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MasterScript.R
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MasterScript.R
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### AE Melton
# This is a "master" script for a group of functions I wrote to help with ENM/SDM analyses.
#
#############################################################################################
##### Install / load libraries and set folder paths #####
#############################################################################################
#
source("~/Dropbox/Niche_Things/RequiredLibraries.R") # and sources function scripts
sessionInfo()
project.folder <- "~/Dropbox/UF_Research/EA_ENA_ENM/XX_Revisions/" # Set the main "umbrella" folder that all subfolders are in
setwd(project.folder)
#
#
num.cores <- detectCores() # For running functions in parallel
#
#############################################################################################
##### Obtain occurrence data #####
#############################################################################################
#
setwd(project.folder)
Point.Data.folder <- paste0(project.folder, "Point_Data/")
dir.create(Point.Data.folder)
setwd(Point.Data.folder)
#
#
GetOccurrenceData(method = "scrubr",
usr.query = "Genus species",
usr.db = c("idigbio", "gbif"),# See spocc package for details and more options
#user.ref = bio1,
usr.output = "Genus_species.csv")
#
#############################################################################################
##### Data prep #####
#############################################################################################
setwd(project.folder)
dir.create("Species_Layers")
dir.create("ENMEval_Outputs")
Species.Layers.folder <- paste0(project.folder, "Species_Layers/")
ENMEval.Outputs.folder <- paste0(project.folder, "ENMEval_Outputs/")
#
#
setwd(Point.Data.folder)
point.data.list <- list.files(pattern = ".csv", full.names = TRUE) # Make a list of all species output folders
point.data.list
species.list <- gsub(pattern = "./", replacement = '', x = point.data.list)
species.list <- gsub(pattern = ".csv", replacement = '', x = species.list)
species.list
#
#
#############################################################################################
##### Data prep #####
#############################################################################################
#
species.name <- ""
#
#
print(species.name)
species.folder <- species.name
print("Loading unclipped rasters")
# Load environmental rasters
#setwd("PATH_TO_ENVIRONMENTAL_DATA_FOLDER")
#env.files <- list.files(pattern = ".tif", full.names = TRUE)
#envStack <- stack(env.files)
#names(envStack) <- c("bio1", "bio10", "bio11", "bio12", "bio13", "bio14", "bio15", "bio16", "bio17", "bio18", "bio19",
# "bio2", "bio3", "bio4", "bio5", "bio6", "bio7", "bio8", "bio9",
# "CEC", "Clay", "Elevation", "OCD", "ORC", "PH", "Sand", "Silt", "Taxonomy")
#envStack <- setMinMax(envStack)
#projection(envStack) <- "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"
#plot(envStack[[1]])
#
#
setwd(Point.Data.folder)
#
#
occ.dat <- read.csv(point.data.list[1])[,2:3] #change [#] as needed
#plot(envStack[[1]])
#points(occ.dat, pch = 19)
#nrow(occ.dat)
#
#
setwd(Species.Layers.folder)
dir.create(species.name)
setwd(species.name)
#
#
print("Clipping raster layers")
# Functions to prepare raster layers
#shpfile <- readOGR(dsn = "PATH_TO_SHAPEFILE")
#plot(shpfile)
#training.region <- RasterLayerProcessing(usr.env = envStack, mask.method = "shapefile", buffer.size = 0, shape = shpfile)
#RasterLayerProcessingByLoopMaskByShape(shape = shpfile,
# rasters.folder = "PATH_TO_ENVIRONMENTAL_DATA",
# na.flag = "NA",
# output.format = "GTiff",
# output.folder = "OUTPUT FOLDER")
RasterLayerProcessingByLoop(usr.occs = occ.dat, buffer.size = 0.25, path.to.rasters = "PATH_TO_ENVIRONMENTAL_DATA", output.format = "GTiff")
#plot(training.region[[1]])
#writeRaster(x = training.region, filename = species.name, bylayer = T, suffix=names(training.region), format = "GTiff", NAFlag = "NA", overwrite = T)
#
#
#graphics.off()
#
#
#rm(list = ls())
#
#############################################################################################
##### Modeling #####
#############################################################################################
# Load species-specific raster layers
print("Loading clipped layers")
setwd(project.folder)
setwd(Species.Layers.folder)
setwd(species.name)
env.files <- list.files(pattern = ".tif", full.names = TRUE)
envStack <- stack(env.files)
envStack <- setMinMax(envStack)
#
#
print("Predictor variable reduction")
#
red.envStack <- ReduceEnvPred(usr.env = envStack, stat = "pearson", corr.cutoff = 0.8) # Uses the caret package to get identify and drop highly correlated layers
KeptLayers <- names(red.envStack)
rm(envStack)
#plot(red.envStack[[1]])
#
#
#graphics.off()
#
#
#N <- ncell(red.envStack[[1]])
#NAcount <- cellStats(x = red.envStack[[1]], stat = "countNA")
#cell.count <- N - NAcount
#cell.count
#
#
#setwd(Point.Data.folder)
#
# Load occurrence data
#occ.dat <- read.csv(point.data.list[1])[,2:3]
#plot(red.envStack[[1]])
#points(occ.dat, pch = 19)
#nrow(occ.dat)
#
#
#cell.count/nrow(occ.dat)
#cell.count/1500
#
#
#graphics.off()
#
#
print("Rarefy point data... Sample background points")
# Make sure you don't have occurrences lacking environmental data
occ.dat.Extract <- raster::extract(red.envStack, sapply(occ.dat, as.numeric))
occ.dat.Extract <- cbind(occ.dat, occ.dat.Extract)
clean_occ.dat.Extract <- occ.dat.Extract[complete.cases(occ.dat.Extract),]
occ.dat <- clean_occ.dat.Extract[,1:2]
#nrow(occ.dat)
#head(occ.dat)
rm(occ.dat.Extract)
rm(clean_occ.dat.Extract)
#
# Rarefy occurrence data to one point per cell or to a maximum number with distances maximized between points #used all.m for oligocephala
rare.pts <- RarefyOccurrences(usr.occ = occ.dat, usr.ref = red.envStack[[1]], usr.max = nrow(occ.dat)) # this reduces to just one per cell, can be set to any max number though
bg.pts <- GetBackgroundPoints(usr.occ = rare.pts, samp.method = "all.m", n = 10000, usr.env = red.envStack)
#plot(red.envStack[[1]])
#points(bg.pts, col = "red", pch = 19)
#points(occ.dat, col = "black", pch = 19)
#points(rare.pts, col = "blue", pch = 19)
colnames(rare.pts) <- c("longitude", "latitude")
#nrow(occ.dat)
#nrow(rare.pts)
#nrow(bg.pts)
