Schefferville - transitions; additive partitioning of beta diversity
Date: February 25, 2015
Tammy L. Elliott
R version 3.1
Show data structure for vascular abundance data
#add 0 values for 11 species to large grid community matrix
abd.sp.allsp<-cbind(abd.sp, sp.zero.com.matrix.zeros)
head(abd.sp.allsp)## AchMil AgrMer AlnVir AmeBar AndPol AnePar AntAlp AntMon ArcAlp ArnAng
## EA1 0.00 0.00 0 0 0.00 0.00 0 0 0 0
## EB1 0.25 0.00 0 0 0.00 0.25 0 0 0 0
## EC1 0.25 0.25 0 0 0.00 0.75 0 0 0 0
## ED1 0.00 0.00 0 0 0.00 0.00 0 0 0 0
## EE1 0.00 0.00 0 0 0.25 0.00 0 0 0 0
## EF1 0.00 0.00 0 0 0.00 0.00 0 0 0 0
## BarAlp BetGla BisViv CalCan CarAqu CarBig CarBru CarCap CarCat CarDef
## EA1 0.00 0.0 0.00 0.0 0 0 0 0 0 0
## EB1 0.25 0.0 0.25 0.0 0 0 0 0 0 0
## EC1 0.25 0.5 0.25 0.0 0 0 0 0 0 0
## ED1 0.00 0.0 0.25 0.0 0 0 0 0 0 0
## EE1 0.00 0.0 0.00 0.5 0 0 0 0 0 0
## EF1 0.00 0.0 0.00 0.0 0 0 0 0 0 0
## CarDis CarGyn CarLep CarLim CarMag CarSci CarTri CarUtr CarVag CasSep
## EA1 0 0.25 0 0.0 0 0 0 0 6.00 0
## EB1 0 0.00 0 0.0 0 0 0 0 0.50 0
## EC1 0 0.00 0 0.0 0 0 0 0 2.00 0
## ED1 0 0.25 0 0.0 0 0 0 0 7.00 0
## EE1 0 0.25 0 0.5 0 0 0 0 0.75 0
## EF1 0 0.00 0 0.0 0 0 0 0 0.00 0
## CerAlp ChaAng CopTri CorCan CysMon DasFru AveFle DiaLap DipCom DryExp
## EA1 0 0 0.50 0.25 0 0 0 0 0 0
## EB1 0 0 0.25 0.00 0 0 0 0 0 0
## EC1 0 0 0.00 0.00 0 5 0 0 0 0
## ED1 0 0 0.50 0.00 0 0 0 0 0 0
## EE1 0 0 0.25 0.25 0 0 0 0 0 0
## EF1 0 0 0.00 0.50 0 0 0 0 0 0
## DryInt ElyTra EmpNig EpiHor EquArv EquSci EquSyl EquVar EriVir EurRad
## EA1 0 0.00 0.75 0 0 0.25 0.75 0 0.00 0.00
## EB1 0 0.00 0.50 0 0 0.00 0.00 0 0.00 0.25
## EC1 0 0.25 0.00 0 0 0.00 0.00 0 0.00 0.25
## ED1 0 0.00 8.00 0 0 0.00 0.75 0 0.00 0.00
## EE1 0 0.00 2.00 0 0 0.00 0.25 0 0.25 0.75
## EF1 0 0.00 3.00 0 0 0.00 0.00 0 0.00 0.00
## FraVir GauHis GeoLiv HupApr JunCom JunTri KalPol LinBor LisCor LonVil
## EA1 0 0.25 0.25 0.0 0 0 0.25 0.25 0 0.00
## EB1 0 0.00 0.25 0.5 7 0 0.00 0.00 0 0.75
## EC1 0 0.00 0.00 0.0 0 0 0.00 0.00 0 0.00
## ED1 0 0.00 0.00 0.0 0 0 0.00 1.00 0 0.00
## EE1 0 0.25 0.00 0.0 0 0 0.25 0.25 0 0.00
## EF1 0 0.00 0.50 0.0 0 0 0.25 0.00 0 0.00
## LuzPar LycAno MaiTri MinBif MitNud MoeMac MonUni MyrGal OrtSec PacAur
## EA1 0 0 0.00 0 0.00 0 0 0 0.00 0
## EB1 0 0 0.00 0 0.00 0 0 0 0.00 0
## EC1 0 0 0.00 0 0.00 0 0 0 0.00 0
## ED1 0 0 0.00 0 0.25 0 0 0 0.25 0
## EE1 0 0 0.25 0 0.00 0 0 0 0.00 0
## EF1 0 0 0.00 0 0.00 0 0 0 0.00 0
## ParKot PetFri PhyCae PoaArc PyrAsa PyrGra RhiMin RhoGro RhoLap RibGla
## EA1 0 0.00 0 0 0 0 0 1 0 0
## EB1 0 0.00 0 0 0 0 0 1 0 0
## EC1 0 0.25 0 0 0 0 0 0 0 0
## ED1 0 0.25 0 0 0 0 0 2 0 0
## EE1 0 0.00 0 0 0 0 0 2 0 0
## EF1 0 0.00 0 0 0 0 0 25 0 0
## RubArc RubCha RubIda SalArc SalArg SalGla SalHum SalPed SalPla SalUva
## EA1 0.00 0 0 0.5 0 0 0 0 0 0
## EB1 0.00 0 0 0.5 0 0 0 0 0 0
## EC1 0.50 0 0 0.5 0 0 0 0 0 0
## ED1 0.25 0 0 1.0 0 0 0 0 0 0
## EE1 0.25 0 0 0.0 0 0 0 0 0 0
## EF1 0.00 0 0 0.0 0 0 0 0 0 0
## SalVes SchPur SelSel SolMac SolMul SteBor SteLon TofPus TriAlp TriBor
## EA1 0 0 0.00 0 0.00 0 0 0 0 0.00
## EB1 0 0 0.25 0 0.25 0 0 0 0 0.00
## EC1 0 0 0.00 0 0.25 0 0 0 0 0.00
## ED1 0 0 0.00 0 0.00 0 0 0 0 0.00
## EE1 0 0 0.00 0 0.00 0 0 0 0 0.25
## EF1 0 0 0.00 0 0.00 0 0 0 0 0.00
## TriCes TriSpi VacCes VacMyr VacOxy VacUli VacVit VibEdu VioAdu VioRen
## EA1 0 0 0.25 0 0.00 0.00 0.00 0 0.00 0
## EB1 12 0 0.00 0 0.25 1.00 0.00 0 0.75 0
## EC1 6 0 1.00 0 0.00 0.00 0.00 0 0.50 0
## ED1 0 0 0.75 0 0.25 1.00 0.25 0 0.00 0
## EE1 3 0 0.00 0 0.25 0.75 0.00 0 0.00 0
## EF1 0 0 0.00 0 0.00 0.00 0.50 0 0.00 0
## AbiBal LarLar PicGla PicMar AreHum BetMin BetPum CarBel CarGla CopLap
## EA1 0 0 0 25 0 0 0 0 0 0
## EB1 0 0 3 0 0 0 0 0 0 0
## EC1 0 0 0 0 0 0 0 0 0 0
## ED1 0 0 2 4 0 0 0 0 0 0
## EE1 0 0 0 0 0 0 0 0 0 0
## EF1 0 0 0 18 0 0 0 0 0 0
## CorTri LuzSpi PoaAlp SalHer SalMyr
## EA1 0 0 0 0 0
## EB1 0 0 0 0 0
## EC1 0 0 0 0 0
## ED1 0 0 0 0 0
## EE1 0 0 0 0 0
## EF1 0 0 0 0 0#again, find difference in what is in phylogeny and plots
xx<-names(abd.sp.allsp)
yy<-trans.hundred.trees[[1]]$tip.label
xxyy<-setdiff(xx,yy)
yyxx<-setdiff(yy,xx)
xxyy## character(0)yyxx## character(0)Calculate Simpson’s beta diversity
Beta diversity; ratio of total number of species in a collection of sites (S) and the average richness per one site
Beta = S/alpha(mean) -1; subtraction of one means that beta=0 when there are no excess species or no heterogeneity between sites
The advantage of additive partitioning of gamma diversity is that beta diversity can be calculated for several different nested levels
First isolate plots that are associated with each sampling band
vasc.800<-abd.sp.allsp[grepl("800*", rownames(abd.sp.allsp)),]
head(vasc.800)## AchMil AgrMer AlnVir AmeBar AndPol AnePar AntAlp AntMon ArcAlp
