. Holcus.lanatus Holcus.mollis
## 1 0 0 0 56
## 2 0 0 0 17
## 3 0 0 0 4
## 4 0 0 0 0
## 5 0 0 0 0
## Homalothecium.lutescens Hylocomium.splendens Hypericum.perforatum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Hypnum.cupressiforme Hypnum.imponens Hypnum.jutlandicum Hypochaeris.radicata
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Kindbergia.praelonga Lathyrus.pratensis Leontodon.hispidus
## 1 0 0 0
## 2 0 0 0
## 3 50 0 0
## 4 50 0 0
## 5 0 0 0
## Leontodon.saxatilis Leucanthemum.vulgare Linum.catharticum Lolium.perenne
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Lophocolea.semiteres Lotus.corniculatus Luzula.multiflora Lysimachia.maritima
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 12 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Medicago.lupulina Myosotis.arvensis Ononis.repens Oxalis.acetosella
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 113
## 5 0 3 0 0
## Pastinaca.sativa Pentaglottis.sempervirens Pillosella.officinarum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 4 0
## Plantago.coronopus Plantago.lanceolata Pleurozium.schreberi Poa.annua
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Poa.spp. Polytrichum.commune Polytrichum.commune.agg. Polytrichum.formosum
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Potentilla.reptans Prunella.vulgaris Pseudoscleropidum.purum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Pteridium.aquilinum Ranunculus.acris Ranunculus.parviflorus Ranunculus.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 4 0 0 0
## 5 0 0 0 0
## Reseda.lutea Rhinanthus.minor Rhizomnium.punctatum Rhytidadelphus.squarrosus
## 1 0 0 0 0
## 2 0 0 0 140
## 3 0 0 0 20
## 4 0 0 0 0
## 5 0 0 0 0
## Rhytidadelphus.triquetris Rubus.fruticosus Sanguisorba.minor.spp.minor
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Saniona.uncinata Sedum.anglicum Senecio.jacobaea Senecio.vulgaris
## 1 0 0 0 0
## 2 0 0 1 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Sonchus.arvensis Stachys.sylvatica Stellaria.apetala Taraxacum.agg.
## 1 0 1 0 0
## 2 0 1 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Thuidium.tamariscinum Thymus.polytrichus Trichostomum.crispulum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## Trifolium.campestre Trifolium.dubium Trifolium.pratense Trifolium.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## Trisetum.flavescens Tussilago.farfara Urtica.diocia Veronica.officinalis
## 1 0 0 0 0
## 2 0 15 0 0
## 3 0 4 0 0
## 4 0 0 0 0
## 5 0 0 55 0
## Vicia.sativa Viola.riviniana Weissia.controversa Zygodon.stirtonii
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
head(PenicuikPC)
## pH SiO2 Al2O3 Fe2O3 CaO MgO Na2O K2O
## 1 8.048 330943.9 55835.91 31054.49 18296.084 1266.375 1261.157 14776.630
## 2 8.075 224368.8 69067.17 28396.68 64393.638 4221.251 4970.441 10459.862
## 3 7.024 272514.6 60863.79 28886.27 8218.944 4040.341 5489.741 11622.069
## 4 5.474 255686.9 58217.54 35460.87 5288.712 4341.858 8160.426 9048.611
## 5 7.222 150514.0 44457.03 21682.19 101486.089 3377.001 3635.099 6475.153
## Cr2O3 TiO2 MnO P2O5 SrO BaO LOI Ag As B
## 1 61.57822 3416.255 1394.0235 899.8838 42.27973 806.0862 6.58 0.20 4 5
## 2 102.63036 4674.875 774.4575 1599.7934 84.55946 626.9560 21.00 0.30 9 10
## 3 95.78834 4495.072 929.3490 2299.7030 84.55946 537.3908 22.80 0.15 8 10
## 4 102.63036 5334.152 1006.7947 1899.7547 84.55946 358.2605 24.70 0.20 9 5
## 5 75.26227 3655.992 464.6745 4199.4577 169.11891 268.6954 39.00 0.30 7 10
## Be Bi Cd Co Cu Ga Hg La Li Mo Ni Pb S Sb Sc Th Tl U V W Zn
## 1 0.25 1.5 0.70 8 30 5 0.5 10 5 0.5 24 28 300 1.5 4 10 5 5 13 5 257
## 2 1.40 1.5 0.80 14 86 10 0.5 10 20 1.0 35 96 500 1.5 4 10 5 5 40 5 337
## 3 1.00 1.5 0.25 13 35 5 0.5 10 10 1.0 28 66 800 2.0 3 10 5 5 37 5 136
## 4 0.70 1.5 0.25 13 21 10 0.5 10 20 1.0 23 66 700 1.5 1 10 5 5 51 5 141
## 5 1.90 1.5 1.50 13 74 5 0.5 10 10 1.0 77 133 1200 1.5 1 10 5 5 33 5 342
## Akermanite Albite Aluminium.oxide.hydroxide Anhydrite Aragonite Biotite
## 1 0 0 0 0 0 0
## 2 0 0 0 1 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 0 0 0 0 0 0
## Birnessite Calcite Clinochlore Cuspidine Diaspore Dickite Gehlenite Goethite
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 1 0 0 0 0 0 0
## Haematite Illite Kaolinite Langite Linnaeite Magnesioferrite Melilite
## 1 0 0 1 0 0 0 0
## 2 0 0 1 0 0 0 0
## 3 0 0 1 0 0 0 0
## 4 0 0 1 0 0 0 0
## 5 0 0 1 0 0 0 0
## Merwinite Microcline Mullite Muscovite Orthoclase Orthopyroxene Periclase
## 1 0 0 0 1 0 0 0
## 2 0 0 0 1 0 0 0
## 3 0 0 0 0 0 0 1
## 4 0 0 0 0 0 0 0
## 5 0 0 0 1 0 0 0
## Phengite Pseudowollastonite Quartz Staurolite Valentinite
## 1 0 0 1 0 0
## 2 0 0 1 0 0
## 3 0 0 1 0 0
## 4 0 0 1 0 0
## 5 0 0 1 0 0
rowSums(PenicuikPS)
## [1] 76 299 319 324 110
#Now for some ANOSIMs…
#anoPSiO2 <- anosim(PenicuikPS, PenicuikPC$SiO2, distance = “bray”, permutations = 9999)
#When this ANOSIM is run, the error message: “there should be replicates within groups” #comes up. This error message came up for other substrate variables for other #data. From now on, only variables that were tested successfully will be included.
anoPCr2O3 <- anosim(PenicuikPS, PenicuikPC$Cr2O3, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoPCr2O3
##
## Call:
## anosim(x = PenicuikPS, grouping = PenicuikPC$Cr2O3, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1111
## Significance: 0.6
##
## Permutation: free
## Number of permutations: 119
anoPSrO <- anosim(PenicuikPS, PenicuikPC$SrO, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoPSrO
##
## Call:
## anosim(x = PenicuikPS, grouping = PenicuikPC$SrO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7143
## Significance: 0.2
##
## Permutation: free
## Number of permutations: 119
#This has a high R statistic of 1, but with a non-significant p value of 0.2
anoPAg <- anosim(PenicuikPS, PenicuikPC$Ag, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoPAg
##
## Call:
## anosim(x = PenicuikPS, grouping = PenicuikPC$Ag, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.3125
## Significance: 0.86667
##
## Permutation: free
## Number of permutations: 119
anoPAs <- anosim(PenicuikPS, PenicuikPC$As, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoPAs
##
## Call:
## anosim(x = PenicuikPS, grouping = PenicuikPC$As, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1111
## Significance: 0.6
##
## Permutation: free
## Number of permutations: 119
anoPCd <- anosim(PenicuikPS, PenicuikPC$Cd, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoPCd
##
## Call:
## anosim(x = PenicuikPS, grouping = PenicuikPC$Cd, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7778
## Significance: 0.2
##
## Permutation: free
## Number of permutations: 119
anoPCo <- anosim(PenicuikPS, PenicuikPC$Co, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoPCo
##
## Call:
## anosim(x = PenicuikPS, grouping = PenicuikPC$Co, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.2381
## Significance: 0.6
##
## Permutation: free
## Number of permutations: 119
#This has a high R statistic of 1, but with a non-significant p value of 0.1
anoPPb <- anosim(PenicuikPS, PenicuikPC$Pb, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoPPb
##
## Call:
## anosim(x = PenicuikPS, grouping = PenicuikPC$Pb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7778
## Significance: 0.2
##
## Permutation: free
## Number of permutations: 119
anoPSc <- anosim(PenicuikPS, PenicuikPC$Sc, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoPSc
##
## Call:
## anosim(x = PenicuikPS, grouping = PenicuikPC$Sc, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.0625
## Significance: 0.6
##
## Permutation: free
## Number of permutations: 119
#None of the ANOSIM tests led to stastistically significant results. Because of this, #CCAs might not be entirely appropriate to represent the data visually. #NMDS analyses will be utilised instead to visualise the plant data.
PenicuikNMDS <- metaMDS(PenicuikPS)
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0
## Run 1 stress 0
## ... Procrustes: rmse 0.1588239 max resid 0.2751202
## Run 2 stress 0
## ... Procrustes: rmse 0.2787275 max resid 0.4391253
## Run 3 stress 0
## ... Procrustes: rmse 0.2494537 max resid 0.3687649
## Run 4 stress 0
## ... Procrustes: rmse 0.2214514 max resid 0.3343612
## Run 5 stress 0
## ... Procrustes: rmse 0.1535876 max resid 0.1988528
## Run 6 stress 8.963861e-05
## ... Procrustes: rmse 0.2974179 max resid 0.4857913
## Run 7 stress 0
## ... Procrustes: rmse 0.3039324 max resid 0.449431
## Run 8 stress 0
## ... Procrustes: rmse 0.299959 max resid 0.4700047
## Run 9 stress 0
## ... Procrustes: rmse 0.1344095 max resid 0.2117445
## Run 10 stress 0
## ... Procrustes: rmse 0.1316192 max resid 0.1933083
## Run 11 stress 0
## ... Procrustes: rmse 0.3264276 max resid 0.4058194
## Run 12 stress 0
## ... Procrustes: rmse 0.3043311 max resid 0.4089408
## Run 13 stress 0
## ... Procrustes: rmse 0.2645414 max resid 0.3669792
## Run 14 stress 0
## ... Procrustes: rmse 0.1243563 max resid 0.1700068
## Run 15 stress 0
## ... Procrustes: rmse 0.2803937 max resid 0.4341262
## Run 16 stress 0
## ... Procrustes: rmse 0.2683513 max resid 0.4773628
## Run 17 stress 0
## ... Procrustes: rmse 0.1155863 max resid 0.1910522
## Run 18 stress 0
## ... Procrustes: rmse 0.2050943 max resid 0.3011
## Run 19 stress 0
## ... Procrustes: rmse 0.3337995 max resid 0.4228298
## Run 20 stress 0
## ... Procrustes: rmse 0.1104326 max resid 0.1692845
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 20: stress < smin
## Warning in metaMDS(PenicuikPS): stress is (nearly) zero: you may have
## insufficient data
## Warning in postMDS(out$points, dis, plot = max(0, plot - 1), ...): skipping
## half-change scaling: too few points below threshold
plot(PenicuikNMDS)
#As in the previous NMDS plots, plant species are represented by red crosses, while
#plant communities are represented by circles.
PenicuikNMDS
##
## Call:
## metaMDS(comm = PenicuikPS)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(PenicuikPS))
## Distance: bray
##
## Dimensions: 2
## Stress: 0
## Stress type 1, weak ties
## Best solution was not repeated after 20 tries
## The best solution was from try 0 (metric scaling or null solution)
## Scaling: centring, PC rotation
## Species: expanded scores based on 'wisconsin(sqrt(PenicuikPS))'
plot(PenicuikNMDS, "sites") # Produces distance
plot(PenicuikNMDS, "species", xlim = c(-0.2, 0.5), ylim = c(-0.4, 0.8))
orditorp(PenicuikNMDS, "species")
#The ordiplot function here shows some differences between species on South Bank Wood in
#Penicuik. U. dioica, for example, plots separately from all of the other species, although
#not all of the species on-site are represented on this plot.
#It might give further context to look at a CCA of this site as well, especially to look
#at plants and substrate.
PenciuikCCA <- cca(PenicuikPS, PenicuikPC)
##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'CaO', 'MgO', 'Na2O',
## 'K2O', 'Cr2O3', 'TiO2', 'MnO', 'P2O5', 'SrO', 'BaO', 'LOI', 'Ag', 'As', 'B',
## 'Be', 'Bi', 'Cd', 'Co', 'Cu', 'Ga', 'Hg', 'La', 'Li', 'Mo', 'Ni', 'Pb', 'S',
## 'Sb', 'Sc', 'Th', 'Tl', 'U', 'V', 'W', 'Zn', 'Akermanite', 'Albite',
## 'Aluminium.oxide.hydroxide', 'Anhydrite', 'Aragonite', 'Biotite', 'Birnessite',
## 'Calcite', 'Clinochlore', 'Cuspidine', 'Diaspore', 'Dickite', 'Gehlenite',
## 'Goethite', 'Haematite', 'Illite', 'Kaolinite', 'Langite', 'Linnaeite',
## 'Magnesioferrite', 'Melilite', 'Merwinite', 'Microcline', 'Mullite',
## 'Muscovite', 'Orthoclase', 'Orthopyroxene', 'Periclase', 'Phengite',
## 'Pseudowollastonite', 'Quartz', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component
print(PenciuikCCA)
## Call: cca(X = PenicuikPS, Y = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.854 1.000
## Constrained 2.854 1.000 4
## Unconstrained 0.000 0.000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## Some constraints or conditions were aliased because they were redundant.
## This can happen if terms are linearly dependent (collinear): 'CaO', 'MgO',
## 'Na2O', 'K2O', 'Cr2O3', 'TiO2', 'MnO', 'P2O5', 'SrO', 'BaO', 'LOI', 'Ag',
## 'As', 'B', 'Be', 'Bi', 'Cd', 'Co', 'Cu', 'Ga', 'Hg', 'La', 'Li', 'Mo',
## 'Ni', 'Pb', 'S', 'Sb', 'Sc', 'Th', 'Tl', 'U', 'V', 'W', 'Zn', 'Akermanite',
## 'Albite', 'Aluminium.oxide.hydroxide', 'Anhydrite', 'Aragonite', 'Biotite',
## 'Birnessite', 'Calcite', 'Clinochlore', 'Cuspidine', 'Diaspore', 'Dickite',
## 'Gehlenite', 'Goethite', 'Haematite', 'Illite', 'Kaolinite', 'Langite',
## 'Linnaeite', 'Magnesioferrite', 'Melilite', 'Merwinite', 'Microcline',
## 'Mullite', 'Muscovite', 'Orthoclase', 'Orthopyroxene', 'Periclase',
## 'Phengite', 'Pseudowollastonite', 'Quartz', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component.
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 1.0000 0.8144 0.6265 0.4135
plot(PenciuikCCA)
summary(PenciuikCCA)
##
## Call:
## cca(X = PenicuikPS, Y = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.854 1
## Constrained 2.854 1
## Unconstrained 0.000 0
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 1.0000 0.8144 0.6265 0.4135
## Proportion Explained 0.3503 0.2853 0.2195 0.1449
## Cumulative Proportion 0.3503 0.6357 0.8551 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 1.0000 0.8144 0.6265 0.4135
## Proportion Explained 0.3503 0.2853 0.2195 0.1449
## Cumulative Proportion 0.3503 0.6357 0.8551 1.0000
PenicuikCCA2 <- cca(PenicuikPS ~ Al2O3 + pH + Fe2O3 + SiO2, data = PenicuikPC)
##
## The model is overfitted with no unconstrained (residual) component
print(PenicuikCCA2)
## Call: cca(formula = PenicuikPS ~ Al2O3 + pH + Fe2O3 + SiO2, data =
## PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.854 1.000
## Constrained 2.854 1.000 4
## Unconstrained 0.000 0.000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## The model is overfitted with no unconstrained (residual) component.
