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TrawlModel_GOA_nl.R
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################# Kotaro Code for loading packages ######################
# Setting lists of required packages & installing it
list_rpackage <- c("rgdal", "raster", "FNN", "xlsx", "lubridate", "xtable","gridGraphics", "maps",
"grid", "gridExtra", "mgcv", "dismo", "rJava", "PresenceAbsence", "sp", "here", "maptools",
"viridis")
which_not_installed <- which(list_rpackage %in% rownames(installed.packages()) == FALSE)
if(length(which_not_installed) > 1){
install.packages(list_rpackage[which_not_installed], dep = TRUE)
# not necessary unless simulating populations or running VAST
# if ("INLA" %in% list_rpackage[which_not_installed]){
# install.packages("INLA", repos=c(getOption("repos"),
# INLA = "https://inla.r-inla-download.org/R/stable"), dep = TRUE, quiet = TRUE)
# }
}
# load the packages from the list
lapply(list_rpackage, require, character.only = TRUE)
#################################################################################
#GOAData<-read.csv("E://EFH Descriptions 2015/Variables/Variables_raw_data/GOA_Trawldata.csv",header=TRUE,stringsAsFactors=FALSE)
#data1<-read.csv("E://Data1.csv",header=TRUE)
#GOAData<-merge(GOAData,data1,by.x="hauljoin",by.y="hauljoin",all.x=TRUE)
#indexes = sample(1:nrow(GOAData), size=0.2*nrow(GOAData))
#test.data = GOAData[indexes,]
#training.data = GOAData[-indexes,]
#write.csv(test.data,"E://EFH Descriptions 2015/Variables/Variables_raw_data/GOA_Trawl_testdata.csv", row.names=FALSE)
#write.csv(training.data,"E://EFH Descriptions 2015/Variables/Variables_raw_data/GOA_Trawl_trainingdata.csv", row.names=FALSE)
home.dir <- here::here()
training.data <- read.csv(paste0(home.dir, "/variables/Variables_raw_data/GOA_Trawl_trainingdata.csv"),
header = TRUE, stringsAsFactors = FALSE)
# substituting raster temp for btemp where btemp is null
for(i in 1:length(training.data$btemp)){
if(is.na(training.data[i, "btemp"]) == TRUE){
training.data[i, "btemp"] <- training.data[i, "rtemp"]
}
}
# eliminating data with bogus? slopes - Ask ROOPER why no negative slopes
training.data <- subset(training.data, training.data$slope >= 0)
# combine adults and juveniles for certain rockfish species
training.data$canrf <- training.data$a_canrf + training.data$j_canrf
training.data$qurf <- training.data$a_qurf + training.data$j_qurf
training.data$blackrf <- training.data$a_blackrf + training.data$j_blackrf
training.data$dbrf <- training.data$a_dbrf + training.data$jdbrf
training.data$darkrf <- training.data$a_darkrf + training.data$j_darkrf
training.data$atka <- training.data$a_atka + training.data$j_atka
training.data$gstrf <- training.data$a_gstrf + training.data$j_gstrf
test.data <- read.csv(paste0(home.dir, "/variables/Variables_raw_data/GOA_Trawl_testdata.csv"),
header = TRUE, stringsAsFactors = FALSE)
# substituting raster temp for btemp where btemp is null
for(i in 1:length(test.data$btemp)){
if(is.na(test.data[i, "btemp"]) == TRUE){
test.data[i, "btemp"] <- test.data[i, "rtemp"]
}
}
test.data <- subset(test.data,test.data$slope >= 0)
test.data$canrf <- test.data$a_canrf + test.data$j_canrf
test.data$qurf <- test.data$a_qurf + test.data$j_qurf
test.data$blackrf <- test.data$a_blackrf + test.data$j_blackrf
test.data$dbrf <- test.data$a_dbrf + test.data$jdbrf
test.data$darkrf <- test.data$a_darkrf + test.data$j_darkrf
test.data$atka <- test.data$a_atka + test.data$j_atka
test.data$gstrf <- test.data$a_gstrf + test.data$j_gstrf
###MAKE THE 1 KM BASE RASTERS#####
ak.coast <- rgdal::readOGR(dsn = "//akc0ss-n086/SEA_Programs/RACE_EFH_variables/shapefiles", layer = "namerica_dcw", verbose = F)
# (network location is \\akc0ss-n086/SEA_Programs/RACE_EFH_variables)
bathy <- raster("//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Bathy")
slope <- raster("//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Slope")
color <- raster("//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Color")
tmax <- raster("//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Tmax")
bcurrent <- raster("//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Bcurrent")
btemp <- raster("//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Btemp")
lat <- init(bathy, v ='y')
lat <- mask(lat, bathy, filename = "//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Lat",
overwrite = TRUE)
lon <- init(bathy, v ='x')
lon <- mask(lon, bathy, filename = "//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Lon",
overwrite = TRUE)
coral <- raster("//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Coralfactor")
sponge <- raster("//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Spongefactor")
whips <- raster("//akc0ss-n086/SEA_Programs/RACE_EFH_variables/Variables/Variables_GOA_1km/Whipsfactor")
raster.stack <- stack(lon, lat, bathy, slope, btemp, color, bcurrent, tmax, sponge, coral, whips)
names(raster.stack) <- c("lon","lat","bdepth","slope","btemp","color","speed", "tmax", "sponge", "coral", "pen")
ModelPlan <- read.csv(paste0(home.dir, "/ModelPlan.csv"), header = TRUE, stringsAsFactors = FALSE)
ModelPlan <- subset(ModelPlan,ModelPlan$Region == "GOA")
species <- c(ModelPlan$Trawl_name_juvenile, ModelPlan$Trawl_name_adult)
models <- c(ModelPlan$Trawl_model_juvenile, ModelPlan$Trawl_model_adult)
start_year <- rep(ModelPlan$Start_year, 2)
species_name <- c(paste("Juvenile_",ModelPlan$Species, sep = ""), paste("Adult_", ModelPlan$Species,sep = ""))
species_name <- species_name[species != ""]
models <- models[species != ""]
start_year <- start_year[species != ""]
species <- species[species != ""]
#j<-35
#k<-1
species_pct <- species
for(j in 1:length(species)){
if(start_year[j] > 1990){
training.dat <- subset(training.data, training.data$year >= start_year[j])
test.dat <- subset(test.data, test.data$year >= start_year[j])
}
if(start_year[j] == 0){
training.dat <- training.data
test.dat <- test.data
}
t1 <- (length(subset(training.dat[,species[j]], training.dat[,species[j]] > 0)) + length(subset(test.dat[,species[j]],
test.dat[,species[j]] > 0)))/(length(training.dat[,1]) + length(test.dat[,1]))
species_pct[j] <- t1
print(paste(species_name[j], round(t1, 2)))
# NOTE - all but the last element in the dir.create path must exist for function to create terminal directory
results.path <- paste0(home.dir, "/Trawl_models/GOA/", species_name[j])
# if writing to an existing directory this will allow original directory to stand and suppress the warning that it exists
# will need to be paired with overwrite = T down the line where necessary
dir.create(results.path)
####################################################################################################################################
####################################################################################################################################
##################################################CPUE MODEL######################################################################
####################################################################################################################################
if(models[j] == "gam"){
# yvar <- training.dat[species[j]]^1
yvar <- species[j]