## E800A1 0 0 0 0 0 0 0 0 1.00
## E800B2 0 0 0 0 0 0 0 0 0.00
## E800C3 0 0 0 0 0 0 0 0 0.50
## E800D4 0 0 0 0 0 0 0 0 0.00
## E800E5 0 0 0 0 0 0 0 0 0.00
## E800F6 0 0 0 0 0 0 0 0 0.25
## ArnAng BarAlp BetGla BisViv CalCan CarAqu CarBig CarBru CarCap
## E800A1 0 0 0 0.00 0 0 0.00 0 0.00
## E800B2 0 0 0 0.00 0 0 2.00 0 0.00
## E800C3 0 0 0 0.25 0 0 1.00 0 0.00
## E800D4 0 0 0 0.50 0 0 1.00 0 2.00
## E800E5 0 0 0 0.00 0 0 0.00 0 0.25
## E800F6 0 0 0 0.25 0 0 0.75 0 0.25
## CarCat CarDef CarDis CarGyn CarLep CarLim CarMag CarSci CarTri
## E800A1 0.0 0 0 0 0 0 0 0.00 0
## E800B2 0.0 0 0 0 0 0 0 0.75 0
## E800C3 0.0 0 0 0 0 0 0 4.00 0
## E800D4 0.0 0 0 0 0 0 0 2.00 0
## E800E5 0.0 0 0 0 0 0 0 2.00 0
## E800F6 0.5 0 0 0 0 0 0 2.00 0
## CarUtr CarVag CasSep CerAlp ChaAng CopTri CorCan CysMon DasFru
## E800A1 0 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 1 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0 0
## AveFle DiaLap DipCom DryExp DryInt ElyTra EmpNig EpiHor EquArv
## E800A1 0 0 0 0 0 0 8 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 0 0 0 1 0 0 0 0
## E800D4 0 0 0 0 1 0 0 0 0
## E800E5 0 8 0 0 2 0 0 0 0
## E800F6 0 0 0 0 1 0 0 0 0
## EquSci EquSyl EquVar EriVir EurRad FraVir GauHis GeoLiv HupApr
## E800A1 0 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 0 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0 0
## JunCom JunTri KalPol LinBor LisCor LonVil LuzPar LycAno MaiTri
## E800A1 0 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 0 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0 0
## MinBif MitNud MoeMac MonUni MyrGal OrtSec PacAur ParKot PetFri
## E800A1 0 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 0 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0 0
## PhyCae PoaArc PyrAsa PyrGra RhiMin RhoGro RhoLap RibGla RubArc
## E800A1 0 0 0 0 0 0 0.00 0 0
## E800B2 0 0 0 0 0 0 2.00 0 0
## E800C3 0 0 0 0 0 0 0.75 0 0
## E800D4 0 0 0 0 0 0 2.00 0 0
## E800E5 0 0 0 0 0 0 0.00 0 0
## E800F6 0 0 0 0 0 0 0.00 0 0
## RubCha RubIda SalArc SalArg SalGla SalHum SalPed SalPla SalUva
## E800A1 0 0 0 0 0 0 0 0 5
## E800B2 0 0 0 0 0 0 0 0 2
## E800C3 0 0 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 2
## E800E5 0 0 0 0 0 0 0 0 2
## E800F6 0 0 0 0 0 0 0 0 0
## SalVes SchPur SelSel SolMac SolMul SteBor SteLon TofPus TriAlp
## E800A1 0 0 0 0 0 0 0 0.00 0
## E800B2 0 0 0 0 0 0 0 0.00 0
## E800C3 10 0 0 0 0 0 0 0.00 0
## E800D4 0 0 0 0 0 0 0 0.25 0
## E800E5 0 0 0 0 0 0 0 0.00 0
## E800F6 12 0 0 0 0 0 0 0.50 0
## TriBor TriCes TriSpi VacCes VacMyr VacOxy VacUli VacVit VibEdu
## E800A1 0 0 0 0 0 0 29.0 0.25 0
## E800B2 0 0 0 0 0 0 3.0 0.25 0
## E800C3 0 0 0 0 0 0 3.0 0.00 0
## E800D4 0 0 0 0 0 0 0.5 0.00 0
## E800E5 0 0 0 0 0 0 2.0 0.00 0
## E800F6 0 2 0 0 0 0 2.0 0.00 0
## VioAdu VioRen AbiBal LarLar PicGla PicMar AreHum BetMin BetPum
## E800A1 0 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 0 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0 0
## CarBel CarGla CopLap CorTri LuzSpi PoaAlp SalHer SalMyr
## E800A1 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0
## E800C3 0 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0vasc.775<-abd.sp.allsp[grepl("775*", rownames(abd.sp.allsp)),]
vasc.745<-abd.sp.allsp[grepl("745*", rownames(abd.sp.allsp)),]
vasc.710<-abd.sp.allsp[grepl("710*", rownames(abd.sp.allsp)),]
vasc.675<-abd.sp.allsp[grepl("675*", rownames(abd.sp.allsp)),]
vasc.645<-abd.sp.allsp[grepl("645*", rownames(abd.sp.allsp)),]
vasc.615<-abd.sp.allsp[grepl("615*", rownames(abd.sp.allsp)),]
vasc.600<-abd.sp.allsp[grepl("600*", rownames(abd.sp.allsp)),]
vasc.1<-abd.sp.allsp[c(1:11),]
vasc.2<-abd.sp.allsp[c(12:22),]
vasc.3<-abd.sp.allsp[c(23:33),]
vasc.4<-abd.sp.allsp[c(34:44),]
vasc.5<-abd.sp.allsp[c(45:55),]
vasc.6<-abd.sp.allsp[c(56:66),]
vasc.7<-abd.sp.allsp[c(67:77),]
vasc.8<-abd.sp.allsp[c(78:88),]
#isolate the alpha values for each plot per sampling band; these will be used to calculate within beta diversity
alphas.vasc.800<-mean(diversity(vasc.800))
head(alphas.vasc.800)## [1] 1.5538alphas.vasc.775<-mean(diversity(vasc.775))
alphas.vasc.745<-mean(diversity(vasc.745))
alphas.vasc.710<-mean(diversity(vasc.710))
alphas.vasc.675<-mean(diversity(vasc.675))
alphas.vasc.645<-mean(diversity(vasc.645))
alphas.vasc.615<-mean(diversity(vasc.615))
alphas.vasc.600<-mean(diversity(vasc.600))
alphas.vasc.8<-mean(diversity(vasc.8))
alphas.vasc.7<-mean(diversity(vasc.7))
alphas.vasc.6<-mean(diversity(vasc.6))
alphas.vasc.5<-mean(diversity(vasc.5))
alphas.vasc.4<-mean(diversity(vasc.4))
alphas.vasc.3<-mean(diversity(vasc.3))
alphas.vasc.2<-mean(diversity(vasc.2))
alphas.vasc.1<-mean(diversity(vasc.1))
#Calculate gammas per each sampling band; these will be used to caluclate within-band beta diversity
gamma.vasc.800<-diversity(colMeans(vasc.800/rowSums(vasc.800)))
head(gamma.vasc.800)## [1] 2.446765gamma.vasc.775<-diversity(colMeans(vasc.775/rowSums(vasc.775)))
gamma.vasc.745<-diversity(colMeans(vasc.745/rowSums(vasc.745)))
gamma.vasc.710<-diversity(colMeans(vasc.710/rowSums(vasc.710)))
gamma.vasc.675<-diversity(colMeans(vasc.675/rowSums(vasc.675)))
gamma.vasc.645<-diversity(colMeans(vasc.645/rowSums(vasc.645)))
gamma.vasc.615<-diversity(colMeans(vasc.615/rowSums(vasc.615)))
gamma.vasc.600<-diversity(colMeans(vasc.600/rowSums(vasc.600)))
gamma.vasc.8<-diversity(colMeans(vasc.8/rowSums(vasc.8)))
gamma.vasc.7<-diversity(colMeans(vasc.7/rowSums(vasc.7)))
gamma.vasc.6<-diversity(colMeans(vasc.6/rowSums(vasc.6)))
gamma.vasc.5<-diversity(colMeans(vasc.5/rowSums(vasc.5)))
