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MaxEnt_HSMs.R
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MaxEnt_HSMs.R
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#### DESCRIPTION ####
# This script is part of Courtney Stuart's first MSc Chapter in the Lab of
# Dr. Stephanie Green at the University of Alberta (2019-2021). This script
# examines MaxEnt's predictive performance using threshold-dependent
# assessments and confusion matrices. Response curves are also used to examine the
# relationship between each spatial predictor and the predicted relative habitat
# suitability for sub-adult gray snapper (Lutjanus griseus (lg)) and bluestriped
# grunt (Haemulon sciurus (hs)).
#### TO USE THIS FILE ####
# This is script 4 of 4 in Courtney's data analysis pipeline
# (follow the steps below unless data are provided by directly by Courtney)
# First run the Full_Data_Prep.R file located in the FL_Habitat_Suitability
# GitHub repository/project, as well as the Seafloor_Morphology geoprocessing
# model stored in the accompanying Stuart_MSc_Ch1.gdb ArcGIS geodatabase. These
# two steps are required to create the data used for MaxEnt modeling. Then, run
# the Collinearity_ENMEval.R file to assess collinearity among the 16 available
# spatial predictors and to identify the optimal settings for MaxEnt modeling.
# Next, run a MaxEnt model for each species independently in the MaxEnt GUI using
# the appropriate settings, the sampling bias raster data layer and training
# presence-only records in the Species_Occurrence folder, and the spatial
# predictors in the Spatial_Predictors folder. Finally, return to this file and
# run the code to assess model performance.
#### CONTACT ####
# Courtney Stuart (cestuart@ualberta.ca)
#### SET-UP ####
# working directory
setwd("Z:/Courtney/Stuart_MSc_Ch1/") # main project folder
# data directories
temp_wd = "Z:/Courtney/Stuart_MSc_Ch1/Temporary/" # temporary files
csv_wd = "Z:/Courtney/Stuart_MSc_Ch1/GitHub/FL_Habitat_Suitability/Data/" # for writing tabular data
fish_wd = "Z:/Courtney/Stuart_MSc_Ch1/Species_Occurrence/" # for fish data
plots_wd = "Z:/Courtney/Stuart_MSc_Ch1/GitHub/FL_Habitat_Suitability/Figures/" # final figures
temp_plots = "Z:/Courtney/Stuart_MSc_Ch1/Plots/" # temp plots for Courtney only
HSMs = "Z:/Courtney/Stuart_MSc_Ch1/HSMs/" # habitat suitability maps
# libraries
library(easypackages)
libraries("glmnet", "tidyverse", "tidyr", "caret", "MLeval", "dplyr", "raster",
"sf", "ggplot2", "fs", "purrr", "stringr", "PNWColors", "cowplot",
"dismo", "conflicted")
conflicted::conflict_prefer("select", "dplyr")
conflicted::conflict_prefer("filter", "dplyr")
conflicted::conflict_prefer("ylim", "ggplot2")
conflicted::conflict_prefer("xlim", "ggplot2")
# save PROJ.4 string for standard projection (ESPG:26958 NAD 83/Florida East)
my_crs = CRS("+init=epsg:26958")
#### CONFUSION MATRICES - GRAY SNAPPER ####
# read in sub-adult gray snapper presence-absence records
lg_full = read.csv(paste0(fish_wd, "Presence_Absence/Subadult_Gray_Snapper_PA_Full.csv"))
lg_full = lg_full %>%
mutate(PRES = as.factor(PRES),
PRES2 = as.factor(ifelse(PRES == 1, "PRESENCE", "ABSENCE"))) %>%
relocate(PRES2, .after = PRES)
# repeat the random train-test (70-30) data split so we can isolate
# the testing data for model evaluation (confusion matrices)
library(ISLR)
set.seed(123) # set seed to ensure repeatability
lg_train_id = sample(seq_len(nrow(lg_full)), size = floor(0.70*nrow(lg_full)))
lg_train = lg_full[lg_train_id,] # creates the training dataset (70%)
lg_test = lg_full[-lg_train_id,] # creates the test dataset (30%)
# isolate the coordinates of the training and testing data
lg_train_coords = lg_train %>% dplyr::select(LON_M, LAT_M)
lg_test_coords = lg_test %>% dplyr::select(LON_M, LAT_M)
# read in the continuous MaxEnt suitability predictions (raster data layer)
lg_maxent = raster(paste0(HSMs, "MaxEnt/Subadult_Gray_Snapper/LUT_GRIS_avg.asc"))
# extract the MaxEnt predictions at the testing sites
lg_maxent_test_prob = cbind(lg_test_coords, raster::extract(lg_maxent, lg_test_coords))
# discretize model predictions using default suitability threshold of 0.5 to get binary response
lg_maxent_test_pred = as.factor(ifelse(lg_maxent_test_prob$`raster::extract(lg_maxent, lg_test_coords)`>= 0.5,
