Schefferville - transitions; Beta diversity distance decay
Tammy L. Elliott
Date: February 28, 2015
R version 3.1.0
Data structure
abd.sp<-cbind(sm.abd[,c(2:58, 60:75, 78:114)],lg.abd[,c(1:4)])
head(abd.sp)## 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
## EA1 0 0 0 25
## EB1 0 0 3 0
## EC1 0 0 0 0
## ED1 0 0 2 4
## EE1 0 0 0 0
## EF1 0 0 0 18# This analysis will be based on maximum clade credibility tree because loop through phylosor for many trees is extremely slow.
trans.one.tree<-read.nexus("trans.one.tree.nex")
phy.dist<- cophenetic(trans.one.tree)
#find difference in what is in phylogeny and plots
x<-names(abd.sp)
y<-trans.one.tree$tip.label
xy<-setdiff(x,y)
yx<-setdiff(y,x)
xy## character(0)yx## [1] "AreHum" "BetMin" "BetPum" "CarBel" "CarGla" "ComUmb" "CopLap"
## [8] "CorAlt" "CorSto" "CorTri" "HydArb" "LuzSpi" "PoaAlp" "SalHer"
## [15] "SalMyr" "TheHum" "XimAme"#this is large grid plots; should calculate nri from species pool from all species together in plots
#create community matrix with 0 values for "AreHum" "BetMin" "BetPum" "CarBel" "CarGla" "CopLap" "CorTri" "LuzSpi" "PoaAlp" "SalHer" "SalMyr"
sp.zero.com.matrix.names<-c("AreHum", "BetMin", "BetPum", "CarBel", "CarGla", "CopLap", "CorTri", "LuzSpi", "PoaAlp", "SalHer", "SalMyr")
sp.zero.com.matrix.zeros<-matrix(0, 176, 11)
#add missing species names as colnames and plot numbers as rownames
colnames(sp.zero.com.matrix.zeros) <-sp.zero.com.matrix.names
rownames(sp.zero.com.matrix.zeros)<-rownames(abd.sp)
#add 0 values for 11 species to large grid community matrix
abd.sp.allsp<-cbind(abd.sp, sp.zero.com.matrix.zeros)
#again, find difference in what is in phylogeny and plots
xx<-names(abd.sp.allsp)
yy<-trans.one.tree$tip.label
xxyy<-setdiff(xx,yy)
yyxx<-setdiff(yy,xx)
xxyy## character(0)yyxx## [1] "ComUmb" "CorAlt" "CorSto" "HydArb" "TheHum" "XimAme"Sorensen beta diversity distance decay for all vascular plants data
veg.beta<-betadiver(abd.sp.allsp, "sor")
veg.beta.m<-as.matrix(veg.beta)
beta.800<-veg.beta.m[grepl("800*", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.800.dist<-1-(mean(c(beta.800[1,1],beta.800[2,2], beta.800[3,3], beta.800[4,4], beta.800[5,5], beta.800[6,6],
beta.800[7,7], beta.800[8,8], beta.800[9,9], beta.800[10,10], beta.800[11,11])))
head(beta.800.dist)## [1] 1beta.775<-veg.beta.m[grepl("775*", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.775.dist<-mean(c(beta.775[1,1],beta.775[2,2], beta.775[3,3], beta.775[4,4], beta.775[5,5], beta.775[6,6],
beta.775[7,7], beta.775[8,8], beta.775[9,9], beta.775[10,10], beta.775[11,11]))
beta.745<-veg.beta.m[grepl("745*", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.745.dist<-mean(c(beta.745[1,1],beta.745[2,2], beta.745[3,3], beta.745[4,4], beta.745[5,5], beta.745[6,6],
beta.745[7,7], beta.745[8,8], beta.745[9,9], beta.745[10,10], beta.745[11,11]))
beta.710<-veg.beta.m[grepl("710*", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.710.dist<-mean(c(beta.710[1,1],beta.710[2,2], beta.710[3,3], beta.710[4,4], beta.710[5,5], beta.710[6,6],
beta.710[7,7], beta.710[8,8], beta.710[9,9], beta.710[10,10], beta.710[11,11]))
beta.675<-veg.beta.m[grepl("675*", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.675.dist<-mean(c(beta.675[1,1],beta.675[2,2], beta.675[3,3], beta.675[4,4], beta.675[5,5], beta.675[6,6],
beta.675[7,7], beta.675[8,8], beta.675[9,9], beta.675[10,10], beta.675[11,11]))
beta.645<-veg.beta.m[grepl("645*", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.645.dist<-mean(c(beta.645[1,1],beta.645[2,2], beta.645[3,3], beta.645[4,4], beta.645[5,5], beta.645[6,6],
beta.645[7,7], beta.645[8,8], beta.645[9,9], beta.645[10,10], beta.645[11,11]))
beta.615<-veg.beta.m[grepl("615*", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.615.dist<-mean(c(beta.615[1,1],beta.615[2,2], beta.615[3,3], beta.615[4,4], beta.615[5,5], beta.615[6,6],
beta.615[7,7], beta.615[8,8], beta.615[9,9], beta.615[10,10], beta.615[11,11]))