#
#
#graphics.off() # Remove plots
#
# Pick a data partition method for model evaluation. This is a simple example for when I'm not tranferring models.
# Other uses may require different partitioning methods.
if(nrow(rare.pts) <= 50){
eval.meth <- "jackknife"
}else{
eval.meth <- "checkerboard2"
}
#
#
print("Running ENMEval")
#
modeval <- ENMevaluate(occ = rare.pts[, c("longitude", "latitude")],
env = red.envStack,
bg.coords = bg.pts,
#categoricals = "Taxonomy",
algorithm = 'maxent.jar',
#RMvalues = seq(0.5, 4, 0.5), # DEFAULT
#fc = c("L", "LQ", "H", "LQH", "LQHP", "LQHPT"), # DEFAULT
method = eval.meth,
#overlap = TRUE, # DEFAULT
#clamp = TRUE, # DEFAULT
#rasterPreds = TRUE, # DEFAULT
parallel = TRUE,
numCores = num.cores,
bin.output = TRUE)
#progbar = TRUE # DEFAULT)
#
#
results <- modeval@results
results
#
#
if(nrow(rare.pts) < 20){
or.threshold <- 1/(nrow(rare.pts))
}else{
or.threshold <- 0.05
}
which(results$avg.test.orMTP <= or.threshold & results$delta.AICc <= 2) # For < 20 pts, results$avg.test.orMTP <= (1/(nrow(rare.pts)))
mod.num <- which(results$avg.test.orMTP <= or.threshold & results$delta.AICc <= 2) # For < 20 pts, results$avg.test.orMTP <= (1/(nrow(rare.pts)))
mod.num
#
#
results$delta.AICc[is.na(results$delta.AICc)] <- 999 # Models with param # > samp size get an NA for dAICc, which throws off the foreach loop
#
#
print("Calculating CBI")
# Parallelized for multiple models
CBI <- GetCBIv2(modeval.usr.sp = modeval,
usr.occs = occ.dat,
usr.raster = red.envStack,
mod.results = results,
or.threshold = or.threshold,
d.AICc.threshold = 2,
num.clust = num.cores)#,
# nclass = 100,
# window.w = "default",
# res = 100)
#CBI
#
# One model; Way more mem efficient. RAM usage drops and slowly builds back up when you start a new one, unlike with the parallelized version
#CBI <- GetCBI(modeval.usr.sp = modeval,
# usr.occs = occ.dat,
# usr.raster = red.envStack,
# mod.results = results,
# mod.num = mod.num[1])
#CBI.b <- GetCBI(modeval.usr.sp = modeval,
# usr.occs = occ.dat,
# usr.raster = red.envStack,
# mod.results = results,
# mod.num = mod.num[2])
#CBI.c <- GetCBI(modeval.usr.sp = modeval,
# usr.occs = occ.dat,
# usr.raster = red.envStack,
# mod.results = results,
# mod.num = mod.num[3])
#CBI.d <- GetCBI(modeval.usr.sp = modeval,
# usr.occs = occ.dat,
# usr.raster = red.envStack,
# mod.results = results,
# mod.num = mod.num[4])
#CBI.e <- GetCBI(modeval.usr.sp = modeval,
# usr.occs = occ.dat,
# usr.raster = red.envStack,
# mod.results = results,
# mod.num = mod.num[5])
#CBI.f <- GetCBI(modeval.usr.sp = modeval,
# usr.occs = occ.dat,
# usr.raster = red.envStack,
# mod.results = results,
# mod.num = mod.num[6])
#CBI <- rbind(CBI, CBI.b)#, CBI.c)#, CBI.d, CBI.e)#, CBI.f)
#CBI
#
#
results.trim <- results[mod.num,]
candidate.model.results <- cbind(results.trim, CBI)
#
#
print("Generating prediction rasters and outputs")
#g <- max(CBI$cbi.training.Spearman.cor, na.rm = T)
#g
#beep <- which(CBI$cbi.training.Spearman.cor == g)[1]
opt.AICc <- which(results$dAICc == 0)
opt.AICc
#results[23:24,]
modeval@predictions[[opt.AICc]] # raw output; Check model name!