"PRESENCE", "ABSENCE"))
# construct confusion matrix at the default suitability threshold of 0.5
lg_maxent_test_CM = confusionMatrix(data = lg_maxent_test_pred, reference = lg_test$PRES2,
positive = "PRESENCE")
lg_maxent_test_CM
# now assess performance using the Max SSS threshold (max sensitivity + sensitivity)
# Max SSS optimizes discrimination between presence and absence sites
# first extract the Max SSS threshold from the MaxEnt results .csv file
lg_results = read.csv(paste0(HSMs, "MaxEnt/Subadult_Gray_Snapper/maxentResults.csv"))
lg_maxSSS = lg_results %>%
filter(Species == "LUT_GRIS (average)") %>%
dplyr::select(Maximum.training.sensitivity.plus.specificity.Cloglog.threshold)
# discretize model predictions using the Max SSS threshold to get binary response
lg_maxent_test_predSSS = as.factor(ifelse(lg_maxent_test_prob$`raster::extract(lg_maxent, lg_test_coords)`>=
lg_maxSSS$Maximum.training.sensitivity.plus.specificity.Cloglog.threshold,
"PRESENCE", "ABSENCE"))
# construct confusion matrix at the Max SSS threshold
lg_maxent_test_CM_SSS = confusionMatrix(data = lg_maxent_test_predSSS, reference = lg_test$PRES2,
positive = "PRESENCE")
lg_maxent_test_CM_SSS
#### CONFUSION MATRICES - BLUESTRIPED GRUNT ####
# read in sub-adult bluestriped grunt presence-absence records
hs_full = read.csv(paste0(fish_wd, "Presence_Absence/Subadult_Bluestriped_Grunt_PA_Full.csv"))
hs_full = hs_full %>%
mutate(PRES = as.factor(PRES),
PRES2 = as.factor(ifelse(PRES == 1, "PRESENCE", "ABSENCE"))) %>%
relocate(PRES2, .after = PRES)
# repeat the random train-test (70-30) data split so we can isolate
# the testing data for model evaluation
library(ISLR)
set.seed(123) # set seed to ensure repeatability
hs_train_id = sample(seq_len(nrow(hs_full)), size = floor(0.70*nrow(hs_full)))
hs_train = hs_full[hs_train_id,] # creates the training dataset (70%)
hs_test = hs_full[-hs_train_id,] # creates the test dataset (30%)
# isolate the coordinates of the training and testing data
hs_train_coords = hs_train %>% dplyr::select(LON_M, LAT_M)
hs_test_coords = hs_test %>% dplyr::select(LON_M, LAT_M)
# read in the continuous MaxEnt suitability predictions (raster data layer)
hs_maxent = raster(paste0(HSMs, "MaxEnt/Subadult_Bluestriped_Grunt/HAE_SCIU_avg.asc"))
# extract the MaxEnt predictions at the testing sites
hs_maxent_test_prob = cbind(hs_test_coords, raster::extract(hs_maxent, hs_test_coords))
# discretize model predictions using default suitability threshold of 0.5 to get binary response
hs_maxent_test_pred = as.factor(ifelse(hs_maxent_test_prob$`raster::extract(hs_maxent, hs_test_coords)`>= 0.5,
"PRESENCE", "ABSENCE"))
# construct confusion matrix at the default suitability threshold of 0.5
hs_maxent_test_CM = confusionMatrix(data = hs_maxent_test_pred, reference = hs_test$PRES2,
positive = "PRESENCE")
hs_maxent_test_CM
# now assess performance using the Max SSS threhold (max sensitivity + sensitivity)
# Max SSS optimizes discrimination between presence and absence sites
# first extract the Max SSS threshold from the MaxEnt results .csv file
hs_results = read.csv(paste0(HSMs, "MaxEnt/Subadult_Bluestriped_Grunt/maxentResults.csv"))
hs_maxSSS = hs_results %>%
filter(Species == "HAE_SCIU (average)") %>%
dplyr::select(Maximum.training.sensitivity.plus.specificity.Cloglog.threshold)
# discretize model predictions using the Max SSS threshold to get binary response
hs_maxent_test_predSSS = as.factor(ifelse(hs_maxent_test_prob$`raster::extract(hs_maxent, hs_test_coords)`>=
hs_maxSSS$Maximum.training.sensitivity.plus.specificity.Cloglog.threshold,
"PRESENCE", "ABSENCE"))
# construct confusion matrix at the Max SSS threshold
hs_maxent_test_CM_SSS = confusionMatrix(data = hs_maxent_test_predSSS, reference = hs_test$PRES2,
positive = "PRESENCE")
hs_maxent_test_CM_SSS
#### RESPONSE CURVES ####
### HABITAT ####
# gray snapper
URM = read.csv(paste0(csv_wd, "URM_ClassLv1_IDs.csv"))
# list and read in the habitat data from the 10 cross validation runs
lg_hab = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots",
pattern = "*Habitat_only.csv")
for(i in 1:length(lg_hab)) {
assign(paste0("lg_hab", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
lg_hab[i])))
}
hab_lg_list = list(lg_hab1, lg_hab2, lg_hab3, lg_hab4, lg_hab5, lg_hab6,
