Schefferville - transitions; Phylogenetic signal of indicator species

Tammy L. Elliott

Date: March 28, 2015

R version 3.1.0

# set global chunk options: 
library(knitr)
opts_chunk$set(cache=FALSE, fig.align='center')

Indicator species analysis

Show sample calculation.

All of the following analyzes are for vasculars only

# Calculate significant indicator species for each grouping
env.dis.inval.3<-multipatt(abd.sp.allsp, vasc.env.clust.3$cluster, control=how(nperm=999))
summary(env.dis.inval.3)

## 
##  Multilevel pattern analysis
##  ---------------------------
## 
##  Association function: IndVal.g
##  Significance level (alpha): 0.05
## 
##  Total number of species: 125
##  Selected number of species: 52 
##  Number of species associated to 1 group: 42 
##  Number of species associated to 2 groups: 10 
## 
##  List of species associated to each combination: 
## 
##  Group 1  #sps.  11 
##         stat p.value    
## CarSci 0.682   0.001 ***
## CarBig 0.537   0.001 ***
## ArcAlp 0.535   0.001 ***
## DryInt 0.458   0.001 ***
## BisViv 0.424   0.003 ** 
## RhoLap 0.356   0.001 ***
## SalUva 0.345   0.002 ** 
## CarCap 0.315   0.005 ** 
## JunTri 0.267   0.016 *  
## PyrGra 0.267   0.014 *  
## CarDef 0.218   0.050 *  
## 
##  Group 2  #sps.  22 
##         stat p.value    
## CarVag 0.759   0.001 ***
## EquSyl 0.647   0.001 ***
## MitNud 0.638   0.001 ***
## CarGyn 0.600   0.001 ***
## GauHis 0.517   0.001 ***
## RubArc 0.484   0.002 ** 
## EquArv 0.470   0.001 ***
## RubCha 0.470   0.001 ***
## OrtSec 0.466   0.001 ***
## VacOxy 0.457   0.001 ***
## PetFri 0.422   0.001 ***
## LarLar 0.403   0.002 ** 
## KalPol 0.389   0.003 ** 
## SalArc 0.389   0.003 ** 
## EurRad 0.374   0.001 ***
## AnePar 0.325   0.023 *  
## SalPla 0.305   0.017 *  
## AchMil 0.285   0.024 *  
## CarAqu 0.285   0.028 *  
## MaiTri 0.285   0.021 *  
## AndPol 0.264   0.037 *  
## CarLim 0.264   0.042 *  
## 
##  Group 3  #sps.  9 
##         stat p.value    
## LycAno 0.651   0.001 ***
## VacCes 0.646   0.001 ***
## SolMac 0.628   0.001 ***
## VacMyr 0.595   0.001 ***
## TriBor 0.557   0.001 ***
## AveFle 0.429   0.015 *  
## RibGla 0.408   0.001 ***
## RubIda 0.318   0.008 ** 
## AlnVir 0.308   0.037 *  
## 
##  Group 1+2  #sps.  3 
##         stat p.value   
## SalVes 0.588   0.002 **
## SolMul 0.356   0.042 * 
## TriCes 0.354   0.019 * 
## 
##  Group 1+3  #sps.  2 
##         stat p.value    
## VacUli 0.792   0.001 ***
## BetGla 0.710   0.001 ***
## 
##  Group 2+3  #sps.  5 
##         stat p.value    
## CorCan 0.775   0.001 ***
## LinBor 0.710   0.001 ***
## PicMar 0.680   0.001 ***
## GeoLiv 0.590   0.001 ***
## PicGla 0.495   0.017 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(env.dis.inval.3, alpha=1)

