From 44d64bb86f981b9d8511e9a8c48132e8ba4ac555 Mon Sep 17 00:00:00 2001
From: Christine Howard <53861305+christinehoward399@users.noreply.github.com>
Date: Wed, 19 Oct 2022 11:02:04 +0100
Subject: [PATCH] Add files via upload

---
 MCMC Extinction models.R | 204 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 204 insertions(+)
 create mode 100644 MCMC Extinction models.R

diff --git a/MCMC Extinction models.R b/MCMC Extinction models.R
new file mode 100644
index 0000000..aa9cba5
--- /dev/null
+++ b/MCMC Extinction models.R	
@@ -0,0 +1,204 @@
+##~~~~~~~~~~~~~~~~~~~ ##
+##  EXTINCTION MODELS _MCMCglmm
+## want to account for both spatial and phylogenetic autocorrelation
+##~~~~~~~~~~~~~~~~~~~ ##
+
+rm(list=ls())
+setwd("X:\\EBBA2")
+
+library(stringr)
+library(MCMCglmm)
+library(reshape2)
+library(phytools)
+library(parallel)
+
+results.dir.path.main<-"X:\\EBBA2\\Model Outputs\\MCMC Extinction models"
+dir.create(results.dir.path.main)
+
+## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~DATA~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ##
+## required data frames 
+cell # cell specific data including land cover, sampling effort etc. 
+species ## species trait data
+
+### ~~~~~~~~~~~~~~~~~~ MODEL FITTING ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ##
+## Need to fit to 10 different absence samples
+
+datFile<-"X:\\EBBA2\\Data\\Extinction Random Samples"
+its<-list.files(datFile)
+its
+
+for(DatI in its){
+  iNum<-gsub(".csv","",strsplit(DatI," ")[[1]][3])
+  
+  ## SAMPLED CELLS
+  extinction<-read.csv(paste0(datFile,"\\",DatI))
+  
+  ## Need to add in the climate suitability for those cells
+  Extinct<-do.call(rbind, lapply(unique(extinction$scientific_name), function(x){
+    print(x)
+    ## reduce down to the species
+    subDat<-extinction[extinction$scientific_name==x,]
+    ## Change in climate suitability
+    climDat<-read.table(paste0("X:\\EBBA2\\Climate Suitability Modelling\\SDM results\\EBBA cells only\\",x,"\\",x," climate prediction summary.txt", sep=""))
+    try(subDat<-merge(subDat, climDat[,c("CGRSNAME", "pred1970","pred1995","ClimChange")], by.x="cell50x50", by.y="CGRSNAME", all.x=T), silent=T)#"pred1960",
+    return(subDat)
+  }))
+  
+  # Format remaining data
+  MyDat<-merge(cells, Extinct[,c("cell50x50","scientific_name","state","point.dist","COG.dist","ClimChange", "pred1970")],
+               by.x="CGRSNAME", by.y="cell50x50", all=T)
+  MyDat$point.dist<-ifelse(MyDat$point.dist=="Inf",NA, MyDat$point.dist)
+  MyDat$COG.dist<-ifelse(MyDat$COG.dist=="Inf",NA, MyDat$COG.dist)
+  
+  ## Add in species traits
+  MyDat<-merge(MyDat, species[,c("Scientific.Name","Mean.Mass","Generation.Length",
+                                 "Clutch","Migratory.Status","rangeSize","HWI","MigDist",
+                                 "MAES.habitat","Policy","HabitatBreadth","dietBreadth",
+                                 "Montane","Persecuted","TipLabel")],
+               by.x="scientific_name", by.y="Scientific.Name")#, all.x=T)
+  MyDat<-droplevels(MyDat)
+  
+  ## For each species want the current extent of of favourable land cover and change in extent of favourable land cover
+  landAsc<-data.frame(MAES=levels(as.factor(MyDat$MAES.habitat) ) )
