diff --git a/R/03_01_presence_preparation.R b/R/03_01_presence_preparation.R
new file mode 100644
index 0000000000000000000000000000000000000000..c8705b9874b716b87aa552304ea415c625a3b27b
--- /dev/null
+++ b/R/03_01_presence_preparation.R
@@ -0,0 +1,68 @@
+# General packages
+library(dplyr)
+library(tidyr)
+library(furrr)
+
+# Geo packages
+library(terra)
+library(CoordinateCleaner)
+library(sf)
+
+# DB packages
+library(Symobio)
+library(DBI)
+
+source("R/utils.R")
+
+con = db_connect() # Connection to Symobio
+sf::sf_use_s2(use_s2 = FALSE) # Don't use spherical geometry
+
+# ---------------------------------------------------------------------------#
+# Prepare Geodata ####
+# ---------------------------------------------------------------------------#
+# TODO: finalize predictor choice
+raster_filepaths = list.files("~/share/groups/mas_data/00_data/processed/CHELSA_v2-1_bioclim/", pattern = ".tif$", full.names = T) %>%
+ stringr::str_sort(numeric = T)
+
+sa_polygon = rnaturalearth::ne_countries() %>%
+ dplyr::filter(continent == "South America") %>%
+ sf::st_union()
+
+# ---------------------------------------------------------------------------#
+# Prepare Occurrence Data ####
+# ---------------------------------------------------------------------------#
+load("data/r_objects/range_maps.RData")
+target_species = unique(range_maps$name_matched[!is.na(range_maps$name_matched)])
+
+# Query species from Symobio
+occs = tbl(con, "species_occurrences") %>%
+ dplyr::filter(species %in% target_species) %>%
+ dplyr::select(-year) %>%
+ dplyr::distinct() %>%
+ collect() %>%
+ sf::st_as_sf(coords = c("longitude", "latitude"), remove = F, crs = sf::st_crs(4326)) %>%
+ sf::st_filter(sa_polygon)
+
+# Flag occurrences using Coordinate cleaner
+occs_flagged = occs %>%
+ dplyr::distinct(species, coordinate_id, longitude, latitude) %>%
+ group_by(species) %>%
+ group_split() %>%
+ purrr::map( # Loop over species individually due to bug in CoordinateCleaner
+ CoordinateCleaner::clean_coordinates,
+ lon = "longitude",
+ lat = "latitude",
+ tests = c("centroids", "gbif", "equal", "institutions", "outliers"),
+ outliers_method = "quantile",
+ verbose = F
+ ) %>%
+ bind_rows() %>%
+ dplyr::filter(.summary == T) %>%
+ dplyr::select(species, coordinate_id, longitude, latitude)
+
+# Finalize and save occurrence dataset
+occs_final = occs %>%
+ inner_join(occs_flagged, by = c("species", "coordinate_id", "longitude", "latitude")) %>%
+ dplyr::select(-coordinate_id)
+
+save(occs_final, file = "data/r_objects/occs_final.RData")
\ No newline at end of file
diff --git a/R/03_02_absence_preparation.R b/R/03_02_absence_preparation.R
new file mode 100644
index 0000000000000000000000000000000000000000..b72d132a3cd8bb01ef3f856fcf55e22d510b9f3f
--- /dev/null
+++ b/R/03_02_absence_preparation.R
@@ -0,0 +1,174 @@
+# General packages
+library(dplyr)
+library(tidyr)
+library(furrr)
+
+# Geo packages
+library(terra)
+library(CoordinateCleaner)
+library(sf)
+library(MASS)
+library(geos)
+
+# DB packages
+library(Symobio)
+library(DBI)
+
+source("R/utils.R")
+
+load("data/r_objects/range_maps.RData")
+load("data/r_objects/occs_final.RData")
+
+# ---------------------------------------------------------------------------#
+# Prepare Geodata ####
+# ---------------------------------------------------------------------------#