beta.600<-veg.beta.m[grepl("600*", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.600.dist<-mean(c(beta.600[1,1],beta.600[2,2], beta.600[3,3], beta.600[4,4], beta.600[5,5], beta.600[6,6],
beta.600[7,7], beta.600[8,8], beta.600[9,9], beta.600[10,10], beta.600[11,11]))
beta.8.first<-veg.beta.m[grepl("8", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.8<-beta.8.first[1:11,1:11]
beta.8.dist<-mean(c(beta.8[1,1],beta.8[2,2], beta.8[3,3], beta.8[4,4], beta.8[5,5], beta.8[6,6],
beta.8[7,7], beta.8[8,8], beta.8[9,9], beta.8[10,10], beta.8[11,11]))
beta.7.first<-veg.beta.m[grepl("7", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.7<-beta.7.first[1:11,1:11]
beta.7.dist<-mean(c(beta.7[1,1],beta.7[2,2], beta.7[3,3], beta.7[4,4], beta.7[5,5], beta.7[6,6],
beta.7[7,7], beta.7[8,8], beta.7[9,9], beta.7[10,10], beta.7[11,11]))
beta.6.first<-veg.beta.m[grepl("6", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.6<-beta.6.first[1:11,1:11]
beta.6.dist<-mean(c(beta.6[1,1],beta.6[2,2], beta.6[3,3], beta.6[4,4], beta.6[5,5], beta.6[6,6],
beta.6[7,7], beta.6[8,8], beta.6[9,9], beta.6[10,10], beta.6[11,11]))
beta.5.first<-veg.beta.m[grepl("5", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.5<-beta.5.first[1:11,1:11]
beta.5.dist<-mean(c(beta.5[1,1],beta.5[2,2], beta.5[3,3], beta.5[4,4], beta.5[5,5], beta.5[6,6],
beta.5[7,7], beta.5[8,8], beta.5[9,9], beta.5[10,10], beta.5[11,11]))
beta.4.first<-veg.beta.m[grepl("4", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.4<-beta.4.first[1:11,1:11]
beta.4.dist<-mean(c(beta.4[1,1],beta.4[2,2], beta.4[3,3], beta.4[4,4], beta.4[5,5], beta.4[6,6],
beta.4[7,7], beta.4[8,8], beta.4[9,9], beta.4[10,10], beta.4[11,11]))
beta.3.first<-veg.beta.m[grepl("3", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.3<-beta.3.first[1:11,1:11]
beta.3.dist<-mean(c(beta.3[1,1],beta.3[2,2], beta.3[3,3], beta.3[4,4], beta.3[5,5], beta.3[6,6],
beta.3[7,7], beta.3[8,8], beta.3[9,9], beta.3[10,10], beta.3[11,11]))
beta.2.first<-veg.beta.m[grepl("2", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.2<-beta.2.first[1:11,1:11]
beta.2.dist<-mean(c(beta.2[1,1],beta.2[2,2], beta.2[3,3], beta.2[4,4], beta.2[5,5], beta.2[6,6],
beta.2[7,7], beta.2[8,8], beta.2[9,9], beta.2[10,10], beta.2[11,11]))
beta.1.first<-veg.beta.m[grepl("1", rownames(veg.beta.m)),grepl("800*", colnames(veg.beta.m)) ]
beta.1<-beta.1.first[1:11,1:11]
beta.1.dist<-mean(c(beta.1[1,1],beta.1[2,2], beta.1[3,3], beta.1[4,4], beta.1[5,5], beta.1[6,6],
beta.1[7,7], beta.1[8,8], beta.1[9,9], beta.1[10,10], beta.1[11,11]))
beta.decay<-c(beta.800.dist, beta.775.dist, beta.745.dist, beta.710.dist, beta.675.dist, beta.645.dist, beta.615.dist, beta.600.dist, beta.8.dist, beta.7.dist,
beta.6.dist, beta.5.dist, beta.4.dist, beta.3.dist, beta.2.dist, beta.1.dist)
beta.decay## [1] 1.00000000 0.52973066 0.22253155 0.20255110 0.07507740 0.17905974
## [7] 0.08080808 0.09533240 0.09571235 0.12017441 0.07424898 0.11296615
## [13] 0.08524447 0.12847775 0.15885705 0.09071299Sorensen phylogenetic beta diversity distance decay for all vascular plants data
#beta.ps.m<-as.matrix(phylosor(abd.sp.allsp, trans.one.tree))
#Save and load R object for phylosor for 176 large grid plots
#saveRDS(beta.ps.m, file="phylosor.large.grid.rds")
beta.ps.m<-readRDS("phylosor.large.grid.rds")
beta.800.ps.m<-beta.ps.m[grepl("800*", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.800.ps.dist<-1-(mean(c(beta.800.ps.m[1,1],beta.800.ps.m[2,2], beta.800.ps.m[3,3], beta.800.ps.m[4,4], beta.800.ps.m[5,5], beta.800.ps.m[6,6],
beta.800.ps.m[7,7], beta.800.ps.m[8,8], beta.800.ps.m[9,9], beta.800.ps.m[10,10], beta.800.ps.m[11,11])))
head(beta.800.ps.dist)## [1] 1beta.775.ps.m<-beta.ps.m[grepl("775*", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.775.ps.dist<-mean(c(beta.775.ps.m[1,1],beta.775.ps.m[2,2], beta.775.ps.m[3,3], beta.775.ps.m[4,4], beta.775.ps.m[5,5], beta.775.ps.m[6,6],
beta.775.ps.m[7,7], beta.775.ps.m[8,8], beta.775.ps.m[9,9], beta.775.ps.m[10,10], beta.775.ps.m[11,11]))