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 1.0000 0.8144 0.6265 0.4135
plot(PenicuikCCA2)
summary(PenicuikCCA2)
##
## Call:
## cca(formula = PenicuikPS ~ Al2O3 + pH + Fe2O3 + SiO2, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.854 1
## Constrained 2.854 1
## Unconstrained 0.000 0
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 1.0000 0.8144 0.6265 0.4135
## Proportion Explained 0.3503 0.2853 0.2195 0.1449
## Cumulative Proportion 0.3503 0.6357 0.8551 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4
## Eigenvalue 1.0000 0.8144 0.6265 0.4135
## Proportion Explained 0.3503 0.2853 0.2195 0.1449
## Cumulative Proportion 0.3503 0.6357 0.8551 1.0000
PenicuikCCA3 <- cca(PenicuikPS ~ Al2O3 + pH + Fe2O3, data = PenicuikPC)
print(PenicuikCCA3)
## Call: cca(formula = PenicuikPS ~ Al2O3 + pH + Fe2O3, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.3694 0.8301 3
## Unconstrained 0.4850 0.1699 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9365 0.8070 0.6259
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.485
plot(PenicuikCCA3)
summary(PenicuikCCA3)
##
## Call:
## cca(formula = PenicuikPS ~ Al2O3 + pH + Fe2O3, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.854 1.0000
## Constrained 2.369 0.8301
## Unconstrained 0.485 0.1699
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9365 0.8070 0.6259 0.4850
## Proportion Explained 0.3281 0.2827 0.2193 0.1699
## Cumulative Proportion 0.3281 0.6108 0.8301 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9365 0.8070 0.6259
## Proportion Explained 0.3953 0.3406 0.2642
## Cumulative Proportion 0.3953 0.7358 1.0000
anova(PenicuikCCA3)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ Al2O3 + pH + Fe2O3, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.36939 1.6284 0.01667 *
## Residual 1 0.48501
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.6284 and a low p value of 0.01667.
PenicuikCCA4 <- cca(PenicuikPS ~ Al2O3 + SrO + Fe2O3, data = PenicuikPC)
print(PenicuikCCA4)
## Call: cca(formula = PenicuikPS ~ Al2O3 + SrO + Fe2O3, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.3362 0.8185 3
## Unconstrained 0.5182 0.1815 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9824 0.8037 0.5501
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.5182
plot(PenicuikCCA4)
summary(PenicuikCCA4)
##
## Call:
## cca(formula = PenicuikPS ~ Al2O3 + SrO + Fe2O3, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.8544 1.0000
## Constrained 2.3362 0.8185
## Unconstrained 0.5182 0.1815
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9824 0.8037 0.5501 0.5182
## Proportion Explained 0.3442 0.2816 0.1927 0.1815
## Cumulative Proportion 0.3442 0.6257 0.8185 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9824 0.8037 0.5501
## Proportion Explained 0.4205 0.3440 0.2355
## Cumulative Proportion 0.4205 0.7645 1.0000
anova(PenicuikCCA4)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ Al2O3 + SrO + Fe2O3, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.3362 1.5028 0.05 *
## Residual 1 0.5182
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.5028 and a p value of 0.008333.
PenicuikCCA5 <- cca(PenicuikPS ~ pH + SrO + Fe2O3, data = PenicuikPC)
print(PenicuikCCA5)
## Call: cca(formula = PenicuikPS ~ pH + SrO + Fe2O3, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.1812 0.7641 3
## Unconstrained 0.6732 0.2359 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9529 0.8141 0.4142
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.6732
plot(PenicuikCCA5)
summary(PenicuikCCA5)
##
## Call:
## cca(formula = PenicuikPS ~ pH + SrO + Fe2O3, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.8544 1.0000
## Constrained 2.1812 0.7641
## Unconstrained 0.6732 0.2359
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9529 0.8141 0.4142 0.6732
## Proportion Explained 0.3338 0.2852 0.1451 0.2359
## Cumulative Proportion 0.3338 0.6190 0.7641 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9529 0.8141 0.4142
## Proportion Explained 0.4369 0.3732 0.1899
## Cumulative Proportion 0.4369 0.8101 1.0000
anova(PenicuikCCA5)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ pH + SrO + Fe2O3, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.18115 1.0799 0.2083
## Residual 1 0.67325
#An F statistic of 1.0799 and a p value of 0.2.
PenicuikCCA6 <- cca(PenicuikPS ~ pH + SrO + Al2O3, data = PenicuikPC)
print(PenicuikCCA6)
## Call: cca(formula = PenicuikPS ~ pH + SrO + Al2O3, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.3811 0.8342 3
## Unconstrained 0.4733 0.1658 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9925 0.8118 0.5768
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.4733
plot(PenicuikCCA6)
summary(PenicuikCCA6)
##
## Call:
## cca(formula = PenicuikPS ~ pH + SrO + Al2O3, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.8544 1.0000
## Constrained 2.3811 0.8342
## Unconstrained 0.4733 0.1658
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9925 0.8118 0.5768 0.4733
## Proportion Explained 0.3477 0.2844 0.2021 0.1658
## Cumulative Proportion 0.3477 0.6321 0.8342 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9925 0.8118 0.5768
## Proportion Explained 0.4168 0.3409 0.2423
## Cumulative Proportion 0.4168 0.7577 1.0000
anova(PenicuikCCA6)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ pH + SrO + Al2O3, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.38108 1.6768 0.008333 **
## Residual 1 0.47332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.6768 and a p value of 0.008333.
PenicuikCCA7 <- cca(PenicuikPS ~ MgO + SrO + As, data = PenicuikPC)
print(PenicuikCCA7)
## Call: cca(formula = PenicuikPS ~ MgO + SrO + As, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.0683 0.7246 3
## Unconstrained 0.7861 0.2754 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9997 0.6551 0.4135
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.7861
plot(PenicuikCCA7)
summary(PenicuikCCA7)
##
## Call:
## cca(formula = PenicuikPS ~ MgO + SrO + As, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.8544 1.0000
## Constrained 2.0683 0.7246
## Unconstrained 0.7861 0.2754
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9997 0.6551 0.4135 0.7861
## Proportion Explained 0.3502 0.2295 0.1449 0.2754
## Cumulative Proportion 0.3502 0.5797 0.7246 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9997 0.6551 0.4135
## Proportion Explained 0.4834 0.3167 0.1999
## Cumulative Proportion 0.4834 0.8001 1.0000
anova(PenicuikCCA7)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ MgO + SrO + As, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.06828 0.877 0.5917
## Residual 1 0.78612
#An F statistic of 0.877 and a p value of 0.4833.
PenicuikCCA8 <- cca(PenicuikPS ~ pH + SrO + As, data = PenicuikPC)
print(PenicuikCCA8)
## Call: cca(formula = PenicuikPS ~ pH + SrO + As, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.3787 0.8333 3
## Unconstrained 0.4757 0.1667 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9920 0.8118 0.5749
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.4757
plot(PenicuikCCA8)
summary(PenicuikCCA8)
##
## Call:
## cca(formula = PenicuikPS ~ pH + SrO + As, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.8544 1.0000
## Constrained 2.3787 0.8333
## Unconstrained 0.4757 0.1667
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9920 0.8118 0.5749 0.4757
## Proportion Explained 0.3475 0.2844 0.2014 0.1667
## Cumulative Proportion 0.3475 0.6319 0.8333 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9920 0.8118 0.5749
## Proportion Explained 0.4171 0.3413 0.2417
## Cumulative Proportion 0.4171 0.7583 1.0000
anova(PenicuikCCA8)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ pH + SrO + As, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.3787 1.6668 0.01667 *
## Residual 1 0.4757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.6668 and a p value of 0.008333.
PenicuikCCA9 <- cca(PenicuikPS ~ Al2O3 + SrO + As, data = PenicuikPC)
print(PenicuikCCA9)
## Call: cca(formula = PenicuikPS ~ Al2O3 + SrO + As, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.3803 0.8339 3
## Unconstrained 0.4741 0.1661 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9920 0.8125 0.5758
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.4741
plot(PenicuikCCA9)
summary(PenicuikCCA9)
##
## Call:
## cca(formula = PenicuikPS ~ Al2O3 + SrO + As, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.8544 1.0000
## Constrained 2.3803 0.8339
## Unconstrained 0.4741 0.1661
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9920 0.8125 0.5758 0.4741
## Proportion Explained 0.3475 0.2846 0.2017 0.1661
## Cumulative Proportion 0.3475 0.6322 0.8339 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9920 0.8125 0.5758
## Proportion Explained 0.4168 0.3413 0.2419
## Cumulative Proportion 0.4168 0.7581 1.0000
anova(PenicuikCCA9)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ Al2O3 + SrO + As, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.38029 1.6735 0.008333 **
## Residual 1 0.47411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.6735 and a p value of 0.008333.
PenicuikCCA10 <- cca(PenicuikPS ~ pH + SrO + Co, data = PenicuikPC)
print(PenicuikCCA10)
## Call: cca(formula = PenicuikPS ~ pH + SrO + Co, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.4085 0.8438 3
## Unconstrained 0.4459 0.1562 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9973 0.8116 0.5997
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.4459
plot(PenicuikCCA10)
summary(PenicuikCCA10)
##
## Call:
## cca(formula = PenicuikPS ~ pH + SrO + Co, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.8544 1.0000
## Constrained 2.4085 0.8438
## Unconstrained 0.4459 0.1562
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9973 0.8116 0.5997 0.4459
## Proportion Explained 0.3494 0.2843 0.2101 0.1562
## Cumulative Proportion 0.3494 0.6337 0.8438 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9973 0.8116 0.5997
## Proportion Explained 0.4141 0.3370 0.2490
## Cumulative Proportion 0.4141 0.7510 1.0000
anova(PenicuikCCA10)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ pH + SrO + Co, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.40854 1.8007 0.008333 **
## Residual 1 0.44586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.8007 and a p value of 0.008333.
PenicuikCCA11 <- cca(PenicuikPS ~ pH + SrO + Pb, data = PenicuikPC)
print(PenicuikCCA11)
## Call: cca(formula = PenicuikPS ~ pH + SrO + Pb, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.2654 0.7937 3
## Unconstrained 0.5890 0.2063 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9714 0.8128 0.4812
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.589
plot(PenicuikCCA11)
summary(PenicuikCCA11)
##
## Call:
## cca(formula = PenicuikPS ~ pH + SrO + Pb, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.854 1.0000
## Constrained 2.265 0.7937
## Unconstrained 0.589 0.2063
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9714 0.8128 0.4812 0.5890
## Proportion Explained 0.3403 0.2848 0.1686 0.2063
## Cumulative Proportion 0.3403 0.6251 0.7937 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9714 0.8128 0.4812
## Proportion Explained 0.4288 0.3588 0.2124
## Cumulative Proportion 0.4288 0.7876 1.0000
anova(PenicuikCCA11)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ pH + SrO + Pb, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.26542 1.2821 0.1583
## Residual 1 0.58898
#An F statistic of 1.2821 and a p value of 0.1.
PenicuikCCA12 <- cca(PenicuikPS ~ pH + SrO + V, data = PenicuikPC)
print(PenicuikCCA12)
## Call: cca(formula = PenicuikPS ~ pH + SrO + V, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.3476 0.8224 3
## Unconstrained 0.5068 0.1776 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9863 0.8120 0.5493
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.5068
plot(PenicuikCCA12)
summary(PenicuikCCA12)
##
## Call:
## cca(formula = PenicuikPS ~ pH + SrO + V, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.8544 1.0000
## Constrained 2.3476 0.8224
## Unconstrained 0.5068 0.1776
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9863 0.8120 0.5493 0.5068
## Proportion Explained 0.3455 0.2845 0.1924 0.1776
## Cumulative Proportion 0.3455 0.6300 0.8224 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9863 0.8120 0.5493
## Proportion Explained 0.4201 0.3459 0.2340
## Cumulative Proportion 0.4201 0.7660 1.0000
anova(PenicuikCCA12)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ pH + SrO + V, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.34759 1.544 0.01667 *
## Residual 1 0.50681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.544 and a p value of 0.01667.
PenicuikCCA13 <- cca(PenicuikPS ~ pH + SrO, data = PenicuikPC)
print(PenicuikCCA13)
## Call: cca(formula = PenicuikPS ~ pH + SrO, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 1.7630 0.6176 2
## Unconstrained 1.0914 0.3824 2
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2
## 0.9515 0.8115
##
## Eigenvalues for unconstrained axes:
## CA1 CA2
## 0.6734 0.4180
plot(PenicuikCCA13)
summary(PenicuikCCA13)
##
## Call:
## cca(formula = PenicuikPS ~ pH + SrO, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.854 1.0000
## Constrained 1.763 0.6176
## Unconstrained 1.091 0.3824
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CA1 CA2
## Eigenvalue 0.9515 0.8115 0.6734 0.4180
## Proportion Explained 0.3333 0.2843 0.2359 0.1464
## Cumulative Proportion 0.3333 0.6176 0.8536 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2
## Eigenvalue 0.9515 0.8115
## Proportion Explained 0.5397 0.4603
## Cumulative Proportion 0.5397 1.0000
anova(PenicuikCCA13)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ pH + SrO, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 2 1.7630 1.6153 0.008333 **
## Residual 2 1.0914
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.6153 and a p value of 0.008333.
PenicuikCCA14 <- cca(PenicuikPS ~ pH + SrO + BaO, data = PenicuikPC)
print(PenicuikCCA14)
## Call: cca(formula = PenicuikPS ~ pH + SrO + BaO, data = PenicuikPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8544 1.0000
## Constrained 2.2647 0.7934 3
## Unconstrained 0.5897 0.2066 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 112 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9712 0.8128 0.4806
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.5897
plot(PenicuikCCA14)
summary(PenicuikCCA14)
##
## Call:
## cca(formula = PenicuikPS ~ pH + SrO + BaO, data = PenicuikPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 2.8544 1.0000
## Constrained 2.2647 0.7934
## Unconstrained 0.5897 0.2066
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CA1
## Eigenvalue 0.9712 0.8128 0.4806 0.5897
## Proportion Explained 0.3403 0.2848 0.1684 0.2066
## Cumulative Proportion 0.3403 0.6250 0.7934 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3
## Eigenvalue 0.9712 0.8128 0.4806
## Proportion Explained 0.4289 0.3589 0.2122
## Cumulative Proportion 0.4289 0.7878 1.0000
anova(PenicuikCCA14)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 119
##
## Model: cca(formula = PenicuikPS ~ pH + SrO + BaO, data = PenicuikPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.26468 1.2801 0.1583
## Residual 1 0.58972
#An F statistic of 1.2801 and a p value of 0.1083.