##########################################################################
## To cope with the possibility that a presence-absence factor could
## have zero contrast (i.e., 100% present or 100% absent), I built a test
## to sequentially identify which factors had contrast and thus could be
## included in the formula.
# continuous x variables
base.form <- c("s(lon,lat,k=10)", "s(slope,k=4)", "s(tmax,k=4)", "s(color,k=4)", "s(speed,k=4)", "s(btemp,k=4)",
"s(bdepth,k=4)")
# empty vector to accept strings below
factor.form <- vector()
# testing factors - if length of unique values == 1 then no contrast. So if >= 2 then include factor term
# written to serially include and accumulate terms as they qualify
if(length(unique(training.dat$sponge)) >= 2){
factor.form <- "as.factor(sponge)"
}
if(length(unique(training.dat$coral)) >= 2){
factor.form <- c(factor.form, "as.factor(coral)")
}
if(length(unique(training.dat$pen)) >= 2){
factor.form <- c(factor.form, "as.factor(pen)")
}
xvars <- c(base.form, factor.form)
gam.form <- as.formula(paste(yvar, "~", paste(xvars, collapse = "+")))
for(i in 1:length(xvars)){
cpue.gam <- gam(gam.form, family = gaussian, data = training.dat)
gcv_gam <- cpue.gam$gcv.ubre
pvals <- summary(cpue.gam)$s.pv
pvals <- c(pvals, summary(cpue.gam)$p.pv[-1])
least_sig <- which.max(pvals)
xvars1 <- xvars[-least_sig]
gam.form1 <- as.formula(paste(yvar, "~", paste(xvars1, collapse = "+")))
cpue.gam1 <- gam(gam.form1, family = gaussian, data = training.dat)
gcv_gam1 <- cpue.gam1$gcv.ubre
if(gcv_gam > gcv_gam1){
gam.form <- gam.form1
gcv_gam <- gcv_gam1
xvars1 <- xvars[-least_sig]
print(summary(cpue.gam1))
}
if(gcv_gam < gcv_gam1)break
}
######## Training Data
pred2 <- predict(cpue.gam, training.dat, fun = predict, type = "response")
cpue.obs.pred <- data.frame(cbind(x = training.dat[species[j]]^1, y = pred2))
colnames(cpue.obs.pred)[1] <- "x"
######### Test Data
pred4 <- predict(cpue.gam, test.dat, fun = predict, type = "response")
t.yvar <- test.dat[species[j]]
t.cpue.obs.pred <- data.frame(cbind(x = t.yvar, y = pred4))
colnames(t.cpue.obs.pred)[1] <- "x"
######## Prediction Map
predict.CPUE.raster1 <- predict(raster.stack, cpue.gam, fun = predict, na.rm = TRUE, overwrite = TRUE, progress = "text",
type = "response", newdata.guaranteed = TRUE)
predict.CPUE.raster <- mask(predict.CPUE.raster1, ak.coast, inverse = TRUE, overwrite = TRUE, progress = "text",
filename = paste(results.path, "/CPUEpredict", sep = ""))
sample1 <- sampleRandom(predict.CPUE.raster, 600000, na.rm = TRUE)
sample1[sample1<=0] <- NA
breaks <- quantile(sample1, probs = c(0,0.05,0.25,0.5,0.75,1), na.rm = TRUE, names = FALSE)
EFH.raster <- cut(predict.CPUE.raster, breaks = breaks, overwrite = TRUE, filename = paste(results.path,
"/EFHmap", sep = ""), progress = "text")
save.image(paste(results.path, "/", species_name[j], ".RData", sep = ""))
####FIGURE 1 - GAM DIAGNOSTICS PLOTS
dir.create(paste(results.path, "/Figures", sep = ""))
png(filename = paste(results.path, "/Figures/CPUEGAMdiagnostics.png", sep = ""), width = 8.5, height = 11, res = 300,
units ="in")
par(mfcol = c(3,2), family = "sans", mar = c(4,4,3,1))
qqnorm((cpue.obs.pred$x - cpue.obs.pred$y), main = "Training data")
qqline((cpue.obs.pred$x - cpue.obs.pred$y))
hist((cpue.obs.pred$x - cpue.obs.pred$y), xlab = "Residuals", main = "")
regr <- lm(y~x, data = cpue.obs.pred)
rsqr <- summary(lm(y~x, data = cpue.obs.pred))$r.squared
adj.rsqr <- summary(lm(y~x, data = cpue.obs.pred))$adj.r.squared
pred.max <- max(cpue.obs.pred$y)
obs.max <- max(cpue.obs.pred$x)
if(pred.max > obs.max){
plot.max = pred.max + (0.04*pred.max)
}else{
plot.max = obs.max + (0.04*obs.max)
}
par(xaxs = "i", yaxs = "i")
plot(cpue.obs.pred$x, cpue.obs.pred$y, ylim = c(0,plot.max), xlim = c(0,plot.max), ylab = "Predicted", xlab = "Observed",
main = "", pch = 20)
abline(coef = c(0,1), lty = 2)
lines(cpue.obs.pred$x, regr$fitted.values)
text(1, plot.max-1, paste("R-squared = ", round(rsqr,2)), pos = 4)
#Plots for test data
qqnorm((t.cpue.obs.pred$x - t.cpue.obs.pred$y), main = "Test data")
qqline((t.cpue.obs.pred$x - t.cpue.obs.pred$y))
hist((t.cpue.obs.pred$x - t.cpue.obs.pred$y), xlab = "Residuals", main = "")
regr <- lm(y~x, data = t.cpue.obs.pred)
rsqr <- summary(lm(y~x, data = t.cpue.obs.pred))$r.squared
adj.rsqr <- summary(lm(y~x, data = t.cpue.obs.pred))$adj.r.squared
pred.max <- max(t.cpue.obs.pred$y)
obs.max <- max(t.cpue.obs.pred$x)
if(pred.max > obs.max){
plot.max = pred.max + (0.04*pred.max)
}else{
plot.max = obs.max + (0.04*obs.max)
}
par(xaxs = "i", yaxs = "i")
plot(t.cpue.obs.pred$x, t.cpue.obs.pred$y, ylim = c(0,plot.max), xlim = c(0,plot.max), ylab = "Predicted", xlab = "Observed",
main = "", pch = 20)
abline(coef = c(0,1), lty = 2)
lines(t.cpue.obs.pred$x, regr$fitted.values)
text(1, plot.max - 1, paste("R-squared = ", round(rsqr,2)), pos = 4)
dev.off()
####FIGURE 2 - GAM EFFECTS PLOT################################
png(filename = paste(results.path, "/Figures/CPUEGAMplots.png", sep = ""), width = 8.5, height = 11, res = 300, units = "in")
n1 <- summary(cpue.gam)$m
n2 <- length(summary(cpue.gam)$p.t)
n2 <- ifelse(n2 > 0, n2-1, 0) + n1
v1 <- all.vars(formula(cpue.gam))
v1 <- v1[-1]
if(v1[1] == "lon"){v1[1] <- "lonlat"
v1 <- v1[-2]
}
par(mfrow = c(ceiling(n2/3), 3), mar = c(4,4,2,.01))
for(i in 1:n2){
if(v1[i] == "lonlat"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, xlab = "Longitude (northings)",
ylab = "Latitude (eastings)") }
if(v1[i] == "slope"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Slope (%)") }
if(v1[i] == "tmax"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Tidal current speed (cm/s)")}
if(v1[i] == "color"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Ocean color") }