p <- predict(modeval@models[[opt.AICc]], red.envStack) # Convert prediction to different output
plot(p) # logistic output
points(occ.dat, pch = 19, cex = 0.25)
#
#
setwd(ENMEval.Outputs.folder)
dir.create(species.name)
setwd(species.name)
#
#
write.csv(x = rare.pts, file = "rare_pts.csv")[,2:3]
write.csv(x = bg.pts, file = "bg_pts.csv")[,2:3]
write.csv(x = results, file = "ModelEvals_ENMEval.csv")
write.csv(x = CBI, file = "ModelEvals_CBI.csv")
write.csv(x = candidate.model.results, file = "CandidateModelResults.csv")
write.csv(x = KeptLayers, file = "KeptLayers.csv")
#
#
pdf("SuitPlot.pdf")
plot(p) # logistic output
points(occ.dat, pch = 19, cex = 0.25)
dev.off()
#
#
var.imp <- var.importance(modeval@models[[max.CBI]])
write.csv(x = var.imp, file = "Variable_Importance.csv")
lambdas <- parse_lambdas(modeval@models[[max.CBI]])
save(lambdas, file = "Lambdas.RDA")
save(modeval, file = "ENMEval.RDA")
writeRaster(x = p, filename = species.name, format = "GTiff", NAFlag = "-9999", overwrite = T)
#
#
bin.mod <- ThresholdModel(usr.raster = p, usr.occs = occ.dat, method = "95pct", output.type = "binary")
plot(bin.mod)
points(occ.dat)
binary.raster.name <- paste0(species.name, "_BIN")
writeRaster(x = bin.mod, filename = binary.raster.name, format = "GTiff", NAFlag = "-9999", overwrite = T)
dev.off()
pdf("BinPlot.pdf")
plot(bin.mod) # logistic output
points(rare.pts, pch = 19, cex = 0.25)
dev.off()
#
#
graphics.off()
#
#}
#############################################################################################
#
setwd(project.folder)
#
#
setwd(project.folder)
setwd("Point_Data/")
occ.dat <- read.csv("SPECIES_NAME.csv")[,2:3]
#
#
setwd(project.folder)
setwd("Species_Layers/SPECIES_NAME/")
env.files <- list.files(pattern = ".tif", full.names = TRUE)
envStack <- stack(env.files)
envStack <- setMinMax(envStack)
#
#
print("Predictor variable reduction")
#
red.envStack <- ReduceEnvPred(usr.env = envStack, stat = "pearson", corr.cutoff = 0.8) # Uses the caret package to get identify and drop highly correlated layers
KeptLayers <- names(red.envStack)
rm(envStack)
#plot(red.envStack[[1]])
setwd(project.folder)
setwd("ENMEval_Outputs/SPECIES_NAME/")
load("SPECIES_NAME_ENMEval.RDA")
results <- read.csv("SPECIES_NAME_ModelEvals_ENMEval.csv")
#
#
print("Generating prediction rasters and outputs")
#g <- max(CBI$cbi.training.Spearman.cor, na.rm = T)
#g
#beep <- which(CBI$cbi.training.Spearman.cor == g)[1]
opt.AICc <- which(results$delta.AICc == 0)
opt.AICc
results[opt.AICc,]
#opt.AICc <- 47
modeval@predictions[[opt.AICc]] # raw output; Check model name!
p <- predict(modeval@models[[opt.AICc]], red.envStack) # Convert prediction to different output
plot(p) # logistic output
points(occ.dat, pch = 19, cex = 0.25)
#
#
candidate.model.results <- results[opt.AICc,]
write.csv(x = candidate.model.results, file = "CandidateModelResults.csv")
write.csv(x = KeptLayers, file = "KeptLayers.csv")
#
#
pdf("SuitPlot.pdf")
plot(p) # logistic output
points(occ.dat, pch = 19, cex = 0.25)
dev.off()
#
#
var.imp <- var.importance(modeval@models[[opt.AICc]])
write.csv(x = var.imp, file = "Variable_Importance.csv")
lambdas <- parse_lambdas(modeval@models[[opt.AICc]])
save(lambdas, file = "Lambdas.RDA")
writeRaster(x = p, filename = "SPECIES_NAME", format = "GTiff", NAFlag = "-9999", overwrite = T)
#
#
graphics.off()
#############################################################################################
##### Post-Modeling #####
#############################################################################################
#############################################################################################
# Turn all the ENM prediction rasters into binary estimated distributions
# Assumes nested series of folders and file names
setwd(ENMEval.Outputs.folder)
#
#
folder.list <- list.files(pattern = "*", full.names = TRUE) # Make a list of all species output folders
folder.list
species.list <- gsub(pattern = "./", replacement = '', x = folder.list)
#
#
GetThresholdedPredictionRasters()
#
#############################################################################################
#############################################################################################
# Remake spreadsheet with all the ENMEval stats and some modeling input data
# Assumes nested series of folders and file names
setwd(ENMEval.Outputs.folder)