beta.745.ps.m<-beta.ps.m[grepl("745*", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.745.ps.dist<-mean(c(beta.745.ps.m[1,1],beta.745.ps.m[2,2], beta.745.ps.m[3,3], beta.745.ps.m[4,4], beta.745.ps.m[5,5], beta.745.ps.m[6,6],
beta.745.ps.m[7,7], beta.745.ps.m[8,8], beta.745.ps.m[9,9], beta.745.ps.m[10,10], beta.745.ps.m[11,11]))
beta.710.ps.m<-beta.ps.m[grepl("710*", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.710.ps.dist<-mean(c(beta.710.ps.m[1,1],beta.710.ps.m[2,2], beta.710.ps.m[3,3], beta.710.ps.m[4,4], beta.710.ps.m[5,5], beta.710.ps.m[6,6],
beta.710.ps.m[7,7], beta.710.ps.m[8,8], beta.710.ps.m[9,9], beta.710.ps.m[10,10], beta.710.ps.m[11,11]))
beta.675.ps.m<-beta.ps.m[grepl("675*", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.675.ps.dist<-mean(c(beta.675.ps.m[1,1],beta.675.ps.m[2,2], beta.675.ps.m[3,3], beta.675.ps.m[4,4], beta.675.ps.m[5,5], beta.675.ps.m[6,6],
beta.675.ps.m[7,7], beta.675.ps.m[8,8], beta.675.ps.m[9,9], beta.675.ps.m[10,10], beta.675.ps.m[11,11]))
beta.645.ps.m<-beta.ps.m[grepl("645*", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.645.ps.dist<-mean(c(beta.645.ps.m[1,1],beta.645.ps.m[2,2], beta.645.ps.m[3,3], beta.645.ps.m[4,4], beta.645.ps.m[5,5], beta.645.ps.m[6,6],
beta.645.ps.m[7,7], beta.645.ps.m[8,8], beta.645.ps.m[9,9], beta.645.ps.m[10,10], beta.645.ps.m[11,11]))
beta.615.ps.m<-beta.ps.m[grepl("615*", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.615.ps.dist<-mean(c(beta.615.ps.m[1,1],beta.615.ps.m[2,2], beta.615.ps.m[3,3], beta.615.ps.m[4,4], beta.615.ps.m[5,5], beta.615.ps.m[6,6],
beta.615.ps.m[7,7], beta.615.ps.m[8,8], beta.615.ps.m[9,9], beta.615.ps.m[10,10], beta.615.ps.m[11,11]))
beta.600.ps.m<-beta.ps.m[grepl("600*", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.600.ps.dist<-mean(c(beta.600.ps.m[1,1],beta.600.ps.m[2,2], beta.600.ps.m[3,3], beta.600.ps.m[4,4], beta.600.ps.m[5,5], beta.600.ps.m[6,6],
beta.600.ps.m[7,7], beta.600.ps.m[8,8], beta.600.ps.m[9,9], beta.600.ps.m[10,10], beta.600.ps.m[11,11]))
beta.8.ps.first<-beta.ps.m[grepl("8", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.8.ps.m<-beta.8.ps.first[1:11,1:11]
beta.8.ps.dist<-mean(c(beta.8.ps.m[1,1],beta.8.ps.m[2,2], beta.8.ps.m[3,3], beta.8.ps.m[4,4], beta.8.ps.m[5,5], beta.8.ps.m[6,6],
beta.8.ps.m[7,7], beta.8.ps.m[8,8], beta.8.ps.m[9,9], beta.8.ps.m[10,10], beta.8.ps.m[11,11]))
beta.7.ps.first<-beta.ps.m[grepl("7", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.7.ps.m<-beta.7.ps.first[1:11,1:11]
beta.7.ps.dist<-mean(c(beta.7.ps.m[1,1],beta.7.ps.m[2,2], beta.7.ps.m[3,3], beta.7.ps.m[4,4], beta.7.ps.m[5,5], beta.7.ps.m[6,6],
beta.7.ps.m[7,7], beta.7.ps.m[8,8], beta.7.ps.m[9,9], beta.7.ps.m[10,10], beta.7.ps.m[11,11]))
beta.6.ps.first<-beta.ps.m[grepl("6", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.6.ps.m<-beta.6.ps.first[1:11,1:11]
beta.6.ps.dist<-mean(c(beta.6.ps.m[1,1],beta.6.ps.m[2,2], beta.6.ps.m[3,3], beta.6.ps.m[4,4], beta.6.ps.m[5,5], beta.6.ps.m[6,6],
beta.6.ps.m[7,7], beta.6.ps.m[8,8], beta.6.ps.m[9,9], beta.6.ps.m[10,10], beta.6.ps.m[11,11]))
beta.5.ps.first<-beta.ps.m[grepl("5", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.5.ps.m<-beta.5.ps.first[1:11,1:11]
beta.5.ps.dist<-mean(c(beta.5.ps.m[1,1],beta.5.ps.m[2,2], beta.5.ps.m[3,3], beta.5.ps.m[4,4], beta.5.ps.m[5,5], beta.5.ps.m[6,6],
beta.5.ps.m[7,7], beta.5.ps.m[8,8], beta.5.ps.m[9,9], beta.5.ps.m[10,10], beta.5.ps.m[11,11]))
beta.4.ps.first<-beta.ps.m[grepl("4", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.4.ps.m<-beta.4.ps.first[1:11,1:11]
beta.4.ps.dist<-mean(c(beta.4.ps.m[1,1],beta.4.ps.m[2,2], beta.4.ps.m[3,3], beta.4.ps.m[4,4], beta.4.ps.m[5,5], beta.4.ps.m[6,6],
beta.4.ps.m[7,7], beta.4.ps.m[8,8], beta.4.ps.m[9,9], beta.4.ps.m[10,10], beta.4.ps.m[11,11]))
beta.3.ps.first<-beta.ps.m[grepl("3", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.3.ps.m<-beta.3.ps.first[1:11,1:11]
beta.3.ps.dist<-mean(c(beta.3.ps.m[1,1],beta.3.ps.m[2,2], beta.3.ps.m[3,3], beta.3.ps.m[4,4], beta.3.ps.m[5,5], beta.3.ps.m[6,6],
