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##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 Barrow- #in-Furness slag bank.
#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… Make sure to set your directory before loading data on your #system.
BarrowPS <-read.csv("BarrowPlantSpecies.csv", header = TRUE)
BarrowPC <-read.csv("BARROW_PLANT_CHEMISTRY_MG_KG.csv", header = TRUE)
head(BarrowPS)
## Anthyllis.vulneraria Aphanes.arvensis Arrhenatherum.elatius Bellis.perennis
## 1 4 0 0 4
## 2 0 0 0 6
## 3 127 0 0 1
## 4 0 0 0 0
## 5 32 0 0 6
## 6 0 0 8 0
## Blackstonia.perfoliata Brachythecium.albicans Brachythecium.rutabulum
## 1 0 0 0
## 2 0 30 30
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Briza.media Bromus.hordeaceus Calliergonella.cupsidata Carex.flacca
## 1 0 0 0 0
## 2 0 0 30 0
## 3 138 0 20 0
## 4 9 0 110 0
## 5 8 0 0 0
## 6 0 1 0 0
## Carlina.vulgaris Centaurium.erythraea Cerastium.fontanum Cynosurus.cristatus
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 1 0
## 4 0 0 2 0
## 5 0 0 1 0
## 6 0 0 0 0
## Daucus.carrota Erigeron.acer Euphrasia.agg Festuca.ovina Festuca.rubra
## 1 0 0 0 66 0
## 2 0 0 0 0 19
## 3 0 0 14 0 1
## 4 0 0 0 0 39
## 5 2 0 0 2 2
## 6 0 0 0 0 54
## Galium.verum Helicotrichon.spp. Hieracium.spp. Holcus.spp. Holcus.lanatus
## 1 0 0 0 0 0
## 2 87 0 0 55 5
## 3 88 0 0 0 1
## 4 0 0 0 50 25
## 5 1 0 1 3 2
## 6 0 1 0 169 0
## Holcus.mollis Hypericum.perforatum Hypochaeris.radicata Lathyrus.pratensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 1 0
## 5 0 0 0 0
## 6 0 0 0 0
## Leontodon.hispidus Leontodon.saxatilis Leucanthemum.vulgare Lolium.perenne
## 1 18 1 0 0
## 2 0 0 12 0
## 3 0 0 0 0
## 4 0 0 1 0
## 5 30 0 3 0
## 6 0 0 2 85
## Lotus.corniculatus Medicago.lupulina Ononis.repens Pastinaca.sativa
## 1 0 3 0 0
## 2 67 3 0 0
## 3 18 0 0 0
## 4 31 0 0 0
## 5 12 0 0 0
## 6 140 0 0 0
## Pillosella.officinarum Plantago.lanceolata Potentilla.reptans
## 1 22 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 4 3 0
## 6 0 0 0
## Prunella.vulgaris Pseudoscleropidum.purum Ranunculus.repens Reseda.lutea
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 1 0
## 5 0 160 0 0
## 6 0 0 0 0
## Sanguisorba.minor.spp.minor Sedum.anglicum Senecio.jacobaea Senecio.vulgaris
## 1 0 0 0 4
## 2 34 0 0 0
## 3 10 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 1 0
## Stellaria.apetala Taraxacum.agg. Trifolium.campestre Trifolium.pratense
## 1 0 2 8 0
## 2 0 0 64 2
## 3 0 1 22 0
## 4 0 0 8 0
## 5 0 0 2 0
## 6 0 0 0 0
## Trifolium.repens Trisetum.flavescens
## 1 0 0
## 2 0 0
## 3 4 0
## 4 17 0
## 5 0 3
## 6 0 0
head(BarrowPC)
## pH_level SiO2 Al2O3 Fe2O3 CaO MgO Na2O K2O
## 1 9.921 143502.5 47579.61 8393.106 255144.60 18513.202 4302.770 3569.635
## 2 8.850 221096.7 53189.66 27907.078 95053.87 8201.288 5415.555 15772.808
## 3 7.701 215954.9 88649.43 53506.051 34876.91 7960.074 8308.797 23161.123
## 4 8.295 260828.7 43292.68 22381.616 79688.02 6633.395 5934.855 14444.571
## 5 8.363 188376.3 63774.67 25389.146 147226.30 7115.824 2893.242 8218.463
## 6 8.047 284200.4 51760.68 31474.148 30874.64 9045.538 6602.526 16602.956
## Cr2O3 TiO2 MnO P2O5 SrO BaO LOI Ag As B
## 1 20.52607 2936.780 4491.853 599.9225 676.47564 3045.2147 16.55 0.2 5 30
## 2 95.78834 3536.123 1239.132 1399.8192 169.11891 626.9560 19.60 0.2 15 20
## 3 191.57668 8031.195 2710.601 3699.5223 84.55946 447.8257 15.80 0.2 60 10
## 4 75.26227 3056.649 619.566 1399.8192 84.55946 447.8257 15.80 0.2 11 10
## 5 75.26227 2876.846 2942.938 1499.8063 253.67837 3224.3449 18.60 0.5 8 20
## 6 102.63036 3056.649 1239.132 1599.7934 84.55946 806.0862 14.80 0.7 12 10
## Be Bi Cd Co Cu Ga Hg La Li Mo Ni Pb S Sb Sc Th Tl U V W Zn
## 1 10.3 1.5 0.25 2 28 5 0.5 40 70 0.5 6 11 7000 1.5 15 10 5 5 18 5 24
## 2 2.1 1.5 0.25 11 44 5 0.5 20 30 1.0 29 61 1600 3.0 5 10 5 5 25 5 88
## 3 1.1 1.5 0.25 31 97 5 0.5 20 40 1.0 60 14 500 5.0 15 10 5 5 39 5 57
## 4 0.7 1.5 0.25 9 37 5 0.5 10 20 1.0 23 43 600 1.5 3 10 5 5 21 5 68
## 5 5.8 1.5 1.00 4 32 5 0.5 20 40 1.0 10 158 2400 5.0 5 10 5 5 11 5 210
## 6 1.5 1.5 1.80 9 41 5 1.0 10 20 1.0 24 240 900 5.0 3 10 5 5 23 5 544
## Akermanite Albite Aluminium.oxide.hydroxide Anhydrite Aragonite Augite
## 1 0 0 0 0 0 0
## 2 0 0 0 0 0 0
## 3 0 1 0 0 0 1
## 4 0 0 1 0 0 0
## 5 0 0 0 0 0 0
## 6 0 0 0 0 0 0
## Biotite Birnessite Calcite Clinochlore Cuspidine Diaspore Dickite Gehlenite
## 1 0 0 1 0 0 0 0 1
## 2 0 0 1 0 0 0 0 0
## 3 0 0 1 1 0 0 0 0
## 4 0 0 1 0 0 0 0 0
## 5 0 0 1 0 0 1 0 1
## 6 0 0 0 0 0 0 0 0
## Goethite Haematite Illite Kaolinite Langite Linnaeite Magnesioferrite
## 1 0 0 0 0 0 0 0
## 2 0 0 0 1 0 0 0
## 3 0 0 1 0 0 0 0
## 4 0 0 0 1 0 0 0
## 5 0 0 0 1 0 0 0
## 6 0 0 0 1 0 0 0
## Melilite Merwinite Microcline Mullite Muscovite Nitratine Orthoclase
## 1 1 0 0 0 0 0 0
## 2 0 0 0 0 1 1 0
## 3 0 0 0 0 1 0 0
## 4 0 0 0 0 1 0 0
## 5 0 0 0 1 0 0 0
## 6 0 0 0 0 1 0 0
## Orthopyroxene Periclase Pigeonite Phengite Pseudowollastonite Quartz
## 1 0 0 1 0 0 0
## 2 0 0 1 0 0 1
## 3 0 0 0 0 0 1
## 4 0 1 1 0 0 1
## 5 0 0 0 0 0 1
## 6 0 0 1 0 0 1
## Staurolite Valentinite
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
sapply(BarrowPS, class)