#After doing multiple CCAs, the one with the highest F statistic and lowest p value is #PenicuikCCA10. However, the plot for this has a very short arrow for Co, which indicates #that Co may not be such an important variable for the CCA. PenicuikCCA6, meanwhile, has #a lower F statistic of 1.6768, the same p value of 0.00833 and BaO and longer arrows #for each of the variables. The plot for PenicuikCCA14 can be improved to better show the #relationships between substrate and plant species.
options(max.print=1000000)
PenicuikCCA6$CCA$v
## CCA1 CCA2 CCA3
## Agrostis.spp. -0.3170951 -0.57943764 -0.30710845
## Agrostis.canina -0.5240142 1.50571071 -4.28411086
## Alchemilla.mollis -0.3633211 1.20199944 1.35332240
## Angelica.sylvestris -0.2521188 0.65770840 0.58100562
## Arrhenatherum.elatius -0.3633211 1.20199944 1.35332240
## Brachythecium.rutabulum -0.2521188 0.65770840 0.58100562
## Calliergonella.cupsidata -0.2104180 0.45359926 0.29138682
## Calypogeia.arguta -0.1409166 0.11341736 -0.19131116
## Cirsium.arvense -0.2768440 0.68216459 -0.59796882
## Dactylis.glomerata -0.4134295 -1.38753418 -0.10386446
## Epilobium.montanum -0.5240142 1.50571071 -4.28411086
## Equisetum.arvense -0.3465359 1.11984231 1.23674628
## Filipendula.ulmaria -0.4436677 1.35385508 -1.46539423
## Fragaria.vesca -0.4315678 -1.55229439 -0.01997496
## Galium.aparine 3.0294405 -0.06426004 -0.10500748
## Galium.arvense -0.5240142 1.50571071 -4.28411086
## Geum.urbanum -0.4315678 -1.55229439 -0.01997496
## Glechoma.hederacea -0.4315678 -1.55229439 -0.01997496
## Heracleum.sphondylium 3.0294405 -0.06426004 -0.10500748
## Holcus.mollis -0.4686354 1.36633065 -2.82686977
## Kindbergia.praelonga -0.2862422 -0.71943851 -0.10564306
## Lotus.corniculatus -0.1409166 0.11341736 -0.19131116
## Myosotis.arvensis 3.0294405 -0.06426004 -0.10500748
## Oxalis.acetosella -0.4315678 -1.55229439 -0.01997496
## Pentaglottis.sempervirens 3.0294405 -0.06426004 -0.10500748
## Pteridium.aquilinum -0.4315678 -1.55229439 -0.01997496
## Rhytidadelphus.squarrosus -0.3355205 1.06592668 1.16024320
## Senecio.jacobaea -0.3633211 1.20199944 1.35332240
## Stachys.sylvatica -0.4436677 1.35385508 -1.46539423
## Tussilago.farfara -0.3164991 0.97282427 1.02813638
## Urtica.diocia 3.0294405 -0.06426004 -0.10500748
## attr(,"na.action")
## Alopercus.pratensis Anthoxanthum.odoratum
## 4 6
## Anthyllis.vulneraria Aphanes.arvensis
## 7 8
## Atrichum.undulatum Avenula.pratensis
## 10 11
## Bellis.perennis Betula.pubescens
## 12 13
## Blackstonia.perfoliata Brachythecium.albicans
## 14 15
## Brachythecium.glareosum Brachythecium.mildeanum
## 16 17
## Briza.media Bromus.hordeaceus
## 19 20
## Bryum.c.f..caespiticium Bryum.c.f..pallescens
## 21 22
## Bryum.capillare Bryum.spp.
## 23 24
## Campanula.rotundifolia Carex.distans
## 27 28
## Carex.flacca Carex.panicea
## 29 30
## Carlina.vulgaris Centaurea.nigra
## 31 32
## Centaurium.erythraea Centaurium.littorale
## 33 34
## Centaurium.pulchellum Cerastium.fontanum
## 35 36
## Chamaenerion.angustifolium Cirriphyllum.piliferum
## 37 38
## Cirsium.palustre Crepis.capillaris
## 40 41
## Cynosurus.cristatus Dactylorhiza.fuchsii
## 42 44
## Danthonia.decumbens Daucus.carrota
## 45 46
## Dicranella.spp. Dicranum.scoparium
## 47 48
## Equisteum.variegatum Erigeron.acer
## 51 52
## Euphrasia.agg Festuca.ovina
## 53 54
## Festuca.rubra Festuca.rubra.agg.
## 55 56
## Fissidens.adianthoides Fissidens.dubius
## 58 59
## Fissidens.exilis Galium.saxatile
## 60 64
## Galium.verum Helicotrichon.spp.
## 65 68
## Hieracium.spp. Holcus.spp.
## 70 71
## Holcus.lanatus Homalothecium.lutescens
## 72 74
## Hylocomium.splendens Hypericum.perforatum
## 75 76
## Hypnum.cupressiforme Hypnum.imponens
## 77 78
## Hypnum.jutlandicum Hypochaeris.radicata
## 79 80
## Lathyrus.pratensis Leontodon.hispidus
## 82 83
## Leontodon.saxatilis Leucanthemum.vulgare
## 84 85
## Linum.catharticum Lolium.perenne
## 86 87
## Lophocolea.semiteres Luzula.multiflora
## 88 90
## Lysimachia.maritima Medicago.lupulina
## 91 92
## Ononis.repens Pastinaca.sativa
## 94 96
## Pillosella.officinarum Plantago.coronopus
## 98 99
## Plantago.lanceolata Pleurozium.schreberi
## 100 101
## Poa.annua Poa.spp.
## 102 103
## Polytrichum.commune Polytrichum.commune.agg.
## 104 105
## Polytrichum.formosum Potentilla.reptans
## 106 107
## Prunella.vulgaris Pseudoscleropidum.purum
## 108 109
## Ranunculus.acris Ranunculus.parviflorus
## 111 112
## Ranunculus.repens Reseda.lutea
## 113 114
## Rhinanthus.minor Rhizomnium.punctatum
## 115 116
## Rhytidadelphus.triquetris Rubus.fruticosus
## 118 119
## Sanguisorba.minor.spp.minor Saniona.uncinata
## 120 121
## Sedum.anglicum Senecio.vulgaris
## 122 124
## Sonchus.arvensis Stellaria.apetala
## 125 127
## Taraxacum.agg. Thuidium.tamariscinum
## 128 129
## Thymus.polytrichus Trichostomum.crispulum
## 130 131
## Trifolium.campestre Trifolium.dubium
## 132 133
## Trifolium.pratense Trifolium.repens
## 134 135
## Trisetum.flavescens Veronica.officinalis
## 136 139
## Vicia.sativa Viola.riviniana
## 140 141
## Weissia.controversa Zygodon.stirtonii
## 142 143
## attr(,"class")
## [1] "exclude"
#The "v values" show the positions of individual species on the graph, with the CCA1 value
#representing the x axis and the CCA2 value representing the y axis, in the case of the
#graph created in R.
plot(PenicuikCCA6, xlim = c(-5, 5), ylim = c(-5, 5))
#Increasing the xlim and ylim to give more room for writing on graph
plot(PenicuikCCA6, choices = c(1,2), display = c("wa", "bp"), xlim = c(-4, 3),
ylim = c(-3, 2))
#Increasing the xlim and ylim to give more room for writing on graph
points(x = -0.4315678, y = -1.55229439, pch = 15, col = "black")
#This point represents Glechoma hederacea
text('G. hederacea', x = -0.4315678, y = -1.55229439, cex = 0.88, pos = 2, col = "black")
#Adding text to this point.
points(x = -0.5240142 , y = 1.50571071, pch = 15, col = "black")
#This point represents Epilobium montanum
text('E. montanum', x = -0.5240142 , y = 1.50571071, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = 3.0421284 , y = 1.075808e-15, pch = 15, col = "black")
#This point represents Heracleum sphondylium
text('H. sphondylium', x = 3.0294405, y = -0.06426004, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = 3.0294405 , y = -0.06426004, pch = 15, col = "black")
#This point represents Angelica sylvestris
text('A. sylvestris', x = -0.2521188, y = 0.65770840, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = -0.2521188, y = 0.65770840, pch = 15, col = "black")
##Carnforth Data
urlfile11 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/CarnforthPlantSpecies.csv'
CarnforthPS <- read.csv(urlfile11, header = TRUE, colClasses = c("numeric"))
urlfile12 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/CARNFORTH_PLANT_CHEMISTRY_MG_KG.csv'
CarnforthPC <-read.csv(urlfile12, header = TRUE, colClasses = c("numeric"))
head(CarnforthPS)
## Agrostis.spp. Agrostis.canina Alchemilla.mollis Alopercus.pratensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 27
## Angelica.sylvestris Anthoxanthum.odoratum Anthyllis.vulneraria
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Aphanes.arvensis Arrhenatherum.elatius Atrichum.undulatum Avenula.pratensis
## 1 0 1 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 11
## Bellis.perennis Betula.pubescens Blackstonia.perfoliata
## 1 0 0 0
## 2 0 0 0
## 3 11 0 0
## 4 0 0 0
## 5 0 0 0
## 6 10 0 0
## Brachythecium.albicans Brachythecium.glareosum Brachythecium.mildeanum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Brachythecium.rutabulum Briza.media Bromus.hordeaceus Bryum.c.f..caespiticium
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Bryum.c.f..pallescens Bryum.capillare Bryum.spp. Calliergonella.cupsidata
## 1 60 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 20 120
## 5 0 0 0 0
## 6 0 0 0 10
## Calypogeia.arguta Campanula.rotundifolia Carex.distans Carex.flacca
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 23 0
## 4 0 0 0 24
## 5 0 0 0 0
## 6 0 0 0 0
## Carex.panicea Carlina.vulgaris Centaurea.nigra Centaurium.erythraea
## 1 0 11 0 0
## 2 0 5 0 0
## 3 0 4 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Centaurium.littorale Centaurium.pulchellum Cerastium.fontanum
## 1 0 0 1
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Chamaenerion.angustifolium Cirriphyllum.piliferum Cirsium.arvense
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Cirsium.palustre Crepis.capillaris Cynosurus.cristatus Dactylis.glomerata
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Dactylorhiza.fuchsii Danthonia.decumbens Daucus.carrota Dicranella.spp.
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Dicranum.scoparium Epilobium.montanum Equisetum.arvense Equisteum.variegatum
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Erigeron.acer Euphrasia.agg Festuca.ovina Festuca.rubra Festuca.rubra.agg.
## 1 0 16 0 0 0
## 2 0 0 0 0 0
## 3 0 0 17 0 0
## 4 0 0 55 0 0
## 5 0 0 27 0 0
## 6 0 0 5 0 0
## Filipendula.ulmaria Fissidens.adianthoides Fissidens.dubius Fissidens.exilis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 20 0
## 5 0 0 0 0
## 6 0 0 0 0
## Fragaria.vesca Galium.aparine Galium.arvense Galium.saxatile Galium.verum
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 1 0
## 6 0 0 0 0 0
## Geum.urbanum Glechoma.hederacea Helicotrichon.spp. Heracleum.sphondylium
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Hieracium.spp. Holcus.spp. Holcus.lanatus Holcus.mollis
## 1 0 42 0 0
## 2 0 0 0 0
## 3 0 40 8 0
## 4 0 58 3 0
## 5 0 70 0 0
## 6 0 23 0 0
## Homalothecium.lutescens Hylocomium.splendens Hypericum.perforatum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 160 0 0
## 6 10 0 0
## Hypnum.cupressiforme Hypnum.imponens Hypnum.jutlandicum Hypochaeris.radicata
## 1 60 0 0 0
## 2 150 0 0 0
## 3 60 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Kindbergia.praelonga Lathyrus.pratensis Leontodon.hispidus
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Leontodon.saxatilis Leucanthemum.vulgare Linum.catharticum Lolium.perenne
## 1 0 0 5 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Lophocolea.semiteres Lotus.corniculatus Luzula.multiflora Lysimachia.maritima
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 220 0 0
## 4 0 19 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Medicago.lupulina Myosotis.arvensis Ononis.repens Oxalis.acetosella
## 1 0 0 0 0
## 2 0 1 0 0
## 3 5 0 0 0
## 4 0 0 0 0
## 5 0 6 2 0
## 6 0 4 63 0
## Pastinaca.sativa Pentaglottis.sempervirens Pillosella.officinarum
## 1 0 0 33
## 2 0 0 286
## 3 0 0 2
## 4 0 0 0
## 5 0 0 54
## 6 0 0 0
## Plantago.coronopus Plantago.lanceolata Pleurozium.schreberi Poa.annua
## 1 0 1 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 1 0 0 0
## 5 19 0 0 0
## 6 0 1 0 0
## Poa.spp. Polytrichum.commune Polytrichum.commune.agg. Polytrichum.formosum
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Potentilla.reptans Prunella.vulgaris Pseudoscleropidum.purum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 30
## 6 0 0 0
## Pteridium.aquilinum Ranunculus.acris Ranunculus.parviflorus Ranunculus.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Reseda.lutea Rhinanthus.minor Rhizomnium.punctatum Rhytidadelphus.squarrosus
## 1 0 0 0 60
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 140
## 6 0 0 0 20
## Rhytidadelphus.triquetris Rubus.fruticosus Sanguisorba.minor.spp.minor
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Saniona.uncinata Sedum.anglicum Senecio.jacobaea Senecio.vulgaris
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Sonchus.arvensis Stachys.sylvatica Stellaria.apetala Taraxacum.agg.