+  landAsc$ESAgrp<-c("Croplnd","Croplnd","Grsslnd","Shrblnd","Wtr_bds","Sprsly_","Urban","Wetland","Forest")
+  
+  MyDat$CurrEoFLC<-0
+  MyDat$ChangeEoFLC<-0
+  MyDat<-MyDat[complete.cases(MyDat$scientific_name),]
+  MyDat<-MyDat[complete.cases(MyDat$MAES.habitat),]
+  
+  for (x in 1:length(MyDat$CurrEoFLC)){
+    print(x)
+    LC<-landAsc[landAsc$MAES==as.character(MyDat[x,"MAES.habitat"]),"ESAgrp"]
+    MyDat$ChangeEoFLC[x]<-MyDat[x,paste0(LC,"_change")]
+    MyDat$CurrEoFLC[x]<-MyDat[x,paste0(LC,"_1992")]
+  }
+  
+  MyDat$animal<-MyDat$TipLabel
+  MyDat<-MyDat[!MyDat$animal=="",]
+  
+  data.model<-MyDat[,c("scientific_name","CGRSNAME","lon","lat","point.dist","rangeSize","pred1970","COG.dist",#"code",
+                       "ClimChange","NAME_FAO","Migratory.Status", "alt_range","IUCN",#"alt_mean",,
+                       "Generation.Length","Mean.Mass","Clutch","Policy","CurrEoFLC","ChangeEoFLC", "HWI","MigDist",
+                       "Shannon_change","Shannon_1992","totalConfidence","HabitatBreadth","dietBreadth","Persecuted", 
+                       "MAES.habitat","Montane","state","animal")]#,"EBBA1abund","EBBA1SAC","code",
+  
+  ## Subset down to cells with similar sampling effor between the two years
+  data.model<-data.model[data.model$totalConfidence==2,]
+  formula <- as.formula(state~ClimChange+point.dist+CurrEoFLC+Mean.Mass+Policy+Montane+COG.dist+
+                          MigDist+Generation.Length+alt_range+IUCN+HWI+
+                          HabitatBreadth+dietBreadth+Clutch+#MAES.habitat+
+                          ChangeEoFLC+pred1970+Persecuted+
+                          Shannon_1992+rangeSize)#+EBBA1SAC )
+  data.model<-data.model[complete.cases(data.model$scientific_name),]
+  data.model<-data.model[complete.cases(data.model$HabitatBreadth),]
+  data.model<-data.model[complete.cases(data.model$ClimChange),]
+  data.model<-droplevels(data.model)
+  missSp<-unique(data.model[is.na(data.model$animal),"scientific_name"])
+  data.model$animal<-ifelse(is.na(data.model$animal), data.model$scientific_name, data.model$animal)
+  data.model$animal<-gsub(" ","_",data.model$animal)
+  data.model<-droplevels(data.model)
+  data.model$Montane<-as.factor(as.character(data.model$Montane))
+  data.model$Policy<-as.factor(as.character(data.model$Policy))
+  data.model$Persecuted<-as.factor(as.character(data.model$Persecuted))
+  
+  ## Standardised variables
+  cont<-c("ClimChange","Mean.Mass","point.dist","Generation.Length","COG.dist",
+          "alt_range","pred1970","IUCN","MigDist","HWI",
+          "Clutch","CurrEoFLC","ChangeEoFLC","Shannon_change","Shannon_1992",
+          "rangeSize","HabitatBreadth","dietBreadth")
+  for ( i in cont){data.model[,i]<-scale(as.numeric(as.character(data.model[,i])))}
+  
+  data.model<-data.model[complete.cases(data.model),]
+  data.model<-droplevels(data.model)
+  
+  results.dir.path.sub<-paste0(results.dir.path.main,"\\Final")
+  dir.create(results.dir.path.sub)
+  
+  ## Set up parallel
+  clust <- makeCluster(10)
+  clusterExport(clust, varlist = c("trlist","missSp","data.model","formula","results.dir.path.sub","DatI", "iNum"))
+  clusterEvalQ(clust, library(phytools))
+  clusterEvalQ(clust, library(MCMCglmm))
+  
+  parLapply(clust, 1:10,function(x){