## 
##  Multilevel pattern analysis
##  ---------------------------
## 
##  Association function: IndVal.g
##  Significance level (alpha): 1
## 
##  Total number of species: 125
##  Selected number of species: 104 
##  Number of species associated to 1 group: 85 
##  Number of species associated to 2 groups: 19 
## 
##  List of species associated to each combination: 
## 
##  Group 1  #sps.  20 
##         stat p.value    
## CarSci 0.682   0.001 ***
## CarBig 0.537   0.001 ***
## ArcAlp 0.535   0.001 ***
## DryInt 0.458   0.001 ***
## BisViv 0.424   0.003 ** 
## RhoLap 0.356   0.001 ***
## SalUva 0.345   0.002 ** 
## CarCap 0.315   0.005 ** 
## JunTri 0.267   0.016 *  
## PyrGra 0.267   0.014 *  
## CarDef 0.218   0.050 *  
## DiaLap 0.218   0.057 .  
## DipCom 0.196   0.128    
## HupApr 0.164   0.394    
## AntAlp 0.154   0.251    
## AntMon 0.154   0.251    
## ArnAng 0.154   0.251    
## CarCat 0.154   0.241    
## PoaArc 0.154   0.247    
## TriSpi 0.154   0.234    
## 
##  Group 2  #sps.  49 
##         stat p.value    
## CarVag 0.759   0.001 ***
## EquSyl 0.647   0.001 ***
## MitNud 0.638   0.001 ***
## CarGyn 0.600   0.001 ***
## GauHis 0.517   0.001 ***
## RubArc 0.484   0.002 ** 
## EquArv 0.470   0.001 ***
## RubCha 0.470   0.001 ***
## OrtSec 0.466   0.001 ***
## VacOxy 0.457   0.001 ***
## PetFri 0.422   0.001 ***
## LarLar 0.403   0.002 ** 
## KalPol 0.389   0.003 ** 
## SalArc 0.389   0.003 ** 
## EurRad 0.374   0.001 ***
## VioAdu 0.359   0.118    
## AnePar 0.325   0.023 *  
## SalPla 0.305   0.017 *  
## AchMil 0.285   0.024 *  
## CarAqu 0.285   0.028 *  
## MaiTri 0.285   0.021 *  
## AndPol 0.264   0.037 *  
## CarLim 0.264   0.042 *  
## EquVar 0.241   0.066 .  
## LonVil 0.241   0.072 .  
## LisCor 0.240   0.124    
## CysMon 0.216   0.133    
## EquSci 0.216   0.132    
## AbiBal 0.201   0.295    
## CarDis 0.187   0.256    
## MyrGal 0.187   0.257    
## AgrMer 0.152   0.379    
## CarMag 0.152   0.339    
## CarTri 0.152   0.385    
## CasSep 0.152   0.357    
## EriVir 0.152   0.367    
## FraVir 0.152   0.367    
## LuzPar 0.152   0.352    
## RhiMin 0.152   0.344    
## SchPur 0.152   0.367    
## CarLep 0.108   1.000    
## CarUtr 0.108   1.000    
## DasFru 0.108   1.000    
## EpiHor 0.108   1.000    
## MonUni 0.108   1.000    
## PacAur 0.108   1.000    
## SalPed 0.108   1.000    
## SelSel 0.108   1.000    
## TriAlp 0.108   1.000    
## 
##  Group 3  #sps.  16 
##         stat p.value    
## LycAno 0.651   0.001 ***
## VacCes 0.646   0.001 ***
## SolMac 0.628   0.001 ***
## VacMyr 0.595   0.001 ***
## TriBor 0.557   0.001 ***
## AveFle 0.429   0.015 *  
## RibGla 0.408   0.001 ***
## RubIda 0.318   0.008 ** 
## AlnVir 0.308   0.037 *  
## ChaAng 0.204   0.127    
## DryExp 0.204   0.116    
## AmeBar 0.144   0.512    
## CerAlp 0.144   0.513    
## MinBif 0.144   0.513    
## SalArg 0.144   0.508    
## SalHum 0.144   0.523    
## 
##  Group 1+2  #sps.  8 
##         stat p.value   
## SalVes 0.588   0.002 **
## SolMul 0.356   0.042 * 
## TriCes 0.354   0.019 * 
## VioRen 0.352   0.099 . 
## SalGla 0.250   0.175   
## BarAlp 0.234   0.227   
## ElyTra 0.175   0.716   
## SteBor 0.125   0.739   
## 
##  Group 1+3  #sps.  2 
##         stat p.value    
## VacUli 0.792   0.001 ***
## BetGla 0.710   0.001 ***
## 
##  Group 2+3  #sps.  9 
##         stat p.value    
## CorCan 0.775   0.001 ***
## LinBor 0.710   0.001 ***
## PicMar 0.680   0.001 ***
## GeoLiv 0.590   0.001 ***
## PicGla 0.495   0.017 *  
## CalCan 0.273   0.225    
## VibEdu 0.173   0.750    
## PyrAsa 0.150   0.811    
## CarBru 0.122   1.000    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

summary(env.dis.inval.3, invalcomp=TRUE)