lg_hab7, lg_hab8, lg_hab9, lg_hab10)
# make a list that corresponds with the ID number you want for each dataframe
hab_lg_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
# iterate across each dataframe in the list
hab_map = Map(cbind, hab_lg_list, ID = hab_lg_IDs)
# combine all habitat data
lg_hab_df = do.call("rbind", list(hab_map[[1]], hab_map[[2]], hab_map[[3]],
hab_map[[4]], hab_map[[5]], hab_map[[6]],
hab_map[[7]], hab_map[[8]], hab_map[[9]],
hab_map[[10]]))
rm(list = c("lg_hab1", "lg_hab2", "lg_hab3", "lg_hab4", "lg_hab5", "lg_hab6", "lg_hab7",
"lg_hab8", "lg_hab9", "lg_hab10", "hab_lg_list", "hab_lg_IDs", "hab_map"))
lg_hab_df$ClassLv1_ID = URM$ClassLv1[match(lg_hab_df$x, URM$ID)] # match IDs
lg_hab_df$ID = as.factor(lg_hab_df$ID)
lg_hab_df$Species = rep("Lutjanus griseus", nrow(lg_hab_df))
# clean and summarize
lg_hab_df = lg_hab_df %>%
rename(Variable = variable,
Hab_ID = x,
HSI = y,
Fold = ID,
Hab_Name = ClassLv1_ID)
lg_hab_summary = lg_hab_df %>%
group_by(Hab_Name) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, Hab_ID, Hab_Name, Mean_HSI, SD_HSI) %>%
distinct()
# repeat for bluestriped grunts
hs_hab = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
pattern = "*Habitat_only.csv")
for(i in 1:length(hs_hab)) {
assign(paste0("hs_hab", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
hs_hab[i])))
}
hab_hs_list = list(hs_hab1, hs_hab2, hs_hab3, hs_hab4, hs_hab5, hs_hab6,
hs_hab7, hs_hab8, hs_hab9, hs_hab10)
# make a list that corresponds with the ID number you want for each dataframe
hab_hs_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
# Iterate across each dataframe in the list
hab_map = Map(cbind, hab_hs_list, ID = hab_hs_IDs)
hs_hab_df = do.call("rbind", list(hab_map[[1]], hab_map[[2]], hab_map[[3]],
hab_map[[4]], hab_map[[5]], hab_map[[6]],
hab_map[[7]], hab_map[[8]], hab_map[[9]],
hab_map[[10]]))
rm(list = c("hs_hab1", "hs_hab2", "hs_hab3", "hs_hab4", "hs_hab5", "hs_hab6", "hs_hab7",
"hs_hab8", "hs_hab9", "hs_hab10", "hab_hs_list", "hab_hs_IDs", "hab_map"))
hs_hab_df$ClassLv1_ID = URM$ClassLv1[match(hs_hab_df$x, URM$ID)] # match IDs
hs_hab_df$ID = as.factor(hs_hab_df$ID)
hs_hab_df$Species = rep("Haemulon sciurus", nrow(hs_hab_df))
hs_hab_df = hs_hab_df %>%
rename(Variable = variable,
Hab_ID = x,
HSI = y,
Fold = ID,
Hab_Name = ClassLv1_ID)
hs_hab_summary = hs_hab_df %>%
group_by(Hab_Name) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, Hab_ID, Hab_Name, Mean_HSI, SD_HSI) %>%
distinct()
# combine habitat results from both species
hab_summary = rbind(lg_hab_summary, hs_hab_summary)
# palette for plotting
my_pal = pnw_palette("Bay",8)
my_pal
# plot habitat relationships
hab_plot = ggplot(hab_summary, aes(x = Hab_Name, y = Mean_HSI, fill = Species, width = 0.5)) +
geom_bar(stat = "identity", color = "black",
position = position_dodge()) +
xlab(" ") + ylab("Relative habitat suitability") +
scale_fill_manual(values = c(my_pal[1], my_pal[5])) +
geom_errorbar(aes(ymin = Mean_HSI-SD_HSI, ymax = Mean_HSI+SD_HSI), width = 0.3,
position = position_dodge(0.5)) +
theme_bw() + theme(legend.position = "top", legend.title = element_blank(),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.y = element_text(color = "black"),
axis.line = element_line(color = "black"),
axis.text.x = element_text(color = "black", angle = 30, vjust = 1,
hjust = 1, size = 7)) +
theme(legend.text = element_text(face = "italic")) +
scale_x_discrete(labels = function(x) str_wrap(str_replace_all(x, "foo" , " "),
width = 25))
hab_plot
# save plot
ggsave(filename = paste0(temp_plots, "Habitat_Curve.png"), hab_plot, width = 6,
height = 4, units = "in", dpi = 300)
#### MANGROVE DISTANCE ####
# gray snapper
lg_mg = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
pattern = "*Mangrove_Dist_only.csv")
for(i in 1:length(lg_mg)) {
assign(paste0("lg_mg", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
lg_mg[i])))
}
mg_lg_list = list(lg_mg1, lg_mg2, lg_mg3, lg_mg4, lg_mg5, lg_mg6,
lg_mg7, lg_mg8, lg_mg9, lg_mg10)
mg_lg_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
mg_map = Map(cbind, mg_lg_list, ID = mg_lg_IDs)
lg_mg_df = do.call("rbind", list(mg_map[[1]], mg_map[[2]], mg_map[[3]],
mg_map[[4]], mg_map[[5]], mg_map[[6]],
mg_map[[7]], mg_map[[8]], mg_map[[9]],
mg_map[[10]]))
lg_mg_df$ID = as.factor(lg_mg_df$ID)