+      
+      # ConsensusTree<-consensus(trlist, p=0.5)
+      ConsensusTree<-trlist[[x]]
+      
+      # ~~~~~~ FORMAT THE CONSENSUS TREE
+      ## Add missing species
+      for(k in missSp){
+        ConsensusTree<-add.species.to.genus(ConsensusTree,k)
+      }
+      
+      ## trim the consensus tree
+      tr<-drop.tip(ConsensusTree,ConsensusTree$tip.label[-match(unique(data.model$animal),ConsensusTree$tip.label)])
+      # Prune tree to just include species of interest
+      
+      # Remove node names from phylogeny
+      tr$node.label <- NULL
+      # MCMCglmm needs ultrametric trees
+      if(is.ultrametric(tr)==FALSE){
+        tr <- chronos(tr)
+        class(tr) <- "phylo"
+      }
+      
+      # check again
+      is.ultrametric(tr)
+      
+      ## Define the covariance matrix
+      inv.phylo<-inverseA(tr,nodes="TIPS",scale=TRUE)
+      
+      ## Re-order trait data to match order of tree (essential for running 'gls'):
+      # data.model <- data.model[match(tr$tip.label, levels(data.model$TipLabel)),] 
+      data.model<-data.model[complete.cases(data.model),]
+      data.model<-droplevels(data.model)
+      
+      ## ~~~~~~~~~~~~~~~~~~~ Model Fitting M1 ~~~~~~~~~~~~~~~~~~~~~~~ ##
+      ## params for sample size etc.
+      THIN <- 200
+      BURNIN <- 20000
+      NITT <-220000
+      
+      # PRIORS- Model M1 is a simple phylogenetic model without intraspecific correlation structure
+      priorpr.m1 <- list(R = list(V = 1, nu = 1),#0.002),
+                         G = list(G1 = list(V = 1, nu = 0.002),
+                                  G2 = list(V = 1, nu = 0.002),
+                                  G3 = list(V = 1, nu = 0.002),
+                                  G4 = list(V = 1, nu = 0.002)))#,
+      
+      #Then you can run your global model: 
+      system.time(mmF<- MCMCglmm(formula,#+X2010.IUCN.Red.List.category,
+                                 random=~animal+idv(NAME_FAO)+idv(scientific_name)+idv(CGRSNAME),
+                                 family="categorical",ginverse=list(animal=inv.phylo$Ainv),prior=priorpr.m1,
+                                 data=data.model,nitt=NITT,burnin=BURNIN,thin=THIN, singular.ok = T))# 
+      
+      ## ~~~~~~~~~~~~~~~~~~~ Model Performance M1 ~~~~~~~~~~~~~~~~~~~~~~~ ##
+      # Calculation of the variance in fitted values
+      mVarF <- var(as.vector(apply(mmF$Sol,2,mean) %*% t(mmF$X)))
+      # MCMCglmm - marginal
+      vmVarF<-numeric(dim(mmF$Sol)[1])
+      for(i in 1:length(vmVarF)){
+        Var<-var(as.vector(mmF$Sol[i,] %*% t(mmF$X)))
+        vmVarF[i]<-Var}
+      R2m<-vmVarF/(vmVarF+mmF$VCV[,1]+mmF$VCV[,2]+mmF$VCV[,3]+mmF$VCV[,4]+mmF$VCV[,5])
+      margR2M1<-mean(R2m);margR2M1
+      
+      ##Save block models
+      mod<-list(mod1=mmF,MarginalR2M1=margR2M1)
+      
+      save(mod, file=paste(results.dir.path.sub,"\\Extinction_MCMCglmm_iteration_",iNum,"_phylo_",x,".rda",sep=""),compress="bzip2")
+    })
+}
+
+
+plot(M1)
+library(coda)
+gelman.plot(x=mcmc.list(M1$Sol, M2$Sol))
+gelman.diag(mcmc.list(M1$Sol, M2$Sol))
+
+library(BayesianTools)
+correlationPlot(data.frame(mmF$Sol)[,1:10])
+correlationPlot(data.frame(mmF$Sol)[,11:19])
+correlationPlot(data.frame(mmF$Sol))
\ No newline at end of file