## 
##  Multilevel pattern analysis
##  ---------------------------
## 
##  Association function: IndVal.g
##  Significance level (alpha): 0.05
## 
##  Total number of species: 125
##  Selected number of species: 52 
##  Number of species associated to 1 group: 42 
##  Number of species associated to 2 groups: 10 
## 
##  List of species associated to each combination: 
## 
##  Group 1  #sps.  11 
##         stat p.value    
## CarSci 0.682   0.001 ***
## CarBig 0.537   0.001 ***
## ArcAlp 0.535   0.001 ***
## DryInt 0.458   0.001 ***
## BisViv 0.424   0.003 ** 
## RhoLap 0.356   0.001 ***
## SalUva 0.345   0.002 ** 
## CarCap 0.315   0.005 ** 
## JunTri 0.267   0.016 *  
## PyrGra 0.267   0.014 *  
## CarDef 0.218   0.050 *  
## 
##  Group 2  #sps.  22 
##         stat p.value    
## CarVag 0.759   0.001 ***
## EquSyl 0.647   0.001 ***
## MitNud 0.638   0.001 ***
## CarGyn 0.600   0.001 ***
## GauHis 0.517   0.001 ***
## RubArc 0.484   0.002 ** 
## EquArv 0.470   0.001 ***
## RubCha 0.470   0.001 ***
## OrtSec 0.466   0.001 ***
## VacOxy 0.457   0.001 ***
## PetFri 0.422   0.001 ***
## LarLar 0.403   0.002 ** 
## KalPol 0.389   0.003 ** 
## SalArc 0.389   0.003 ** 
## EurRad 0.374   0.001 ***
## AnePar 0.325   0.023 *  
## SalPla 0.305   0.017 *  
## AchMil 0.285   0.024 *  
## CarAqu 0.285   0.028 *  
## MaiTri 0.285   0.021 *  
## AndPol 0.264   0.037 *  
## CarLim 0.264   0.042 *  
## 
##  Group 3  #sps.  9 
##         stat p.value    
## LycAno 0.651   0.001 ***
## VacCes 0.646   0.001 ***
## SolMac 0.628   0.001 ***
## VacMyr 0.595   0.001 ***
## TriBor 0.557   0.001 ***
## AveFle 0.429   0.015 *  
## RibGla 0.408   0.001 ***
## RubIda 0.318   0.008 ** 
## AlnVir 0.308   0.037 *  
## 
##  Group 1+2  #sps.  3 
##         stat p.value   
## SalVes 0.588   0.002 **
## SolMul 0.356   0.042 * 
## TriCes 0.354   0.019 * 
## 
##  Group 1+3  #sps.  2 
##         stat p.value    
## VacUli 0.792   0.001 ***
## BetGla 0.710   0.001 ***
## 
##  Group 2+3  #sps.  5 
##         stat p.value    
## CorCan 0.775   0.001 ***
## LinBor 0.710   0.001 ***
## PicMar 0.680   0.001 ***
## GeoLiv 0.590   0.001 ***
## PicGla 0.495   0.017 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#Analyzing species ecological preferences with correlation indices
# Negative values indicate that a species tends to 'avoid' certain habitats
env.dis.inval.3.phi<-multipatt(abd.sp.allsp, vasc.env.clust.3$cluster, func="r.g", control=how(nperm=999))
summary(env.dis.inval.3.phi)