gamma.vasc.4<-diversity(colMeans(vasc.4/rowSums(vasc.4)))
gamma.vasc.3<-diversity(colMeans(vasc.3/rowSums(vasc.3)))
gamma.vasc.2<-diversity(colMeans(vasc.2/rowSums(vasc.2)))
gamma.vasc.1<-diversity(colMeans(vasc.1/rowSums(vasc.1)))
#Calculate beta diversity per each sampling band
bd.800<-gamma.vasc.800-alphas.vasc.800
bd.775<-gamma.vasc.775-alphas.vasc.775
bd.745<-gamma.vasc.745-alphas.vasc.745
bd.710<-gamma.vasc.710-alphas.vasc.710
bd.675<-gamma.vasc.675-alphas.vasc.675
bd.645<-gamma.vasc.645-alphas.vasc.645
bd.615<-gamma.vasc.615-alphas.vasc.615
bd.600<-gamma.vasc.600-alphas.vasc.600
bd.8<-gamma.vasc.8-alphas.vasc.8
bd.7<-gamma.vasc.7-alphas.vasc.7
bd.6<-gamma.vasc.6-alphas.vasc.6
bd.5<-gamma.vasc.5-alphas.vasc.5
bd.4<-gamma.vasc.4-alphas.vasc.4
bd.3<-gamma.vasc.3-alphas.vasc.3
bd.2<-gamma.vasc.2-alphas.vasc.2
bd.1<-gamma.vasc.1-alphas.vasc.1
#Calculate grid beta
#overall gamma diversity
grid.gamma<-diversity(colSums(abd.sp.allsp)/sum(colSums(abd.sp.allsp)))
grid.alpha<-mean(gamma.vasc.800, gamma.vasc.775, gamma.vasc.745, gamma.vasc.710, gamma.vasc.675, gamma.vasc.645, gamma.vasc.615, gamma.vasc.600, gamma.vasc.8, gamma.vasc.7, gamma.vasc.6, gamma.vasc.5, gamma.vasc.4, gamma.vasc.3, gamma.vasc.2, gamma.vasc.1)
#grid beta; this is among habitat betadiversity
grid.beta<-(grid.gamma-grid.alpha)Simpon’s among- and within- diversity table
#show results in table
grid.alphas.band<-round(rbind(alphas.vasc.800, alphas.vasc.775, alphas.vasc.745, alphas.vasc.710, alphas.vasc.675,alphas.vasc.645, alphas.vasc.615, alphas.vasc.600, alphas.vasc.8, alphas.vasc.7, alphas.vasc.6, alphas.vasc.5, alphas.vasc.4, alphas.vasc.3, alphas.vasc.2, alphas.vasc.1), 1)
grid.gamma.vasc.band<-round(rbind(gamma.vasc.800, gamma.vasc.775, gamma.vasc.745, gamma.vasc.710, gamma.vasc.675, gamma.vasc.645, gamma.vasc.615, gamma.vasc.600, gamma.vasc.8, gamma.vasc.7, gamma.vasc.6, gamma.vasc.5, gamma.vasc.4, gamma.vasc.3, gamma.vasc.2, gamma.vasc.1),1)
grid.bd.within<-round(rbind(bd.800, bd.775, bd.745, bd.710, bd.675, bd.645, bd.615, bd.600, bd.8, bd.7, bd.6, bd.5, bd.4, bd.3, bd.2, bd.1),1)
grid.alpha.beta.gamma<-cbind(grid.alphas.band, grid.gamma.vasc.band, grid.bd.within)
colnames(grid.alpha.beta.gamma)<-c("Mean alpha", "Gamma", "Beta diversity (within bands)")
rownames(grid.alpha.beta.gamma)<-c("800 metres", "775 metres", "745 metres", "710 metres", "675 metres", "645 metres", "615 metres", "600 metres", "8", "7", "6","5", "4", "3", "2", "1")
grid.alpha.beta.gamma## Mean alpha Gamma Beta diversity (within bands)
## 800 metres 1.6 2.4 0.9
## 775 metres 1.4 2.3 0.9
## 745 metres 1.0 2.0 1.0
## 710 metres 1.2 2.0 0.8
## 675 metres 1.2 2.0 0.8
## 645 metres 1.4 2.5 1.1
## 615 metres 1.2 2.5 1.3
## 600 metres 1.5 2.7 1.2
## 8 1.7 2.8 1.1
## 7 1.5 2.7 1.3
## 6 1.4 2.7 1.2
## 5 1.8 2.7 1.0
## 4 1.7 2.7 1.0
## 3 1.8 3.1 1.3
## 2 2.2 3.4 1.1
## 1 1.8 2.9 1.1Simpson’s beta diversity with angiosperms only
#subset transdata to remove angiosperms
angio.sp.abd<-abd.sp.allsp[,-match(c("LycAno", "HupApr", "DipCom", "SelSel", "CysMon", "DryExp", "EquSyl", "EquArv", "EquSci", "EquVar", "JunCom", "PicMar", "PicGla", "AbiBal", "LarLar"), names(abd.sp.allsp))]
head(angio.sp.abd)## AchMil AgrMer AlnVir AmeBar AndPol AnePar AntAlp AntMon ArcAlp ArnAng
## EA1 0.00 0.00 0 0 0.00 0.00 0 0 0 0
## EB1 0.25 0.00 0 0 0.00 0.25 0 0 0 0
## EC1 0.25 0.25 0 0 0.00 0.75 0 0 0 0
## ED1 0.00 0.00 0 0 0.00 0.00 0 0 0 0
## EE1 0.00 0.00 0 0 0.25 0.00 0 0 0 0
## EF1 0.00 0.00 0 0 0.00 0.00 0 0 0 0
## BarAlp BetGla BisViv CalCan CarAqu CarBig CarBru CarCap CarCat CarDef
## EA1 0.00 0.0 0.00 0.0 0 0 0 0 0 0
## EB1 0.25 0.0 0.25 0.0 0 0 0 0 0 0
## EC1 0.25 0.5 0.25 0.0 0 0 0 0 0 0
## ED1 0.00 0.0 0.25 0.0 0 0 0 0 0 0
## EE1 0.00 0.0 0.00 0.5 0 0 0 0 0 0
## EF1 0.00 0.0 0.00 0.0 0 0 0 0 0 0
## CarDis CarGyn CarLep CarLim CarMag CarSci CarTri CarUtr CarVag CasSep
## EA1 0 0.25 0 0.0 0 0 0 0 6.00 0
## EB1 0 0.00 0 0.0 0 0 0 0 0.50 0
## EC1 0 0.00 0 0.0 0 0 0 0 2.00 0
## ED1 0 0.25 0 0.0 0 0 0 0 7.00 0
## EE1 0 0.25 0 0.5 0 0 0 0 0.75 0
## EF1 0 0.00 0 0.0 0 0 0 0 0.00 0
## CerAlp ChaAng CopTri CorCan DasFru AveFle DiaLap DryInt ElyTra EmpNig
## EA1 0 0 0.50 0.25 0 0 0 0 0.00 0.75
## EB1 0 0 0.25 0.00 0 0 0 0 0.00 0.50
## EC1 0 0 0.00 0.00 5 0 0 0 0.25 0.00
## ED1 0 0 0.50 0.00 0 0 0 0 0.00 8.00
## EE1 0 0 0.25 0.25 0 0 0 0 0.00 2.00
## EF1 0 0 0.00 0.50 0 0 0 0 0.00 3.00
## EpiHor EriVir EurRad FraVir GauHis GeoLiv JunTri KalPol LinBor LisCor
## EA1 0 0.00 0.00 0 0.25 0.25 0 0.25 0.25 0
## EB1 0 0.00 0.25 0 0.00 0.25 0 0.00 0.00 0
## EC1 0 0.00 0.25 0 0.00 0.00 0 0.00 0.00 0
## ED1 0 0.00 0.00 0 0.00 0.00 0 0.00 1.00 0
## EE1 0 0.25 0.75 0 0.25 0.00 0 0.25 0.25 0
## EF1 0 0.00 0.00 0 0.00 0.50 0 0.25 0.00 0
## LonVil LuzPar MaiTri MinBif MitNud MoeMac MonUni MyrGal OrtSec PacAur
## EA1 0.00 0 0.00 0 0.00 0 0 0 0.00 0
## EB1 0.75 0 0.00 0 0.00 0 0 0 0.00 0
## EC1 0.00 0 0.00 0 0.00 0 0 0 0.00 0
## ED1 0.00 0 0.00 0 0.25 0 0 0 0.25 0
## EE1 0.00 0 0.25 0 0.00 0 0 0 0.00 0
## EF1 0.00 0 0.00 0 0.00 0 0 0 0.00 0
## ParKot PetFri PhyCae PoaArc PyrAsa PyrGra RhiMin RhoGro RhoLap RibGla
## EA1 0 0.00 0 0 0 0 0 1 0 0
## EB1 0 0.00 0 0 0 0 