Data Analysis for Block and Meave (2015, Plant Eco & Div)
This report shows the data analysis done for the paper Block and Meave (201X), published in the journal Plant Ecology & Diversity.
The analysis was reproduced for the last time on March 9th, 2015. I used R version 3.1.2 (2014-10-31) – “Pumpkin Helmet” and worked in a platform x86_64-apple-darwin13.4.0 (64-bit).
Redundancy analysis
First, load the packages required for the analysis.
library(ade4)
library(vegan)## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.2-0
##
## Attaching package: 'vegan'
##
## The following object is masked from 'package:ade4':
##
## ccalibrary(MASS)
library(ellipse)
library(FactoMineR)##
## Attaching package: 'FactoMineR'
##
## The following object is masked from 'package:ade4':
##
## reconstTo load the data, all files must be in the working directory.
structure <- read.csv("str.csv")
env <- read.csv("env.csv")
asp <- read.csv("aspect.csv")I did some modifications to the data. First, I combined all the quantitative variables of the env data (i.e., all except the geomorphological unit) with the asp data into a single object called env.quant. Then, I normalized the data in env.quant to allow comparison between variables with very different units. Finally, I added the geomorphological unit column to the normalized quantitative environmental variables to create the data frame env.ra, which was used for the redundancy analysis.
env.quant <- cbind(env[,-5],asp)
env.z <- scale(env.quant)
unit <- env[,5]
env.ra <- data.frame(env.z,unit)Structural data
I did a redundancy analysis on vegetation structure using the environmental data env.ra as explanatory variables. I assigned the results to the object str.rda.
str.rda <- rda(structure~., env.ra, scale=T)
summary(str.rda)##
## Call:
## rda(formula = structure ~ altitude + slope + rock + disturbance + aspect + unit, data = env.ra, scale = T)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 4.000 1.000
## Constrained 1.028 0.257
## Unconstrained 2.972 0.743
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## RDA1 RDA2 RDA3 RDA4 PC1 PC2 PC3
## Eigenvalue 0.7970 0.14050 0.08005 0.01048 1.756 0.9250 0.18135
## Proportion Explained 0.1992 0.03512 0.02001 0.00262 0.439 0.2313 0.04534
## Cumulative Proportion 0.1992 0.23436 0.25438 0.25699 0.696 0.9273 0.97263
## PC4
## Eigenvalue 0.10948
## Proportion Explained 0.02737
## Cumulative Proportion 1.00000
##
## Accumulated constrained eigenvalues
## Importance of components:
## RDA1 RDA2 RDA3 RDA4
## Eigenvalue 0.7970 0.1405 0.08005 0.01048
## Proportion Explained 0.7753 0.1367 0.07787 0.01019
## Cumulative Proportion 0.7753 0.9119 0.98981 1.00000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 3.919476
##
##
## Species scores
##
## RDA1 RDA2 RDA3 RDA4 PC1 PC2
## ba -0.9246 0.1831 -0.02910 -0.16244 1.63093 -0.08778
## cov -0.9044 -0.6180 0.07885 0.01387 1.43470 0.38987
## den -0.3918 0.2247 0.50312 0.03641 -0.04655 1.82720
## hei -1.1110 0.2714 -0.21738 0.11105 1.42278 -0.23274
##
##
## Site scores (weighted sums of species scores)
##
## RDA1 RDA2 RDA3 RDA4 PC1 PC2
## row1 0.22785 0.270852 2.93543 2.99485 -0.440562 0.691015
## row2 0.83904 0.438888 -0.53142 2.84411 -0.759571 -0.351583
## row3 -1.30198 1.170564 3.16299 1.65683 0.284619 1.128730
## row4 0.51936 0.503734 -0.37649 2.81833 -0.935897 -0.192376
## row5 0.39212 -0.484969 -2.11758 1.83449 -0.592485 -0.763339
## row6 -1.52395 -0.677108 -0.48953 -4.36253 0.589039 0.035292
## row7 -0.23288 0.871005 1.84044 3.50893 -0.548670 0.598587
## row8 -0.82872 0.698119 -1.63213 -1.49074 -0.049693 -0.295598
## row9 -3.19451 1.823282 -5.33611 -0.57433 1.713923 -0.896904
## row10 -1.66047 0.044759 -0.36296 -6.08025 0.739296 0.046176
## row11 0.74386 0.321653 -0.81193 0.46492 -0.238842 -0.599955
## row12 0.02928 0.938983 2.76807 -0.95225 -0.210929 0.652865
## row13 -0.49777 0.166082 2.19364 0.12887 0.248290 0.562664
## row14 -0.35294 0.077719 3.97498 1.43795 0.224860 1.009485
## row15 0.43781 0.620014 -1.26618 1.60703 -0.192672 -0.531850
## row16 -0.62458 0.144980 -1.01823 1.59121 0.470039 -0.317093
## row17 -0.15330 0.301143 -0.74566 -4.02228 0.038103 -0.363888
## row18 0.21503 -0.238104 -0.37917 -1.79550 -0.035170 -0.384009
## row19 -0.21140 0.049961 2.81896 1.24119 -0.035303 0.669403
## row20 0.48369 -0.357933 -1.39662 -1.97098 -0.268375 -0.697621
## row21 0.40529 0.118357 -0.46167 -0.80829 -0.273158 -0.369210
## row22 -0.73699 -0.487500 -1.28856 2.06542 0.129132 0.123463
## row23 0.98976 0.520984 0.09964 1.19454 -0.822201 -0.233775
## row24 -0.17926 0.974587 0.27170 1.30371 0.003687 0.105831
## row25 -1.81344 0.004611 -0.02022 -6.25932 1.276900 0.162542
## row26 -0.37324 0.539169 0.41698 -0.10356 0.099255 0.131802
## row27 0.76246 0.011700 -1.92192 0.05080 -0.305235 -0.801849
## row28 0.30313 0.554482 -0.42097 0.79339 -0.409757 -0.177813
## row29 0.54160 0.869045 4.25898 2.25532 -0.625595 1.073244
## row30 -1.23394 -0.489382 -0.52002 -1.97958 0.926973 -0.014235
## row31 0.39780 0.552798 1.04122 -0.51695 0.135709 -0.007307
## row32 -0.37463 0.175404 -0.79200 0.96691 0.142803 -0.201658
## row33 1.69139 -0.090545 -1.93650 -1.03120 -1.122280 -1.016715
## row34 0.48151 0.486157 2.11958 3.75873 -0.646170 0.514701
## row35 0.06236 -0.286263 -0.37509 -3.19727 -0.255766 -0.296253
## row36 -0.38132 -0.526112 5.32994 4.00103 -0.266117 1.559362
## row37 -1.09137 -1.156933 -2.18382 -3.11015 0.346459 -0.507656
## row38 -1.42084 -3.343145 -0.38820 -0.11040 0.809729 0.022932
## row39 -0.98507 -1.798210 -0.28107 -0.48119 0.529806 -0.021936
## row40 -0.68616 -1.811207 -0.07914 0.02499 0.325827 -0.045470
## row41 0.84738 -0.042626 -0.13673 0.45189 0.000903 0.139604
## row42 0.52173 -0.462560 -1.05166 0.27212 0.122909 -0.043106
## row43 0.87014 0.140114 -0.09022 -1.07335 0.002405 0.129693
## row44 1.42033 0.232568 -1.46279 -0.57770 -0.413797 -0.363876
## row45 0.62162 0.462679 0.26658 1.24125 0.323972 