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TS_basicSEM_longterm.R
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TS_basicSEM_longterm.R
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###############################################
# sensitivity analysis of long-term data sets #
# using basic model in 'TS_basicSEM.R' #
# for each combination of #
# phylogenetic and functional #
# diversity metric: #
# 1) FDis + eMNTD #
# 2) FRic + eMNTD #
# 3) FDis + eMPD #
# 4) FRic + eMPD #
###############################################
require(devtools)
install_github("jslefche/piecewiseSEM")
require(dplyr)
require(piecewiseSEM)
library(semPlot)
library(lmerTest)
library(nlme)
library(car)
# Data
stab<-read.delim("data.csv",sep=",",header=T)
stab<-filter(stab,Site!="BIODEPTH_GR") # should get rid of site where we didn't have good trait coverage
stab_4<-select(stab,Site,Study_length,UniqueID,SppN,eMPD,eMNTD,ePSE,sMPD,sMNTD,FDis4,FRic4,PCAdim1_4trts,SLA, LDMC, LeafN, LeafP,
Plot_TempStab,Plot_Biomassxbar, Plot_Biomasssd,Gross_synchrony,
mArid,sdAridity)
# for plots with ONLY 1 spp, we assume that a species #is perfectly synchronized with itself
stab_4$Gross_synchrony<-ifelse(is.na(stab_4$Gross_synchrony)==TRUE,1,stab_4$Gross_synchrony)
# convert synchrony metrics to different scale
stab_4$GrossAsynchrony_s<-stab_4$Gross_synchrony*-1
# further adjustments
stab_4$SppN<-as.numeric(stab_4$SppN)
stab_4$TS_lg2<-log(stab_4$Plot_TempStab,base=2)
stab_4$lg2SppN <- log(stab_4$SppN,2)
stab_4$lg2_mArid <-log(stab_4$mArid,base=2)
# Filter out NAs for Asynchrony and FRic4
stab_4<-filter(stab_4, is.na(PlotAsynchrony_s)==FALSE)
stab_444<-filter(stab_4, is.na(FRic4)==FALSE)
####################
# Data subsets ####
####################
study_duration<-summarize(group_by(stab_444, Site), Study_Duration=max(Study_length))
study_duration$Study_Length<-ifelse(study_duration$Study_Duration>4,1,0)
longterm<-filter(study_duration, Study_Length==1)
longterm<-as.character(longterm$Site)
stab_666<- filter(stab_444,as.character(Site) %in% longterm)
######################
# piecewise SEM #####
# with synchrony ####
######################
# Control list set up for LMM in nlme
bb<-lmeControl(msMaxIter=0,msVerbose = TRUE,opt="optim",maxIter=100,optimMEthod="L-BFGS-B") ######## "msMaxIter=0" is important in here!!!
cc<-lmeControl(opt="optim")
##################
# FDis_eMNTD #####
##################
modList2=list(
lme(eMNTD~lg2SppN,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(FDis4~lg2SppN,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(GrossAsynchrony_s~lg2SppN+FDis4+eMNTD+sdAridity+lg2_mArid,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(TS_lg2~GrossAsynchrony_s+PCAdim1_4trts+eMNTD+FDis4+lg2SppN+sdAridity+lg2_mArid,random=~1+lg2SppN|Site, control=cc,data=stab_666)
)
lapply(modList2, plot)
# Explore distribution of residuals
lapply(modList2, function(i) hist(resid(i)))
# Look at variance inflation factors
lapply(modList2[3:4], vif)
sem.fit(modList2,stab_666,corr.errors=c("eMNTD~~FDis4"),conditional=T,
model.control = list(lmeControl(opt = "optim"))) #naive model
emntdfdis.fit<-sem.fit(modList2,stab_666,
corr.errors=c("eMNTD~~FDis4","GrossAsynchrony_s~~PCAdim1_4trts",
"eMNTD~~PCAdim1_4trts"),conditional=T,
model.control = list(lmeControl(opt = "optim")))