0 1 0 0
## EC1 0 0.25 0 0 0 0 0 0 0 0
## ED1 0 0.25 0 0 0 0 0 2 0 0
## EE1 0 0.00 0 0 0 0 0 2 0 0
## EF1 0 0.00 0 0 0 0 0 25 0 0
## RubArc RubCha RubIda SalArc SalArg SalGla SalHum SalPed SalPla SalUva
## EA1 0.00 0 0 0.5 0 0 0 0 0 0
## EB1 0.00 0 0 0.5 0 0 0 0 0 0
## EC1 0.50 0 0 0.5 0 0 0 0 0 0
## ED1 0.25 0 0 1.0 0 0 0 0 0 0
## EE1 0.25 0 0 0.0 0 0 0 0 0 0
## EF1 0.00 0 0 0.0 0 0 0 0 0 0
## SalVes SchPur SolMac SolMul SteBor SteLon TofPus TriAlp TriBor TriCes
## EA1 0 0 0 0.00 0 0 0 0 0.00 0
## EB1 0 0 0 0.25 0 0 0 0 0.00 12
## EC1 0 0 0 0.25 0 0 0 0 0.00 6
## ED1 0 0 0 0.00 0 0 0 0 0.00 0
## EE1 0 0 0 0.00 0 0 0 0 0.25 3
## EF1 0 0 0 0.00 0 0 0 0 0.00 0
## TriSpi VacCes VacMyr VacOxy VacUli VacVit VibEdu VioAdu VioRen AreHum
## EA1 0 0.25 0 0.00 0.00 0.00 0 0.00 0 0
## EB1 0 0.00 0 0.25 1.00 0.00 0 0.75 0 0
## EC1 0 1.00 0 0.00 0.00 0.00 0 0.50 0 0
## ED1 0 0.75 0 0.25 1.00 0.25 0 0.00 0 0
## EE1 0 0.00 0 0.25 0.75 0.00 0 0.00 0 0
## EF1 0 0.00 0 0.00 0.00 0.50 0 0.00 0 0
## BetMin BetPum CarBel CarGla CopLap CorTri LuzSpi PoaAlp SalHer SalMyr
## EA1 0 0 0 0 0 0 0 0 0 0
## EB1 0 0 0 0 0 0 0 0 0 0
## EC1 0 0 0 0 0 0 0 0 0 0
## ED1 0 0 0 0 0 0 0 0 0 0
## EE1 0 0 0 0 0 0 0 0 0 0
## EF1 0 0 0 0 0 0 0 0 0 0#The advantage of additive partitioning of gamma diversity is that beta diversity can be calculated for several different nested levels
#First isolate plots that are associated with each sampling band
angio.800<-angio.sp.abd[grepl("800*", rownames(angio.sp.abd)),]
head(angio.800)## AchMil AgrMer AlnVir AmeBar AndPol AnePar AntAlp AntMon ArcAlp
## E800A1 0 0 0 0 0 0 0 0 1.00
## E800B2 0 0 0 0 0 0 0 0 0.00
## E800C3 0 0 0 0 0 0 0 0 0.50
## E800D4 0 0 0 0 0 0 0 0 0.00
## E800E5 0 0 0 0 0 0 0 0 0.00
## E800F6 0 0 0 0 0 0 0 0 0.25
## ArnAng BarAlp BetGla BisViv CalCan CarAqu CarBig CarBru CarCap
## E800A1 0 0 0 0.00 0 0 0.00 0 0.00
## E800B2 0 0 0 0.00 0 0 2.00 0 0.00
## E800C3 0 0 0 0.25 0 0 1.00 0 0.00
## E800D4 0 0 0 0.50 0 0 1.00 0 2.00
## E800E5 0 0 0 0.00 0 0 0.00 0 0.25
## E800F6 0 0 0 0.25 0 0 0.75 0 0.25
## CarCat CarDef CarDis CarGyn CarLep CarLim CarMag CarSci CarTri
## E800A1 0.0 0 0 0 0 0 0 0.00 0
## E800B2 0.0 0 0 0 0 0 0 0.75 0
## E800C3 0.0 0 0 0 0 0 0 4.00 0
## E800D4 0.0 0 0 0 0 0 0 2.00 0
## E800E5 0.0 0 0 0 0 0 0 2.00 0
## E800F6 0.5 0 0 0 0 0 0 2.00 0
## CarUtr CarVag CasSep CerAlp ChaAng CopTri CorCan DasFru AveFle
## E800A1 0 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 1 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0 0
## DiaLap DryInt ElyTra EmpNig EpiHor EriVir EurRad FraVir GauHis
## E800A1 0 0 0 8 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 1 0 0 0 0 0 0 0
## E800D4 0 1 0 0 0 0 0 0 0
## E800E5 8 2 0 0 0 0 0 0 0
## E800F6 0 1 0 0 0 0 0 0 0
## GeoLiv JunTri KalPol LinBor LisCor LonVil LuzPar MaiTri MinBif
## E800A1 0 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 0 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0 0
## MitNud MoeMac MonUni MyrGal OrtSec PacAur ParKot PetFri PhyCae
## E800A1 0 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 0 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0 0
## PoaArc PyrAsa PyrGra RhiMin RhoGro RhoLap RibGla RubArc RubCha
## E800A1 0 0 0 0 0 0.00 0 0 0
## E800B2 0 0 0 0 0 2.00 0 0 0
## E800C3 0 0 0 0 0 0.75 0 0 0
## E800D4 0 0 0 0 0 2.00 0 0 0
## E800E5 0 0 0 0 0 0.00 0 0 0
## E800F6 0 0 0 0 0 0.00 0 0 0
## RubIda SalArc SalArg SalGla SalHum SalPed SalPla SalUva SalVes
## E800A1 0 0 0 0 0 0 0 5 0
## E800B2 0 0 0 0 0 0 0 2 0
## E800C3 0 0 0 0 0 0 0 0 10
## E800D4 0 0 0 0 0 0 0 2 0
## E800E5 0 0 0 0 0 0 0 2 0
## E800F6 0 0 0 0 0 0 0 0 12
## SchPur SolMac SolMul SteBor SteLon TofPus TriAlp TriBor TriCes
## E800A1 0 0 0 0 0 0.00 0 0 0
## E800B2 0 0 0 0 0 0.00 0 0 0
## E800C3 0 0 0 0 0 0.00 0 0 0
## E800D4 0 0 0 0 0 0.25 0 0 0
## E800E5 0 0 0 0 0 0.00 0 0 0
## E800F6 0 0 0 0 0 0.50 0 0 2
## TriSpi VacCes VacMyr VacOxy VacUli VacVit VibEdu VioAdu VioRen
## E800A1 0 0 0 0 29.0 0.25 0 0 0
## E800B2 0 0 0 0 3.0 0.25 0 0 0
## E800C3 0 0 0 0 3.0 0.00 0 0 0
## E800D4 0 0 0 0 0.5 0.00 0 0 0
## E800E5 0 0 0 0 2.0 0.00 0 0 0
## E800F6 0 0 0 0 2.0 0.00 0 0 0
## AreHum BetMin BetPum CarBel CarGla CopLap CorTri LuzSpi PoaAlp
## E800A1 0 0 0 0 0 0 0 0 0
## E800B2 0 0 0 0 0 0 0 0 0
## E800C3 0 0 0 0 0 0 0 0 0
## E800D4 0 0 0 0 0 0 0 0 0
## E800E5 0 0 0 0 0 0 0 0 0
## E800F6 0 0 0 0 0 0 0 0 0
## SalHer SalMyr
## E800A1 0 0
## E800B2 0 0
## E800C3 0 0
## E800D4 0 0
## E800E5 0 0
## E800F6 0 0angio.775<-angio.sp.abd[grepl("775*", rownames(angio.sp.abd)),]
angio.745<-angio.sp.abd[grepl("745*", rownames(angio.sp.abd)),]
angio.710<-angio.sp.abd[grepl("710*", rownames(angio.sp.abd)),]
angio.675<-angio.sp.abd[grepl("675*", rownames(angio.sp.abd)),]
angio.645<-angio.sp.abd[grepl("645*", rownames(angio.sp.abd)),]
angio.615<-angio.sp.abd[grepl("615*", rownames(angio.sp.abd)),]
angio.600<-angio.sp.abd[grepl("600*", rownames(angio.sp.abd)),]
angio.1<-angio.sp.abd[c(1:11),]
angio.2<-angio.sp.abd[c(12:22),]
angio.3<-angio.sp.abd[c(23:33),]
angio.4<-angio.sp.abd[c(34:44),]
angio.5<-angio.sp.abd[c(45:55),]
angio.6<-angio.sp.abd[c(56:66),]
angio.7<-angio.sp.abd[c(67:77),]