## 1 0 0 0 0
## 2 0 0 0 1
## 3 0 0 0 3
## 4 0 0 0 2
## 5 0 0 0 1
## 6 0 0 0 0
## Thuidium.tamariscinum Thymus.polytrichus Trichostomum.crispulum
## 1 0 283 60
## 2 0 7 0
## 3 0 159 0
## 4 0 0 0
## 5 0 0 0
## 6 0 20 0
## Trifolium.campestre Trifolium.dubium Trifolium.pratense Trifolium.repens
## 1 0 162 0 0
## 2 0 118 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 2
## Trisetum.flavescens Tussilago.farfara Urtica.diocia Veronica.officinalis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 7 0
## 6 0 0 0 0
## Vicia.sativa Viola.riviniana Weissia.controversa Zygodon.stirtonii
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
head(CarnforthPC)
## pH_level SiO2 Al2O3 Fe2O3 CaO MgO Na2O K2O
## 1 9.008 182299.6 47632.53 153873.611 164378.9 6030.359 2967.428 10791.921
## 2 10.175 159862.7 52131.16 7903.508 262291.5 8924.931 2151.385 3486.621
## 3 10.589 170613.7 44139.48 20283.340 204401.6 8140.985 2299.756 5561.990
## 4 9.516 181832.2 35936.10 7833.566 200828.1 8080.681 2596.499 4648.828
## 5 10.133 142567.7 43927.78 4826.036 250141.8 9769.182 1186.971 2407.429
## 6 8.790 200997.0 31755.02 181850.631 121497.4 6030.359 3709.284 13282.364
## Cr2O3 TiO2 MnO P2O5 SrO BaO LOI Ag As B Be
## 1 342.10121 3596.057 5421.202 999.8709 287.5021 1791.3027 22.86 0.00 0 0 0.0
## 2 27.36810 4495.072 1858.698 399.9484 338.2378 1433.0422 13.90 0.15 3 50 12.8
## 3 20.52607 2637.109 1936.144 599.9225 253.6784 716.5211 19.00 1.30 11 60 8.4
## 4 41.05215 2457.306 1781.252 499.9354 253.6784 985.2165 20.70 0.30 7 40 8.6
## 5 13.68405 3356.320 929.349 599.9225 253.6784 716.5211 22.20 0.30 3 40 11.4
## 6 68.42024 1798.029 2323.372 999.8709 211.3986 806.0862 20.61 0.00 0 0 0.0
## Bi Cd Co Cu Ga Hg La Li Mo Ni Pb S Sb Sc Th Tl U V W Zn
## 1 0.0 0.00 0 0 0 0.0 0 0 0.0 0 0 10000 0.0 0 0 0 0 100 0 6000
## 2 1.5 0.25 1 14 5 0.5 30 50 0.5 7 11 6000 2.0 11 10 5 10 10 5 36
## 3 2.0 2.70 3 53 10 0.5 30 40 0.5 8 212 4300 16.0 7 10 5 5 12 5 503
## 4 1.5 0.60 36 30 5 0.5 20 40 0.5 6 71 2700 2.0 5 10 5 5 11 5 120
## 5 1.5 0.25 4 17 5 0.5 30 40 0.5 3 28 8100 1.5 8 10 5 5 5 5 46
## 6 0.0 0.00 0 0 0 0.0 0 0 0.0 0 0 5000 0.0 0 0 0 0 100 0 2000
## Akermanite Albite Aluminium.oxide.hydroxide Anhydrite Aragonite Biotite
## 1 1 0 0 0 0 1
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 0 0 0 0 0
## 5 1 0 0 0 0 0
## 6 0 0 0 0 0 0
## Birnessite Calcite Clinochlore Cuspidine Diaspore Dickite Gehlenite Goethite
## 1 0 1 0 0 0 0 0 0
## 2 0 1 0 1 0 0 1 0
## 3 0 1 0 1 0 0 1 0
## 4 0 1 0 0 0 0 1 0
## 5 0 1 0 1 0 0 0 0
## 6 0 1 0 0 0 0 0 0
## Haematite Illite Kaolinite Langite Linnaeite Magnesioferrite Melilite
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 1 0
## Merwinite Microcline Mullite Muscovite Orthoclase Orthopyroxene Periclase
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 1
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 1 0 0 1
## 6 1 0 0 0 0 0 0
## Phengite Pseudowollastonite Quartz Staurolite Valentinite
## 1 0 1 1 0 0
## 2 0 0 1 0 0
## 3 0 0 1 0 0
## 4 0 0 1 0 0
## 5 0 0 0 0 0
## 6 0 0 1 0 0
rowSums(CarnforthPS)
## [1] 795 568 552 322 517 206 319 343
#Now for some ANOSIMs…
#anoCSiO2 <- anosim(CarnforthPS, CarnforthPC$SiO2, distance = “bray”, permutations = 9999)
#When this ANOSIM is run, the error message: “there should be replicates within groups” #comes up. This error message comes up for another substrate variables for the Barrow #data. From now on, only variables that were tested successfully will be included.
anoCMgO <- anosim(CarnforthPS, CarnforthPC$MgO, distance = "bray", permutations = 9999)
anoCMgO
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$MgO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4287
##
## Permutation: free
## Number of permutations: 9999
anoCCr2O3 <- anosim(CarnforthPS, CarnforthPC$Cr2O3, distance = "bray", permutations = 9999)
anoCCr2O3
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Cr2O3, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0
## Significance: 0.5006
##
## Permutation: free
## Number of permutations: 9999
anoCP2O5 <- anosim(CarnforthPS, CarnforthPC$P2O5, distance = "bray", permutations = 9999)
anoCP2O5
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$P2O5, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.03846
## Significance: 0.4723
##
## Permutation: free
## Number of permutations: 9999
anoCSrO <- anosim(CarnforthPS, CarnforthPC$SrO, distance = "bray", permutations = 9999)
anoCSrO
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$SrO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.07576
## Significance: 0.5985
##
## Permutation: free
## Number of permutations: 9999
anoCBaO <- anosim(CarnforthPS, CarnforthPC$BaO, distance = "bray", permutations = 9999)
anoCBaO
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$BaO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1923
## Significance: 0.6727
##
## Permutation: free
## Number of permutations: 9999
anoCAg <- anosim(CarnforthPS, CarnforthPC$Ag, distance = "bray", permutations = 9999)
anoCAg
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Ag, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.01333
## Significance: 0.497
##
## Permutation: free
## Number of permutations: 9999
anoCAs <- anosim(CarnforthPS, CarnforthPC$As, distance = "bray", permutations = 9999)
anoCAs
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$As, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0
## Significance: 0.4966
##
## Permutation: free
## Number of permutations: 9999
anoCB <- anosim(CarnforthPS, CarnforthPC$B, distance = "bray", permutations = 9999)
anoCB
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$B, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1875
## Significance: 0.7076
##
## Permutation: free
## Number of permutations: 9999
anoCBe <- anosim(CarnforthPS, CarnforthPC$Be, distance = "bray", permutations = 9999)
anoCBe
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Be, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4274
##
## Permutation: free
## Number of permutations: 9999
anoCCd <- anosim(CarnforthPS, CarnforthPC$Cd, distance = "bray", permutations = 9999)
anoCCd
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Cd, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1042
## Significance: 0.6296
##
## Permutation: free
## Number of permutations: 9999
anoCCo <- anosim(CarnforthPS, CarnforthPC$Co, distance = "bray", permutations = 9999)
anoCCo
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Co, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1538
## Significance: 0.3512
##
## Permutation: free
## Number of permutations: 9999
anoCCu <- anosim(CarnforthPS, CarnforthPC$Cu, distance = "bray", permutations = 9999)
anoCCu
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Cu, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4232
##
## Permutation: free
## Number of permutations: 9999
anoCMo <- anosim(CarnforthPS, CarnforthPC$Mo, distance = "bray", permutations = 9999)
anoCMo
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Mo, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.3542
## Significance: 0.8562
##
## Permutation: free
## Number of permutations: 9999
anoCNi <- anosim(CarnforthPS, CarnforthPC$Ni, distance = "bray", permutations = 9999)
anoCNi
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Ni, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.2267
## Significance: 0.7544
##
## Permutation: free
## Number of permutations: 9999
anoCPb <- anosim(CarnforthPS, CarnforthPC$Pb, distance = "bray", permutations = 9999)
anoCPb
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Pb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4296
##
## Permutation: free
## Number of permutations: 9999
anoCSb <- anosim(CarnforthPS, CarnforthPC$Sb, distance = "bray", permutations = 9999)
anoCSb
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Sb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.01333
## Significance: 0.5111
##
## Permutation: free
## Number of permutations: 9999
anoCSc <- anosim(CarnforthPS, CarnforthPC$Sc, distance = "bray", permutations = 9999)
anoCSc
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Sc, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4217
##
## Permutation: free
## Number of permutations: 9999
anoCV <- anosim(CarnforthPS, CarnforthPC$V, distance = "bray", permutations = 9999)
anoCV
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$V, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4271
##
## Permutation: free
## Number of permutations: 9999
#None of the ANOSIM tests led to stastistically significant results. Because of this,
#CCAs might not be entirely appropriate to represent the data visually.
#NMDS analyses will be utilised to visualise the plant data.
CarnforthNMDS <- metaMDS(CarnforthPS)
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1027624
## Run 1 stress 0.1175384
## Run 2 stress 0.05547408
## ... New best solution
## ... Procrustes: rmse 0.2042017 max resid 0.4524411
## Run 3 stress 0.1027624
## Run 4 stress 0.1027625
## Run 5 stress 0.186362
## Run 6 stress 0.05547398
## ... New best solution
## ... Procrustes: rmse 4.411432e-05 max resid 7.488946e-05
## ... Similar to previous best
## Run 7 stress 0.05547398
## ... New best solution
## ... Procrustes: rmse 6.750473e-06 max resid 1.270373e-05
## ... Similar to previous best
## Run 8 stress 0.05547398
## ... New best solution
## ... Procrustes: rmse 6.782685e-06 max resid 1.228053e-05
## ... Similar to previous best
## Run 9 stress 0.190284
## Run 10 stress 0.05547399
## ... Procrustes: rmse 3.218193e-05 max resid 5.714836e-05
## ... Similar to previous best
## Run 11 stress 0.055474
## ... Procrustes: rmse 5.876103e-05 max resid 0.0001036368
## ... Similar to previous best
## Run 12 stress 0.05547398
## ... Procrustes: rmse 5.877481e-06 max resid 1.059789e-05
## ... Similar to previous best
## Run 13 stress 0.08975528
## Run 14 stress 0.2848522
## Run 15 stress 0.055474
## ... Procrustes: rmse 5.699742e-05 max resid 9.960538e-05
## ... Similar to previous best
## Run 16 stress 0.1027624
## Run 17 stress 0.05547399
## ... Procrustes: rmse 2.927659e-05 max resid 5.150946e-05
## ... Similar to previous best
## Run 18 stress 0.3083098
## Run 19 stress 0.05547399
## ... Procrustes: rmse 2.263375e-05 max resid 3.966542e-05
## ... Similar to previous best
## Run 20 stress 0.05547399
## ... Procrustes: rmse 3.515576e-05 max resid 6.215593e-05
## ... Similar to previous best
## *** Best solution repeated 8 times
plot(CarnforthNMDS)
#As in the other NMDS plots, species are represented by red crosses and communities are #represented by circles.
CarnforthNMDS
##
## Call:
## metaMDS(comm = CarnforthPS)
##
## global Multidimensional Scaling using monoMDS
##
## Data: wisconsin(sqrt(CarnforthPS))
## Distance: bray
##
## Dimensions: 2
## Stress: 0.05547398
## Stress type 1, weak ties
## Best solution was repeated 8 times in 20 tries
## The best solution was from try 8 (random start)
## Scaling: centring, PC rotation, halfchange scaling
## Species: expanded scores based on 'wisconsin(sqrt(CarnforthPS))'
plot(CarnforthNMDS, "sites") # Produces distance
plot(CarnforthNMDS, "species")
orditorp(CarnforthNMDS, "species")
#What is interesting about this NMDS is that most of the species are separated out, with #very little clustering or ‘clumping’. This contrasts greatly with the NMDS plots #for Fallin Bing, Addiewell Bing and South Bank Wood (Penicuik). It might still be #useful to look at a CCA for Carnforth as well…
CarnforthCCA <- cca(CarnforthPS, CarnforthPC)
##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'K2O', 'Cr2O3', 'TiO2',
## 'MnO', 'P2O5', 'SrO', 'BaO', 'LOI', 'Ag', 'As', 'B', 'Be', 'Bi', 'Cd', 'Co',
## 'Cu', 'Ga', 'Hg', 'La', 'Li', 'Mo', 'Ni', 'Pb', 'S', 'Sb', 'Sc', 'Th', 'Tl',
## 'U', 'V', 'W', 'Zn', 'Akermanite', 'Albite', 'Aluminium.oxide.hydroxide',
## 'Anhydrite', 'Aragonite', 'Biotite', 'Birnessite', 'Calcite', 'Clinochlore',
## 'Cuspidine', 'Diaspore', 'Dickite', 'Gehlenite', 'Goethite', 'Haematite',
## 'Illite', 'Kaolinite', 'Langite', 'Linnaeite', 'Magnesioferrite', 'Melilite',
## 'Merwinite', 'Microcline', 'Mullite', 'Muscovite', 'Orthoclase',
## 'Orthopyroxene', 'Periclase', 'Phengite', 'Pseudowollastonite', 'Quartz',
## 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component
print(CarnforthCCA)
## Call: cca(X = CarnforthPS, Y = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.868 1.000
## Constrained 3.868 1.000 7
## Unconstrained 0.000 0.000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## Some constraints or conditions were aliased because they were redundant.
## This can happen if terms are linearly dependent (collinear): 'K2O',
## 'Cr2O3', 'TiO2', 'MnO', 'P2O5', 'SrO', 'BaO', 'LOI', 'Ag', 'As', 'B', 'Be',
## 'Bi', 'Cd', 'Co', 'Cu', 'Ga', 'Hg', 'La', 'Li', 'Mo', 'Ni', 'Pb', 'S',
## 'Sb', 'Sc', 'Th', 'Tl', 'U', 'V', 'W', 'Zn', 'Akermanite', 'Albite',
## 'Aluminium.oxide.hydroxide', 'Anhydrite', 'Aragonite', 'Biotite',
## 'Birnessite', 'Calcite', 'Clinochlore', 'Cuspidine', 'Diaspore', 'Dickite',
## 'Gehlenite', 'Goethite', 'Haematite', 'Illite', 'Kaolinite', 'Langite',
## 'Linnaeite', 'Magnesioferrite', 'Melilite', 'Merwinite', 'Microcline',
## 'Mullite', 'Muscovite', 'Orthoclase', 'Orthopyroxene', 'Periclase',
## 'Phengite', 'Pseudowollastonite', 'Quartz', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component.
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.8089 0.6910 0.6454 0.5739 0.4932 0.4219 0.2338
plot(CarnforthCCA)
CarnforthCCA1 <- cca(CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Calcite,
data = CarnforthPC)
print(CarnforthCCA1)
## Call: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Calcite, data =
## CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.8682 1.0000
## Constrained 3.0308 0.7835 5
## Unconstrained 0.8374 0.2165 2
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.7958 0.6891 0.6261 0.5340 0.3858
##
## Eigenvalues for unconstrained axes:
## CA1 CA2
## 0.5070 0.3303
plot(CarnforthCCA1)
anova(CarnforthCCA1)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Calcite, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 5 3.03084 1.4478 0.005 **
## Residual 2 0.83737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##An F statistic of 1.4478 and a low p value of 0.008.
CarnforthCCA2 <- cca(CarnforthPS ~ Na2O + Co +SrO + Al2O3 + Calcite + MgO,
data = CarnforthPC)
print(CarnforthCCA2)
## Call: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Calcite + MgO,
## data = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.86821 1.00000
## Constrained 3.52185 0.91046 6
## Unconstrained 0.34635 0.08954 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.7968 0.6898 0.6295 0.5737 0.4903 0.3417
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.3464
plot(CarnforthCCA2)
anova(CarnforthCCA2)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Calcite + MgO, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.5219 1.6947 0.001 ***
## Residual 1 0.3464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##An F statistic of 1.6947 and a low p value of 0.002.