emntdfdis.fit<-cbind(emntdfdis.fit$Fisher.C,emntdfdis.fit$AIC)
emntdfdis.fit$ModClass<-"FDis_eMNTD_LongTerm"
ts_emntd2<-sem.coefs(modList2,stab_666,standardize="scale",corr.errors=c("eMNTD ~~ FDis4","GrossAsynchrony_s ~~ PCAdim1_4trts",
"eMNTD ~~ PCAdim1_4trts"))
ts_emntd2$ModClass<-"FDis_eMNTD_LongTerm"
mf_ts_emntd<-sem.model.fits(modList2)
mf_ts_emntd$ResponseVars<-c("eMNTD","FDis4","Asynchrony","Temp_Stability")
mf_ts_emntd$PredVars<-c("lg2SppN","lg2SppN","lg2SppN,eMNTD,FDis4, lg2_mArid, Arid_sd","eMNTD,FDis4,eMNTD,Asynchrony,lg2SppN,lg2_mArid, Arid_sd")
mf_ts_emntd$ModClass<-"FDis_eMNTD_LongTerm"
# write results to file
write.table(ts_emntd2,"TS_emntd_fdis_sem_coefs_LongTerm_SIMPLE.csv",sep=",",row.names=F) # sem path coefficients
write.table(mf_ts_emntd,"TS_emntd_fdis_model_fits_LongTerm_SIMPLE.csv",sep=",",row.names=F) # model fits (marginal + conditional R2)
write.table(emntdfdis.fit,"TS_emntd_fdis_semfit_LongTerm_SIMPLE.csv",sep=",",row.names=F) # sem model fit
#######################
## FRic4 - eMNTD ###
#######################
modList22=list(
lme(eMNTD~lg2SppN,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(FRic4~lg2SppN,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(GrossAsynchrony_s~lg2SppN+FRic4+eMNTD+sdAridity+lg2_mArid,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(TS_lg2~GrossAsynchrony_s+PCAdim1_4trts+eMNTD+FRic4+lg2SppN+sdAridity+lg2_mArid,random=~1+lg2SppN|Site, control=cc,data=stab_666)
)
lapply(modList22, plot)
# Explore distribution of residuals
lapply(modList22, function(i) hist(resid(i)))
# Look at variance inflation factors
lapply(modList22[3:4], vif)
sem.fit(modList22,stab_666,corr.errors=c("eMNTD~~FRic4"),conditional=T,
model.control = list(lmeControl(opt = "optim"))) #naive model
emntdfric.fit<-sem.fit(modList22,stab_666,corr.errors=c("FRic4~~eMNTD", " eMNTD ~~ PCAdim1_4trts",
"GrossAsynchrony_s ~~ PCAdim1_4trts"),conditional=T,
model.control = list(lmeControl(opt = "optim"))) # add eMNTD as predictor of TS
emntdfric.fit<-cbind(emntdfric.fit$Fisher.C,emntdfric.fit$AIC)
emntdfric.fit$ModClass<-"FRic_eMNTD_LongTerm"
ts_emntd2<-sem.coefs(modList22,stab_666,standardize="scale",corr.errors=c("FRic4~~eMNTD", " eMNTD ~~ PCAdim1_4trts",
"GrossAsynchrony_s ~~ PCAdim1_4trts"))
ts_emntd2$ModClass<-"FRic_eMNTD_LongTerm"
mf_ts_emntd<-sem.model.fits(modList22)
mf_ts_emntd$ResponseVars<-c("eMNTD","FRic4","Asynchrony","Temp_Stability")
mf_ts_emntd$PredVars<-c("lg2SppN","lg2SppN","lg2SppN,eMNTD,FRic4,lg2_mArid, Arid_sd","Asynchrony,FRic4, eMNTD,lg2SppN,lg2_mArid, Arid_sd")
mf_ts_emntd$ModClass<-"FRic_eMNTD_LongTerm"
# write results to file
write.table(ts_emntd2,"TS_emntd_fric_sem_coefs_LongTerm_SIMPLE.csv",sep=",",row.names=F) # sem path coefficients
write.table(mf_ts_emntd,"TS_emntd_fric_model_fits_LongTerm_SIMPLE.csv",sep=",",row.names=F) # model fits (marginal + conditional R2)
write.table(emntdfric.fit,"TS_emntd_fric_semfit_LongTerm_SIMPLE.csv",sep=",",row.names=F) # sem model fit
##################
# FDis_eMPD ######
##################
modList3=list(
lme(eMPD~lg2SppN,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(FDis4~lg2SppN,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(GrossAsynchrony_s~lg2SppN+FDis4+eMPD+sdAridity+lg2_mArid,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(TS_lg2~GrossAsynchrony_s+FDis4+PCAdim1_4trts+lg2SppN+FDis4+eMPD+sdAridity+lg2_mArid,random=~1+lg2SppN|Site, control=cc,data=stab_666)