angio.8<-angio.sp.abd[c(78:88),]
#isolate the alpha values for each plot per sampling band; these will be used to calculate within beta diversity
alphas.angio.800<-mean(diversity(angio.800))
head(alphas.angio.800)## [1] 1.543359alphas.angio.775<-mean(diversity(angio.775))
alphas.angio.745<-mean(diversity(angio.745))
alphas.angio.710<-mean(diversity(angio.710))
alphas.angio.675<-mean(diversity(angio.675))
alphas.angio.645<-mean(diversity(angio.645))
alphas.angio.615<-mean(diversity(angio.615))
alphas.angio.600<-mean(diversity(angio.600))
alphas.angio.8<-mean(diversity(angio.8))
alphas.angio.7<-mean(diversity(angio.7))
alphas.angio.6<-mean(diversity(angio.6))
alphas.angio.5<-mean(diversity(angio.5))
alphas.angio.4<-mean(diversity(angio.4))
alphas.angio.3<-mean(diversity(angio.3))
alphas.angio.2<-mean(diversity(angio.2))
alphas.angio.1<-mean(diversity(angio.1))
#Calculate gammas per each sampling band; these will be used to caluclate within-band beta diversity
gamma.angio.800<-diversity(colMeans(angio.800/rowSums(angio.800)))
head(gamma.angio.800)## [1] 2.433709gamma.angio.775<-diversity(colMeans(angio.775/rowSums(angio.775)))
gamma.angio.745<-diversity(colMeans(angio.745/rowSums(angio.745)))
gamma.angio.710<-diversity(colMeans(angio.710/rowSums(angio.710)))
gamma.angio.675<-diversity(colMeans(angio.675/rowSums(angio.675)))
gamma.angio.645<-diversity(colMeans(angio.645/rowSums(angio.645)))
gamma.angio.615<-diversity(colMeans(angio.615/rowSums(angio.615)))
gamma.angio.600<-diversity(colMeans(angio.600/rowSums(angio.600)))
gamma.angio.8<-diversity(colMeans(angio.8/rowSums(angio.8)))
gamma.angio.7<-diversity(colMeans(angio.7/rowSums(angio.7)))
gamma.angio.6<-diversity(colMeans(angio.6/rowSums(angio.6)))
gamma.angio.5<-diversity(colMeans(angio.5/rowSums(angio.5)))
gamma.angio.4<-diversity(colMeans(angio.4/rowSums(angio.4)))
gamma.angio.3<-diversity(colMeans(angio.3/rowSums(angio.3)))
gamma.angio.2<-diversity(colMeans(angio.2/rowSums(angio.2)))
gamma.angio.1<-diversity(colMeans(angio.1/rowSums(angio.1)))
#Calculate beta diversity per each sampling band
bd.800.angio<-gamma.angio.800-alphas.angio.800
bd.775.angio<-gamma.angio.775-alphas.angio.775
bd.745.angio<-gamma.angio.745-alphas.angio.745
bd.710.angio<-gamma.angio.710-alphas.angio.710
bd.675.angio<-gamma.angio.675-alphas.angio.675
bd.645.angio<-gamma.angio.645-alphas.angio.645
bd.615.angio<-gamma.angio.615-alphas.angio.615
bd.600.angio<-gamma.angio.600-alphas.angio.600
bd.8.angio<-gamma.angio.8-alphas.angio.8
bd.7.angio<-gamma.angio.7-alphas.angio.7
bd.6.angio<-gamma.angio.6-alphas.angio.6
bd.5.angio<-gamma.angio.5-alphas.angio.5
bd.4.angio<-gamma.angio.4-alphas.angio.4
bd.3.angio<-gamma.angio.3-alphas.angio.3
bd.2.angio<-gamma.angio.2-alphas.angio.2
bd.1.angio<-gamma.angio.1-alphas.angio.1
#Calculate grid beta
#overall gamma diversity
grid.gamma.angio<-diversity(colSums(angio.sp.abd)/sum(colSums(angio.sp.abd)))
grid.alpha.angio<-mean(gamma.angio.800, gamma.angio.775, gamma.angio.745, gamma.angio.710, gamma.angio.675, gamma.angio.645, gamma.angio.615, gamma.angio.600, gamma.angio.8, gamma.angio.7, gamma.angio.6, gamma.angio.5, gamma.angio.4, gamma.angio.3, gamma.angio.2, gamma.angio.1)
#grid beta; this is among habitat betadiversity
grid.beta.angio<-(grid.gamma.angio-grid.alpha.angio)Simpson’s angiosperm table
#show results in table
grid.alphas.angio.band<-round(rbind(alphas.angio.800, alphas.angio.775, alphas.angio.745, alphas.angio.710, alphas.angio.675,alphas.angio.645, alphas.angio.615, alphas.angio.600, alphas.angio.8, alphas.angio.7, alphas.angio.6, alphas.angio.5, alphas.angio.4, alphas.angio.3, alphas.angio.2, alphas.angio.1), 1)
grid.gamma.angio.band<-round(rbind(gamma.angio.800, gamma.angio.775, gamma.angio.745, gamma.angio.710, gamma.angio.675, gamma.angio.645, gamma.angio.615, gamma.angio.600, gamma.angio.8, gamma.angio.7, gamma.angio.6, gamma.angio.5, gamma.angio.4, gamma.angio.3, gamma.angio.2, gamma.angio.1),1)
grid.bd.within.angio<-round(rbind(bd.800.angio, bd.775.angio, bd.745.angio, bd.710.angio, bd.675.angio, bd.645.angio, bd.615.angio, bd.600.angio, bd.8.angio, bd.7.angio, bd.6.angio, bd.5.angio, bd.4.angio, bd.3.angio, bd.2.angio, bd.1.angio),1)
grid.alpha.beta.gamma.angio<-cbind(grid.alphas.angio.band, grid.gamma.angio.band, grid.bd.within.angio)
colnames(grid.alpha.beta.gamma.angio)<-c("Mean alpha", "Gamma", "Beta diversity (within bands)")
rownames(grid.alpha.beta.gamma.angio)<-c("800 metres", "775 metres", "745 metres", "710 metres", "675 metres", "645 metres", "615 metres", "600 metres", "8", "7", "6","5", "4", "3", "2", "1")
grid.alpha.beta.gamma.angio## Mean alpha Gamma Beta diversity (within bands)
## 800 metres 1.5 2.4 0.9
## 775 metres 1.4 2.2 0.9
## 745 metres 1.0 2.0 1.0
## 710 metres 1.0 1.7 0.7
## 675 metres 1.0 1.8 0.8
## 645 metres 1.3 2.3 1.0
## 615 metres 1.2 2.4 1.2
## 600 metres 1.3 2.5 1.2
## 8 1.5 2.7 1.2
## 7 1.4 2.7 1.3
## 6 1.2 2.5 1.3
## 5 1.6 2.5 0.9
## 4 1.7 2.7 1.0
## 3 1.6 2.9 1.3
## 2 2.1 3.1 1.1
## 1 1.6 2.7 1.1Rao’s phylogenetic diversity
Rao’s quadratic entropy is a measure of within- and among-community diversity taking species dissimilarities into account.