if(v1[i] == "speed"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Current speed (m/s)")}
if(v1[i] == "btemp"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Temperature (C)")}
if(v1[i] == "bdepth"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Depth (m)")}
if(v1[i] == "sponge"){plot(cpue.gam, select = i, all.terms = TRUE, ylabs = "Variable effect", xlabs = "Sponge presence")}
if(v1[i] == "coral"){plot(cpue.gam, select = i, all.terms = TRUE, ylabs = "Variable effect", xlabs = "Coral presence")}
if(v1[i] == "pen"){plot(cpue.gam, select = i, all.terms = TRUE, ylabs = "Variable effect", xlabs = "Pennatulacean presence")
}
}
dev.off()
######FIGURE 3 - PREDICTED ABUNDANCE MAP ##################################################
png(filename = paste(results.path, "/Figures/CPUEGAMmap.png", sep = ""), width = 7.1, height = 5, res = 300, units = "in")
par(mfrow = c(1,1), mar = c(5,4,1,1), family = "sans")
plot(predict.CPUE.raster, main = "", xaxt = "n", yaxt = "n", box = F, col = plasma(255), ext = GOA.ext.pol,
legend.shrink = 0.5, axis.args = list(cex.axis = 0.65), legend.args = list(text = "Abundance", cex = 0.65,
cex.lab = 0.65, side = 1, line = 2), horiz = TRUE, ylab = "Latitude", xlab = "Longitude",
zlim = c(0, maxValue(predict.CPUE.raster)))
plot(ak.coast, col = "black", add = TRUE)
plot(wrld_grd_proj2, lty = 3, col = "lightgrey", add = T)
axis(1, at = GOA.xaxis.ticks, labels = GOA.xaxis)
axis(2, at = GOA.yaxis.ticks, labels = GOA.yaxis)
dev.off()
######FIGURE 4 - EFH MAP ##################################################
png(filename=paste(results.path, "/Figures/EFHmap.png", sep = ""),width=7.1,height=5,res=300,units="in")
par(mfrow=c(1,1),mar=c(5,4,1,1),family="sans")
plot(EFH.raster, main = "", xaxt = "n", yaxt = "n", box = F, col = colorRampPalette(c("grey", "coral3"))(4),
ext = GOA.ext.pol, legend = FALSE, ylab = "Latitude", xlab = "Longitude", horiz = TRUE,
zlim = c(2, maxValue(EFH.raster)))
plot(ak.coast, col = "black", add = TRUE)
plot(wrld_grd_proj2, lty = 3, col = "lightgrey", add = TRUE)
axis(1, at = GOA.xaxis.ticks, labels = GOA.xaxis)
axis(2, at = GOA.yaxis.ticks, labels = GOA.yaxis)
legVals <- c("95%","75%","50%","25%")
legend("bottomright", legend = legVals, pch = 15, col = colorRampPalette(c("grey", "coral3"))(4), bty = "n", pt.cex = 2,
title = "Percentiles", cex = 1)
depth.contour <- contour(raster.stack[["bdepth"]], levels = c(100,300,500), col = "black", lwd = 0.05, drawlabels = FALSE,
add = TRUE)
dev.off()
##########################WRITE RASTERS TO ArcGIS FORMAT GeoTIFF's############################################
writeRaster(predict.CPUE.raster, filename = paste0(home.dir, "/GeoTiffs/GeoTiffs_GOA/", species_name[j],
"_CPUE_map.tif", sep = ""), overwrite = TRUE, format = "GTiff", options = c("PROFILE=GeoTIFF", "TFW=YES"),
NAflag = -999)
writeRaster(EFH.raster, filename = paste0(home.dir, "/GeoTiffs/GeoTiffs_GOA/", species_name[j], "_EFHmap.tif",
sep = ""), overwrite = TRUE, format = "GTiff", options = c("PROFILE=GeoTIFF", "TFW=YES"), NAflag = -999)
########TABLE 1 - SUMMARY TABLE FOR CPUE GAM#######################
n1 <- summary(cpue.gam)$m
n2 <- length(summary(cpue.gam)$p.t)
n2 <- ifelse(n2>0,n2-1,0)+n1
v1 <- all.vars(formula(cpue.gam))
v1 <- v1[-1]
if(v1[1] == "lon"){
v1[1] <- "lonlat"
v1<-v1[-2]
}
table1 <- array("", dim=c(length(v1)+2,6))
table1[1,] <- c(" "," "," "," ","Training data","Test data")
table1[2,] <- c("Model term","EDF","p-value","Deviance explained","rsq","rsq")
table1[3,4] <- round(summary(cpue.gam)$dev.expl,3)
table1[3,5] <- rsqr <- round(summary(lm(y~x, data = cpue.obs.pred))$r.squared,2)
table1[3,6] <- rsqr <- round(summary(lm(y~x, data = t.cpue.obs.pred))$r.squared,2)
table1a <- v1
table1a[table1a == "lonlat"] <- "Longitude*latitude"
table1a[table1a == "slope"] <- "slope"
table1a[table1a == "pen"] <- "Pennatulacean present"
table1a[table1a == "coral"] <- "Coral present"
table1a[table1a == "sponge"] <- "Sponge present"
table1a[table1a == "color"] <- "Ocean color"
table1a[table1a == "speed"] <- "Current speed"
table1a[table1a == "bdepth"] <- "Depth"
table1a[table1a == "tmax"] <- "Tidal current"
table1a[table1a == "btemp"] <- "Temperature"
table1b <- c(summary(cpue.gam)$edf,summary(cpue.gam)$pTerms.df)
table1c <- c(summary(cpue.gam)$s.pv,summary(cpue.gam)$pTerms.pv)
table1[3:(length(v1)+2),1] <- table1a[order(table1c)]
table1[3:(length(v1)+2),2] <- signif(table1b[order(table1c)],digits=2)
table1[3:(length(v1)+2),3] <- signif(table1c[order(table1c)],digits=3)
cpue.table <- xtable::xtable(table1)
xtable::print.xtable(cpue.table, type = "html", file = paste(results.path, "/Figures/cpuegamtable.html", sep = ""),
include.rownames = getOption("xtable.include.rownames", FALSE), html.table.attributes = 2,
include.colnames = getOption("xtable.include.colnames", FALSE), hline.after = getOption("xtable.hline.after",
c(-1, 1, nrow(table1))))
print(paste(species[j], "Catch model complete"))
}
####################################################################################################################################
####################################################################################################################################
##################################################HURDLE MODEL######################################################################
####################################################################################################################################
if(models[j] == "hgam"){
######PRESENCE ABSENCE PART#########
# yvar <- training.dat[species[j]]^1
#### strategy here is to capture the name of the column and then use it inline to convert cpue to binary p-a
yvar <- species[j]
pa.dat <- training.dat
eval(parse(text = paste0("pa.dat$", yvar, "<- ifelse(", "pa.dat$", yvar, "> 0, 1, 0)")))