## Anthyllis.vulneraria Aphanes.arvensis
## "integer" "integer"
## Arrhenatherum.elatius Bellis.perennis
## "integer" "integer"
## Blackstonia.perfoliata Brachythecium.albicans
## "integer" "integer"
## Brachythecium.rutabulum Briza.media
## "integer" "integer"
## Bromus.hordeaceus Calliergonella.cupsidata
## "integer" "integer"
## Carex.flacca Carlina.vulgaris
## "integer" "integer"
## Centaurium.erythraea Cerastium.fontanum
## "integer" "integer"
## Cynosurus.cristatus Daucus.carrota
## "integer" "integer"
## Erigeron.acer Euphrasia.agg
## "integer" "integer"
## Festuca.ovina Festuca.rubra
## "integer" "integer"
## Galium.verum Helicotrichon.spp.
## "integer" "integer"
## Hieracium.spp. Holcus.spp.
## "integer" "integer"
## Holcus.lanatus Holcus.mollis
## "integer" "integer"
## Hypericum.perforatum Hypochaeris.radicata
## "integer" "integer"
## Lathyrus.pratensis Leontodon.hispidus
## "integer" "integer"
## Leontodon.saxatilis Leucanthemum.vulgare
## "integer" "integer"
## Lolium.perenne Lotus.corniculatus
## "integer" "integer"
## Medicago.lupulina Ononis.repens
## "integer" "integer"
## Pastinaca.sativa Pillosella.officinarum
## "integer" "integer"
## Plantago.lanceolata Potentilla.reptans
## "integer" "integer"
## Prunella.vulgaris Pseudoscleropidum.purum
## "integer" "integer"
## Ranunculus.repens Reseda.lutea
## "integer" "integer"
## Sanguisorba.minor.spp.minor Sedum.anglicum
## "integer" "integer"
## Senecio.jacobaea Senecio.vulgaris
## "integer" "integer"
## Stellaria.apetala Taraxacum.agg.
## "integer" "integer"
## Trifolium.campestre Trifolium.pratense
## "integer" "integer"
## Trifolium.repens Trisetum.flavescens
## "integer" "integer"
sapply(BarrowPC, 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" "integer" "integer"
## Be Bi Cd
## "numeric" "numeric" "numeric"
## Co Cu Ga
## "integer" "integer" "integer"
## Hg La Li
## "numeric" "integer" "integer"
## Mo Ni Pb
## "numeric" "integer" "integer"
## S Sb Sc
## "integer" "numeric" "integer"
## Th Tl U
## "integer" "integer" "integer"
## V W Zn
## "integer" "integer" "integer"
## Akermanite Albite Aluminium.oxide.hydroxide
## "integer" "integer" "integer"
## Anhydrite Aragonite Augite
## "integer" "integer" "integer"
## Biotite Birnessite Calcite
## "integer" "integer" "integer"
## Clinochlore Cuspidine Diaspore
## "integer" "integer" "integer"
## Dickite Gehlenite Goethite
## "integer" "integer" "integer"
## Haematite Illite Kaolinite
## "integer" "integer" "integer"
## Langite Linnaeite Magnesioferrite
## "integer" "integer" "integer"
## Melilite Merwinite Microcline
## "integer" "integer" "integer"
## Mullite Muscovite Nitratine
## "integer" "integer" "integer"
## Orthoclase Orthopyroxene Periclase
## "integer" "integer" "integer"
## Pigeonite Phengite Pseudowollastonite
## "integer" "integer" "integer"
## Quartz Staurolite Valentinite
## "integer" "integer" "integer"
rowSums(BarrowPS)
## [1] 132 444 446 294 277 461 328 428 82 250 155
BarrowPSna <- na.omit(BarrowPS)
print(BarrowPS)