rm(list = c("lg_mg1", "lg_mg2", "lg_mg3", "lg_mg4", "lg_mg5", "lg_mg6", "lg_mg7",
"lg_mg8","lg_mg9", "lg_mg10", "mg_lg_list", "mg_lg_IDs", "mg_map"))
lg_mg_df$Species = rep("Lutjanus griseus", nrow(lg_mg_df))
lg_mg_df = lg_mg_df %>%
rename(Variable = variable,
MG_Dist = x,
HSI = y,
Fold = ID) %>%
filter(MG_Dist >= 0)
lg_mg_summary = lg_mg_df %>%
group_by(MG_Dist) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, MG_Dist, Mean_HSI, SD_HSI) %>%
distinct()
# bluestriped grunts
hs_mg = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
pattern = "*Mangrove_Dist_only.csv")
for(i in 1:length(hs_mg)) {
assign(paste0("hs_mg", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
hs_mg[i])))
}
mg_hs_list = list(hs_mg1, hs_mg2, hs_mg3, hs_mg4, hs_mg5, hs_mg6,
hs_mg7, hs_mg8, hs_mg9, hs_mg10)
mg_hs_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
mg_map = Map(cbind, mg_hs_list, ID = mg_hs_IDs)
hs_mg_df = do.call("rbind", list(mg_map[[1]], mg_map[[2]], mg_map[[3]],
mg_map[[4]], mg_map[[5]], mg_map[[6]],
mg_map[[7]], mg_map[[8]], mg_map[[9]],
mg_map[[10]]))
hs_mg_df$ID = as.factor(hs_mg_df$ID)
rm(list = c("hs_mg1", "hs_mg2", "hs_mg3", "hs_mg4", "hs_mg5", "hs_mg6", "hs_mg7",
"hs_mg8","hs_mg9", "hs_mg10", "mg_hs_list", "mg_hs_IDs", "mg_map"))
hs_mg_df$Species = rep("Haemulon sciurus", nrow(hs_mg_df))
hs_mg_df = hs_mg_df %>%
rename(Variable = variable,
MG_Dist = x,
HSI = y,
Fold = ID) %>%
filter(MG_Dist >= 0)
hs_mg_summary = hs_mg_df %>%
group_by(MG_Dist) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, MG_Dist, Mean_HSI, SD_HSI) %>%
distinct()
# combined mangrove distance results
mg_summary = rbind(lg_mg_summary, hs_mg_summary)
# plot relationships
mg_plot =
ggplot(mg_summary, aes(y = Mean_HSI, x = MG_Dist, group = Species,
color = Species, fill = Species)) +
geom_point(aes(color = Species), size = 0.5) +
geom_line(aes(color = Species)) +
scale_color_manual(values = c(my_pal[1], my_pal[5])) +
scale_fill_manual(values = c(my_pal[1], my_pal[5])) +
geom_ribbon(aes(ymin = Mean_HSI - SD_HSI, ymax = Mean_HSI + SD_HSI),
alpha = 0.3, show.legend = FALSE) +
xlab("Distance to nearest mangrove (m)") +
ylab("Relative habitat suitability") + ylim(0, 1) +
scale_x_continuous(breaks=seq(0, 20000, 5000)) +
theme_bw() + theme(legend.position = "top", legend.title = element_blank(),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.text = element_text(face = "italic"))
mg_plot
# save plot
ggsave(filename = paste0(temp_plots, "Mangrove_Dist_Curve2.png"), mg_plot,
width = 3.15, height = 3.15, units = "in", dpi = 300)
#### DEPTH ####
# gray snapper
lg_depth = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
pattern = "*Depth_only.csv")
for(i in 1:length(lg_depth)) {
assign(paste0("lg_depth", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
lg_depth[i])))
}
depth_lg_list = list(lg_depth1, lg_depth2, lg_depth3, lg_depth4, lg_depth5, lg_depth6,
lg_depth7, lg_depth8, lg_depth9, lg_depth10)
depth_lg_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
depth_map = Map(cbind, depth_lg_list, ID = depth_lg_IDs)
lg_depth_df = do.call("rbind", list(depth_map[[1]], depth_map[[2]], depth_map[[3]],
depth_map[[4]], depth_map[[5]], depth_map[[6]],
depth_map[[7]], depth_map[[8]], depth_map[[9]],
depth_map[[10]]))
lg_depth_df$ID = as.factor(lg_depth_df$ID)
rm(list = c("lg_depth1", "lg_depth2", "lg_depth3", "lg_depth4", "lg_depth5", "lg_depth6", "lg_depth7",
"lg_depth8","lg_depth9", "lg_depth10", "depth_lg_list", "depth_lg_IDs", "depth_map"))
lg_depth_df$Species = rep("Lutjanus griseus", nrow(lg_depth_df))
lg_depth_df = lg_depth_df %>%
rename(Variable = variable,
Depth = x,
HSI = y,
Fold = ID) %>%
filter(Depth <= 0) %>% # remove extrapolations to 0+ depth (above ground)
mutate(Depth = Depth*-1) # remove negative sign, assume negative considering this is depth
lg_depth_summary = lg_depth_df %>%
group_by(Depth) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, Depth, Mean_HSI, SD_HSI) %>%
distinct()
# bluestriped grunt
hs_depth = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
pattern = "*Depth_only.csv")
for(i in 1:length(hs_depth)) {
assign(paste0("hs_depth", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
hs_depth[i])))
}
depth_hs_list = list(hs_depth1, hs_depth2, hs_depth3, hs_depth4, hs_depth5, hs_depth6,