beta.3.ps.m[7,7], beta.3.ps.m[8,8], beta.3.ps.m[9,9], beta.3.ps.m[10,10], beta.3.ps.m[11,11]))
beta.2.ps.first<-beta.ps.m[grepl("2", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.2.ps.m<-beta.2.ps.first[1:11,1:11]
beta.2.ps.dist<-mean(c(beta.2.ps.m[1,1],beta.2.ps.m[2,2], beta.2.ps.m[3,3], beta.2.ps.m[4,4], beta.2.ps.m[5,5], beta.2.ps.m[6,6],
beta.2.ps.m[7,7], beta.2.ps.m[8,8], beta.2.ps.m[9,9], beta.2.ps.m[10,10], beta.2.ps.m[11,11]))
beta.1.ps.first<-beta.ps.m[grepl("1", rownames(beta.ps.m)),grepl("800*", colnames(beta.ps.m)) ]
beta.1.ps.m<-beta.1.ps.first[1:11,1:11]
beta.1.ps.dist<-mean(c(beta.1.ps.m[1,1],beta.1.ps.m[2,2], beta.1.ps.m[3,3], beta.1.ps.m[4,4], beta.1.ps.m[5,5], beta.1.ps.m[6,6],
beta.1.ps.m[7,7], beta.1.ps.m[8,8], beta.1.ps.m[9,9], beta.1.ps.m[10,10], beta.1.ps.m[11,11]))
beta.ps.decay<-c(beta.800.ps.dist, beta.775.ps.dist, beta.745.ps.dist, beta.710.ps.dist, beta.675.ps.dist, beta.645.ps.dist, beta.615.ps.dist, beta.600.ps.dist, beta.8.ps.dist, beta.7.ps.dist,
beta.6.ps.dist, beta.5.ps.dist, beta.4.ps.dist, beta.3.ps.dist, beta.2.ps.dist, beta.1.ps.dist)
beta.ps.decay## [1] 1.0000000 0.7425462 0.5927562 0.5228959 0.4437030 0.4567980 0.4164349
## [8] 0.4170008 0.4704402 0.4615182 0.4522095 0.4593093 0.4590692 0.5180568
## [15] 0.5040371 0.4732519Plot distance decay beta diversity for vascular plants
#dev.new(width=11.8, height=4)
par(mfrow=c(1,2))
par(mar=c(5.1,5.1,5.5,2.1))
plot(beta.decay, type="l",axes=FALSE, col="black", lwd=2, ylab="Beta diversity", xlab="Sampling Band",las=1, cex.axis=1, cex.lab=1.25,
main="Distance decay of \nSorensen's beta diversity (vasculars)", cex.main=1.5, ylim=c(0,1))
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"), lwd=2)
axis(2, lwd=2)
box(bty="l", lwd=2)
plot(beta.ps.decay, type="l",axes=FALSE, col="black", lwd=2, ylab="Phylogenetic beta diversity", xlab="Sampling Band",las=1, cex.axis=1, cex.lab=1.25,
main="Distance decay of Sorensen's \nphylogenetic beta diversity (vasculars)", cex.main=1.5, ylim=c(0.4,1))
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"), lwd=2)
axis(2, lwd=2)
box(bty="l", lwd=2)
#plot Distance decay of beta diversity and phylogenetic beta diversity together
#dev.new(width=5.9, height=4)
par(mar=c(5.1,5.1,5.5,2.1))
plot(beta.decay, type="l",axes=FALSE, col="black", lwd=2, ylab="Beta diversity", xlab="Sampling Band",las=1, cex.axis=1, cex.lab=1.25,
main="Distance decay of Sorensen's beta diversity and \nSorensen's phylogenetic beta diversity (vasculars)", cex.main=1.25, ylim=c(0,1))
lines(beta.ps.decay, type="l",col="grey70", lwd=2)
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"), lwd=2)
axis(2, lwd=2)
legend("topright", c("Sorensen's beta diversity", "Sorensen's phylogenetic beta diversity"), col = c("black","grey70" ), cex=1.1,
lty = c(1, 1), pch = c(NA, NA), lwd=3 , bg = "white", bty="n")
box(bty="l", lwd=2)
***
Distance decay beta diversity for angiosperm only data
#Analyze angiosperm only data
#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))]Sorensen’s beta diversity for angiosperm only day
#sorensen comparison
angio.beta<-betadiver(angio.sp.abd, "sor")
angio.beta.m<-as.matrix(angio.beta)
beta.800.angio<-angio.beta.m[grepl("800*", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.800.dist.angio<-1-(mean(c(beta.800.angio[1,1],beta.800.angio[2,2], beta.800.angio[3,3], beta.800.angio[4,4], beta.800.angio[5,5], beta.800.angio[6,6],
beta.800.angio[7,7], beta.800.angio[8,8], beta.800.angio[9,9], beta.800.angio[10,10], beta.800.angio[11,11])))
head(beta.800.dist.angio)## [1] 1beta.775.angio<-angio.beta.m[grepl("775*", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.775.dist.angio<-mean(c(beta.775.angio[1,1],beta.775.angio[2,2], beta.775.angio[3,3], beta.775.angio[4,4], beta.775.angio[5,5], beta.775.angio[6,6],
beta.775.angio[7,7], beta.775.angio[8,8], beta.775.angio[9,9], beta.775.angio[10,10], beta.775.angio[11,11]))
beta.745.angio<-angio.beta.m[grepl("745*", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.745.dist.angio<-mean(c(beta.745.angio[1,1],beta.745.angio[2,2], beta.745.angio[3,3], beta.745.angio[4,4], beta.745.angio[5,5], beta.745.angio[6,6],