xvars <- c("s(lon,lat, k=10)", "s(slope,k=4)", "s(tmax,k=4)", "s(color,k=4)", "s(speed,k=4)", "s(btemp,k=4)",
"s(bdepth,k=4)", "as.factor(sponge)", "as.factor(coral)", "as.factor(pen)")
gam.form <- as.formula(paste(yvar, "~", paste(xvars, collapse = "+")))
# gam.form <- as.formula(paste(yvar, "~", paste(xvars,collapse = "+")))
for(i in 1:length(xvars)){
pa.gam <- gam(gam.form, family = binomial, data = pa.dat)
gcv_gam <- pa.gam$gcv.ubre
pvals <- summary(pa.gam)$s.pv
pvals <- c(pvals,summary(pa.gam)$p.pv[-1])
least_sig <- which.max(pvals)
xvars1 <- xvars[-least_sig]
gam.form1 <- as.formula(paste(yvar, "~", paste(xvars1, collapse = "+")))
pa.gam1 <- gam(gam.form1, family = binomial, data = pa.dat)
gcv_gam1 <- pa.gam1$gcv.ubre
if(gcv_gam > gcv_gam1){
gam.form <- gam.form1
gcv_gam <- gcv_gam1
xvars <- xvars[-least_sig]
print(summary(pa.gam1))
}
if(gcv_gam < gcv_gam1)
break
}
######## Training Data
pred2 <- predict(pa.gam, pa.dat, fun = predict, na.rm = TRUE, overwrite = TRUE, progress = "text",
type = "response", newdata.guaranteed = TRUE)
eval(parse(text = paste0("pa.obs.pred <- data.frame(cbind(x = pa.dat$", yvar, ", y = pred2))")))
colnames(pa.obs.pred)[1] <- "x"
auc.dat <- data.frame(cbind(seq(1,length(pred2)), pa.obs.pred))
print(auc(auc.dat, na.rm = TRUE))
print(optimal.thresholds(auc.dat, opt.methods = c(seq(1:9))))
thresh <- optimal.thresholds(auc.dat, opt.methods = 2)
thresh <- thresh[,2]
print(cmx(auc.dat, threshold = thresh))
######### Test Data
t.pa.dat <- test.dat
eval(parse(text = paste0("t.pa.dat$", yvar, "<- ifelse(", "t.pa.dat$", yvar, "> 0, 1, 0)")))
pred4 <- predict(pa.gam, t.pa.dat, fun = predict, na.rm = TRUE, overwrite = TRUE, progress = "text",
type = "response", newdata.guaranteed = TRUE)
# .pa.obs.pred <- data.frame(cbind(x = t.yvar, y = pred4))
eval(parse(text = paste0("t.pa.obs.pred <- data.frame(cbind(x = t.pa.dat$", yvar, ", y = pred4))")))
colnames(t.pa.obs.pred)[1] <- "x"
t.auc.dat <- data.frame(cbind(seq(1,length(pred4)), t.pa.obs.pred))
print(auc(t.auc.dat, na.rm = TRUE))
print(cmx(t.auc.dat, threshold = thresh))
######## Prediction Map
predict.pa.raster1 <- predict(raster.stack, pa.gam, fun = predict, na.rm = TRUE, overwrite = TRUE,
progress = "text", type = "response", newdata.guaranteed = TRUE)
predict.pa.raster <- mask(predict.pa.raster1, ak.coast, inverse = TRUE, overwrite = TRUE, progress = "text",
filename = paste(results.path, "/PApredict" , sep = ""))
breaks = c(thresh,1)
pa.raster.cut <- cut(predict.pa.raster, breaks = breaks)
######CPUE PART##########
training.dat.catch <- subset(training.dat, training.dat[species[j]] > 0)
test.dat.catch <- subset(test.dat, test.dat[species[j]] > 0)
# yvar <- species[j]
##########################################################################
## To cope with the possibility that a presence-absence factor could
## have zero contrast (i.e., 100% present or 100% absent), I built a test
## to sequentially identify which factors had contrast and thus could be
## included in the formula.
## Does not appaer to be a problem for the pagam with a binomial distribution
# continuous x variables
base.form <- c("s(lon,lat,k=10)", "s(slope,k=4)", "s(tmax,k=4)", "s(color,k=4)", "s(speed,k=4)", "s(btemp,k=4)",
"s(bdepth,k=4)")
# empty vector to accept strings below
factor.form <- vector()
# testing factors - if length of unique values == 1 then no contrast. So if >= 2 then include factor term
# written to serially include and accumulate terms as they qualify
if(length(unique(training.dat.catch$sponge)) >= 2){
factor.form <- "as.factor(sponge)"
}
if(length(unique(training.dat.catch$coral)) >= 2){
factor.form <- c(factor.form, "as.factor(coral)")
}
if(length(unique(training.dat.catch$pen)) >= 2){
factor.form <- c(factor.form, "as.factor(pen)")
}
xvars <- c(base.form, factor.form)
gam.form <- as.formula(paste(yvar, "~", paste(xvars, collapse = "+")))
for(i in 1:length(xvars)){
cpue.gam <- gam(gam.form, family = gaussian, data = training.dat.catch)
gcv_gam <- cpue.gam$gcv.ubre
pvals <- summary(cpue.gam)$s.pv
pvals <- c(pvals,summary(cpue.gam)$p.pv[-1])
least_sig <- which.max(pvals)
xvars1 <- xvars[-least_sig]
gam.form1 <- as.formula(paste(yvar, "~", paste(xvars1, collapse = "+")))
cpue.gam1 <- gam(gam.form1, family = gaussian, data = training.dat.catch)
gcv_gam1 <- cpue.gam1$gcv.ubre
if(gcv_gam > gcv_gam1){
gam.form <- gam.form1
gcv_gam <- gcv_gam1
xvars <- xvars[-least_sig]
print(summary(cpue.gam1))
}
if(gcv_gam < gcv_gam1)
break
}
######## Training Data
pred2 <- predict(cpue.gam, training.dat.catch, fun = predict, na.rm = TRUE, overwrite = TRUE, progress = "text",
type = "response", newdata.guaranteed = TRUE)
# cpue.obs.pred <- data.frame(cbind(x = yvar, y = pred2))
eval(parse(text = paste0("cpue.obs.pred <- data.frame(cbind(x = training.dat.catch$", yvar, ", y = pred2))")))
colnames(cpue.obs.pred)[1] <- "x"
######### Test Data
pred4 <- predict(cpue.gam, test.dat.catch, fun = predict, na.rm = TRUE, overwrite = TRUE, progress = "text",
type = "response", newdata.guaranteed = TRUE)
t.yvar <- test.dat.catch[species[j]]
t.cpue.obs.pred <- data.frame(cbind(x = t.yvar, y = pred4))
colnames(t.cpue.obs.pred)[1] <- "x"
######## Prediction Map
predict.CPUE.raster1 <- predict(raster.stack, cpue.gam, fun = predict, na.rm = TRUE, overwrite = TRUE,
progress = "text", type = "response", newdata.guaranteed = TRUE)
predict.CPUE.raster2 <- mask(predict.CPUE.raster1, ak.coast, inverse = TRUE, overwrite = TRUE, progress = "text")
predict.CPUE.raster <- mask(predict.CPUE.raster2, pa.raster.cut, overwrite = TRUE, progress = "text",
filename = paste(results.path, "/CPUEpredict", sep = ""))
sample1 <- sampleRandom(predict.CPUE.raster, 600000, na.rm = TRUE)
sample1[sample1 <= 0] <- NA
