itsdm calls isolation forest and variations such as SCiForest and EIF to model species distribution. It provides features including:
Install the CRAN release of itsdm with
You can install the development version of itsdm from GitHub with:
This is a basic example which shows you how to solve a common problem:
library(itsdm)
# Using a pseudo presence-only occurrence dataset of
# virtual species provided in this package
data("occ_virtual_species")
# Split to training and test
occ_virtual_species <- occ_virtual_species %>%
mutate(id = row_number())
set.seed(11)
occ <- occ_virtual_species %>% sample_frac(0.7)
occ_test <- occ_virtual_species %>% filter(! id %in% occ$id)
occ <- occ %>% select(-id)
occ_test <- occ_test %>% select(-id)
# Get environmental variables
env_vars <- system.file(
'extdata/bioclim_tanzania_10min.tif',
package = 'itsdm') %>% read_stars() %>%
%>% slice('band', c(1, 6, 12, 15))
# Train the model
mod <- isotree_po(
occ = occ, occ_test = occ_test,
variables = env_vars, ntrees = 200,
sample_rate = 0.8, ndim = 2L,
seed = 123L)
# Check results
## Suitability
ggplot() +
geom_stars(data = mod$prediction) +
scale_fill_viridis_c('Predicted suitability',
na.value = 'transparent') +
coord_equal() +
theme_linedraw()
## Plot independent response curves
plot(mod$independent_responses,
target_var = c('bio1', 'bio12'))This package is part of project “Combining Spatially-explicit Simulation of Animal Movement and Earth Observation to Reconcile Agriculture and Wildlife Conservation”. This project is funded by NASA FINESST program (award number: 80NSSC20K1640).