#
#
folder.list <- list.files(pattern = "*", full.names = TRUE) # Make a list of all species output folders
folder.list
species.list <- gsub(pattern = "./", replacement = '', x = folder.list)
species.list
#
#
df <- data.frame(species = character(),
rare.pts = character(),
bg.pts = character(),
partition.method = character(),
train.AUC = character(),
avg.test.AUC = character(),
avg.diff.AUC = character(),
avg.test.orMTP = character(),
delta.AICc = character(),
g = character()) # Generate empty data frame to put results in
#
#
for(i in 1:length(folder.list)){
#
setwd(folder.list[i])
tmp.species <- gsub(pattern = "_", replacement = " ", x = species.name)
rare.pts <- read.csv("rare_pts.csv")[,2:3]
colnames(rare.pts) <- c("longitude", "latitude")
bg.pts <- read.csv("bg_pts.csv")[,2:3]
colnames(bg.pts) <- c("longitude", "latitude")
mod.file <- paste0(species.name, "_ENMEval.RDA")
load(file = mod.file)
partition.method <- modeval@partition.method[1]
results.file <- paste0(species.name, "_ModelEvals_ENMEval.csv")
results <- read.csv(results.file)
CBI.file <- paste0(species.name, "_ModelEvals_CBI.csv")
CBI <- read.csv(CBI.file)
g <- max(CBI$cbi.training.Spearman.cor, na.rm = T)
beep <- which(CBI$cbi.training.Spearman.cor == g)[1]
mod.num <- CBI$mod.num[beep]
results.trim <- results[mod.num,]
candidate.model.results <- cbind(results.trim, g, partition.method)
candidate.model.results <- candidate.model.results[,c("partition.method", "train.AUC", "avg.test.AUC", "avg.diff.AUC", "avg.test.orMTP", "delta.AICc", "g")]
tmp.file.name <- paste0(species.name, "CandidateModelResults.csv")
write.csv(x = candidate.model.results, file = tmp.file.name)
tmp <- cbind(tmp.species, nrow(rare.pts), nrow(bg.pts), candidate.model.results)
df <- rbind(df, tmp)
setwd("~/Dropbox/UF_Research/EA_ENA_ENM/GEB_Revisions/ENMEval_Outputs/")
}
#
#
colnames(df) <- c("Species", "Rarefied Points", "Background Points", "Parition Method", "Training AUC", "Average Test AUC", "Average AUC Difference", "Averate Test orMTP", "Delta AICc", "CBI")
df
write.csv(x = df, file = "../ENM_Data_and_Evaluations.csv", row.names = F)
#
#############
csv <- read.csv("ENM_Data_and_Evaluations_TRIM.csv")
head(csv)
EA <- csv[csv$Region == "EA",]
ENA <- csv[csv$Region == "ENA",]
#
#
df <- data.frame(mean(csv$Rarefied.Points), sd(csv$Rarefied.Points), range(csv$Rarefied.Points),
mean(csv$Training.AUC), sd(csv$Training.AUC), range(csv$Training.AUC),
mean(csv$Average.Test.AUC), sd(csv$Average.Test.AUC), range(csv$Average.Test.AUC),
mean(csv$Average.AUC.Difference), sd(csv$Average.AUC.Difference), range(csv$Average.AUC.Difference),
mean(csv$Average.Test.orMTP), sd(csv$Average.Test.orMTP), range(csv$Average.Test.orMTP),
mean(csv$Delta.AICc), sd(csv$Delta.AICc), range(csv$Delta.AICc),
mean(csv$CBI), sd(csv$CBI), range(csv$CBI))
df
write.csv(x = df, file = "Summary_Stats_for_Model_Evals_XX_REV.csv", row.names = F)
low.or <- subset(x = csv$Average.Test.orMTP, csv$Average.Test.orMTP <= 0.05) # 58 >= 0.7
length(low.or)
high.test.AUC <- subset(x = csv$Average.Test.AUC, csv$Average.Test.AUC >= 0.5) # 58 >= 0.7
length(high.test.AUC)
low.dAUC <- subset(x = csv$Average.AUC.Difference, csv$Average.AUC.Difference <= 0.05)
length(low.dAUC)
low.dAICc <- subset(x = csv$Delta.AICc, csv$Delta.AICc == 0)
length(low.dAICc)
low.cbi <- subset(x = csv$CBI, csv$CBI <= 0.7)
length(low.cbi)
#
#############
csv <- read.csv("ENM_Data_and_Evaluations_TRIM.csv")
head(csv)
csv <- csv[csv$Region == "EA",]
#ENA <- csv[csv$Region == "ENA",]
#
#
df <- data.frame(mean(csv$Rarefied.Points), sd(csv$Rarefied.Points), range(csv$Rarefied.Points),
mean(csv$Training.AUC), sd(csv$Training.AUC), range(csv$Training.AUC),
mean(csv$Average.Test.AUC), sd(csv$Average.Test.AUC), range(csv$Average.Test.AUC),
mean(csv$Average.AUC.Difference), sd(csv$Average.AUC.Difference), range(csv$Average.AUC.Difference),
mean(csv$Average.Test.orMTP), sd(csv$Average.Test.orMTP), range(csv$Average.Test.orMTP),
mean(csv$Delta.AICc), sd(csv$Delta.AICc), range(csv$Delta.AICc),
mean(csv$CBI), sd(csv$CBI), range(csv$CBI))
df
write.csv(x = df, file = "EA_Summary_Stats_for_Model_Evals_XX_REV.csv", row.names = F)
#
#
csv <- read.csv("ENM_Data_and_Evaluations_TRIM.csv")
head(csv)
#EA <- csv[csv$Region == "EA",]
csv <- csv[csv$Region == "ENA",]