CarnforthCCA3 <- cca(CarnforthPS ~ Na2O + Co + P2O5 + Al2O3, data = CarnforthPC)
print(CarnforthCCA3)
## Call: cca(formula = CarnforthPS ~ Na2O + Co + P2O5 + Al2O3, data =
## CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.8682 1.0000
## Constrained 2.2333 0.5774 4
## Unconstrained 1.6349 0.4226 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.7660 0.6623 0.5402 0.2648
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.6517 0.5161 0.4670
plot(CarnforthCCA3)
anova(CarnforthCCA3)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ Na2O + Co + P2O5 + Al2O3, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 4 2.2333 1.0245 0.332
## Residual 3 1.6349
#An F statistic of 1.0245 and a p value of 0.382.
CarnforthCCA4 <- cca(CarnforthPS ~ pH_level + Co +SrO + Al2O3 + Calcite + MgO,
data = CarnforthPC)
print(CarnforthCCA4)
## Call: cca(formula = CarnforthPS ~ pH_level + Co + SrO + Al2O3 + Calcite +
## MgO, data = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.8682 1.0000
## Constrained 3.4512 0.8922 6
## Unconstrained 0.4170 0.1078 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8029 0.6899 0.6387 0.5456 0.4825 0.2915
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.417
plot(CarnforthCCA4)
anova(CarnforthCCA4)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ pH_level + Co + SrO + Al2O3 + Calcite + MgO, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.4512 1.3792 0.004 **
## Residual 1 0.4170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.3792 and a p value of 0.01.
CarnforthCCA5 <- cca(CarnforthPS ~ pH_level + Co + As + Al2O3 + Calcite + MgO,
data = CarnforthPC)
print(CarnforthCCA5)
## Call: cca(formula = CarnforthPS ~ pH_level + Co + As + Al2O3 + Calcite +
## MgO, data = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.8682 1.0000
## Constrained 3.4109 0.8818 6
## Unconstrained 0.4573 0.1182 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8071 0.6868 0.6199 0.5552 0.4863 0.2556
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.4573
plot(CarnforthCCA5)
anova(CarnforthCCA5)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ pH_level + Co + As + Al2O3 + Calcite + MgO, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.4109 1.243 0.075 .
## Residual 1 0.4573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.243 and a p value of 0.089.
CarnforthCCA6 <- cca(CarnforthPS ~ Na2O + Co +SrO + Al2O3 + Calcite + V,
data = CarnforthPC)
print(CarnforthCCA6)
## Call: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Calcite + V,
## data = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.8682 1.0000
## Constrained 3.3915 0.8768 6
## Unconstrained 0.4767 0.1232 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.7998 0.6892 0.6332 0.5414 0.4791 0.2487
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.4767
plot(CarnforthCCA6)
anova(CarnforthCCA6)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Calcite + V, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.3915 1.1857 0.1
## Residual 1 0.4767
#An F statistic of 1.1857 and a p value of 0.104.
CarnforthCCA7 <- cca(CarnforthPS ~ Na2O + Co +SrO + Al2O3 + Ag + MgO,
data = CarnforthPC)
print(CarnforthCCA7)
## Call: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Ag + MgO, data
## = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.86821 1.00000
## Constrained 3.50666 0.90653 6
## Unconstrained 0.36155 0.09347 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8011 0.6893 0.6378 0.5672 0.4924 0.3189
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.3615
plot(CarnforthCCA7)
anova(CarnforthCCA7)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Ag + MgO, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.5067 1.6165 0.002 **
## Residual 1 0.3615
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.6165 and a p value of 0.003.
CarnforthCCA8 <- cca(CarnforthPS ~ Na2O + Co +SrO + Al2O3 + BaO + MgO,
data = CarnforthPC)
print(CarnforthCCA8)
## Call: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + BaO + MgO, data
## = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.8682 1.0000
## Constrained 3.3792 0.8736 6
## Unconstrained 0.4890 0.1264 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8089 0.6901 0.6451 0.5427 0.4532 0.2392
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.489
plot(CarnforthCCA8)
anova(CarnforthCCA8)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + BaO + MgO, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.3792 1.1518 0.184
## Residual 1 0.4890
#An F statistic of 1.1518 and a p value of 0.147.
CarnforthCCA9 <- cca(CarnforthPS ~ Na2O + Co +SrO + Al2O3 + Cr2O3 + MgO,
data = CarnforthPC)
print(CarnforthCCA9)
## Call: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Cr2O3 + MgO,
## data = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.8682 1.0000
## Constrained 3.3565 0.8677 6
## Unconstrained 0.5117 0.1323 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8077 0.6905 0.6435 0.5408 0.4375 0.2365
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.5117
plot(CarnforthCCA9)
anova(CarnforthCCA9)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Cr2O3 + MgO, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.3565 1.0933 0.285
## Residual 1 0.5117
#An F statistic of 1.6442 and a p value of 0.002.
CarnforthCCA9 <- cca(CarnforthPS ~ Na2O + Co +SrO + Al2O3 + Cd + MgO,
data = CarnforthPC)
print(CarnforthCCA9)
## Call: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Cd + MgO, data
## = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.86821 1.00000
## Constrained 3.51219 0.90796 6
## Unconstrained 0.35601 0.09204 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8002 0.6893 0.6361 0.5695 0.4930 0.3240
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.356
plot(CarnforthCCA9)
anova(CarnforthCCA9)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Al2O3 + Cd + MgO, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.5122 1.6442 0.003 **
## Residual 1 0.3560
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.634 and a low p value of 0.002.
CarnforthCCA10 <- cca(CarnforthPS ~ Na2O + Co +SrO + Cd + Calcite + MgO,
data = CarnforthPC)
print(CarnforthCCA10)
## Call: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Cd + Calcite + MgO,
## data = CarnforthPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 3.86821 1.00000
## Constrained 3.51017 0.90744 6
## Unconstrained 0.35804 0.09256 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 102 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8014 0.6909 0.6376 0.5676 0.4932 0.3195
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.358
plot(CarnforthCCA10)
anova(CarnforthCCA10)
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: cca(formula = CarnforthPS ~ Na2O + Co + SrO + Cd + Calcite + MgO, data = CarnforthPC)
## Df ChiSquare F Pr(>F)
## Model 6 3.5102 1.634 0.003 **
## Residual 1 0.3580
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#An F statistic of 1.6287 and a low p value of 0.001.
#After doing several CCAs with different combinations of chemical variables, the one with #the highest F statistic, 1.6947, is CarnforthCCA2. This CCA also has a low p value of #0.002. The plot for this one looks good, but it can be made to be a little neater and #easier to interpret…
options(max.print=1000000)
CarnforthCCA2$CCA$v
## CCA1 CCA2 CCA3 CCA4
## Alopercus.pratensis 0.70197267 1.9144914 0.58018924 -3.19668010
## Arrhenatherum.elatius -0.92434356 -0.3623170 -0.30215207 0.13427454
## Avenula.pratensis 0.70197267 1.9144914 0.58018924 -3.19668010
## Bellis.perennis 0.25570592 1.0509552 0.19022470 -0.86331856
## Brachythecium.mildeanum 2.78627371 -2.1170901 0.13902509 0.32569124
## Bryum.c.f..pallescens -0.97459212 -0.7902055 -1.38783174 -0.32053435
## Bryum.spp. 1.08992217 2.5870088 -1.28669429 1.76431490
## Calliergonella.cupsidata 1.06007990 2.5352767 -1.14308786 1.38269990
## Carex.distans 1.88581916 -1.3862996 0.04600892 0.61157449
## Carex.flacca 1.08992217 2.5870088 -1.28669429 1.76431490
## Carlina.vulgaris -0.76941458 -0.3533724 -0.05304103 0.32526965
## Centaurium.pulchellum 2.78627371 -2.1170901 0.13902509 0.32569124
## Cerastium.fontanum -0.92434356 -0.3623170 -0.30215207 0.13427454
## Dicranella.spp. -1.03489039 -1.3036717 -2.69064735 -0.86630502
## Euphrasia.agg -0.92434356 -0.3623170 -0.30215207 0.13427454
## Festuca.ovina 1.06512484 0.8841181 -0.43752785 0.47189414
## Fissidens.dubius 1.08992217 2.5870088 -1.28669429 1.76431490
## Fissidens.exilis -1.03489039 -1.3036717 -2.69064735 -0.86630502
## Galium.saxatile 0.25327885 0.3925877 0.39790464 -1.84098085
## Holcus.spp. 0.35971488 0.7701560 -0.20685038 -0.16208029
## Holcus.lanatus 0.18816704 0.8989459 -0.47039918 1.39602713
## Homalothecium.lutescens 0.27967260 0.4821115 0.40862726 -1.92072787
## Hypnum.cupressiforme -0.66364332 -0.1652824 1.09999378 0.70620087
## Linum.catharticum -0.92434356 -0.3623170 -0.30215207 0.13427454
## Lotus.corniculatus -0.05142062 0.4504439 -0.25351743 1.29817661
## Lysimachia.maritima 2.78627371 -2.1170901 0.13902509 0.32569124
## Medicago.lupulina -0.14999113 0.2659224 -0.16428852 1.25791922
## Myosotis.arvensis 0.32388542 0.8867765 0.62497725 -2.10166559
## Ononis.repens 0.68816670 1.8676636 0.57458048 -3.15496628
## Pillosella.officinarum -0.63005543 -0.1744372 1.67056970 0.29508742
## Plantago.coronopus 2.41445838 -1.7261350 0.16509219 0.02921254
## Plantago.lanceolata -0.11118545 0.7760872 0.13901858 -1.53120278
## Pseudoscleropidum.purum 1.26647679 -0.6112834 0.29435282 -0.97431202
## Rhytidadelphus.squarrosus -0.02710055 0.3250595 0.22355141 -1.42552022
## Taraxacum.agg. 0.17404653 0.8722031 -0.07168349 0.88224152
## Thymus.polytrichus -0.62903152 -0.1603932 -0.40066206 0.27255158
## Trichostomum.crispulum -0.99587386 -0.9714288 -1.84764902 -0.51315929
## Trifolium.campestre 2.78627371 -2.1170901 0.13902509 0.32569124
## Trifolium.dubium -0.85711750 -0.3187554 0.73823585 0.37870712
## Trifolium.repens 0.70197267 1.9144914 0.58018924 -3.19668010
## Urtica.diocia 0.25327885 0.3925877 0.39790464 -1.84098085
## CCA5 CCA6
## Alopercus.pratensis 2.23753647 -2.73079659
## Arrhenatherum.elatius 0.39151162 -1.93124381
## Avenula.pratensis 2.23753647 -2.73079659
## Bellis.perennis 2.36187593 -0.19603802
## Brachythecium.mildeanum 0.05140906 -0.77970802
## Bryum.c.f..pallescens -0.30152871 -0.74573425
## Bryum.spp. -1.85436517 -0.70523196
## Calliergonella.cupsidata -1.53960351 -0.86104462
## Carex.distans 0.79461657 0.10594407
## Carex.flacca -1.85436517 -0.70523196
## Carlina.vulgaris 0.20674996 -0.49095930
## Centaurium.pulchellum 0.05140906 -0.77970802
## Cerastium.fontanum 0.39151162 -1.93124381
## Dicranella.spp. -1.13317712 0.67687722
## Euphrasia.agg 0.39151162 -1.93124381
## Festuca.ovina -0.57422053 0.23471256
## Fissidens.dubius -1.85436517 -0.70523196
## Fissidens.exilis -1.13317712 0.67687722
## Galium.saxatile -0.98632494 2.45820246
## Holcus.spp. -0.03668418 0.24977834
## Holcus.lanatus 1.29419989 1.34096433
## Homalothecium.lutescens -0.79668603 2.15296723
## Hypnum.cupressiforme -0.03540532 -0.01756412
## Linum.catharticum 0.39151162 -1.93124381
## Lotus.corniculatus 2.13074333 1.88461900
## Lysimachia.maritima 0.05140906 -0.77970802
## Medicago.lupulina 2.47491179 2.10828794
## Myosotis.arvensis 0.16562703 0.33850867
## Ononis.repens 2.13834073 -2.57113508
## Pillosella.officinarum -1.01774072 0.11864224
## Plantago.coronopus -0.10995452 -0.32004552
## Plantago.lanceolata 1.31452405 -2.33102020
## Pseudoscleropidum.purum -0.57123134 1.16303827
## Rhytidadelphus.squarrosus -0.31747302 0.78935357
## Taraxacum.agg. 0.21705445 1.03859562
## Thymus.polytrichus 0.95251835 -0.45952861
## Trichostomum.crispulum -0.59505168 -0.24363609
## Trifolium.campestre 0.05140906 -0.77970802
## Trifolium.dubium -0.28353711 -1.16053071
## Trifolium.repens 2.23753647 -2.73079659
## Urtica.diocia -0.98632494 2.45820246
## attr(,"na.action")
## Agrostis.spp. Agrostis.canina
## 1 2
## Alchemilla.mollis Angelica.sylvestris
## 3 5
## Anthoxanthum.odoratum Anthyllis.vulneraria
## 6 7
## Aphanes.arvensis Atrichum.undulatum
## 8 10
## Betula.pubescens Blackstonia.perfoliata
## 13 14
## Brachythecium.albicans Brachythecium.glareosum
## 15 16
## Brachythecium.rutabulum Briza.media
## 18 19
## Bromus.hordeaceus Bryum.c.f..caespiticium
## 20 21
## Bryum.capillare Calypogeia.arguta
## 23 26
## Campanula.rotundifolia Carex.panicea
## 27 30
## Centaurea.nigra Centaurium.erythraea
## 32 33
## Centaurium.littorale Chamaenerion.angustifolium
## 34 37
## Cirriphyllum.piliferum Cirsium.arvense
## 38 39
## Cirsium.palustre Crepis.capillaris
## 40 41
## Cynosurus.cristatus Dactylis.glomerata
## 42 43
## Dactylorhiza.fuchsii Danthonia.decumbens
## 44 45
## Daucus.carrota Dicranum.scoparium
## 46 48
## Epilobium.montanum Equisetum.arvense
## 49 50
## Equisteum.variegatum Erigeron.acer
## 51 52
## Festuca.rubra Festuca.rubra.agg.
## 55 56
## Filipendula.ulmaria Fissidens.adianthoides
## 57 58
## Fragaria.vesca Galium.aparine
## 61 62
## Galium.arvense Galium.verum
## 63 65
## Geum.urbanum Glechoma.hederacea
## 66 67
## Helicotrichon.spp. Heracleum.sphondylium
## 68 69
## Hieracium.spp. Holcus.mollis
## 70 73
## Hylocomium.splendens Hypericum.perforatum
## 75 76
## Hypnum.imponens Hypnum.jutlandicum
## 78 79
## Hypochaeris.radicata Kindbergia.praelonga
## 80 81
## Lathyrus.pratensis Leontodon.hispidus
## 82 83
## Leontodon.saxatilis Leucanthemum.vulgare
## 84 85
## Lolium.perenne Lophocolea.semiteres
## 87 88
## Luzula.multiflora Oxalis.acetosella
## 90 95
## Pastinaca.sativa Pentaglottis.sempervirens
## 96 97
## Pleurozium.schreberi Poa.annua
## 101 102
## Poa.spp. Polytrichum.commune
## 103 104
## Polytrichum.commune.agg. Polytrichum.formosum
## 105 106
## Potentilla.reptans Prunella.vulgaris
## 107 108
## Pteridium.aquilinum Ranunculus.acris
## 110 111
## Ranunculus.parviflorus Ranunculus.repens
## 112 113
## Reseda.lutea Rhinanthus.minor
## 114 115
## Rhizomnium.punctatum Rhytidadelphus.triquetris
## 116 118
## Rubus.fruticosus Sanguisorba.minor.spp.minor
## 119 120
## Saniona.uncinata Sedum.anglicum
## 121 122
## Senecio.jacobaea Senecio.vulgaris
## 123 124
## Sonchus.arvensis Stachys.sylvatica
## 125 126
## Stellaria.apetala Thuidium.tamariscinum
## 127 129
## Trifolium.pratense Trisetum.flavescens
## 134 136
## Tussilago.farfara Veronica.officinalis
## 137 139
## Vicia.sativa Viola.riviniana
## 140 141
## Weissia.controversa Zygodon.stirtonii
## 142 143
## attr(,"class")
## [1] "exclude"
#The "v values" show the positions of individual species on the graph, with the CCA1 value
#representing the x axis and the CCA2 value representing the y axis, in the case of the
#graph created in R.