0.125759
## row46 1.32867 0.077666 -2.41439 0.63104 -0.200387 -0.580246
## row47 0.54648 -0.163968 -0.69925 -0.21887 0.039691 0.120521
## row48 0.46100 0.013093 0.45371 -0.29796 0.234807 0.395313
## row49 1.05549 -0.033624 -1.55023 -1.65233 0.001161 -0.424578
## row50 0.75562 1.056595 1.12386 -0.91787 -0.111664 0.500916
## row51 0.98951 1.138993 0.93102 3.75441 -0.657122 0.126073
## row52 0.57955 -0.383280 0.27890 -0.11564 -0.293886 0.029130
## row53 -0.43264 -0.752247 -2.01169 -0.89838 0.414836 -0.373897
## row54 0.87810 -0.025614 0.30465 0.53367 -0.210496 -0.041841
## row55 -0.02590 -0.776680 0.30696 1.36464 0.357604 0.136570
## row56 -0.10845 -0.122607 0.89605 0.20977 -0.079788 0.340771
## row57 -0.35294 -0.551753 0.88240 0.41618 0.235583 0.283178
## row58 -0.05423 -0.484832 -1.23636 -3.23534 0.278158 -0.304450
## row59 0.66189 -0.416249 0.24633 1.42364 -0.385505 0.097930
## row60 -0.22794 -0.407290 -1.13650 -1.00796 0.340615 -0.293465
##
##
## Site constraints (linear combinations of constraining variables)
##
## RDA1 RDA2 RDA3 RDA4 PC1 PC2
## row1 -0.26089 0.62605 0.22986 0.85861 -0.440562 0.691015
## row2 -0.32384 0.26106 -0.01812 1.23257 -0.759571 -0.351583
## row3 -0.60263 0.51133 -0.20105 0.56560 0.284619 1.128730
## row4 -0.85656 0.49559 -0.51524 -0.15249 -0.935897 -0.192376
## row5 -0.67594 0.78606 -0.23465 0.66850 -0.592485 -0.763339
## row6 -0.74300 0.04717 -0.36697 -0.13296 0.589039 0.035292
## row7 -0.85423 0.59937 -0.50007 0.08323 -0.548670 0.598587
## row8 -0.97219 0.44412 -0.73927 -0.28430 -0.049693 -0.295598
## row9 -0.81074 0.66824 -0.36931 0.17432 1.713923 -0.896904
## row10 -0.66412 0.22014 -0.19257 0.13661 0.739296 0.046176
## row11 0.25869 0.64577 0.95246 0.10841 -0.238842 -0.599955
## row12 -0.11740 0.06356 0.40479 -0.55878 -0.210929 0.652865
## row13 -0.01715 0.40916 0.55454 -0.30658 0.248290 0.562664
## row14 0.21229 -0.03820 0.89201 0.26072 0.224860 1.009485
## row15 0.07986 0.18115 0.46875 0.41073 -0.192672 -0.531850
## row16 0.01796 0.40511 0.63890 0.08011 0.470039 -0.317093
## row17 -0.21855 0.14193 0.31071 -0.71155 0.038103 -0.363888
## row18 0.03804 0.29169 0.71545 -0.31724 -0.035170 -0.384009
## row19 -0.08269 -0.18752 0.68556 -0.79016 -0.035303 0.669403
## row20 -0.10137 0.11185 0.51471 -0.44550 -0.268375 -0.697621
## row21 -0.05828 -0.11958 0.55695 -1.24800 -0.273158 -0.369210
## row22 -0.50030 -0.42731 -1.47362 0.64197 0.129132 0.123463
## row23 -0.23768 0.36247 0.16373 -0.69325 -0.822201 -0.233775
## row24 -0.09425 0.14008 0.12153 -0.15297 0.003687 0.105831
## row25 -0.01921 0.59298 0.26521 0.21092 1.276900 0.162542
## row26 -0.20630 0.73432 0.05741 -0.16163 0.099255 0.131802
## row27 0.13877 0.06953 0.44495 0.29022 -0.305235 -0.801849
## row28 -0.30743 0.36311 -0.12611 -0.72674 -0.409757 -0.177813
## row29 -0.12292 0.75749 0.11158 0.16304 -0.625595 1.073244
## row30 0.07298 0.14294 0.29231 0.18886 0.926973 -0.014235
## row31 0.58637 0.46632 1.15850 0.37005 0.135709 -0.007307