hs_depth7, hs_depth8, hs_depth9, hs_depth10)
depth_hs_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
depth_map = Map(cbind, depth_hs_list, ID = depth_hs_IDs)
hs_depth_df = do.call("rbind", list(depth_map[[1]], depth_map[[2]], depth_map[[3]],
depth_map[[4]], depth_map[[5]], depth_map[[6]],
depth_map[[7]], depth_map[[8]], depth_map[[9]],
depth_map[[10]]))
hs_depth_df$ID = as.factor(hs_depth_df$ID)
rm(list = c("hs_depth1", "hs_depth2", "hs_depth3", "hs_depth4", "hs_depth5", "hs_depth6", "hs_depth7",
"hs_depth8","hs_depth9", "hs_depth10", "depth_hs_list", "depth_hs_IDs", "depth_map"))
hs_depth_df$Species = rep("Haemulon sciurus", nrow(hs_depth_df))
hs_depth_df = hs_depth_df %>%
rename(Variable = variable,
Depth = x,
HSI = y,
Fold = ID) %>%
filter(Depth <= 0) %>% # remove extrapolations to 0+ depth (above ground)
mutate(Depth = Depth*-1) # remove negative sign, assume negative considering this is depth
hs_depth_summary = hs_depth_df %>%
group_by(Depth) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, Depth, Mean_HSI, SD_HSI) %>%
distinct()
# combined depth results
depth_summary = rbind(lg_depth_summary, hs_depth_summary)
depth_plot = ggplot(depth_summary, aes(y = Mean_HSI, x = Depth, group = Species,
color = Species, fill = Species)) +
geom_point(aes(color = Species), size = 0.5) +
geom_line(aes(color = Species)) +
scale_color_manual(values = c(my_pal[1], my_pal[5])) +
scale_fill_manual(values = c(my_pal[1], my_pal[5])) +
geom_ribbon(aes(ymin = Mean_HSI - SD_HSI, ymax = Mean_HSI + SD_HSI),
alpha = 0.3, show.legend = FALSE) +
xlab("Depth (m)") +
ylab("Relative habitat suitability") + ylim(0, 1) +
scale_x_continuous(breaks = seq(0, 60, 10)) +
theme_bw() + theme(legend.position = "top", legend.title = element_blank(),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.text = element_text(face = "italic"))
depth_plot
# save plot
ggsave(filename = paste0(temp_plots, "Depth_Curve.png"), depth_plot,
width = 3.15, height = 3.15, units = "in", dpi = 300)
#### SLOPE ####
# gray snapper
lg_slope = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
pattern = "*Slope_only.csv")
for(i in 1:length(lg_slope)) {
assign(paste0("lg_slope", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
lg_slope[i])))
}
slope_lg_list = list(lg_slope1, lg_slope2, lg_slope3, lg_slope4, lg_slope5, lg_slope6,
lg_slope7, lg_slope8, lg_slope9, lg_slope10)
slope_lg_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
slope_map = Map(cbind, slope_lg_list, ID = slope_lg_IDs)
lg_slope_df = do.call("rbind", list(slope_map[[1]], slope_map[[2]], slope_map[[3]],
slope_map[[4]], slope_map[[5]], slope_map[[6]],
slope_map[[7]], slope_map[[8]], slope_map[[9]],
slope_map[[10]]))
lg_slope_df$ID = as.factor(lg_slope_df$ID)
rm(list = c("lg_slope1", "lg_slope2", "lg_slope3", "lg_slope4", "lg_slope5", "lg_slope6", "lg_slope7",
"lg_slope8","lg_slope9", "lg_slope10", "slope_lg_list", "slope_lg_IDs", "slope_map"))
lg_slope_df$Species = rep("Lutjanus griseus", nrow(lg_slope_df))
lg_slope_df = lg_slope_df %>%
rename(Variable = variable,
Slope = x,
HSI = y,
Fold = ID) %>%
filter(Slope >= 0)
lg_slope_summary = lg_slope_df %>%
group_by(Slope) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, Slope, Mean_HSI, SD_HSI) %>%
distinct()
# bluestriped grunt
hs_slope = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
pattern = "*Slope_only.csv")
for(i in 1:length(hs_slope)) {
assign(paste0("hs_slope", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
hs_slope[i])))
}
slope_hs_list = list(hs_slope1, hs_slope2, hs_slope3, hs_slope4, hs_slope5, hs_slope6,
hs_slope7, hs_slope8, hs_slope9, hs_slope10)
slope_hs_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
slope_map = Map(cbind, slope_hs_list, ID = slope_hs_IDs)
hs_slope_df = do.call("rbind", list(slope_map[[1]], slope_map[[2]], slope_map[[3]],
slope_map[[4]], slope_map[[5]], slope_map[[6]],
slope_map[[7]], slope_map[[8]], slope_map[[9]],
slope_map[[10]]))
hs_slope_df$ID = as.factor(hs_slope_df$ID)
rm(list = c("hs_slope1", "hs_slope2", "hs_slope3", "hs_slope4", "hs_slope5", "hs_slope6", "hs_slope7",
"hs_slope8","hs_slope9", "hs_slope10", "slope_hs_list", "slope_hs_IDs", "slope_map"))
hs_slope_df$Species = rep("Haemulon sciurus", nrow(hs_slope_df))
hs_slope_df = hs_slope_df %>%