plot(CarnforthCCA2, xlim = c(-5, 5), ylim = c(-5, 5))
#Increasing the xlim and ylim to give more room for writing on graph
plot(CarnforthCCA2, choices = c(1,2), display = c("wa", "bp"), xlim = c(-4, 3),
ylim = c(-3, 2))
#Increasing the xlim and ylim to give more room for writing on graph Note that Community
#1 and Community 2 are grouped/clustered together in this CCA plot.
points(x = 0.70197267, y = 1.9144914, pch = 15, col = "black")
#This point represents Alopercus pratensis
text('A. pratensis', x = 0.70197267, y = 1.9144914, cex = 0.88, pos = 2, col = "black")
#Adding text to this point.
points(x = -0.63005543, y = -0.1744372, pch = 15, col = "black")
#This point represents Pilosella officinarum
text('P. officinarum', x = -0.63005543, y = -0.1744372, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = 2.78627371 , y = -2.1170901, pch = 15, col = "black")
#This point represents Centaurium pulchellum
text('C. pulchellum', x = 2.78627371, y = -2.1170901, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = -1.03489039, y = -1.3036717, pch = 15, col = "black")
#This point represents Fissidens exilis
text('F. exilis', x = -1.03489039, y = -1.3036717, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = 0.27967260, y = 0.4821115, pch = 15, col = "black")
#This point represents Homalothecium lutescens
text('H. lutescens', x = 0.27967260, y = 0.4821115, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
##Hodbarrow Data##
urlfile13 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/HodbarrowPlantSpecies.csv'
HodbarrowPS <- read.csv(urlfile13, header = TRUE, colClasses = c("numeric"))
urlfile14 <- 'https://raw.githubusercontent.com/Savannankvm/Canonical-Correspondence-analyses-six-study-sites-PhD/PhD-files/HODBARROW_PLANT_CHEMISTRY_MG_KG.csv'
HodbarrowPC <-read.csv(urlfile14, header = TRUE,
colClasses = c("numeric"))
head(HodbarrowPS)
## Agrostis.spp. Agrostis.canina Alchemilla.mollis Alopercus.pratensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Angelica.sylvestris Anthoxanthum.odoratum Anthyllis.vulneraria
## 1 0 0 0
## 2 0 0 70
## 3 0 0 189
## 4 0 7 0
## 5 0 0 0
## 6 0 0 0
## Aphanes.arvensis Arrhenatherum.elatius Atrichum.undulatum Avenula.pratensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Bellis.perennis Betula.pubescens Blackstonia.perfoliata
## 1 0 0 0
## 2 0 0 0
## 3 1 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Brachythecium.albicans Brachythecium.glareosum Brachythecium.mildeanum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Brachythecium.rutabulum Briza.media Bromus.hordeaceus Bryum.c.f..caespiticium
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Bryum.c.f..pallescens Bryum.capillare Bryum.spp. Calliergonella.cupsidata
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 60 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Calypogeia.arguta Campanula.rotundifolia Carex.distans Carex.flacca
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 42 0 0
## 5 0 0 0 3
## 6 0 0 0 0
## Carex.panicea Carlina.vulgaris Centaurea.nigra Centaurium.erythraea
## 1 0 0 0 0
## 2 0 0 0 0
## 3 1 0 0 0
## 4 0 3 0 0
## 5 0 2 1 0
## 6 0 1 0 0
## Centaurium.littorale Centaurium.pulchellum Cerastium.fontanum
## 1 0 0 0
## 2 4 0 0
## 3 0 0 0
## 4 4 0 1
## 5 5 0 0
## 6 0 0 0
## Chamaenerion.angustifolium Cirriphyllum.piliferum Cirsium.arvense
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Cirsium.palustre Crepis.capillaris Cynosurus.cristatus Dactylis.glomerata
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Dactylorhiza.fuchsii Danthonia.decumbens Daucus.carrota Dicranella.spp.
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Dicranum.scoparium Epilobium.montanum Equisetum.arvense Equisteum.variegatum
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 10 31
## 5 0 0 0 0
## 6 0 0 0 0
## Erigeron.acer Euphrasia.agg Festuca.ovina Festuca.rubra Festuca.rubra.agg.
## 1 0 0 0 10 0
## 2 0 0 0 0 0
## 3 0 0 0 4 0
## 4 0 0 0 2 0
## 5 0 36 0 44 0
## 6 0 0 0 0 0
## Filipendula.ulmaria Fissidens.adianthoides Fissidens.dubius Fissidens.exilis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Fragaria.vesca Galium.aparine Galium.arvense Galium.saxatile Galium.verum
## 1 0 0 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 5 0 0 0 0 0
## 6 0 0 0 0 0
## Geum.urbanum Glechoma.hederacea Helicotrichon.spp. Heracleum.sphondylium
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Hieracium.spp. Holcus.spp. Holcus.lanatus Holcus.mollis
## 1 0 47 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 18 0
## 5 0 23 0 0
## 6 0 78 0 0
## Homalothecium.lutescens Hylocomium.splendens Hypericum.perforatum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Hypnum.cupressiforme Hypnum.imponens Hypnum.jutlandicum Hypochaeris.radicata
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 160 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Kindbergia.praelonga Lathyrus.pratensis Leontodon.hispidus
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 16
## 6 0 0 2
## Leontodon.saxatilis Leucanthemum.vulgare Linum.catharticum Lolium.perenne
## 1 0 2 0 0
## 2 0 0 2 0
## 3 0 2 0 0
## 4 0 1 0 0
## 5 0 6 1 0
## 6 0 0 6 0
## Lophocolea.semiteres Lotus.corniculatus Luzula.multiflora Lysimachia.maritima
## 1 0 0 0 2
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 57 0 0
## 6 0 0 0 0
## Medicago.lupulina Myosotis.arvensis Ononis.repens Oxalis.acetosella
## 1 0 0 0 0
## 2 0 0 5 0
## 3 0 0 1 0
## 4 91 0 111 0
## 5 0 0 2 0
## 6 0 0 0 0
## Pastinaca.sativa Pentaglottis.sempervirens Pillosella.officinarum
## 1 0 0 2
## 2 0 0 0
## 3 0 0 0
## 4 0 0 91
## 5 0 0 39
## 6 0 0 0
## Plantago.coronopus Plantago.lanceolata Pleurozium.schreberi Poa.annua
## 1 0 0 0 0
## 2 0 0 0 69
## 3 0 0 0 46
## 4 0 5 0 0
## 5 50 20 0 55
## 6 0 0 0 0
## Poa.spp. Polytrichum.commune Polytrichum.commune.agg. Polytrichum.formosum
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Potentilla.reptans Prunella.vulgaris Pseudoscleropidum.purum
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Pteridium.aquilinum Ranunculus.acris Ranunculus.parviflorus Ranunculus.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Reseda.lutea Rhinanthus.minor Rhizomnium.punctatum Rhytidadelphus.squarrosus
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Rhytidadelphus.triquetris Rubus.fruticosus Sanguisorba.minor.spp.minor
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Saniona.uncinata Sedum.anglicum Senecio.jacobaea Senecio.vulgaris
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Sonchus.arvensis Stachys.sylvatica Stellaria.apetala Taraxacum.agg.
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 2
## Thuidium.tamariscinum Thymus.polytrichus Trichostomum.crispulum
## 1 0 0 150
## 2 0 216 30
## 3 0 253 60
## 4 0 0 0
## 5 0 0 70
## 6 0 7 0
## Trifolium.campestre Trifolium.dubium Trifolium.pratense Trifolium.repens
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## Trisetum.flavescens Tussilago.farfara Urtica.diocia Veronica.officinalis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 3 0 0
## 6 0 0 0 0
## Vicia.sativa Viola.riviniana Weissia.controversa Zygodon.stirtonii
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 10
head(HodbarrowPC)
## pH SiO2 Al2O3 Fe2O3 CaO MgO Na2O K2O
## 1 10.660 153318.7 46679.88 7833.566 265864.97 14111.040 2077.199 4067.724
## 2 8.381 324399.8 36782.90 26648.112 18867.84 5487.627 6379.969 14278.542
## 3 8.873 324399.8 37947.25 30005.354 27086.78 6754.002 6899.269 14029.497
## 4 8.806 284667.9 52660.41 25039.433 45096.99 13930.129 13353.424 14444.571
## 5 10.346 203334.2 43980.70 14128.395 162234.80 8201.288 8605.540 8052.433
## 6 9.966 134153.8 47579.61 7413.910 260862.13 12603.450 1854.642 2739.488
## Cr2O3 TiO2 MnO P2O5 SrO BaO LOI Ag As B
## 1 13.68405 1258.620 1781.2522 199.9742 338.23782 4746.9523 15.05 0.30 6 70
## 2 68.42024 2697.043 774.4575 999.8709 84.55946 716.5211 11.80 0.20 16 10
## 3 68.42024 2816.912 929.3490 999.8709 84.55946 716.5211 9.88 0.20 19 10
## 4 109.47239 3056.649 929.3490 1699.7805 169.11891 1343.4771 11.55 0.15 7 10
## 5 20.52607 1138.752 1239.1320 699.9096 169.11891 2866.0844 17.55 0.40 10 40
## 6 13.68405 1258.620 1471.4692 199.9742 253.67837 3134.7798 22.00 0.30 6 30
## Be Bi Cd Co Cu Ga Hg La Li Mo Ni Pb S Sb Sc Th Tl U V W Zn
## 1 11.4 1.5 0.25 1 17 5 0.5 40 80 0.5 5 74 7400 3.0 10 10 5 5 6 5 73
## 2 1.8 1.5 0.25 5 14 5 0.5 10 20 1.0 13 39 700 5.0 2 10 5 5 18 5 61
## 3 2.0 1.5 0.25 6 18 5 0.5 10 20 1.0 13 51 700 6.0 3 10 5 5 19 5 74
## 4 1.9 1.5 0.25 10 24 10 0.5 20 30 0.5 33 16 1100 1.5 3 10 5 5 31 5 46
## 5 6.8 1.5 0.25 2 22 5 0.5 20 40 0.5 6 56 5300 4.0 6 10 5 5 7 5 58
## 6 11.6 1.5 0.25 1 18 5 0.5 50 60 0.5 4 41 3500 3.0 12 10 5 5 4 5 51
## Akermanite Albite Aluminium.oxide.hydroxide Anhydrite Aragonite Biotite
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 0 0 0 0 0
## 4 0 1 0 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Birnessite Calcite Clinochlore Cuspidine Diaspore Dickite Gehlenite Goethite
## 1 0 1 0 0 0 0 1 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 1 0 1 0 0 1 0
## 6 0 1 0 0 0 1 0 0
## Haematite Illite Kaolinite Langite Linnaeite Magnesioferrite Melilite
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 1 0 0
## Merwinite Microcline Mullite Muscovite Orthoclase Orthopyroxene Periclase
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 1 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 1 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## Phengite Pseudowollastonite Quartz Staurolite Valentinite
## 1 0 0 1 0 0
## 2 0 0 1 0 0
## 3 0 0 1 0 1
## 4 0 0 1 0 0
## 5 0 0 1 0 0
## 6 0 0 0 0 0
rowSums(HodbarrowPS)
## [1] 213 396 617 577 433 106 216
#Again, ANOSIMs will be carried out to see if there are any statistically significant
#differences between plants in different concentrations of elements.
anoHSiO2 <- anosim(HodbarrowPS, HodbarrowPC$SiO2, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHSiO2
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$SiO2, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 1
## Significance: 0.047619
##
## Permutation: free
## Number of permutations: 5039
#This ANOSIM has a high ANOSIM statistic of 1 and a low p value of 0.047619, showing that
#there are statistically significant differences between plants in different silicon
#concentrations on the Hodbarrow site.
#anoHAl2O3 <- anosim(HodbarrowPS, HodbarrowPC$Al2O3, distance = "bray", permutations = 9999)
#When this ANOSIM is run, the error message: "there should be replicates within groups"
#comes up. This error message comes up for another substrate variables for the Hodbarrow
#data. From now on, only variables that were tested successfully will be included.
anoHCr2O3 <- anosim(HodbarrowPS, HodbarrowPC$Cr2O3, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHCr2O3
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$Cr2O3, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.963
## Significance: 0.0095238
##
## Permutation: free
## Number of permutations: 5039
#This ANOSIM has a high R statistic of 0.963 and a low p value of 0.0095238. This shows that
#there are significant differences between plants growing in different concentrations of
#Cr2O3.
anoHTiO2 <- anosim(HodbarrowPS, HodbarrowPC$TiO2, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHTiO2
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$TiO2, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7
## Significance: 0.19048
##
## Permutation: free
## Number of permutations: 5039
anoHMnO <- anosim(HodbarrowPS, HodbarrowPC$MnO, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHMnO
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$MnO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.5263
## Significance: 0.91429
##
## Permutation: free
## Number of permutations: 5039
anoHP2O5 <- anosim(HodbarrowPS, HodbarrowPC$P2O5, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHP2O5
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$P2O5, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.963
## Significance: 0.0095238
##
## Permutation: free
## Number of permutations: 5039
#This ANOSIM has a high R statistic of 0.963 and a low p value of 0.0095238. There are
#significant differences between plants in different concentrations of P2O5 in
#Hodbarrow.
anoHSrO <- anosim(HodbarrowPS, HodbarrowPC$SrO, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHSrO
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$SrO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.5556
## Significance: 0.047619
##
## Permutation: free
## Number of permutations: 5039
#This ANOSIM has an R statistic of 0.5556 and a p value of 0.047619. This shows that there
#are significant differences between plants growing in different concentrations of SrO
#in Hodbarrow, although the R statistic indicates that this isn't quite as strong as the
#differences seen in, say, SiO2, or Cr2O3.
anoHBaO <- anosim(HodbarrowPS, HodbarrowPC$BaO, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHBaO
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$BaO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.4737
## Significance: 0.15238
##
## Permutation: free
## Number of permutations: 5039
anoHAg <- anosim(HodbarrowPS, HodbarrowPC$Ag, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHAg
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$Ag, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.8947
## Significance: 0.028571
##
## Permutation: free
## Number of permutations: 5039
#This ANOSIM has a high R statistic of 0.8947 and a low p value of 0.028571. This
#demonstrates that plants are significantly different in differing concentrations of Ag
#at Hodbarrow.