beta.745.angio[7,7], beta.745.angio[8,8], beta.745.angio[9,9], beta.745.angio[10,10], beta.745.angio[11,11]))
beta.710.angio<-angio.beta.m[grepl("710*", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.710.dist.angio<-mean(c(beta.710.angio[1,1],beta.710.angio[2,2], beta.710.angio[3,3], beta.710.angio[4,4], beta.710.angio[5,5], beta.710.angio[6,6],
beta.710.angio[7,7], beta.710.angio[8,8], beta.710.angio[9,9], beta.710.angio[10,10], beta.710.angio[11,11]))
beta.675.angio<-angio.beta.m[grepl("675*", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.675.dist.angio<-mean(c(beta.675.angio[1,1],beta.675.angio[2,2], beta.675.angio[3,3], beta.675.angio[4,4], beta.675.angio[5,5], beta.675.angio[6,6],
beta.675.angio[7,7], beta.675.angio[8,8], beta.675.angio[9,9], beta.675.angio[10,10], beta.675.angio[11,11]))
beta.645.angio<-angio.beta.m[grepl("645*", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.645.dist.angio<-mean(c(beta.645.angio[1,1],beta.645.angio[2,2], beta.645.angio[3,3], beta.645.angio[4,4], beta.645.angio[5,5], beta.645.angio[6,6],
beta.645.angio[7,7], beta.645.angio[8,8], beta.645.angio[9,9], beta.645.angio[10,10], beta.645.angio[11,11]))
beta.615.angio<-angio.beta.m[grepl("615*", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.615.dist.angio<-mean(c(beta.615.angio[1,1],beta.615.angio[2,2], beta.615.angio[3,3], beta.615.angio[4,4], beta.615.angio[5,5], beta.615.angio[6,6],
beta.615.angio[7,7], beta.615.angio[8,8], beta.615.angio[9,9], beta.615.angio[10,10], beta.615.angio[11,11]))
beta.600.angio<-angio.beta.m[grepl("600*", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.600.dist.angio<-mean(c(beta.600.angio[1,1],beta.600.angio[2,2], beta.600.angio[3,3], beta.600.angio[4,4], beta.600.angio[5,5], beta.600.angio[6,6],
beta.600.angio[7,7], beta.600.angio[8,8], beta.600.angio[9,9], beta.600.angio[10,10], beta.600.angio[11,11]))
beta.8.angio<-angio.beta.m[grepl("8", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.8<-beta.8.angio[1:11,1:11]
beta.8.dist.angio<-mean(c(beta.8.angio[1,1],beta.8.angio[2,2], beta.8.angio[3,3], beta.8.angio[4,4], beta.8.angio[5,5], beta.8.angio[6,6],
beta.8.angio[7,7], beta.8.angio[8,8], beta.8.angio[9,9], beta.8.angio[10,10], beta.8.angio[11,11]))
beta.7.angio<-angio.beta.m[grepl("7", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.7<-beta.7.angio[1:11,1:11]
beta.7.dist.angio<-mean(c(beta.7.angio[1,1],beta.7.angio[2,2], beta.7.angio[3,3], beta.7.angio[4,4], beta.7.angio[5,5], beta.7.angio[6,6],
beta.7.angio[7,7], beta.7.angio[8,8], beta.7.angio[9,9], beta.7.angio[10,10], beta.7.angio[11,11]))
beta.6.angio<-angio.beta.m[grepl("6", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.6<-beta.6.angio[1:11,1:11]
beta.6.dist.angio<-mean(c(beta.6.angio[1,1],beta.6.angio[2,2], beta.6.angio[3,3], beta.6.angio[4,4], beta.6.angio[5,5], beta.6.angio[6,6],
beta.6.angio[7,7], beta.6.angio[8,8], beta.6.angio[9,9], beta.6.angio[10,10], beta.6.angio[11,11]))
beta.5.angio<-angio.beta.m[grepl("5", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.5<-beta.5.angio[1:11,1:11]
beta.5.dist.angio<-mean(c(beta.5.angio[1,1],beta.5.angio[2,2], beta.5.angio[3,3], beta.5.angio[4,4], beta.5.angio[5,5], beta.5.angio[6,6],
beta.5.angio[7,7], beta.5.angio[8,8], beta.5.angio[9,9], beta.5.angio[10,10], beta.5.angio[11,11]))
beta.4.angio<-angio.beta.m[grepl("4", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.4<-beta.4.angio[1:11,1:11]
beta.4.dist.angio<-mean(c(beta.4.angio[1,1],beta.4.angio[2,2], beta.4.angio[3,3], beta.4.angio[4,4], beta.4.angio[5,5], beta.4.angio[6,6],
beta.4.angio[7,7], beta.4.angio[8,8], beta.4.angio[9,9], beta.4.angio[10,10], beta.4.angio[11,11]))
beta.3.angio<-angio.beta.m[grepl("3", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.3<-beta.3.angio[1:11,1:11]
beta.3.dist.angio<-mean(c(beta.3.angio[1,1],beta.3.angio[2,2], beta.3.angio[3,3], beta.3.angio[4,4], beta.3.angio[5,5], beta.3.angio[6,6],
