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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 Warton slag bank #and the Hodbarrow RSPB Reserve.
#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 beforehand before running this #on your own system.
##Carnforth (Warton) Data
CarnforthPS <- read.csv("CarnforthPlantSpecies.csv", header = TRUE, colClasses = c("numeric"))
CarnforthPC <-read.csv("CARNFORTH_PLANT_CHEMISTRY_MG_KG.csv", 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$MgO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4279
##
## Permutation: free
## Number of permutations: 9999
anoCCr2O3 <- anosim(CarnforthPS, CarnforthPC$Cr2O3, distance = "bray", permutations = 9999)
anoCCr2O3
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Cr2O3, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0
## Significance: 0.5117
##
## Permutation: free
## Number of permutations: 9999
anoCP2O5 <- anosim(CarnforthPS, CarnforthPC$P2O5, distance = "bray", permutations = 9999)
anoCP2O5
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$P2O5, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.03846
## Significance: 0.4561
##
## Permutation: free
## Number of permutations: 9999
anoCSrO <- anosim(CarnforthPS, CarnforthPC$SrO, distance = "bray", permutations = 9999)
anoCSrO
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$SrO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.07576
## Significance: 0.5927
##
## Permutation: free
## Number of permutations: 9999
anoCBaO <- anosim(CarnforthPS, CarnforthPC$BaO, distance = "bray", permutations = 9999)
anoCBaO
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$BaO, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1923
## Significance: 0.6707
##
## Permutation: free
## Number of permutations: 9999
anoCAg <- anosim(CarnforthPS, CarnforthPC$Ag, distance = "bray", permutations = 9999)
anoCAg
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Ag, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.01333
## Significance: 0.502
##
## Permutation: free
## Number of permutations: 9999
anoCAs <- anosim(CarnforthPS, CarnforthPC$As, distance = "bray", permutations = 9999)
anoCAs
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$As, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0
## Significance: 0.4984
##
## Permutation: free
## Number of permutations: 9999
anoCB <- anosim(CarnforthPS, CarnforthPC$B, distance = "bray", permutations = 9999)
anoCB
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$B, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1875
## Significance: 0.713
##
## Permutation: free
## Number of permutations: 9999
anoCBe <- anosim(CarnforthPS, CarnforthPC$Be, distance = "bray", permutations = 9999)
anoCBe
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Be, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4344
##
## Permutation: free
## Number of permutations: 9999
anoCCd <- anosim(CarnforthPS, CarnforthPC$Cd, distance = "bray", permutations = 9999)
anoCCd
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Cd, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.1042
## Significance: 0.6265
##
## Permutation: free
## Number of permutations: 9999
anoCCo <- anosim(CarnforthPS, CarnforthPC$Co, distance = "bray", permutations = 9999)
anoCCo
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Co, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1538
## Significance: 0.348
##
## Permutation: free
## Number of permutations: 9999
anoCCu <- anosim(CarnforthPS, CarnforthPC$Cu, distance = "bray", permutations = 9999)
anoCCu
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Cu, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.425
##
## Permutation: free
## Number of permutations: 9999
anoCMo <- anosim(CarnforthPS, CarnforthPC$Mo, distance = "bray", permutations = 9999)
anoCMo
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Mo, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.3542
## Significance: 0.8575
##
## Permutation: free
## Number of permutations: 9999
anoCNi <- anosim(CarnforthPS, CarnforthPC$Ni, distance = "bray", permutations = 9999)
anoCNi
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Ni, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.2267
## Significance: 0.7485
##
## Permutation: free
## Number of permutations: 9999
anoCPb <- anosim(CarnforthPS, CarnforthPC$Pb, distance = "bray", permutations = 9999)
anoCPb
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Pb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4157
##
## Permutation: free
## Number of permutations: 9999
anoCSb <- anosim(CarnforthPS, CarnforthPC$Sb, distance = "bray", permutations = 9999)
anoCSb
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Sb, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: -0.01333
## Significance: 0.5254
##
## Permutation: free
## Number of permutations: 9999
anoCSc <- anosim(CarnforthPS, CarnforthPC$Sc, distance = "bray", permutations = 9999)
anoCSc
##
## Call:
## anosim(x = CarnforthPS, grouping = CarnforthPC$Sc, permutations = 9999, distance = "bray")
## Dissimilarity: bray
##
## ANOSIM statistic R: 0.1852
## Significance: 0.4302
##
## Permutation: free
## Number of permutations: 9999
anoCV <- anosim(CarnforthPS, CarnforthPC$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: 0.4355
##
## 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 0.2488059
## Run 2 stress 0.08821447
## ... New best solution
## ... Procrustes: rmse 0.2066695 max resid 0.412472
## Run 3 stress 0.05547403
## ... New best solution
## ... Procrustes: rmse 0.1751812 max resid 0.3454967
## Run 4 stress 0.190284
## Run 5 stress 0.1027624
## Run 6 stress 0.05547404
## ... Procrustes: rmse 0.0002151524 max resid 0.0003792484
## ... Similar to previous best
## Run 7 stress 0.08821452
## Run 8 stress 0.1027624
## Run 9 stress 0.05547398
## ... New best solution
## ... Procrustes: rmse 0.0001165222 max resid 0.0002066861
## ... Similar to previous best
## Run 10 stress 0.05547399
## ... Procrustes: rmse 4.74301e-06 max resid 8.551239e-06
## ... Similar to previous best
## Run 11 stress 0.0882145
## Run 12 stress 0.1770037
## Run 13 stress 0.1320888
## Run 14 stress 0.1027625
## Run 15 stress 0.05547401
## ... Procrustes: rmse 2.280356e-05 max resid 3.852681e-05
## ... Similar to previous best
## Run 16 stress 0.1027625
## Run 17 stress 0.261886
## Run 18 stress 0.08821447
## Run 19 stress 0.08975528
## Run 20 stress 0.09407489
## *** Best solution repeated 3 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 3 times in 20 tries
## The best solution was from try 9 (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 0.316
## 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 0.008 **
## 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 0.097 .
## 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 0.085 .
## Residual 1 0.4767
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#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 0.153
## 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 0.32
## 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 0.004 **
## 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##
HodbarrowPS <- read.csv("HodbarrowPlantSpecies.csv", header = TRUE, colClasses = c("numeric"))
HodbarrowPC <-read.csv("HODBARROW_PLANT_CHEMISTRY_MG_KG.csv", 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 0.007 **
## 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 0.082 .
## 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 0.059 .
## 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 0.025 *
## 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 0.008 **
## 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 0.026 *
## 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 0.013 *
## 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 0.051 .
## 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 0.026 *
## 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 0.027 *
## 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 0.07 .
## 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 0.004 **
## 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 0.016 *
## 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 0.095 .
## 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 0.088 .
## 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 0.014 *
## 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