## row32 -0.14828 -0.93963 0.22870 0.27441 0.142803 -0.201658
## row33 -0.16662 -0.97979 0.30111 0.51927 -1.122280 -1.016715
## row34 -0.25937 -0.76233 0.08377 0.22608 -0.646170 0.514701
## row35 -0.40073 -0.88495 0.20936 -0.16872 -0.255766 -0.296253
## row36 -0.36371 -1.10373 0.11447 -0.01730 -0.266117 1.559362
## row37 -0.74130 -0.68708 -0.31597 -0.75265 0.346459 -0.507656
## row38 -0.31602 -1.03833 0.11786 0.01573 0.809729 0.022932
## row39 -0.23871 -1.04597 0.26583 -0.39551 0.529806 -0.021936
## row40 -0.25796 -0.82257 0.29131 0.23314 0.325827 -0.045470
## row41 0.88182 -0.02301 -0.58901 0.13887 0.000903 0.139604
## row42 0.69650 -0.12574 -0.74241 -0.72514 0.122909 -0.043106
## row43 0.88549 0.19463 -0.59175 -0.26733 0.002405 0.129693
## row44 0.73552 0.16822 -0.66685 -0.56148 -0.413797 -0.363876
## row45 1.15459 0.32377 0.30476 -0.15235 0.323972 0.125759
## row46 0.92171 -0.03042 -0.62613 0.30953 -0.200387 -0.580246
## row47 0.62954 -0.07726 -1.06231 -0.40526 0.039691 0.120521
## row48 0.86907 0.30125 -0.71024 0.27567 0.234807 0.395313
## row49 0.94693 0.05214 -0.20401 -0.47669 0.001161 -0.424578
## row50 0.70728 0.49635 -0.67316 -0.27760 -0.111664 0.500916
## row51 0.14766 0.05499 0.19300 0.06012 -0.657122 0.126073
## row52 0.14628 -0.48898 -0.16414 0.46062 -0.293886 0.029130
## row53 0.07475 -0.43552 -0.40563 0.06615 0.414836 -0.373897
## row54 0.57010 -0.51296 0.24191 1.05029 -0.210496 -0.041841
## row55 0.52471 -0.42227 0.22236 0.84924 0.357604 0.136570
## row56 -0.14403 -0.05262 -0.28757 -0.70911 -0.079788 0.340771
## row57 0.05615 -0.23937 0.20518 -0.52407 0.235583 0.283178
## row58 0.21229 -0.14090 -0.25033 0.99828 0.278158 -0.304450
## row59 0.14413 -0.60001 -0.36816 -0.10289 -0.385505 0.097930
## row60 0.17493 0.05607 0.07514 0.29637 0.340615 -0.293465
##
##
## Biplot scores for constraining variables
##
## RDA1 RDA2 RDA3 RDA4 PC1 PC2
## altitude -0.052022 0.83310 -0.16162 -0.26436 0 0
## slope -0.126811 -0.01535 -0.25691 -0.44548 0 0
## rock 0.702348 0.03640 -0.60210 -0.09455 0 0
## disturbance 0.136109 0.09902 0.43460 0.08652 0 0
## aspect -0.305177 -0.12562 -0.06303 -0.07136 0 0
## unitLO -0.203837 -0.68921 0.21697 0.02691 0 0
## unitOC -0.597827 0.41178 -0.25696 0.27837 0 0
## unitSU 0.006159 0.17893 0.54248 -0.20061 0 0
## unitTE 0.168540 -0.24584 -0.04757 0.21609 0 0
## unitUO -0.117956 0.23121 0.03658 -0.13147 0 0
##
##
## Centroids for factor constraints
##
## RDA1 RDA2 RDA3 RDA4 PC1 PC2
## unitCH 0.842845 0.1280 -0.55611 -0.21418 0 0
## unitLO -0.230633 -0.7798 0.24549 0.03045 0 0
## unitOC -0.676414 0.4659 -0.29074 0.31497 0 0
## unitSU 0.006969 0.2024 0.61379 -0.22698 0 0
## unitTE 0.190696 -0.2782 -0.05382 0.24450 0 0
## unitUO -0.133462 0.2616 0.04139 -0.14876 0 0I then got the R squared and the adjusted R squared for this analysis.
(R2 <- RsquareAdj(str.rda)$r.squared)## [1] 0.2569949(R2adj <- RsquareAdj(str.rda)$adj.r.squared)## [1] 0.1053612To make a triplot with scaling 2.
