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Commit 025428c9 authored by ye87zine's avatar ye87zine
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merged latest version of main 

Merge branch 'main' into alternative-model-evaluation

# Conflicts:
#	R/03_presence_absence_preparation.R
parents fae69c94 0105831c
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1 merge request!2Alternative model evaluation
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R/01_range_map_preparation.R → R/01_01_range_map_preparation.R
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R/01_02_raster_preparation.R 0 → 100644
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library(tidyverse)
library(terra)
library(rnaturalearth)
library(furrr)
# Define Study extent ####
study_extent = rnaturalearth::ne_countries() %>%
dplyr::filter(continent == "South America") %>%
sf::st_union() %>%
sf::st_bbox() %>%
ext()
# CHELSA bioclim target variables ####
chelsa_urls = c("https://os.zhdk.cloud.switch.ch/chelsav2/GLOBAL/climatologies/1981-2010/bio/CHELSA_cmi_mean_1981-2010_V.2.1.tif",
"https://os.zhdk.cloud.switch.ch/chelsav2/GLOBAL/climatologies/1981-2010/bio/CHELSA_rsds_1981-2010_mean_V.2.1.tif",
"https://os.zhdk.cloud.switch.ch/chelsav2/GLOBAL/climatologies/1981-2010/bio/CHELSA_bio6_1981-2010_V.2.1.tif",
"https://os.zhdk.cloud.switch.ch/chelsav2/GLOBAL/climatologies/1981-2010/bio/CHELSA_bio17_1981-2010_V.2.1.tif")
chelsa = terra::rast(chelsa_urls) %>%
crop(study_extent)
set.names(chelsa, c("cmi", "rsds", "bio6", "bio17"))
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)
# Seasonal inundation ####
igsw = list.files("I:/mas_data/00_data/processed/iGSW/", pattern = "seasonal_(199[0-9]|200[0-9]|2010)", full.names = TRUE) %>%
terra::rast() %>%
terra::resample(chelsa, threads = 32) %>%
app(mean, na.rm = TRUE) %>%
subst(NA, 0)
terra::set.names(igsw, "igsw")
terra::writeRaster(igsw, filename = file.path("data", "geospatial", "raster", "mean_seasonalInundation_1992-2010.tif"), overwrite = T)
# Distance to freshwater ####
dtfw = list.files("I:/mas_data/00_data/processed/linearDistance/", pattern = "lake|reservoirs|river", full.names = TRUE) %>%
terra::rast() %>%
terra::resample(chelsa) %>%
app(mean, na.rm = TRUE)
terra::set.names(dtfw, "dtfw")
terra::writeRaster(dtfw, filename = file.path("data", "geospatial", "raster", "mean_distanceToWater.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)
R/03_presence_absence_preparation.R → R/03_01_presence_preparation.R
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......@@ -20,26 +20,22 @@ 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$", full.names = T) %>%
stringr::str_sort(numeric = T)
sa_polygon = rnaturalearth::ne_countries() %>%
dplyr::filter(continent == "South America") %>%
sf::st_union()
raster_filepaths = list.files("~/symobio-modeling/data/geospatial/raster/", full.names = T) %>%
stringr::str_sort(numeric = T)
raster_data = terra::rast(raster_filepaths)
# ---------------------------------------------------------------------------#
# Prepare Occurrence Data ####
# ---------------------------------------------------------------------------#
load("data/r_objects/range_maps.RData")
target_species = unique(range_maps$name_matched[!is.na(range_maps$name_matched)])
# Query species from Symobio
occs = tbl(con, "species_occurrences") %>%
dplyr::filter(species %in% target_species) %>%
dplyr::select(-year) %>%
......@@ -48,6 +44,7 @@ occs = tbl(con, "species_occurrences") %>%
sf::st_as_sf(coords = c("longitude", "latitude"), remove = F, crs = sf::st_crs(4326)) %>%
sf::st_filter(sa_polygon)
# Flag occurrences using Coordinate cleaner
occs_flagged = occs %>%
dplyr::distinct(species, coordinate_id, longitude, latitude) %>%
group_by(species) %>%
......@@ -64,92 +61,23 @@ occs_flagged = occs %>%
dplyr::filter(.summary == T) %>%
dplyr::select(species, coordinate_id, longitude, latitude)
env_vars = tbl(con, "raster_extracts_num") %>%
dplyr::filter(
coordinate_id %in% occs$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()
# Subset species occurrences to validated records
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")
# Extract environmental data
proj_string = "+proj=tcea +lon_0=-58.0704167 +datum=WGS84 +units=m +no_defs"
# ---------------------------------------------------------------------------#
# 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$x
) %>%
tibble() %>%
bind_cols(sample_points)
# Create presence-absence dataframe
pa_spec = occs_spec %>%