## 
##  Multilevel pattern analysis
##  ---------------------------
## 
##  Association function: r.g
##  Significance level (alpha): 0.05
## 
##  Total number of species: 125
##  Selected number of species: 49 
##  Number of species associated to 1 group: 43 
##  Number of species associated to 2 groups: 6 
## 
##  List of species associated to each combination: 
## 
##  Group 1  #sps.  12 
##         stat p.value    
## VacUli 0.423   0.001 ***
## CarSci 0.419   0.001 ***
## ArcAlp 0.331   0.001 ***
## CarBig 0.278   0.001 ***
## SalUva 0.255   0.002 ** 
## RhoLap 0.244   0.004 ** 
## DryInt 0.237   0.005 ** 
## BisViv 0.235   0.002 ** 
## PyrGra 0.216   0.011 *  
## JunTri 0.211   0.007 ** 
## CarDef 0.178   0.047 *  
## CarCap 0.174   0.041 *  
## 
##  Group 2  #sps.  23 
##         stat p.value    
## CarVag 0.498   0.001 ***
## CarGyn 0.419   0.001 ***
## MitNud 0.407   0.001 ***
## PicMar 0.392   0.001 ***
## VacOxy 0.371   0.001 ***
## EquSyl 0.364   0.001 ***
## RubCha 0.351   0.001 ***
## EquArv 0.322   0.001 ***
## LarLar 0.306   0.001 ***
## PetFri 0.305   0.001 ***
## OrtSec 0.302   0.001 ***
## RubArc 0.264   0.004 ** 
## SalArc 0.263   0.003 ** 
## KalPol 0.251   0.002 ** 
## EurRad 0.251   0.004 ** 
## MaiTri 0.221   0.015 *  
## RhoGro 0.214   0.018 *  
## SalPla 0.213   0.012 *  
## AchMil 0.212   0.012 *  
## AndPol 0.211   0.026 *  
## AnePar 0.207   0.012 *  
## GauHis 0.205   0.010 ** 
## CarAqu 0.181   0.034 *  
## 
##  Group 3  #sps.  8 
##         stat p.value    
## VacCes 0.377   0.001 ***
## VacMyr 0.361   0.001 ***
## SolMac 0.316   0.001 ***
## TriBor 0.311   0.001 ***
## RibGla 0.256   0.001 ***
## LycAno 0.247   0.002 ** 
## AveFle 0.161   0.040 *  
## RubIda 0.153   0.022 *  
## 
##  Group 1+2  #sps.  1 
##         stat p.value   
## SalVes 0.237   0.008 **
## 
##  Group 1+3  #sps.  1 
##         stat p.value    
## BetGla 0.324   0.001 ***
## 
##  Group 2+3  #sps.  4 
##         stat p.value    
## CorCan 0.353   0.001 ***
## GeoLiv 0.237   0.002 ** 
## PicGla 0.218   0.008 ** 
## LinBor 0.213   0.011 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

round(head(env.dis.inval.3.phi$str),3)

##             1      2      3    1+2    1+3   2+3
## AchMil -0.106  0.212 -0.106  0.106 -0.212 0.106
## AgrMer -0.062  0.124 -0.062  0.062 -0.124 0.062
## AlnVir -0.032 -0.114  0.146 -0.146  0.114 0.032
## AmeBar -0.059 -0.059  0.118 -0.118  0.059 0.059
## AndPol -0.106  0.211 -0.106  0.106 -0.211 0.106
## AnePar -0.067  0.207 -0.140  0.140 -0.207 0.067

#Indicator species analysis without site groups combinations
env.dis.indvalori.3<-multipatt(abd.sp.allsp, vasc.env.clust.3$cluster, duleg=TRUE, control=how(nperm=999))
summary(env.dis.indvalori.3)