#
#
df <- data.frame(mean(csv$Rarefied.Points), sd(csv$Rarefied.Points), range(csv$Rarefied.Points),
mean(csv$Training.AUC), sd(csv$Training.AUC), range(csv$Training.AUC),
mean(csv$Average.Test.AUC), sd(csv$Average.Test.AUC), range(csv$Average.Test.AUC),
mean(csv$Average.AUC.Difference), sd(csv$Average.AUC.Difference), range(csv$Average.AUC.Difference),
mean(csv$Average.Test.orMTP), sd(csv$Average.Test.orMTP), range(csv$Average.Test.orMTP),
mean(csv$Delta.AICc), sd(csv$Delta.AICc), range(csv$Delta.AICc),
mean(csv$CBI), sd(csv$CBI), range(csv$CBI))
df
write.csv(x = df, file = "ENA_Summary_Stats_for_Model_Evals_XX_REV.csv", row.names = F)
#
#
csv <- read.csv("ENM_Data_and_Evaluations_TRIM.csv")
head(csv)
EA <- csv[csv$Region == "EA",]
ENA <- csv[csv$Region == "ENA",]
t.test(EA$Training.AUC, ENA$Training.AUC)
t.test(EA$Average.Test.AUC, ENA$Average.Test.AUC)
t.test(EA$Average.Test.orMTP, ENA$Average.Test.orMTP)
t.test(EA$CBI, ENA$CBI)
#
#############################################################################################
#
setwd(ENMEval.Outputs.folder)
#
#
folder.list <- list.files(pattern = "*", full.names = TRUE) # Make a list of all species output folders
folder.list
#
#
df <- data.frame(longitude = character(), latitude = character())
for(i in 1:length(folder.list)){
#
setwd(folder.list[i])
bg.pts <- read.csv("bg_pts.csv")[,2:3]
colnames(bg.pts) <- c("longitude", "latitude")
df <- rbind(df, bg.pts)
setwd(project.folder)
}
df
write.csv(x = df, file = "../all_bg_points.csv")
big.bg.pts.trim <- gridSample(xy = big.bg.pts, r = env[[1]], n = 1)
write.csv(x = big.bg.pts.trim, file = "all_bg_points_ONE_PER_CELL.csv")
#
#############################################################################################
#############################################################################################
setwd("PCA_Layers_nSamp_1mil/")
env.files <- list.files(pattern = ".tif", full.names = TRUE)
env <- stack(env.files)
env <- setMinMax(env)
setwd(project.folder)
big.bg.pts <- read.csv(file = "all_bg_points_ONE_PER_CELL.csv", header = TRUE)
df <- GetNicheAreas(env = env, big.bg.pts = big.bg.pts)
write.csv(x = df, file = "niche_areas.csv", row.names = FALSE)
#
#############################################################################################
#
setwd("SDM_95pct/")
df <- GetGeographicAreaSizes()
df
colnames(df) <- c("Species", "Geographic Area")
write.csv(x = df, file = "../Geographic_Area_Estimate.csv", row.names = F)
#
#############################################################################################
#############################################################################################
##### Statistical tests #####
#############################################################################################
#
setwd(project.folder)
#
#
csv <- read.csv(file = "All_Breadth_Metrics_V2_TRIM.csv")
csv$Genus <- gsub(pattern = " .*", replacement = "", x = csv$Species)
csv$Niche_per_Geography <- csv$NicheArea/csv$GeographicArea
EA <- csv[csv$Region == "EA",]
ENA <- csv[csv$Region == "ENA",]
#
#
data.frame(min(csv$NicheArea), max(csv$NicheArea), mean(csv$NicheArea), sd(csv$NicheArea))
niche.comp <- wilcox.test(x = EA$NicheArea, y = ENA$NicheArea) # B2 is reference by DLW in a warren et al paper
ea.df <- data.frame(mean(EA$NicheArea), sd(EA$NicheArea))
ena.df <- data.frame(mean(ENA$NicheArea), sd(ENA$NicheArea))
#wilcox.test(x = EA$NicheArea, y = ENA$NicheArea) # Mann-Whitney test
niche.area.comp <- wilcox.test(x = EA$NicheArea, y = ENA$NicheArea) # B2 is reference by DLW in a warren et al paper
ea.df <- data.frame(mean(EA$NicheArea), sd(EA$NicheArea))
ena.df <- data.frame(mean(ENA$NicheArea), sd(ENA$NicheArea))
#
#
niche.area.df <- cbind(ea.df$mean.EA.NicheArea., ena.df$mean.ENA.NicheArea., niche.area.comp$statistic[[1]], niche.area.comp$null.value[[1]], niche.area.comp$p.value)
colnames(niche.area.df) <- c("EA", "ENA", "W", "Null", "P-Value")
write.csv(x = niche.area.df, file = "Niche_Area_Comp.csv")
#
#
#csv <- read.csv(file = "Niche_Area_and_95pct_G_Breadth_N_nucifera_CORRECTED.csv")
#data.frame(min(csv$GeographicArea), max(csv$GeographicArea), mean(csv$GeographicArea), sd(csv$GeographicArea))
#EA <- csv[csv$Region == "EA",]
#ENA <- csv[csv$Region == "ENA",]
Geo.comp <- wilcox.test(x = EA$GeographicArea, y = ENA$GeographicArea) # B2 is reference by DLW in a warren et al paper