dplyr::mutate(present = 1) %>%
bind_rows(abs_spec)
raster_data = terra::project(raster_data, proj_string)
occs_final = st_transform(occs_final, proj_string)
env_data = extract(raster_data, vect(occs_final), ID = F)
# Define cross-validation folds
folds_eval = tryCatch({
spatial_folds = suppressMessages(
blockCV::cv_spatial(
pa_spec,
column = "present",
k = 5,
progress = F, plot = F, report = F
)
)
spatial_folds$folds_ids
}, warning = function(w){
NA
}, error = function(e){
NA
})
pa_spec$fold_eval = folds_eval
return(pa_spec)
})
# Merge data + final processing
occs_final = bind_cols(occs_final, env_data) %>%
drop_na() %>% # Remove records with NA env vars
group_by(species) %>%
distinct() # Remove duplicate records
model_data = bind_rows(model_data)
save(model_data, file = "data/r_objects/nest_cv/model_data.RData")
# Save occurrences
save(occs_final, file = "data/r_objects/occs_final.RData")
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# General packages
library(dplyr)
library(tidyr)
library(furrr)
# Geo packages
library(terra)
library(CoordinateCleaner)
library(sf)
library(geos)
# Stat packages
library(MASS)
library(dismo)
library(spatialEco)
library(blockCV)
library(caret)
source("R/utils.R")
load("data/r_objects/range_maps.RData")
load("data/r_objects/occs_final.RData")
# ---------------------------------------------------------------------------#
# Prepare Geodata ####
# ---------------------------------------------------------------------------#
target_species = unique(occs_final$species)
sa_polygon = rnaturalearth::ne_countries() %>%
dplyr::filter(continent == "South America") %>%
sf::st_union()
raster_filepaths = list.files("~/symobio-modeling/data/geospatial/raster/", full.names = T) %>%
#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)
# 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)
# ---------------------------------------------------------------------------#
# Sample absences ####
# #
# Absences should be predominantly sampled from regions that are #
# geographically close (reproduce sampling biases) but environmentally #
# dissimilar (avoid false negatives) to the known occurrences #
# ---------------------------------------------------------------------------#
future::plan("multisession", workers = 16)
model_data = furrr::future_walk(
.x = target_species,
.options = furrr::furrr_options(seed = 42, scheduling = FALSE), # make sure workers get constant stream of work
.env_globals = c(raster_filepaths, sa_polygon, occs_final, range_maps, proj_string),
.f = function(species){
# Prepare species occurrence data #####
occs_spec = dplyr::filter(occs_final, species == !!species)
if(nrow(occs_spec) <= 1){
warning("Skipping species ", species, ": Can't process species with a single presence record." )
return()
}
occs_multipoint = occs_spec %>%
summarise(geometry = st_combine(geometry))
range_spec = dplyr::filter(range_maps, name_matched == !!species) %>%
rename(species = name_matched)
# Define model/sampling region
# 1. Union all points from range polygon and occurrences records
# 2. Build concave polygon from unionen points
# 3. Buffer by 100 km to extend sampling region into new environments
# 4. Crop by South America outline
sampling_region = st_cast(range_spec, "MULTIPOINT") %>%
st_union(occs_multipoint) %>%
geos::geos_concave_hull(ratio = 0.3) %>% # Concave hull in sf requires geos 3.11
st_as_sf() %>%
st_set_crs(proj_string) %>%
st_buffer(dist = 100000) %>%
st_intersection(sa_polygon)
# Load raster data
raster_data = terra::rast(raster_filepaths) %>%
terra::project(proj_string) %>%
terra::crop(sf::st_bbox(sampling_region))
# Define spatial KDE
ref = st_bbox(sampling_region)[c(1,3,2,4)]
# Function to estimate bandwidth
# ---> this function is the default estimation of the sf.kde package but with a minimum of 50000 = 50km)
calc_bw <- function(x) {
r <- stats::quantile(x, c(0.25, 0.75))
h <- (r[2] - r[1])/1.34
result = 4 * 1.06 * min(sqrt(stats::var(x)), h) * length(x)^(-1/5)
return(max(result, 50000))
}
bw_x = calc_bw(st_coordinates(occs_spec)[,"X"])
bw_y = calc_bw(st_coordinates(occs_spec)[,"Y"])
spatial_kde = spatialEco::sf.kde(occs_spec, bw = c(bw_x, bw_y), res = 10000, ref = ref, standardize = T, scale.factor = 1) %>%
crop(sampling_region, mask = T, touches = F)
# Define reverse niche
env_sampling_region = terra::crop(raster_data, sampling_region, mask = T) %>%
terra::resample(spatial_kde) %>%
raster::stack()