## Anthyllis.vulneraria Aphanes.arvensis Arrhenatherum.elatius Bellis.perennis
## 1 4 0 0 4
## 2 0 0 0 6
## 3 127 0 0 1
## 4 0 0 0 0
## 5 32 0 0 6
## 6 0 0 8 0
## 7 0 1 0 0
## 8 0 5 0 2
## 9 0 0 0 0
## 10 0 0 5 0
## 11 0 0 0 4
## Blackstonia.perfoliata Brachythecium.albicans Brachythecium.rutabulum
## 1 0 0 0
## 2 0 30 30
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 2 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## Briza.media Bromus.hordeaceus Calliergonella.cupsidata Carex.flacca
## 1 0 0 0 0
## 2 0 0 30 0
## 3 138 0 20 0
## 4 9 0 110 0
## 5 8 0 0 0
## 6 0 1 0 0
## 7 0 0 10 120
## 8 0 0 0 40
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## Carlina.vulgaris Centaurium.erythraea Cerastium.fontanum Cynosurus.cristatus
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 1 0
## 4 0 0 2 0
## 5 0 0 1 0
## 6 0 0 0 0
## 7 0 0 1 54
## 8 0 14 0 15
## 9 2 1 0 0
## 10 0 0 0 0
## 11 5 0 0 0
## Daucus.carrota Erigeron.acer Euphrasia.agg Festuca.ovina Festuca.rubra
## 1 0 0 0 66 0
## 2 0 0 0 0 19
## 3 0 0 14 0 1
## 4 0 0 0 0 39
## 5 2 0 0 2 2
## 6 0 0 0 0 54
## 7 0 0 0 0 42
## 8 0 0 7 0 19
## 9 0 25 2 0 22
## 10 1 0 1 0 41
## 11 0 6 4 0 44
## Galium.verum Helicotrichon.spp. Hieracium.spp. Holcus.spp. Holcus.lanatus
## 1 0 0 0 0 0
## 2 87 0 0 55 5
## 3 88 0 0 0 1
## 4 0 0 0 50 25
## 5 1 0 1 3 2
## 6 0 1 0 169 0
## 7 0 0 0 10 0
## 8 0 0 0 30 0
## 9 0 0 0 0 0
## 10 2 0 0 82 5
## 11 0 0 0 4 0
## Holcus.mollis Hypericum.perforatum Hypochaeris.radicata Lathyrus.pratensis
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 1 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 42
## 8 0 0 0 0
## 9 0 0 0 0
## 10 10 0 0 0
## 11 0 22 0 0
## Leontodon.hispidus Leontodon.saxatilis Leucanthemum.vulgare Lolium.perenne
## 1 18 1 0 0
## 2 0 0 12 0
## 3 0 0 0 0
## 4 0 0 1 0
## 5 30 0 3 0
## 6 0 0 2 85
## 7 0 0 0 2
## 8 0 0 0 0
## 9 0 0 2 2
## 10 0 0 0 5
## 11 0 0 24 0
## Lotus.corniculatus Medicago.lupulina Ononis.repens Pastinaca.sativa
## 1 0 3 0 0
## 2 67 3 0 0
## 3 18 0 0 0
## 4 31 0 0 0
## 5 12 0 0 0
## 6 140 0 0 0
## 7 0 0 0 0
## 8 14 0 153 0
## 9 2 3 0 1
## 10 98 0 0 0
## 11 17 0 0 0
## Pillosella.officinarum Plantago.lanceolata Potentilla.reptans
## 1 22 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 4 3 0
## 6 0 0 0
## 7 0 1 37
## 8 0 3 1
## 9 3 0 0
## 10 0 0 0
## 11 14 3 2
## Prunella.vulgaris Pseudoscleropidum.purum Ranunculus.repens Reseda.lutea
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 1 0
## 5 0 160 0 0
## 6 0 0 0 0
## 7 2 0 0 0
## 8 0 0 0 0
## 9 5 0 0 1
## 10 0 0 0 0
## 11 0 0 0 0
## Sanguisorba.minor.spp.minor Sedum.anglicum Senecio.jacobaea Senecio.vulgaris
## 1 0 0 0 4
## 2 34 0 0 0
## 3 10 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 1 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 6 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## Stellaria.apetala Taraxacum.agg. Trifolium.campestre Trifolium.pratense
## 1 0 2 8 0
## 2 0 0 64 2
## 3 0 1 22 0
## 4 0 0 8 0
## 5 0 0 2 0
## 6 0 0 0 0
## 7 2 0 2 0
## 8 0 0 0 0
## 9 0 4 0 0
## 10 0 0 0 0
## 11 0 6 0 0
## Trifolium.repens Trisetum.flavescens
## 1 0 0
## 2 0 0
## 3 4 0
## 4 17 0
## 5 0 3
## 6 0 0
## 7 2 0
## 8 0 123
## 9 0 1
## 10 0 0
## 11 0 0
#Need to do this to make sure the datasets are good to go.