breaks <- quantile(sample1, probs = c(0,0.05,0.25,0.5,0.75,1), na.rm = TRUE, names = FALSE)
EFH.raster <- cut(predict.CPUE.raster, breaks = breaks, overwrite = TRUE, filename = paste(results.path, "/EFHmap",
sep = ""), progress = "text")
save.image(paste(results.path, "/", species_name[j], ".RData", sep = ""))
####FIGURE 1 - PA GAM EFFECTS PLOT################################
dir.create(paste(results.path, "/Figures", sep = ""))
png(filename = paste(results.path, "/Figures/PAGAMplots.png", sep = ""), width = 8.5, height = 11, res = 300, units = "in")
n1 <- summary(pa.gam)$m
n2 <- length(summary(pa.gam)$p.t)
n2 <- ifelse(n2 > 0, n2-1, 0) + n1
v1 <- all.vars(formula(pa.gam))
v1 <- v1[-1]
if(v1[1] == "lon"){
v1[1] <- "lonlat"
v1 <- v1[-2]
}
par(mfrow = c(ceiling(n2/3), 3), mar = c(4,4,2,.01))
for(i in 1:n2){
if(v1[i] == "lonlat"){plot(pa.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, xlab = "Longitude (northings)",
ylab = "Latitude (eastings)") }
if(v1[i] == "slope"){plot(pa.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Slope (%)") }
if(v1[i] == "tmax"){plot(pa.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Tidal current speed (cm/s)")}
if(v1[i] == "color"){plot(pa.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Ocean color") }
if(v1[i] == "speed"){plot(pa.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Current speed (m/s)")}
if(v1[i] == "btemp"){plot(pa.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Temperature (C)")}
if(v1[i] == "bdepth"){plot(pa.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Depth (m)")}
if(v1[i] == "sponge"){plot(pa.gam, select = i, all.terms = TRUE, ylabs = "Variable effect", xlabs = "Sponge presence")}
if(v1[i] == "coral"){plot(pa.gam, select = i, all.terms = TRUE, ylabs = "Variable effect", xlabs = "Coral presence")}
if(v1[i] == "pen"){plot(pa.gam, select = i, all.terms = TRUE, ylabs = "Variable effect", xlabs = "Pennatulacean presence")}}
dev.off()
####FIGURE 2 - PA GAM DIAGNOSTICS PLOTS
#Plots for training data
png(filename = paste(results.path, "/Figures/PAGAMdiagnostics.png", sep = ""), width = 8.5, height = 11, res = 300, units = "in")
par(mfcol = c(3,2),family = "sans", mar = c(4,4,1,.01))
PresenceAbsence::auc.roc.plot(auc.dat, opt.methods = 2, main = "", add.legend = F, xlab = "Specificity", ylab = "Sensitivity",
add.opt.legend = F)
text(0, 0.95, "Training data", pos = 4, cex = 1.25)
PresenceAbsence::calibration.plot(auc.dat, N.bins = 10, xlab = "Predicted occurence", ylab = "Proportion of observed occurence",
main = "")
PresenceAbsence::presence.absence.hist(auc.dat, truncate.tallest = TRUE, main = "", ylab = "Number of observations",
xlab = "Predicted probability")
#Plots for test data
PresenceAbsence::auc.roc.plot(t.auc.dat, main = "", add.legend = F, xlab = "Specificity", ylab = "Sensitivity", add.opt.legend = F)
text(0, 0.95, "Test data", pos = 4, cex = 1.25)
PresenceAbsence::calibration.plot(t.auc.dat, N.bins = 10, xlab = "Predicted occurence", ylab = "Proportion of observed occurence",
main = "")
PresenceAbsence::presence.absence.hist(t.auc.dat, truncate.tallest = TRUE, main = "", ylab = "Number of observations",
xlab = "Predicted probability")
dev.off()
######FIGURE 3 - PREDICTED PRESENCE MAP ##################################################
png(filename = paste(results.path, "/Figures/PAGAMmap.png", sep = ""), width = 7.1, height = 5, res = 300, units = "in")
par(mfrow = c(1,1), mar = c(5,4,1,1), family = "sans")
raster::plot(predict.pa.raster, main = "", xaxt = "n", yaxt = "n", box = F, col = jet.colors(255), ext = GOA.ext.pol,
legend.shrink = 0.5, axis.args = list(cex.axis = 0.65), legend.args = list(text = "Probability of presence", cex = 0.65,
cex.lab = 0.65, side = 1, line = 2), horiz = TRUE, ylab = "Latitude", xlab = "Longitude", zlim = c(0,1))
raster::plot(ak.coast, col = "grey80", add = TRUE)
raster::plot(wrld_grd_proj2, lty = 3, col = "lightgrey", add = T)
axis(1, at = GOA.xaxis.ticks, labels = GOA.xaxis)
axis(2, at = GOA.yaxis.ticks, labels = GOA.yaxis)
dev.off()
####FIGURE 4 - CPUE GAM EFFECTS PLOT################################
png(filename = paste(results.path, "/Figures/CPUEGAMplots.png", sep = ""), width = 8.5, height = 11, res = 300, units = "in")
n1 <- summary(cpue.gam)$m
n2 <- length(summary(cpue.gam)$p.t)
n2 <- ifelse(n2 > 0, n2-1, 0) + n1
v1 <- all.vars(formula(cpue.gam))
v1 <- v1[-1]
if(v1[1] == "lon"){
v1[1] <- "lonlat"
v1 <- v1[-2]
}
par(mfrow = c(ceiling(n2/3), 3), mar = c(4,4,2,.01))
for(i in 1:n2){
if(v1[i] == "lonlat"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, xlab = "Longitude (northings)",
ylab = "Latitude (eastings)") }
if(v1[i] == "slope"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Slope (%)") }
if(v1[i] == "tmax"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Tidal current speed (cm/s)")}
if(v1[i] == "color"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Ocean color") }
if(v1[i] == "speed"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Current speed (m/s)")}
if(v1[i] == "btemp"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Temperature (C)")}
if(v1[i] == "bdepth"){plot(cpue.gam, scale = 0, shade = TRUE, select = i, all.terms = TRUE, ylab = "Variable effect",
xlab = "Depth (m)")}
if(v1[i] == "sponge"){plot(cpue.gam, select = i, all.terms = TRUE, ylabs = "Variable effect", xlabs = "Sponge presence")}
if(v1[i] == "coral"){plot(cpue.gam, select = i, all.terms = TRUE, ylabs = "Variable effect", xlabs = "Coral presence")}
if(v1[i] == "pen"){plot(cpue.gam, select = i, all.terms = TRUE, ylabs = "Variable effect", xlabs = "Pennatulacean presence")}}
dev.off()
####FIGURE 5 - CPUE GAM DIAGNOSTICS PLOTS
#Plots for training data
png(filename = paste(results.path, "/Figures/CPUEGAMdiagnostics.png", sep = ""), width = 8.5, height = 11, res = 300, units = "in")