par(mar=c(4,4,2,2))
plot(str.rda, xlab="RDA1 (19.92 %)", ylab="RDA2 (3.51 %)",
display=c("cn", "lc", "sp"), type="n", xlim=c(-1.3,1.3))
sites.sc <- scores(str.rda, choices=1:2, scaling=2, display="lc")
points(sites.sc, pch=1, cex=0.5)
va.sc <- scores(str.rda, choices=1:2, scaling=2, display="sp")
text(va.sc, row.names(va.sc), cex=0.9)
env.sc <- scores(str.rda, choices=1:2, scaling=2, display="bp")
arrows(0,0, env.sc[1:5,1], env.sc[1:5,2], lty=1, lwd=2.5, length=0.1)
env.names <- c("Elevation", "Slope", "Rock", "CDI", "Aspect")
text(env.sc[c(1,3,4),], env.names[c(1,3,4)], cex=0.9, font=2, pos=4)
text(env.sc[c(2,5),], env.names[c(2,5)], cex=0.9, font=2, pos=2)
unit.names <- c("CH", "LO", "OC", "SU", "TE", "UO")
unit.sc <- scores(str.rda, choices=1:2, scaling=2, display="cn")
points(unit.sc, pch=23)
text(unit.sc[c(1,2,4,5),], unit.names[c(1,2,4,5)], cex=0.7, font=2, pos=4)
text(unit.sc[c(3,6),], unit.names[c(3,6)], cex=0.7, font=2, pos=3)
Composition data
First I loaded the species data. Again, the file PA_total.csv must be in the working directory. To reduce the importance of large abundances, I applied the Hellinger transformation to the species data, as recommended by Borcard et al. (2011, Numerical Ecology in R) .
spe <- read.csv("PA_total.csv", sep=",", header=T, row.names=1)
spe.he <- decostand(spe,"hellinger")Then I did a redundancy analysis using all the environmental variables as explanatory variables.
spe.rda <- rda(spe.he~., env.ra)
summary.sperda <- summary(spe.rda)
head(summary.sperda)##
## Call:
## rda(formula = spe.he ~ altitude + slope + rock + disturbance + aspect + unit, data = env.ra)
##
## Partitioning of variance:
## Inertia Proportion
## Total 0.8593 1.0000
## Constrained 0.2709 0.3153
## Unconstrained 0.5884 0.6847
##
## Eigenvalues, and their contribution to the variance
##
## Importance of components:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## Eigenvalue 0.06795 0.05164 0.04341 0.02369 0.01885 0.01825
## Proportion Explained 0.07908 0.06010 0.05052 0.02757 0.02194 0.02124
## Cumulative Proportion 0.07908 0.13917 0.18970 0.21727 0.23920 0.26044
## RDA7 RDA8 RDA9 RDA10 PC1 PC2
## Eigenvalue 0.01308 0.01264 0.01228 0.00910 0.03710 0.02956
## Proportion Explained 0.01522 0.01471 0.01430 0.01059 0.04318 0.03440
## Cumulative Proportion 0.27566 0.29037 0.30467 0.31526 0.35844 0.39283
## PC3 PC4 PC5 PC6 PC7 PC8
## Eigenvalue 0.02726 0.02463 0.02326 0.02174 0.02054 0.01957
## Proportion Explained 0.03173 0.02867 0.02708 0.02530 0.02390 0.02278
## Cumulative Proportion 0.42456 0.45323 0.48030 0.50561 0.52951 0.55228
## PC9 PC10 PC11 PC12 PC13 PC14
## Eigenvalue 0.01858 0.01787 0.0171 0.01676 0.01637 0.01541
## Proportion Explained 0.02162 0.02080 0.0199 0.01950 0.01905 0.01794
## Cumulative Proportion 0.57390 0.59470 0.6146 0.63410 0.65315 0.67109
## PC15 PC16 PC17 PC18 PC19 PC20
## Eigenvalue 0.01469 0.01398 0.01316 0.01289 0.01257 0.01183
## Proportion Explained 