Analysis for Climbing impact manuscript
Richness and Diversity Components
Code by Georgia Harrison
March 11, 2021
library packages
library(vegan) #community ecology
library(ggplot2) #data viz
library(dplyr) #data manipulation
library(tidyr) #data manipulation
library(ggpubr) #for combining multiple ggplots into one figure
library(ddpcr) #for joining data setsImport the data
Remove bluff mountain, only want face plots
data <- read.csv("~/Research/Manuscript/HARRISON_LGWA_MATRIX.csv", header=TRUE)
data <- data %>%
group_by(T_LOCATION) %>%
filter(T_LOCATION %in% c("HB", "TR")| n() == 1)
#remove bluff mtn
data <- data %>%
group_by(P_LOCATION) %>%
filter(P_LOCATION %in% c("FACE")| n() == 1)import data in long format too
long <- read.csv("~/Research/Manuscript/Long_data.csv", header=TRUE)Create matrix for each taxa group
First, determine which columns start and end each taxa group and then create a matrix for each taxa group
which( colnames(data)=="BLAFLA") #54
## [1] 54
which( colnames(data)=="YELPDR") #133
## [1] 133
which( colnames(data)=="Moss_2") #134
## [1] 134
which( colnames(data)=="Wei_controversa") #174
## [1] 174
which( colnames(data)=="Ace_saccharum") #175
## [1] 175
which( colnames(data)=="Plant_5") #212
## [1] 212
total.abundance.matrix <- data[,54:212]
lichen.abundance.matrix <- data[,54:133]
moss.abundance.matrix <- data [,134:174]
plant.abundance.matrix <- data [,175:212]Create a new data frame to input richness, diveristy and abundance values (but keep the important metadata)
indic <- data[,c("NAME","ROUTE","PITCH_NUM", "CL_UNCL", "PLOT_NUM", "P_LOCATION", "T_LOCATION", "ROUTE_TYPE",
"GRADE_CAT", "STARS", "TOT_HEIGHT", "SLOPE", "ASPECT_NORTH", "ASPECT_EAST")]
indic$T_LOCATION <- as.factor((indic$T_LOCATION))
indic$CL_UNCL <- as.factor((indic$CL_UNCL))Determine speciess richnes, shannon and total abundance for each
the cover will be used as a proxy for abundance
#all
indic$AllRichness <- rowSums(total.abundance.matrix>0)
indic$AllShannon <- diversity(total.abundance.matrix) # shannon is default
indic$AllCover <- rowSums(total.abundance.matrix)
#lichens
indic$LRichness <- rowSums(lichen.abundance.matrix>0)
indic$LShannon <- diversity(lichen.abundance.matrix) # shannon is default
indic$LCover <- rowSums(lichen.abundance.matrix)
#mosses
indic$MRichness <- rowSums(moss.abundance.matrix>0)
indic$MShannon <- diversity(moss.abundance.matrix) # shannon is default
indic$MCover <- rowSums(moss.abundance.matrix)
#plants
indic$PRichness <- rowSums(plant.abundance.matrix>0)
indic$PShannon <- diversity(plant.abundance.matrix) # shannon is default
indic$PCover <- rowSums(plant.abundance.matrix)Compare richness and diversity by climbing and site
Using ANOVAs
##all species
Ranova <- aov(AllRichness~T_LOCATION*CL_UNCL, data=indic)
summary(Ranova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 261.2 261.16 47.297 3.07e-11 ***
## CL_UNCL 1 38.5 38.46 6.966 0.0087 **
## T_LOCATION:CL_UNCL 1 24.4 24.39 4.416 0.0364 *
## Residuals 330 1822.2 5.52
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Ranova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = AllRichness ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 2.274312 1.623768 2.924856 0
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 0.6962638 0.1744206 1.218107 0.0090763
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 1.7244318 0.5583611 2.8905025 0.0009184
## HB:UNCLIMBED-HB:CLIMBED -0.4187500 -1.9607514 1.1232514 0.8965542
## TR:UNCLIMBED-HB:CLIMBED 2.7083333 1.4697680 3.9468987 0.0000002
## HB:UNCLIMBED-TR:CLIMBED -2.1431818 -3.3416905 -0.9446731 0.0000329
## TR:UNCLIMBED-TR:CLIMBED 0.9839015 0.2140321 1.7537709 0.0058824
## TR:UNCLIMBED-HB:UNCLIMBED 3.1270833 1.8579315 4.3962352 0.0000000Danova <- aov(AllShannon~T_LOCATION*CL_UNCL, data=indic)
summary(Danova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 4.494 4.494 49.593 1.11e-11 ***
## CL_UNCL 1 0.672 0.672 7.414 0.00682 **
## T_LOCATION:CL_UNCL 1 0.174 0.174 1.917 0.16712
## Residuals 330 29.907 0.091
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Danova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = AllShannon ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 0.2983548 0.2150119 0.3816977 0
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 0.092023 0.0251683 0.1588777 0.007127
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 0.256342485 0.10695409 0.4057309 0.0000752
## HB:UNCLIMBED-HB:CLIMBED -0.001722832 -0.19927270 0.1958270 0.9999959
## TR:UNCLIMBED-HB:CLIMBED 0.373015457 0.21433959 0.5316913 0.0000000
## HB:UNCLIMBED-TR:CLIMBED -0.258065317 -0.41160943 -0.1045212 0.0001113
## TR:UNCLIMBED-TR:CLIMBED 0.116672973 0.01804298 0.2153030 0.0129632
## TR:UNCLIMBED-HB:UNCLIMBED 0.374738289 0.21214390 0.5373327 0.0000000Canova <- aov(AllCover~T_LOCATION*CL_UNCL, data=indic)
summary(Canova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 2487 2487.4 22.254 3.53e-06 ***
## CL_UNCL 1 479 479.3 4.288 0.0391 *
## T_LOCATION:CL_UNCL 1 60 60.2 0.539 0.4634
## Residuals 330 36885 111.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Canova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = AllCover ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 7.018857 4.091959 9.945755 3.5e-06
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 2.457938 0.1100848 4.805791 0.0402387
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 6.3352273 1.0888946 11.5815600 0.0106248
## HB:UNCLIMBED-HB:CLIMBED 0.7208333 -6.2168698 7.6585364 0.9932344
## TR:UNCLIMBED-HB:CLIMBED 9.2604167 3.6879194 14.8329139 0.0001370
## HB:UNCLIMBED-TR:CLIMBED -5.6143939 -11.0066702 -0.2221177 0.0376453
## TR:UNCLIMBED-TR:CLIMBED 2.9251894 -0.5385721 6.3889509 0.1307549
## TR:UNCLIMBED-HB:UNCLIMBED 8.5395833 2.8294729 14.2496938 0.0007785#lichens only
Ranova <- aov(LRichness~T_LOCATION*CL_UNCL, data=indic)
summary(Ranova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 78.5 78.55 16.080 7.51e-05 ***
## CL_UNCL 1 9.1 9.13 1.869 0.173
## T_LOCATION:CL_UNCL 1 11.6 11.60 2.374 0.124
## Residuals 330 1612.0 4.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Ranova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LRichness ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 1.247272 0.6353986 1.859146 7.51e-05
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 0.3392085 -0.1516148 0.8300318 0.1749103
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 0.8494318 -0.2473243 1.9461880 0.1900978
## HB:UNCLIMBED-HB:CLIMBED -0.4312500 -1.8815905 1.0190905 0.8689201
## TR:UNCLIMBED-HB:CLIMBED 1.3854167 0.2204752 2.5503582 0.0123223
## HB:UNCLIMBED-TR:CLIMBED -1.2806818 -2.4079478 -0.1534159 0.0187182
## TR:UNCLIMBED-TR:CLIMBED 0.5359848 -0.1881213 1.2600910 0.2252303
## TR:UNCLIMBED-HB:UNCLIMBED 1.8166667 0.6229569 3.0103765 0.0005975Danova <- aov(LShannon~T_LOCATION*CL_UNCL, data=indic)
summary(Danova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 1.46 1.4554 11.824 0.00066 ***
## CL_UNCL 1 0.41 0.4130 3.355 0.06791 .
## T_LOCATION:CL_UNCL 1 0.13 0.1255 1.019 0.31341
## Residuals 330 40.62 0.1231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Danova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LShannon ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 0.1697808 0.07265164 0.26691 0.0006597
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 0.07214359 -0.005769985 0.1500572 0.0694366
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 0.133275405 -0.04082431 0.30737512 0.1989072
## HB:UNCLIMBED-HB:CLIMBED -0.007591612 -0.23781950 0.22263628 0.9997784
## TR:UNCLIMBED-HB:CLIMBED 0.226299590 0.04137610 0.41122308 0.0093014
## HB:UNCLIMBED-TR:CLIMBED -0.140867017 -0.31980987 0.03807584 0.1780946
## TR:UNCLIMBED-TR:CLIMBED 0.093024185 -0.02192085 0.20796922 0.1585729
## TR:UNCLIMBED-HB:UNCLIMBED 0.233891202 0.04440101 0.42338140 0.0085199Canova <- aov(LCover~T_LOCATION*CL_UNCL, data=indic)
summary(Canova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 801 800.5 5.960 0.0152 *
## CL_UNCL 1 220 219.7 1.636 0.2018
## T_LOCATION:CL_UNCL 1 25 24.6 0.183 0.6687
## Residuals 330 44324 134.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Canova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LCover ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 3.981855 0.7733694 7.19034 0.0151576
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 1.664146 -0.9095868 4.237879 0.2042847
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 3.5568182 -2.1942478 9.307884 0.3817414
## HB:UNCLIMBED-HB:CLIMBED 0.5541667 -7.0509912 8.159325 0.9976339
## TR:UNCLIMBED-HB:CLIMBED 5.5208333 -0.5877765 11.629443 0.0925522
## HB:UNCLIMBED-TR:CLIMBED -3.0026515 -8.9137018 2.908399 0.5561287
## TR:UNCLIMBED-TR:CLIMBED 1.9640152 -1.8329841 5.761014 0.5408758
## TR:UNCLIMBED-HB:UNCLIMBED 4.9666667 -1.2927957 11.226129 0.1724029###PLANTS
Ranova <- aov(PRichness~T_LOCATION*CL_UNCL, data=indic)
summary(Ranova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 5.56 5.556 8.954 0.00298 **
## CL_UNCL 1 3.34 3.337 5.378 0.02101 *
## T_LOCATION:CL_UNCL 1 0.82 0.815 1.314 0.25256
## Residuals 330 204.75 0.620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Ranova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PRichness ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 0.3317125 0.1136442 0.5497808 0.002977
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 0.205068 0.03014135 0.3799946 0.0217221
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 0.241477273 -0.1494003468 0.6323549 0.3827417
## HB:UNCLIMBED-HB:CLIMBED 0.002083333 -0.5148097116 0.5189764 0.9999996
## TR:UNCLIMBED-HB:CLIMBED 0.500000000 0.0848215153 0.9151785 0.0109102
## HB:UNCLIMBED-TR:CLIMBED -0.239393939 -0.6411450715 0.1623572 0.4155760
## TR:UNCLIMBED-TR:CLIMBED 0.258522727 0.0004554369 0.5165900 0.0494078
## TR:UNCLIMBED-HB:UNCLIMBED 0.497916667 0.0724853175 0.9233480 0.0143253Danova <- aov(PShannon~T_LOCATION*CL_UNCL, data=indic)
summary(Danova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 0.243 0.24262 4.555 0.0336 *
## CL_UNCL 1 0.036 0.03590 0.674 0.4122
## T_LOCATION:CL_UNCL 1 0.009 0.00895 0.168 0.6821
## Residuals 330 17.577 0.05326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Danova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PShannon ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 0.06932017 0.005428011 0.1332123 0.0335547
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 0.02127177 -0.02998024 0.07252378 0.4148243
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 5.983645e-02 -0.05468736 0.17436027 0.5323095
## HB:UNCLIMBED-HB:CLIMBED -3.105155e-16 -0.15144527 0.15144527 1.0000000
## TR:UNCLIMBED-HB:CLIMBED 8.670698e-02 -0.03493679 0.20835074 0.2562351
## HB:UNCLIMBED-TR:CLIMBED -5.983645e-02 -0.17754612 0.05787321 0.5555253
## TR:UNCLIMBED-TR:CLIMBED 2.687052e-02 -0.04874100 0.10248205 0.7954492
## TR:UNCLIMBED-HB:UNCLIMBED 8.670698e-02 -0.03794079 0.21135475 0.2769769Canova <- aov(PCover~T_LOCATION*CL_UNCL, data=indic)
summary(Canova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 27.8 27.84 5.496 0.0197 *
## CL_UNCL 1 54.9 54.95 10.848 0.0011 **
## T_LOCATION:CL_UNCL 1 12.2 12.24 2.417 0.1209
## Residuals 330 1671.5 5.07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Canova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PCover ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 0.7425285 0.1194709 1.365586 0.0196511
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 0.8321697 0.3323751 1.331964 0.0011672
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 0.39772727 -0.7190754 1.5145300 0.7944030
## HB:UNCLIMBED-HB:CLIMBED 0.04583333 -1.4310165 1.5226832 0.9998152
## TR:UNCLIMBED-HB:CLIMBED 1.43750000 0.2512656 2.6237344 0.0102608
## HB:UNCLIMBED-TR:CLIMBED -0.35189394 -1.4997641 0.7959762 0.8582491
## TR:UNCLIMBED-TR:CLIMBED 1.03977273 0.3024313 1.7771141 0.0017823
## TR:UNCLIMBED-HB:UNCLIMBED 1.39166667 0.1761382 2.6071952 0.0174879##MOSSES
Ranova <- aov(MRichness~T_LOCATION*CL_UNCL, data=indic)
summary(Ranova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 24.41 24.411 29.664 1.01e-07 ***
## CL_UNCL 1 1.83 1.833 2.227 0.137
## T_LOCATION:CL_UNCL 1 0.40 0.397 0.482 0.488
## Residuals 330 271.57 0.823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Ranova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MRichness ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 0.6953273 0.4441867 0.9464679 1e-07
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 0.1519873 -0.04946876 0.3534433 0.13873
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 0.63352273 0.1833645 1.0836810 0.0018305
## HB:UNCLIMBED-HB:CLIMBED 0.01041667 -0.5848686 0.6057019 0.9999668
## TR:UNCLIMBED-HB:CLIMBED 0.82291667 0.3447721 1.3010613 0.0000710
## HB:UNCLIMBED-TR:CLIMBED -0.62310606 -1.0857869 -0.1604252 0.0032050
## TR:UNCLIMBED-TR:CLIMBED 0.18939394 -0.1078119 0.4865998 0.3546015
## TR:UNCLIMBED-HB:UNCLIMBED 0.81250000 0.3225476 1.3024524 0.0001423Danova <- aov(MShannon~T_LOCATION*CL_UNCL, data=indic)
summary(Danova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 0.823 0.8227 9.208 0.00260 **
## CL_UNCL 1 0.719 0.7192 8.049 0.00484 **
## T_LOCATION:CL_UNCL 1 0.073 0.0734 0.822 0.36533
## Residuals 330 29.487 0.0894
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Danova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MShannon ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 0.1276512 0.04489631 0.2104061 0.0026014
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED 0.09520647 0.02882346 0.1615895 0.0050722
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 0.10502347 -0.04331091 0.25335786 0.2619015
## HB:UNCLIMBED-HB:CLIMBED 0.03465736 -0.16149869 0.23081341 0.9683832
## TR:UNCLIMBED-HB:CLIMBED 0.21664944 0.05909310 0.37420577 0.0024737
## HB:UNCLIMBED-TR:CLIMBED -0.07036612 -0.22282689 0.08209466 0.6324877
## TR:UNCLIMBED-TR:CLIMBED 0.11162596 0.01369185 0.20956007 0.0181909
## TR:UNCLIMBED-HB:UNCLIMBED 0.18199208 0.02054488 0.34343928 0.0200291Canova <- aov(MCover~T_LOCATION*CL_UNCL, data=indic)
summary(Canova)## Df Sum Sq Mean Sq F value Pr(>F)
## T_LOCATION 1 266 265.82 24.269 1.33e-06 ***
## CL_UNCL 1 0 0.12 0.011 0.918
## T_LOCATION:CL_UNCL 1 0 0.49 0.045 0.832
## Residuals 330 3614 10.95
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(Canova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MCover ~ T_LOCATION * CL_UNCL, data = indic)
##
## $T_LOCATION
## diff lwr upr p adj
## TR-HB 2.294473 1.378256 3.210691 1.3e-06
##
## $CL_UNCL
## diff lwr upr p adj
## UNCLIMBED-CLIMBED -0.0383777 -0.7733349 0.6965795 0.9182465
##
## $`T_LOCATION:CL_UNCL`
## diff lwr upr p adj
## TR:CLIMBED-HB:CLIMBED 2.38068182 0.7384028 4.0229608 0.0012220
## HB:UNCLIMBED-HB:CLIMBED 0.12083333 -2.0509016 2.2925683 0.9989408
## TR:UNCLIMBED-HB:CLIMBED 2.30208333 0.5577038 4.0464628 0.0040837
## HB:UNCLIMBED-TR:CLIMBED -2.25984848 -3.9478127 -0.5718842 0.0034428
## TR:UNCLIMBED-TR:CLIMBED -0.07859848 -1.1628726 1.0056756 0.9976702
## TR:UNCLIMBED-HB:UNCLIMBED 2.18125000 0.3937929 3.9687071 0.0095692Figures for diversity, richness and cover for all taxa by climbing and site
theme_set(
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.text = element_text(size=11, colour = "black"),
axis.line = element_line(colour = "black"),
axis.title = element_text(size=16, colour = "black"),
legend.title = element_text(size = 16, colour = "black"),
legend.text = element_text(size=16, colour= "black")
))
#richness
p <- ggplot(indic, aes(x=T_LOCATION, y=AllRichness, fill=CL_UNCL))
p <- p + geom_violin(trim=FALSE)+theme_classic()
p <- p + labs(y="Species Richness", x="Site")
pr <- p + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
#diversity
p <- ggplot(indic, aes(x=T_LOCATION, y=AllShannon, fill=CL_UNCL))
p <- p + geom_violin(trim=FALSE)+theme_classic()
p <- p + labs(y="Shannon Diversity", x="Site")
pd <- p + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
#abundance
p <- ggplot(indic, aes(x=T_LOCATION, y=AllCover, fill=CL_UNCL))
p <- p + geom_violin(trim=FALSE)+theme_classic()
p <- p + labs(y="Relative Abundance", x="Site")
pa <- p + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
all_taxa_summary <- ggarrange(pr, pd, pa, ncol=3, common.legend = TRUE)
all_taxa_summary 
ggsave("alltaxa_summary.jpeg", plot = all_taxa_summary, device = "jpeg", width = 8, height = 4, dpi = 300, scale = 1)Figure for species richness for all groups
If needed, we could do the same thing for diversity and cover - but might be better to just put this info into a table?