maxent_fit <- maxent(env_sampling_region, st_coordinates(occs_spec))
env_kde <- (1-predict(env_sampling_region, maxent_fit)) %>%
terra::rast()
# Sample pseudo absences
abs_prob = spatial_kde * env_kde
abs_spec_list = list()
samples_required = nrow(occs_spec)
while(samples_required != 0){
sample_points = spatSample(abs_prob, size = samples_required, method = "weights", xy = T, as.df = T) %>%
dplyr::select(-z) %>%
dplyr::mutate(x = x + runif(nrow(.), -5000, 5000), # Add jitter (res/2) to cell centroids
y = y + runif(nrow(.), -5000, 5000)) %>% # Add jitter (res/2) to cell centroids
st_as_sf(coords = c("x", "y"), crs = proj_string)
sample_abs = bind_cols(
terra::extract(raster_data, terra::vect(sample_points), ID = FALSE)
) %>%
bind_cols(sample_points) %>%
drop_na()
abs_spec_list[[length(abs_spec_list)+1]] = sample_points
samples_required = samples_required - nrow(sample_points) # Sometimes there are no env data for sample points, so keep sampling
}
abs_spec = bind_rows(abs_spec_list)
abs_spec = abs_spec %>%
bind_cols(
st_coordinates(st_transform(abs_spec, "EPSG:4326"))
) %>%
dplyr::rename(
longitude = X,
latitude = Y
) %>%
dplyr::mutate(
species = !!species,
present = 0
)
# Create presence-absence dataframe
pa_spec = occs_spec %>%
dplyr::mutate(present = 1) %>%
bind_rows(abs_spec)
ggplot() +
ggtitle(species) +
geom_sf(data = st_crop(sa_polygon, ref), fill = "#756445") +
tidyterra::geom_spatraster(data = abs_prob) +
scale_fill_gradient(low="darkblue", high="green", na.value=NA, name = "Absence sampling probability") +
geom_sf(data = range_spec, alpha = 0, color = "black") +
geom_sf(data = pa_spec, aes(color = as.factor(present)), size = 0.7) +
scale_color_discrete(name = "Presence") +
theme_minimal()
try({
ggsave(paste0(species, ".pdf"), path = "plots/pa_sampling", device = "pdf", scale = 2)
})
# 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$fold_eval = folds
pa_spec$geometry = NULL
save(pa_spec, file = paste0("data/r_objects/pa_sampling/", species, ".RData"))
}
)
# Combine presence-absence data
files = list.files("data/r_objects/pa_sampling/", full.names = T)
model_data = lapply(files, function(f){load(f); return(pa_spec)}) %>%
bind_rows()
save(model_data, file = "data/r_objects/model_data_pa_sampling.RData")
renv.lock
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......@@ -8,7 +8,29 @@
}
]
},
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......@@ -42,6 +64,17 @@
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......@@ -211,6 +244,25 @@
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......@@ -411,7 +463,7 @@
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......@@ -433,7 +485,7 @@
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......@@ -560,6 +612,28 @@
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......@@ -584,6 +658,20 @@
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......@@ -744,6 +832,20 @@
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......@@ -850,6 +952,17 @@
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......@@ -876,6 +989,16 @@
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......@@ -1572,6 +1695,18 @@
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......@@ -1620,6 +1755,16 @@
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......@@ -1716,6 +1861,18 @@
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......@@ -1767,6 +1924,62 @@
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"foreach",
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"numDeriv",
"optimParallel",
"parallel",
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"scatterplot3d",
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],
"Hash": "592feaaac935ea62afa4399fd83201fd"
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......@@ -1969,6 +2182,16 @@
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"Hash": "5f919ae5e7f83a6f91dcf2288943370d"
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......@@ -2305,6 +2528,19 @@
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"Hash": "10ee4b91ec812690bd55d9bf51edccee"
},
"secretbase": {
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......@@ -2364,6 +2600,49 @@
],
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},
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......@@ -2889,6 +3168,18 @@
],
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"yaml": {
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......
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