It measures the diversity of species among communities, similar to analyzes of diversity among populations.
This measure will be equivalent to Simpson’s diversity if no phylogeny is supplied.
Including phylogenies, metrics are equivalent to the mean pairwise phylogenetic distance between to individuals drawn from a community.
I split my data into sampling bands where I calculate within-band beta diversity to compare to the among-band beta diversity calculated for the entire dataset.
Warning - be careful how to interpret beta diversity values because we shouldn’t compare within- verses among-communities diversities with these values.
#first upload one phylogenetic tree
#Load maximum clade credibility tree
one.tree<-read.nexus("trans.one.tree.nex")
#drop extra tips from tree
trans.one.tree<-drop.tip(one.tree,c("ComUmb", "TheHum", "XimAme", "CorAlt", "CorSto", "HydArb"))
#make tree ultrametric
trans.one.ultra <- chronopl(trans.one.tree, lambda=0)
#show ultrametric tree with lambda =0
#Incorporate phylogenetic analysis into comparisons; this is entire grid comparison
vasc.phy.div<-raoD(abd.sp.allsp,trans.one.ultra)
#Split into individual elevation bands
vasc.800.phy.div<-raoD(vasc.800, trans.one.ultra)
vasc.775.phy.div<-raoD(vasc.775, trans.one.ultra)
vasc.745.phy.div<-raoD(vasc.745, trans.one.ultra)
vasc.710.phy.div<-raoD(vasc.710, trans.one.ultra)
vasc.675.phy.div<-raoD(vasc.675, trans.one.ultra)
vasc.645.phy.div<-raoD(vasc.645, trans.one.ultra)
vasc.615.phy.div<-raoD(vasc.615, trans.one.ultra)
vasc.600.phy.div<-raoD(vasc.600, trans.one.ultra)
vasc.8.phy.div<-raoD(vasc.8, trans.one.ultra)
vasc.7.phy.div<-raoD(vasc.7, trans.one.ultra)
vasc.6.phy.div<-raoD(vasc.6, trans.one.ultra)
vasc.5.phy.div<-raoD(vasc.5, trans.one.ultra)
vasc.4.phy.div<-raoD(vasc.4, trans.one.ultra)
vasc.3.phy.div<-raoD(vasc.3, trans.one.ultra)
vasc.2.phy.div<-raoD(vasc.2, trans.one.ultra)
vasc.1.phy.div<-raoD(vasc.1, trans.one.ultra)Rao’s phylogenetic diversity table for vascular plants
#Table including within band beta diversity
grid.vasc.p.div.alpha<-round(rbind(vasc.800.phy.div$alpha, vasc.775.phy.div$alpha, vasc.745.phy.div$alpha, vasc.710.phy.div$alpha, vasc.675.phy.div$alpha, vasc.645.phy.div$alpha, vasc.615.phy.div$alpha, vasc.600.phy.div$alpha,
vasc.8.phy.div$alpha, vasc.7.phy.div$alpha, vasc.6.phy.div$alpha, vasc.5.phy.div$alpha, vasc.4.phy.div$alpha, vasc.3.phy.div$alpha, vasc.2.phy.div$alpha, vasc.1.phy.div$alpha),2)
grid.vasc.p.div.gamma<-round(rbind(vasc.800.phy.div$total, vasc.775.phy.div$total, vasc.745.phy.div$total, vasc.710.phy.div$total, vasc.675.phy.div$total, vasc.645.phy.div$total, vasc.615.phy.div$total, vasc.600.phy.div$total,
vasc.8.phy.div$total, vasc.7.phy.div$total, vasc.6.phy.div$total, vasc.5.phy.div$total, vasc.4.phy.div$total, vasc.3.phy.div$total, vasc.2.phy.div$total, vasc.1.phy.div$total),2)
grid.vasc.p.div.beta<-round(rbind(vasc.800.phy.div$beta, vasc.775.phy.div$beta, vasc.745.phy.div$beta, vasc.710.phy.div$beta, vasc.675.phy.div$beta, vasc.645.phy.div$beta, vasc.615.phy.div$beta, vasc.600.phy.div$beta,
vasc.8.phy.div$beta, vasc.7.phy.div$beta, vasc.6.phy.div$beta, vasc.5.phy.div$beta, vasc.4.phy.div$beta, vasc.3.phy.div$beta, vasc.2.phy.div$beta, vasc.1.phy.div$beta),2)
grid.alpha.beta.gamma.vasc.phy.div<-cbind(grid.vasc.p.div.alpha, grid.vasc.p.div.gamma, grid.vasc.p.div.beta)
colnames(grid.alpha.beta.gamma.vasc.phy.div)<-c("Mean alpha", "Gamma", "Beta diversity (within bands)")
rownames(grid.alpha.beta.gamma.vasc.phy.div)<-c("800 metres", "775 metres", "745 metres", "710 metres", "675 metres", "645 metres", "615 metres", "600 metres", "8", "7", "6","5", "4", "3", "2", "1")
grid.alpha.beta.gamma.vasc.phy.div## Mean alpha Gamma Beta diversity (within bands)
## 800 metres 0.61 0.75 0.14
## 775 metres 0.52 0.72 0.20
## 745 metres 0.40 0.65 0.25
## 710 metres 0.53 0.71 0.18
## 675 metres 0.48 0.62 0.14
## 645 metres 0.55 0.77 0.22
## 615 metres 0.39 0.79 0.40
## 600 metres 0.58 0.80 0.22
## 8 0.60 0.72 0.12
## 7 0.55 0.80 0.25
## 6 0.58 0.81 0.23
## 5 0.65 0.81 0.16
## 4 0.59 0.83 0.23
## 3 0.65 0.85 0.20
## 2 0.75 0.85 0.10
## 1 0.63 0.82 0.18Rao’s phylogenetic diversity for angiosperms only
#drop extra tips from tree to make angiosperm only tree
angio.one.tree<-drop.tip(trans.one.tree,c("LycAno", "HupApr", "DipCom", "SelSel", "CysMon", "DryExp", "EquSyl", "EquArv", "EquSci", "EquVar", "JunCom", "PicMar", "PicGla", "AbiBal", "LarLar"))
#make tree ultrametric
angio.one.ultra <- chronopl(angio.one.tree, lambda=0)
angio.phy.div<-raoD(angio.sp.abd,angio.one.ultra)
#Split into individual elevation bands
angio.800.phy.div<-raoD(angio.800, angio.one.ultra)
angio.775.phy.div<-raoD(angio.775, angio.one.ultra)
angio.745.phy.div<-raoD(angio.745, angio.one.ultra)
angio.710.phy.div<-raoD(angio.710, angio.one.ultra)
angio.675.phy.div<-raoD(angio.675, angio.one.ultra)
angio.645.phy.div<-raoD(angio.645, angio.one.ultra)
angio.615.phy.div<-raoD(angio.615, angio.one.ultra)
angio.600.phy.div<-raoD(angio.600, angio.one.ultra)
angio.8.phy.div<-raoD(angio.8, angio.one.ultra)
angio.7.phy.div<-raoD(angio.7, angio.one.ultra)
angio.6.phy.div<-raoD(angio.6, angio.one.ultra)
angio.5.phy.div<-raoD(angio.5, angio.one.ultra)
angio.4.phy.div<-raoD(angio.4, angio.one.ultra)
angio.3.phy.div<-raoD(angio.3, angio.one.ultra)
angio.2.phy.div<-raoD(angio.2, angio.one.ultra)
angio.1.phy.div<-raoD(angio.1, angio.one.ultra)Rao’s phylogenetic beta diversity with angiosperms only - Table
#Table including within band beta diversity