theme_set(
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.text = element_text(size=11, colour = "black"),
axis.line = element_line(colour = "black"), axis.title = element_text(size=16, colour = "black"),
legend.title = element_text(size = 16, colour = "black"), legend.text = element_text(size=16, colour= "black")))
all <- ggplot(indic, aes(x=T_LOCATION, y=AllRichness, fill=factor(CL_UNCL)))
all <- all + geom_violin(trim=FALSE)+theme_classic()
all <- all + labs(y="Species Richness", x="Site", title="All Taxa")
all <- all + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(all)
L <- ggplot(indic, aes(x=T_LOCATION, y=LRichness, fill=factor(CL_UNCL)))
L <- L + geom_violin(trim=FALSE) +theme_classic()
L <- L + labs(y="Species Richness", x="Site", title="Lichens")
L <- L + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(L)
M <- ggplot(indic, aes(x=T_LOCATION, y=MRichness, fill=factor(CL_UNCL)))
M <- M + geom_violin(trim=FALSE)+theme_classic()
M <- M + labs(y="Species Richness", x="Site", title="Bryophytes")
M <- M + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(M)
P <- ggplot(indic, aes(x=T_LOCATION, y=PRichness, fill=factor(CL_UNCL)))
P <- P + geom_violin(trim=FALSE)+theme_classic()
P <- P + labs(y="Species Richness", x="Site", title="Vascular Plants")
P <- P + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(P)
#combine into one figure
combined_rich <- ggarrange(all, L, M, P, common.legend = TRUE)
combined_rich 
ggsave("combined_rich.jpeg", plot = combined_rich, device = "jpeg", width = 8, height = 6, dpi = 300, scale = 1)Figure for diversity for all groups
If needed, we could do the same thing for diversity and cover - but might be better to just put this info into a table?
theme_set(
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.text = element_text(size=11, colour = "black"),
axis.line = element_line(colour = "black"), axis.title = element_text(size=16, colour = "black"),
legend.title = element_text(size = 16, colour = "black"), legend.text = element_text(size=16, colour= "black")))
all <- ggplot(indic, aes(x=T_LOCATION, y=AllShannon, fill=factor(CL_UNCL)))
all <- all + geom_violin(trim=TRUE)+theme_classic()
all <- all + labs(y="Species Diversity", x="Site", title="All Taxa")
all <- all + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(all)
L <- ggplot(indic, aes(x=T_LOCATION, y=LShannon, fill=factor(CL_UNCL)))
L <- L + geom_violin(trim=TRUE) +theme_classic()
L <- L + labs(y="Species Diversity", x="Site", title="Lichens")
L <- L + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(L)
M <- ggplot(indic, aes(x=T_LOCATION, y=MShannon, fill=factor(CL_UNCL)))
M <- M + geom_violin(trim=TRUE)+theme_classic()
M <- M + labs(y="Species Diversity", x="Site", title="Bryophytes")
M <- M + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(M)
P <- ggplot(indic, aes(x=T_LOCATION, y=PShannon, fill=factor(CL_UNCL)))
P <- P + geom_violin(trim=TRUE)+theme_classic()
P <- P + labs(y="Species Diversity", x="Site", title="Vascular Plants")
P <- P + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(P)
#combine into one figure
combined_div <- ggarrange(all, L, M, P, common.legend = TRUE)
combined_div
ggsave("combined_div.jpeg", plot = combined_div, device = "jpeg", width = 8, height = 6, dpi = 300, scale = 1)Difference by climbing grade
anova <- aov(AllShannon~GRADE_CAT, data = indic)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## GRADE_CAT 3 4.041 1.3470 14.24 9.46e-09 ***
## Residuals 330 31.206 0.0946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = AllShannon ~ GRADE_CAT, data = indic)
##
## $GRADE_CAT
## diff lwr upr p adj
## EASY-ADVANCED 0.21365488 0.09494404 0.33236571 0.0000288
## MODERATE-ADVANCED -0.21865412 -0.45112896 0.01382071 0.0737271
## UNCLIMBED-ADVANCED 0.17954095 0.06117056 0.29791134 0.0006291
## MODERATE-EASY -0.43230900 -0.65618834 -0.20842965 0.0000059
## UNCLIMBED-EASY -0.03411393 -0.13455775 0.06632989 0.8167916
## UNCLIMBED-MODERATE 0.39819507 0.17449606 0.62189409 0.0000362anova <- aov(AllRichness~GRADE_CAT, data = indic)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## GRADE_CAT 3 202.1 67.37 11.44 3.75e-07 ***
## Residuals 330 1944.1 5.89
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = AllRichness ~ GRADE_CAT, data = indic)
##
## $GRADE_CAT
## diff lwr upr p adj
## EASY-ADVANCED 1.5746544 0.6376834 2.5116253 0.0001115
## MODERATE-ADVANCED -1.3285714 -3.1634686 0.5063257 0.2431802
## UNCLIMBED-ADVANCED 1.3539683 0.4196844 2.2882522 0.0012269
## MODERATE-EASY -2.9032258 -4.6702797 -1.1361719 0.0001682
## UNCLIMBED-EASY -0.2206861 -1.0134777 0.5721054 0.8895860
## UNCLIMBED-MODERATE 2.6825397 0.9169091 4.4481703 0.0006132#LIchens only
anova <- aov(LShannon~GRADE_CAT, data = indic)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## GRADE_CAT 3 2.44 0.8146 6.692 0.000214 ***
## Residuals 330 40.17 0.1217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LShannon ~ GRADE_CAT, data = indic)
##
## $GRADE_CAT
## diff lwr upr p adj
## EASY-ADVANCED 0.097965599 -0.03671967 0.23265087 0.2394276
## MODERATE-ADVANCED -0.303666686 -0.56742472 -0.03990865 0.0166397
## UNCLIMBED-ADVANCED 0.095803177 -0.03849584 0.23010219 0.2555723
## MODERATE-EASY -0.401632285 -0.65563817 -0.14762640 0.0003245
## UNCLIMBED-EASY -0.002162422 -0.11612256 0.11179772 0.9999577
## UNCLIMBED-MODERATE 0.399469863 0.14566858 0.65327115 0.0003500anova <- aov(LRichness~GRADE_CAT, data = indic)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## GRADE_CAT 3 87.2 29.061 5.905 0.000618 ***
## Residuals 330 1624.1 4.921
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = LRichness ~ GRADE_CAT, data = indic)
##
## $GRADE_CAT
## diff lwr upr p adj
## EASY-ADVANCED 0.7470046 -0.1093884 1.60339764 0.1115802
## MODERATE-ADVANCED -1.6285714 -3.3056705 0.04852767 0.0605945
## UNCLIMBED-ADVANCED 0.5698413 -0.2840958 1.42377833 0.3132870
## MODERATE-EASY -2.3755760 -3.9906663 -0.76048576 0.0009934
## UNCLIMBED-EASY -0.1771633 -0.9017762 0.54744947 0.9219017
## UNCLIMBED-MODERATE 2.1984127 0.5846233 3.81220206 0.0027823 #MOSSES only
anova <- aov(MShannon~GRADE_CAT, data = indic)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## GRADE_CAT 3 1.482 0.4940 5.503 0.00106 **
## Residuals 330 29.620 0.0898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MShannon ~ GRADE_CAT, data = indic)
##
## $GRADE_CAT
## diff lwr upr p adj
## EASY-ADVANCED 0.134023611 0.01836889 0.2496783 0.0156905
## MODERATE-ADVANCED -0.009614452 -0.23610442 0.2168755 0.9995280
## UNCLIMBED-ADVANCED 0.163703537 0.04838049 0.2790266 0.0016311
## MODERATE-EASY -0.143638063 -0.36175382 0.0744777 0.3250570
## UNCLIMBED-EASY 0.029679927 -0.06817805 0.1275379 0.8620765
## UNCLIMBED-MODERATE 0.173317990 -0.04462208 0.3912581 0.1707966anova <- aov(MRichness~GRADE_CAT, data = indic)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## GRADE_CAT 3 12.88 4.293 4.965 0.0022 **
## Residuals 330 285.33 0.865
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = MRichness ~ GRADE_CAT, data = indic)
##
## $GRADE_CAT
## diff lwr upr p adj
## EASY-ADVANCED 0.52188940 0.16293345 0.8808454 0.0011722
## MODERATE-ADVANCED 0.35714286 -0.34581091 1.0600966 0.5559845
## UNCLIMBED-ADVANCED 0.42857143 0.07064489 0.7864980 0.0115415
## MODERATE-EASY -0.16474654 -0.84170940 0.5122163 0.9228781
## UNCLIMBED-EASY -0.09331797 -0.39703843 0.2104025 0.8574377
## UNCLIMBED-MODERATE 0.07142857 -0.60498901 0.7478461 0.9929037#plants only
anova <- aov(PShannon~GRADE_CAT, data = indic)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## GRADE_CAT 3 0.30 0.10016 1.882 0.132
## Residuals 330 17.56 0.05322TukeyHSD(anova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PShannon ~ GRADE_CAT, data = indic)
##
## $GRADE_CAT
## diff lwr upr p adj
## EASY-ADVANCED 0.073745052 -0.01531350 0.16280361 0.1432576
## MODERATE-ADVANCED -0.007148606 -0.18155454 0.16725733 0.9995750
## UNCLIMBED-ADVANCED 0.058913853 -0.02988930 0.14771701 0.3184780
## MODERATE-EASY -0.080893658 -0.24885113 0.08706381 0.5995154
## UNCLIMBED-EASY -0.014831199 -0.09018559 0.06052319 0.9570997
## UNCLIMBED-MODERATE 0.066062459 -0.10175973 0.23388465 0.7399239anova <- aov(PRichness~GRADE_CAT, data = indic)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## GRADE_CAT 3 7.48 2.4925 3.974 0.00837 **
## Residuals 330 206.98 0.6272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(anova)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = PRichness ~ GRADE_CAT, data = indic)
##
## $GRADE_CAT
## diff lwr upr p adj
## EASY-ADVANCED 0.30576037 3.365767e-05 0.6114871 0.0499629
## MODERATE-ADVANCED -0.05714286 -6.558563e-01 0.5415706 0.9947336
## UNCLIMBED-ADVANCED 0.35555556 5.070561e-02 0.6604055 0.0147913
## MODERATE-EASY -0.36290323 -9.394799e-01 0.2136734 0.3658079
## UNCLIMBED-EASY 0.04979519 -2.088868e-01 0.3084772 0.9596981
## UNCLIMBED-MODERATE 0.41269841 -1.634138e-01 0.9888107 0.2520661figures for by climbing grade category
All taxa richness, diversity and cover by climbing grade
level_order <- c("EASY", "MODERATE", "ADVANCED", "UNCLIMBED")
#how to arranged factors of x axis in order of increasing difficulty
d <- ggplot(indic, aes(x= factor(GRADE_CAT, level = level_order), y=AllShannon, fill=CL_UNCL)) +
labs(y="Shannon Diversity", x="Climbing Grade") +
geom_violin(trim=FALSE) +
labs(title="All Taxa")+
scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))+
theme_classic()+
theme(axis.text.x = element_text(angle=45, hjust=1))+
ylim(0,3)
r<- ggplot(indic, aes(x= factor(GRADE_CAT, level = level_order), y=AllRichness, fill=CL_UNCL)) +
labs(y="Species Richness", x="Climbing Grade") +
geom_violin(trim=FALSE) +
labs(title="All Taxa")+
scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))+
theme_classic()+
theme(axis.text.x = element_text(angle=45, hjust=1))+
ylim(0,18)
a <- ggplot(indic, aes(x= factor(GRADE_CAT, level = level_order), y=AllCover, fill=CL_UNCL)) +
labs(y="Abundance", x="Climbing Grade") +
geom_violin(trim=FALSE) +
labs(title="All Taxa")+
scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))+
theme_classic()+
theme(axis.text.x = element_text(angle=45, hjust=1))
climbing_level <- ggarrange(d, r, a, ncol=3,common.legend = TRUE)## Warning: Removed 18 rows containing missing values (geom_violin).climbing_level
Richness of all taxa groups by climbing grade
theme_set(
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.text = element_text(size=11, colour = "black"),
axis.line = element_line(colour = "black"), axis.title = element_text(size=16, colour = "black"),
legend.title = element_text(size = 16, colour = "black"), legend.text = element_text(size=16, colour= "black")))
all <- ggplot(indic, aes(x= factor(GRADE_CAT, level = level_order), y=AllRichness, fill=CL_UNCL))
all <- all + geom_violin(trim=FALSE)+theme_classic()
all <- all + labs(y="Species Richness", x="Site", title="All Taxa")
all <- all + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(all)
L <- ggplot(indic, aes(x= factor(GRADE_CAT, level = level_order), y=LRichness, fill=CL_UNCL))
L <- L + geom_violin(trim=FALSE) +theme_classic()
L <- L + labs(y="Species Richness", x="Site", title="Lichens")
L <- L + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(L)
M <- ggplot(indic, aes(x= factor(GRADE_CAT, level = level_order), y=MRichness, fill=CL_UNCL))
M <- M + geom_violin(trim=FALSE)+theme_classic()
M <- M + labs(y="Species Richness", x="Site", title="Bryophytes")
M <- M + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(M)
P <- ggplot(indic, aes(x= factor(GRADE_CAT, level = level_order), y=PRichness, fill=CL_UNCL))
P <- P + geom_violin(trim=FALSE)+theme_classic()
P <- P + labs(y="Species Richness", x="Site", title="Vascular Plants")
P <- P + scale_fill_grey(name="Climbing", labels = c("Climbed", "Unclimbed"))
plot(P)
#combine into one figure
combined_rich_grade <- ggarrange(all, L, M, P, common.legend = TRUE)
combined_rich_grade 
create a summary table for richness, cover, and diveristy
indic$T_LOCATION <- as.factor((indic$T_LOCATION))
indic$CL_UNCL <- as.factor((indic$CL_UNCL))
t_all <- indic %>%
group_by(T_LOCATION, CL_UNCL) %>%
summarise(
mean_rich=mean(AllRichness), sd_rich=sd(AllRichness), min_rich=min(AllRichness), max_rich=max(AllRichness),
mean_div=mean(AllShannon), sd_div=sd(AllShannon), min_div=min(AllShannon), max_div=max(AllShannon),
mean_abun=mean(AllCover), sd_abun=sd(AllCover), min_abun=min(AllCover), max_abun=max(AllCover)
)## `summarise()` has grouped output by 'T_LOCATION'. You can override using the `.groups` argument.head(t_all)## # A tibble: 4 x 14
## # Groups: T_LOCATION [2]
## T_LOCATION CL_UNCL mean_rich sd_rich min_rich max_rich mean_div sd_div min_div
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 HB CLIMBED 6.22 1.70 3 9 1.57 0.291 0.830
## 2 HB UNCLIM~ 5.8 1.73 4 10 1.57 0.225 1.20
## 3 TR CLIMBED 7.94 2.42 2 16 1.82 0.319 0.673
## 4 TR UNCLIM~ 8.93 2.56 4 15 1.94 0.290 1.21
## # ... with 5 more variables: max_div <dbl>, mean_abun <dbl>, sd_abun <dbl>,
## # min_abun <dbl>, max_abun <dbl>t_lichen <- indic %>%
group_by(T_LOCATION, CL_UNCL) %>%
summarise(
mean_rich=mean(LRichness), sd_rich=sd(LRichness), min_rich=min(LRichness), max_rich=max(LRichness),
mean_div=mean(LShannon), sd_div=sd(LShannon), min_div=min(LShannon), max_div=max(LShannon),
mean_abun=mean(LCover), sd_abun=sd(LCover), min_abun=min(LCover), max_abun=max(LCover)
)## `summarise()` has grouped output by 'T_LOCATION'. You can override using the `.groups` argument.t_lichen$taxa <- "lichen"
t_moss <- indic %>%
group_by(T_LOCATION, CL_UNCL) %>%
summarise(
mean_rich=mean(MRichness), sd_rich=sd(MRichness), min_rich=min(MRichness), max_rich=max(MRichness),
mean_div=mean(MShannon), sd_div=sd(MShannon), min_div=min(MShannon), max_div=max(MShannon),
mean_abun=mean(MCover), sd_abun=sd(MCover), min_abun=min(MCover), max_abun=max(MCover)
)## `summarise()` has grouped output by 'T_LOCATION'. You can override using the `.groups` argument.t_moss$taxa <- "moss"
t_plants <- indic %>%
group_by(T_LOCATION, CL_UNCL) %>%
summarise(
mean_rich=mean(PRichness), sd_rich=sd(PRichness), min_rich=min(PRichness), max_rich=max(PRichness),
mean_div=mean(PShannon), sd_div=sd(PShannon), min_div=min(PShannon), max_div=max(PShannon),
mean_abun=mean(PCover), sd_abun=sd(PCover), min_abun=min(PCover), max_abun=max(PCover)
)## `summarise()` has grouped output by 'T_LOCATION'. You can override using the `.groups` argument.t_plants$taxa <- "plants"summary_stats<-
merge_dfs_overwrite_col(
t_all, t_lichen,
bycol = c("CL_UNCL", "T_LOCATION"))## Warning: `select_()` was deprecated in dplyr 0.7.0.