ea.df <- data.frame(mean(EA$GeographicArea), sd(EA$GeographicArea))
ena.df <- data.frame(mean(ENA$GeographicArea), sd(ENA$GeographicArea))
geo.df <- cbind(ea.df$mean.EA.GeographicArea., ena.df$mean.ENA.GeographicArea., Geo.comp$statistic[[1]], Geo.comp$null.value[[1]], Geo.comp$p.value)
geo.df
colnames(geo.df) <- c("EA", "ENA", "W", "Null", "P-Value")
write.csv(x = geo.df, file = "Geographic_Sampling_Comp.csv")
#
#setwd(project.folder)
#csv <- read.csv(file = "All_Breadth_Metrics_V2_TRIM.csv")
#head(csv)
#csv$Genus <- gsub(pattern = " .*", replacement = "", x = csv$Species)
#csv$Niche_per_Geography <- csv$NicheArea/csv$GeographicArea
#
#
NPG.comp <- wilcox.test(x = EA$Niche_per_Geography, y = ENA$Niche_per_Geography)
ea.df <- data.frame(mean(EA$Niche_per_Geography), sd(EA$Niche_per_Geography))
ena.df <- data.frame(mean(ENA$Niche_per_Geography), sd(ENA$Niche_per_Geography))
NPG.df <- cbind(ea.df$mean.EA.Niche_per_Geography., ena.df$mean.ENA.Niche_per_Geography., NPG.comp$statistic[[1]], NPG.comp$null.value[[1]], NPG.comp$p.value)
NPG.df
colnames(NPG.df) <- c("EA", "ENA", "W", "Null", "P-Value")
write.csv(x = NPG.df, file = "Niche_Area_Per_Geographic_Sampling_Comp.csv")
#
#
EA.df <- EA %>%
group_by(Genus) %>%
summarise(meanNicheArea = mean(NicheArea), sdNicheArea = sd(NicheArea),
meanG = mean(GeographicArea), sdG = sd(GeographicArea),
meanNPG = mean(Niche_per_Geography), sdNPG = sd(Niche_per_Geography))
EA.df
write.csv(x = EA.df, file = "EA_Metrics_Per_GENUS_V3_XX_REV.csv")
ENA.df <- ENA %>%
group_by(Genus) %>%
summarise(meanNicheArea = mean(NicheArea), sdNicheArea = sd(NicheArea),
meanG = mean(GeographicArea), sdG = sd(GeographicArea),
meanNPG = mean(Niche_per_Geography), sdNPG = sd(Niche_per_Geography))
ENA.df
write.csv(x = ENA.df, file = "ENA_Metrics_Per_GENUS_V3_XX_REV.csv")
all.df <- csv %>%
#group_by(Genus) %>%
summarise(minNicheArea = min(NicheArea), maxNicheArea = max(NicheArea), meanNicheArea = mean(NicheArea), sdNicheArea = sd(NicheArea),
minGeographicArea = min(GeographicArea), maxGeographicArea = max(GeographicArea), meanGeographicArea = mean(GeographicArea), sdGeographicArea = sd(GeographicArea),
minNPG = min(Niche_per_Geography), maxNPG = max(Niche_per_Geography), meanNPG = mean(Niche_per_Geography), sdNPG = sd(Niche_per_Geography))
all.df
write.csv(x = all.df, file = "All_Metrics_Summary_V3_XX_REV.csv") #"All_Metrics_Per_GENUS_V3_XX_REV.csv"
#
#############################################################################################
### Background tests
#
setwd(ENMEval.Outputs.folder)
folder.list <- list.files(pattern = "*", full.names = TRUE) # Make a list of all species output folders
folder.list
species.list <- gsub(pattern = "./", replacement = '', x = folder.list)
species.list
genera.list.all <- gsub(pattern = "_.*", replacement = '', x = species.list)
genera.list <- unique(genera.list.all)
genera.list
#
#
setwd("~/Dropbox/UF_Research/EA_ENA_ENM/GEB_Revisions/PCA_Layers_nSamp_1mil/")
env.files <- list.files(pattern = ".tif", full.names = TRUE)
env <- stack(env.files)
env <- setMinMax(env)
#
#
output.folder <- "~/Dropbox/UF_Research/EA_ENA_ENM/GEB_Revisions/ENMEval_Outputs/" # Set the main "umbrella" folder that all subfolders are in
setwd(output.folder)
ecospat.folder <- "~/Dropbox/UF_Research/EA_ENA_ENM/GEB_Revisions/EcoSpat_BG_Test_Outputs/"
#
#
species.list
species.1 <- "Gymnocladus_chinensis" #
species.2 <- "Gymnocladus_dioicus" #
DoEcoSpatBGTest(species.1 = species.1, species.2 = species.2, env = env,
test.type = "symmetric", bg.source = "points",
output.folder = output.folder, ecospat.folder = ecospat.folder,
R = 100, th.sp = 0, th.env = 0, nreps = 1000)
#
#
setwd(ecospat.folder)
output.files <- list.files(pattern = "*.csv", full.names = TRUE)
df <- data.frame(Species.1 = character(), Species.2 = character(), Obs.D = character(), Obs.I = character(), p.D = character(), p.I = character())
for(i in 1:length(output.files)){
#
csv.filename <- output.files[i]
Species.Names <- gsub(pattern = "_BG_Test_Out.csv", replacement = "", x = csv.filename)
Species.Names <- gsub(pattern = "./", replacement = "", x = Species.Names)
boop <- strsplit(sub('(^[^_]+_[^_]+)_(.*)$', '\\1 \\2', Species.Names), ' ') # Thank you akron on https://stackoverflow.com/questions/32398427/r-split-a-character-string-on-the-second-underscore # OK, this didn't work for taxa w/ subspecies. Oops.