#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$K2O, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.3704
## Significance: 0.324
##
## Permutation: free
## Number of permutations: 9999
#Not statistically significant, will mention statistically significant results when #they are generated.
anoBCr2O3 <- anosim(BarrowPS, BarrowPC$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$TiO2, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.01701
## Significance: 0.5136
##
## Permutation: free
## Number of permutations: 9999
anoBMnO <- anosim(BarrowPS, BarrowPC$MnO, distance = "bray", permutations = 9999)
anoBMnO
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$MnO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2051
## Significance: 0.322
##
## Permutation: free
## Number of permutations: 9999
anoBP2O5 <- anosim(BarrowPS, BarrowPC$P2O5, distance = "bray", permutations = 9999)
anoBP2O5
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$P2O5, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.6981
## Significance: 0.045
##
## Permutation: free
## Number of permutations: 9999
#R statistic of 0.6981 and p value of 0.0456 for P2O5.
anoBSrO <- anosim(BarrowPS, BarrowPC$SrO, distance = "bray", permutations = 9999)
anoBSrO
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$SrO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1506
## Significance: 0.2081
##
## Permutation: free
## Number of permutations: 9999
anoBBaO <- anosim(BarrowPS, BarrowPC$BaO, distance = "bray", permutations = 9999)
anoBBaO
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$BaO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.3333
## Significance: 0.3542
##
## Permutation: free
## Number of permutations: 9999
anoBAg <- anosim(BarrowPS, BarrowPC$Ag, distance = "bray", permutations = 9999)
anoBAg
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Ag, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2128
## Significance: 0.1986
##
## Permutation: free
## Number of permutations: 9999
anoBAs <- anosim(BarrowPS, BarrowPC$As, distance = "bray", permutations = 9999)
anoBAs
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$As, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.2564
## Significance: 0.2248
##
## Permutation: free
## Number of permutations: 9999
anoBB <- anosim(BarrowPS, BarrowPC$B, distance = "bray", permutations = 9999)
anoBB
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$B, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.09158
## Significance: 0.3112
##
## Permutation: free
## Number of permutations: 9999
anoBCd <- anosim(BarrowPS, BarrowPC$Cd, distance = "bray", permutations = 9999)
anoBCd
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Cd, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.01333
## Significance: 0.4531
##
## Permutation: free
## Number of permutations: 9999
anoBCo <- anosim(BarrowPS, BarrowPC$Co, distance = "bray", permutations = 9999)
anoBCo
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Co, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.5
## Significance: 0.0612
##
## Permutation: free
## Number of permutations: 9999
#R statistic of 0.5 and a close-to-significant p value of 0.0586 for Co.
anoBCu <- anosim(BarrowPS, BarrowPC$Cu, distance = "bray", permutations = 9999)
anoBCu
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Cu, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.4444
## Significance: 0.7262
##
## Permutation: free
## Number of permutations: 9999
anoBNi <- anosim(BarrowPS, BarrowPC$Ni, distance = "bray", permutations = 9999)
anoBNi
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Ni, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.434
## Significance: 0.1584
##
## Permutation: free
## Number of permutations: 9999
anoBS <- anosim(BarrowPS, BarrowPC$S, distance = "bray", permutations = 9999)
anoBS
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$S, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.4444
## Significance: 0.7284
##
## Permutation: free
## Number of permutations: 9999
anoBSb <- anosim(BarrowPS, BarrowPC$Sb, distance = "bray", permutations = 9999)
anoBSb
##
## Call:
## anosim(x = BarrowPS, grouping = BarrowPC$Sb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1795
## Significance: 0.8406
##
## Permutation: free
## Number of permutations: 9999
anoBSc <- anosim(BarrowPS, BarrowPC$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: 0.0201