## Please use `select()` instead.## Warning: `rename_()` was deprecated in dplyr 0.7.0.
## Please use `rename()` instead.head(summary_stats)## # A tibble: 4 x 15
## # Groups: T_LOCATION [2]
## T_LOCATION CL_UNCL mean_rich sd_rich min_rich max_rich mean_div sd_div min_div
## <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 HB CLIMBED 6.03 1.77 3 9 1.54 0.300 0.830
## 2 HB UNCLIM~ 5.6 1.75 4 10 1.53 0.239 1.14
## 3 TR CLIMBED 6.88 2.38 0 15 1.67 0.395 0
## 4 TR UNCLIM~ 7.42 2.15 2 12 1.77 0.306 0.693
## # ... with 6 more variables: max_div <dbl>, mean_abun <dbl>, sd_abun <dbl>,
## # min_abun <dbl>, max_abun <dbl>, taxa <chr>summary_stats<-
merge_dfs_overwrite_col(
summary_stats, t_moss,
bycol = c("CL_UNCL", "T_LOCATION",
"mean_rich", "sd_rich", "min_rich", "max_rich",
"mean_div", "sd_div", "min_div", "max_div",
"mean_abun", "sd_abun", "min_abun", "max_abun"))
summary_stats<-
merge_dfs_overwrite_col(
summary_stats, t_plants,
bycol = c("CL_UNCL", "T_LOCATION"))Rank abundance curves
library(BiodiversityR)## Warning: package 'BiodiversityR' was built under R version 4.0.4## Loading required package: tcltk## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car## BiodiversityR 2.12-3: Use command BiodiversityRGUI() to launch the Graphical User Interface;
## to see changes use BiodiversityRGUI(changeLog=TRUE, backward.compatibility.messages=TRUE)library(tibble)
#load in community data and environment
data1 <- data %>% column_to_rownames(var="NAME")
com <- data1[,53:211]
head(com)## BLAFLA BLASM BLASQ BLAWHT BLBRDOT BLGRDOT BLKPDR BLWHDOT
## JIM DANDY_1_CLIMBED_1 0 0 0 0 8 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 9 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 9 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 9 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 9 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 9 0 0 0
## BRBLDOT BRBLKBDR BRBLKCRST BRGCRUST BRNFOL BRNGRCRST
## JIM DANDY_1_CLIMBED_1 0 0 0 1 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 1 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 1 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 2 0 0
## BRNGRFOL BRNREIN BRNWART BRNYELL BROPDR BUBLGUM CLAD
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0 0
## CLADBRS CLADBSTK CLADFLA CLADPIX CLADRC CLADSQ CLADSTLK
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0 0
## GRBBRD GREEREIN GREEWART GREMED GREPAPER GRESMSQ
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0
## GREYBLDOT GREYFOL GREYPDR GREYREIN GRMED GRMEDSQ GRNCIL
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0 0
## GRNCRST GRNFOL GRNPDR GRNSM GRNSQB GRNWART GRNWDE GRWIDE
## JIM DANDY_1_CLIMBED_1 0 0 3 0 6 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 4 0 9 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 2 0 3 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 5 0 4 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 3 0 0 0 0 0
## GRYBR GRYBRN JETBLK MINGREY MINTPDR PAPER PNKBLA PNKCRST
## JIM DANDY_1_CLIMBED_1 0 0 0 9 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 9 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 9 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 8 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 3 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 1 0 0 0 0
## PSYCHO PUFFHYP RAMALI RCKTRIPC REDCRST REINDEER ROCKTRP
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 0 5
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0 9
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0 3
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0 9
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0 0
## SHIELD SOILCR TANBUB TANCUP TEALCRST TNYBRNCH TOADGR
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 6 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 9 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 5 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 7 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 4 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 9 0
## TOADSK USNEA WHBLDOT WHITBUB WHITEFOL WHITREIN WHPDR
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0 1
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0 0
## YELLWART YELPDR Moss_2 Moss_3 Moss_4 Moss_5 Moss_6 Moss_7
## JIM DANDY_1_CLIMBED_1 0 1 0 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 9 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0 0 0
## Moss_8 Moss_9 Moss_10 Moss_11 Moss_12 Moss_13 Moss_14
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0 0
## Moss_15 Moss_16 And_rothii Atr_angustatum Bry_sp
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0
## Buc_venusta Cam_tallulensis Cer_purpureus Dic_heteromalla
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Dic_montanum Dic_scoparium Dic_varia Dip_apiculatum
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Dit_lineare Dit_pusillum Gri_pilifera Hup_appalachiana
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Leu_albidum Leu_glaucum Poc_juniperinum Poh_nutans
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Pol_commune Pol_juniperinum Pol_piliferum Pol_strictum
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Pse_elegans Rac_heterostichum Wei_controversa
## JIM DANDY_1_CLIMBED_1 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 1
## JIM DANDY_3_CLIMBED_1 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 1
## Ace_saccharum Agr_parennans And_virginicus.var.virginicus
## JIM DANDY_1_CLIMBED_1 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0
## Bet_alleghaniensis Car_2 Car_3 Car_4 Car_5 Car_6 Car_7
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0 0
## Car_umbellata Cor_major Den_punctilobula Dic_accumulatum
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Gal_urceolata Graminoid_1 Hyd_petiolaris Kal_buxifolia
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Kal_latifolia Kri_dandelion Nys_sylvatica Oxy_arboreum
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Par_argrocoma Pin_rigida Rho_major Rho_minus
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Rub_allegheniensis Sel_tortipila Sib_tridentata Sol_1
## JIM DANDY_1_CLIMBED_1 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0
## Sor_americana Spo_1 Sym_1 Vac_corymbosum Plant_1 Plant_3
## JIM DANDY_1_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_2_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_3_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_4_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_5_CLIMBED_1 0 0 0 0 0 0
## JIM DANDY_6_CLIMBED_1 0 0 0 0 0 0
## Plant_4 Plant_5
## JIM DANDY_1_CLIMBED_1 0 0
## JIM DANDY_2_CLIMBED_1 0 0
## JIM DANDY_3_CLIMBED_1 0 0
## JIM DANDY_4_CLIMBED_1 0 0
## JIM DANDY_5_CLIMBED_1 0 0
## JIM DANDY_6_CLIMBED_1 0 0#environmental factors: climbing and site
env <- data1[,c("CL_UNCL", "T_LOCATION")]
env$Climbing <- as.factor(env$CL_UNCL)
env$Site <- as.factor(env$T_LOCATION)
RankAbun1 <- rankabundance(com, y=env)
RankAbun1## rank abundance proportion plower pupper accumfreq
## TOADSK 1 1791 15.6 14.6 16.6 15.6
## WHPDR 2 1363 11.9 10.9 12.8 27.5
## MINGREY 3 1282 11.2 10.1 12.3 38.6
## TNYBRNCH 4 961 8.4 7.4 9.3 47.0
## BLBRDOT 5 658 5.7 4.7 6.7 52.7
## ROCKTRP 6 612 5.3 4.5 6.2 58.1
## GRNSQB 7 561 4.9 4.2 5.6 63.0
## GRNPDR 8 434 3.8 3.2 4.4 66.7
## BRGCRUST 9 401 3.5 2.8 4.2 70.2
## BRBLKBDR 10 389 3.4 2.5 4.2 73.6
## BRBLKCRST 11 264 2.3 1.5 3.1 75.9
## Moss_6 12 263 2.3 1.7 2.9 78.2
## CLADSQ 13 219 1.9 1.5 2.3 80.1
## Sel_tortipila 14 158 1.4 0.9 1.8 81.5
## PAPER 15 120 1.0 0.6 1.5 82.5
## WHITBUB 16 96 0.8 0.5 1.1 83.4
## BLWHDOT 17 91 0.8 0.4 1.2 84.2
## BLGRDOT 18 88 0.8 0.3 1.2 84.9
## GRMEDSQ 19 87 0.8 0.4 1.1 85.7
## BLKPDR 20 76 0.7 0.3 1.0 86.3
## BRBLDOT 21 75 0.7 0.3 1.0 87.0
## Moss_15 22 75 0.7 0.2 1.1 87.7
## USNEA 23 67 0.6 0.4 0.8 88.2
## GREYPDR 24 59 0.5 0.2 0.8 88.7
## GRBBRD 25 54 0.5 0.1 0.8 89.2
## Moss_5 26 53 0.5 0.2 0.7 89.7
## BRNWART 27 52 0.5 0.2 0.7 90.1
## Cam_tallulensis 28 49 0.4 0.2 0.6 90.6
## GRWIDE 29 47 0.4 0.2 0.6 91.0
## GRNWDE 30 45 0.4 0.2 0.6 91.4
## CLADBRS 31 44 0.4 0.2 0.6 91.7
## GRNCRST 32 44 0.4 0.1 0.7 92.1
## GRNWART 33 44 0.4 0.1 0.7 92.5
## Hyd_petiolaris 34 39 0.3 0.2 0.5 92.8
## JETBLK 35 38 0.3 0.1 0.6 93.2
## CLADPIX 36 35 0.3 0.2 0.5 93.5
## WHITREIN 37 33 0.3 0.1 0.5 93.8
## Wei_controversa 38 33 0.3 0.1 0.4 94.1
## CLADSTLK 39 31 0.3 0.1 0.4 94.3
## GREEREIN 40 29 0.3 0.1 0.4 94.6
## Moss_2 41 27 0.2 0.1 0.4 94.8
## Moss_10 42 27 0.2 0.0 0.4 95.1
## GRMED 43 26 0.2 0.0 0.4 95.3
## And_rothii 44 26 0.2 0.0 0.4 95.5
## GREMED 45 25 0.2 0.0 0.4 95.7
## YELLWART 46 22 0.2 0.1 0.3 95.9
## Bry_sp 47 20 0.2 0.0 0.3 96.1
## GRNFOL 48 19 0.2 0.0 0.3 96.3
## Moss_7 49 19 0.2 0.0 0.3 96.4
## Moss_8 50 19 0.2 0.0 0.3 96.6
## GRESMSQ 51 18 0.2 0.0 0.3 96.7
## Dic_montanum 52 15 0.1 0.0 0.3 96.9
## BLASQ 53 15 0.1 0.0 0.2 97.0
## GREYREIN 54 14 0.1 0.0 0.2 97.1
## Moss_11 55 14 0.1 0.0 0.2 97.2
## YELPDR 56 13 0.1 0.0 0.3 97.4
## Buc_venusta 57 13 0.1 0.0 0.2 97.5
## Agr_parennans 58 13 0.1 0.0 0.2 97.6
## TEALCRST 59 12 0.1 0.0 0.2 97.7
## Pol_juniperinum 60 12 0.1 0.0 0.2 97.8
## MINTPDR 61 12 0.1 0.0 0.2 97.9
## BUBLGUM 62 11 0.1 0.0 0.2 98.0
## WHBLDOT 63 11 0.1 0.0 0.2 98.1
## Pol_commune 64 10 0.1 0.0 0.2 98.2
## Dic_scoparium 65 7 0.1 0.0 0.1 98.2
## TANCUP 66 7 0.1 0.0 0.1 98.3
## GREPAPER 67 7 0.1 0.0 0.1 98.4
## PNKCRST 68 7 0.1 0.0 0.1 98.4
## GRNSM 69 6 0.1 0.0 0.1 98.5
## Moss_12 70 6 0.1 0.0 0.1 98.5
## Dic_varia 71 6 0.1 0.0 0.1 98.6
## Dit_lineare 72 6 0.1 0.0 0.1 98.6
## Leu_albidum 73 6 0.1 0.0 0.1 98.7
## Car_umbellata 74 6 0.1 0.0 0.1 98.7
## Dic_accumulatum 75 6 0.1 0.0 0.1 98.8
## Sol_1 76 6 0.1 0.0 0.1 98.8
## RAMALI 77 5 0.0 0.0 0.1 98.9
## Pol_piliferum 78 5 0.0 0.0 0.1 98.9
## Moss_13 79 5 0.0 0.0 0.1 99.0
## BLAFLA 80 5 0.0 0.0 0.1 99.0
## Moss_4 81 5 0.0 0.0 0.1 99.1
## Gal_urceolata 82 5 0.0 0.0 0.1 99.1
## Car_2 83 4 0.0 0.0 0.1 99.1
## Graminoid_1 84 4 0.0 0.0 0.1 99.2
## Kal_buxifolia 85 4 0.0 0.0 0.1 99.2
## Rho_minus 86 4 0.0 0.0 0.1 99.2
## Cer_purpureus 87 4 0.0 0.0 0.1 99.3
## GREEWART 88 4 0.0 0.0 0.1 99.3
## Oxy_arboreum 89 4 0.0 0.0 0.1 99.3
## Leu_glaucum 90 4 0.0 0.0 0.1 99.4
## PSYCHO 91 3 0.0 0.0 0.1 99.4
## SOILCR 92 3 0.0 0.0 0.1 99.4
## WHITEFOL 93 3 0.0 0.0 0.1 99.5
## Pin_rigida 94 3 0.0 0.0 0.1 99.5
## Dic_heteromalla 95 3 0.0 0.0 0.1 99.5
## Moss_16 96 3 0.0 0.0 0.1 99.5
## GRYBR 97 3 0.0 0.0 