rename(Variable = variable,
Slope = x,
HSI = y,
Fold = ID)%>%
filter(Slope >= 0)
hs_slope_summary = hs_slope_df %>%
group_by(Slope) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, Slope, Mean_HSI, SD_HSI) %>%
distinct()
# combined slope results
slope_summary = rbind(lg_slope_summary, hs_slope_summary)
slope_plot = ggplot(slope_summary, aes(y = Mean_HSI, x = Slope, group = Species,
color = Species, fill = Species)) +
geom_point(aes(color = Species), size = 0.5) +
geom_line(aes(color = Species)) +
scale_color_manual(values = c(my_pal[1], my_pal[5])) +
scale_fill_manual(values = c(my_pal[1], my_pal[5])) +
geom_ribbon(aes(ymin = Mean_HSI - SD_HSI, ymax = Mean_HSI + SD_HSI),
alpha = 0.3, show.legend = FALSE) +
xlab("Slope (degrees)") +
ylab("Relative habitat suitability") + ylim(0, 1) +
theme_bw() + theme(legend.position = "top", legend.title = element_blank(),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.text = element_text(face = "italic"))
slope_plot
# save plot
ggsave(filename = paste0(temp_plots, "Slope_Curve.png"), slope_plot,
width = 3.15, height = 3.15, units = "in", dpi = 300)
#### RUGOSITY ####
# gray snapper
lg_rug = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
pattern = "*Rugosity_only.csv")
for(i in 1:length(lg_rug)) {
assign(paste0("lg_rug", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
lg_rug[i])))
}
rug_lg_list = list(lg_rug1, lg_rug2, lg_rug3, lg_rug4, lg_rug5, lg_rug6,
lg_rug7, lg_rug8, lg_rug9, lg_rug10)
rug_lg_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
rug_map = Map(cbind, rug_lg_list, ID = rug_lg_IDs)
lg_rug_df = do.call("rbind", list(rug_map[[1]], rug_map[[2]], rug_map[[3]],
rug_map[[4]], rug_map[[5]], rug_map[[6]],
rug_map[[7]], rug_map[[8]], rug_map[[9]],
rug_map[[10]]))
lg_rug_df$ID = as.factor(lg_rug_df$ID)
rm(list = c("lg_rug1", "lg_rug2", "lg_rug3", "lg_rug4", "lg_rug5", "lg_rug6", "lg_rug7",
"lg_rug8","lg_rug9", "lg_rug10", "rug_lg_list", "rug_lg_IDs", "rug_map"))
lg_rug_df$Species = rep("Lutjanus griseus", nrow(lg_rug_df))
lg_rug_df = lg_rug_df %>%
rename(Variable = variable,
Rug = x,
HSI = y,
Fold = ID)
lg_rug_summary = lg_rug_df %>%
group_by(Rug) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>% ### NOTE, these are all zero because rugosity was set to 0 by L1 reg
ungroup() %>%
dplyr::select(Species, Variable, Rug, Mean_HSI, SD_HSI) %>%
distinct()
# bluestriped grunt
hs_rug = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
pattern = "*Rugosity_only.csv")
for(i in 1:length(hs_rug)) {
assign(paste0("hs_rug", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
hs_rug[i])))
}
rug_hs_list = list(hs_rug1, hs_rug2, hs_rug3, hs_rug4, hs_rug5, hs_rug6,
hs_rug7, hs_rug8, hs_rug9, hs_rug10)
rug_hs_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
rug_map = Map(cbind, rug_hs_list, ID = rug_hs_IDs)
hs_rug_df = do.call("rbind", list(rug_map[[1]], rug_map[[2]], rug_map[[3]],
rug_map[[4]], rug_map[[5]], rug_map[[6]],
rug_map[[7]], rug_map[[8]], rug_map[[9]],
rug_map[[10]]))
hs_rug_df$ID = as.factor(hs_rug_df$ID)
rm(list = c("hs_rug1", "hs_rug2", "hs_rug3", "hs_rug4", "hs_rug5", "hs_rug6", "hs_rug7",
"hs_rug8","hs_rug9", "hs_rug10", "rug_hs_list", "rug_hs_IDs", "rug_map"))
hs_rug_df$Species = rep("Haemulon sciurus", nrow(hs_rug_df))
hs_rug_df = hs_rug_df %>%
rename(Variable = variable,
Rug = x,
HSI = y,
Fold = ID)
hs_rug_summary = hs_rug_df %>%
group_by(Rug) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>% ### NOTE, these are all zero because rugosity was set to 0 by L1 reg
ungroup() %>%
dplyr::select(Species, Variable, Rug, Mean_HSI, SD_HSI) %>%
distinct()
# combined rugosity results
rug_summary = rbind(lg_rug_summary, hs_rug_summary)
rug_plot = ggplot(rug_summary, aes(y = Mean_HSI, x = Rug, group = Species,
color = Species, fill = Species)) +
geom_point(aes(color = Species), size = 0.5) +
geom_line(aes(color = Species)) +
scale_color_manual(values = c(my_pal[1], my_pal[5])) +
scale_fill_manual(values = c(my_pal[1], my_pal[5])) +
geom_ribbon(aes(ymin = Mean_HSI - SD_HSI, ymax = Mean_HSI + SD_HSI),
alpha = 0.3, show.legend = FALSE) +
xlab("Rugosity (Slope-Corrected SA:PA)") +
ylab("Relative habitat suitability") + ylim(0, 1) +
theme_bw() + theme(legend.position = "top", legend.title = element_blank(),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.text = element_text(face = "italic"))