## 
##  Multilevel pattern analysis
##  ---------------------------
## 
##  Association function: IndVal.g
##  Significance level (alpha): 0.05
## 
##  Total number of species: 125
##  Selected number of species: 50 
##  Number of species associated to 1 group: 50 
##  Number of species associated to 2 groups: 0 
## 
##  List of species associated to each combination: 
## 
##  Group 1  #sps.  11 
##         stat p.value    
## VacUli 0.771   0.001 ***
## CarSci 0.682   0.001 ***
## CarBig 0.537   0.001 ***
## ArcAlp 0.535   0.001 ***
## DryInt 0.458   0.001 ***
## BisViv 0.424   0.001 ***
## RhoLap 0.356   0.002 ** 
## SalUva 0.345   0.001 ***
## CarCap 0.315   0.002 ** 
## JunTri 0.267   0.015 *  
## PyrGra 0.267   0.009 ** 
## 
##  Group 2  #sps.  28 
##         stat p.value    
## CarVag 0.759   0.001 ***
## PicMar 0.662   0.001 ***
## EquSyl 0.647   0.001 ***
## MitNud 0.638   0.001 ***
## CarGyn 0.600   0.001 ***
## RhoGro 0.595   0.002 ** 
## LinBor 0.587   0.006 ** 
## GauHis 0.517   0.001 ***
## EmpNig 0.491   0.033 *  
## RubArc 0.484   0.002 ** 
## EquArv 0.470   0.001 ***
## RubCha 0.470   0.001 ***
## OrtSec 0.466   0.001 ***
## VacOxy 0.457   0.001 ***
## GeoLiv 0.454   0.037 *  
## PetFri 0.422   0.001 ***
## PicGla 0.411   0.038 *  
## LarLar 0.403   0.001 ***
## KalPol 0.389   0.002 ** 
## SalArc 0.389   0.003 ** 
## EurRad 0.374   0.002 ** 
## VioAdu 0.359   0.031 *  
## AnePar 0.325   0.024 *  
## SalPla 0.305   0.015 *  
## AchMil 0.285   0.028 *  
## CarAqu 0.285   0.034 *  
## MaiTri 0.285   0.019 *  
## AndPol 0.264   0.041 *  
## 
##  Group 3  #sps.  11 
##         stat p.value    
## LycAno 0.651   0.001 ***
## VacCes 0.646   0.001 ***
## SolMac 0.628   0.001 ***
## CorCan 0.609   0.001 ***
## VacMyr 0.595   0.001 ***
## TriBor 0.557   0.001 ***
## BetGla 0.533   0.011 *  
## AveFle 0.429   0.008 ** 
## RibGla 0.408   0.001 ***
## RubIda 0.318   0.008 ** 
## AlnVir 0.308   0.022 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Get values corresponding to each cluster; these are numeric
env.no.na<-subset(env.dis.inval.3.phi$str, !(is.na(env.dis.inval.3.phi$str[,1])))
env.dist.inv.1.3<-env.no.na[,1]
env.dist.inv.2.3<-env.no.na[,2]
env.dist.inv.3.3<-env.no.na[,3]

names(which(env.dis.inval.3.phi$str[,1]=="NaN"))

##  [1] "MoeMac" "ParKot" "PhyCae" "SteLon" "AreHum" "BetMin" "BetPum"
##  [8] "CarBel" "CarGla" "CopLap" "CorTri" "LuzSpi" "PoaAlp" "SalHer"
## [15] "SalMyr"

# drop tips from vascular phylogeny so that it matches species with usable indicator species

#drop extra tips from tree
trans.ind.3.tree<-drop.tip(trans.one.tree,c("MoeMac", "ParKot", "PhyCae", "SteLon", "AreHum", "BetMin", "BetPum", "CarBel", "CarGla", "CopLap", "CorTri", "LuzSpi", "PoaAlp", "SalHer", "SalMyr"))

#set colours for map
env.1.3.map<-contMap(trans.ind.3.tree,env.dist.inv.1.3,lwd=4,res=100, fsize=0.35, ftype="b", sig=2)

env.2.3.map<-contMap(trans.ind.3.tree,env.dist.inv.2.3,lwd=4,res=100, fsize=0.35, ftype="b", sig=2)

env.3.3.map<-contMap(trans.ind.3.tree,env.dist.inv.3.3,lwd=4,res=100, fsize=0.35, ftype="b", sig=2)

Phylogenetic signal of indicator species for environmental distance clusters (vasculars)

Three environmental distance clusters

#plot contMaps side-by-side
#dev.new(width=11.8, height=8)
par(mfrow=c(1,3), mar=c(4, 4, 0, 1), mai=c(4, 0, 0, 0))
#Black and white ContMap
env.1.3.map$cols[]<-grey(seq(1,0,length.out=length(env.1.3.map$cols)))
plot(env.1.3.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, env.1.3.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

#Black and white ContMap for angiosperms only
env.2.3.map$cols[]<-grey(seq(1,0,length.out=length(env.2.3.map$cols)))
plot(env.2.3.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, env.2.3.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

env.3.3.map$cols[]<-grey(seq(1,0,length.out=length(env.3.3.map$cols)))
plot(env.3.3.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)