0.1 99.6
## Pol_strictum 98 3 0.0 0.0 0.1 99.6
## Pse_elegans 99 3 0.0 0.0 0.1 99.6
## BLAWHT 100 3 0.0 0.0 0.1 99.6
## Kal_latifolia 101 3 0.0 0.0 0.1 99.7
## Poh_nutans 102 3 0.0 0.0 0.1 99.7
## BRNFOL 103 2 0.0 0.0 0.1 99.7
## BROPDR 104 2 0.0 0.0 0.1 99.7
## CLADBSTK 105 2 0.0 0.0 0.0 99.7
## TANBUB 106 2 0.0 0.0 0.1 99.8
## Bet_alleghaniensis 107 2 0.0 0.0 0.1 99.8
## Car_7 108 2 0.0 0.0 0.1 99.8
## Moss_9 109 2 0.0 0.0 0.1 99.8
## Car_3 110 2 0.0 0.0 0.1 99.8
## PNKBLA 111 2 0.0 0.0 0.1 99.9
## BRNGRFOL 112 1 0.0 0.0 0.0 99.9
## BRNYELL 113 1 0.0 0.0 0.0 99.9
## CLAD 114 1 0.0 0.0 0.0 99.9
## GRNCIL 115 1 0.0 0.0 0.0 99.9
## REDCRST 116 1 0.0 0.0 0.0 99.9
## SHIELD 117 1 0.0 0.0 0.0 99.9
## Car_5 118 1 0.0 0.0 0.0 99.9
## Car_6 119 1 0.0 0.0 0.0 99.9
## Rho_major 120 1 0.0 0.0 0.0 99.9
## Rub_allegheniensis 121 1 0.0 0.0 0.0 99.9
## Plant_5 122 1 0.0 0.0 0.0 99.9
## Moss_14 123 1 0.0 0.0 0.0 100.0
## CLADRC 124 1 0.0 0.0 0.0 100.0
## Rac_heterostichum 125 1 0.0 0.0 0.0 100.0
## BLASM 126 1 0.0 0.0 0.0 100.0
## Cor_major 127 1 0.0 0.0 0.0 100.0
## Nys_sylvatica 128 1 0.0 0.0 0.0 100.0
## BRNGRCRST 129 0 0.0 0.0 0.0 100.0
## BRNREIN 130 0 0.0 0.0 0.0 100.0
## CLADFLA 131 0 0.0 0.0 0.0 100.0
## PUFFHYP 132 0 0.0 0.0 0.0 100.0
## RCKTRIPC 133 0 0.0 0.0 0.0 100.0
## REINDEER 134 0 0.0 0.0 0.0 100.0
## TOADGR 135 0 0.0 0.0 0.0 100.0
## Gri_pilifera 136 0 0.0 0.0 0.0 100.0
## Car_4 137 0 0.0 0.0 0.0 100.0
## Den_punctilobula 138 0 0.0 0.0 0.0 100.0
## Par_argrocoma 139 0 0.0 0.0 0.0 100.0
## Sib_tridentata 140 0 0.0 0.0 0.0 100.0
## Spo_1 141 0 0.0 0.0 0.0 100.0
## Sym_1 142 0 0.0 0.0 0.0 100.0
## Vac_corymbosum 143 0 0.0 0.0 0.0 100.0
## Plant_1 144 0 0.0 0.0 0.0 100.0
## Plant_3 145 0 0.0 0.0 0.0 100.0
## Plant_4 146 0 0.0 0.0 0.0 100.0
## Dip_apiculatum 147 0 0.0 0.0 0.0 100.0
## Atr_angustatum 148 0 0.0 0.0 0.0 100.0
## GRYBRN 149 0 0.0 0.0 0.0 100.0
## Moss_3 150 0 0.0 0.0 0.0 100.0
## Ace_saccharum 151 0 0.0 0.0 0.0 100.0
## And_virginicus.var.virginicus 152 0 0.0 0.0 0.0 100.0
## Sor_americana 153 0 0.0 0.0 0.0 100.0
## GREYBLDOT 154 0 0.0 0.0 0.0 100.0
## GREYFOL 155 0 0.0 0.0 0.0 100.0
## Dit_pusillum 156 0 0.0 0.0 0.0 100.0
## Hup_appalachiana 157 0 0.0 0.0 0.0 100.0
## Kri_dandelion 158 0 0.0 0.0 0.0 100.0
## Poc_juniperinum 159 0 0.0 0.0 0.0 100.0
## logabun rankfreq
## TOADSK 3.3 0.6
## WHPDR 3.1 1.3
## MINGREY 3.1 1.9
## TNYBRNCH 3.0 2.5
## BLBRDOT 2.8 3.1
## ROCKTRP 2.8 3.8
## GRNSQB 2.7 4.4
## GRNPDR 2.6 5.0
## BRGCRUST 2.6 5.7
## BRBLKBDR 2.6 6.3
## BRBLKCRST 2.4 6.9
## Moss_6 2.4 7.5
## CLADSQ 2.3 8.2
## Sel_tortipila 2.2 8.8
## PAPER 2.1 9.4
## WHITBUB 2.0 10.1
## BLWHDOT 2.0 10.7
## BLGRDOT 1.9 11.3
## GRMEDSQ 1.9 11.9
## BLKPDR 1.9 12.6
## BRBLDOT 1.9 13.2
## Moss_15 1.9 13.8
## USNEA 1.8 14.5
## GREYPDR 1.8 15.1
## GRBBRD 1.7 15.7
## Moss_5 1.7 16.4
## BRNWART 1.7 17.0
## Cam_tallulensis 1.7 17.6
## GRWIDE 1.7 18.2
## GRNWDE 1.7 18.9
## CLADBRS 1.6 19.5
## GRNCRST 1.6 20.1
## GRNWART 1.6 20.8
## Hyd_petiolaris 1.6 21.4
## JETBLK 1.6 22.0
## CLADPIX 1.5 22.6
## WHITREIN 1.5 23.3
## Wei_controversa 1.5 23.9
## CLADSTLK 1.5 24.5
## GREEREIN 1.5 25.2
## Moss_2 1.4 25.8
## Moss_10 1.4 26.4
## GRMED 1.4 27.0
## And_rothii 1.4 27.7
## GREMED 1.4 28.3
## YELLWART 1.3 28.9
## Bry_sp 1.3 29.6
## GRNFOL 1.3 30.2
## Moss_7 1.3 30.8
## Moss_8 1.3 31.4
## GRESMSQ 1.3 32.1
## Dic_montanum 1.2 32.7
## BLASQ 1.2 33.3
## GREYREIN 1.1 34.0
## Moss_11 1.1 34.6
## YELPDR 1.1 35.2
## Buc_venusta 1.1 35.8
## Agr_parennans 1.1 36.5
## TEALCRST 1.1 37.1
## Pol_juniperinum 1.1 37.7
## MINTPDR 1.1 38.4
## BUBLGUM 1.0 39.0
## WHBLDOT 1.0 39.6
## Pol_commune 1.0 40.3
## Dic_scoparium 0.8 40.9
## TANCUP 0.8 41.5
## GREPAPER 0.8 42.1
## PNKCRST 0.8 42.8
## GRNSM 0.8 43.4
## Moss_12 0.8 44.0
## Dic_varia 0.8 44.7
## Dit_lineare 0.8 45.3
## Leu_albidum 0.8 45.9
## Car_umbellata 0.8 46.5
## Dic_accumulatum 0.8 47.2
## Sol_1 0.8 47.8
## RAMALI 0.7 48.4
## Pol_piliferum 0.7 49.1
## Moss_13 0.7 49.7
## BLAFLA 0.7 50.3
## Moss_4 0.7 50.9
## Gal_urceolata 0.7 51.6
## Car_2 0.6 52.2
## Graminoid_1 0.6 52.8
## Kal_buxifolia 0.6 53.5
## Rho_minus 0.6 54.1
## Cer_purpureus 0.6 54.7
## GREEWART 0.6 55.3
## Oxy_arboreum 0.6 56.0
## Leu_glaucum 0.6 56.6
## PSYCHO 0.5 57.2
## SOILCR 0.5 57.9
## WHITEFOL 0.5 58.5
## Pin_rigida 0.5 59.1
## Dic_heteromalla 0.5 59.7
## Moss_16 0.5 60.4
## GRYBR 0.5 61.0
## Pol_strictum 0.5 61.6
## Pse_elegans 0.5 62.3
## BLAWHT 0.5 62.9
## Kal_latifolia 0.5 63.5
## Poh_nutans 0.5 64.2
## BRNFOL 0.3 64.8
## BROPDR 0.3 65.4
## CLADBSTK 0.3 66.0
## TANBUB 0.3 66.7
## Bet_alleghaniensis 0.3 67.3
## Car_7 0.3 67.9
## Moss_9 0.3 68.6
## Car_3 0.3 69.2
## PNKBLA 0.3 69.8
## BRNGRFOL 0.0 70.4
## BRNYELL 0.0 71.1
## CLAD 0.0 71.7
## GRNCIL 0.0 72.3
## REDCRST 0.0 73.0
## SHIELD 0.0 73.6
## Car_5 0.0 74.2
## Car_6 0.0 74.8
## Rho_major 0.0 75.5
## Rub_allegheniensis 0.0 76.1
## Plant_5 0.0 76.7
## Moss_14 0.0 77.4
## CLADRC 0.0 78.0
## Rac_heterostichum 0.0 78.6
## BLASM 0.0 79.2
## Cor_major 0.0 79.9
## Nys_sylvatica 0.0 80.5
## BRNGRCRST -Inf 81.1
## BRNREIN -Inf 81.8
## CLADFLA -Inf 82.4
## PUFFHYP -Inf 83.0
## RCKTRIPC -Inf 83.6
## REINDEER -Inf 84.3
## TOADGR -Inf 84.9
## Gri_pilifera -Inf 85.5
## Car_4 -Inf 86.2
## Den_punctilobula -Inf 86.8
## Par_argrocoma -Inf 87.4
## Sib_tridentata -Inf 88.1
## Spo_1 -Inf 88.7
## Sym_1 -Inf 89.3
## Vac_corymbosum -Inf 89.9
## Plant_1 -Inf 90.6
## Plant_3 -Inf 91.2
## Plant_4 -Inf 91.8
## Dip_apiculatum -Inf 92.5
## Atr_angustatum -Inf 93.1
## GRYBRN -Inf 93.7
## Moss_3 -Inf 94.3
## Ace_saccharum -Inf 95.0
## And_virginicus.var.virginicus -Inf 95.6
## Sor_americana -Inf 96.2
## GREYBLDOT -Inf 96.9
## GREYFOL -Inf 97.5
## Dit_pusillum -Inf 98.1
## Hup_appalachiana -Inf 98.7
## Kri_dandelion -Inf 99.4
## Poc_juniperinum -Inf 100.0rankabunplot(RankAbun1, scale='abundance', addit=FALSE, specnames=c(1,2,3))
Rank abundance curves by climbing and site
rankabuncomp(com, y=env, factor="Site", scale='proportion', legend=FALSE)
## Grouping species labelit rank abundance proportion plower pupper
## 1 HB TOADSK TRUE 1 335 18.9 16.2 21.5
## 2 HB BRBLKBDR TRUE 2 301 16.9 14.0 19.9
## 3 HB WHPDR TRUE 3 281 15.8 13.6 18.0
## 4 HB BRBLKCRST FALSE 4 255 14.4 10.7 18.0
## 5 HB BLBRDOT FALSE 5 105 5.9 2.9 8.9
## 6 HB BLGRDOT FALSE 6 81 4.6 2.1 7.0
## 7 HB CLADSQ FALSE 7 62 3.5 2.3 4.7
## 8 HB GRBBRD FALSE 8 54 3.0 0.8 5.3
## 9 HB PAPER FALSE 9 52 2.9 1.2 4.7
## 10 HB BLWHDOT FALSE 10 36 2.0 0.7 3.3
## 11 HB TNYBRNCH FALSE 11 28 1.6 0.6 2.5
## 12 HB ROCKTRP FALSE 12 24 1.4 0.2 2.5
## 13 HB MINGREY FALSE 13 17 1.0 -0.1 2.0
## 14 HB BRGCRUST FALSE 14 16 0.9 0.2 1.6
## 15 HB Dic_montanum FALSE 15 14 0.8 0.0 1.6
## 16 HB Sel_tortipila FALSE 16 13 0.7 -0.3 1.7
## 17 HB YELLWART FALSE 17 12 0.7 0.0 1.4
## 18 HB TEALCRST FALSE 18 11 0.6 -0.2 1.5
## 19 HB CLADSTLK FALSE 19 9 0.5 0.1 0.9
## 20 HB MINTPDR FALSE 20 9 0.5 0.0 1.0
## 21 HB GRMEDSQ FALSE 21 8 0.5 0.0 0.9
## 22 HB USNEA FALSE 22 8 0.5 0.0 0.9
## 23 HB GREPAPER FALSE 23 7 0.4 -0.1 0.9
## 24 HB GRNPDR FALSE 24 6 0.3 0.1 0.6
## 25 HB TANCUP FALSE 25 6 0.3 -0.1 0.8
## 26 HB Pol_commune FALSE 26 5 0.3 -0.3 0.8
## 27 HB CLADPIX FALSE 27 4 0.2 0.0 0.4
## 28 HB GRNSQB FALSE 28 3 0.2 -0.1 0.4
## 29 HB JETBLK FALSE 29 3 0.2 -0.1 0.4
## 30 HB RAMALI FALSE 30 3 0.2 -0.2 0.5
## 31 HB TANBUB FALSE 31 2 0.1 -0.1 0.3
## 32 HB WHITBUB FALSE 32 2 0.1 -0.1 0.3
## 33 HB Moss_9 FALSE 33 2 0.1 -0.1 0.3
## 34 HB Moss_8 FALSE 34 1 0.1 -0.1 0.2
## 35 HB Leu_albidum FALSE 35 1 0.1 -0.1 0.2
## 36 HB REDCRST FALSE 36 1 0.1 -0.1 0.2
## 37 TR TOADSK TRUE 1 1456 15.0 13.9 16.1
## 38 TR MINGREY TRUE 2 1265 13.0 11.9 14.2
## 39 TR WHPDR TRUE 3 1082 11.1 10.1 12.2
## 40 TR TNYBRNCH FALSE 4 933 9.6 8.6 10.7
## 41 TR ROCKTRP FALSE 5 588 6.1 5.1 7.1
## 42 TR GRNSQB FALSE 6 558 5.7 4.9 6.6
## 43 TR BLBRDOT FALSE 7 553 5.7 4.7 6.7
## 44 TR GRNPDR FALSE 8 428 4.4 3.7 5.1
## 45 TR BRGCRUST FALSE 9 385 4.0 3.2 4.8
## 46 TR Moss_6 FALSE 10 263 2.7 2.0 3.4
## 47 TR CLADSQ FALSE 11 157 1.6 1.2 2.0
## 48 TR Sel_tortipila FALSE 12 145 1.5 1.0 2.0
## 49 TR WHITBUB FALSE 13 94 1.0 0.6 1.3
## 50 TR BRBLKBDR FALSE 14 88 0.9 0.5 1.4
## 51 TR GRMEDSQ FALSE 15 79 0.8 0.4 1.2
## 52 TR BLKPDR FALSE 16 76 0.8 0.3 1.2
## 53 TR BRBLDOT FALSE 17 75 0.8 0.3 1.2
## 54 TR Moss_15 FALSE 18 75 0.8 0.3 1.3
## 55 TR PAPER FALSE 19 68 0.7 0.3 1.1
## 56 TR USNEA FALSE 20 59 0.6 0.4 0.9
## 57 TR GREYPDR FALSE 21 59 0.6 0.2 1.0
## 58 TR BLWHDOT FALSE 22 55 0.6 0.2 1.0
## 59 TR Moss_5 FALSE 23 53 0.5 0.2 0.9
## 60 TR BRNWART FALSE 24 52 0.5 0.2 0.9
## 61 TR Cam_tallulensis FALSE 25 49 0.5 0.3 0.7
## 62 TR GRWIDE FALSE 26 47 0.5 0.2 0.7
## 63 TR GRNWDE FALSE 27 45 0.5 0.2 0.7
## 64 TR CLADBRS FALSE 28 44 0.5 0.2 0.7
## 65 TR GRNCRST FALSE 29 44 0.5 0.1 0.8
## 66 TR GRNWART FALSE 30 44 0.5 0.1 0.8
## 67 TR Hyd_petiolaris FALSE 31 39 0.4 0.2 0.6
## 68 TR JETBLK FALSE 32 35 0.4 0.0 0.7
## 69 TR WHITREIN FALSE 33 33 0.3 0.1 0.6
## 70 TR Wei_controversa FALSE 34 33 0.3 0.2 0.5
## 71 TR CLADPIX FALSE 35 31 0.3 0.1 0.5
## 72 TR GREEREIN FALSE 36 29 0.3 0.1 0.5
## 73 TR Moss_2 FALSE 37 27 0.3 0.1 0.5
## 74 TR Moss_10 FALSE 38 27 0.3 0.1 0.5
## 75 TR GRMED FALSE 39 26 0.3 0.0 0.5
## 76 TR And_rothii FALSE 40 26 0.3 0.0 0.5
## 77 TR GREMED FALSE 41 25 0.3 0.0 0.5
## 78 TR CLADSTLK FALSE 42 22 0.2 0.1 0.4
## 79 TR Bry_sp FALSE 43 20 0.2 0.0 0.4
## 80 TR GRNFOL FALSE 44 19 0.2 0.0 0.4
## 81 TR Moss_7 FALSE 45 19 0.2 0.0 0.4
## 82 TR Moss_8 FALSE 46 18 0.2 0.0 0.3
## 83 TR GRESMSQ FALSE 47 18 0.2 0.0 0.4
## 84 TR BLASQ FALSE 48 15 0.2 0.0 0.3
## 85 TR Moss_11 FALSE 49 14 0.1 0.0 0.3
## 86 TR GREYREIN FALSE 50 14 0.1 0.0 0.3
## 87 TR Agr_parennans FALSE 51 13 0.1 0.0 0.3
## 88 TR Buc_venusta FALSE 52 13 0.1 0.0 0.3
## 89 TR YELPDR FALSE 53 13 0.1 -0.1 0.3
## 90 TR Pol_juniperinum FALSE 54 12 0.1 0.0 0.3
## 91 TR BUBLGUM FALSE 55 11 0.1 0.0 0.2
## 92 TR WHBLDOT FALSE 56 11 0.1 0.0 0.2
## 93 TR YELLWART FALSE 57 10 0.1 0.0 0.2
## 94 TR BRBLKCRST FALSE 58 9 0.1 0.0 0.2
## 95 TR BLGRDOT FALSE 59 7 0.1 0.0 0.2
## 96 TR PNKCRST FALSE 60 7 0.1 0.0 0.1
## 97 TR Dic_scoparium FALSE 61 7 0.1 0.0 0.1
## 98 TR GRNSM FALSE 62 6 0.1 0.0 0.1
## 99 TR Dit_lineare FALSE 63 6 0.1 0.0 0.1
## 100 TR Dic_accumulatum FALSE 64 6 0.1 0.0 0.1
## 101 TR Sol_1 FALSE 65 6 0.1 0.0 0.2
## 102 TR Moss_12 FALSE 66 6 0.1 0.0 0.1
## 103 TR Dic_varia FALSE 67 6 0.1 0.0 0.1
## 104 TR Car_umbellata FALSE 68 6 0.1 0.0 0.1
## 105 TR Pol_commune FALSE 69 5 0.1 0.0 0.1
## 