+target_species = unique(occs_final$species)
+
+sa_polygon = rnaturalearth::ne_countries() %>%
+ dplyr::filter(continent == "South America") %>%
+ sf::st_union()
+
+raster_filepaths = list.files("~/share/groups/mas_data/00_data/processed/CHELSA_v2-1_bioclim/", pattern = ".tif$", full.names = T) %>% # TODO: finalize predictor choice
+ stringr::str_sort(numeric = T)
+
+raster_data = terra::rast(raster_filepaths) %>%
+ terra::crop(sf::st_bbox(sa_polygon))
+
+# Project (proj string generated with SA bbox coordinates on https://projectionwizard.org)
+proj_string = "+proj=tcea +lon_0=-58.0704167 +datum=WGS84 +units=m +no_defs"
+
+raster_data = terra::project(raster_data, proj_string)
+sa_polygon = st_transform(sa_polygon, proj_string)
+occs_final = st_transform(occs_final, proj_string)
+range_maps = st_transform(range_maps, proj_string)
+
+# ---------------------------------------------------------------------------#
+# 1. Distance-based sampling ####
+# #
+# Absences should be predominantly sampled from regions that are #
+# geographically close (reproduce sampling biases) but environmentally #
+# dissimilar (avoid false negatives) to the known occurrences #
+# ---------------------------------------------------------------------------#
+# Geographic distance
+future::plan("multisession", workers = 16)
+pseudo_absences = furrr::future_map(
+ .x = target_species,
+ .options = furrr::furrr_options(seed = 42),
+ .f = function(species){
+ # Prepare occurrences
+ occs_spec = dplyr::filter(occs_final, species == !!species)
+
+ occs_poly = occs_spec %>%
+ summarise(geometry = st_combine(geometry)) %>%
+ st_concave_hull(ratio = 0.5)
+
+ occs_bff = occs_spec %>%
+ st_buffer(10000) %>% # Buffer by 10 kilometers as exclusion radius for absence sampling
+ st_union()
+
+ range_spec = dplyr::filter(range_maps, name_matched == !!species) %>%
+ rename(species = name_matched)
+
+ # Define model/sampling region
+ sampling_region = st_union(range_spec, occs_poly) %>% # Union occs poly and expert range
+ st_buffer(dist = 100000) %>% # Buffer by 100 km to extend sampling region into new environments
+ st_intersection(sa_polygon) %>% # Crop to South America
+ st_difference(occs_bff) # Crop out buffered presences
+
+ # Define KDE
+ ref = st_bbox(sampling_region)[c(1,3,2,4)]
+ min_extent = min(ref[2]-ref[1], ref[4]-ref[3])
+ occs_kde = spatialEco::sf.kde(occs_spec, bw = min_extent/2, res = 10000, ref = ref, standardize = T, scale.factor = 1) %>%
+ crop(sampling_region, mask = T, touches = F)
+
+ # Sampling of pseudo absence
+ abs_spec = st_sf(geometry = st_sfc(), crs = proj_string)
+ while(nrow(abs_spec) < nrow(occs_spec)){
+ sample_points_raw = st_sample(sampling_region, nrow(occs_spec))
+ p = extract(occs_kde, vect(sample_points_raw))$z
+ x = runif(length(p))
+
+ sample_points = sample_points_raw[x<p]
+ samples_required = nrow(occs_spec) - nrow(abs_spec)
+
+ if(length(sample_points) > samples_required){
+ sample_points = sample(sample_points, samples_required)
+ }
+
+ abs_spec = bind_rows(abs_spec, st_sf(geometry = sample_points))
+ }
+
+ plot(sa_polygon)
+ plot(occs_kde, add = T, legend = F)
+ plot(range_spec, add = T, col = "#00000000")
+ plot(abs_spec, add = T, col = "black", pch = 3, cex = 0.5)
+ plot(occs_spec, add = T, col = "white", pch = 3, cex = 0.5)
+
+
+ sample_points = st_as_sf(st_sample(sample_region, nrow(abs_spec))) %>%
+ dplyr::mutate(
+ species = occs_spec$species[1],
+ longitude = sf::st_coordinates(.)[,1],
+ latitude = sf::st_coordinates(.)[,2]
+ ) %>%
+ dplyr::rename(geometry = "x")
+
+ abs_spec = terra::rast(raster_filepaths) %>%
+ setNames(paste0("layer_", 1:19)) %>%
+ terra::extract(sample_points) %>%
+ dplyr::select(-ID) %>%
+ dplyr::mutate(
+ present = 0,
+ geometry = sample_points$x
+ ) %>%
+ tibble() %>%
+ bind_cols(sample_points)
+
+ # Create presence-absence dataframe
+ pa_spec = occs_spec %>%
+ dplyr::mutate(present = 1) %>%
+ bind_rows(abs_spec)
+
+ # Split into train and test datasets
+ train_index = createDataPartition(pa_spec$present, p = 0.7, list = FALSE)
+ pa_spec$train = 0
+ pa_spec$train[train_index] = 1
+
+ # Define cross-validation folds
+ folds = tryCatch({
+ spatial_folds = suppressMessages(
+ blockCV::cv_spatial(
+ pa_spec,
+ column = "present",
+ k = 5,
+ progress = F, plot = F, report = F
+ )
+ )
+
+ spatial_folds$folds_ids
+ }, warning = function(w){
+ NA
+ }, error = function(e){
+ NA
+ })
+
+ pa_spec$folds = folds
+ pa_spec$geometry = NULL
+
+ return(pa_spec)
+ })
+
+model_data = bind_rows(model_data)
+save(model_data, file = "data/r_objects/model_data.RData")
+
+
+
+
+ggplot() +
+ geom_sf(data = sa_polygon, fill = "red") +
+ geom_sf(data = range_spec, fill = "green") +
+ geom_sf(data = occs_spec) +
+ # facet_wrap("species", nrow = 2) +
+ # theme_minimal()
+ #
+
\ No newline at end of file
diff --git a/R/03_presence_absence_preparation.R b/R/03_presence_absence_preparation.R
deleted file mode 100644
index 9fd817c71fa460cf27c87aa9d023296999392b67..0000000000000000000000000000000000000000
--- a/R/03_presence_absence_preparation.R
+++ /dev/null
@@ -1,160 +0,0 @@
-# General packages
-library(dplyr)
-library(tidyr)
-library(furrr)
-
-# Geo packages
-library(terra)
-library(CoordinateCleaner)
-library(sf)
-
-# DB packages
-library(Symobio)
-library(DBI)
-
-source("R/utils.R")
-
-con = db_connect() # Connection to Symobio
-sf::sf_use_s2(use_s2 = FALSE) # Don't use spherical geometry
-
-# ---------------------------------------------------------------------------#
-# Prepare Geodata ####
-# ---------------------------------------------------------------------------#
-raster_info = tbl(con, "datasets") %>%
- dplyr::filter(stringr::str_detect(name, "CHELSA")) %>%
- collect()
-
-# raster_filepaths = list.files(raster_info$raw_data_path, pattern = ".tif$", full.names = T) %>%
-# stringr::str_sort(numeric = T)
-
-raster_filepaths = list.files("I:/mas_data/00_data/processed/CHELSA_v2-1_bioclim", pattern = ".tif$", full.names = T) %>%
- stringr::str_sort(numeric = T)
-
-sa_polygon = rnaturalearth::ne_countries() %>%
- dplyr::filter(continent == "South America") %>%
- sf::st_union()
-
-# ---------------------------------------------------------------------------#
-# Prepare Occurrence Data ####
-# ---------------------------------------------------------------------------#
-load("data/r_objects/range_maps.RData")
-target_species = unique(range_maps$name_matched[!is.na(range_maps$name_matched)])
-
-occs = tbl(con, "species_occurrences") %>%
- dplyr::filter(species %in% target_species) %>%
- dplyr::select(-year) %>%
- dplyr::distinct() %>%
- collect() %>%