#add.color.bar(420, env.3.3.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

Four environmental distance clusters

#plot all contMaps side-by-side for four clusters
#dev.new(width=11.8, height=8)
par(mfrow=c(1,4), mar=c(4, 4, 0, 1), mai=c(4, 0, 0, 0))
#Black and white ContMap
env.1.4.map$cols[]<-grey(seq(1,0,length.out=length(env.1.4.map$cols)))
plot(env.1.4.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, env.1.4.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

env.2.4.map$cols[]<-grey(seq(1,0,length.out=length(env.2.4.map$cols)))
plot(env.2.4.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, env.2.4.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

env.3.4.map$cols[]<-grey(seq(1,0,length.out=length(env.3.4.map$cols)))
plot(env.3.4.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, env.3.4.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

env.4.4.map$cols[]<-grey(seq(1,0,length.out=length(env.4.4.map$cols)))
plot(env.4.4.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)

#add.color.bar(420, env.4.4.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

Five environmental distance clusters

#plot all  angiosperm contMaps side-by-side for four clusters
#dev.new(width=11.8, height=8)
par(mfrow=c(1,5), mar=c(4, 4, 0, 1), mai=c(4, 0, 0, 0))
#Black and white ContMap
env.1.5.map$cols[]<-grey(seq(1,0,length.out=length(env.1.5.map$cols)))
plot(env.1.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, env.1.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

env.2.5.map$cols[]<-grey(seq(1,0,length.out=length(env.2.5.map$cols)))
plot(env.2.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, env.2.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

env.3.5.map$cols[]<-grey(seq(1,0,length.out=length(env.3.5.map$cols)))
plot(env.3.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, env.3.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

env.4.5.map$cols[]<-grey(seq(1,0,length.out=length(env.4.5.map$cols)))
plot(env.4.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, env.4.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

env.5.5.map$cols[]<-grey(seq(1,0,length.out=length(env.5.5.map$cols)))
plot(env.5.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)

#add.color.bar(420, env.5.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

Phylogenetic signal for indicator species across Tammy’s classifications

Five clusters for Tammy’s classifications

I only completed an analysis for five clusters since that is the number I reduced the groupings to when I aggregated the preexisting clusters from before.

#plot all  contMaps side-by-side for fiveclusters
#dev.new(width=11.8, height=8)
par(mfrow=c(1,5), mar=c(4, 4, 0, 1), mai=c(4, 0, 0, 0))
#Black and white ContMap
tc.1.5.map$cols[]<-grey(seq(1,0,length.out=length(tc.1.5.map$cols)))
plot(tc.1.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, tc.1.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

tc.2.5.map$cols[]<-grey(seq(1,0,length.out=length(tc.2.5.map$cols)))
plot(tc.2.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, tc.2.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

tc.3.5.map$cols[]<-grey(seq(1,0,length.out=length(tc.3.5.map$cols)))
plot(tc.3.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, tc.3.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

tc.4.5.map$cols[]<-grey(seq(1,0,length.out=length(tc.4.5.map$cols)))
plot(tc.4.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)
#add.color.bar(420, tc.4.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

tc.5.5.map$cols[]<-grey(seq(1,0,length.out=length(tc.5.5.map$cols)))
plot(tc.5.5.map,outline=TRUE,lwd=3, res=10,  ftype="b",sig=2,legend=FALSE, fsize=0.35)

#add.color.bar(420, tc.5.5.map$cols, lims=c(0,0.106),title="Species contribution to beta diversity", digits=3, prompt=TRUE, lwd=3, outline=TRUE, fsize=0.6, ftype="b")

Blomberg’s K for indicator species for both environmental distance clusters and Tammy’s habitat classification clusters

env.clust.K.3

##          Group 1 Group 2 Group 3
## Env.dist   0.019    0.02   0.013

env.clust.K.4

##          Group 1 Group 2 Group 3 Group 4
## Env.dist   0.019   0.015   0.021    0.02

tc.env.clust.K.5

##             Group 1 Group 2 Group 3 Group 4 Group 5
## Env.dist      0.015   0.019   0.014   0.017   0.019
## Tammy class   0.014   0.018   0.021   0.019   0.019