106 TR Moss_13 FALSE 70 5 0.1 0.0 0.1
## 107 TR Pol_piliferum FALSE 71 5 0.1 -0.1 0.2
## 108 TR BLAFLA FALSE 72 5 0.1 0.0 0.1
## 109 TR Moss_4 FALSE 73 5 0.1 0.0 0.2
## 110 TR Leu_albidum FALSE 74 5 0.1 0.0 0.1
## 111 TR Gal_urceolata FALSE 75 5 0.1 -0.1 0.2
## 112 TR Car_2 FALSE 76 4 0.0 0.0 0.1
## 113 TR Graminoid_1 FALSE 77 4 0.0 0.0 0.1
## 114 TR Kal_buxifolia FALSE 78 4 0.0 0.0 0.1
## 115 TR Rho_minus FALSE 79 4 0.0 0.0 0.1
## 116 TR Cer_purpureus FALSE 80 4 0.0 0.0 0.1
## 117 TR Oxy_arboreum FALSE 81 4 0.0 0.0 0.1
## 118 TR GREEWART FALSE 82 4 0.0 0.0 0.1
## 119 TR Leu_glaucum FALSE 83 4 0.0 0.0 0.1
## 120 TR BLAWHT FALSE 84 3 0.0 0.0 0.1
## 121 TR PSYCHO FALSE 85 3 0.0 0.0 0.1
## 122 TR SOILCR FALSE 86 3 0.0 0.0 0.1
## 123 TR WHITEFOL FALSE 87 3 0.0 0.0 0.1
## 124 TR Poh_nutans FALSE 88 3 0.0 0.0 0.1
## 125 TR Kal_latifolia FALSE 89 3 0.0 0.0 0.1
## 126 TR Pin_rigida FALSE 90 3 0.0 0.0 0.1
## 127 TR Dic_heteromalla FALSE 91 3 0.0 0.0 0.1
## 128 TR Moss_16 FALSE 92 3 0.0 0.0 0.1
## 129 TR GRYBR FALSE 93 3 0.0 0.0 0.1
## 130 TR Pol_strictum FALSE 94 3 0.0 0.0 0.1
## 131 TR Pse_elegans FALSE 95 3 0.0 0.0 0.1
## 132 TR MINTPDR FALSE 96 3 0.0 0.0 0.1
## 133 TR BRNFOL FALSE 97 2 0.0 0.0 0.1
## 134 TR BROPDR FALSE 98 2 0.0 0.0 0.1
## 135 TR PNKBLA FALSE 99 2 0.0 0.0 0.1
## 136 TR RAMALI FALSE 100 2 0.0 0.0 0.1
## 137 TR CLADBSTK FALSE 101 2 0.0 0.0 0.0
## 138 TR Bet_alleghaniensis FALSE 102 2 0.0 0.0 0.1
## 139 TR Car_3 FALSE 103 2 0.0 0.0 0.1
## 140 TR Car_7 FALSE 104 2 0.0 0.0 0.1
## 141 TR BRNGRFOL FALSE 105 1 0.0 0.0 0.0
## 142 TR BRNYELL FALSE 106 1 0.0 0.0 0.0
## 143 TR CLAD FALSE 107 1 0.0 0.0 0.0
## 144 TR GRNCIL FALSE 108 1 0.0 0.0 0.0
## 145 TR SHIELD FALSE 109 1 0.0 0.0 0.0
## 146 TR TANCUP FALSE 110 1 0.0 0.0 0.0
## 147 TR TEALCRST FALSE 111 1 0.0 0.0 0.0
## 148 TR Car_6 FALSE 112 1 0.0 0.0 0.0
## 149 TR Cor_major FALSE 113 1 0.0 0.0 0.0
## 150 TR Nys_sylvatica FALSE 114 1 0.0 0.0 0.0
## 151 TR Rho_major FALSE 115 1 0.0 0.0 0.0
## 152 TR Rub_allegheniensis FALSE 116 1 0.0 0.0 0.0
## 153 TR Plant_5 FALSE 117 1 0.0 0.0 0.0
## 154 TR Dic_montanum FALSE 118 1 0.0 0.0 0.0
## 155 TR Moss_14 FALSE 119 1 0.0 0.0 0.0
## 156 TR BLASM FALSE 120 1 0.0 0.0 0.0
## 157 TR CLADRC FALSE 121 1 0.0 0.0 0.0
## 158 TR Rac_heterostichum FALSE 122 1 0.0 0.0 0.0
## 159 TR Car_5 FALSE 123 1 0.0 0.0 0.0
## accumfreq logabun rankfreq
## 1 18.9 2.5 2.8
## 2 35.8 2.5 5.6
## 3 51.6 2.4 8.3
## 4 66.0 2.4 11.1
## 5 71.9 2.0 13.9
## 6 76.4 1.9 16.7
## 7 79.9 1.8 19.4
## 8 82.9 1.7 22.2
## 9 85.9 1.7 25.0
## 10 87.9 1.6 27.8
## 11 89.5 1.4 30.6
## 12 90.8 1.4 33.3
## 13 91.8 1.2 36.1
## 14 92.7 1.2 38.9
## 15 93.5 1.1 41.7
## 16 94.2 1.1 44.4
## 17 94.9 1.1 47.2
## 18 95.5 1.0 50.0
## 19 96.0 1.0 52.8
## 20 96.5 1.0 55.6
## 21 97.0 0.9 58.3
## 22 97.4 0.9 61.1
## 23 97.8 0.8 63.9
## 24 98.1 0.8 66.7
## 25 98.5 0.8 69.4
## 26 98.8 0.7 72.2
## 27 99.0 0.6 75.0
## 28 99.2 0.5 77.8
## 29 99.3 0.5 80.6
## 30 99.5 0.5 83.3
## 31 99.6 0.3 86.1
## 32 99.7 0.3 88.9
## 33 99.8 0.3 91.7
## 34 99.9 0.0 94.4
## 35 99.9 0.0 97.2
## 36 100.0 0.0 100.0
## 37 15.0 3.2 0.8
## 38 28.0 3.1 1.6
## 39 39.2 3.0 2.4
## 40 48.8 3.0 3.3
## 41 54.9 2.8 4.1
## 42 60.6 2.7 4.9
## 43 66.3 2.7 5.7
## 44 70.7 2.6 6.5
## 45 74.7 2.6 7.3
## 46 77.4 2.4 8.1
## 47 79.0 2.2 8.9
## 48 80.5 2.2 9.8
## 49 81.5 2.0 10.6
## 50 82.4 1.9 11.4
## 51 83.2 1.9 12.2
## 52 84.0 1.9 13.0
## 53 84.8 1.9 13.8
## 54 85.5 1.9 14.6
## 55 86.2 1.8 15.4
## 56 86.8 1.8 16.3
## 57 87.4 1.8 17.1
## 58 88.0 1.7 17.9
## 59 88.6 1.7 18.7
## 60 89.1 1.7 19.5
## 61 89.6 1.7 20.3
## 62 90.1 1.7 21.1
## 63 90.5 1.7 22.0
## 64 91.0 1.6 22.8
## 65 91.4 1.6 23.6
## 66 91.9 1.6 24.4
## 67 92.3 1.6 25.2
## 68 92.7 1.5 26.0
## 69 93.0 1.5 26.8
## 70 93.3 1.5 27.6
## 71 93.7 1.5 28.5
## 72 94.0 1.5 29.3
## 73 94.2 1.4 30.1
## 74 94.5 1.4 30.9
## 75 94.8 1.4 31.7
## 76 95.1 1.4 32.5
## 77 95.3 1.4 33.3
## 78 95.5 1.3 34.1
## 79 95.7 1.3 35.0
## 80 95.9 1.3 35.8
## 81 96.1 1.3 36.6
## 82 96.3 1.3 37.4
## 83 96.5 1.3 38.2
## 84 96.7 1.2 39.0
## 85 96.8 1.1 39.8
## 86 97.0 1.1 40.7
## 87 97.1 1.1 41.5
## 88 97.2 1.1 42.3
## 89 97.4 1.1 43.1
## 90 97.5 1.1 43.9
## 91 97.6 1.0 44.7
## 92 97.7 1.0 45.5
## 93 97.8 1.0 46.3
## 94 97.9 1.0 47.2
## 95 98.0 0.8 48.0
## 96 98.0 0.8 48.8
## 97 98.1 0.8 49.6
## 98 98.2 0.8 50.4
## 99 98.2 0.8 51.2
## 100 98.3 0.8 52.0
## 101 98.4 0.8 52.8
## 102 98.4 0.8 53.7
## 103 98.5 0.8 54.5
## 104 98.5 0.8 55.3
## 105 98.6 0.7 56.1
## 106 98.7 0.7 56.9
## 107 98.7 0.7 57.7
## 108 98.8 0.7 58.5
## 109 98.8 0.7 59.3
## 110 98.9 0.7 60.2
## 111 98.9 0.7 61.0
## 112 98.9 0.6 61.8
## 113 99.0 0.6 62.6
## 114 99.0 0.6 63.4
## 115 99.1 0.6 64.2
## 116 99.1 0.6 65.0
## 117 99.2 0.6 65.9
## 118 99.2 0.6 66.7
## 119 99.2 0.6 67.5
## 120 99.3 0.5 68.3
## 121 99.3 0.5 69.1
## 122 99.3 0.5 69.9
## 123 99.4 0.5 70.7
## 124 99.4 0.5 71.5
## 125 99.4 0.5 72.4
## 126 99.5 0.5 73.2
## 127 99.5 0.5 74.0
## 128 99.5 0.5 74.8
## 129 99.5 0.5 75.6
## 130 99.6 0.5 76.4
## 131 99.6 0.5 77.2
## 132 99.6 0.5 78.0
## 133 99.7 0.3 78.9
## 134 99.7 0.3 79.7
## 135 99.7 0.3 80.5
## 136 99.7 0.3 81.3
## 137 99.7 0.3 82.1
## 138 99.8 0.3 82.9
## 139 99.8 0.3 83.7
## 140 99.8 0.3 84.6
## 141 99.8 0.0 85.4
## 142 99.8 0.0 86.2
## 143 99.8 0.0 87.0
## 144 99.8 0.0 87.8
## 145 99.9 0.0 88.6
## 146 99.9 0.0 89.4
## 147 99.9 0.0 90.2
## 148 99.9 0.0 91.1
## 149 99.9 0.0 91.9
## 150 99.9 0.0 92.7
## 151 99.9 0.0 93.5
## 152 99.9 0.0 94.3
## 153 99.9 0.0 95.1
## 154 99.9 0.0 95.9
## 155 100.0 0.0 96.7
## 156 100.0 0.0 97.6
## 157 100.0 0.0 98.4
## 158 100.0 0.0 99.2
## 159 100.0 0.0 100.0RA.Site <- rankabuncomp(com, y=env, factor='Site',
return.data=TRUE, specnames=c(1:10), legend=FALSE)
RA.Site## Grouping species labelit rank abundance proportion plower pupper
## 1 HB TOADSK TRUE 1 335 18.9 16.2 21.5
## 2 HB BRBLKBDR TRUE 2 301 16.9 14.0 19.9
## 3 HB WHPDR TRUE 3 281 15.8 13.6 18.0
## 4 HB BRBLKCRST TRUE 4 255 14.4 10.7 18.0
## 5 HB BLBRDOT TRUE 5 105 5.9 2.9 8.9
## 6 HB BLGRDOT TRUE 6 81 4.6 2.1 7.0
## 7 HB CLADSQ TRUE 7 62 3.5 2.3 4.7
## 8 HB GRBBRD TRUE 8 54 3.0 0.8 5.3
## 9 HB PAPER TRUE 9 52 2.9 1.2 4.7
## 10 HB BLWHDOT TRUE 10 36 2.0 0.7 3.3
## 11 HB TNYBRNCH FALSE 11 28 1.6 0.6 2.5
## 12 HB ROCKTRP FALSE 12 24 1.4 0.2 2.5
## 13 HB MINGREY FALSE 13 17 1.0 -0.1 2.0
## 14 HB BRGCRUST FALSE 14 16 0.9 0.2 1.6
## 15 HB Dic_montanum FALSE 15 14 0.8 0.0 1.6
## 16 HB Sel_tortipila FALSE 16 13 0.7 -0.3 1.7
## 17 HB YELLWART FALSE 17 12 0.7 0.0 1.4
## 18 HB TEALCRST FALSE 18 11 0.6 -0.2 1.5
## 19 HB CLADSTLK FALSE 19 9 0.5 0.1 0.9
## 20 HB MINTPDR FALSE 20 9 0.5 0.0 1.0
## 21 HB GRMEDSQ FALSE 21 8 0.5 0.0 0.9
## 22 HB USNEA FALSE 22 8 0.5 0.0 0.9
## 23 HB GREPAPER FALSE 23 7 0.4 -0.1 0.9
## 24 HB GRNPDR FALSE 24 6 0.3 0.1 0.6
## 25 HB TANCUP FALSE 25 6 0.3 -0.1 0.8
## 26 HB Pol_commune FALSE 26 5 0.3 -0.3 0.8
## 27 HB CLADPIX FALSE 27 4 0.2 0.0 0.4
## 28 HB GRNSQB FALSE 28 3 0.2 -0.1 0.4
## 29 HB JETBLK FALSE 29 3 0.2 -0.1 0.4
## 30 HB RAMALI FALSE 30 3 0.2 -0.2 0.5
## 31 HB TANBUB FALSE 31 2 0.1 -0.1 0.3
## 32 HB WHITBUB FALSE 32 2 0.1 -0.1 0.3
## 33 HB Moss_9 FALSE 33 2 0.1 -0.1 0.3
## 34 HB Moss_8 FALSE 34 1 0.1 -0.1 0.2
## 35 HB Leu_albidum FALSE 35 1 0.1 -0.1 0.2
## 36 HB REDCRST FALSE 36 1 0.1 -0.1 0.2
## 37 TR TOADSK TRUE 1 1456 15.0 13.9 16.1
## 38 TR MINGREY TRUE 2 1265 13.0 11.9 14.2
## 39 TR WHPDR TRUE 3 1082 11.1 10.1 12.2
## 40 TR TNYBRNCH TRUE 4 933 9.6 8.6 10.7
## 41 TR ROCKTRP TRUE 5 588 6.1 5.1 7.1
## 42 TR GRNSQB TRUE 6 558 5.7 4.9 6.6
## 43 TR BLBRDOT TRUE 7 553 5.7 4.7 6.7
## 44 TR GRNPDR TRUE 8 428 4.4 3.7 5.1
## 45 TR BRGCRUST TRUE 9 385 4.0 3.2 4.8
## 46 TR Moss_6 TRUE 10 263 2.7 2.0 3.4
## 47 TR CLADSQ FALSE 11 157 1.6 1.2 2.0
## 48 TR Sel_tortipila FALSE 12 145 1.5 1.0 2.0
## 49 TR WHITBUB FALSE 13 94 1.0 0.6 1.3
## 50 TR BRBLKBDR FALSE 14 88 0.9 0.5 1.4
## 51 TR GRMEDSQ FALSE 15 79 0.8 0.4 1.2
## 52 TR BLKPDR FALSE 16 76 0.8 0.3 1.2
## 53 TR BRBLDOT FALSE 17 75 0.8 0.3 1.2
## 54 TR Moss_15 FALSE 18 75 0.8 0.3 1.3
## 55 TR PAPER FALSE 19 68 0.7 0.3 1.1
## 56 TR USNEA FALSE 20 59 0.6 0.4 0.9
## 57 TR GREYPDR FALSE 21 59 0.6 0.2 1.0
## 58 TR BLWHDOT FALSE 22 55 0.6 0.2 1.0
## 59 TR Moss_5 FALSE 23 53 0.5 0.2 0.9
## 60 TR BRNWART FALSE 24 52 0.5 0.2 0.9
## 61 TR Cam_tallulensis FALSE 25 49 0.5 0.3 0.7
## 62 TR GRWIDE FALSE 26 47 0.5 0.2 0.7
## 63 TR GRNWDE FALSE 27 45 0.5 0.2 0.7
## 64 TR CLADBRS FALSE 28 44 0.5 0.2 0.7
## 65 TR GRNCRST FALSE 29 44 0.5 0.1 0.8
## 66 TR GRNWART FALSE 30 44 0.5 0.1 0.8
## 67 TR Hyd_petiolaris FALSE 31 39 0.4 0.2 0.6
## 68 TR JETBLK FALSE 32 35 0.4 0.0 0.7
## 69 TR WHITREIN FALSE 33 33 0.3 0.1 0.6
## 70 TR Wei_controversa FALSE 34 33 0.3 0.2 0.5
## 71 TR CLADPIX FALSE 35 31 0.3 0.1 0.5
## 72 TR GREEREIN FALSE 36 29 0.3 0.1 0.5
## 73 TR Moss_2 FALSE 37 27 0.3 0.1 0.5
## 74 TR Moss_10 FALSE 38 27 0.3 0.1 0.5
## 75 TR GRMED FALSE 39 26 0.3 0.0 0.5
## 76 TR And_rothii FALSE 40 26 0.3 0.0 0.5
## 77 TR GREMED FALSE 41 25 0.3 0.0 0.5
## 78 TR CLADSTLK FALSE 42 22 0.2 0.1 0.4
## 79 TR Bry_sp FALSE 43 20 0.2 0.0 0.4
## 80 TR GRNFOL FALSE 44 19 0.2 0.0 0.4
## 81 TR Moss_7 FALSE 45 19 0.2 0.0 0.4
## 82 TR Moss_8 FALSE 46 18 0.2 0.0 0.3
## 83 TR GRESMSQ FALSE 47 18 0.2 0.0 0.4
## 84 TR BLASQ FALSE 48 15 0.2 0.0 0.3
## 85 TR Moss_11 FALSE 49 14 0.1 0.0 0.3
## 86 TR GREYREIN FALSE 50 14 0.1 0.0 0.3
## 87 TR Agr_parennans FALSE 51 13 0.1 0.0 0.3
## 88 TR Buc_venusta FALSE 52 13 0.1 0.0 0.3
## 89 TR YELPDR FALSE 53 13 0.1 -0.1 0.3
## 90 TR Pol_juniperinum FALSE 54 12 0.1 0.0 0.3
## 91 TR BUBLGUM FALSE 55 11 0.1 0.0 0.2
## 92 TR WHBLDOT FALSE 56 11 0.1 0.0 0.2
## 93 TR YELLWART FALSE 57 10 0.1 0.0 0.2
## 94 TR BRBLKCRST FALSE 58 9 0.1 0.0 0.2
## 95 TR BLGRDOT FALSE 59 7 0.1 0.0 0.2
## 96 TR PNKCRST FALSE 60 7 0.1 0.0 0.1
## 97 TR Dic_scoparium FALSE 61 7 0.1 0.0 0.1
## 98 TR GRNSM FALSE 62 6 0.1 0.0 0.1
## 99 TR Dit_lineare FALSE 63 6 0.1 0.0 0.1
## 100 TR Dic_accumulatum FALSE 64 6 0.1 0.0 0.1
## 101 TR Sol_1 FALSE 65 6 0.1 0.0 0.2
## 102 TR Moss_12 FALSE 66 6 0.1 0.0 0.1
## 103 TR Dic_varia FALSE 67 6 0.1 0.0 0.1
## 104 TR Car_umbellata FALSE 68 6 0.1 0.0 0.1
## 105 TR Pol_commune FALSE 69 5 0.1 0.0 0.1
## 106 TR Moss_13 FALSE 70 5 0.1 0.0 0.1
## 107 TR Pol_piliferum FALSE 71 5 0.1 -0.1 0.2
## 108 TR BLAFLA FALSE 72 5 0.1 0.0 0.1
## 109 TR Moss_4 FALSE 73 5 0.1 0.0 0.2
## 110 TR Leu_albidum FALSE 74 5 0.1 0.0 0.1
## 111 TR Gal_urceolata FALSE 75 5 0.1 -0.1 0.2
## 112 TR Car_2 FALSE 76 4 0.0 