##
## 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 4.781 1.000
## Constrained 4.781 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.
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10
## 0.8172 0.7280 0.6486 0.5715 0.5650 0.4821 0.3262 0.2994 0.2659 0.0770
summary(BarrowCCA)
##
## Call:
## cca(X = BarrowPS, Y = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.781 1
## Constrained 4.781 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 0.7280 0.6486 0.5715 0.5650 0.4821 0.32617 0.29936
## Proportion Explained 0.1709 0.1523 0.1357 0.1195 0.1182 0.1008 0.06822 0.06262
## Cumulative Proportion 0.1709 0.3232 0.4589 0.5784 0.6966 0.7974 0.86565 0.92827
## CCA9 CCA10
## Eigenvalue 0.26592 0.07702
## Proportion Explained 0.05562 0.01611
## Cumulative Proportion 0.98389 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8
## Eigenvalue 0.8172 0.7280 0.6486 0.5715 0.5650 0.4821 0.32617 0.29936
## Proportion Explained 0.1709 0.1523 0.1357 0.1195 0.1182 0.1008 0.06822 0.06262
## Cumulative Proportion 0.1709 0.3232 0.4589 0.5784 0.6966 0.7974 0.86565 0.92827
## CCA9 CCA10
## Eigenvalue 0.26592 0.07702
## Proportion Explained 0.05562 0.01611
## Cumulative Proportion 0.98389 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 4.781 1.000
## Constrained 3.394 0.710 6
## Unconstrained 1.387 0.290 4
##
## Inertia is scaled Chi-square
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8007 0.6692 0.6292 0.5455 0.4623 0.2875
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3 CA4
## 0.5419 0.3504 0.2938 0.2003
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 4.781 1.00
## Constrained 3.394 0.71
## Unconstrained 1.386 0.29
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1 CA2
## Eigenvalue 0.8007 0.6692 0.6292 0.5455 0.46235 0.28751 0.5419 0.3504
## Proportion Explained 0.1675 0.1400 0.1316 0.1141 0.09671 0.06014 0.1134 0.0733
## Cumulative Proportion 0.1675 0.3075 0.4391 0.5531 0.64986 0.70999 0.8233 0.8966
## CA3 CA4
## Eigenvalue 0.29381 0.2003
## Proportion Explained 0.06145 0.0419
## Cumulative Proportion 0.95810 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.8007 0.6692 0.6292 0.5455 0.4623 0.2875
## Proportion Explained 0.2359 0.1971 0.1854 0.1607 0.1362 0.0847
## Cumulative Proportion 0.2359 0.4330 0.6184 0.7791 0.9153 1.0000
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 3.3944 1.6321 0.001 ***
## Residual 4 1.3865
## ---
## 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 4.7808 1.0000
## Constrained 3.8197 0.7990 7
## Unconstrained 0.9612 0.2010 3
##
## Inertia is scaled Chi-square
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.7990 0.7089 0.6200 0.5650 0.4758 0.3491 0.3018
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4930 0.3464 0.1218
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 4.7808 1.000
## Constrained 3.8197 0.799
## Unconstrained 0.9612 0.201
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7990 0.7089 0.6200 0.5650 0.47584 0.34908 0.30177
## Proportion Explained 0.1671 0.1483 0.1297 0.1182 0.09953 0.07302 0.06312
## Cumulative Proportion 0.1671 0.3154 0.4451 0.5633 0.66282 0.73583 0.79895
## CA1 CA2 CA3
## Eigenvalue 0.4930 0.34639 0.12182
## Proportion Explained 0.1031 0.07245 0.02548
## Cumulative Proportion 0.9021 0.97452 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7990 0.7089 0.6200 0.5650 0.4758 0.34908 0.30177
## Proportion Explained 0.2092 0.1856 0.1623 0.1479 0.1246 0.09139 0.07901
## Cumulative Proportion 0.2092 0.3948 0.5571 0.7050 0.8296 0.92099 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 3.8197 1.7031 0.001 ***
## Residual 3 0.9612
## ---
## 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 4.7808 1.0000
## Constrained 3.7670 0.7879 7
## Unconstrained 1.0138 0.2121 3
##
## Inertia is scaled Chi-square
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.8040 0.6864 0.6149 0.5649 0.4426 0.3389 0.3153
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4643 0.4117 0.1378
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 4.781 1.0000
## Constrained 3.767 0.7879
## Unconstrained 1.014 0.2121
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8040 0.6864 0.6149 0.5649 0.44262 0.3389 0.31525
## Proportion Explained 0.1682 0.1436 0.1286 0.1182 0.09258 0.0709 0.06594
## Cumulative Proportion 0.1682 0.3118 0.4404 0.5585 0.65111 0.7220 0.78794
## CA1 CA2 CA3
## Eigenvalue 0.46430 0.41167 0.13785
## Proportion Explained 0.09712 0.08611 0.02883
## Cumulative Proportion 0.88506 0.97117 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8040 0.6864 0.6149 0.5649 0.4426 0.33894 0.31525
## Proportion Explained 0.2134 0.1822 0.1632 0.1500 0.1175 0.08998 0.08369
## Cumulative Proportion 0.2134 0.3957 0.5589 0.7088 0.8263 0.91631 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 3.7670 1.5925 0.002 **
## Residual 3 1.0138
## ---
## 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 4.781 1.000
## Constrained 2.792 0.584 5
## Unconstrained 1.989 0.416 5
##
## Inertia is scaled Chi-square
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5