rug_plot
# save plot
ggsave(filename = paste0(temp_plots, "Rugosity_Curve.png"), rug_plot,
width = 3.15, height = 3.15, units = "in", dpi = 300)
#### CURVATURE ####
# gray snapper
lg_curv = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
pattern = "*Curvature_only.csv")
for(i in 1:length(lg_curv)) {
assign(paste0("lg_curv", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
lg_curv[i])))
}
curv_lg_list = list(lg_curv1, lg_curv2, lg_curv3, lg_curv4, lg_curv5, lg_curv6,
lg_curv7, lg_curv8, lg_curv9, lg_curv10)
curv_lg_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
curv_map = Map(cbind, curv_lg_list, ID = curv_lg_IDs)
lg_curv_df = do.call("rbind", list(curv_map[[1]], curv_map[[2]], curv_map[[3]],
curv_map[[4]], curv_map[[5]], curv_map[[6]],
curv_map[[7]], curv_map[[8]], curv_map[[9]],
curv_map[[10]]))
lg_curv_df$ID = as.factor(lg_curv_df$ID)
rm(list = c("lg_curv1", "lg_curv2", "lg_curv3", "lg_curv4", "lg_curv5", "lg_curv6", "lg_curv7",
"lg_curv8","lg_curv9", "lg_curv10", "curv_lg_list", "curv_lg_IDs", "curv_map"))
lg_curv_df$Species = rep("Lutjanus griseus", nrow(lg_curv_df))
lg_curv_df = lg_curv_df %>%
rename(Variable = variable,
Curvature = x,
HSI = y,
Fold = ID)
lg_curv_summary = lg_curv_df %>%
group_by(Curvature) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, Curvature, Mean_HSI, SD_HSI) %>%
distinct()
# bluestriped grunt
hs_curv = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
pattern = "*Curvature_only.csv")
for(i in 1:length(hs_curv)) {
assign(paste0("hs_curv", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
hs_curv[i])))
}
curv_hs_list = list(hs_curv1, hs_curv2, hs_curv3, hs_curv4, hs_curv5, hs_curv6,
hs_curv7, hs_curv8, hs_curv9, hs_curv10)
curv_hs_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
curv_map = Map(cbind, curv_hs_list, ID = curv_hs_IDs)
hs_curv_df = do.call("rbind", list(curv_map[[1]], curv_map[[2]], curv_map[[3]],
curv_map[[4]], curv_map[[5]], curv_map[[6]],
curv_map[[7]], curv_map[[8]], curv_map[[9]],
curv_map[[10]]))
hs_curv_df$ID = as.factor(hs_curv_df$ID)
rm(list = c("hs_curv1", "hs_curv2", "hs_curv3", "hs_curv4", "hs_curv5", "hs_curv6", "hs_curv7",
"hs_curv8","hs_curv9", "hs_curv10", "curv_hs_list", "curv_hs_IDs", "curv_map"))
hs_curv_df$Species = rep("Haemulon sciurus", nrow(hs_curv_df))
hs_curv_df = hs_curv_df %>%
rename(Variable = variable,
Curvature = x,
HSI = y,
Fold = ID)
hs_curv_summary = hs_curv_df %>%
group_by(Curvature) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, Curvature, Mean_HSI, SD_HSI) %>%
distinct()
# combined curvature results
curv_summary = rbind(lg_curv_summary, hs_curv_summary)
curv_plot = ggplot(curv_summary, aes(y = Mean_HSI, x = Curvature, group = Species,
color = Species, fill = Species)) +
geom_point(aes(color = Species), size = 0.5) +
geom_line(aes(color = Species)) +
scale_color_manual(values = c(my_pal[1], my_pal[5])) +
scale_fill_manual(values = c(my_pal[1], my_pal[5])) +
geom_ribbon(aes(ymin = Mean_HSI - SD_HSI, ymax = Mean_HSI + SD_HSI),
alpha = 0.3, show.legend = FALSE) +
xlab("Curvature") +
ylab("Relative habitat suitability") + ylim(0, 1) +
theme_bw() + theme(legend.position = "top", legend.title = element_blank(),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.text = element_text(face = "italic"))
curv_plot
# save plot
ggsave(filename = paste0(temp_plots, "Curvature_Curve.png"), curv_plot,
width = 3.15, height = 3.15, units = "in", dpi = 300)
#### BPI FINE ####
# gray snapper
lg_bpiF = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
pattern = "*BPI_Fine_only.csv")
for(i in 1:length(lg_bpiF)) {
assign(paste0("lg_bpiF", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
lg_bpiF[i])))
}
bpiF_lg_list = list(lg_bpiF1, lg_bpiF2, lg_bpiF3, lg_bpiF4, lg_bpiF5, lg_bpiF6,
lg_bpiF7, lg_bpiF8, lg_bpiF9, lg_bpiF10)
bpiF_lg_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
bpiF_map = Map(cbind, bpiF_lg_list, ID = bpiF_lg_IDs)
lg_bpiF_df = do.call("rbind", list(bpiF_map[[1]], bpiF_map[[2]], bpiF_map[[3]],
bpiF_map[[4]], bpiF_map[[5]], bpiF_map[[6]],
bpiF_map[[7]], bpiF_map[[8]], bpiF_map[[9]],
bpiF_map[[10]]))
lg_bpiF_df$ID = as.factor(lg_bpiF_df$ID)
rm(list = c("lg_bpiF1", "lg_bpiF2", "lg_bpiF3", "lg_bpiF4", "lg_bpiF5", "lg_bpiF6", "lg_bpiF7",