0.0 0.1
## 113 TR Graminoid_1 FALSE 77 4 0.0 0.0 0.1
## 114 TR Kal_buxifolia FALSE 78 4 0.0 0.0 0.1
## 115 TR Rho_minus FALSE 79 4 0.0 0.0 0.1
## 116 TR Cer_purpureus FALSE 80 4 0.0 0.0 0.1
## 117 TR Oxy_arboreum FALSE 81 4 0.0 0.0 0.1
## 118 TR GREEWART FALSE 82 4 0.0 0.0 0.1
## 119 TR Leu_glaucum FALSE 83 4 0.0 0.0 0.1
## 120 TR BLAWHT FALSE 84 3 0.0 0.0 0.1
## 121 TR PSYCHO FALSE 85 3 0.0 0.0 0.1
## 122 TR SOILCR FALSE 86 3 0.0 0.0 0.1
## 123 TR WHITEFOL FALSE 87 3 0.0 0.0 0.1
## 124 TR Poh_nutans FALSE 88 3 0.0 0.0 0.1
## 125 TR Kal_latifolia FALSE 89 3 0.0 0.0 0.1
## 126 TR Pin_rigida FALSE 90 3 0.0 0.0 0.1
## 127 TR Dic_heteromalla FALSE 91 3 0.0 0.0 0.1
## 128 TR Moss_16 FALSE 92 3 0.0 0.0 0.1
## 129 TR GRYBR FALSE 93 3 0.0 0.0 0.1
## 130 TR Pol_strictum FALSE 94 3 0.0 0.0 0.1
## 131 TR Pse_elegans FALSE 95 3 0.0 0.0 0.1
## 132 TR MINTPDR FALSE 96 3 0.0 0.0 0.1
## 133 TR BRNFOL FALSE 97 2 0.0 0.0 0.1
## 134 TR BROPDR FALSE 98 2 0.0 0.0 0.1
## 135 TR PNKBLA FALSE 99 2 0.0 0.0 0.1
## 136 TR RAMALI FALSE 100 2 0.0 0.0 0.1
## 137 TR CLADBSTK FALSE 101 2 0.0 0.0 0.0
## 138 TR Bet_alleghaniensis FALSE 102 2 0.0 0.0 0.1
## 139 TR Car_3 FALSE 103 2 0.0 0.0 0.1
## 140 TR Car_7 FALSE 104 2 0.0 0.0 0.1
## 141 TR BRNGRFOL FALSE 105 1 0.0 0.0 0.0
## 142 TR BRNYELL FALSE 106 1 0.0 0.0 0.0
## 143 TR CLAD FALSE 107 1 0.0 0.0 0.0
## 144 TR GRNCIL FALSE 108 1 0.0 0.0 0.0
## 145 TR SHIELD FALSE 109 1 0.0 0.0 0.0
## 146 TR TANCUP FALSE 110 1 0.0 0.0 0.0
## 147 TR TEALCRST FALSE 111 1 0.0 0.0 0.0
## 148 TR Car_6 FALSE 112 1 0.0 0.0 0.0
## 149 TR Cor_major FALSE 113 1 0.0 0.0 0.0
## 150 TR Nys_sylvatica FALSE 114 1 0.0 0.0 0.0
## 151 TR Rho_major FALSE 115 1 0.0 0.0 0.0
## 152 TR Rub_allegheniensis FALSE 116 1 0.0 0.0 0.0
## 153 TR Plant_5 FALSE 117 1 0.0 0.0 0.0
## 154 TR Dic_montanum FALSE 118 1 0.0 0.0 0.0
## 155 TR Moss_14 FALSE 119 1 0.0 0.0 0.0
## 156 TR BLASM FALSE 120 1 0.0 0.0 0.0
## 157 TR CLADRC FALSE 121 1 0.0 0.0 0.0
## 158 TR Rac_heterostichum FALSE 122 1 0.0 0.0 0.0
## 159 TR Car_5 FALSE 123 1 0.0 0.0 0.0
## accumfreq logabun rankfreq
## 1 18.9 2.5 2.8
## 2 35.8 2.5 5.6
## 3 51.6 2.4 8.3
## 4 66.0 2.4 11.1
## 5 71.9 2.0 13.9
## 6 76.4 1.9 16.7
## 7 79.9 1.8 19.4
## 8 82.9 1.7 22.2
## 9 85.9 1.7 25.0
## 10 87.9 1.6 27.8
## 11 89.5 1.4 30.6
## 12 90.8 1.4 33.3
## 13 91.8 1.2 36.1
## 14 92.7 1.2 38.9
## 15 93.5 1.1 41.7
## 16 94.2 1.1 44.4
## 17 94.9 1.1 47.2
## 18 95.5 1.0 50.0
## 19 96.0 1.0 52.8
## 20 96.5 1.0 55.6
## 21 97.0 0.9 58.3
## 22 97.4 0.9 61.1
## 23 97.8 0.8 63.9
## 24 98.1 0.8 66.7
## 25 98.5 0.8 69.4
## 26 98.8 0.7 72.2
## 27 99.0 0.6 75.0
## 28 99.2 0.5 77.8
## 29 99.3 0.5 80.6
## 30 99.5 0.5 83.3
## 31 99.6 0.3 86.1
## 32 99.7 0.3 88.9
## 33 99.8 0.3 91.7
## 34 99.9 0.0 94.4
## 35 99.9 0.0 97.2
## 36 100.0 0.0 100.0
## 37 15.0 3.2 0.8
## 38 28.0 3.1 1.6
## 39 39.2 3.0 2.4
## 40 48.8 3.0 3.3
## 41 54.9 2.8 4.1
## 42 60.6 2.7 4.9
## 43 66.3 2.7 5.7
## 44 70.7 2.6 6.5
## 45 74.7 2.6 7.3
## 46 77.4 2.4 8.1
## 47 79.0 2.2 8.9
## 48 80.5 2.2 9.8
## 49 81.5 2.0 10.6
## 50 82.4 1.9 11.4
## 51 83.2 1.9 12.2
## 52 84.0 1.9 13.0
## 53 84.8 1.9 13.8
## 54 85.5 1.9 14.6
## 55 86.2 1.8 15.4
## 56 86.8 1.8 16.3
## 57 87.4 1.8 17.1
## 58 88.0 1.7 17.9
## 59 88.6 1.7 18.7
## 60 89.1 1.7 19.5
## 61 89.6 1.7 20.3
## 62 90.1 1.7 21.1
## 63 90.5 1.7 22.0
## 64 91.0 1.6 22.8
## 65 91.4 1.6 23.6
## 66 91.9 1.6 24.4
## 67 92.3 1.6 25.2
## 68 92.7 1.5 26.0
## 69 93.0 1.5 26.8
## 70 93.3 1.5 27.6
## 71 93.7 1.5 28.5
## 72 94.0 1.5 29.3
## 73 94.2 1.4 30.1
## 74 94.5 1.4 30.9
## 75 94.8 1.4 31.7
## 76 95.1 1.4 32.5
## 77 95.3 1.4 33.3
## 78 95.5 1.3 34.1
## 79 95.7 1.3 35.0
## 80 95.9 1.3 35.8
## 81 96.1 1.3 36.6
## 82 96.3 1.3 37.4
## 83 96.5 1.3 38.2
## 84 96.7 1.2 39.0
## 85 96.8 1.1 39.8
## 86 97.0 1.1 40.7
## 87 97.1 1.1 41.5
## 88 97.2 1.1 42.3
## 89 97.4 1.1 43.1
## 90 97.5 1.1 43.9
## 91 97.6 1.0 44.7
## 92 97.7 1.0 45.5
## 93 97.8 1.0 46.3
## 94 97.9 1.0 47.2
## 95 98.0 0.8 48.0
## 96 98.0 0.8 48.8
## 97 98.1 0.8 49.6
## 98 98.2 0.8 50.4
## 99 98.2 0.8 51.2
## 100 98.3 0.8 52.0
## 101 98.4 0.8 52.8
## 102 98.4 0.8 53.7
## 103 98.5 0.8 54.5
## 104 98.5 0.8 55.3
## 105 98.6 0.7 56.1
## 106 98.7 0.7 56.9
## 107 98.7 0.7 57.7
## 108 98.8 0.7 58.5
## 109 98.8 0.7 59.3
## 110 98.9 0.7 60.2
## 111 98.9 0.7 61.0
## 112 98.9 0.6 61.8
## 113 99.0 0.6 62.6
## 114 99.0 0.6 63.4
## 115 99.1 0.6 64.2
## 116 99.1 0.6 65.0
## 117 99.2 0.6 65.9
## 118 99.2 0.6 66.7
## 119 99.2 0.6 67.5
## 120 99.3 0.5 68.3
## 121 99.3 0.5 69.1
## 122 99.3 0.5 69.9
## 123 99.4 0.5 70.7
## 124 99.4 0.5 71.5
## 125 99.4 0.5 72.4
## 126 99.5 0.5 73.2
## 127 99.5 0.5 74.0
## 128 99.5 0.5 74.8
## 129 99.5 0.5 75.6
## 130 99.6 0.5 76.4
## 131 99.6 0.5 77.2
## 132 99.6 0.5 78.0
## 133 99.7 0.3 78.9
## 134 99.7 0.3 79.7
## 135 99.7 0.3 80.5
## 136 99.7 0.3 81.3
## 137 99.7 0.3 82.1
## 138 99.8 0.3 82.9
## 139 99.8 0.3 83.7
## 140 99.8 0.3 84.6
## 141 99.8 0.0 85.4
## 142 99.8 0.0 86.2
## 143 99.8 0.0 87.0
## 144 99.8 0.0 87.8
## 145 99.9 0.0 88.6
## 146 99.9 0.0 89.4
## 147 99.9 0.0 90.2
## 148 99.9 0.0 91.1
## 149 99.9 0.0 91.9
## 150 99.9 0.0 92.7
## 151 99.9 0.0 93.5
## 152 99.9 0.0 94.3
## 153 99.9 0.0 95.1
## 154 99.9 0.0 95.9
## 155 100.0 0.0 96.7
## 156 100.0 0.0 97.6
## 157 100.0 0.0 98.4
## 158 100.0 0.0 99.2
## 159 100.0 0.0 100.0rankabuncomp(com, y=env, factor="Climbing", scale='proportion', legend=FALSE)
## Grouping species labelit rank abundance proportion plower
## 1 CLIMBED MINGREY TRUE 1 997 14.2 12.9
## 2 CLIMBED TOADSK TRUE 2 988 14.1 12.8
## 3 CLIMBED TNYBRNCH TRUE 3 747 10.7 9.5
## 4 CLIMBED WHPDR FALSE 4 699 10.0 8.8
## 5 CLIMBED BLBRDOT FALSE 5 479 6.8 5.5
## 6 CLIMBED GRNSQB FALSE 6 389 5.6 4.6
## 7 CLIMBED ROCKTRP FALSE 7 271 3.9 2.9
## 8 CLIMBED BRGCRUST FALSE 8 269 3.8 2.9
## 9 CLIMBED GRNPDR FALSE 9 241 3.4 2.8
## 10 CLIMBED BRBLKBDR FALSE 10 211 3.0 2.0
## 11 CLIMBED Moss_6 FALSE 11 198 2.8 1.9
## 12 CLIMBED BRBLKCRST FALSE 12 135 1.9 1.0
## 13 CLIMBED CLADSQ FALSE 13 126 1.8 1.3
## 14 CLIMBED BLKPDR FALSE 14 76 1.1 0.5
## 15 CLIMBED Moss_15 FALSE 15 75 1.1 0.4
## 16 CLIMBED PAPER FALSE 16 71 1.0 0.4
## 17 CLIMBED WHITBUB FALSE 17 65 0.9 0.5
## 18 CLIMBED GRMEDSQ FALSE 18 64 0.9 0.5
## 19 CLIMBED GREYPDR FALSE 19 59 0.8 0.3
## 20 CLIMBED BLGRDOT FALSE 20 56 0.8 0.3
## 21 CLIMBED Sel_tortipila FALSE 21 54 0.8 0.4
## 22 CLIMBED BRNWART FALSE 22 52 0.7 0.3
## 23 CLIMBED GRNCRST FALSE 23 44 0.6 0.2
## 24 CLIMBED Moss_5 FALSE 24 43 0.6 0.2
## 25 CLIMBED GRWIDE FALSE 25 43 0.6 0.3
## 26 CLIMBED GRNWDE FALSE 26 38 0.5 0.2
## 27 CLIMBED Wei_controversa FALSE 27 33 0.5 0.2
## 28 CLIMBED GRBBRD FALSE 28 32 0.5 0.0
## 29 CLIMBED BLWHDOT FALSE 29 31 0.4 0.1
## 30 CLIMBED USNEA FALSE 30 30 0.4 0.2
## 31 CLIMBED JETBLK FALSE 31 29 0.4 0.0
## 32 CLIMBED Hyd_petiolaris FALSE 32 21 0.3 0.1
## 33 CLIMBED Bry_sp FALSE 33 20 0.3 0.1
## 34 CLIMBED GRNFOL FALSE 34 19 0.3 0.0
## 35 CLIMBED Moss_7 FALSE 35 19 0.3 0.0
## 36 CLIMBED GRNWART FALSE 36 19 0.3 0.1
## 37 CLIMBED GREMED FALSE 37 16 0.2 0.0
## 38 CLIMBED YELLWART FALSE 38 14 0.2 0.0
## 39 CLIMBED Moss_11 FALSE 39 14 0.2 0.0
## 40 CLIMBED YELPDR FALSE 40 13 0.2 -0.1
## 41 CLIMBED BLASQ FALSE 41 13 0.2 0.0
## 42 CLIMBED Buc_venusta FALSE 42 13 0.2 0.0
## 43 CLIMBED TEALCRST FALSE 43 12 0.2 0.0
## 44 CLIMBED BUBLGUM FALSE 44 11 0.2 0.0
## 45 CLIMBED And_rothii FALSE 45 11 0.2 -0.1
## 46 CLIMBED CLADSTLK FALSE 46 9 0.1 0.0
## 47 CLIMBED Moss_2 FALSE 47 8 0.1 0.0
## 48 CLIMBED MINTPDR FALSE 48 7 0.1 0.0
## 49 CLIMBED Pol_commune FALSE 49 7 0.1 -0.1
## 50 CLIMBED Car_umbellata FALSE 50 6 0.1 0.0
## 51 CLIMBED Moss_12 FALSE 51 6 0.1 0.0
## 52 CLIMBED GRNSM FALSE 52 5 0.1 0.0
## 53 CLIMBED Moss_4 FALSE 53 5 0.1 -0.1
## 54 CLIMBED Agr_parennans FALSE 54 5 0.1 0.0
## 55 CLIMBED Moss_13 FALSE 55 5 0.1 0.0
## 56 CLIMBED Cam_tallulensis FALSE 56 5 0.1 -0.1
## 57 CLIMBED TANCUP FALSE 57 4 0.1 0.0
## 58 CLIMBED Graminoid_1 FALSE 58 4 0.1 0.0
## 59 CLIMBED Oxy_arboreum FALSE 59 4 0.1 -0.1
## 60 CLIMBED Rho_minus FALSE 60 4 0.1 -0.1
## 61 CLIMBED GREPAPER FALSE 61 4 0.1 0.0
## 62 CLIMBED Dic_montanum FALSE 62 4 0.1 0.0
## 63 CLIMBED PSYCHO FALSE 63 3 0.0 0.0
## 64 CLIMBED WHBLDOT FALSE 64 3 0.0 0.0
## 65 CLIMBED Dic_heteromalla FALSE 65 3 0.0 0.0
## 66 CLIMBED Dit_lineare FALSE 66 3 0.0 0.0
## 67 CLIMBED Pse_elegans FALSE 67 3 0.0 0.0
## 68 CLIMBED Moss_8 FALSE 68 3 0.0 0.0
## 69 CLIMBED Poh_nutans FALSE 69 3 0.0 0.0
## 70 CLIMBED GRYBR FALSE 70 3 0.0 0.0
## 71 CLIMBED BRNFOL FALSE 71 2 0.0 0.0
## 72 CLIMBED BROPDR FALSE 72 2 0.0 0.0
## 73 CLIMBED CLADPIX FALSE 73 2 0.0 0.0
## 74 CLIMBED PNKCRST FALSE 74 2 0.0 0.0
## 75 CLIMBED Car_7 FALSE 75 2 0.0 0.0
## 76 CLIMBED Dic_accumulatum FALSE 76 2 0.0 0.0
## 77 CLIMBED CLADBSTK FALSE 77 2 0.0 0.0
## 78 CLIMBED Car_3 FALSE 78 2 0.0 0.0
## 79 CLIMBED BRNYELL FALSE 79 1 0.0 0.0
## 80 CLIMBED CLAD FALSE 80 1 0.0 0.0
## 81 CLIMBED GRNCIL FALSE 81 1 0.0 0.0
## 82 CLIMBED SHIELD FALSE 82 1 0.0 0.0
## 83 CLIMBED Car_5 FALSE 83 1 0.0 0.0
## 84 CLIMBED Rub_allegheniensis FALSE 84 1 0.0 0.0
## 85 CLIMBED Plant_5 FALSE 85 1 0.0 0.0
## 86 CLIMBED Moss_14 FALSE 86 1 0.0 0.0
## 87 CLIMBED Leu_albidum FALSE 87 1 0.0 0.0
## 88 CLIMBED Pol_strictum FALSE 88 1 0.0 0.0
## 89 CLIMBED Nys_sylvatica FALSE 89 1 0.0 0.0
## 90 CLIMBED Car_2 FALSE 90 1 0.0 0.0
## 91 UNCLIMBED TOADSK TRUE 1 803 17.9 16.3
## 92 UNCLIMBED WHPDR TRUE 2 664 14.8 13.4
## 93 UNCLIMBED ROCKTRP TRUE 3 341 7.6 6.0
## 94 UNCLIMBED MINGREY FALSE 4 285 6.4 4.9
## 95 UNCLIMBED TNYBRNCH FALSE 5 214 4.8 3.5
## 96 UNCLIMBED GRNPDR FALSE 6 193 4.3 3.1
## 97 UNCLIMBED BLBRDOT FALSE 7 179 4.0 2.7
## 98 UNCLIMBED BRBLKBDR FALSE 8 178 4.0 2.5
## 99 UNCLIMBED GRNSQB FALSE 9 172 3.8 2.8
## 100 UNCLIMBED BRGCRUST FALSE 10 132 2.9 2.0
## 101 UNCLIMBED BRBLKCRST FALSE 11 129 2.9 1.5
## 102 UNCLIMBED Sel_tortipila FALSE 12 104 2.3 1.4
## 103 UNCLIMBED CLADSQ FALSE 13 93 2.1 1.5
## 104 UNCLIMBED BRBLDOT FALSE 14 75 1.7 0.7
## 105 UNCLIMBED Moss_6 FALSE 15 65 1.5 0.7
## 106 UNCLIMBED BLWHDOT FALSE 16 60 1.3 0.5
## 107 UNCLIMBED PAPER FALSE 17 49 1.1 0.4
## 108 UNCLIMBED CLADBRS FALSE 18 44 1.0 0.5
## 109 UNCLIMBED Cam_tallulensis FALSE 19 44 1.0 0.6
## 110 UNCLIMBED USNEA FALSE 20 37 0.8 0.4
## 111 UNCLIMBED CLADPIX FALSE 21 33 0.7 0.4
## 112 UNCLIMBED WHITREIN FALSE 22 33 0.7 0.2
## 113 UNCLIMBED BLGRDOT FALSE 23 32 0.7 0.0
## 114 UNCLIMBED WHITBUB FALSE 24 31 0.7 0.2
## 115 UNCLIMBED GREEREIN FALSE 25 29 0.6 0.2
## 116 UNCLIMBED Moss_10 FALSE 