par(mfcol = c(3,2), family = "sans", mar = c(4,4,3,1))
qqnorm((cpue.obs.pred$x - cpue.obs.pred$y), main = "Training data")
qqline((cpue.obs.pred$x - cpue.obs.pred$y))
hist((cpue.obs.pred$x - cpue.obs.pred$y), xlab = "Residuals", main = "")
regr <- lm(y~x, data = cpue.obs.pred)
rsqr <- summary(lm(y~x, data = cpue.obs.pred))$r.squared
adj.rsqr <- summary(lm(y~x, data = cpue.obs.pred))$adj.r.squared
pred.max <- max(cpue.obs.pred$y)
obs.max <- max(cpue.obs.pred$x)
if(pred.max > obs.max){
plot.max = pred.max + (0.04*pred.max)
}else{
plot.max = obs.max + (0.04*obs.max)
}
par(xaxs = "i", yaxs = "i")
plot(cpue.obs.pred$x, cpue.obs.pred$y, ylim = c(0,plot.max), xlim = c(0,plot.max), ylab = "Predicted", xlab = "Observed",
main = "", pch = 20)
abline(coef = c(0,1), lty = 2)
lines(cpue.obs.pred$x, regr$fitted.values)
text(1, plot.max-1, paste("R-squared = ", round(rsqr,2)), pos = 4)
#Plots for test data
qqnorm((t.cpue.obs.pred$x - t.cpue.obs.pred$y), main = "Test data")
qqline((t.cpue.obs.pred$x-t.cpue.obs.pred$y))
hist((t.cpue.obs.pred$x - t.cpue.obs.pred$y), xlab = "Residuals", main = "")
regr <- lm(y~x, data = t.cpue.obs.pred)
rsqr <- summary(lm(y~x, data = t.cpue.obs.pred))$r.squared
adj.rsqr <- summary(lm(y~x, data = t.cpue.obs.pred))$adj.r.squared
pred.max <- max(t.cpue.obs.pred$y)
obs.max <- max(t.cpue.obs.pred$x)
if(pred.max > obs.max){
plot.max = pred.max + (0.04*pred.max)
}else{
plot.max = obs.max + (0.04*obs.max)
}
par(xaxs = "i", yaxs = "i")
plot(t.cpue.obs.pred$x, t.cpue.obs.pred$y, ylim = c(0,plot.max), xlim = c(0,plot.max), ylab = "Predicted", xlab = "Observed",
main = "", pch = 20)
abline(coef = c(0,1), lty = 2)
lines(t.cpue.obs.pred$x, regr$fitted.values)
text(1, plot.max-1, paste("R-squared = ", round(rsqr,2)), pos = 4)
dev.off()
######FIGURE 6 - PREDICTED ABUNDANCE MAP ##################################################
png(filename = paste(results.path, "/Figures/CPUEGAMmap.png", sep = ""), width = 7.1, height = 5, res = 300, units = "in")
par(mfrow = c(1,1), mar = c(5,4,1,1), family = "sans")
plot(predict.CPUE.raster, main = "", xaxt = "n", yaxt = "n", box = F, col = jet.colors(255), ext = GOA.ext.pol,legend.shrink = 0.5,
axis.args = list(cex.axis = 0.65), legend.args = list(text = "Abundance", cex = 0.65, cex.lab = 0.65, side = 1, line = 2),
horiz = TRUE, ylab = "Latitude", xlab = "Longitude", zlim = c(0, maxValue(predict.CPUE.raster)))
plot(ak.coast, col = "grey80", add = TRUE)
plot(wrld_grd_proj2, lty = 3, col = "lightgrey", add = T)
axis(1, at = GOA.xaxis.ticks, labels = GOA.xaxis)
axis(2,at = GOA.yaxis.ticks, labels = GOA.yaxis)
dev.off()
######FIGURE 7 - EFH MAP ##################################################
png(filename = paste(results.path, "/Figures/EFHmap.png", sep = ""), width = 7.1, height = 5, res = 300, units = "in")
par(mfrow = c(1,1), mar = c(5,4,1,1), family = "sans")
plot(EFH.raster, main = "", xaxt = "n", yaxt = "n", box = F, col = colorRampPalette(c("grey", "coral3"))(4), ext = GOA.ext.pol,
legend = FALSE, ylab = "Latitude", xlab = "Longitude", horiz = TRUE, zlim = c(2, maxValue(EFH.raster)))
plot(ak.coast, col = "black", add = TRUE)
plot(wrld_grd_proj2, lty = 3, col = "lightgrey", add = T)
axis(1, at = GOA.xaxis.ticks, labels = GOA.xaxis)
axis(2 ,at = GOA.yaxis.ticks, labels = GOA.yaxis)
legVals <- c("95%","75%","50%","25%")
legend("bottomleft", legend = legVals, pch = 15, col = colorRampPalette(c("grey", "coral3"))(4), bty = "n", pt.cex = 2 ,
title = "Percentiles", cex = 1)
depth.contour <- contour(raster.stack[["bdepth"]], levels = c(100,300,500), col = "black", lwd = 0.05, drawlabels = FALSE, add = TRUE)
dev.off()
##########################WRITE RASTERS TO ArcGIS FORMAT GeoTIFF's############################################
# dir.create(paste0(results.path, "/GeoTiffs"))
raster::writeRaster(predict.CPUE.raster, filename = paste0(home.dir, "/GeoTiffs/GeoTiffs_GOA/", species_name[j], "_CPUE_map.tif"),
overwrite = TRUE, format = "GTiff", options = c("PROFILE=GeoTIFF","TFW=YES"), NAflag = -999)
raster::writeRaster(predict.pa.raster, filename = paste0(home.dir, "/GeoTiffs/GeoTiffs_GOA/", species_name[j], "_PA_map.tif"),
overwrite = TRUE, format = "GTiff", options = c("PROFILE=GeoTIFF","TFW=YES"), NAflag = -999)
raster::writeRaster(EFH.raster, filename = paste0(home.dir, "/GeoTiffs/GeoTiffs_GOA/", species_name[j], "_EFHmap.tif"),
overwrite = TRUE, format = "GTiff", options = c("PROFILE=GeoTIFF","TFW=YES"), NAflag = -999)
########TABLE 1 - SUMMARY TABLE FOR PRESENCE-ABSENCE GAM#######################
n1 <- summary(pa.gam)$m
n2 <- length(summary(pa.gam)$p.t)
n2 <- ifelse(n2 > 0, n2-1, 0) + n1
v1 <- all.vars(formula(pa.gam))
v1 <- v1[-1]
if(v1[1] == "lon"){
v1[1] <- "lonlat"
v1 <- v1[-2]
}
table1 <- array("", dim = c(length(v1) + 2, 9))
table1[1,] <- c(" ", " ", " ", " ", "Training data", " ", " ", "Test data", " ")
table1[2,] <- c("Model term", "EDF", "p-value", "Deviance explained", "AUC", "Optimum threshold", "Percent correctly classified",
"AUC", "Percent correctly classified")
table1[3,4] <- round(summary(pa.gam)$dev.expl, 3)
table1[3,5] <- round(auc(auc.dat)$AUC, 2)
table1[3,6] <- thresh
table1[3,7] <- round(pcc(cmx(auc.dat, threshold = thresh))$PCC,2)
table1[3,8] <- round(auc(t.auc.dat)$AUC, 2)
table1[3,9] <- round(pcc(cmx(t.auc.dat, threshold = thresh))$PCC,2)
table1a <- v1
table1a[table1a == "lonlat"] <- "Longitude*latitude"
table1a[table1a == "slope"] <- "Slope"
table1a[table1a == "pen"] <- "Pennatulacean present"
table1a[table1a == "coral"] <- "Coral present"
table1a[table1a == "sponge"] <- "Sponge present"
table1a[table1a == "color"] <- "Ocean color"
table1a[table1a == "speed"] <- "Current speed"
table1a[table1a == "bdepth"] <- "Depth"
table1a[table1a == "tmax"] <- "Tidal current"
table1a[table1a == "btemp"] <- "Temperature"