## 0.7867 0.6794 0.6048 0.3841 0.3370
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3 CA4 CA5
## 0.6168 0.5218 0.4209 0.3203 0.1090
summary(BarrowCCA1.4)
##
## Call:
## cca(formula = BarrowPS ~ pH_level + K2O + Al2O3 + BaO + Calcite, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.781 1.000
## Constrained 2.792 0.584
## Unconstrained 1.989 0.416
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CA1 CA2
## Eigenvalue 0.7867 0.6794 0.6048 0.38411 0.33701 0.6168 0.5218
## Proportion Explained 0.1646 0.1421 0.1265 0.08034 0.07049 0.1290 0.1091
## Cumulative Proportion 0.1646 0.3067 0.4332 0.51353 0.58402 0.7130 0.8222
## CA3 CA4 CA5
## Eigenvalue 0.42091 0.32028 0.10897
## Proportion Explained 0.08804 0.06699 0.02279
## Cumulative Proportion 0.91021 0.97721 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5
## Eigenvalue 0.7867 0.6794 0.6048 0.3841 0.3370
## Proportion Explained 0.2818 0.2433 0.2166 0.1376 0.1207
## Cumulative Proportion 0.2818 0.5251 0.7417 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 2.7921 1.4039 0.005 **
## Residual 5 1.9888
## ---
## 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 4.7808 1.0000
## Constrained 3.8369 0.8026 7
## Unconstrained 0.9439 0.1974 3
##
## Inertia is scaled Chi-square
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.7987 0.7203 0.6122 0.5661 0.5003 0.3363 0.3030
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4612 0.3326 0.1501
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 4.7808 1.0000
## Constrained 3.8369 0.8026
## Unconstrained 0.9439 0.1974
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7987 0.7203 0.6122 0.5661 0.5003 0.33630 0.30305
## Proportion Explained 0.1671 0.1507 0.1281 0.1184 0.1047 0.07034 0.06339
## Cumulative Proportion 0.1671 0.3177 0.4458 0.5642 0.6688 0.73917 0.80256
## CA1 CA2 CA3
## Eigenvalue 0.46124 0.33258 0.1501
## Proportion Explained 0.09648 0.06956 0.0314
## Cumulative Proportion 0.89904 0.96860 1.0000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7987 0.7203 0.6122 0.5661 0.5003 0.33630 0.30305
## Proportion Explained 0.2082 0.1877 0.1596 0.1475 0.1304 0.08765 0.07898
## Cumulative Proportion 0.2082 0.3959 0.5554 0.7030 0.8334 0.92102 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 3.8369 1.7421 0.001 ***
## Residual 3 0.9439
## ---
## 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 4.7808 1.0000
## Constrained 3.8557 0.8065 7
## Unconstrained 0.9251 0.1935 3
##
## Inertia is scaled Chi-square
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.8032 0.7096 0.6098 0.5606 0.5210 0.3367 0.3147
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4590 0.3245 0.1417
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 4.7808 1.0000
## Constrained 3.8557 0.8065
## Unconstrained 0.9251 0.1935
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8032 0.7096 0.6098 0.5606 0.5210 0.33671 0.31475
## Proportion Explained 0.1680 0.1484 0.1276 0.1173 0.1090 0.07043 0.06584
## Cumulative Proportion 0.1680 0.3164 0.4440 0.5613 0.6702 0.74065 0.80649
## CA1 CA2 CA3
## Eigenvalue 0.45900 0.32449 0.14165
## Proportion Explained 0.09601 0.06787 0.02963
## Cumulative Proportion 0.90250 0.97037 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8032 0.7096 0.6098 0.5606 0.5210 0.33671 0.31475
## Proportion Explained 0.2083 0.1840 0.1582 0.1454 0.1351 0.08733 0.08163
## Cumulative Proportion 0.2083 0.3924 0.5505 0.6959 0.8310 0.91837 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 3.8557 1.7862 0.001 ***
## Residual 3 0.9251
## ---
## 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 4.7808 1.0000
## Constrained 3.7645 0.7874 7
## Unconstrained 1.0163 0.2126 3
##
## Inertia is scaled Chi-square
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.8129 0.7055 0.6141 0.5341 0.4616 0.3363 0.2999
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4718 0.3935 0.1510
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 4.781 1.0000
## Constrained 3.764 0.7874
## Unconstrained 1.016 0.2126
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8129 0.7055 0.6141 0.5341 0.46162 0.33626 0.29990
## Proportion Explained 0.1700 0.1476 0.1284 0.1117 0.09656 0.07034 0.06273
## Cumulative Proportion 0.1700 0.3176 0.4461 0.5578 0.65435 0.72468 0.78741
## CA1 CA2 CA3
## Eigenvalue 0.47176 0.39355 0.15103
## Proportion Explained 0.09868 0.08232 0.03159
## Cumulative Proportion 0.88609 0.96841 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.8129 0.7055 0.6141 0.5341 0.4616 0.33626 0.29990
## Proportion Explained 0.2160 0.1874 0.1631 0.1419 0.1226 0.08933 0.07967
## Cumulative Proportion 0.2160 0.4034 0.5665 0.7084 0.8310 0.92033 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 3.7645 1.5874 0.003 **
## Residual 3 1.0163
## ---
## 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 4.7808 1.0000
## Constrained 3.8030 0.7955 7
## Unconstrained 0.9778 0.2045 3
##
## Inertia is scaled Chi-square
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## 0.7980 0.6956 0.6116 0.5703 0.4899 0.3362 0.3015