beta.3.angio[7,7], beta.3.angio[8,8], beta.3.angio[9,9], beta.3.angio[10,10], beta.3.angio[11,11]))
beta.2.angio<-angio.beta.m[grepl("2", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.2<-beta.2.angio[1:11,1:11]
beta.2.dist.angio<-mean(c(beta.2.angio[1,1],beta.2.angio[2,2], beta.2.angio[3,3], beta.2.angio[4,4], beta.2.angio[5,5], beta.2.angio[6,6],
beta.2.angio[7,7], beta.2.angio[8,8], beta.2.angio[9,9], beta.2.angio[10,10], beta.2.angio[11,11]))
beta.1.angio<-angio.beta.m[grepl("1", rownames(angio.beta.m)),grepl("800*", colnames(angio.beta.m)) ]
beta.1<-beta.1.angio[1:11,1:11]
beta.1.dist.angio<-mean(c(beta.1.angio[1,1],beta.1.angio[2,2], beta.1.angio[3,3], beta.1.angio[4,4], beta.1.angio[5,5], beta.1.angio[6,6],
beta.1.angio[7,7], beta.1.angio[8,8], beta.1.angio[9,9], beta.1.angio[10,10], beta.1.angio[11,11]))
beta.decay.angio<-c(beta.800.dist.angio, beta.775.dist.angio, beta.745.dist.angio, beta.710.dist.angio, beta.675.dist.angio, beta.645.dist.angio, beta.615.dist.angio, beta.600.dist.angio, beta.8.dist.angio, beta.7.dist.angio,
beta.6.dist.angio, beta.5.dist.angio, beta.4.dist.angio, beta.3.dist.angio, beta.2.dist.angio, beta.1.dist.angio)
beta.decay.angio## [1] 1.00000000 0.54084821 0.22415493 0.22259162 0.07619048 0.19233197
## [7] 0.08845664 0.10611741 0.11294217 0.13398268 0.08126359 0.12805272
## [13] 0.10216760 0.13076419 0.16598296 0.10056749Sorensen’s phylogenetic beta diversity for angiosperms only
#beta.ps.angio.m<-as.matrix(phylosor(angio.sp.abd, trans.one.tree))
#Save and load R object for phylosor for 176 large grid plots
#saveRDS(beta.ps.angio.m, file="phylosor.large.grid.angio.rds")
beta.ps.angio.m<-readRDS("phylosor.large.grid.angio.rds")
beta.800.ps.angio.m<-beta.ps.angio.m[grepl("800*", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.800.ps.angio.dist<-1-(mean(c(beta.800.ps.angio.m[1,1],beta.800.ps.angio.m[2,2], beta.800.ps.angio.m[3,3], beta.800.ps.angio.m[4,4], beta.800.ps.angio.m[5,5], beta.800.ps.angio.m[6,6],
beta.800.ps.angio.m[7,7], beta.800.ps.angio.m[8,8], beta.800.ps.angio.m[9,9], beta.800.ps.angio.m[10,10], beta.800.ps.angio.m[11,11])))
head(beta.800.ps.angio.dist)## [1] 1beta.775.ps.angio.m<-beta.ps.angio.m[grepl("775*", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.775.ps.angio.dist<-mean(c(beta.775.ps.angio.m[1,1],beta.775.ps.angio.m[2,2], beta.775.ps.angio.m[3,3], beta.775.ps.angio.m[4,4], beta.775.ps.angio.m[5,5], beta.775.ps.angio.m[6,6],
beta.775.ps.angio.m[7,7], beta.775.ps.angio.m[8,8], beta.775.ps.angio.m[9,9], beta.775.ps.angio.m[10,10], beta.775.ps.angio.m[11,11]))
beta.745.ps.angio.m<-beta.ps.angio.m[grepl("745*", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.745.ps.angio.dist<-mean(c(beta.745.ps.angio.m[1,1],beta.745.ps.angio.m[2,2], beta.745.ps.angio.m[3,3], beta.745.ps.angio.m[4,4], beta.745.ps.angio.m[5,5], beta.745.ps.angio.m[6,6],
beta.745.ps.angio.m[7,7], beta.745.ps.angio.m[8,8], beta.745.ps.angio.m[9,9], beta.745.ps.angio.m[10,10], beta.745.ps.angio.m[11,11]))
beta.710.ps.angio.m<-beta.ps.angio.m[grepl("710*", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.710.ps.angio.dist<-mean(c(beta.710.ps.angio.m[1,1],beta.710.ps.angio.m[2,2], beta.710.ps.angio.m[3,3], beta.710.ps.angio.m[4,4], beta.710.ps.angio.m[5,5], beta.710.ps.angio.m[6,6],
beta.710.ps.angio.m[7,7], beta.710.ps.angio.m[8,8], beta.710.ps.angio.m[9,9], beta.710.ps.angio.m[10,10], beta.710.ps.angio.m[11,11]))
beta.675.ps.angio.m<-beta.ps.angio.m[grepl("675*", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.675.ps.angio.dist<-mean(c(beta.675.ps.angio.m[1,1],beta.675.ps.angio.m[2,2], beta.675.ps.angio.m[3,3], beta.675.ps.angio.m[4,4], beta.675.ps.angio.m[5,5], beta.675.ps.angio.m[6,6],
beta.675.ps.angio.m[7,7], beta.675.ps.angio.m[8,8], beta.675.ps.angio.m[9,9], beta.675.ps.angio.m[10,10], beta.675.ps.angio.m[11,11]))
beta.645.ps.angio.m<-beta.ps.angio.m[grepl("645*", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.645.ps.angio.dist<-mean(c(beta.645.ps.angio.m[1,1],beta.645.ps.angio.m[2,2], beta.645.ps.angio.m[3,3], beta.645.ps.angio.m[4,4], beta.645.ps.angio.m[5,5], beta.645.ps.angio.m[6,6],