0.01710 0.01627 0.01531 0.01500 0.01463 0.01376
## Cumulative Proportion 0.68819 0.70445 0.71976 0.73477 0.74940 0.76316
## PC21 PC22 PC23 PC24 PC25 PC26
## Eigenvalue 0.01164 0.01084 0.01070 0.01049 0.01013 0.009514
## Proportion Explained 0.01354 0.01262 0.01245 0.01221 0.01178 0.011070
## Cumulative Proportion 0.77670 0.78932 0.80177 0.81399 0.82577 0.836840
## PC27 PC28 PC29 PC30 PC31
## Eigenvalue 0.009407 0.008943 0.008698 0.008482 0.007893
## Proportion Explained 0.010950 0.010410 0.010120 0.009870 0.009190
## Cumulative Proportion 0.847790 0.858200 0.868320 0.878190 0.887380
## PC32 PC33 PC34 PC35 PC36
## Eigenvalue 0.007565 0.007349 0.007034 0.006934 0.006694
## Proportion Explained 0.008800 0.008550 0.008190 0.008070 0.007790
## Cumulative Proportion 0.896180 0.904740 0.912920 0.920990 0.928780
## PC37 PC38 PC39 PC40 PC41
## Eigenvalue 0.006353 0.005784 0.005622 0.005219 0.004988
## Proportion Explained 0.007390 0.006730 0.006540 0.006070 0.005810
## Cumulative Proportion 0.936180 0.942910 0.949450 0.955520 0.961330
## PC42 PC43 PC44 PC45 PC46
## Eigenvalue 0.004838 0.004789 0.004715 0.004561 0.003928
## Proportion Explained 0.005630 0.005570 0.005490 0.005310 0.004570
## Cumulative Proportion 0.966960 0.972530 0.978020 0.983330 0.987900
## PC47 PC48 PC49
## Eigenvalue 0.003677 0.003378 0.003343
## Proportion Explained 0.004280 0.003930 0.003890
## Cumulative Proportion 0.992180 0.996110 1.000000
##
## Accumulated constrained eigenvalues
## Importance of components:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## Eigenvalue 0.06795 0.05164 0.04341 0.02369 0.01885 0.01825
## Proportion Explained 0.25083 0.19063 0.16025 0.08746 0.06958 0.06736
## Cumulative Proportion 0.25083 0.44146 0.60172 0.68918 0.75876 0.82611
## RDA7 RDA8 RDA9 RDA10
## Eigenvalue 0.01308 0.01264 0.01228 0.00910
## Proportion Explained 0.04829 0.04665 0.04535 0.03359
## Cumulative Proportion 0.87441 0.92106 0.96641 1.00000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 2.668355
##
##
## Species scores
##
## RDA1 RDA2 RDA3
## Acacia.cochliacantha -0.003359 -0.013391 -0.006752
## Acacia.penatula -0.006813 -0.010297 -0.004528
## Acaciella.angustissima.var..angustissima -0.027410 -0.054519 -0.055067
## Acaciella.painteri.var..houghii -0.001005 0.004012 -0.010721
## Acalypha.aff..triloba -0.006813 -0.010297 -0.004528
## Acalypha.langiana -0.012237 -0.050560 -0.070710
## ....
## RDA4 RDA5 RDA6
## Acacia.cochliacantha 0.010861 -0.010221 0.001266
## Acacia.penatula -0.018864 0.001808 -0.005013
## Acaciella.angustissima.var..angustissima -0.012860 0.033837 -0.012646
## Acaciella.painteri.var..houghii 0.005222 -0.021100 -0.010779
## Acalypha.aff..triloba -0.018864 0.001808 -0.005013
## Acalypha.langiana 0.063054 0.018409 0.080199
## ....
##
##
## Site scores (weighted sums of species scores)
##
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## OC1 -0.4770 -0.09291 0.7532 0.2125 -0.46119 0.3947