grid.angio.p.div.alpha<-round(rbind(angio.800.phy.div$alpha, angio.775.phy.div$alpha, angio.745.phy.div$alpha, angio.710.phy.div$alpha, angio.675.phy.div$alpha, angio.645.phy.div$alpha, angio.615.phy.div$alpha, angio.600.phy.div$alpha,
angio.8.phy.div$alpha, angio.7.phy.div$alpha, angio.6.phy.div$alpha, angio.5.phy.div$alpha, angio.4.phy.div$alpha, angio.3.phy.div$alpha, angio.2.phy.div$alpha, angio.1.phy.div$alpha),2)
grid.angio.p.div.gamma<-round(rbind(angio.800.phy.div$total, angio.775.phy.div$total, angio.745.phy.div$total, angio.710.phy.div$total, angio.675.phy.div$total, angio.645.phy.div$total, angio.615.phy.div$total, angio.600.phy.div$total,
angio.8.phy.div$total, angio.7.phy.div$total, angio.6.phy.div$total, angio.5.phy.div$total, angio.4.phy.div$total, angio.3.phy.div$total, angio.2.phy.div$total, angio.1.phy.div$total),2)
grid.angio.p.div.beta<-round(rbind(angio.800.phy.div$beta, angio.775.phy.div$beta, angio.745.phy.div$beta, angio.710.phy.div$beta, angio.675.phy.div$beta, angio.645.phy.div$beta, angio.615.phy.div$beta, angio.600.phy.div$beta,
angio.8.phy.div$beta, angio.7.phy.div$beta, angio.6.phy.div$beta, angio.5.phy.div$beta, angio.4.phy.div$beta, angio.3.phy.div$beta, angio.2.phy.div$beta, angio.1.phy.div$beta),2)
grid.alpha.beta.gamma.angio.phy.div<-cbind(grid.angio.p.div.alpha, grid.angio.p.div.gamma, grid.angio.p.div.beta)
colnames(grid.alpha.beta.gamma.angio.phy.div)<-c("Mean alpha", "Gamma", "Beta diversity (within bands)")
rownames(grid.alpha.beta.gamma.angio.phy.div)<-c("800 metres", "775 metres", "745 metres", "710 metres", "675 metres", "645 metres", "615 metres", "600 metres", "8", "7", "6","5", "4", "3", "2", "1")
grid.alpha.beta.gamma.angio.phy.div## Mean alpha Gamma Beta diversity (within bands)
## 800 metres 0.64 0.78 0.15
## 775 metres 0.54 0.75 0.21
## 745 metres 0.40 0.67 0.27
## 710 metres 0.47 0.67 0.20
## 675 metres 0.44 0.60 0.16
## 645 metres 0.46 0.71 0.25
## 615 metres 0.39 0.75 0.36
## 600 metres 0.50 0.78 0.28
## 8 0.46 0.61 0.15
## 7 0.49 0.77 0.28
## 6 0.48 0.79 0.31
## 5 0.65 0.81 0.16
## 4 0.62 0.79 0.17
## 3 0.58 0.83 0.25
## 2 0.73 0.86 0.13
## 1 0.56 0.81 0.25Rao’s diversity with abundance data for vascular plants
#Calculate diversity indices with raoD diversity
vasc.raoD.div<-raoD(abd.sp.allsp)
#Split into individual elevation bands
vasc.800.raoD.div<-raoD(vasc.800)
vasc.775.raoD.div<-raoD(vasc.775)
vasc.745.raoD.div<-raoD(vasc.745)
vasc.710.raoD.div<-raoD(vasc.710)
vasc.675.raoD.div<-raoD(vasc.675)
vasc.645.raoD.div<-raoD(vasc.645)
vasc.615.raoD.div<-raoD(vasc.615)
vasc.600.raoD.div<-raoD(vasc.600)
vasc.8.raoD.div<-raoD(vasc.8)
vasc.7.raoD.div<-raoD(vasc.7)
vasc.6.raoD.div<-raoD(vasc.6)
vasc.5.raoD.div<-raoD(vasc.5)
vasc.4.raoD.div<-raoD(vasc.4)
vasc.3.raoD.div<-raoD(vasc.3)
vasc.2.raoD.div<-raoD(vasc.2)
vasc.1.raoD.div<-raoD(vasc.1)Rao’s beta diversity for angiosperms only based on abundance data
#Table including within band beta diversity
grid.vasc.raoD.div.alpha<-round(rbind(vasc.800.raoD.div$alpha, vasc.775.raoD.div$alpha, vasc.745.raoD.div$alpha, vasc.710.raoD.div$alpha, vasc.675.raoD.div$alpha, vasc.645.raoD.div$alpha, vasc.615.raoD.div$alpha, vasc.600.raoD.div$alpha,
vasc.8.raoD.div$alpha, vasc.7.raoD.div$alpha, vasc.6.raoD.div$alpha, vasc.5.raoD.div$alpha, vasc.4.raoD.div$alpha, vasc.3.raoD.div$alpha, vasc.2.raoD.div$alpha, vasc.1.raoD.div$alpha),2)
grid.vasc.raoD.div.gamma<-round(rbind(vasc.800.raoD.div$total, vasc.775.raoD.div$total, vasc.745.raoD.div$total, vasc.710.raoD.div$total, vasc.675.raoD.div$total, vasc.645.raoD.div$total, vasc.615.raoD.div$total, vasc.600.raoD.div$total,
vasc.8.raoD.div$total, vasc.7.raoD.div$total, vasc.6.raoD.div$total, vasc.5.raoD.div$total, vasc.4.raoD.div$total, vasc.3.raoD.div$total, vasc.2.raoD.div$total, vasc.1.raoD.div$total),2)
grid.vasc.raoD.div.beta<-round(rbind(vasc.800.raoD.div$beta, vasc.775.raoD.div$beta, vasc.745.raoD.div$beta, vasc.710.raoD.div$beta, vasc.675.raoD.div$beta, vasc.645.raoD.div$beta, vasc.615.raoD.div$beta, vasc.600.raoD.div$beta,
vasc.8.raoD.div$beta, vasc.7.raoD.div$beta, vasc.6.raoD.div$beta, vasc.5.raoD.div$beta, vasc.4.raoD.div$beta, vasc.3.raoD.div$beta, vasc.2.raoD.div$beta, vasc.1.raoD.div$beta),2)
grid.alpha.beta.gamma.vasc.raoD.div<-cbind(grid.vasc.raoD.div.alpha, grid.vasc.raoD.div.gamma, grid.vasc.raoD.div.beta)
colnames(grid.alpha.beta.gamma.vasc.raoD.div)<-c("Mean alpha", "Gamma", "Beta diversity (within bands)")
rownames(grid.alpha.beta.gamma.vasc.raoD.div)<-c("800 metres", "775 metres", "745 metres", "710 metres", "675 metres", "645 metres", "615 metres", "600 metres", "8", "7", "6","5", "4", "3", "2", "1")
grid.alpha.beta.gamma.vasc.raoD.div## Mean alpha Gamma Beta diversity (within bands)
## 800 metres 0.69 0.85 0.17
## 775 metres 0.59 0.81 0.22
## 745 metres 0.44 0.76 0.32
## 710 metres 0.58 0.77 0.19
## 675 metres 0.52 0.69 0.17
## 645 metres 0.66 0.88 0.22
## 615 metres 0.47 0.89 0.42
## 600 metres 0.69 0.91 0.22
## 8 0.70 0.83 0.14
## 7 0.61 0.88 0.27
## 6 0.63 0.89 0.26
## 5 0.73 0.91 0.17
## 4 0.64 0.89 0.24
## 3 0.72 0.93 0.21
## 2 0.81 0.94 0.12
## 1 0.69 0.90 0.21Rao’s diversity based on abundance data for angiosperms only
angio.raoD.div<-raoD(angio.sp.abd)
#Split into individual elevation bands
angio.800.raoD.div<-raoD(angio.800)
angio.775.raoD.div<-raoD(angio.775)
angio.745.raoD.div<-raoD(angio.745)
angio.710.raoD.div<-raoD(angio.710)
angio.675.raoD.div<-raoD(angio.675)
angio.645.raoD.div<-raoD(angio.645)
angio.615.raoD.div<-raoD(angio.615)
angio.600.raoD.div<-raoD(angio.600)
angio.8.raoD.div<-raoD(angio.8)
angio.7.raoD.div<-raoD(angio.7)
angio.6.raoD.div<-raoD(angio.6)