26 27 0.6 0.1
## 117 UNCLIMBED GRMED FALSE 27 26 0.6 0.0
## 118 UNCLIMBED GRNWART FALSE 28 25 0.6 -0.1
## 119 UNCLIMBED GRMEDSQ FALSE 29 23 0.5 0.1
## 120 UNCLIMBED CLADSTLK FALSE 30 22 0.5 0.2
## 121 UNCLIMBED GRBBRD FALSE 31 22 0.5 -0.1
## 122 UNCLIMBED Moss_2 FALSE 32 19 0.4 0.0
## 123 UNCLIMBED GRESMSQ FALSE 33 18 0.4 0.0
## 124 UNCLIMBED Hyd_petiolaris FALSE 34 18 0.4 0.1
## 125 UNCLIMBED Moss_8 FALSE 35 16 0.4 0.0
## 126 UNCLIMBED And_rothii FALSE 36 15 0.3 0.0
## 127 UNCLIMBED GREYREIN FALSE 37 14 0.3 0.0
## 128 UNCLIMBED Pol_juniperinum FALSE 38 12 0.3 -0.1
## 129 UNCLIMBED Dic_montanum FALSE 39 11 0.2 -0.1
## 130 UNCLIMBED Moss_5 FALSE 40 10 0.2 0.0
## 131 UNCLIMBED GREMED FALSE 41 9 0.2 -0.2
## 132 UNCLIMBED JETBLK FALSE 42 9 0.2 -0.2
## 133 UNCLIMBED WHBLDOT FALSE 43 8 0.2 0.0
## 134 UNCLIMBED YELLWART FALSE 44 8 0.2 0.0
## 135 UNCLIMBED Agr_parennans FALSE 45 8 0.2 0.0
## 136 UNCLIMBED Dic_scoparium FALSE 46 7 0.2 0.0
## 137 UNCLIMBED GRNWDE FALSE 47 7 0.2 0.0
## 138 UNCLIMBED Sol_1 FALSE 48 6 0.1 -0.1
## 139 UNCLIMBED Dic_varia FALSE 49 6 0.1 0.0
## 140 UNCLIMBED MINTPDR FALSE 50 5 0.1 0.0
## 141 UNCLIMBED PNKCRST FALSE 51 5 0.1 0.0
## 142 UNCLIMBED RAMALI FALSE 52 5 0.1 0.0
## 143 UNCLIMBED Pol_piliferum FALSE 53 5 0.1 -0.1
## 144 UNCLIMBED Gal_urceolata FALSE 54 5 0.1 -0.1
## 145 UNCLIMBED Leu_albidum FALSE 55 5 0.1 0.0
## 146 UNCLIMBED BLAFLA FALSE 56 5 0.1 0.0
## 147 UNCLIMBED GREEWART FALSE 57 4 0.1 0.0
## 148 UNCLIMBED GRWIDE FALSE 58 4 0.1 0.0
## 149 UNCLIMBED Dic_accumulatum FALSE 59 4 0.1 -0.1
## 150 UNCLIMBED Kal_buxifolia FALSE 60 4 0.1 -0.1
## 151 UNCLIMBED Leu_glaucum FALSE 61 4 0.1 0.0
## 152 UNCLIMBED Cer_purpureus FALSE 62 4 0.1 -0.1
## 153 UNCLIMBED BLAWHT FALSE 63 3 0.1 0.0
## 154 UNCLIMBED GREPAPER FALSE 64 3 0.1 -0.1
## 155 UNCLIMBED SOILCR FALSE 65 3 0.1 0.0
## 156 UNCLIMBED TANCUP FALSE 66 3 0.1 -0.1
## 157 UNCLIMBED Kal_latifolia FALSE 67 3 0.1 -0.1
## 158 UNCLIMBED Pin_rigida FALSE 68 3 0.1 -0.1
## 159 UNCLIMBED Moss_16 FALSE 69 3 0.1 0.0
## 160 UNCLIMBED Dit_lineare FALSE 70 3 0.1 0.0
## 161 UNCLIMBED Pol_commune FALSE 71 3 0.1 0.0
## 162 UNCLIMBED WHITEFOL FALSE 72 3 0.1 -0.1
## 163 UNCLIMBED Car_2 FALSE 73 3 0.1 -0.1
## 164 UNCLIMBED PNKBLA FALSE 74 2 0.0 0.0
## 165 UNCLIMBED TANBUB FALSE 75 2 0.0 0.0
## 166 UNCLIMBED Bet_alleghaniensis FALSE 76 2 0.0 0.0
## 167 UNCLIMBED Moss_9 FALSE 77 2 0.0 0.0
## 168 UNCLIMBED BLASQ FALSE 78 2 0.0 0.0
## 169 UNCLIMBED Pol_strictum FALSE 79 2 0.0 0.0
## 170 UNCLIMBED BRNGRFOL FALSE 80 1 0.0 0.0
## 171 UNCLIMBED CLADRC FALSE 81 1 0.0 0.0
## 172 UNCLIMBED REDCRST FALSE 82 1 0.0 0.0
## 173 UNCLIMBED Rac_heterostichum FALSE 83 1 0.0 0.0
## 174 UNCLIMBED Car_6 FALSE 84 1 0.0 0.0
## 175 UNCLIMBED Cor_major FALSE 85 1 0.0 0.0
## 176 UNCLIMBED Rho_major FALSE 86 1 0.0 0.0
## 177 UNCLIMBED BLASM FALSE 87 1 0.0 0.0
## 178 UNCLIMBED GRNSM FALSE 88 1 0.0 0.0
## pupper accumfreq logabun rankfreq
## 1 15.6 14.2 3.0 1.1
## 2 15.4 28.3 3.0 2.2
## 3 11.9 39.0 2.9 3.3
## 4 11.1 49.0 2.8 4.4
## 5 8.2 55.8 2.7 5.6
## 6 6.5 61.4 2.6 6.7
## 7 4.8 65.2 2.4 7.8
## 8 4.8 69.1 2.4 8.9
## 9 4.1 72.5 2.4 10.0
## 10 4.0 75.5 2.3 11.1
## 11 3.7 78.4 2.3 12.2
## 12 2.8 80.3 2.1 13.3
## 13 2.3 82.1 2.1 14.4
## 14 1.7 83.2 1.9 15.6
## 15 1.8 84.3 1.9 16.7
## 16 1.6 85.3 1.9 17.8
## 17 1.3 86.2 1.8 18.9
## 18 1.4 87.1 1.8 20.0
## 19 1.4 87.9 1.8 21.1
## 20 1.3 88.7 1.7 22.2
## 21 1.1 89.5 1.7 23.3
## 22 1.2 90.3 1.7 24.4
## 23 1.1 90.9 1.6 25.6
## 24 1.1 91.5 1.6 26.7
## 25 0.9 92.1 1.6 27.8
## 26 0.9 92.7 1.6 28.9
## 27 0.7 93.1 1.5 30.0
## 28 0.9 93.6 1.5 31.1
## 29 0.8 94.0 1.5 32.2
## 30 0.6 94.5 1.5 33.3
## 31 0.8 94.9 1.5 34.4
## 32 0.5 95.2 1.3 35.6
## 33 0.5 95.5 1.3 36.7
## 34 0.5 95.7 1.3 37.8
## 35 0.5 96.0 1.3 38.9
## 36 0.5 96.3 1.3 40.0
## 37 0.5 96.5 1.2 41.1
## 38 0.4 96.7 1.1 42.2
## 39 0.4 96.9 1.1 43.3
## 40 0.4 97.1 1.1 44.4
## 41 0.3 97.3 1.1 45.6
## 42 0.4 97.5 1.1 46.7
## 43 0.4 97.6 1.1 47.8
## 44 0.3 97.8 1.0 48.9
## 45 0.4 97.9 1.0 50.0
## 46 0.2 98.1 1.0 51.1
## 47 0.2 98.2 0.9 52.2
## 48 0.2 98.3 0.8 53.3
## 49 0.3 98.4 0.8 54.4
## 50 0.2 98.5 0.8 55.6
## 51 0.2 98.6 0.8 56.7
## 52 0.2 98.6 0.7 57.8
## 53 0.2 98.7 0.7 58.9
## 54 0.2 98.8 0.7 60.0
## 55 0.2 98.8 0.7 61.1
## 56 0.2 98.9 0.7 62.2
## 57 0.1 99.0 0.6 63.3
## 58 0.1 99.0 0.6 64.4
## 59 0.2 99.1 0.6 65.6
## 60 0.2 99.1 0.6 66.7
## 61 0.1 99.2 0.6 67.8
## 62 0.1 99.3 0.6 68.9
## 63 0.1 99.3 0.5 70.0
## 64 0.1 99.3 0.5 71.1
## 65 0.1 99.4 0.5 72.2
## 66 0.1 99.4 0.5 73.3
## 67 0.1 99.5 0.5 74.4
## 68 0.1 99.5 0.5 75.6
## 69 0.1 99.6 0.5 76.7
## 70 0.1 99.6 0.5 77.8
## 71 0.1 99.6 0.3 78.9
## 72 0.1 99.7 0.3 80.0
## 73 0.1 99.7 0.3 81.1
## 74 0.1 99.7 0.3 82.2
## 75 0.1 99.7 0.3 83.3
## 76 0.1 99.8 0.3 84.4
## 77 0.1 99.8 0.3 85.6
## 78 0.1 99.8 0.3 86.7
## 79 0.0 99.8 0.0 87.8
## 80 0.0 99.9 0.0 88.9
## 81 0.0 99.9 0.0 90.0
## 82 0.0 99.9 0.0 91.1
## 83 0.0 99.9 0.0 92.2
## 84 0.0 99.9 0.0 93.3
## 85 0.0 99.9 0.0 94.4
## 86 0.0 99.9 0.0 95.6
## 87 0.0 100.0 0.0 96.7
## 88 0.0 100.0 0.0 97.8
## 89 0.0 100.0 0.0 98.9
## 90 0.0 100.0 0.0 100.0
## 91 19.6 17.9 2.9 1.1
## 92 16.3 32.8 2.8 2.3
## 93 9.2 40.4 2.5 3.4
## 94 7.8 46.7 2.5 4.5
## 95 6.0 51.5 2.3 5.7
## 96 5.5 55.8 2.3 6.8
## 97 5.3 59.8 2.3 8.0
## 98 5.4 63.8 2.3 9.1
## 99 4.9 67.6 2.2 10.2
## 100 3.9 70.6 2.1 11.4
## 101 4.3 73.5 2.1 12.5
## 102 3.3 75.8 2.0 13.6
## 103 2.7 77.9 2.0 14.8
## 104 2.6 79.5 1.9 15.9
## 105 2.2 81.0 1.8 17.0
## 106 2.2 82.3 1.8 18.2
## 107 1.8 83.4 1.7 19.3
## 108 1.5 84.4 1.6 20.5
## 109 1.4 85.4 1.6 21.6
## 110 1.3 86.2 1.6 22.7
## 111 1.1 87.0 1.5 23.9
## 112 1.3 87.7 1.5 25.0
## 113 1.4 88.4 1.5 26.1
## 114 1.2 89.1 1.5 27.3
## 115 1.1 89.7 1.5 28.4
## 116 1.1 90.4 1.4 29.5
## 117 1.1 90.9 1.4 30.7
## 118 1.2 91.5 1.4 31.8
## 119 1.0 92.0 1.4 33.0
## 120 0.8 92.5 1.3 34.1
## 121 1.0 93.0 1.3 35.2
## 122 0.8 93.4 1.3 36.4
## 123 0.8 93.8 1.3 37.5
## 124 0.7 94.2 1.3 38.6
## 125 0.7 94.6 1.2 39.8
## 126 0.7 94.9 1.2 40.9
## 127 0.6 95.2 1.1 42.0
## 128 0.6 95.5 1.1 43.2
## 129 0.5 95.7 1.0 44.3
## 130 0.5 96.0 1.0 45.5
## 131 0.6 96.2 1.0 46.6
## 132 0.6 96.4 1.0 47.7
## 133 0.4 96.5 0.9 48.9
## 134 0.4 96.7 0.9 50.0
## 135 0.4 96.9 0.9 51.1
## 136 0.3 97.1 0.8 52.3
## 137 0.3 97.2 0.8 53.4
## 138 0.3 97.3 0.8 54.5
## 139 0.3 97.5 0.8 55.7
## 140 0.2 97.6 0.7 56.8
## 141 0.2 97.7 0.7 58.0
## 142 0.3 97.8 0.7 59.1
## 143 0.3 97.9 0.7 60.2
## 144 0.3 98.0 0.7 61.4
## 145 0.3 98.1 0.7 62.5
## 146 0.2 98.3 0.7 63.6
## 147 0.2 98.3 0.6 64.8
## 148 0.2 98.4 0.6 65.9
## 149 0.2 98.5 0.6 67.0
## 150 0.2 98.6 0.6 68.2
## 151 0.2 98.7 0.6 69.3
## 152 0.3 98.8 0.6 70.5
## 153 0.2 98.9 0.5 71.6
## 154 0.2 98.9 0.5 72.7
## 155 0.2 99.0 0.5 73.9
## 156 0.2 99.1 0.5 75.0
## 157 0.2 99.1 0.5 76.1
## 158 0.2 99.2 0.5 77.3
## 159 0.2 99.3 0.5 78.4
## 160 0.2 99.3 0.5 79.5
## 161 0.2 99.4 0.5 80.7
## 162 0.2 99.5 0.5 81.8
## 163 0.2 99.5 0.5 83.0
## 164 0.1 99.6 0.3 84.1
## 165 0.1 99.6 0.3 85.2
## 166 0.1 99.7 0.3 86.4
## 167 0.1 99.7 0.3 87.5
## 168 0.1 99.8 0.3 88.6
## 169 0.1 99.8 0.3 89.8
## 170 0.1 99.8 0.0 90.9
## 171 0.1 99.8 0.0 92.0
## 172 0.1 99.9 0.0 93.2
## 173 0.1 99.9 0.0 94.3
## 174 0.1 99.9 0.0 95.5
## 175 0.1 99.9 0.0 96.6
## 176 0.1 100.0 0.0 97.7
## 177 0.1 100.0 0.0 98.9
## 178 0.1 100.0 0.0 100.0RA.Cl <- rankabuncomp(com, y=env, factor='Climbing',
return.data=TRUE, specnames=c(1:10), legend=FALSE)
RA.Cl## Grouping species labelit rank abundance proportion plower
## 1 CLIMBED MINGREY TRUE 1 997 14.2 12.9
## 2 CLIMBED TOADSK TRUE 2 988 14.1 12.8
## 3 CLIMBED TNYBRNCH TRUE 3 747 10.7 9.5
## 4 CLIMBED WHPDR TRUE 4 699 10.0 8.8
## 5 CLIMBED BLBRDOT TRUE 5 479 6.8 5.5
## 6 CLIMBED GRNSQB TRUE 6 389 5.6 4.6
## 7 CLIMBED ROCKTRP TRUE 7 271 3.9 2.9
## 8 CLIMBED BRGCRUST TRUE 8 269 3.8 2.9
## 9 CLIMBED GRNPDR TRUE 9 241 3.4 2.8
## 10 CLIMBED BRBLKBDR TRUE 10 211 3.0 2.0
## 11 CLIMBED Moss_6 FALSE 11 198 2.8 1.9
## 12 CLIMBED BRBLKCRST FALSE 12 135 1.9 1.0
## 13 CLIMBED CLADSQ FALSE 13 126 1.8 1.3
## 14 CLIMBED BLKPDR FALSE 14 76 1.1 0.5
## 15 CLIMBED Moss_15 FALSE 15 75 1.1 0.4
## 16 CLIMBED PAPER FALSE 16 71 1.0 0.4
## 17 CLIMBED WHITBUB FALSE 17 65 0.9 0.5
## 18 CLIMBED GRMEDSQ FALSE 18 64 0.9 0.5
## 19 CLIMBED GREYPDR FALSE 19 59 0.8 0.3
## 20 CLIMBED BLGRDOT FALSE 20 56 0.8 0.3
## 21 CLIMBED Sel_tortipila FALSE 21 54 0.8 0.4
## 22 CLIMBED BRNWART FALSE 22 52 0.7 0.3
## 23 CLIMBED GRNCRST FALSE 23 44 0.6 0.2
## 24 CLIMBED Moss_5 FALSE 24 43 0.6 0.2
## 25 CLIMBED GRWIDE FALSE 25 43 0.6 0.3
## 26 CLIMBED GRNWDE FALSE 26 38 0.5 0.2
## 27 CLIMBED Wei_controversa FALSE 27 33 0.5 0.2
## 28 CLIMBED GRBBRD FALSE 28 32 0.5 0.0
## 29 CLIMBED BLWHDOT FALSE 29 31 0.4 0.1
## 30 CLIMBED USNEA FALSE 30 30 0.4 0.2
## 31 CLIMBED JETBLK FALSE 31 29 0.4 0.0
## 32 CLIMBED Hyd_petiolaris FALSE 32 21 0.3 0.1
## 33 CLIMBED Bry_sp FALSE 33 20 0.3 0.1
## 34 CLIMBED GRNFOL FALSE 34 19 0.3 0.0
## 35 CLIMBED Moss_7 FALSE 35 19 0.3 0.0
## 36 CLIMBED GRNWART FALSE 36 19 0.3 0.1
## 37 CLIMBED GREMED FALSE 37 16 0.2 0.0
## 38 CLIMBED YELLWART FALSE 38 14 0.2 0.0
## 39 CLIMBED Moss_11 FALSE 39 14 0.2 0.0
## 40 CLIMBED YELPDR FALSE 40 13 0.2 -0.1
## 41 CLIMBED BLASQ FALSE 41 13 0.2 0.0
## 42 CLIMBED Buc_venusta FALSE 42 13 0.2 0.0
## 43 CLIMBED TEALCRST FALSE 43 12 0.2 0.0
## 44 CLIMBED BUBLGUM FALSE 44 11 0.2 0.0
## 45 CLIMBED And_rothii FALSE 45 11 0.2 -0.1
## 46 CLIMBED CLADSTLK FALSE 46 9 0.1 0.0
## 47 CLIMBED Moss_2 FALSE 47 8 0.1 0.0
## 48 CLIMBED MINTPDR FALSE 48 7 0.1 0.0
## 49 CLIMBED Pol_commune FALSE 49 7 0.1 -0.1
## 50 CLIMBED Car_umbellata FALSE 50 6 0.1 0.0
## 51 CLIMBED Moss_12 FALSE 51 6 0.1 0.0
## 52 CLIMBED GRNSM FALSE 52 5 0.1 0.0
## 53 CLIMBED Moss_4 FALSE 53 5 0.1 -0.1
## 54 CLIMBED Agr_parennans FALSE 54 5 0.1 0.0
## 55 CLIMBED Moss_13 FALSE 55 5 0.1 0.0
## 56 CLIMBED Cam_tallulensis FALSE 56 5 0.1 -0.1
## 57 CLIMBED TANCUP FALSE 57 4 0.1 0.0
## 58 CLIMBED Graminoid_1 FALSE 58 4 0.1 0.0
## 59 CLIMBED Oxy_arboreum FALSE 59 4 0.1 -0.1
## 60 CLIMBED Rho_minus FALSE 60 4 0.1 -0.1
## 61 CLIMBED GREPAPER FALSE 61 4 0.1 0.0
## 62 CLIMBED Dic_montanum FALSE 62 4 0.1 0.0
## 63 CLIMBED PSYCHO FALSE 63 3 0.0 0.0
## 64 CLIMBED WHBLDOT FALSE 64 3 0.0 0.0
## 65 CLIMBED Dic_heteromalla FALSE 65 3 0.0 0.0
## 66 CLIMBED Dit_lineare FALSE 66 3 0.0 0.0
## 67 CLIMBED Pse_elegans FALSE 67 3 0.0 0.0
## 68 CLIMBED Moss_8 FALSE 68 3 0.0 0.0
## 69 CLIMBED Poh_nutans FALSE 69 3 0.0 0.0
## 70 CLIMBED GRYBR FALSE 70 3 0.0 0.0
## 71 CLIMBED BRNFOL