beta.645.ps.angio.m[7,7], beta.645.ps.angio.m[8,8], beta.645.ps.angio.m[9,9], beta.645.ps.angio.m[10,10], beta.645.ps.angio.m[11,11]))
beta.615.ps.angio.m<-beta.ps.angio.m[grepl("615*", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.615.ps.angio.dist<-mean(c(beta.615.ps.angio.m[1,1],beta.615.ps.angio.m[2,2], beta.615.ps.angio.m[3,3], beta.615.ps.angio.m[4,4], beta.615.ps.angio.m[5,5], beta.615.ps.angio.m[6,6],
beta.615.ps.angio.m[7,7], beta.615.ps.angio.m[8,8], beta.615.ps.angio.m[9,9], beta.615.ps.angio.m[10,10], beta.615.ps.angio.m[11,11]))
beta.600.ps.angio.m<-beta.ps.angio.m[grepl("600*", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.600.ps.angio.dist<-mean(c(beta.600.ps.angio.m[1,1],beta.600.ps.angio.m[2,2], beta.600.ps.angio.m[3,3], beta.600.ps.angio.m[4,4], beta.600.ps.angio.m[5,5], beta.600.ps.angio.m[6,6],
beta.600.ps.angio.m[7,7], beta.600.ps.angio.m[8,8], beta.600.ps.angio.m[9,9], beta.600.ps.angio.m[10,10], beta.600.ps.angio.m[11,11]))
beta.8.ps.angio.first<-beta.ps.angio.m[grepl("8", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.8.ps.angio.m<-beta.8.ps.angio.first[1:11,1:11]
beta.8.ps.angio.dist<-mean(c(beta.8.ps.angio.m[1,1],beta.8.ps.angio.m[2,2], beta.8.ps.angio.m[3,3], beta.8.ps.angio.m[4,4], beta.8.ps.angio.m[5,5], beta.8.ps.angio.m[6,6],
beta.8.ps.angio.m[7,7], beta.8.ps.angio.m[8,8], beta.8.ps.angio.m[9,9], beta.8.ps.angio.m[10,10], beta.8.ps.angio.m[11,11]))
beta.7.ps.angio.first<-beta.ps.angio.m[grepl("7", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.7.ps.angio.m<-beta.7.ps.angio.first[1:11,1:11]
beta.7.ps.angio.dist<-mean(c(beta.7.ps.angio.m[1,1],beta.7.ps.angio.m[2,2], beta.7.ps.angio.m[3,3], beta.7.ps.angio.m[4,4], beta.7.ps.angio.m[5,5], beta.7.ps.angio.m[6,6],
beta.7.ps.angio.m[7,7], beta.7.ps.angio.m[8,8], beta.7.ps.angio.m[9,9], beta.7.ps.angio.m[10,10], beta.7.ps.angio.m[11,11]))
beta.6.ps.angio.first<-beta.ps.angio.m[grepl("6", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.6.ps.angio.m<-beta.6.ps.angio.first[1:11,1:11]
beta.6.ps.angio.dist<-mean(c(beta.6.ps.angio.m[1,1],beta.6.ps.angio.m[2,2], beta.6.ps.angio.m[3,3], beta.6.ps.angio.m[4,4], beta.6.ps.angio.m[5,5], beta.6.ps.angio.m[6,6],
beta.6.ps.angio.m[7,7], beta.6.ps.angio.m[8,8], beta.6.ps.angio.m[9,9], beta.6.ps.angio.m[10,10], beta.6.ps.angio.m[11,11]))
beta.5.ps.angio.first<-beta.ps.angio.m[grepl("5", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.5.ps.angio.m<-beta.5.ps.angio.first[1:11,1:11]
beta.5.ps.angio.dist<-mean(c(beta.5.ps.angio.m[1,1],beta.5.ps.angio.m[2,2], beta.5.ps.angio.m[3,3], beta.5.ps.angio.m[4,4], beta.5.ps.angio.m[5,5], beta.5.ps.angio.m[6,6],
beta.5.ps.angio.m[7,7], beta.5.ps.angio.m[8,8], beta.5.ps.angio.m[9,9], beta.5.ps.angio.m[10,10], beta.5.ps.angio.m[11,11]))
beta.4.ps.angio.first<-beta.ps.angio.m[grepl("4", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.4.ps.angio.m<-beta.4.ps.angio.first[1:11,1:11]
beta.4.ps.angio.dist<-mean(c(beta.4.ps.angio.m[1,1],beta.4.ps.angio.m[2,2], beta.4.ps.angio.m[3,3], beta.4.ps.angio.m[4,4], beta.4.ps.angio.m[5,5], beta.4.ps.angio.m[6,6],
beta.4.ps.angio.m[7,7], beta.4.ps.angio.m[8,8], beta.4.ps.angio.m[9,9], beta.4.ps.angio.m[10,10], beta.4.ps.angio.m[11,11]))
beta.3.ps.angio.first<-beta.ps.angio.m[grepl("3", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.3.ps.angio.m<-beta.3.ps.angio.first[1:11,1:11]
beta.3.ps.angio.dist<-mean(c(beta.3.ps.angio.m[1,1],beta.3.ps.angio.m[2,2], beta.3.ps.angio.m[3,3], beta.3.ps.angio.m[4,4], beta.3.ps.angio.m[5,5], beta.3.ps.angio.m[6,6],
beta.3.ps.angio.m[7,7], beta.3.ps.angio.m[8,8], beta.3.ps.angio.m[9,9], beta.3.ps.angio.m[10,10], beta.3.ps.angio.m[11,11]))
beta.2.ps.angio.first<-beta.ps.angio.m[grepl("2", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.2.ps.angio.m<-beta.2.ps.angio.first[1:11,1:11]