anoHAs <- anosim(HodbarrowPS, HodbarrowPC$As, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHAs
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$As, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7
## Significance: 0.19048
##
## Permutation: free
## Number of permutations: 5039
anoHB <- anosim(HodbarrowPS, HodbarrowPC$B, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHB
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$B, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1481
## Significance: 0.57143
##
## Permutation: free
## Number of permutations: 5039
anoHCo <- anosim(HodbarrowPS, HodbarrowPC$Co, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHCo
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$Co, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.8421
## Significance: 0.038095
##
## Permutation: free
## Number of permutations: 5039
#This ANOSIM has a high R statistic of 0.8421 and a low p value of 0.038095. This
#demonstrates that plants are significantly different in differing concentrations of Co at
#Hodbarrow.
anoHCu <- anosim(HodbarrowPS, HodbarrowPC$Cu, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHCu
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$Cu, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.4
## Significance: 0.71429
##
## Permutation: free
## Number of permutations: 5039
anoHNi <- anosim(HodbarrowPS, HodbarrowPC$Ni, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHNi
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$Ni, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 1
## Significance: 0.047619
##
## Permutation: free
## Number of permutations: 5039
#This ANOSIM has a high R statistic of 1 and a low p value of 0.047619. This demonstrates
#significant differences between plants growing in different concentrations of Ni at
#Hodbarrow.
anoHS <- anosim(HodbarrowPS, HodbarrowPC$S, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHS
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$S, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 1
## Significance: 0.047619
##
## Permutation: free
## Number of permutations: 5039
#With a high R statistic of 1 and a low p value of 0.047619, this ANOSIM shows that there
#are significant differences between plants growing in different concentraitons of S at
#Hodbarrow.
anoHSb <- anosim(HodbarrowPS, HodbarrowPC$Sb, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHSb
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$Sb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.8421
## Significance: 0.038095
##
## Permutation: free
## Number of permutations: 5039
#This ANOSIM has a high R statistic of 0.8421 and a low p value of 0.038095, showing that
#plants significantly differ in different concentrations of Sb at Hodbarrow.
anoHSc <- anosim(HodbarrowPS, HodbarrowPC$Sc, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHSc
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$Sc, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.9
## Significance: 0.95238
##
## Permutation: free
## Number of permutations: 5039
anoHTi <- anosim(HodbarrowPS, HodbarrowPC$Ti, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHTi
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$Ti, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.7
## Significance: 0.19048
##
## Permutation: free
## Number of permutations: 5039
anoHV <- anosim(HodbarrowPS, HodbarrowPC$V, distance = "bray", permutations = 9999)
## 'nperm' >= set of all permutations: complete enumeration.
## Set of permutations < 'minperm'. Generating entire set.
anoHV
##
## Call:
## anosim(x = HodbarrowPS, grouping = HodbarrowPC$V, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.9
## Significance: 0.095238
##
## Permutation: free
## Number of permutations: 5039
#After carrying ANOSIMs for all of the geochemical variables, several of them demonstrated
#significant differences in terms of their relation to plant growth. CCA plots will better
#represent the data than NMDS plots, so some will be generated now to find the most
#appropriate CCA for the data.
HodbarrowCCA <- cca(HodbarrowPS, HodbarrowPC)
##
## Some constraints or conditions were aliased because they were redundant. This
## can happen if terms are linearly dependent (collinear): 'Na2O', 'K2O', 'Cr2O3',
## 'TiO2', 'MnO', 'P2O5', 'SrO', 'BaO', 'LOI', 'Ag', 'As', 'B', 'Be', 'Bi', 'Cd',
## 'Co', 'Cu', 'Ga', 'Hg', 'La', 'Li', 'Mo', 'Ni', 'Pb', 'S', 'Sb', 'Sc', 'Th',
## 'Tl', 'U', 'V', 'W', 'Zn', 'Akermanite', 'Albite', 'Aluminium.oxide.hydroxide',
## 'Anhydrite', 'Aragonite', 'Biotite', 'Birnessite', 'Calcite', 'Clinochlore',
## 'Cuspidine', 'Diaspore', 'Dickite', 'Gehlenite', 'Goethite', 'Haematite',
## 'Illite', 'Kaolinite', 'Langite', 'Linnaeite', 'Magnesioferrite', 'Melilite',
## 'Merwinite', 'Microcline', 'Mullite', 'Muscovite', 'Orthoclase',
## 'Orthopyroxene', 'Periclase', 'Phengite', 'Pseudowollastonite', 'Quartz',
## 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component
print(HodbarrowCCA)
## Call: cca(X = HodbarrowPS, Y = HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.811 1.000
## Constrained 2.811 1.000 6
## Unconstrained 0.000 0.000 0
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## Some constraints or conditions were aliased because they were redundant.
## This can happen if terms are linearly dependent (collinear): 'Na2O', 'K2O',
## 'Cr2O3', 'TiO2', 'MnO', 'P2O5', 'SrO', 'BaO', 'LOI', 'Ag', 'As', 'B', 'Be',
## 'Bi', 'Cd', 'Co', 'Cu', 'Ga', 'Hg', 'La', 'Li', 'Mo', 'Ni', 'Pb', 'S',
## 'Sb', 'Sc', 'Th', 'Tl', 'U', 'V', 'W', 'Zn', 'Akermanite', 'Albite',
## 'Aluminium.oxide.hydroxide', 'Anhydrite', 'Aragonite', 'Biotite',
## 'Birnessite', 'Calcite', 'Clinochlore', 'Cuspidine', 'Diaspore', 'Dickite',
## 'Gehlenite', 'Goethite', 'Haematite', 'Illite', 'Kaolinite', 'Langite',
## 'Linnaeite', 'Magnesioferrite', 'Melilite', 'Merwinite', 'Microcline',
## 'Mullite', 'Muscovite', 'Orthoclase', 'Orthopyroxene', 'Periclase',
## 'Phengite', 'Pseudowollastonite', 'Quartz', 'Staurolite', 'Valentinite'
##
## The model is overfitted with no unconstrained (residual) component.
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.9051 0.7597 0.5296 0.3423 0.1971 0.0769
plot(HodbarrowCCA)
HodbarrowCCA1 <- cca(HodbarrowPS ~ CaO + Al2O3 + SiO2,
data = HodbarrowPC)
print(HodbarrowCCA1)
## Call: cca(formula = HodbarrowPS ~ CaO + Al2O3 + SiO2, data = HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 2.1059 0.7492 3
## Unconstrained 0.7048 0.2508 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.9009 0.7536 0.4514
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4050 0.2115 0.0883
plot(HodbarrowCCA1)
anova(HodbarrowCCA1)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ CaO + Al2O3 + SiO2, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.10594 2.9878 0.001 ***
## Residual 3 0.70484
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#High F statistic of 2.9878 and low p value of 0.001.
#With such a high F statistic, it may be tempting to say that this is the most suitable #CCA, but a few more can be constructed, including with some of the variables that #were shown to be significant in the ANOSIMs.
HodbarrowCCA2 <- cca(HodbarrowPS ~ SiO2 + Cr2O3 + P2O5,
data = HodbarrowPC)
print(HodbarrowCCA2)
## Call: cca(formula = HodbarrowPS ~ SiO2 + Cr2O3 + P2O5, data = HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 2.0587 0.7324 3
## Unconstrained 0.7521 0.2676 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.8796 0.7534 0.4257
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.3760 0.2961 0.0800
plot(HodbarrowCCA2)
anova(HodbarrowCCA2)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ SiO2 + Cr2O3 + P2O5, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 3 2.05870 2.7373 0.003 **
## Residual 3 0.75208
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#High F statistic of 2.7373 and low p value of 0.003.
HodbarrowCCA3 <- cca(HodbarrowPS ~ SrO + Co + Ni,
data = HodbarrowPC)
print(HodbarrowCCA3)
## Call: cca(formula = HodbarrowPS ~ SrO + Co + Ni, data = HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.811 1.000
## Constrained 1.748 0.622 3
## Unconstrained 1.063 0.378 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.8780 0.6717 0.1984
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.5164 0.3677 0.1785
plot(HodbarrowCCA3)
anova(HodbarrowCCA3)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ SrO + Co + Ni, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.7482 1.6452 0.075 .
## Residual 3 1.0626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.6452 and p value of 0.05.
HodbarrowCCA3 <- cca(HodbarrowPS ~ SrO + Co + Ni,
data = HodbarrowPC)
print(HodbarrowCCA3)
## Call: cca(formula = HodbarrowPS ~ SrO + Co + Ni, data = HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.811 1.000
## Constrained 1.748 0.622 3
## Unconstrained 1.063 0.378 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.8780 0.6717 0.1984
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.5164 0.3677 0.1785
plot(HodbarrowCCA3)
anova(HodbarrowCCA3)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ SrO + Co + Ni, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.7482 1.6452 0.073 .
## Residual 3 1.0626
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.6452 and p value of 0.053.
HodbarrowCCA4 <- cca(HodbarrowPS ~ S + Sb + Ni,
data = HodbarrowPC)
print(HodbarrowCCA4)
## Call: cca(formula = HodbarrowPS ~ S + Sb + Ni, data = HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 1.8699 0.6653 3
## Unconstrained 0.9409 0.3347 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.8873 0.6816 0.3010
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.5717 0.2049 0.1643
plot(HodbarrowCCA4)
anova(HodbarrowCCA4)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ S + Sb + Ni, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.86990 1.9874 0.02 *
## Residual 3 0.94087
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#High F statistic of 1.9874 and p value of 0.015.
HodbarrowCCA5 <- cca(HodbarrowPS ~ Ag + Al2O3 + As,
data = HodbarrowPC)
print(HodbarrowCCA5)
## Call: cca(formula = HodbarrowPS ~ Ag + Al2O3 + As, data = HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 1.8945 0.6740 3
## Unconstrained 0.9163 0.3260 3
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3
## 0.8562 0.7281 0.3102
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4214 0.2884 0.2065
plot(HodbarrowCCA5)
anova(HodbarrowCCA5)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Ag + Al2O3 + As, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 3 1.89445 2.0674 0.006 **
## Residual 3 0.91632
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#High F statistic of 2.0674 and low p value of 0.001.
HodbarrowCCA6 <- cca(HodbarrowPS ~ Ag + Al2O3 + As + SiO2,
data = HodbarrowPC)
print(HodbarrowCCA6)
## Call: cca(formula = HodbarrowPS ~ Ag + Al2O3 + As + SiO2, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 2.2526 0.8014 4
## Unconstrained 0.5582 0.1986 2
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.9044 0.7482 0.5126 0.0874
##
## Eigenvalues for unconstrained axes:
## CA1 CA2
## 0.3508 0.2074
plot(HodbarrowCCA6)
anova(HodbarrowCCA6)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Ag + Al2O3 + As + SiO2, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 4 2.25255 2.0176 0.017 *
## Residual 2 0.55822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#High F statistic of 2.0176 and low p value of 0.018.
HodbarrowCCA7 <- cca(HodbarrowPS ~ Ag + SrO + As + Cr2O3,
data = HodbarrowPC)
print(HodbarrowCCA7)
## Call: cca(formula = HodbarrowPS ~ Ag + SrO + As + Cr2O3, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 2.3134 0.8230 4
## Unconstrained 0.4974 0.1770 2
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.8793 0.7505 0.4893 0.1942
##
## Eigenvalues for unconstrained axes:
## CA1 CA2
## 0.3884 0.1090
plot(HodbarrowCCA7)
anova(HodbarrowCCA7)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Ag + SrO + As + Cr2O3, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 4 2.31341 2.3256 0.01 **
## Residual 2 0.49737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#High F statistic of 2.3256 and low p value of 0.003.
HodbarrowCCA8 <- cca(HodbarrowPS ~ SiO2 + SrO + As + Cr2O3,
data = HodbarrowPC)
print(HodbarrowCCA8)
## Call: cca(formula = HodbarrowPS ~ SiO2 + SrO + As + Cr2O3, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 2.1942 0.7807 4
## Unconstrained 0.6165 0.2193 2
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.8831 0.7572 0.3596 0.1944
##
## Eigenvalues for unconstrained axes:
## CA1 CA2
## 0.4734 0.1431
plot(HodbarrowCCA8)
anova(HodbarrowCCA8)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ SiO2 + SrO + As + Cr2O3, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 4 2.19424 1.7795 0.049 *
## Residual 2 0.61653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.7795 and p value of 0.024.
HodbarrowCCA9 <- cca(HodbarrowPS ~ Ag + SrO + As + P2O5,
data = HodbarrowPC)
print(HodbarrowCCA9)
## Call: cca(formula = HodbarrowPS ~ Ag + SrO + As + P2O5, data = HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 2.3322 0.8297 4
## Unconstrained 0.4786 0.1703 2
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4
## 0.8963 0.7483 0.4980 0.1896
##
## Eigenvalues for unconstrained axes:
## CA1 CA2
## 0.3691 0.1094
plot(HodbarrowCCA9)
anova(HodbarrowCCA9)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Ag + SrO + As + P2O5, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 4 2.33219 2.4366 0.004 **
## Residual 2 0.47858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#High F statistic of 2.4366 and low p value of 0.005.
HodbarrowCCA10 <- cca(HodbarrowPS ~ Ag + SrO + As + Cr2O3 + SiO2,
data = HodbarrowPC)
print(HodbarrowCCA10)
## Call: cca(formula = HodbarrowPS ~ Ag + SrO + As + Cr2O3 + SiO2, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.81078 1.00000
## Constrained 2.58639 0.92017 5
## Unconstrained 0.22438 0.07983 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.8831 0.7596 0.5080 0.2421 0.1937
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.22438
plot(HodbarrowCCA10)
anova(HodbarrowCCA10)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Ag + SrO + As + Cr2O3 + SiO2, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 5 2.58639 2.3054 0.027 *
## Residual 1 0.22438
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 2.3054 and p value of 0.008.
HodbarrowCCA11 <- cca(HodbarrowPS ~ Cd + Mo + BaO + V + Na2O,
data = HodbarrowPC)
print(HodbarrowCCA11)
## Call: cca(formula = HodbarrowPS ~ Cd + Mo + BaO + V + Na2O, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.81078 1.00000
## Constrained 2.72396 0.96911 5
## Unconstrained 0.08682 0.03089 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.9037 0.7596 0.5262 0.3394 0.1950
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.08682
plot(HodbarrowCCA11)
anova(HodbarrowCCA11)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Cd + Mo + BaO + V + Na2O, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 5 2.72396 6.275 0.03 *
## Residual 1 0.08682
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Particularly high F statistic of 6.275 and p value of 0.001.
HodbarrowCCA12 <- cca(HodbarrowPS ~ Cr2O3 + Be + BaO + Zn + TiO2,
data = HodbarrowPC)
print(HodbarrowCCA12)
## Call: cca(formula = HodbarrowPS ~ Cr2O3 + Be + BaO + Zn + TiO2, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 2.5217 0.8971 5
## Unconstrained 0.2891 0.1029 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.9047 0.7359 0.5184 0.2112 0.1515
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.28911
plot(HodbarrowCCA12)
anova(HodbarrowCCA12)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Cr2O3 + Be + BaO + Zn + TiO2, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 5 2.52167 1.7444 0.069 .