table1b <- c(summary(pa.gam)$edf, summary(pa.gam)$pTerms.df)
table1c <- c(summary(pa.gam)$s.pv, summary(pa.gam)$pTerms.pv)
table1[3:(length(v1) + 2), 1] <- table1a[order(table1c)]
table1[3:(length(v1) + 2), 2] <- signif(table1b[order(table1c)], digits = 2)
table1[3:(length(v1) + 2), 3] <- signif(table1c[order(table1c)], digits = 3)
pa.table <- xtable::xtable(table1)
xtable::print.xtable(pa.table,type = "html", file = paste(results.path, "/Figures/pagamtable.html", sep = ""),
include.rownames = getOption("xtable.include.rownames", FALSE), html.table.attributes = 2,
include.colnames = getOption("xtable.include.colnames", FALSE), hline.after = getOption("xtable.hline.after",
c(-1, 1, nrow(table1))))
########TABLE 2 - SUMMARY TABLE FOR CPUE GAM#######################
n1 <- summary(cpue.gam)$m
n2 <- length(summary(cpue.gam)$p.t)
n2 <- ifelse(n2 > 0, n2-1, 0) + n1
v1 <- all.vars(formula(cpue.gam))
v1 <- v1[-1]
if(v1[1] == "lon"){
v1[1] <- "lonlat"
v1 <- v1[-2]
}
table1 <- array("", dim = c(length(v1) + 2, 6))
table1[1,] <- c(" ", " ", " ", " ", "Training data", "Test data")
table1[2,] <- c("Model term", "EDF", "p-value", "Deviance explained", "rsq", "rsq")
table1[3,4] <- round(summary(cpue.gam)$dev.expl, 3)
table1[3,5] <- rsqr <- round(summary(lm(y~x, data = cpue.obs.pred))$r.squared,2)
table1[3,6] <- rsqr <- round(summary(lm(y~x, data = t.cpue.obs.pred))$r.squared,2)
table1a <- v1
table1a[table1a == "lonlat"] <- "Longitude*latitude"
table1a[table1a == "slope"] <- "Slope"
table1a[table1a == "pen"] <- "Pennatulacean present"
table1a[table1a == "coral"] <- "Coral present"
table1a[table1a == "sponge"] <- "Sponge present"
table1a[table1a == "color"] <- "Ocean color"
table1a[table1a == "speed"] <- "Current speed"
table1a[table1a == "bdepth"] <- "Depth"
table1a[table1a == "tmax"] <- "Tidal current"
table1a[table1a == "btemp"] <- "Temperature"
table1b <- c(summary(cpue.gam)$edf, summary(cpue.gam)$pTerms.df)
table1c <- c(summary(cpue.gam)$s.pv, summary(cpue.gam)$pTerms.pv)
table1[3:(length(v1) + 2), 1] <- table1a[order(table1c)]
table1[3:(length(v1) + 2), 2] <- signif(table1b[order(table1c)], digits = 2)
table1[3:(length(v1) + 2), 3] <- signif(table1c[order(table1c)], digits = 3)
cpue.table <- xtable::xtable(table1)
xtable::print.xtable(cpue.table, type = "html", file = paste(results.path, "/Figures/cpuegamtable.html", sep = ""),
include.rownames = getOption("xtable.include.rownames", FALSE), html.table.attributes = 2,
include.colnames = getOption("xtable.include.colnames", FALSE), hline.after = getOption("xtable.hline.after",
c(-1, 1, nrow(table1))))
print(paste(species[j], "hurdle model complete"))
}
####################################################################################################################################
####################################################################################################################################
##################################################MAXENT MODEL######################################################################
####################################################################################################################################
if(models[j] == "maxent"){
training.pos <- subset(training.dat, training.dat[species[j]] > 0)
training.pos <- cbind(training.pos$lon, training.pos$lat)
test.pos <- subset(test.dat, test.dat[species[j]] > 0)
test.pos <- cbind(test.pos$lon, test.pos$lat)
maxent.stack <- stack(bathy, slope, btemp, color, bcurrent, tmax)
names(maxent.stack) <- c("Depth", "Slope", "Temperature", "Color", "Current_speed", "Tidal_current")
maxent.model <- dismo::maxent(maxent.stack, training.pos, args = c("-P", "-J"), path = results.path)
# had to put maxent.jar in library/dismo/java/ used maxent.zip@C:/Program Files/R/3.5.1
habitat.prediction1 <- dismo::predict(maxent.model, maxent.stack, overwrite = TRUE, progress = "text")
habitat.prediction <- raster::mask(habitat.prediction1, ak.coast, inverse = TRUE, overwrite = TRUE, progress = "text",
filename = paste(results.path, "/SuitableHabitat", sep = ""))
######STEP 5 - TEST THE PREDICTIONS AGAINST THE TRAINING DATA############################
#Extract the predictions at the training points
train.predicted <- raster::extract(habitat.prediction, training.pos)
#Create a vector of 1's (presence points for training data)
train.observed <- rep(1, length(train.predicted))
#Choose a random vector of absence points
train.background <- dismo::randomPoints(habitat.prediction, length(training.pos[,1]), training.pos, tryf = 50)
#Extract the predictions at these points
train.background.predicted <- raster::extract(habitat.prediction, train.background)
#Create a vector of 0's (absence points for training data)
train.background.observed <- rep(0, length(train.background.predicted))
#Bind the observations and predictions together and create a dataframe
train.predicted <- c(train.predicted, train.background.predicted)
train.observed <- c(train.observed, train.background.observed)
train.auc_data <- data.frame(cbind(seq(1, length(train.predicted), 1), train.observed, train.predicted))
train.auc_data <- subset(train.auc_data, train.auc_data$train.predicted >= 0)
#Calculate the AUC
PresenceAbsence::auc(train.auc_data, na.rm = TRUE)
#Estimate the thresholds and calculate diagnostics
PresenceAbsence::optimal.thresholds(train.auc_data, opt.methods = c(seq(1:9)))
train.threshold <- PresenceAbsence::optimal.thresholds(train.auc_data, opt.methods = 2)
train.threshold <- train.threshold[, 2]
PresenceAbsence::cmx(train.auc_data, threshold = train.threshold)
#######STEP 6 - TEST THE PREDICTIONS AGAINST THE TEST DATA FOR FALL############################
#Extract the predictions at the testing points
test.predicted <- raster::extract(habitat.prediction, test.pos)