Species.1 <- boop[[1]][1]
Species.2 <- boop[[1]][2]
csv <- read.csv(csv.filename)
colnames(csv) <- c("Obs.D", "Obs.I", "p.D", "p.I")
tmp <- data.frame(Species.1, Species.2, csv)
#colnames(bg.pts) <- c("longitude", "latitude")
df <- rbind(df, tmp)
}
df
write.csv(x = df, file = "All_BG_Tests_Out.csv", row.names = FALSE)
#
#############################################################################################
setwd(project.folder)
csv <- read.csv(file = "All_Breadth_Metrics_V2_TRIM.csv")
head(csv)
EA <- csv[csv$Region == "EA",]
ENA <- csv[csv$Region == "ENA",]
#wilcox.test(x = EA$B1, y = ENA$B1) # Mann-Whitney test
niche.area.comp <- wilcox.test(x = EA$GeographicArea, y = ENA$GeographicArea)
data.frame(min(csv$GeographicArea), max(csv$GeographicArea), mean(csv$GeographicArea), sd(csv$GeographicArea))
ea.df <- data.frame(mean(EA$GeographicArea), sd(EA$GeographicArea))
ena.df <- data.frame(mean(ENA$GeographicArea), sd(ENA$GeographicArea))
#
#
df <- cbind(niche.area.comp$statistic,
niche.area.comp$null.value,
niche.area.comp$p.value,
ea.df$mean.EA.GeographicArea.,
ea.df$sd.EA.GeographicArea.,
ena.df$mean.ENA.GeographicArea.,
ena.df$sd.ENA.GeographicArea.)
df
write.csv(x = df, file = "FILE.csv", row.names = FALSE)
#############################################################################################
#############################################################################################
csv <- read.csv(file = "All_BG_Tests_Out_w_Regions_R2_TRIM.csv")
head(csv)
EA.EA <- csv[csv$Comparison == "EA-EA",]
EA.ENA <- csv[csv$Comparison == "EA-ENA",]
ENA.ENA <- csv[csv$Comparison == "ENA-ENA",]
nrow(csv)
nrow(EA.EA) + nrow(EA.ENA) + nrow(ENA.ENA) # Just making sure everyhing made it in....
D.Comp <- kruskal.test(Obs.D ~ Comparison, data = csv)
D.comp.df <- cbind(D.Comp$statistic, D.Comp$parameter, D.Comp$p.value)
colnames(D.comp.df) <- c("Statistic", "Parameter", "P-value")
write.csv(x = D.comp.df, file = "Background_Test_Comp_XX_REV.csv", row.names = FALSE)
#
#
ea.ea.D.mean <- mean(EA.EA$Obs.D)
ea.ea.D.var <- var(EA.EA$Obs.D)
ea.ea.D.sd <- sd(EA.EA$Obs.D)
ea.ena.D.mean <- mean(EA.ENA$Obs.D)
ea.ena.D.var <- var(EA.ENA$Obs.D)
ea.ena.D.sd <- sd(EA.ENA$Obs.D)
ena.ena.D.mean <- mean(ENA.ENA$Obs.D)
ena.ena.D.var <- var(ENA.ENA$Obs.D)
ena.ena.D.sd <- sd(ENA.ENA$Obs.D)
df <- data.frame(ea.ea.D.mean, ea.ea.D.var, ea.ea.D.sd,
ea.ena.D.mean, ea.ena.D.var, ea.ena.D.sd,
ena.ena.D.mean, ena.ena.D.var, ena.ena.D.sd)
df
write.csv(x = df, file = "BackGround_Test_Comparison_Summary_Stats_XX_REV.csv", row.names = F)
#
#
sig <- subset(csv, p.D <= 0.05)
nrow(csv[csv$Comparison == "EA-EA",])
nrow(csv[csv$Comparison == "EA-ENA",])
nrow(csv[csv$Comparison == "ENA-ENA",])
EA.EA <- sig[sig$Comparison == "EA-EA",]
EA.ENA <- sig[sig$Comparison == "EA-ENA",]
ENA.ENA <- sig[sig$Comparison == "ENA-ENA",]
nrow(EA.EA)
nrow(EA.ENA)
nrow(ENA.ENA)
#
#
pdf("~/Dropbox/Manuscripts/UFL/EA_ENA_ENM/XX_REVISION/Figures/Background_Test_Results_XX_REV.pdf") #width = 8.5, height = 8.5, units = 'in', res = 300)
ggplot(csv, aes(x = Comparison, y = Obs.D, fill = Comparison)) +
geom_violin() +
ylab("Schoener's D") +
ylim(0, 1) +
xlab("Ecospat Background Test Results by Regional Comparison") +
#scale_fill_grey(start=0.75, end=0.5) +
theme_classic() +
theme(text = element_text(size=15)) +
theme(legend.position="none") +
geom_point(shape = 21, size = 2, position = position_jitterdodge(), color = "black", alpha = 1) +
theme_pubr() +
stat_summary(fun = "mean", geom = "point", shape = 19, size = 2, color = "black")
dev.off()
#
#############################################################################################
