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24_forecasted_presences_gm.R
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24_forecasted_presences_gm.R
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rm(list=ls())
library(here)
setwd(paste0(here(), '/data'))
presences_time<-list()
missing<-to_rm<-list()
#Read in Data
data<-read.csv('countydatanorm_march.csv', stringsAsFactors = FALSE) # spatial data
data2<-read.csv('spdat_clean_gdk.csv', stringsAsFactors = FALSE) # species data, see Hudgins et al. corrigendum for information on why ALB (spp=3) cannot be accurately fit
host.density2<-read.csv('hostden_clean_gdk.csv') # tree host density by pest species
prez<-read.csv('prez_clean_gdk.csv') # invasible host range (from FIA)
prez2<-read.csv('prez2_clean_gdk.csv') # pest presences in 2000 for the 3 species examined here, in 2009 for all other species
new_hwa<-read.csv('gm_5yr_fitting_july.csv')[,3:29]
L<-rep(0,64) # size of each pest's host range
ic=F
for (sppp in 1:64)
{
L[sppp]<-length(which(prez[,sppp]!=0))
}
#sources<-as.list(read.csv('Psources_notypos.csv')[,1])
currpopden<-as.matrix(read.csv("currpopden_5.csv", stringsAsFactors = FALSE))
currpopden2<-as.matrix(read.csv("future_scaled_pop2.csv"))
twenty35<-rowMeans(cbind(currpopden[,47], currpopden2[,4]))
twenty45<-rowMeans(cbind(currpopden2[,4], currpopden2[,5]))
twenty55<-rowMeans(cbind(currpopden2[,5], currpopden2[,6]))
currpopden<-cbind(currpopden, twenty35, currpopden2[,4], twenty45, currpopden2[,5], twenty55, currpopden2[,6])
#host.density2<-read.csv("hostden_clean_gdk.csv", stringsAsFactors = FALSE)
Tr1<-function(x)
{
sqrt((data$X_coord-data$X_coord[x])^2+(data$Y_coord-data$Y_coord[x])^2)
}
dists<-sapply(1:3372, Tr1)
T1<-exp(-dists/50000)
rm(dists)
YEARS<-data2$YEAR
LLfit=function(par)
{
pars<-rep(0,32)
temp_t=F # minimum temperature threshold, should be set to true for spp=48
hum_t=F # maximum humidity threshold, should be set to true for spp=44
if (i %in% c(44,48,49)==F)
{
pars[c(1,21,22,4,18,20,8)]<-as.numeric(c(par,c(0.000538410692229749, 0.299034706404549, -0.525670755351726, 15.6132848183217,-0.163552592351765, 0.323831382884772)))
}
if (i==44)
{
hum_t=T
pars[c(1,21,22,4,18,20,8,32)]<-as.numeric(c(par[1],c(0.000538410692229749, 0.299034706404549, -0.525670755351726, 15.6132848183217,-0.163552592351765, 0.323831382884772, par[2])))
}
if (i %in% c(48,49)==T)
{
temp_t=T
pars[c(1,21,22,4,18,20,8,31)]<-as.numeric(c(par[1],c(0.000538410692229749, 0.299034706404549, -0.525670755351726, 15.6132848183217,-0.163552592351765, 0.323831382884772, par[2])))
}
par<-pars
par[21]<-abs(par[21])
par[22]<-abs(par[22])+1
if (ic==F)
{
par[1]<-1.74213662335515
}
spp=i
current_temp<-current_temp2<-0
if (temp_t==T)
{
temp2<-temp<-read.csv('bio6_5yr.csv')
current_temp<-current_temp2<-temp2[,1]
}
hum=0
if (hum_t==T)
{
hum<-read.csv('interp_rel_humidity.csv')[,1]
}
#Pest Parameters
Pfull<<-matrix(0, 3372,64)
Pfull_time<<-Pfull
constpD=rep(0,64)
constpD=matrix(rep(constpD),3372,64, byrow=TRUE)
constpD2<-matrix(rep(par[9]*data[,18]+par[10]*data[,16]+par[16]*data[,19]+par[17]*data[,20]+par[18]*data[,21]),3372,64)+par[19]*host.density2
constpD<-as.numeric(constpD)+constpD2
constpD3<-matrix(rep(par[4]*data[,19]+par[12]*data[,20]+par[3]*data[,21]+par[13]*data[,18]+par[15]*data[,16]),3372,64)+par[5]*host.density2
#Pest Parameters
if (i %in%startpt==F)
{
sources<-as.list(read.csv('Psources_notypos.csv')[,1])
}
if (i %in%startpt==T)
{
load("Psources_closest_march.Rdata")
}
Psource=sources[[spp]]
YEAR<-YEARS[spp]
Discovery<-2009-YEAR
Pfull<<-matrix(0, 3372, 64)
T2<-T1[prez[1:L[spp],spp],prez[1:L[spp],spp]]
vecP<-rep(0,L[spp])
for (rrr in 1:length(Psource))
{vecP[which(prez[,spp]==Psource[rrr])]=1}
r0<-par[22]
for (time in 1:(floor(YEAR/5)+9))
{
if (temp_t==T)
{
if (time<(floor(YEAR/5)-4))
{
current_temp<-temp2[,1]