## OC2 -0.5046 -0.19478 0.7846 0.1383 -0.55588 0.1253
## OC3 -0.3533 0.06168 0.6782 0.1060 -0.21171 0.1414
## OC4 -0.4736 -0.19019 0.6348 0.1188 0.08922 -0.1280
## OC5 -0.3711 -0.01763 0.7193 0.3439 0.24355 0.2106
## OC6 -0.4631 -0.27424 0.5637 0.1864 -0.02151 -0.4369
## ....
##
##
## Site constraints (linear combinations of constraining variables)
##
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## OC1 -0.3943 -0.141786 0.5903 0.22991 -0.423332 0.472054
## OC2 -0.4585 -0.200318 0.7284 0.06717 -0.413690 0.062382
## OC3 -0.4085 -0.036607 0.6309 0.16138 -0.002839 0.073781
## OC4 -0.3810 0.002168 0.5868 0.16456 0.099085 0.003786
## OC5 -0.4050 0.106489 0.6316 0.18617 0.402167 0.169800
## OC6 -0.3762 -0.198568 0.5686 0.17043 -0.445906 -0.295726
## ....
##
##
## Biplot scores for constraining variables
##
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## altitude 0.231336 0.79690 0.4754 0.20805 0.18250 -0.04546
## slope -0.003372 -0.11149 -0.1998 0.67388 -0.01662 -0.39309
## rock 0.908813 -0.19820 0.3002 -0.06050 0.02770 -0.04448
## disturbance -0.080655 -0.07863 -0.1951 -0.16376 -0.32443 0.85189
## aspect -0.346488 -0.13290 0.1872 -0.33498 -0.12575 0.13342
## unitLO -0.255897 -0.41885 -0.2968 -0.77653 0.09837 0.10760
## unitOC -0.515829 -0.05691 0.7944 0.21470 -0.03435 0.03652
## unitSU 0.051974 0.70402 -0.2385 -0.03111 -0.39614 -0.01947
## unitTE -0.092466 -0.44377 -0.4847 0.68834 0.13124 -0.03868
## unitUO -0.099056 0.39376 -0.1009 -0.05393 0.11655 -0.12238
##
##
## Centroids for factor constraints
##
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
## unitCH 0.70194 -0.13731 0.2514 -0.03194 0.06495 0.02806
## unitLO -0.19711 -0.32263 -0.2286 -0.59815 0.07577 0.08288
## unitOC -0.39734 -0.04384 0.6119 0.16538 -0.02646 0.02813
## unitSU 0.04003 0.54230 -0.1837 -0.02396 -0.30514 -0.01500
## unitTE -0.07123 -0.34183 -0.3733 0.53022 0.10109 -0.02980
## unitUO -0.07630 0.30331 -0.0777 -0.04154 0.08978 -0.09427# Get the R^2 and an adjusted R^2
(spR2 <- RsquareAdj(spe.rda)$r.squared)## [1] 0.3152582(spR2adj <- RsquareAdj(spe.rda)$adj.r.squared)## [1] 0.175515To make a triplot of the RDA on species data with scaling 2.
# Make plotting space
par(mar=c(4,4,2,2))
plot(spe.rda, scaling=2, display=c("cn", "lc", "sp"), type="n",
xlab="RDA1 (7.91 %)", ylab="RDA2 (6.01 %)")
# Plot points for sites
spsites.sc <- scores(spe.rda, choices=1:2, scaling=2, display="lc")
points(spsites.sc, pch=1, cex=0.5)
# Plot points for species
sp.sc <- scores(spe.rda, choices=1:2, scaling=2, display="sp")
points(sp.sc, pch=4, cex=0.5, col="gray50")
# Plot arrows of quantitative explanatory variables and their labels
spenv.sc <- scores(spe.rda, choices=1:2, scaling=2, display="bp")
arrows(0,0, spenv.sc[1:5,1], spenv.sc[1:5,2], lty=1, lwd=2.5, length=0.1, col="black")
env.names <- c("Elevation", "Slope", "Rock", "CDI", "Aspect")
text(spenv.sc[1:3,], env.names[1:3], cex=0.9, font=2, pos=4)
text(spenv.sc[4:5,], env.names[4:5], cex=0.9, font=2, pos=1)
# Plot point for geomorphological unit centroids and their labels
unit.names <- c("CH", "LO", "OC", "SU", "TE", "UO")
spunit.sc <- scores(spe.rda, choices=1:2, scaling=2, display="cn")
points(spunit.sc, pch=23)
text(spunit.sc[c(2,3,4,6),], unit.names[c(2,3,4,6)], cex=0.7, font=2, pos=2)
text(spunit.sc[c(1,5),], unit.names[c(1,5)], cex=0.7, font=2, pos=3)
Plot for the paper
This is the code to make the figure for the paper.