angio.5.raoD.div<-raoD(angio.5)
angio.4.raoD.div<-raoD(angio.4)
angio.3.raoD.div<-raoD(angio.3)
angio.2.raoD.div<-raoD(angio.2)
angio.1.raoD.div<-raoD(angio.1)Table for Rao’s diversity based on abundance data for angiosperms only
#Table including within band beta diversity
grid.angio.raoD.div.alpha<-round(rbind(angio.800.raoD.div$alpha, angio.775.raoD.div$alpha, angio.745.raoD.div$alpha, angio.710.raoD.div$alpha, angio.675.raoD.div$alpha, angio.645.raoD.div$alpha, angio.615.raoD.div$alpha, angio.600.raoD.div$alpha,
angio.8.raoD.div$alpha, angio.7.raoD.div$alpha, angio.6.raoD.div$alpha, angio.5.raoD.div$alpha, angio.4.raoD.div$alpha, angio.3.raoD.div$alpha, angio.2.raoD.div$alpha, angio.1.raoD.div$alpha),2)
grid.angio.raoD.div.gamma<-round(rbind(angio.800.raoD.div$total, angio.775.raoD.div$total, angio.745.raoD.div$total, angio.710.raoD.div$total, angio.675.raoD.div$total, angio.645.raoD.div$total, angio.615.raoD.div$total, angio.600.raoD.div$total,
angio.8.raoD.div$total, angio.7.raoD.div$total, angio.6.raoD.div$total, angio.5.raoD.div$total, angio.4.raoD.div$total, angio.3.raoD.div$total, angio.2.raoD.div$total, angio.1.raoD.div$total),2)
grid.angio.raoD.div.beta<-round(rbind(angio.800.raoD.div$beta, angio.775.raoD.div$beta, angio.745.raoD.div$beta, angio.710.raoD.div$beta, angio.675.raoD.div$beta, angio.645.raoD.div$beta, angio.615.raoD.div$beta, angio.600.raoD.div$beta,
angio.8.raoD.div$beta, angio.7.raoD.div$beta, angio.6.raoD.div$beta, angio.5.raoD.div$beta, angio.4.raoD.div$beta, angio.3.raoD.div$beta, angio.2.raoD.div$beta, angio.1.raoD.div$beta),2)
grid.alpha.beta.gamma.angio.raoD.div<-cbind(grid.angio.raoD.div.alpha, grid.angio.raoD.div.gamma, grid.angio.raoD.div.beta)
colnames(grid.alpha.beta.gamma.angio.raoD.div)<-c("Mean alpha", "Gamma", "Beta diversity (within bands)")
rownames(grid.alpha.beta.gamma.angio.raoD.div)<-c("800 metres", "775 metres", "745 metres", "710 metres", "675 metres", "645 metres", "615 metres", "600 metres", "8", "7", "6","5", "4", "3", "2", "1")
grid.alpha.beta.gamma.angio.raoD.div## Mean alpha Gamma Beta diversity (within bands)
## 800 metres 0.69 0.85 0.17
## 775 metres 0.59 0.81 0.22
## 745 metres 0.42 0.76 0.34
## 710 metres 0.50 0.71 0.21
## 675 metres 0.47 0.65 0.18
## 645 metres 0.57 0.84 0.27
## 615 metres 0.47 0.86 0.39
## 600 metres 0.62 0.89 0.27
## 8 0.58 0.74 0.16
## 7 0.56 0.86 0.30
## 6 0.53 0.87 0.34
## 5 0.72 0.87 0.15
## 4 0.66 0.83 0.18
## 3 0.65 0.90 0.26
## 2 0.79 0.92 0.13
## 1 0.61 0.87 0.26Plot within and among beta diversity values
#Plot
#dev.new(width=11.8, height=8)
par(mfrow=c(2,2))
plot(grid.alpha.beta.gamma[,3], axes=FALSE, col="black", pch=16, cex=1.25, ylab="Beta diversity", xlab="Sampling band",las=1, cex.axis=0.65, cex.lab=1,
main="Beta diversity within and among \nsampling bands (vasculars) using Simpson's diversity", ylim=c(-1,1.5),cex.main=1, bty="c")
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"))
axis(2)
abline(h=grid.beta, lwd=2, lty=2)
box(bty="l", lwd=3)
legend("bottomleft", c("Beta diversity (among)", "Beta diversity (within)"), col = c("black","black"), cex=1,
lty = c(2, 0),lwd=c(2,2), pch = c(NA, 16), bg = "white", bty="n")
plot(grid.alpha.beta.gamma.angio[,3], axes=FALSE, col="black", pch=16, cex=1.25,ylab="Beta diversity", xlab="Sampling band",las=1, cex.axis=0.65, cex.lab=1,
main="Beta diversity within and among \nsampling bands (angiosperms) using Simpson's diversity", ylim=c(-1,1.5),cex.main=1, bty="c")
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"))
axis(2)
abline(h=grid.beta.angio, lwd=2, lty=2)
box(bty="l", lwd=3)
legend("bottomleft", c("Beta diversity (among)", "Beta diversity (within)"), col = c("black","black"), cex=1,
lty = c(2, 0),lwd=c(2,2), pch = c(NA, 16), bg = "white", bty="n")
plot(grid.alpha.beta.gamma.vasc.raoD.div[,3], axes=FALSE, col="black", pch=16, cex=1.25, ylab="Beta diversity", xlab="Sampling band",las=1, cex.axis=0.65, cex.lab=1,
main="Phylogenetic beta diversity compared to beta diversity within and \namong sampling bands (vasculars) using Rao's quadratic entropy", ylim=c(0,0.75),cex.main=1, bty="c")
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"))
axis(2)
points(grid.alpha.beta.gamma.vasc.phy.div[,3], col="gray70", pch=16, cex=1.25)
abline(h=vasc.raoD.div$beta, lwd=2, lty=2)
abline(h=vasc.phy.div$beta, lwd=2, lty=2, col="gray70")
box(bty="l", lwd=3)
legend("topright", c("Beta diversity (among)", "Beta diversity (within)", "Phylogenetic beta diversity (among)", "Phylogenetic beta diversity (within)"), col = c("black","black", "gray70", "gray70"), cex=1,
lty = c(2, 0,2,0),lwd=c(2,2,2,2), pch = c(NA, 16, NA, 16), bg = "white", bty="n")
plot(grid.alpha.beta.gamma.angio.raoD.div[,3], axes=FALSE, col="black", pch=16, cex=1.25, ylab="Beta diversity", xlab="Sampling band",las=1, cex.axis=0.65, cex.lab=1,
main="Phylogenetic beta diversity compared to beta diversity within and \namong sampling bands (angiosperms) using Rao's quadratic entropy", ylim=c(0,0.75),cex.main=1, bty="c")
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"))
axis(2)
points(grid.alpha.beta.gamma.angio.phy.div[,3], col="gray70", pch=16, cex=1.25)
abline(h=angio.raoD.div$beta, lwd=2, lty=2)
abline(h=angio.phy.div$beta, lwd=2, lty=2, col="gray70")
box(bty="l", lwd=3)
legend("topright", c("Beta diversity (among)", "Beta diversity (within)", "Phylogenetic beta diversity (among)", "Phylogenetic beta diversity (within)"), col = c("black","black", "gray70", "gray70"), cex=1,
lty = c(2, 0,2,0),lwd=c(2,2,2,2), pch = c(NA, 16, NA, 16), bg = "white", bty="n")