#Create a vector of 1's (presence points for testing data)
test.observed <- rep(1, length(test.predicted))
#Choose a random vector of absence points
test.background <- dismo::randomPoints(habitat.prediction, length(test.pos[, 1]), test.pos, tryf = 50)
#Extract the predictions at these points
test.background.predicted <- raster::extract(habitat.prediction, test.background)
#Create a vector of 0's (absence points for testing data)
test.background.observed <- rep(0, length(test.background.predicted))
#Bind the observations and predictions together and create a dataframe
test.predicted <- c(test.predicted, test.background.predicted)
test.observed <- c(test.observed, test.background.observed)
test.auc_data <- data.frame(cbind(seq(1, length(test.predicted), 1), test.observed, test.predicted))
test.auc_data <- subset(test.auc_data, test.auc_data$test.predicted >= 0)
#Calculate the AUC
PresenceAbsence::auc(test.auc_data, na.rm = TRUE)
PresenceAbsence::cmx(test.auc_data,threshold = train.threshold)
####FIGURE 2 - MAXENT EFFECTS PLOT################################
dir.create(paste(results.path, "/Figures", sep = ""))
png(filename = paste(results.path, "/Figures/VariableEffectplots.png", sep = ""), width = 8.5, height = 11, res = 300, units = "in")
par(mfrow = c(2,3), mar = c(4,4,2,.01))
response(maxent.model, var = "Depth", col = 1, rug = FALSE)
response(maxent.model, var = "Tidal_current", col = 1, rug = FALSE)
response(maxent.model, var = "Slope", col = 1, rug = FALSE)
response(maxent.model, var = "Color", col = 1, rug = FALSE)
response(maxent.model, var = "Temperature", col = 1, rug = FALSE)
response(maxent.model, var = "Current_speed", col = 1, rug = FALSE)
dev.off()
####FIGURE 3 - MAXENT DIAGNOSTICS PLOTS
#Plots for training data
png(filename = paste(results.path, "/Figures/maxentdiagnostics.png", sep = ""), width = 8.5, height = 11, res = 300, units = "in")
par(mfcol = c(3,2), family = "sans", mar = c(4,4,1,.01))
PresenceAbsence::auc.roc.plot(train.auc_data,opt.methods = 2, main = "", add.legend = F, xlab = "Specificity",
ylab = "Sensitivity", add.opt.legend = F)
text(0, 0.95, "Training data", pos = 4, cex = 1.25)
PresenceAbsence::calibration.plot(train.auc_data, N.bins = 10, xlab = "Predicted occurence",
ylab = "Proportion of observed occurence", main = "")
PresenceAbsence::presence.absence.hist(train.auc_data, truncate.tallest = TRUE, main = "", ylab = "Number of observations",
xlab = "Predicted probability")
#Plots for test data
PresenceAbsence::auc.roc.plot(test.auc_data, main = "", add.legend = F, xlab = "Specificity", ylab = "Sensitivity", add.opt.legend = F)
text(0, 0.95, "Test data", pos = 4, cex = 1.25)
PresenceAbsence::calibration.plot(test.auc_data, N.bins = 10, xlab = "Predicted occurence",
ylab = "Proportion of observed occurence", main = "")
PresenceAbsence::presence.absence.hist(test.auc_data, truncate.tallest = TRUE, main = "", ylab = "Number of observations",
xlab = "Predicted probability")
dev.off()
######FIGURE 4 - PREDICTED PRESENCE MAP ##################################################
png(filename = paste(results.path, "/Figures/SuitableHabitatmap.png", sep = ""), width = 7.1, height = 5, res = 300, units = "in")
par(mfrow = c(1,1), mar = c(5,4,1,1), family = "sans")
plot(habitat.prediction, main = "", xaxt = "n", yaxt = "n", box = F, col = jet.colors(255), ext = GOA.ext.pol, legend.shrink = 0.5,
axis.args = list(cex.axis = 0.65), legend.args = list(text = "Probability of suitable habitat", cex = 0.65, cex.lab = 0.65,
side = 1, line = 2), horiz = TRUE, ylab = "Latitude", xlab = "Longitude", zlim = c(0,1))
plot(ak.coast, col = "grey80", add = TRUE)
plot(wrld_grd_proj2, lty = 3, col = "lightgrey", add = T)
axis(1, at = GOA.xaxis.ticks,labels = GOA.xaxis)
axis(2, at = GOA.yaxis.ticks, labels = GOA.yaxis)
dev.off()
######FIGURE 5 - EFH MAP ##################################################
sample1 <- sampleRandom(habitat.prediction, 300000, na.rm = TRUE)
sample1[sample1 <= 0] <- NA
breaks <- quantile(sample1, probs = c(0,0.05,0.25,0.5,0.75,1), na.rm = TRUE, names = FALSE)
EFH.raster <- cut(habitat.prediction, breaks = breaks, overwrite = TRUE, filename = paste(results.path, "/EFHmap", sep = ""))
save.image(paste(results.path, "/", species_name[j], ".RData", sep = ""))
png(filename = paste(results.path, "/Figures/EFHmap.png", sep = ""), width = 7.1, height = 5, res = 300, units = "in")
par(mfrow = c(1,1), mar = c(5,4,1,1), family = "sans")
plot(EFH.raster, main = "", xaxt = "n", yaxt = "n", box = F, col = colorRampPalette(c("grey", "coral3"))(4), ext = GOA.ext.pol,
legend = FALSE, ylab = "Latitude", xlab = "Longitude", horiz = TRUE, zlim = c(2, maxValue(EFH.raster)))
plot(ak.coast, col = "black", add = TRUE)
plot(wrld_grd_proj2, lty = 3, col ="lightgrey", add = T)
axis(1, at = GOA.xaxis.ticks, labels = GOA.xaxis)
axis(2, at = GOA.yaxis.ticks, labels = GOA.yaxis)
legVals <- c("95%","75%","50%","25%")
legend("bottomleft", legend = legVals, pch = 15, col = colorRampPalette(c("grey", "coral3"))(4), bty = "n", pt.cex = 2 ,
title = "Percentiles", cex = 1)
depth.contour <- contour(maxent.stack[["Depth"]], levels = c(100,300,500), col = "black", lwd = 0.05, drawlabels = FALSE, add = TRUE)
dev.off()
#######FIGURE 1 - POINTS ON DEPTH MAP#################################################
png(filename = paste(results.path, "/Figures/Mappts.png", sep = ""), width = 7.1, height = 5, res = 300, units = "in")
par(mfrow = c(1,1), mar = c(5,4,1,1), family = "sans")
plot(maxent.stack[["Depth"]], main = "", xaxt = "n", yaxt = "n", box = F, ext = GOA.ext.pol, legend.shrink = 0.5,
axis.args = list(cex.axis = 0.65), legend.args = list(text = "Depth", cex = 0.65, cex.lab = 0.65, side = 1, line = 2),
horiz = TRUE, ylab = "Latitude", xlab = "Longitude")