par(mfrow=c(2,1))
# Fig 3a - Structure RDA triplot - Scaling 2 (Correlation triplot)
par(mar=c(4,4,2,1))
plot(1, xlab="RDA1 (19.92 %)", ylab="RDA2 (3.51 %)", type="n",
xlim=c(-1.3,1.3), ylim=c(-1.1,1.1))
abline(v=0, lty=3); abline(h=0, lty=3); mtext("a", side=3, adj=0, cex=1.25)
sites.sc <- scores(str.rda, choices=1:2, scaling=2, display="lc")
points(sites.sc, pch=1, cex=0.5)
va.sc <- scores(str.rda, choices=1:2, scaling=2, display="sp")
text(va.sc, row.names(va.sc), cex=0.9)
env.sc <- scores(str.rda, choices=1:2, scaling=2, display="bp")
arrows(0,0, env.sc[1:5,1], env.sc[1:5,2], lty=1, lwd=2.5, length=0.1)
env.names <- c("Elevation", "Slope", "Rock", "CDI", "Aspect")
text(env.sc[c(3,4),], env.names[c(3,4)], cex=0.9, font=2, pos=4)
text(env.sc[c(2,5),], env.names[c(2,5)], cex=0.9, font=2, pos=2)
text(env.sc[1,1], env.sc[1,2], env.names[1], cex=0.9, font=2, pos=3)
unit.names <- c("CH", "LO", "OC", "SU", "TE", "UO")
unit.sc <- scores(str.rda, choices=1:2, scaling=2, display="cn")
points(unit.sc, pch=18)
text(unit.sc[c(2,4,5),], unit.names[c(2,4,5)], cex=0.7, font=2, pos=4)
text(unit.sc[c(1,3,6),], unit.names[c(1,3,6)], cex=0.7, font=2, pos=3)
# Fig 3b - Species composition RDA triplot - Scaling 2 (Correlation triplot)
# Make plotting space
par(mar=c(4,4,2,1))
plot(1, type="n", xlim=c(-0.5,1), ylim=c(-0.6,0.8),
xlab="RDA1 (7.91 %)", ylab="RDA2 (6.01 %)")
abline(v=0, lty=3); abline(h=0, lty=3)
mtext("b", side=3, adj=0, cex=1.25)
# Plot points for sites
spsites.sc <- scores(spe.rda, choices=1:2, scaling=2, display="lc")
points(spsites.sc, pch=1, cex=0.5)
# Plot points for species
sp.sc <- scores(spe.rda, choices=1:2, scaling=2, display="sp")
points(sp.sc, pch=4, cex=0.5, col="gray50")
# Plot arrows of quantitative explanatory variables and their labels
spenv.sc <- scores(spe.rda, choices=1:2, scaling=2, display="bp")
arrows(0,0, spenv.sc[1:5,1], spenv.sc[1:5,2], lty=1, lwd=2.5, length=0.1, col="black")
env.names <- c("Elevation", "Slope", "Rock", "CDI", "Aspect")
text(spenv.sc[1:3,], env.names[1:3], cex=0.9, font=2, pos=4)
text(spenv.sc[4:5,], env.names[4:5], cex=0.9, font=2, pos=1)
# Plot point for geomorphological unit centroids and their labels
unit.names <- c("CH", "LO", "OC", "SU", "TE", "OU")
spunit.sc <- scores(spe.rda, choices=1:2, scaling=2, display="cn")
points(spunit.sc, pch=18)
text(spunit.sc[c(2,3,4,6),], unit.names[c(2,3,4,6)], cex=0.7, font=2, pos=2)
text(spunit.sc[c(1,5),], unit.names[c(1,5)], cex=0.7, font=2, pos=3)