FALSE 71 2 0.0 0.0
## 72 CLIMBED BROPDR FALSE 72 2 0.0 0.0
## 73 CLIMBED CLADPIX FALSE 73 2 0.0 0.0
## 74 CLIMBED PNKCRST FALSE 74 2 0.0 0.0
## 75 CLIMBED Car_7 FALSE 75 2 0.0 0.0
## 76 CLIMBED Dic_accumulatum FALSE 76 2 0.0 0.0
## 77 CLIMBED CLADBSTK FALSE 77 2 0.0 0.0
## 78 CLIMBED Car_3 FALSE 78 2 0.0 0.0
## 79 CLIMBED BRNYELL FALSE 79 1 0.0 0.0
## 80 CLIMBED CLAD FALSE 80 1 0.0 0.0
## 81 CLIMBED GRNCIL FALSE 81 1 0.0 0.0
## 82 CLIMBED SHIELD FALSE 82 1 0.0 0.0
## 83 CLIMBED Car_5 FALSE 83 1 0.0 0.0
## 84 CLIMBED Rub_allegheniensis FALSE 84 1 0.0 0.0
## 85 CLIMBED Plant_5 FALSE 85 1 0.0 0.0
## 86 CLIMBED Moss_14 FALSE 86 1 0.0 0.0
## 87 CLIMBED Leu_albidum FALSE 87 1 0.0 0.0
## 88 CLIMBED Pol_strictum FALSE 88 1 0.0 0.0
## 89 CLIMBED Nys_sylvatica FALSE 89 1 0.0 0.0
## 90 CLIMBED Car_2 FALSE 90 1 0.0 0.0
## 91 UNCLIMBED TOADSK TRUE 1 803 17.9 16.3
## 92 UNCLIMBED WHPDR TRUE 2 664 14.8 13.4
## 93 UNCLIMBED ROCKTRP TRUE 3 341 7.6 6.0
## 94 UNCLIMBED MINGREY TRUE 4 285 6.4 4.9
## 95 UNCLIMBED TNYBRNCH TRUE 5 214 4.8 3.5
## 96 UNCLIMBED GRNPDR TRUE 6 193 4.3 3.1
## 97 UNCLIMBED BLBRDOT TRUE 7 179 4.0 2.7
## 98 UNCLIMBED BRBLKBDR TRUE 8 178 4.0 2.5
## 99 UNCLIMBED GRNSQB TRUE 9 172 3.8 2.8
## 100 UNCLIMBED BRGCRUST TRUE 10 132 2.9 2.0
## 101 UNCLIMBED BRBLKCRST FALSE 11 129 2.9 1.5
## 102 UNCLIMBED Sel_tortipila FALSE 12 104 2.3 1.4
## 103 UNCLIMBED CLADSQ FALSE 13 93 2.1 1.5
## 104 UNCLIMBED BRBLDOT FALSE 14 75 1.7 0.7
## 105 UNCLIMBED Moss_6 FALSE 15 65 1.5 0.7
## 106 UNCLIMBED BLWHDOT FALSE 16 60 1.3 0.5
## 107 UNCLIMBED PAPER FALSE 17 49 1.1 0.4
## 108 UNCLIMBED CLADBRS FALSE 18 44 1.0 0.5
## 109 UNCLIMBED Cam_tallulensis FALSE 19 44 1.0 0.6
## 110 UNCLIMBED USNEA FALSE 20 37 0.8 0.4
## 111 UNCLIMBED CLADPIX FALSE 21 33 0.7 0.4
## 112 UNCLIMBED WHITREIN FALSE 22 33 0.7 0.2
## 113 UNCLIMBED BLGRDOT FALSE 23 32 0.7 0.0
## 114 UNCLIMBED WHITBUB FALSE 24 31 0.7 0.2
## 115 UNCLIMBED GREEREIN FALSE 25 29 0.6 0.2
## 116 UNCLIMBED Moss_10 FALSE 26 27 0.6 0.1
## 117 UNCLIMBED GRMED FALSE 27 26 0.6 0.0
## 118 UNCLIMBED GRNWART FALSE 28 25 0.6 -0.1
## 119 UNCLIMBED GRMEDSQ FALSE 29 23 0.5 0.1
## 120 UNCLIMBED CLADSTLK FALSE 30 22 0.5 0.2
## 121 UNCLIMBED GRBBRD FALSE 31 22 0.5 -0.1
## 122 UNCLIMBED Moss_2 FALSE 32 19 0.4 0.0
## 123 UNCLIMBED GRESMSQ FALSE 33 18 0.4 0.0
## 124 UNCLIMBED Hyd_petiolaris FALSE 34 18 0.4 0.1
## 125 UNCLIMBED Moss_8 FALSE 35 16 0.4 0.0
## 126 UNCLIMBED And_rothii FALSE 36 15 0.3 0.0
## 127 UNCLIMBED GREYREIN FALSE 37 14 0.3 0.0
## 128 UNCLIMBED Pol_juniperinum FALSE 38 12 0.3 -0.1
## 129 UNCLIMBED Dic_montanum FALSE 39 11 0.2 -0.1
## 130 UNCLIMBED Moss_5 FALSE 40 10 0.2 0.0
## 131 UNCLIMBED GREMED FALSE 41 9 0.2 -0.2
## 132 UNCLIMBED JETBLK FALSE 42 9 0.2 -0.2
## 133 UNCLIMBED WHBLDOT FALSE 43 8 0.2 0.0
## 134 UNCLIMBED YELLWART FALSE 44 8 0.2 0.0
## 135 UNCLIMBED Agr_parennans FALSE 45 8 0.2 0.0
## 136 UNCLIMBED Dic_scoparium FALSE 46 7 0.2 0.0
## 137 UNCLIMBED GRNWDE FALSE 47 7 0.2 0.0
## 138 UNCLIMBED Sol_1 FALSE 48 6 0.1 -0.1
## 139 UNCLIMBED Dic_varia FALSE 49 6 0.1 0.0
## 140 UNCLIMBED MINTPDR FALSE 50 5 0.1 0.0
## 141 UNCLIMBED PNKCRST FALSE 51 5 0.1 0.0
## 142 UNCLIMBED RAMALI FALSE 52 5 0.1 0.0
## 143 UNCLIMBED Pol_piliferum FALSE 53 5 0.1 -0.1
## 144 UNCLIMBED Gal_urceolata FALSE 54 5 0.1 -0.1
## 145 UNCLIMBED Leu_albidum FALSE 55 5 0.1 0.0
## 146 UNCLIMBED BLAFLA FALSE 56 5 0.1 0.0
## 147 UNCLIMBED GREEWART FALSE 57 4 0.1 0.0
## 148 UNCLIMBED GRWIDE FALSE 58 4 0.1 0.0
## 149 UNCLIMBED Dic_accumulatum FALSE 59 4 0.1 -0.1
## 150 UNCLIMBED Kal_buxifolia FALSE 60 4 0.1 -0.1
## 151 UNCLIMBED Leu_glaucum FALSE 61 4 0.1 0.0
## 152 UNCLIMBED Cer_purpureus FALSE 62 4 0.1 -0.1
## 153 UNCLIMBED BLAWHT FALSE 63 3 0.1 0.0
## 154 UNCLIMBED GREPAPER FALSE 64 3 0.1 -0.1
## 155 UNCLIMBED SOILCR FALSE 65 3 0.1 0.0
## 156 UNCLIMBED TANCUP FALSE 66 3 0.1 -0.1
## 157 UNCLIMBED Kal_latifolia FALSE 67 3 0.1 -0.1
## 158 UNCLIMBED Pin_rigida FALSE 68 3 0.1 -0.1
## 159 UNCLIMBED Moss_16 FALSE 69 3 0.1 0.0
## 160 UNCLIMBED Dit_lineare FALSE 70 3 0.1 0.0
## 161 UNCLIMBED Pol_commune FALSE 71 3 0.1 0.0
## 162 UNCLIMBED WHITEFOL FALSE 72 3 0.1 -0.1
## 163 UNCLIMBED Car_2 FALSE 73 3 0.1 -0.1
## 164 UNCLIMBED PNKBLA FALSE 74 2 0.0 0.0
## 165 UNCLIMBED TANBUB FALSE 75 2 0.0 0.0
## 166 UNCLIMBED Bet_alleghaniensis FALSE 76 2 0.0 0.0
## 167 UNCLIMBED Moss_9 FALSE 77 2 0.0 0.0
## 168 UNCLIMBED BLASQ FALSE 78 2 0.0 0.0
## 169 UNCLIMBED Pol_strictum FALSE 79 2 0.0 0.0
## 170 UNCLIMBED BRNGRFOL FALSE 80 1 0.0 0.0
## 171 UNCLIMBED CLADRC FALSE 81 1 0.0 0.0
## 172 UNCLIMBED REDCRST FALSE 82 1 0.0 0.0
## 173 UNCLIMBED Rac_heterostichum FALSE 83 1 0.0 0.0
## 174 UNCLIMBED Car_6 FALSE 84 1 0.0 0.0
## 175 UNCLIMBED Cor_major FALSE 85 1 0.0 0.0
## 176 UNCLIMBED Rho_major FALSE 86 1 0.0 0.0
## 177 UNCLIMBED BLASM FALSE 87 1 0.0 0.0
## 178 UNCLIMBED GRNSM FALSE 88 1 0.0 0.0
## pupper accumfreq logabun rankfreq
## 1 15.6 14.2 3.0 1.1
## 2 15.4 28.3 3.0 2.2
## 3 11.9 39.0 2.9 3.3
## 4 11.1 49.0 2.8 4.4
## 5 8.2 55.8 2.7 5.6
## 6 6.5 61.4 2.6 6.7
## 7 4.8 65.2 2.4 7.8
## 8 4.8 69.1 2.4 8.9
## 9 4.1 72.5 2.4 10.0
## 10 4.0 75.5 2.3 11.1
## 11 3.7 78.4 2.3 12.2
## 12 2.8 80.3 2.1 13.3
## 13 2.3 82.1 2.1 14.4
## 14 1.7 83.2 1.9 15.6
## 15 1.8 84.3 1.9 16.7
## 16 1.6 85.3 1.9 17.8
## 17 1.3 86.2 1.8 18.9
## 18 1.4 87.1 1.8 20.0
## 19 1.4 87.9 1.8 21.1
## 20 1.3 88.7 1.7 22.2
## 21 1.1 89.5 1.7 23.3
## 22 1.2 90.3 1.7 24.4
## 23 1.1 90.9 1.6 25.6
## 24 1.1 91.5 1.6 26.7
## 25 0.9 92.1 1.6 27.8
## 26 0.9 92.7 1.6 28.9
## 27 0.7 93.1 1.5 30.0
## 28 0.9 93.6 1.5 31.1
## 29 0.8 94.0 1.5 32.2
## 30 0.6 94.5 1.5 33.3
## 31 0.8 94.9 1.5 34.4
## 32 0.5 95.2 1.3 35.6
## 33 0.5 95.5 1.3 36.7
## 34 0.5 95.7 1.3 37.8
## 35 0.5 96.0 1.3 38.9
## 36 0.5 96.3 1.3 40.0
## 37 0.5 96.5 1.2 41.1
## 38 0.4 96.7 1.1 42.2
## 39 0.4 96.9 1.1 43.3
## 40 0.4 97.1 1.1 44.4
## 41 0.3 97.3 1.1 45.6
## 42 0.4 97.5 1.1 46.7
## 43 0.4 97.6 1.1 47.8
## 44 0.3 97.8 1.0 48.9
## 45 0.4 97.9 1.0 50.0
## 46 0.2 98.1 1.0 51.1
## 47 0.2 98.2 0.9 52.2
## 48 0.2 98.3 0.8 53.3
## 49 0.3 98.4 0.8 54.4
## 50 0.2 98.5 0.8 55.6
## 51 0.2 98.6 0.8 56.7
## 52 0.2 98.6 0.7 57.8
## 53 0.2 98.7 0.7 58.9
## 54 0.2 98.8 0.7 60.0
## 55 0.2 98.8 0.7 61.1
## 56 0.2 98.9 0.7 62.2
## 57 0.1 99.0 0.6 63.3
## 58 0.1 99.0 0.6 64.4
## 59 0.2 99.1 0.6 65.6
## 60 0.2 99.1 0.6 66.7
## 61 0.1 99.2 0.6 67.8
## 62 0.1 99.3 0.6 68.9
## 63 0.1 99.3 0.5 70.0
## 64 0.1 99.3 0.5 71.1
## 65 0.1 99.4 0.5 72.2
## 66 0.1 99.4 0.5 73.3
## 67 0.1 99.5 0.5 74.4
## 68 0.1 99.5 0.5 75.6
## 69 0.1 99.6 0.5 76.7
## 70 0.1 99.6 0.5 77.8
## 71 0.1 99.6 0.3 78.9
## 72 0.1 99.7 0.3 80.0
## 73 0.1 99.7 0.3 81.1
## 74 0.1 99.7 0.3 82.2
## 75 0.1 99.7 0.3 83.3
## 76 0.1 99.8 0.3 84.4
## 77 0.1 99.8 0.3 85.6
## 78 0.1 99.8 0.3 86.7
## 79 0.0 99.8 0.0 87.8
## 80 0.0 99.9 0.0 88.9
## 81 0.0 99.9 0.0 90.0
## 82 0.0 99.9 0.0 91.1
## 83 0.0 99.9 0.0 92.2
## 84 0.0 99.9 0.0 93.3
## 85 0.0 99.9 0.0 94.4
## 86 0.0 99.9 0.0 95.6
## 87 0.0 100.0 0.0 96.7
## 88 0.0 100.0 0.0 97.8
## 89 0.0 100.0 0.0 98.9
## 90 0.0 100.0 0.0 100.0
## 91 19.6 17.9 2.9 1.1
## 92 16.3 32.8 2.8 2.3
## 93 9.2 40.4 2.5 3.4
## 94 7.8 46.7 2.5 4.5
## 95 6.0 51.5 2.3 5.7
## 96 5.5 55.8 2.3 6.8
## 97 5.3 59.8 2.3 8.0
## 98 5.4 63.8 2.3 9.1
## 99 4.9 67.6 2.2 10.2
## 100 3.9 70.6 2.1 11.4
## 101 4.3 73.5 2.1 12.5
## 102 3.3 75.8 2.0 13.6
## 103 2.7 77.9 2.0 14.8
## 104 2.6 79.5 1.9 15.9
## 105 2.2 81.0 1.8 17.0
## 106 2.2 82.3 1.8 18.2
## 107 1.8 83.4 1.7 19.3
## 108 1.5 84.4 1.6 20.5
## 109 1.4 85.4 1.6 21.6
## 110 1.3 86.2 1.6 22.7
## 111 1.1 87.0 1.5 23.9
## 112 1.3 87.7 1.5 25.0
## 113 1.4 88.4 1.5 26.1
## 114 1.2 89.1 1.5 27.3
## 115 1.1 89.7 1.5 28.4
## 116 1.1 90.4 1.4 29.5
## 117 1.1 90.9 1.4 30.7
## 118 1.2 91.5 1.4 31.8
## 119 1.0 92.0 1.4 33.0
## 120 0.8 92.5 1.3 34.1
## 121 1.0 93.0 1.3 35.2
## 122 0.8 93.4 1.3 36.4
## 123 0.8 93.8 1.3 37.5
## 124 0.7 94.2 1.3 38.6
## 125 0.7 94.6 1.2 39.8
## 126 0.7 94.9 1.2 40.9
## 127 0.6 95.2 1.1 42.0
## 128 0.6 95.5 1.1 43.2
## 129 0.5 95.7 1.0 44.3
## 130 0.5 96.0 1.0 45.5
## 131 0.6 96.2 1.0 46.6
## 132 0.6 96.4 1.0 47.7
## 133 0.4 96.5 0.9 48.9
## 134 0.4 96.7 0.9 50.0
## 135 0.4 96.9 0.9 51.1
## 136 0.3 97.1 0.8 52.3
## 137 0.3 97.2 0.8 53.4
## 138 0.3 97.3 0.8 54.5
## 139 0.3 97.5 0.8 55.7
## 140 0.2 97.6 0.7 56.8
## 141 0.2 97.7 0.7 58.0
## 142 0.3 97.8 0.7 59.1
## 143 0.3 97.9 0.7 60.2
## 144 0.3 98.0 0.7 61.4
## 145 0.3 98.1 0.7 62.5
## 146 0.2 98.3 0.7 63.6
## 147 0.2 98.3 0.6 64.8
## 148 0.2 98.4 0.6 65.9
## 149 0.2 98.5 0.6 67.0
## 150 0.2 98.6 0.6 68.2
## 151 0.2 98.7 0.6 69.3
## 152 0.3 98.8 0.6 70.5
## 153 0.2 98.9 0.5 71.6
## 154 0.2 98.9 0.5 72.7
## 155 0.2 99.0 0.5 73.9
## 156 0.2 99.1 0.5 75.0
## 157 0.2 99.1 0.5 76.1
## 158 0.2 99.2 0.5 77.3
## 159 0.2 99.3 0.5 78.4
## 160 0.2 99.3 0.5 79.5
## 161 0.2 99.4 0.5 80.7
## 162 0.2 99.5 0.5 81.8
## 163 0.2 99.5 0.5 83.0
## 164 0.1 99.6 0.3 84.1
## 165 0.1 99.6 0.3 85.2
## 166 0.1 99.7 0.3 86.4
## 167 0.1 99.7 0.3 87.5
## 168 0.1 99.8 0.3 88.6
## 169 0.1 99.8 0.3 89.8
## 170 0.1 99.8 0.0 90.9
## 171 0.1 99.8 0.0 92.0
## 172 0.1 99.9 0.0 93.2
## 173 0.1 99.9 0.0 94.3
## 174 0.1 99.9 0.0 95.5
## 175 0.1 99.9 0.0 96.6
## 176 0.1 100.0 0.0 97.7
## 177 0.1 100.0 0.0 98.9
## 178 0.1 100.0 0.0 100.0FIgures for rank abundance curves by site and climbing
library(ggrepel)
plotgg2 <- ggplot(data=RA.Site, aes(x = rank, y = abundance))
plotgg2 <- plotgg2 + scale_x_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_line(aes(colour=Grouping), size=1) +
geom_point(aes(colour=Grouping), size=5, alpha=0.7) +
geom_text_repel(data=subset(RA.Site, labelit == TRUE),
aes(label=species),
angle=45, nudge_x=1, nudge_y=1, show.legend=FALSE) +
scale_color_brewer(palette = "Dark2") +
labs(x = "rank", y = "abundance", colour = "Site")
plotgg2## Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
plotgg3 <- ggplot(data=RA.Cl, aes(x = rank, y = abundance))
plotgg3 <- plotgg3 + scale_x_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
scale_y_continuous(expand=c(0, 1), sec.axis = dup_axis(labels=NULL, name=NULL)) +
geom_line(aes(colour=Grouping), size=1) +
geom_point(aes(colour=Grouping), size=5, alpha=0.7) +
geom_text_repel(data=subset(RA.Cl, labelit == TRUE),
aes(label=species),
angle=45, nudge_x=1, nudge_y=1, show.legend=FALSE) +
scale_color_brewer(palette = "Dark2") +
labs(x = "rank", y = "abundance", colour = "Site")
plotgg3## Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps