From 9f7c6efbd2e755a09567cd74a0b6bd39fdbe443b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?K=C3=B6nig?= <ye87zine@usr.idiv.de>
Date: Thu, 20 Mar 2025 15:41:30 +0100
Subject: [PATCH] started cleaning up and reorganizing repo
---
.gitignore | 2 +-
...reparation.R => 01_01_range_preparation.R} | 2 +-
R/01_02_raster_preparation.R | 36 -
...eparation.R => 01_02_traits_preparation.R} | 52 +-
...reparation.R => 01_03_phylo_preparation.R} | 10 +-
R/01_04_raster_preparation.R | 20 +
R/02_01_functional_group_assignment.R | 34 -
...on.R => 02_01_presence_data_preparation.R} | 0
...ion.R => 02_02_absence_data_preparation.R} | 0
R/03_03_dataset_exploration.R | 53 --
R/03_03_model_data_finalization.R | 46 +-
R/03_04_dataset_exploration.R | 95 +++
R/04_01_modelling_ssdm.R | 10 +-
R/04_04_modelling_msdm_rf.R | 2 +-
R/05_01_performance_report.qmd | 47 +-
R/05_01_performance_report_rf.qmd | 692 ------------------
R/05_02_publication_analysis.R | 4 +-
R/_publish.yml | 2 -
README.md | 63 +-
occurrences.png | Bin 98073 -> 0 bytes
20 files changed, 274 insertions(+), 896 deletions(-)
rename R/{01_01_range_map_preparation.R => 01_01_range_preparation.R} (99%)
delete mode 100644 R/01_02_raster_preparation.R
rename R/{02_02_functional_traits_preparation.R => 01_02_traits_preparation.R} (64%)
rename R/{02_03_phylo_preparation.R => 01_03_phylo_preparation.R} (84%)
create mode 100644 R/01_04_raster_preparation.R
delete mode 100644 R/02_01_functional_group_assignment.R
rename R/{03_01_presence_preparation.R => 02_01_presence_data_preparation.R} (100%)
rename R/{03_02_absence_preparation.R => 02_02_absence_data_preparation.R} (100%)
delete mode 100644 R/03_03_dataset_exploration.R
create mode 100644 R/03_04_dataset_exploration.R
delete mode 100644 R/05_01_performance_report_rf.qmd
delete mode 100644 occurrences.png
diff --git a/.gitignore b/.gitignore
index 3acf079..4d2ad7c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,4 +13,4 @@ renv/cache/
data/
plots/
R/*/
-R/*.html
\ No newline at end of file
+*.html
\ No newline at end of file
diff --git a/R/01_01_range_map_preparation.R b/R/01_01_range_preparation.R
similarity index 99%
rename from R/01_01_range_map_preparation.R
rename to R/01_01_range_preparation.R
index eb33fdc..4f6a412 100644
--- a/R/01_01_range_map_preparation.R
+++ b/R/01_01_range_preparation.R
@@ -9,7 +9,7 @@ sf::sf_use_s2(use_s2 = FALSE)
# --------------------------------------#
# Process range maps ####
# --------------------------------------#
-# Load range maps
+# Load IUCN range maps
range_maps = st_read(
"~/share/groups/mas_data/Saved_Data_Dropbox_Business/Datensätze/Range Maps/IUCN_range_maps_mammals_version2016/TERRESTRIAL_MAMMALS.shp",
geometry_column = "geometry",
diff --git a/R/01_02_raster_preparation.R b/R/01_02_raster_preparation.R
deleted file mode 100644
index e7876e9..0000000
--- a/R/01_02_raster_preparation.R
+++ /dev/null
@@ -1,36 +0,0 @@
-library(tidyverse)
-library(terra)
-library(rnaturalearth)
-library(furrr)
-
-# Define Study extent ####
-sa_polygon = rnaturalearth::ne_countries(scale = 10, returnclass = "sf") %>%
- dplyr::filter(continent == "South America") %>%
- sf::st_union() %>%
- st_crop(xmin = -82, ymin = -56, xmax = -34, ymax = 13)
-
-save(sa_polygon, file = "data/r_objects/sa_polygon.RData")
-
-# CHELSA bioclim target variables ####
-chelsa_urls = paste0("https://os.zhdk.cloud.switch.ch/chelsav2/GLOBAL/climatologies/1981-2010/bio/CHELSA_bio", 1:19,"_1981-2010_V.2.1.tif")
-
-chelsa = terra::rast(chelsa_urls) %>%
- crop(sa_polygon)
-set.names(chelsa, paste0("bio", 1:19))
-terra::writeRaster(chelsa, filename = file.path("data", "geospatial", "raster", basename(chelsa_urls)), overwrite = T)
-
-# Tree canopy cover ####
-igfc = list.files("I:/mas_data/00_data/processed/iGFC/", pattern = "canopyDensity_(199[0-9]|200[0-9]|2010)", full.names = TRUE) %>%
- terra::rast() %>%
- terra::resample(chelsa) %>%
- app(mean, na.rm = TRUE)
-terra::set.names(igfc, "igfc")
-terra::writeRaster(igfc, filename = file.path("data", "geospatial", "raster", "mean_canopyDensity_1992-2010.tif"), overwrite = T)
-
-# Terrain ####
-roughness = list.files("C:/Users/ye87zine/Downloads", pattern = "roughness", full.names = T) %>%
- terra::rast() %>%
- crop(study_extent) %>%
- terra::resample(chelsa)
-terra::set.names(roughness, "roughness")
-terra::writeRaster(roughness, filename = file.path("data", "geospatial", "raster", "terrain_roughness.tif"), overwrite = T)
diff --git a/R/02_02_functional_traits_preparation.R b/R/01_02_traits_preparation.R
similarity index 64%
rename from R/02_02_functional_traits_preparation.R
rename to R/01_02_traits_preparation.R
index dfec715..0e48083 100644
--- a/R/02_02_functional_traits_preparation.R
+++ b/R/01_02_traits_preparation.R
@@ -1,6 +1,49 @@
library("tidyverse")
library("traitdata")
+library("Symobio")
+library("vegan")
+
+load("data/r_objects/range_maps.RData")
+
+# -------------------------------------------#
+# Assign functional groups ####
+# -------------------------------------------#
+# Get taxonomic information for target species
+names_unique = unique(range_maps$name_matched[!is.na(range_maps$name_matched)])
+
+species_matched = lapply(names_unique, function(name){
+ match_result = Symobio::gbif_match_name(name = name)
+ if(match_result$status != "ACCEPTED"){ # Search for accepted name
+ match_result = gbif_match_name(usageKey = match_result$acceptedUsageKey)
+ }
+ match_result$name_orig = name
+ return(match_result)
+}) %>%
+ bind_rows()
+
+# Assign functional groups
+functional_groups = species_matched %>%
+ mutate(
+ functional_group = case_when(
+ order %in% c("Carnivora", "Artiodactyla", "Cingulata", "Perissodactyla") ~ 1,
+ order %in% c("Rodentia", "Didelphimorphia", "Soricomorpha", "Paucituberculata", "Lagomorpha") ~ 2,
+ order %in% c("Primates", "Pilosa") ~ 3,
+ order %in% c("Chiroptera") ~ 4
+ ),
+ functional_group = factor(functional_group, labels = c("large ground-dwelling", "small ground-dwelling", "arboreal", "flying"))
+ ) %>%
+ dplyr::select(
+ name_orig,
+ name_matched = species,
+ functional_group
+ ) %>%
+ distinct()
+save(functional_groups, file = "data/r_objects/functional_groups.RData")
+
+# ---------------------------------------------------#
+# Process and merge functional traits ####
+# ---------------------------------------------------#
# Match names
traits = traitdata::elton_mammals
names_unique = unique(traits$scientificNameStd)
@@ -69,12 +112,9 @@ traits_proc = traits_matched %>%
save(traits_proc, file = "data/r_objects/traits_proc.RData")
-# Calculate Distances
-library("vegan")
-
-load("data/r_objects/range_maps.RData")
-load("data/r_objects/traits_proc.RData")
-
+# ---------------------------------------------------#
+# Calculate functional dissimilarity ####
+# ---------------------------------------------------#
target_species = unique(range_maps$name_matched[!is.na(range_maps$name_matched)])
traits_target = dplyr::filter(traits_proc, species %in% target_species)
diff --git a/R/02_03_phylo_preparation.R b/R/01_03_phylo_preparation.R
similarity index 84%
rename from R/02_03_phylo_preparation.R
rename to R/01_03_phylo_preparation.R
index a72c833..4ba9a85 100644
--- a/R/02_03_phylo_preparation.R
+++ b/R/01_03_phylo_preparation.R
@@ -6,7 +6,9 @@ library(Symobio)
forest = read.nexus("data/phylogenies/Complete_phylogeny.nex")
load("data/r_objects/range_maps_names_matched.RData")
-# Get taxonomic information for target species
+# ---------------------------------------------------#
+# Match taxonomic names in phylogeny ####
+# ---------------------------------------------------#
phylo_names_unique = unique(forest$UNTITLED$tip.label)
phylo_names_matched = lapply(phylo_names_unique, function(name){
@@ -28,6 +30,10 @@ phylo_names_matched = lapply(phylo_names_unique, function(name){
save(phylo_names_matched, file = "data/r_objects/phylo_names_matched.RData")
+# ---------------------------------------------------#
+# Calculate phylo distances ####
+# ---------------------------------------------------#
+# Calculate pairwise phylogenetic distances across a random sample 50 of forests
# Trim forest to target species
phylo_names_target = dplyr::filter(phylo_names_matched, name_matched %in% range_maps_names_matched$name_matched)
@@ -40,8 +46,6 @@ forest_pruned = lapply(forest, function(x) {
})
class(forest_pruned) <- "multiPhylo"
-
-# Calculate pairwise phylogenetic distances across a random sample 50 of forests
indices = sample(length(forest_pruned), 50)
dists = lapply(indices, function(i){
cophenetic.phylo(forest_pruned[[i]])
diff --git a/R/01_04_raster_preparation.R b/R/01_04_raster_preparation.R
new file mode 100644
index 0000000..aa9e210
--- /dev/null
+++ b/R/01_04_raster_preparation.R
@@ -0,0 +1,20 @@
+library(tidyverse)
+library(terra)
+library(rnaturalearth)
+library(furrr)
+
+# Define Study extent ####
+sa_polygon = rnaturalearth::ne_countries(scale = 10, returnclass = "sf") %>%
+ dplyr::filter(continent == "South America") %>%
+ sf::st_union() %>%
+ st_crop(xmin = -82, ymin = -56, xmax = -34, ymax = 13)
+
+save(sa_polygon, file = "data/r_objects/sa_polygon.RData")
+
+# CHELSA bioclim target variables ####
+chelsa_urls = paste0("https://os.zhdk.cloud.switch.ch/chelsav2/GLOBAL/climatologies/1981-2010/bio/CHELSA_bio", 1:19,"_1981-2010_V.2.1.tif")
+
+chelsa = terra::rast(chelsa_urls) %>%
+ crop(sa_polygon)
+set.names(chelsa, paste0("bio", 1:19))
+terra::writeRaster(chelsa, filename = file.path("data", "geospatial", "raster", basename(chelsa_urls)), overwrite = T)
\ No newline at end of file
diff --git a/R/02_01_functional_group_assignment.R b/R/02_01_functional_group_assignment.R
deleted file mode 100644
index ee2a06f..0000000
--- a/R/02_01_functional_group_assignment.R
+++ /dev/null
@@ -1,34 +0,0 @@
-load("data/r_objects/range_maps.RData")
-
-# Get taxonomic information for target species
-names_unique = unique(range_maps$name_matched[!is.na(range_maps$name_matched)])
-
-species_matched = lapply(names_unique, function(name){
- match_result = Symobio::gbif_match_name(name = name)
- if(match_result$status != "ACCEPTED"){
- match_result = gbif_match_name(usageKey = match_result$acceptedUsageKey)
- }
- match_result$name_orig = name
- return(match_result)
-}) %>%
- bind_rows()
-
-# Assign functional groups
-functional_groups = species_matched %>%
- mutate(
- functional_group = case_when(
- order %in% c("Carnivora", "Artiodactyla", "Cingulata", "Perissodactyla") ~ 1,
- order %in% c("Rodentia", "Didelphimorphia", "Soricomorpha", "Paucituberculata", "Lagomorpha") ~ 2,
- order %in% c("Primates", "Pilosa") ~ 3,
- order %in% c("Chiroptera") ~ 4
- ),
- functional_group = factor(functional_group, labels = c("large ground-dwelling", "small ground-dwelling", "arboreal", "flying"))
- ) %>%
- dplyr::select(
- name_orig,
- name_matched = species,
- functional_group
- ) %>%
- distinct()
-
-save(functional_groups, file = "data/r_objects/functional_groups.RData")
diff --git a/R/03_01_presence_preparation.R b/R/02_01_presence_data_preparation.R
similarity index 100%
rename from R/03_01_presence_preparation.R
rename to R/02_01_presence_data_preparation.R
diff --git a/R/03_02_absence_preparation.R b/R/02_02_absence_data_preparation.R
similarity index 100%
rename from R/03_02_absence_preparation.R
rename to R/02_02_absence_data_preparation.R
diff --git a/R/03_03_dataset_exploration.R b/R/03_03_dataset_exploration.R
deleted file mode 100644
index ad33173..0000000
--- a/R/03_03_dataset_exploration.R
+++ /dev/null
@@ -1,53 +0,0 @@
-library(tidyverse)
-library(sf)
-library(terra)
-
-source("R/utils.R")
-
-load("data/r_objects/model_data.RData")
-load("data/r_objects/sa_polygon.RData")
-
-sa_polygon = sf::st_transform(sa_polygon, crs(model_data))
-
-# ---------------------------------------#
-# Plot all presences in the dataset ####
-# ---------------------------------------#
-data_extent = ext(model_data)
-
-presences = model_data %>%
- dplyr::filter(present == 1)
-
-template_raster <- terra::rast(data_extent, resolution = 1000) # 1000m = 1km
-crs(template_raster) <- st_crs(model_data)$wkt
-
-point_counts <- terra::rasterize(
- vect(presences),
- template_raster,
- field = NULL,
- fun = "count",
- background = 0
-) %>%
- terra::mask(vect(sa_polygon))
-
-length(which(values(point_counts) > 0))
-
-# only 36858 raster cells contain at least 1 presence record
-
-ggplot() +
- tidyterra::geom_spatraster(data = point_counts, maxcell = 5e7) +
- scale_fill_gradientn(
- colors = c("black", "yellow", "darkred"),
- values = scales::rescale(c(0, 1, max(point_counts_df$count, na.rm = TRUE))),
- breaks = c(0, 1, max(point_counts_df$count, na.rm = TRUE)),
- na.value = "transparent"
- ) +
- geom_sf(data = sa_polygon, fill = NA, color = "gray", size = 0.5) +
- theme_minimal() +
- coord_sf() +
- labs(title = "Coordinate distribution and density")
-
-ggsave("coordinate_density.pdf", path = "plots/", device = "pdf", scale = 4)
-
-# ---------------------------------------#
-# Plot all presences in the dataset ####
-# ---------------------------------------#
diff --git a/R/03_03_model_data_finalization.R b/R/03_03_model_data_finalization.R
index 8631c4d..e0addfa 100644
--- a/R/03_03_model_data_finalization.R
+++ b/R/03_03_model_data_finalization.R
@@ -31,49 +31,5 @@ model_data = bind_rows(model_data_core, model_data_background) %>%
relocate(contains("fold"), .after = last_col()) %>%
mutate(species = as.factor(species))
-# Explore folds assignment
-cv_plot(spatial_folds)
-ggsave(filename = "plots/publication/global_folds_map.pdf", device = "pdf", scale = 2.5)
-
-folds_summary = model_data %>%
- dplyr::filter(record_type == "core") %>%
- sf::st_drop_geometry() %>%
- dplyr::group_by(species, fold_global) %>%
- dplyr::summarise(
- n_occ = length(which(present == 1)),
- n_abs = length(which(present == 0))
- )
-
-folds_uniform = folds_summary %>%
- dplyr::group_by(species, fold_global) %>%
- dplyr::summarise(
- is_uniform = n_occ == 0 | n_abs == 0
- ) %>%
- dplyr::group_by(species) %>%
- dplyr::summarise(
- folds_total = n(),
- folds_uniform = length(which(is_uniform)),
- folds_mixed = folds_total - folds_uniform
- ) %>%
- dplyr::ungroup()
-
-plot_1 = ggplot(folds_uniform, aes(x=folds_total)) +
- geom_histogram(binwidth=0.5, fill = "orange") +
- labs(title="Number of folds per species", x="Count", y = "Number of species") +
- theme_minimal()
-plot_2 = ggplot(folds_uniform, aes(x=folds_uniform)) +
- geom_histogram(binwidth=0.5, fill = "violet") +
- labs(title="Number of uniform folds per species", x="Count", y = "Number of species",
- subtitle="Uniform folds contain only absences or only presences and cannot be used in model evaluation.") +
- theme_minimal()
-plot_3 = ggplot(folds_uniform, aes(x=folds_mixed)) +
- geom_histogram(binwidth=0.5, fill = "blue") +
- labs(title="Number of mixed folds per species", x="Count", y = "Number of species",
- subtitle="Mixed folds both absences and presences and can be used in model evaluation.") +
- theme_minimal()
-
-plot_combined = grid.arrange(plot_1, plot_2, plot_3, ncol = 1)
-ggsave(filename = "plots/publication/global_folds_histogram.pdf", plot_combined, device = "pdf", height = 10, width = 7)
-
# Save Model data
-save(model_data, file = "data/r_objects/model_data.RData")
\ No newline at end of file
+save(model_data, file = "data/r_objects/model_data.RData")
diff --git a/R/03_04_dataset_exploration.R b/R/03_04_dataset_exploration.R
new file mode 100644
index 0000000..53a627a
--- /dev/null
+++ b/R/03_04_dataset_exploration.R
@@ -0,0 +1,95 @@
+library(tidyverse)
+library(sf)
+library(terra)
+
+source("R/utils.R")
+
+load("data/r_objects/model_data.RData")
+load("data/r_objects/sa_polygon.RData")
+
+sa_polygon = sf::st_transform(sa_polygon, crs(model_data))
+
+# ---------------------------------------#
+# Plot all presences in the dataset ####
+# ---------------------------------------#
+data_extent = ext(model_data)
+
+presences = model_data %>%
+ dplyr::filter(present == 1)
+
+template_raster <- terra::rast(data_extent, resolution = 1000) # 1000m = 1km
+crs(template_raster) <- st_crs(model_data)$wkt
+
+point_counts <- terra::rasterize(
+ vect(presences),
+ template_raster,
+ field = NULL,
+ fun = "count",
+ background = 0
+) %>%
+ terra::mask(vect(sa_polygon))
+
+length(which(values(point_counts) > 0)) # only 36858 raster cells contain at least 1 presence record
+
+ggplot() +
+ tidyterra::geom_spatraster(data = point_counts, maxcell = 5e7) +
+ scale_fill_gradientn(
+ colors = c("black", "yellow", "darkred"),
+ values = scales::rescale(c(0, 1, max(point_counts_df$count, na.rm = TRUE))),
+ breaks = c(0, 1, max(point_counts_df$count, na.rm = TRUE)),
+ na.value = "transparent"
+ ) +
+ geom_sf(data = sa_polygon, fill = NA, color = "gray", size = 0.5) +
+ theme_minimal() +
+ coord_sf() +
+ labs(title = "Coordinate distribution and density")
+
+ggsave("coordinate_density.pdf", path = "plots/", device = "pdf", scale = 4)
+
+# ---------------------------------------#
+# Explore folds assignment ####
+# ---------------------------------------#
+cv_plot(spatial_folds)
+ggsave(filename = "plots/publication/global_folds_map.pdf", device = "pdf", scale = 2.5)
+
+folds_summary = model_data %>%
+ dplyr::filter(record_type == "core") %>%
+ sf::st_drop_geometry() %>%
+ dplyr::group_by(species, fold_global) %>%
+ dplyr::summarise(
+ n_occ = length(which(present == 1)),
+ n_abs = length(which(present == 0))
+ )
+
+folds_uniform = folds_summary %>%
+ dplyr::group_by(species, fold_global) %>%
+ dplyr::summarise(
+ is_uniform = n_occ == 0 | n_abs == 0
+ ) %>%
+ dplyr::group_by(species) %>%
+ dplyr::summarise(
+ folds_total = n(),
+ folds_uniform = length(which(is_uniform)),
+ folds_mixed = folds_total - folds_uniform
+ ) %>%
+ dplyr::ungroup()
+
+plot_1 = ggplot(folds_uniform, aes(x=folds_total)) +
+ geom_histogram(binwidth=0.5, fill = "orange") +
+ labs(title="Number of folds per species", x="Count", y = "Number of species") +
+ theme_minimal()
+plot_2 = ggplot(folds_uniform, aes(x=folds_uniform)) +
+ geom_histogram(binwidth=0.5, fill = "violet") +
+ labs(title="Number of uniform folds per species", x="Count", y = "Number of species",
+ subtitle="Uniform folds contain only absences or only presences and cannot be used in model evaluation.") +
+ theme_minimal()
+plot_3 = ggplot(folds_uniform, aes(x=folds_mixed)) +
+ geom_histogram(binwidth=0.5, fill = "blue") +
+ labs(title="Number of mixed folds per species", x="Count", y = "Number of species",
+ subtitle="Mixed folds both absences and presences and can be used in model evaluation.") +
+ theme_minimal()
+
+plot_combined = grid.arrange(plot_1, plot_2, plot_3, ncol = 1)
+ggsave(filename = "plots/publication/global_folds_histogram.pdf", plot_combined, device = "pdf", height = 10, width = 7)
+
+
diff --git a/R/04_01_modelling_ssdm.R b/R/04_01_modelling_ssdm.R
index 77c25ec..a62cd3e 100644
--- a/R/04_01_modelling_ssdm.R
+++ b/R/04_01_modelling_ssdm.R
@@ -193,7 +193,7 @@ data_split = model_data %>%
for(pa_spec in data_split){
## Preparations ####
species = pa_spec$species[1]
- print(species)
+ print(as.character(species))
# Create inner CV folds for model training
cv_train = blockCV::cv_spatial(
@@ -231,7 +231,7 @@ for(pa_spec in data_split){
method = "ranger",
trControl = train_ctrl,
tuneLength = 8,
- weights = data_train$weight,
+ weights = pa_spec$weight,
verbose = F
)
save(rf_fit, file = paste0("data/r_objects/ssdm_results/full_models/", species, "_rf_fit.RData"))
@@ -246,7 +246,7 @@ for(pa_spec in data_split){
method = "gbm",
trControl = train_ctrl,
tuneLength = 8,
- weights = data_train$weight,
+ weights = pa_spec$weight,
verbose = F
)
save(gbm_fit, file = paste0("data/r_objects/ssdm_results/full_models/", species, "_gbm_fit.RData"))
@@ -259,7 +259,7 @@ for(pa_spec in data_split){
y = pa_spec$present_fct,
method = "gamSpline",
tuneLength = 8,
- weights = data_train$weight,
+ weights = pa_spec$weight,
trControl = train_ctrl
)
save(gam_fit, file = paste0("data/r_objects/ssdm_results/full_models/", species, "_gam_fit.RData"))
@@ -277,7 +277,7 @@ for(pa_spec in data_split){
burnin = 200L,
early_stopping = 25,
lr = 0.001,
- batchsize = min(ceiling(nrow(data_train)/10), 64),
+ batchsize = min(ceiling(nrow(pa_spec)/10), 64),
dropout = 0.25,
optimizer = config_optimizer("adam"),
validation = 0.2,
diff --git a/R/04_04_modelling_msdm_rf.R b/R/04_04_modelling_msdm_rf.R
index 4544b73..1e03f70 100644
--- a/R/04_04_modelling_msdm_rf.R
+++ b/R/04_04_modelling_msdm_rf.R
@@ -67,7 +67,7 @@ rf_fit = caret::train(
metric = "Accuracy",
trControl = train_ctrl,
tuneLength = 8,
- weights = full_data$weight,
+ weights = model_data$weight,
num.threads = 48
)
diff --git a/R/05_01_performance_report.qmd b/R/05_01_performance_report.qmd
index 8fb626b..38096cb 100644
--- a/R/05_01_performance_report.qmd
+++ b/R/05_01_performance_report.qmd
@@ -12,9 +12,9 @@ library(plotly)
library(DT)
load("../data/r_objects/model_data.RData")
-#load("../data/r_objects/ssdm_performance.RData")
+load("../data/r_objects/ssdm_performance.RData")
load("../data/r_objects/msdm_embed_performance.RData")
-#load("../data/r_objects/msdm_onehot_performance.RData")
+load("../data/r_objects/msdm_onehot_performance.RData")
load("../data/r_objects/msdm_rf_performance.RData")
load("../data/r_objects/range_maps.RData")
@@ -55,11 +55,32 @@ loess_fit = function(x, y, span = 0.75){
)
}
+# Determine valid folds for evaluation
+# A fold is valid if there are at least n presences in the training and validation data
+obs_fold = model_data %>%
+ sf::st_drop_geometry() %>%
+ dplyr::filter(record_type == "core", present == 1) %>%
+ dplyr::group_by(species, fold_global) %>%
+ dplyr::summarise(obs_fold = n())
+
+obs_total = model_data %>%
+ sf::st_drop_geometry() %>%
+ dplyr::filter(record_type == "core", present == 1) %>%
+ dplyr::group_by(species) %>%
+ dplyr::summarise(obs_total = n())
+
+folds_valid = obs_fold %>%
+ dplyr::left_join(obs_total) %>%
+ dplyr::mutate(obs_train = obs_total-obs_fold) %>%
+ dplyr::filter(obs_train > 5 & obs_fold > 5) %>%
+ dplyr::select(species, fold_global)
+
# Performance table
-performance = msdm_embed_performance %>%
- #bind_rows(msdm_embed_performance) %>%
- #bind_rows(msdm_onehot_performance) %>%
+performance = ssdm_performance %>%
+ bind_rows(msdm_embed_performance) %>%
+ bind_rows(msdm_onehot_performance) %>%
bind_rows(msdm_rf_performance) %>%
+ semi_join(folds_valid, by = c("species", "fold_global")) %>%
dplyr::group_by(species, model, metric) %>%
dplyr::summarise(value = mean(value, na.rm = T)) %>%
dplyr::mutate(
@@ -199,7 +220,7 @@ df_join = model_data %>%
dplyr::summarise(n_obs = n())
performance = performance %>%
- dplyr::left_join(df_join, by = "species")
+ dplyr::inner_join(df_join, by = "species")
# Range size
df_join = range_maps %>%
@@ -377,7 +398,7 @@ Range size was calculated based on polygon layers from the IUCN Red List of Thre
```{r echo = FALSE}
df_plot = dplyr::filter(performance, metric == "auc")
-plot = plot_performance(df_plot, y_var = "value", y_label = "AUC", x_var = "range_size", x_label = "Range size [km]")
+plot = plot_performance(df_plot, y_var = "value", y_label = "AUC", x_var = "range_size", x_label = "Range size [km²]")
bslib::card(plot, full_screen = T)
```
@@ -385,7 +406,7 @@ bslib::card(plot, full_screen = T)
```{r echo = FALSE}
df_plot = dplyr::filter(performance, metric == "f1")
-plot = plot_performance(df_plot, y_var = "value", y_label = "F1", x_var = "range_size", x_label = "Range size [km]")
+plot = plot_performance(df_plot, y_var = "value", y_label = "F1", x_var = "range_size", x_label = "Range size [km²]")
bslib::card(plot, full_screen = T)
```
@@ -393,7 +414,7 @@ bslib::card(plot, full_screen = T)
```{r echo = FALSE}
df_plot = dplyr::filter(performance, metric == "kappa")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Kappa", x_var = "range_size", x_label = "Range size [km]")
+plot = plot_performance(df_plot, y_var = "value", y_label = "Kappa", x_var = "range_size", x_label = "Range size [km²]")
bslib::card(plot, full_screen = T)
```
@@ -401,7 +422,7 @@ bslib::card(plot, full_screen = T)
```{r echo = FALSE}
df_plot = dplyr::filter(performance, metric == "accuracy")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Accuracy", x_var = "range_size", x_label = "Range size [km]")
+plot = plot_performance(df_plot, y_var = "value", y_label = "Accuracy", x_var = "range_size", x_label = "Range size [km²]")
bslib::card(plot, full_screen = T)
```
@@ -409,7 +430,7 @@ bslib::card(plot, full_screen = T)
```{r echo = FALSE}
df_plot = dplyr::filter(performance, metric == "precision")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Precision", x_var = "range_size", x_label = "Range size [km]")
+plot = plot_performance(df_plot, y_var = "value", y_label = "Precision", x_var = "range_size", x_label = "Range size [km²]")
bslib::card(plot, full_screen = T)
```
@@ -417,7 +438,7 @@ bslib::card(plot, full_screen = T)
```{r echo = FALSE}
df_plot = dplyr::filter(performance, metric == "recall")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Recall", x_var = "range_size", x_label = "Range size [km]")
+plot = plot_performance(df_plot, y_var = "value", y_label = "Recall", x_var = "range_size", x_label = "Range size [km²]")
bslib::card(plot, full_screen = T)
```
:::
@@ -430,7 +451,7 @@ bslib::card(plot, full_screen = T)
```{r echo = FALSE}
df_plot = dplyr::filter(performance, metric == "auc") %>%
add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "AUC (partial residual)", x_var = "range_size", x_label = "Range size [km]")
+plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "AUC (partial residual)", x_var = "range_size", x_label = "Range size [km²]")
bslib::card(plot, full_screen = T)
```
diff --git a/R/05_01_performance_report_rf.qmd b/R/05_01_performance_report_rf.qmd
deleted file mode 100644
index 534a5f8..0000000
--- a/R/05_01_performance_report_rf.qmd
+++ /dev/null
@@ -1,692 +0,0 @@
----
-title: "SDM Performance report"
-format: html
-editor: visual
-engine: knitr
----
-
-```{r init, echo = FALSE, include = FALSE}
-library(tidyverse)
-library(sf)
-library(plotly)
-library(DT)
-
-load("../data/r_objects/model_data.RData")
-load("../data/r_objects/range_maps.RData")
-load("../data/r_objects/range_maps_gridded.RData")
-load("../data/r_objects/occs_final.RData")
-load("../data/r_objects/functional_groups.RData")
-
-sf::sf_use_s2(use_s2 = FALSE)
-
-# Load performance of different RF versions
-load("../data/r_objects/deprecated/msdm_rf_no_species_random_abs_performance.RData")
-load("../data/r_objects/deprecated/msdm_rf_no_species_performance.RData")
-load("../data/r_objects/msdm_rf_performance.RData")
-
-msdm_rf_no_species_performance$model = "rf_noSpec_oldVars_informedAbs"
-msdm_rf_no_species_performance$metric = tolower(msdm_rf_no_species_performance$metric)
-msdm_rf_no_species_random_abs_performance$model = "rf_noSpec_oldVars_randomAbs"
-msdm_rf_no_species_random_abs_performance$metric = tolower(msdm_rf_no_species_random_abs_performance$metric)
-msdm_rf_performance$model = "rf_noSpec_newVars_randomAbs"
-```
-
-```{r globals, echo = FALSE, cache = TRUE, include = FALSE}
-# Regression functions
-asym_fit = function(x, y){
- nls_fit = nls(y ~ 1 - (1-b) * exp(-c * log(x)), start = list(b = 0.1, c = 0.1))
- new_x = exp(seq(log(min(x)), log(max(x)), length.out = 100))
- data.frame(
- x = new_x,
- fit = predict(nls_fit, newdata = data.frame(x = new_x))
- )
-}
-
-glm_fit = function(x, y, family = "binomial"){
- glm_fit = suppressWarnings(glm(y~x, family = family))
- new_x = seq(min(x), max(x), length.out = 100)
- data.frame(
- x = new_x,
- fit = predict(glm_fit, newdata = data.frame(x = new_x), type = "response")
- )
-}
-
-loess_fit = function(x, y, span = 0.75){
- df = data.frame(x = x, y = y)
- loess_fit = loess(y ~ x, data = df, span = span)
- new_x = seq(min(x), max(x), length.out = 100)
- data.frame(
- x = new_x,
- fit = predict(loess_fit, newdata = data.frame(x = new_x))
- )
-}
-
-# Performance table
-performance = msdm_rf_performance %>%
- bind_rows(msdm_rf_no_species_performance) %>%
- bind_rows(msdm_rf_no_species_random_abs_performance) %>%
- dplyr::group_by(species, model, metric) %>%
- dplyr::summarise(value = mean(value, na.rm = F)) %>%
- dplyr::mutate(
- value = case_when(
- ((is.na(value) | is.nan(value)) & metric %in% c("auc", "f1", "accuracy", "precision", "recall")) ~ 0.5,
- ((is.na(value) | is.nan(value)) & metric %in% c("kappa")) ~ 0,
- .default = value
- )
- ) %>%
- ungroup()
-
-plot_performance = function(df_plot, y_var, y_label, x_var, x_label, x_log = T, reg_func = loess_fit) {
- df_plot = df_plot %>%
- dplyr::rename(
- x_var = !!x_var,
- y_var = !!y_var
- )
-
- # Calculate regression lines for each model and metric combination
- suppressWarnings({
- regression_lines = df_plot %>%
- group_by(model) %>%
- group_modify( ~ reg_func(.x[["x_var"]], .x[["y_var"]]))
- })
-
- # Create base plot
- plot <- plot_ly() %>%
- layout(
- title = paste0("Model Performance vs. ", x_label),
- xaxis = list(title = x_label, type = ifelse(x_log, "log", "linear")),
- yaxis = list(title = y_label),
- legend = list(x = 1.1, y = 0.5), # Move legend to the right of the plot
- margin = list(r = 150), # Add right margin to accommodate legend
- hovermode = 'closest'
- )
-
- # Points
- for (model_name in unique(df_plot$model)) {
- plot = plot %>%
- add_markers(
- data = filter(df_plot, model == model_name),
- x = ~ x_var,
- y = ~ y_var,
- color = model_name, # Set color to match legendgroup
- legendgroup = model_name,
- opacity = 0.6,
- name = ~ model,
- hoverinfo = 'text',
- text = ~ paste(
- "Species:", species, "<br>", x_label, ":", x_var, "<br>Value:", round(y_var, 3)
- )
- )
- }
-
- # Add regression lines
- for (model_name in unique(df_plot$model)) {
- reg_data = dplyr::filter(regression_lines, model == model_name)
- plot = plot %>%
- add_lines(
- data = reg_data,
- x = ~ x,
- y = ~ fit,
- color = model_name, # Set color to match legendgroup
- legendgroup = model_name,
- name = paste(model_name, '(fit)'),
- showlegend = FALSE
- )
- }
-
- return(plot)
-}
-
-add_partial_residuals = function(df, focus_var, control_vars, family = quasibinomial) {
- model_formula <- as.formula(paste("value ~", paste(c(focus_var, control_vars), collapse = " + ")))
- model = glm(model_formula, data = df, family = family)
-
- if (focus_var %in% names(model$coefficients)) {
- df[[paste0(focus_var, "_partial")]] = residuals(model, type = "response") + model$coefficients[focus_var] * df[[focus_var]]
- }
-
- return(df)
-}
-
-```
-
-## Summary
-
-This document contrasts the performance of different variations of random forest models.
-
-## Analysis
-
-### Quantify drivers of model performance
-
-```{r prepare_model_df, echo = FALSE, include = FALSE}
-# Number of records
-df_join = model_data %>%
- sf::st_drop_geometry() %>%
- dplyr::filter(present == 1) %>%
- dplyr::group_by(species) %>%
- dplyr::summarise(n_obs = n())
-
-performance = performance %>%
- dplyr::left_join(df_join, by = "species")
-
-# Range size
-df_join = range_maps %>%
- sf::st_transform("+proj=tcea +lon_0=-58.0704167 +datum=WGS84 +units=m +no_defs") %>%
- dplyr::mutate(range_size = as.numeric(sf::st_area(range_maps) / 1000000)) %>% # range in sqkm
- sf::st_drop_geometry()
-
-performance = performance %>%
- inner_join(df_join, by = c("species" = "name_matched"))
-
-# Range coverage
-range_maps_gridded = range_maps_gridded %>%
- sf::st_transform("+proj=tcea +lon_0=-58.0704167 +datum=WGS84 +units=m +no_defs")
-
-df_cells_total = range_maps_gridded %>%
- dplyr::rename("species" = name_matched) %>%
- group_by(species) %>%
- summarise(cells_total = n()) %>%
- st_drop_geometry()
-
-df_cells_occ = range_maps_gridded %>%
- st_join(occs_final, join = st_intersects) %>%
- filter(name_matched == species) %>% # Filter only intersections of the same species
- group_by(species) %>%
- summarise(cells_occupied = n_distinct(geometry)) %>%
- st_drop_geometry()
-
-df_join = df_cells_total %>%
- dplyr::inner_join(df_cells_occ, by = "species") %>%
- dplyr::mutate(range_cov = cells_occupied / cells_total) %>%
- dplyr::select(species, range_cov)
-
-performance = performance %>%
- inner_join(df_join, by = "species")
-
-# Range coverage bias
-df_occs_total = occs_final %>%
- st_drop_geometry() %>%
- group_by(species) %>%
- summarise(occs_total = n())
-
-df_join = df_occs_total %>%
- dplyr::inner_join(df_cells_total, by = "species") %>%
- dplyr::inner_join(df_cells_occ, by = "species") %>%
- dplyr::mutate(range_bias = 1-((cells_occupied / cells_total) / pmin(occs_total / cells_total, 1))) %>%
- dplyr::select(species, range_bias)
-
-performance = performance %>%
- inner_join(df_join, by = "species")
-```
-
-### Number of records
-
-::: panel-tabset
-#### Marginal Effects
-
-::: panel-tabset
-##### AUC
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "auc")
-plot = plot_performance(df_plot, y_var = "value", y_label = "AUC", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### F1
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "f1")
-plot = plot_performance(df_plot, y_var = "value", y_label = "F1", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### Cohen's kappa
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "kappa")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Kappa", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### Accuracy
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "accuracy")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Accuracy", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### Precision
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "precision")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Precision", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### Recall
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "recall")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Recall", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-:::
-
-#### Partial Effects
-
-::: panel-tabset
-##### AUC
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "auc") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "AUC (partial residual)", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### F1
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "f1") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "F1 (partial residual)", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### Cohen's kappa
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "kappa") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"), family = gaussian)
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Kappa (partial residual)", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### Accuracy
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "accuracy") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Accuracy (partial residual)", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### Precision
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "precision") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Precision (partial residual)", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-
-##### Recall
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "recall") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Recall (partial residual)", x_var = "n_obs", x_label = "Number of records")
-bslib::card(plot, full_screen = T)
-```
-:::
-:::
-
-### Range size
-
-Range size was calculated based on polygon layers from the IUCN Red List of Threatened Species (2016).
-
-::: panel-tabset
-#### Marginal Effects
-
-::: panel-tabset
-##### AUC
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "auc")
-plot = plot_performance(df_plot, y_var = "value", y_label = "AUC", x_var = "range_size", x_label = "Range size [km]")
-bslib::card(plot, full_screen = T)
-```
-
-##### F1
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "f1")
-plot = plot_performance(df_plot, y_var = "value", y_label = "F1", x_var = "range_size", x_label = "Range size [km]")
-bslib::card(plot, full_screen = T)
-```
-
-##### Cohen's kappa
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "kappa")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Kappa", x_var = "range_size", x_label = "Range size [km]")
-bslib::card(plot, full_screen = T)
-```
-
-##### Accuracy
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "accuracy")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Accuracy", x_var = "range_size", x_label = "Range size [km]")
-bslib::card(plot, full_screen = T)
-```
-
-##### Precision
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "precision")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Precision", x_var = "range_size", x_label = "Range size [km]")
-bslib::card(plot, full_screen = T)
-```
-
-##### Recall
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "recall")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Recall", x_var = "range_size", x_label = "Range size [km]")
-bslib::card(plot, full_screen = T)
-```
-:::
-
-#### Partial Effects
-
-::: panel-tabset
-##### AUC
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "auc") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "AUC (partial residual)", x_var = "range_size", x_label = "Range size [km]")
-bslib::card(plot, full_screen = T)
-```
-
-##### F1
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "f1") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "F1 (partial residual)", x_var = "range_size", x_label = "Range size [km²]")
-bslib::card(plot, full_screen = T)
-```
-
-##### Cohen's kappa
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "kappa") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"), family = gaussian)
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Kappa (partial residual)", x_var = "range_size", x_label = "Range size [km²]")
-bslib::card(plot, full_screen = T)
-```
-
-##### Accuracy
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "accuracy") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Accuracy (partial residual)", x_var = "range_size", x_label = "Range size [km²]")
-bslib::card(plot, full_screen = T)
-```
-
-##### Precision
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "precision") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Precision (partial residual)", x_var = "range_size", x_label = "Range size [km²]")
-bslib::card(plot, full_screen = T)
-```
-
-##### Recall
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "recall") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Recall (partial residual)", x_var = "range_size", x_label = "Range size [km²]")
-bslib::card(plot, full_screen = T)
-```
-:::
-:::
-
-### Range coverage
-
-Species ranges were split into continuous hexagonal grid cells of 1 degree diameter. Range coverage was then calculated as the number of grid cells containing at least one occurrence record divided by the number of total grid cells.
-
-$$
-RangeCoverage = \frac{N_{cells\_occ}}{N_{cells\_total}}
-$$
-
-::: panel-tabset
-#### Marginal Effects
-
-::: panel-tabset
-##### AUC
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "auc")
-plot = plot_performance(df_plot, y_var = "value", y_label = "AUC", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### F1
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "f1")
-plot = plot_performance(df_plot, y_var = "value", y_label = "F1", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### Cohen's kappa
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "kappa")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Kappa", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### Accuracy
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "accuracy")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Accuracy", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### Precision
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "precision")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Precision", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### Recall
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "recall")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Recall", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-:::
-
-#### Partial Effects
-
-::: panel-tabset
-##### AUC
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "auc") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "AUC (partial residual)", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### F1
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "f1") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "F1 (partial residual)", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### Cohen's kappa
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "kappa") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"), family = gaussian)
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Kappa (partial residual)", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### Accuracy
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "accuracy") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Accuracy (partial residual)", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### Precision
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "precision") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Precision (partial residual)", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-
-##### Recall
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "recall") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Recall (partial residual)", x_var = "range_cov", x_label = "Range coverage")
-bslib::card(plot, full_screen = T)
-```
-:::
-:::
-
-### Range coverage bias
-
-Range coverage bias was calculated as 1 minus the ratio of the actual range coverage and the hypothetical range coverage if all observations were maximally spread out across the range.
-
-$$
-RangeCoverageBias = 1 - \frac{RangeCoverage}{min({N_{obs\_total}} / {N_{cells\_total}}, 1)}
-$$
-
-Higher bias values indicate that occurrence records are spatially more clustered within the range of the species.
-
-::: panel-tabset
-#### Marginal Effects
-
-::: panel-tabset
-##### AUC
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "auc")
-plot = plot_performance(df_plot, y_var = "value", y_label = "AUC", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### F1
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "f1")
-plot = plot_performance(df_plot, y_var = "value", y_label = "F1", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### Cohen's kappa
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "kappa")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Kappa", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### Accuracy
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "accuracy")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Accuracy", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### Precision
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "precision")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Precision", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### Recall
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "recall")
-plot = plot_performance(df_plot, y_var = "value", y_label = "Recall", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-:::
-
-#### Partial Effects
-
-::: panel-tabset
-##### AUC
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "auc") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "AUC (partial residual)", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### F1
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "f1") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "F1 (partial residual)", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### Cohen's kappa
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "kappa") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"), family = gaussian)
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Kappa (partial residual)", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### Accuracy
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "accuracy") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Accuracy (partial residual)", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### Precision
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "precision") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Precision (partial residual)", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-
-##### Recall
-
-```{r echo = FALSE}
-df_plot = dplyr::filter(performance, metric == "recall") %>%
- add_partial_residuals(focus_var = "n_obs", control_vars = c("range_size", "range_cov", "range_bias"))
-plot = plot_performance(df_plot, y_var = "n_obs_partial", y_label = "Recall (partial residual)", x_var = "range_bias", x_label = "Range coverage bias")
-bslib::card(plot, full_screen = T)
-```
-:::
-:::
diff --git a/R/05_02_publication_analysis.R b/R/05_02_publication_analysis.R
index 4d347c4..8f9bc5e 100644
--- a/R/05_02_publication_analysis.R
+++ b/R/05_02_publication_analysis.R
@@ -242,10 +242,10 @@ raster_data = terra::rast(raster_filepaths) %>%
terra::crop(sf::st_bbox(sa_polygon)) %>%
terra::project(sf::st_crs(model_data)$input)
-specs = c("Histiotus velatus", "Hydrochoerus hydrochaeris", "Euryoryzomys legatus", "Eumops trumbulli")
+specs = c("Aotus lemurinus")
for(spec in specs){
pdf(file = paste0("plots/range_predictions/", spec, ".pdf"), width = 12)
- plots = plot_predictions(spec, model_data, raster_data, algorithms = c("gam", "gbm", "rf", "msdm_onehot", "msdm_embed", "msdm_rf"))
+ plots = plot_predictions(spec, model_data, raster_data, algorithms = c("gam", "gbm", "rf"))
lapply(plots, print)
dev.off()
}
diff --git a/R/_publish.yml b/R/_publish.yml
index 5604c70..1da1e87 100644
--- a/R/_publish.yml
+++ b/R/_publish.yml
@@ -2,8 +2,6 @@
quarto-pub:
- id: 77b30762-b473-447d-be18-d4bbd6261fbf
url: 'https://chrkoenig.quarto.pub/sdm-performance-report'
- - id: 7777d450-82af-4364-b4c1-b52d7139ade0
- url: 'https://chrkoenig.quarto.pub/rf-nospec-performance-report'
- source: 05_01_performance_report_rf.qmd
quarto-pub:
- id: 98e776a5-fb0b-41f9-8733-446bf7ffb272
diff --git a/README.md b/README.md
index aaaa50c..a26b747 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,62 @@
-# Symobio-modeling
+# Codebase Documentation
-Code and data accompanying SSDM vs MSDM model comparison
\ No newline at end of file
+This repository implements a species distribution modeling comparison study for about 600 South American mammal species. Specifically, the
+
+## Project Structure
+
+- **`R/`**: Contains all the R scripts organized by workflow steps.
+- **`Symobio_modeling.Rproj`**: RStudio project file for easy navigation.
+- **`README.md`**: High-level overview of the project.
+- **`renv/`**: Manages package dependencies for reproducibility.
+- **`renv.lock`**: Lockfile for `renv` to ensure consistent package versions.
+
+## Workflow Overview
+
+The workflow is divided into several stages, each represented by scripts in the `R/` directory. Below is a summary of the key steps:
+
+### 1. Preparation of Geographic Data
+- **`01_01_range_map_preparation.R`**: Processes IUCN mammal range maps for South America, converting them to a standardized raster format with consistent projection for downstream analysis.
+- **`01_02_raster_preparation.R`**: Prepares environmental predictor rasters (e.g., climate, elevation, land cover) by cropping to South America extent, resampling to consistent resolution, and performing any necessary transformations.
+
+### 2. Preparation of complementary species-level data
+
+
+- **`02_01_functional_group_assignment.R`**: Assigns mammal species to functional groups based on diet, locomotion, and body size characteristics, creating categorical variables for modeling.
+- **`02_02_functional_traits_preparation.R`**: Cleans and standardizes continuous trait data (body mass, diet breadth, etc.) for all study species, handling missing values through imputation where necessary.
+- **`02_03_phylo_preparation.R`**: Extracts phylogenetic information for target mammal species, computes phylogenetic distance matrices, and prepares the data for inclusion in models.
+
+### 3. Preparation of Presence/Absence Data
+- **`03_01_presence_preparation.R`**: Processes occurrence records from GBIF and other sources, applies spatial filtering to reduce sampling bias, and aligns taxonomic nomenclature.
+- **`03_02_absence_preparation.R`**: Generates pseudo-absence points using a stratified random approach, with constraints based on environmental conditions and range map boundaries.
+- **`03_03_dataset_exploration.R`**: Produces descriptive statistics and visualizations of presence/absence data, environmental variables, and species coverage to assess data quality.
+- **`03_04_model_data_finalization.R`**: Merges all prepared datasets (occurrences, absences, predictors) into final modeling datasets, splits data into training/testing sets, and applies any necessary scaling or transformations.
+
+### 4. Modeling
+- **`04_01_modelling_ssdm.R`**: Implements traditional single-species distribution modeling (SSDM) approaches with selected algorithms (e.g., MaxEnt, random forests), including hyperparameter tuning.
+- **`04_02_modelling_msdm_embed.R`**: Develops multi-species distribution models using neural network approaches that embed species identities into a latent space, capturing inter-species relationships.
+- **`04_03_modelling_msdm_onehot.R`**: Implements multi-species distribution models using one-hot encoding for species identities, enabling joint prediction across all species simultaneously.
+- **`04_04_modelling_msdm_rf.R`**: Implements random forest-based multi-species distribution modeling, incorporating species identity as a predictor variable alongside environmental variables.
+
+### 5. Analysis
+- **`05_01_performance_report.qmd`**: Generates comprehensive reports on model performance metrics (AUC, TSS, etc.) for all modeling approaches, with visualizations comparing performance across species and methods.
+- **`05_02_publication_analysis.qmd`**: Conducts advanced statistical analyses of model results, creates publication-quality figures, and summarizes findings for manuscript preparation.
+
+## Miscellaneous
+- **`utils.R`**: Contains utility functions used across multiple scripts, including data processing helpers, custom evaluation metrics, and visualization functions.
+
+### Miscellaneous
+- **`utils.R`**:
+
+## Getting Started
+
+1. Clone the repository and open the `Symobio_modeling.Rproj` file in RStudio.
+2. Restore the project environment using `renv`:
+ ```r
+ renv::restore()
+ ```
+3. Run the scripts in the R/ directory sequentially. Some scripts, especially for model fitting, may run a long time and benefit from powerful hardware.
+
+## Additional Notes
+- Ensure that all required input data (e.g., range maps, raster files) is available in the expected directories.
+- Outputs from each script are typically saved to disk and used as inputs for subsequent scripts.
+- Refer to the README.md file for any additional project-specific instructions.
diff --git a/occurrences.png b/occurrences.png
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--
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