- sf::st_as_sf(coords = c("longitude", "latitude"), remove = F, crs = sf::st_crs(4326)) %>%
- sf::st_filter(sa_polygon)
-
-occs_flagged = occs %>%
- dplyr::distinct(species, coordinate_id, longitude, latitude) %>%
- group_by(species) %>%
- group_split() %>%
- purrr::map( # Loop over species individually due to bug in CoordinateCleaner
- CoordinateCleaner::clean_coordinates,
- lon = "longitude",
- lat = "latitude",
- tests = c("centroids", "gbif", "equal", "institutions", "outliers"),
- outliers_method = "quantile",
- verbose = F
- ) %>%
- bind_rows() %>%
- dplyr::filter(.summary == T) %>%
- dplyr::select(species, coordinate_id, longitude, latitude)
-
-env_vars = tbl(con, "raster_extracts_num") %>%
- dplyr::filter(
- coordinate_id %in% occs$coordinate_id,
- metric == "mean"
- ) %>%
- arrange(raster_layer_id) %>%
- tidyr::pivot_wider(id_cols = coordinate_id, names_from = raster_layer_id, names_prefix = "layer_", values_from = value) %>%
- collect()
-
-occs_final = occs %>%
- inner_join(occs_flagged, by = c("species", "coordinate_id", "longitude", "latitude")) %>%
- inner_join(env_vars, by = "coordinate_id") %>%
- dplyr::select(-coordinate_id)
-
-save(occs_final, file = "data/r_objects/occs_final.RData")
-
-# ---------------------------------------------------------------------------#
-# Create Modeling dataset ####
-# ---------------------------------------------------------------------------#
-load("data/r_objects/occs_final.RData")
-load("data/r_objects/sa_polygon.RData")
-sf::sf_use_s2(use_s2 = FALSE)
-
-occs_split = split(occs_final, occs_final$species)
-
-future::plan("multisession", workers = 16)
-model_data = furrr::future_map(occs_split, .progress = TRUE, .options = furrr::furrr_options(seed = 42), .f = function(occs_spec){
- # Define model/sampling region
- occs_bbox = occs_spec %>%
- sf::st_bbox() %>%
- expand_bbox(min_span = 1, expansion = 0.25) %>%
- sf::st_as_sfc() %>%
- st_set_crs(st_crs(occs_spec))
-
- sample_region = suppressMessages(
- st_as_sf(st_intersection(occs_bbox, sa_polygon))
- )
-
- # Sample pseudo absence
- sample_points = st_as_sf(st_sample(sample_region, nrow(occs_spec))) %>%
- dplyr::mutate(
- species = occs_spec$species[1],
- longitude = sf::st_coordinates(.)[,1],
- latitude = sf::st_coordinates(.)[,2]
- ) %>%
- dplyr::rename(geometry = "x")
-
- abs_spec = terra::rast(raster_filepaths) %>%
- setNames(paste0("layer_", 1:19)) %>%
- terra::extract(sample_points) %>%
- dplyr::select(-ID) %>%
- dplyr::mutate(
- present = 0,
- geometry = sample_points$x
- ) %>%
- tibble() %>%
- bind_cols(sample_points)
-
- # Create presence-absence dataframe
- pa_spec = occs_spec %>%
- dplyr::mutate(present = 1) %>%
- bind_rows(abs_spec)
-
- # Split into train and test datasets
- train_index = createDataPartition(pa_spec$present, p = 0.7, list = FALSE)
- pa_spec$train = 0
- pa_spec$train[train_index] = 1
-
- # Define cross-validation folds
- folds = tryCatch({
- spatial_folds = suppressMessages(
- blockCV::cv_spatial(
- pa_spec,
- column = "present",
- k = 5,
- progress = F, plot = F, report = F
- )
- )
-
- spatial_folds$folds_ids
- }, warning = function(w){
- NA
- }, error = function(e){
- NA
- })
-
- pa_spec$folds = folds
- pa_spec$geometry = NULL
-
- return(pa_spec)
-})
-
-model_data = bind_rows(model_data)
-save(model_data, file = "data/r_objects/model_data.RData")