## Residual 1 0.28911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.7444 and p value of 0.053.
HodbarrowCCA13 <- cca(HodbarrowPS ~ Cr2O3 + Be + BaO + SiO2 + TiO2,
data = HodbarrowPC)
print(HodbarrowCCA13)
## Call: cca(formula = HodbarrowPS ~ Cr2O3 + Be + BaO + SiO2 + TiO2, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.81078 1.00000
## Constrained 2.72222 0.96850 5
## Unconstrained 0.08855 0.03150 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.9031 0.7595 0.5294 0.3337 0.1966
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.08855
plot(HodbarrowCCA13)
anova(HodbarrowCCA13)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Cr2O3 + Be + BaO + SiO2 + TiO2, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 5 2.72222 6.1483 0.002 **
## Residual 1 0.08855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#High F statistic of 6.1483 and p value of 0.001.
HodbarrowCCA14 <- cca(HodbarrowPS ~ Cr2O3 + SiO2 + BaO + Zn + TiO2,
data = HodbarrowPC)
print(HodbarrowCCA14)
## Call: cca(formula = HodbarrowPS ~ Cr2O3 + SiO2 + BaO + Zn + TiO2, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.81078 1.00000
## Constrained 2.63479 0.93739 5
## Unconstrained 0.17598 0.06261 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.9045 0.7532 0.4861 0.2961 0.1949
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.17598
plot(HodbarrowCCA14)
anova(HodbarrowCCA14)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Cr2O3 + SiO2 + BaO + Zn + TiO2, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 5 2.63479 2.9944 0.018 *
## Residual 1 0.17598
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#High F statistic of 2.9944 and p value of 0.006.
HodbarrowCCA15 <- cca(HodbarrowPS ~ Cr2O3 + SiO2 + BaO + TiO2 + pH,
data = HodbarrowPC)
print(HodbarrowCCA15)
## Call: cca(formula = HodbarrowPS ~ Cr2O3 + SiO2 + BaO + TiO2 + pH, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 2.5082 0.8924 5
## Unconstrained 0.3026 0.1076 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.9035 0.7506 0.3828 0.2748 0.1965
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.30257
plot(HodbarrowCCA15)
anova(HodbarrowCCA15)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Cr2O3 + SiO2 + BaO + TiO2 + pH, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 5 2.50821 1.6579 0.096 .
## Residual 1 0.30257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#F statistic of 1.6579 and p value of 0.052.
HodbarrowCCA16 <- cca(HodbarrowPS ~ Cr2O3 + SiO2 + Be + TiO2 + pH,
data = HodbarrowPC)
print(HodbarrowCCA16)
## Call: cca(formula = HodbarrowPS ~ Cr2O3 + SiO2 + Be + TiO2 + pH, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.8108 1.0000
## Constrained 2.5183 0.8959 5
## Unconstrained 0.2925 0.1041 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.9050 0.7591 0.4787 0.2668 0.1087
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.29251
plot(HodbarrowCCA16)
anova(HodbarrowCCA16)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Cr2O3 + SiO2 + Be + TiO2 + pH, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 5 2.51827 1.7218 0.083 .
## Residual 1 0.29251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#This CCA has a somewhat high F statistic of 1.7218 and a p value of 0.056.
HodbarrowCCA17 <- cca(HodbarrowPS ~ Na2O + SiO2 + Be + BaO + TiO2,
data = HodbarrowPC)
print(HodbarrowCCA17)
## Call: cca(formula = HodbarrowPS ~ Na2O + SiO2 + Be + BaO + TiO2, data =
## HodbarrowPC)
##
## -- Model Summary --
##
## Inertia Proportion Rank
## Total 2.81078 1.00000
## Constrained 2.70680 0.96301 5
## Unconstrained 0.10397 0.03699 1
##
## Inertia is scaled Chi-square
##
## -- Note --
##
## 106 species (variables) deleted due to missingness.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.8977 0.7589 0.5140 0.3406 0.1955
##
## Eigenvalues for unconstrained axes:
## CA1
## 0.10397
plot(HodbarrowCCA17)
anova(HodbarrowCCA17)
## Set of permutations < 'minperm'. Generating entire set.
## Permutation test for cca under reduced model
## Permutation: free
## Number of permutations: 5039
##
## Model: cca(formula = HodbarrowPS ~ Na2O + SiO2 + Be + BaO + TiO2, data = HodbarrowPC)
## Df ChiSquare F Pr(>F)
## Model 5 2.70680 5.2067 0.008 **
## Residual 1 0.10397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#This CCA has a high F statistic of 5.2067 and a low p value of 0.003.
#After doing a few more CCAs, the one with the highest F statistic (6.1483) is #HodbarrowCCA13. The p value of this one is also low, at 0.001. #The plot for this CCA looks OK, but it can be modified to make it easier #to interpret what is going on with some of the different plant species and communities.
options(max.print=1000000)
HodbarrowCCA13$CCA$v
## CCA1 CCA2 CCA3 CCA4
## Anthoxanthum.odoratum -1.8808682 0.515930843 -0.144904225 -0.1398460
## Anthyllis.vulneraria 0.8513919 1.041042224 0.226654799 -0.5136023
## Bellis.perennis 0.9116973 1.047100739 0.187480527 -0.7875399
## Bryum.spp. 0.9116973 1.047100739 0.187480527 -0.7875399
## Campanula.rotundifolia -1.8808682 0.515930843 -0.144904225 -0.1398460
## Carex.flacca 0.1419301 -1.586688931 1.350916103 0.1033922
## Carex.panicea 0.9116973 1.047100739 0.187480527 -0.7875399
## Carlina.vulgaris -0.3164334 -1.387531027 0.971548823 -1.3666677
## Centaurea.nigra 0.1630491 -1.163737359 0.694471519 1.7013144
## Centaurium.littorale -0.2745504 -0.134095913 0.468430358 0.4684153
## Cerastium.fontanum -1.8808682 0.515930843 -0.144904225 -0.1398460
## Dactylis.glomerata 0.1102516 -2.221116290 2.335582980 -2.2934911
## Equisetum.arvense -1.8808682 0.515930843 -0.144904225 -0.1398460
## Equisteum.variegatum -1.8808682 0.515930843 -0.144904225 -0.1398460
## Euphrasia.agg 0.1386204 -1.652972387 1.453791747 -0.1470284
## Festuca.rubra 0.1705365 -1.375764205 0.923216137 0.3245228
## Holcus.spp. 0.3971040 -1.412117935 -2.719427123 -1.1910307
## Holcus.lanatus -1.8808682 0.515930843 -0.144904225 -0.1398460
## Hypnum.cupressiforme -1.8808682 0.515930843 -0.144904225 -0.1398460
## Leontodon.hispidus 0.1346201 -1.927026434 1.675332121 -1.3244222
## Leucanthemum.vulgare 0.1775724 -0.555027518 -0.004501243 1.3982298
## Linum.catharticum 0.3192237 -1.513567295 -0.844415940 -2.6519716
## Lotus.corniculatus 0.1493355 -1.438381237 1.120734236 0.6637026
## Lysimachia.maritima 0.5162381 -0.866505432 -2.223199809 3.4437837
## Medicago.lupulina -1.8808682 0.515930843 -0.144904225 -0.1398460
## Ononis.repens -1.7150902 0.513540930 -0.107948056 -0.0989720
## Pillosella.officinarum -1.2406638 -0.001280767 0.071603827 0.4584307
## Plantago.coronopus 0.1630491 -1.163737359 0.694471519 1.7013144
## Plantago.lanceolata -0.0545567 -1.576064174 1.498089179 -0.6145219
## Poa.annua 0.5789231 0.322731125 0.410337564 0.4290675
## Prunella.vulgaris 0.1102516 -2.221116290 2.335582980 -2.2934911
## Taraxacum.agg. 0.3241573 -1.880643518 -2.336923359 -3.8947193
## Thymus.polytrichus 0.8033777 0.996376956 0.181773550 -0.3850685
## Trichostomum.crispulum 0.5297032 -0.380228576 -0.850468946 1.9199615
## Trifolium.pratense 0.1102516 -2.221116290 2.335582980 -2.2934911
## Tussilago.farfara 0.1630491 -1.163737359 0.694471519 1.7013144
## Zygodon.stirtonii 0.4311102 -1.710407131 -4.673176528 -4.6953334
## CCA5
## Anthoxanthum.odoratum -0.23832396
## Anthyllis.vulneraria -0.15012954
## Bellis.perennis -0.37461452
## Bryum.spp. -0.37461452
## Campanula.rotundifolia -0.23832396
## Carex.flacca 0.76411901
## Carex.panicea -0.37461452
## Carlina.vulgaris -1.13534521
## Centaurea.nigra 3.56015015
## Centaurium.littorale 1.08867477
## Cerastium.fontanum -0.23832396
## Dactylis.glomerata -3.42992771
## Equisetum.arvense -0.23832396
## Equisteum.variegatum -0.23832396
## Euphrasia.agg 0.32593502
## Festuca.rubra -0.01541101
## Holcus.spp. 0.42885209
## Holcus.lanatus -0.23832396
## Hypnum.cupressiforme -0.23832396
## Leontodon.hispidus -1.39073989
## Leucanthemum.vulgare 1.07181145
## Linum.catharticum 0.06404070
## Lotus.corniculatus 1.74454551
## Lysimachia.maritima -4.29171098
## Medicago.lupulina -0.23832396
## Ononis.repens -0.14645693
## Pillosella.officinarum 0.82253753
## Plantago.coronopus 3.56015015
## Plantago.lanceolata -0.54549112
## Poa.annua 1.23552120
## Prunella.vulgaris -3.42992771
## Taraxacum.agg. 0.75314264
## Thymus.polytrichus 0.04963641
## Trichostomum.crispulum -1.30110842
## Trifolium.pratense -3.42992771
## Tussilago.farfara 3.56015015
## Zygodon.stirtonii 2.84467781
## attr(,"na.action")
## Agrostis.spp. Agrostis.canina
## 1 2
## Alchemilla.mollis Alopercus.pratensis
## 3 4
## Angelica.sylvestris Aphanes.arvensis
## 5 8
## Arrhenatherum.elatius Atrichum.undulatum
## 9 10
## Avenula.pratensis Betula.pubescens
## 11 13
## Blackstonia.perfoliata Brachythecium.albicans
## 14 15
## Brachythecium.glareosum Brachythecium.mildeanum
## 16 17
## Brachythecium.rutabulum Briza.media
## 18 19
## Bromus.hordeaceus Bryum.c.f..caespiticium
## 20 21
## Bryum.c.f..pallescens Bryum.capillare
## 22 23
## Calliergonella.cupsidata Calypogeia.arguta
## 25 26
## Carex.distans Centaurium.erythraea
## 28 33
## Centaurium.pulchellum Chamaenerion.angustifolium
## 35 37
## Cirriphyllum.piliferum Cirsium.arvense
## 38 39
## Cirsium.palustre Crepis.capillaris
## 40 41
## Cynosurus.cristatus Dactylorhiza.fuchsii
## 42 44
## Danthonia.decumbens Daucus.carrota
## 45 46
## Dicranella.spp. Dicranum.scoparium
## 47 48
## Epilobium.montanum Erigeron.acer
## 49 52
## Festuca.ovina Festuca.rubra.agg.
## 54 56
## Filipendula.ulmaria Fissidens.adianthoides
## 57 58
## Fissidens.dubius Fissidens.exilis
## 59 60
## Fragaria.vesca Galium.aparine
## 61 62
## Galium.arvense Galium.saxatile
## 63 64
## Galium.verum Geum.urbanum
## 65 66
## Glechoma.hederacea Helicotrichon.spp.
## 67 68
## Heracleum.sphondylium Hieracium.spp.
## 69 70
## Holcus.mollis Homalothecium.lutescens
## 73 74
## Hylocomium.splendens Hypericum.perforatum
## 75 76
## Hypnum.imponens Hypnum.jutlandicum
## 78 79
## Hypochaeris.radicata Kindbergia.praelonga
## 80 81
## Lathyrus.pratensis Leontodon.saxatilis
## 82 84
## Lolium.perenne Lophocolea.semiteres
## 87 88
## Luzula.multiflora Myosotis.arvensis
## 90 93
## Oxalis.acetosella Pastinaca.sativa
## 95 96
## Pentaglottis.sempervirens Pleurozium.schreberi
## 97 101
## Poa.spp. Polytrichum.commune
## 103 104
## Polytrichum.commune.agg. Polytrichum.formosum
## 105 106
## Potentilla.reptans Pseudoscleropidum.purum
## 107 109
## Pteridium.aquilinum Ranunculus.acris
## 110 111
## Ranunculus.parviflorus Ranunculus.repens
## 112 113
## Reseda.lutea Rhinanthus.minor
## 114 115
## Rhizomnium.punctatum Rhytidadelphus.squarrosus
## 116 117
## Rhytidadelphus.triquetris Rubus.fruticosus
## 118 119
## Sanguisorba.minor.spp.minor Saniona.uncinata
## 120 121
## Sedum.anglicum Senecio.jacobaea
## 122 123
## Senecio.vulgaris Sonchus.arvensis
## 124 125
## Stachys.sylvatica Stellaria.apetala
## 126 127
## Thuidium.tamariscinum Trifolium.campestre
## 129 132
## Trifolium.dubium Trifolium.repens
## 133 135
## Trisetum.flavescens Urtica.diocia
## 136 138
## Veronica.officinalis Vicia.sativa
## 139 140
## Viola.riviniana Weissia.controversa
## 141 142
## attr(,"class")
## [1] "exclude"
#The "v values" show the positions of individual species on the graph, with the CCA1 value
#representing the x axis and the CCA2 value representing the y axis, in the case of the
#graph created in R.
plot(HodbarrowCCA13, xlim = c(-5, 5), ylim = c(-5, 5))
#Increasing the xlim and ylim to give more room for writing on graph
plot(HodbarrowCCA13, choices = c(1,2), display = c("wa", "bp"), xlim = c(-4, 3),
ylim = c(-3, 2))
#Increasing the xlim and ylim to give more room for writing on graph Note that Community
#2 and Community 3 are grouped/clustered together in this CCA plot and that Communities
#1, 5, 6 and 7 are grouped together.
points(x = -1.8808682 , y = 0.515930843, pch = 15, col = "black")
#This point represents Equisetum variegatum
text('E. variegatum', x = -1.8808682, y = 0.515930843, cex = 0.88, pos = 2, col = "black")
#Adding text to this point.
points(x = 0.5789231 , y = 0.322731125, pch = 15, col = "black")
#This point represents Poa annua
text('P. annua', x = 0.5789231 , y = 0.322731125, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = 0.1102516, y = -2.221116290, pch = 15, col = "black")
#This point represents Trifolium pratense
text('T. pratense', x = 0.1102516, y = -2.221116290, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = 0.3192237, y = -1.513567295, pch = 15, col = "black")
#This point represents Linum catharticum
text('L. catharticum', x = 0.3192237, y = -1.513567295, cex = 0.88, pos = 2, col = "black")
#Adding text to this point
points(x = 0.8513919, y = 1.041042224, pch = 15, col = "black")
#This point represents Anthyllis vulneraria
text('A. vulneraria', x = 0.8513919, y = 1.041042224, cex = 0.88, pos = 2, col = "black")
#Adding text to this point