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3
## 0.4734 0.3519 0.1524
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 4.7808 1.0000
## Constrained 3.8030 0.7955
## Unconstrained 0.9778 0.2045
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7980 0.6956 0.6116 0.5703 0.4899 0.33619 0.30146
## Proportion Explained 0.1669 0.1455 0.1279 0.1193 0.1025 0.07032 0.06305
## Cumulative Proportion 0.1669 0.3124 0.4403 0.5596 0.6621 0.73242 0.79547
## CA1 CA2 CA3
## Eigenvalue 0.47344 0.35194 0.15244
## Proportion Explained 0.09903 0.07361 0.03189
## Cumulative Proportion 0.89450 0.96811 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
## Eigenvalue 0.7980 0.6956 0.6116 0.5703 0.4899 0.3362 0.30146
## Proportion Explained 0.2098 0.1829 0.1608 0.1499 0.1288 0.0884 0.07927
## Cumulative Proportion 0.2098 0.3927 0.5536 0.7035 0.8323 0.9207 1.00000
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 3.8030 1.6668 0.001 ***
## Residual 3 0.9778
## ---
## 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 4.7808 1.0000
## Constrained 3.3904 0.7092 6
## Unconstrained 1.3905 0.2908 4
##
## Inertia is scaled Chi-square
##
## -- Eigenvalues --
##
## Eigenvalues for constrained axes:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## 0.8022 0.6642 0.6288 0.5404 0.4589 0.2958
##
## Eigenvalues for unconstrained axes:
## CA1 CA2 CA3 CA4
## 0.5674 0.3790 0.3068 0.1372
summary(BarrowCCA2)
##
## Call:
## cca(formula = BarrowPS ~ Na2O + pH_level + MgO + CaO + Al2O3 + Cr2O3, data = BarrowPC)
##
## Partitioning of scaled Chi-square:
## Inertia Proportion
## Total 4.781 1.0000
## Constrained 3.390 0.7092
## Unconstrained 1.390 0.2908
##
## Eigenvalues, and their contribution to the scaled Chi-square
##
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CA1 CA2
## Eigenvalue 0.8022 0.6642 0.6288 0.5404 0.4589 0.29578 0.5674 0.37905
## Proportion Explained 0.1678 0.1389 0.1315 0.1130 0.0960 0.06187 0.1187 0.07928
## Cumulative Proportion 0.1678 0.3067 0.4383 0.5513 0.6473 0.70916 0.8278 0.90713
## CA3 CA4
## Eigenvalue 0.30685 0.13716
## Proportion Explained 0.06418 0.02869
## Cumulative Proportion 0.97131 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
## Eigenvalue 0.8022 0.6642 0.6288 0.5404 0.4589 0.29578
## Proportion Explained 0.2366 0.1959 0.1855 0.1594 0.1354 0.08724
## Cumulative Proportion 0.2366 0.4325 0.6180 0.7774 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 3.3904 1.6255 0.002 **
## Residual 4 1.3905
## ---
## 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 -1.31679829 0.35564071 1.72401074 0.11463673
## Aphanes.arvensis 1.47807999 -1.45442717 0.89999121 -1.01786706
## Arrhenatherum.elatius 0.41742445 0.91955813 -1.21737609 -0.58064722
## Bellis.perennis -0.82627680 -0.77854942 -0.29129238 -0.11027422
## Blackstonia.perfoliata 1.50711422 -1.54071702 1.05177611 -1.85700942
## Brachythecium.albicans 0.10636765 0.52481121 0.38045396 0.28773345
## Brachythecium.rutabulum 0.10636765 0.52481121 0.38045396 0.28773345
## Briza.media -1.08363603 0.84358952 2.03050100 0.26997196
## Bromus.hordeaceus 0.43058718 1.01127788 -0.94046695 -0.91847546
## Calliergonella.cupsidata -0.13143713 1.08575923 0.03857636 0.43413397
## Carex.flacca 1.37646016 -1.15241271 0.36874405 1.91913120
## Carlina.vulgaris -0.07212621 0.11689979 -1.65648422 -0.37684550
## Centaurium.erythraea 1.39303436 -1.43535934 0.79919209 -1.77724344
## Cerastium.fontanum -0.44032882 0.23678114 0.25744161 0.60767346
## Cynosurus.cristatus 1.37077955 -1.13552991 0.33904701 2.08331122
## Daucus.carrota -1.29373458 -0.84396368 -0.69963169 -0.65682312
## Erigeron.acer -0.16832748 0.06058089 -2.44420361 -0.58365314
## Euphrasia.agg -0.16787580 0.14055693 0.99555842 -0.37876225
## Festuca.ovina -2.16514749 -2.37790298 -1.75304460 1.41594491
## Festuca.rubra 0.38635804 0.30320703 -0.76546675 0.12052076
## Galium.verum -0.49095899 0.72477083 1.31531647 0.30455923
## Helicotrichon.spp. 0.43058718 1.01127788 -0.94046695 -0.91847546
## Hieracium.spp. -2.13878391 -1.65234879 -0.21923217 -0.96517365
## Holcus.spp. 0.38548226 0.68261764 -0.67181493 -0.39332973
## Holcus.lanatus -0.17782166 1.06624832 -0.43432521 0.14945173
## Holcus.mollis 0.39636408 0.77280653 -1.66043073 -0.04012204
## Hypericum.perforatum -0.01934321 0.14780042 -1.22428420 -0.26337583
## Hypochaeris.radicata -0.15641500 1.45542663 -0.47962535 0.24111999
## Lathyrus.pratensis 