beta.2.ps.angio.dist<-mean(c(beta.2.ps.angio.m[1,1],beta.2.ps.angio.m[2,2], beta.2.ps.angio.m[3,3], beta.2.ps.angio.m[4,4], beta.2.ps.angio.m[5,5], beta.2.ps.angio.m[6,6],
beta.2.ps.angio.m[7,7], beta.2.ps.angio.m[8,8], beta.2.ps.angio.m[9,9], beta.2.ps.angio.m[10,10], beta.2.ps.angio.m[11,11]))
beta.1.ps.angio.first<-beta.ps.angio.m[grepl("1", rownames(beta.ps.angio.m)),grepl("800*", colnames(beta.ps.angio.m)) ]
beta.1.ps.angio.m<-beta.1.ps.angio.first[1:11,1:11]
beta.1.ps.angio.dist<-mean(c(beta.1.ps.angio.m[1,1],beta.1.ps.angio.m[2,2], beta.1.ps.angio.m[3,3], beta.1.ps.angio.m[4,4], beta.1.ps.angio.m[5,5], beta.1.ps.angio.m[6,6],
beta.1.ps.angio.m[7,7], beta.1.ps.angio.m[8,8], beta.1.ps.angio.m[9,9], beta.1.ps.angio.m[10,10], beta.1.ps.angio.m[11,11]))
beta.ps.angio.decay<-c(beta.800.ps.angio.dist, beta.775.ps.angio.dist, beta.745.ps.angio.dist, beta.710.ps.angio.dist, beta.675.ps.angio.dist, beta.645.ps.angio.dist, beta.615.ps.angio.dist, beta.600.ps.angio.dist, beta.8.ps.angio.dist, beta.7.ps.angio.dist,
beta.6.ps.angio.dist, beta.5.ps.angio.dist, beta.4.ps.angio.dist, beta.3.ps.angio.dist, beta.2.ps.angio.dist, beta.1.ps.angio.dist)
beta.ps.angio.decay## [1] 1.0000000 0.7840362 0.6293877 0.6367411 0.5051795 0.5433688 0.4949520
## [8] 0.5071233 0.6353049 0.5732323 0.5757294 0.5954311 0.5881355 0.6163918
## [15] 0.6136943 0.6102760Plot distance decays for angiosperms only
dev.new(width=11.8, height=4)
par(mfrow=c(1,2))
par(mar=c(5.1,5.1,5.5,2.1))
plot(beta.decay.angio, type="l",axes=FALSE, col="black", lwd=2, ylab="Beta diversity", xlab="Sampling Band",las=1, cex.axis=1, cex.lab=1.25,
main="Distance decay of \nSorensen's beta diversity (angiosperms)", cex.main=1.5, ylim=c(0,1))
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"), lwd=2)
axis(2, lwd=2)
box(bty="l", lwd=2)
plot(beta.ps.angio.decay, type="l",axes=FALSE, col="black", lwd=2, ylab="Phylogenetic beta diversity", xlab="Sampling Band",las=1, cex.axis=1, cex.lab=1.25,
main="Distance decay of Sorensen's \nphylogenetic beta diversity (angiosperms)", cex.main=1.5, ylim=c(0.4,1))
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"), lwd=2)
axis(2, lwd=2)
box(bty="l", lwd=2)Plot Distance decay of beta diversity and phylogenetic beta diversity together for angiosperms
#dev.new(width=5.9, height=4)
par(mar=c(5.1,5.1,5.5,2.1))
plot(beta.decay.angio, type="l",axes=FALSE, col="black", lwd=2, ylab="Beta diversity", xlab="Sampling Band",las=1, cex.axis=1, cex.lab=1.25,
main="Distance decay of Sorensen's beta diversity and \nSorensen's phylogenetic beta diversity (angiosperms)", cex.main=1.25, ylim=c(0,1))
lines(beta.ps.angio.decay, type="l",col="grey70", lwd=2)
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"), lwd=2)
axis(2, lwd=2)
legend("topright", c("Sorensen's beta diversity", "Sorensen's phylogenetic beta diversity"), col = c("black","grey70" ), cex=1.1,
lty = c(1, 1), pch = c(NA, NA), lwd=3 , bg = "white", bty="n")
box(bty="l", lwd=2)
Plot Distance decay of beta diversity and phylogenetic beta diversity together for vasculars and angiosperms
#dev.new(width=11.8, height=8)
par(mar=c(5.1,5.1,5.5,2.1))
plot(beta.decay, type="l",axes=FALSE, col="black", lwd=2, ylab="Beta diversity", xlab="Sampling Band",las=1, cex.axis=1, cex.lab=1.25,
main="Distance decay of Sorensen's beta diversity and \nSorensen's phylogenetic beta diversity (vasculars verses angiosperms)", cex.main=1.25, ylim=c(0,1))
lines(beta.ps.decay, type="l",col="gray70", lwd=2)
lines(beta.decay.angio, type="l",col="black", lwd=2, lty=2)
lines(beta.ps.angio.decay, type="l",col="grey70", lwd=2, lty=2)
axis(1,1:16,labels=c("800m", "775m","745m", "710m", "675m", "645m", "615m", "600m", "8", "7", "6", "5", "4", "3","2", "1"), lwd=2)
axis(2, lwd=2)
legend("topright", c("Sorensen's beta diversity - vasculars", "Sorensen's phylogenetic beta diversity - vasculars","Sorensen's beta diversity - angiosperms", "Sorensen's phylogenetic beta diversity - angiosperms"), col = c("black","grey70" ), cex=1.1,
lty = c(1, 1,2,2), pch = c(NA, NA), lwd=3 , bg = "white", bty="n")
box(bty="l", lwd=2)