setwd(project.folder)
csv <- read.csv(file = "All_Breadth_Metrics_V2_TRIM.csv")
head(csv)
g.plot <- ggplot(csv, aes(x = Region, y = GeographicArea, fill = Region)) +
geom_violin() +
ylab("Geographic Area (km^2)") +
#ylim(0, 1) +
xlab("Sampled G-space") +
#scale_fill_grey(start=0.75, end=0.5) +
theme_classic() +
theme(text = element_text(size=12)) +
theme(legend.position="none") +
geom_point(shape = 21, size = 2, position = position_jitterdodge(), color = "black", alpha = 1) +
theme_pubr() +
stat_summary(fun = "mean", geom = "point", shape = 19, size = 2, color = "black")
e.plot <- ggplot(csv, aes(x = Region, y = NicheArea, fill = Region)) +
geom_violin() +
ylab("Niche Area") +
ylim(0, 30) +
xlab("Niche breadth in E-space") +
#scale_fill_grey(start=0.75, end=0.5) +
theme_classic() +
theme(text = element_text(size=12)) +
theme(legend.position="none") +
geom_point(shape = 21, size = 2, position = position_jitterdodge(), color = "black", alpha = 1) +
theme_pubr() +
stat_summary(fun = "mean", geom = "point", shape = 19, size = 2, color = "black")
npg.plot <- ggplot(csv, aes(x = Region, y = RasterBreadth, fill = Region)) +
geom_violin() +
ylab("Niche Area per Sampled Geographic Area") +
ylim(0, 1) +
xlab("Niche Area per Sampled Geographic Area") +
#scale_fill_grey(start=0.75, end=0.5) +
theme_classic() +
theme(text = element_text(size=12)) +
theme(legend.position="none") +
geom_point(shape = 21, size = 2, position = position_jitterdodge(), color = "black", alpha = 1) +
theme_pubr() +
stat_summary(fun = "mean", geom = "point", shape = 19, size = 2, color = "black")
big.grid <- plot_grid(g.plot, e.plot, ncol = 2, labels = "AUTO")
pdf("~/Dropbox/Manuscripts/UFL/EA_ENA_ENM/XX_REVISION/Figures/Breadth_Metrics_XX_REV.pdf",
width = 8.5, height = 8.5)#, units = 'in', res = 300)
ggdraw(big.grid)
dev.off()
#
#############################################################################################
# Let's make a plot that shows some of the variation in sample size and prediction rasters
# We already have plots for all of our species, but let's make a 2x3 grid with EA v ENA comparisons
output.folder <- "~/Dropbox/UF_Research/EA_ENA_ENM/GEB_Revisions/ENMEval_Outputs/" # Set the main "umbrella" folder that all subfolders are in
species.list <- c("Apios_priceana",
"Apios_fortunei",
"Cornus_alternifolia",
"Cornus_controversa",
"Tipularia_discolor",
"Tipularia_japonica")
# Lowest EA and ENA species vs the highest in that genus, and then one genus with just a ton of points for both species
#apios.fortunei.pts <- read.csv("ENMEval_Outputs/Apios_fortunei/rare_pts.csv")[,2:3]
#apios.priceana.pts <- read.csv("ENMEval_Outputs/Apios_priceana/rare_pts.csv")[,2:3]
#cornus.alternifolia.pts <- read.csv("ENMEval_Outputs/Cornus_alternifolia/rare_pts.csv")[,2:3]
#cornus.controversa.pts <- read.csv("ENMEval_Outputs/Cornus_controversa/rare_pts.csv")[,2:3]
#tipularia.discolor.pts <- read.csv("ENMEval_Outputs/Tipularia_discolor/rare_pts.csv")[,2:3]
#tipularia.japonica.pts <- read.csv("ENMEval_Outputs/Tipularia_japonica/rare_pts.csv")[,2:3]
#
#
#apios.fortunei.rast <- raster("ENMEval_Outputs/Apios_fortunei/Apios_fortunei.tif")
#apios.priceana.rast <- raster("ENMEval_Outputs/Apios_priceana/Apios_priceana.tif")
#cornus.alternifolia.rast <- raster("ENMEval_Outputs/Cornus_alternifolia/Cornus_alternifolia.tif")
#cornus.controversa.rast <- raster("ENMEval_Outputs/Cornus_controversa/Cornus_controversa.tif")
#tipularia.discolor.rast <- raster("ENMEval_Outputs/Tipularia_discolor/Tipularia_discolor.tif")
#tipularia.japonica.rast <- raster("ENMEval_Outputs/Tipularia_japonica/Tipularia_japonica.tif")
#