)
lapply(modList3, plot)
# Explore distribution of residuals
lapply(modList3, function(i) hist(resid(i)))
# Look at variance inflation factors
lapply(modList3[3:4], vif)
sem.fit(modList3,stab_666,corr.errors=c("eMPD~~FDis4"),conditional=T,
model.control = list(lmeControl(opt = "optim"))) #naive model
empdfdis.fit<-sem.fit(modList3,stab_666,corr.errors=c("eMPD~~FDis4","eMPD ~~ PCAdim1_4trts"),conditional=T,
model.control = list(lmeControl(opt = "optim"))) #
empdfdis.fit<-cbind(empdfdis.fit$Fisher.C,empdfdis.fit$AIC)
empdfdis.fit$ModClass<-"FDis_eMPD_LongTerm"
ts_empd2<-sem.coefs(modList3,stab_666,standardize="scale",corr.errors=c("eMPD ~~ FDis4","eMPD ~~ PCAdim1_4trts"))
ts_empd2$ModClass<-"FDis_eMPD_LongTerm"
mf_ts_empd<-sem.model.fits(modList3)
mf_ts_empd$ResponseVars<-c("eMPD","FDis4","Asynchrony","Temp_Stability")
mf_ts_empd$PredVars<-c("lg2SppN","lg2SppN","lg2SppN,eMPD,FDis4,lg2_mArid, Arid_sd","Asynchrony,lg2SppN,FDis4,eMNTD,CV_Precip,lg2_mArid, Arid_sd")
mf_ts_empd$ModClass<-"FDis_eMPD_LongTerm"
# write results to file
write.table(ts_empd2,"TS_empd_fdis_sem_coefs_LongTerm_SIMPLE_April2018.csv",sep=",",row.names=F) # sem path coefficients
write.table(mf_ts_empd,"TS_empd_fdis_model_fits_LongTermSIMPLE_April2018.csv",sep=",",row.names=F) # model fits (marginal + conditional R2)
write.table(empdfdis.fit,"TS_empd_fdis_semfit_LongTerm_SIMPLE_April2018.csv",sep=",",row.names=F) # sem model fit
#######################
## FRic - eMPD #######
#######################
modList33=list(
lme(eMPD~lg2SppN,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(FRic4~lg2SppN,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(GrossAsynchrony_s~lg2SppN+FRic4+eMPD+sdAridity+lg2_mArid,random=~1+lg2SppN|Site,control=cc,data=stab_666),
lme(TS_lg2~GrossAsynchrony_s+FRic4+eMPD+PCAdim1_4trts+lg2SppN+sdAridity+lg2_mArid,random=~1+lg2SppN|Site, control=cc,data=stab_666)
)
lapply(modList33, plot)
# Explore distribution of residuals
lapply(modList33, function(i) hist(resid(i)))
# Look at variance inflation factors
lapply(modList33[3:4], vif)
sem.fit(modList33,stab_666,corr.errors=c("eMPD~~FRic4"),conditional=T,
model.control = list(lmeControl(opt = "optim"))) #naive model
smpdfdis.fit<-sem.fit(modList33,stab_666,corr.errors=c("eMPD~~FRic4","eMPD ~~ PCAdim1_4trts"),conditional=T,
model.control = list(lmeControl(opt = "optim")))
smpdfdis.fit<-cbind(smpdfdis.fit$Fisher.C,smpdfdis.fit$AIC)
smpdfdis.fit$ModClass<-"FRic4_eMPD_LongTerm"
ts_smpd2<-sem.coefs(modList33,stab_666,standardize="scale",corr.errors=c("eMPD~~FRic4","eMPD ~~ PCAdim1_4trts"))
ts_smpd2$ModClass<-"FRic4_eMPD_LongTerm"
mf_ts_smpd<-sem.model.fits(modList33)
mf_ts_smpd$ResponseVars<-c("eMPD","FRic4","Asynchrony","Temp_Stability")
mf_ts_smpd$PredVars<-c("lg2SppN","lg2SppN","lg2SppN,eMPD,FRic4,lg2_mArid, Arid_sd","Asynchrony,FRic4,eMPD,F-S,lg2SppN,lg2_mArid, Arid_sd")
mf_ts_smpd$ModClass<-"FRic4_eMPD_LongTerm"
# write results to file
write.table(ts_smpd2,"TS_empd_fric_sem_coefs_LongTerm_SIMPLE.csv",sep=",",row.names=F) # sem path coefficients
write.table(mf_ts_smpd,"TS_empd_fric_model_fits_LongTerm_SIMPLE.csv",sep=",",row.names=F) # model fits (marginal + conditional R2)
write.table(smpdfdis.fit,"TS_empd_fric_semfit_LongTermSIMPLE.csv",sep=",",row.names=F) # sem model fit