current_temp2<-temp2[,1]
}
if (time>=(floor(YEAR/5)-4))
{
current_temp2<-temp2[,time-floor(YEAR/5)+6]
}
}
vecP[which(prez[,spp]==Psource)]=1
if (temp_t==T)
{vecP[which(current_temp[prez[,spp]]<par[31])]<-0}
if (hum_t==T)
{vecP[which(hum[prez[,spp]]<par[32])]<-0}
vecP[which(prez[,spp]==Psource)]=1
Pnext<-rep(0,L[spp]) # vecP for next timestep
qq<-0 # dispersal kernel
column<-(((Discovery+5*(time-1))-1790)/5)+1 # which year of human population density to consider
qq<-matrix(rep(constpD[prez[which(vecP>=par[21]),spp],spp]+par[8]*currpopden[prez[which(vecP>=par[21]),spp],column], L[spp]), nrow=length(which(vecP>=par[21])), ncol=L[spp])
zzz<-matrix(rep(constpD3[prez[1:L[spp],spp],spp]+par[20]*currpopden[prez[1:L[spp],spp],column], L[spp]), nrow=L[spp], ncol=L[spp], byrow=TRUE) # add in current human populations
qq<-(2*par[1]*exp((zzz[which(vecP>=par[21]),]+qq)))/(1+exp(zzz[which(vecP>=par[21]),]+qq))
qq<-T2[which(vecP>=par[21]),]^qq # add in distance
#scale dispersal kernel
if (length(which(vecP>=par[21]))>1){qq<-qq/rowSums(qq)}
if (length(which(vecP>=par[21]))==1){qq<-qq/sum(qq)}
qq[which(qq<0.001)]=0
Pnext=(vecP[which(vecP>=par[21])])%*%(qq) # dispersal into and out of all sites
if (temp_t==T)
{
Pnext[which(current_temp[prez[,spp]]<par[31])]<-0
}
if (hum_t==T)
{
Pnext[which(hum[prez[,spp]]>par[2])]<-0
}
Pnext[which(prez[,spp]==Psource)]=1
Pfull[,time]<<-c(prez[which(Pnext>=par[21]),spp], rep(0, 3372-length(which(Pnext>=par[21]))))
Pfull_time[,time]<<-c(prez[which(Pnext>=par[21]),spp], rep(0, 3372-length(which(Pnext>=par[21])))) #threshold at 'discoverable' value
#if (time>1)
{
dddd<-which(!(Pfull_time[1:length(which(Pfull_time[,time-1]!=0)),time-1]%in%Pfull_time[1:length(which(Pfull_time[,time]!=0)),time]))
ffff<-which(prez[1:length(which(prez[,spp]!=0)), spp]%in%Pfull_time[dddd,time-1])
Pnext[ffff]<-par[21]
}
if (time<floor(YEAR/5))
{
cccc<-which(!(prez[1:length(which(prez[,spp]!=0)),spp]%in%new_hwa[1:length(which(new_hwa[,4]!=0)),4]))
Pnext[cccc]<-0
}
if (time==floor(YEAR/5))
{
dddd<-which(prez[1:length(which(prez[,spp]!=0)),spp]%in%prez2[1:length(which(prez2[,spp]!=0)),spp])
cccc<-which(!(prez[1:length(which(prez[,spp]!=0)),spp]%in%prez2[1:length(which(prez2[,spp]!=0)),spp]))
eeee<-which(Pnext[dddd]<par[21])
Pnext[dddd[eeee]]<-par[21]
Pnext[cccc]<-0
}
if (time%in%(which(colSums(new_hwa)>0)))
{
dddd<-which(prez[1:length(which(prez[,spp]!=0)),spp]%in%new_hwa[1:length(which(new_hwa[,time]!=0)),time])
cccc<-which(!(prez[1:length(which(prez[,spp]!=0)),spp]%in%new_hwa[1:length(which(new_hwa[,time]!=0)),time]))
eeee<-which(Pnext[dddd]<par[21])
Pnext[dddd[eeee]]<-par[21]
Pnext[cccc]<-0
}
current_temp<-current_temp2
Pnext[which(prez[,spp]==Psource)]=1
Pnext[which(Pnext>=par[21])]=Pnext[which(Pnext>=par[21])]*r0
Pnext[which(Pnext>=1)]<-1
vecP=Pnext
vecP[which(prez[,spp]==Psource)]=1
Pfull_time[,time]<<-c(prez[which(Pnext>=par[21]),spp], rep(0, 3372-length(which(Pnext>=par[21])))) #threshold at 'discoverable' value
Pfull[,time]<<-c(prez[which(Pnext>=par[21]),spp], rep(0, 3372-length(which(Pnext>=par[21])))) #threshold at 'discoverable' value
}
}
startpt<-read.csv('startpt.csv')[,1]
for (i in which(data2$COLUMN_NAM=="GM_pres"))
{
if (i==3)
{
next
}
spp=i
if (i %in% startpt==F)
{
pars<-read.table(paste("./new_adj_inter/par",i,"adj_inter",i, sep="."))[,1]
}
if (i %in% startpt==T)
{
pars<-read.csv(paste("./adj_inter_start/par_GDKic",i,"csv", sep="."))[,2]
}
if (i==48)
{
pars<-read.csv('par_GDK_forecast.48.csv')[,1]
}
if (i==44)
{
pars<-read.csv('par_GDK_forecast.44.csv')[,1]
}
if (i==49)
{
pars<-read.csv("par_GDK_HWA_s_ic_c_forecast_.csv")[,2]
}
if (i==51)
{
pars<-read.csv("par_GDK_GM_s_ic__forecast_.csv")[,1]
}
if (i==54)
{
pars<-read.csv("par_GDK_BBD_s_ic__forecast_.csv")[,1]
}
LLfit(c(pars))
presences_time[[i]]<-Pfull_time
}
saveRDS(presences_time, file="../output/presences_time_gm.rds")