"lg_bpiF8","lg_bpiF9", "lg_bpiF10", "bpiF_lg_list", "bpiF_lg_IDs", "bpiF_map"))
lg_bpiF_df$Species = rep("Lutjanus griseus", nrow(lg_bpiF_df))
lg_bpiF_df = lg_bpiF_df %>%
rename(Variable = variable,
BPI_Fine = x,
HSI = y,
Fold = ID)
lg_bpiF_summary = lg_bpiF_df %>%
group_by(BPI_Fine) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, BPI_Fine, Mean_HSI, SD_HSI) %>%
distinct()
# bluestriped grunt
hs_bpiF = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
pattern = "*BPI_Fine_only.csv")
for(i in 1:length(hs_bpiF)) {
assign(paste0("hs_bpiF", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
hs_bpiF[i])))
}
bpiF_hs_list = list(hs_bpiF1, hs_bpiF2, hs_bpiF3, hs_bpiF4, hs_bpiF5, hs_bpiF6,
hs_bpiF7, hs_bpiF8, hs_bpiF9, hs_bpiF10)
bpiF_hs_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
bpiF_map = Map(cbind, bpiF_hs_list, ID = bpiF_hs_IDs)
hs_bpiF_df = do.call("rbind", list(bpiF_map[[1]], bpiF_map[[2]], bpiF_map[[3]],
bpiF_map[[4]], bpiF_map[[5]], bpiF_map[[6]],
bpiF_map[[7]], bpiF_map[[8]], bpiF_map[[9]],
bpiF_map[[10]]))
hs_bpiF_df$ID = as.factor(hs_bpiF_df$ID)
rm(list = c("hs_bpiF1", "hs_bpiF2", "hs_bpiF3", "hs_bpiF4", "hs_bpiF5", "hs_bpiF6", "hs_bpiF7",
"hs_bpiF8","hs_bpiF9", "hs_bpiF10", "bpiF_hs_list", "bpiF_hs_IDs", "bpiF_map"))
hs_bpiF_df$Species = rep("Haemulon sciurus", nrow(hs_bpiF_df))
hs_bpiF_df = hs_bpiF_df %>%
rename(Variable = variable,
BPI_Fine = x,
HSI = y,
Fold = ID)
hs_bpiF_summary = hs_bpiF_df %>%
group_by(BPI_Fine) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, BPI_Fine, Mean_HSI, SD_HSI) %>%
distinct()
bpiF_summary = rbind(lg_bpiF_summary, hs_bpiF_summary)
bpiF_plot = ggplot(bpiF_summary, aes(y = Mean_HSI, x = BPI_Fine, group = Species,
color = Species, fill = Species)) +
geom_point(aes(color = Species), size = 0.5) +
geom_line(aes(color = Species)) +
scale_color_manual(values = c(my_pal[1], my_pal[5])) +
scale_fill_manual(values = c(my_pal[1], my_pal[5])) +
geom_ribbon(aes(ymin = Mean_HSI - SD_HSI, ymax = Mean_HSI + SD_HSI),
alpha = 0.3, show.legend = FALSE) +
xlab("Fine-Scale BPI") +
ylab("Relative habitat suitability") + ylim(0, 1) +
scale_x_continuous(breaks = seq(-1000, 2000, 500)) +
theme_bw() + theme(legend.position = "top", legend.title = element_blank(),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.text = element_text(face = "italic"))
bpiF_plot
# save plot
ggsave(filename = paste0(temp_plots, "BPI_Fine_Curve.png"), bpiF_plot,
width = 3.15, height = 3.15, units = "in", dpi = 300)
#### BPI BROAD ####
# gray snapper
lg_bpiB = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
pattern = "*BPI_Broad_only.csv")
for(i in 1:length(lg_bpiB)) {
assign(paste0("lg_bpiB", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Gray_Snapper/plots/",
lg_bpiB[i])))
}
bpiB_lg_list = list(lg_bpiB1, lg_bpiB2, lg_bpiB3, lg_bpiB4, lg_bpiB5, lg_bpiB6,
lg_bpiB7, lg_bpiB8, lg_bpiB9, lg_bpiB10)
bpiB_lg_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
bpiB_map = Map(cbind, bpiB_lg_list, ID = bpiB_lg_IDs)
lg_bpiB_df = do.call("rbind", list(bpiB_map[[1]], bpiB_map[[2]], bpiB_map[[3]],
bpiB_map[[4]], bpiB_map[[5]], bpiB_map[[6]],
bpiB_map[[7]], bpiB_map[[8]], bpiB_map[[9]],
bpiB_map[[10]]))
lg_bpiB_df$ID = as.factor(lg_bpiB_df$ID)
rm(list = c("lg_bpiB1", "lg_bpiB2", "lg_bpiB3", "lg_bpiB4", "lg_bpiB5", "lg_bpiB6", "lg_bpiB7",
"lg_bpiB8","lg_bpiB9", "lg_bpiB10", "bpiB_lg_list", "bpiB_lg_IDs", "bpiB_map"))
lg_bpiB_df$Species = rep("Lutjanus griseus", nrow(lg_bpiB_df))
lg_bpiB_df = lg_bpiB_df %>%
rename(Variable = variable,
BPI_Broad = x,
HSI = y,
Fold = ID)
lg_bpiB_summary = lg_bpiB_df %>%
group_by(BPI_Broad) %>%
mutate(Mean_HSI = mean(HSI),
SD_HSI = sd(HSI)) %>%
ungroup() %>%
dplyr::select(Species, Variable, BPI_Broad, Mean_HSI, SD_HSI) %>%
distinct()
# bluestriped grunt
hs_bpiB = list.files("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
pattern = "*BPI_Broad_only.csv")
for(i in 1:length(hs_bpiB)) {
assign(paste0("hs_bpiB", i),
read.csv(paste0("Z:/Courtney/Stuart_MSc_Ch1/HSMs/MaxEnt/Subadult_Bluestriped_Grunt/plots/",
hs_bpiB[i])))
}
bpiB_hs_list = list(hs_bpiB1, hs_bpiB2, hs_bpiB3, hs_bpiB4, hs_bpiB5, hs_bpiB6,
hs_bpiB7, hs_bpiB8, hs_bpiB9, hs_bpiB10)
bpiB_hs_IDs = list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
bpiB_map = Map(cbind, bpiB_hs_list, ID = bpiB_hs_IDs)