1.33290880 -1.02297794 0.14106670 3.17784474
## Leontodon.hispidus -2.14896984 -1.93267655 -0.81184152 -0.04519603
## Leontodon.saxatilis -2.16594639 -2.39988948 -1.79952377 1.48810002
## Leucanthemum.vulgare -0.12062693 0.19193584 -0.75703631 -0.19728658
## Lolium.perenne 0.43446150 0.93463822 -0.99397531 -0.77911036
## Lotus.corniculatus 0.19199985 0.69137600 -0.64189233 -0.35830099
## Medicago.lupulina -0.75455415 -0.61181002 -1.38535136 0.37177126
## Ononis.repens 1.50711422 -1.54071702 1.05177611 -1.85700942
## Pastinaca.sativa -0.20408370 0.03964820 -2.73698427 -0.66051970
## Pillosella.officinarum -1.32765145 -1.33067007 -1.53063682 0.53973687
## Plantago.lanceolata -0.06201299 -1.01587741 -0.10341541 -0.60788320
## Potentilla.reptans 1.26965134 -0.97738250 0.09556689 2.87991235
## Prunella.vulgaris 0.23505701 -0.26395927 -1.91468399 0.43615585
## Pseudoscleropidum.purum -2.13878391 -1.65234879 -0.21923217 -0.96517365
## Ranunculus.repens -0.15641500 1.45542663 -0.47962535 0.24111999
## Reseda.lutea -0.20408370 0.03964820 -2.73698427 -0.66051970
## Sanguisorba.minor.spp.minor -0.16392931 0.62107666 0.82231083 0.30039773
## Sedum.anglicum -0.20408370 0.03964820 -2.73698427 -0.66051970
## Senecio.jacobaea 0.43058718 1.01127788 -0.94046695 -0.91847546
## Senecio.vulgaris -2.16594639 -2.39988948 -1.79952377 1.48810002
## Stellaria.apetala 1.33290880 -1.02297794 0.14106670 3.17784474
## Taraxacum.agg. -0.48824814 -0.21584649 -1.50523581 -0.06943673
## Trifolium.campestre -0.35101669 0.39194248 0.53869115 0.41726491
## Trifolium.pratense 0.10636765 0.52481121 0.38045396 0.28773345
## Trifolium.repens -0.18804319 1.15173102 0.06204345 0.51428497
## Trisetum.flavescens 1.40751665 -1.53091017 0.99191953 -1.82652125
## CCA5 CCA6 CCA7
## Anthyllis.vulneraria -0.16719387 -0.04152376 -1.27618222
## Aphanes.arvensis -0.37097811 0.17174175 0.20841641
## Arrhenatherum.elatius 0.83862301 0.97428159 -1.75348572
## Bellis.perennis -0.15514291 -0.86188280 0.47612436
## Blackstonia.perfoliata -0.65252954 0.24440902 0.34652057
## Brachythecium.albicans -0.47118488 0.08952356 2.10840230
## Brachythecium.rutabulum -0.47118488 0.08952356 2.10840230
## Briza.media -0.53321206 0.00640023 -1.32555398
## Bromus.hordeaceus 1.38806320 1.49439252 -1.35887362
## Calliergonella.cupsidata -0.02684092 0.08724823 2.65116800
## Carex.flacca 0.61445190 -0.08259368 -0.27494814
## Carlina.vulgaris -1.97675239 -4.80039110 -0.83503778
## Centaurium.erythraea -0.93625051 0.26631232 0.27573541
## Cerastium.fontanum 0.59582364 -0.12775295 1.21531912
## Cynosurus.cristatus 0.66953805 -0.09681119 -0.30196852
## Daucus.carrota 1.60851718 -0.43885846 -0.42434891
## Erigeron.acer -4.11397727 -0.88304592 -0.74771359
## Euphrasia.agg -1.00421994 -0.86490300 -1.06311256
## Festuca.ovina -2.37175341 2.71659598 -0.08594059
## Festuca.rubra -0.13686446 -0.72469366 -0.17085161
## Galium.verum -0.58705237 0.06183401 0.12946480
## Helicotrichon.spp. 1.38806320 1.49439252 -1.35887362
## Hieracium.spp. 2.43301643 -0.72933975 0.55590918
## Holcus.spp. 0.51281442 0.62176589 -0.27567993
## Holcus.lanatus 0.12579460 0.07231280 2.50456022
## Holcus.mollis -0.04048131 0.14210410 -2.38486509
## Hypericum.perforatum -0.80411573 -6.94973094 -0.88295016
## Hypochaeris.radicata 0.12883348 0.12027209 3.88873884
## Lathyrus.pratensis 1.03677905 -0.19159458 -0.48210438
## Leontodon.hispidus 0.57662808 0.60204451 0.30792177
## Leontodon.saxatilis -2.51735250 2.82101827 -0.10539058
## Leucanthemum.vulgare -0.55831079 -3.71937057 0.12541430
## Lolium.perenne 1.17063655 1.36698524 -1.38109899
## Lotus.corniculatus 0.36563926 0.27891495 -0.49876707
## Medicago.lupulina -2.63229380 1.16116677 0.42925163
## Ononis.repens -0.65252954 0.24440902 0.34652057
## Pastinaca.sativa -4.90834404 0.57295848 -0.71525682
## Pillosella.officinarum -1.66586841 -0.84726313 -0.33958207
## Plantago.lanceolata 0.39658925 -2.24955796 -0.04236656
## Potentilla.reptans 0.90250159 -0.51860131 -0.48143105
## Prunella.vulgaris -3.20973744 0.35451475 -0.64864183
## Pseudoscleropidum.purum 2.43301643 -0.72933975 0.55590918
## Ranunculus.repens 0.12883348 0.12027209 3.88873884
## Reseda.lutea -4.90834404 0.57295848 -0.71525682
## Sanguisorba.minor.spp.minor -0.53417564 0.07863762 1.22588203
## Sedum.anglicum -4.90834404 0.57295848 -0.71525682
## Senecio.jacobaea 1.38806320 1.49439252 -1.35887362
## Senecio.vulgaris -2.51735250 2.82101827 -0.10539058
## Stellaria.apetala 1.03677905 -0.19159458 -0.48210438
## Taraxacum.agg. -2.32623998 -2.59406844 -0.78032279
## Trifolium.campestre -0.55460345 0.26729925 1.19159464
## Trifolium.pratense -0.47118488 0.08952356 2.10840230
## Trifolium.repens 0.05523263 0.07947556 2.52372191
## Trisetum.flavescens -0.61315298 0.22399408 0.34310631
#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