The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

itsdm

Overview

itsdm calls isolation forest and variations such as SCiForest and EIF to model species distribution. It provides features including:

• A few functions to download environmental variables.
• Outlier tree-based suspicious environmental outliers detection.
• Isolation forest-based environmental suitability modeling.
• Non-spatial response curves of environmental variables.
• Spatial response maps of environmental variables.
• Variable importance analysis.
• Presence-only model evaluation.
• Method to convert predicted suitability to presence-absence map.
• Variable contribution analysis for the target observations.
• Method to analyze the spatial impacts of changing environment.

Installation

Install the CRAN release of itsdm with

install.packages("itsdm")

You can install the development version of itsdm from GitHub with:

# install.packages("remotes")
remotes::install_github("LLeiSong/itsdm")

Example

This is a basic example which shows you how to solve a common problem:

library(itsdm)
library(dplyr)
library(stars)
library(ggplot2)

# Using a pseudo presence-only occurrence dataset of
# virtual species provided in this package
data("occ_virtual_species")
obs_df <- occ_virtual_species %>% filter(usage == "train")
eval_df <- occ_virtual_species %>% filter(usage == "eval")
x_col <- "x"
y_col <- "y"
obs_col <- "observation"
obs_type <- "presence_absence"

# Format the observations
obs_train_eval <- format_observation(
  obs_df = obs_df, eval_df = eval_df,
  x_col = x_col, y_col = y_col, obs_col = obs_col,
  obs_type = obs_type)

# 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(
  obs_mode = "presence_absence",
  obs = obs_train_eval$obs,
  obs_ind_eval = obs_train_eval$eval,
  variables = env_vars, ntrees = 200,
  sample_size = 0.8, ndim = 2,
  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'))

The Shapley values-based analysis can apply to external models. Here is an example to analyze impacts of the bio12 decreasing 200 mm to species distribution based on Random Forest (RF) prediction:

# Prepare data
data("occ_virtual_species")
obs_df <- occ_virtual_species %>% 
  filter(usage == "train")

env_vars <- system.file(
  'extdata/bioclim_tanzania_10min.tif',
  package = 'itsdm') %>% read_stars() %>%
  slice('band', c(1, 5, 12)) %>% 
  split()

model_data <- stars::st_extract(
  env_vars, at = as.matrix(obs_df %>% select(x, y))) %>% 
  as.data.frame()
names(model_data) <- names(env_vars)
model_data <- model_data %>% 
  mutate(occ = obs_df[['observation']])
model_data$occ <- as.factor(model_data$occ)

mod_rf <- randomForest(
  occ ~ .,
  data = model_data,
  ntree = 200)

pfun <- function(X.model, newdata) {
  # for data.frame
  predict(X.model, newdata, type = "prob")[, "1"]
}

# Use a fixed value
climate_changes <- detect_envi_change(
  model = mod_rf,
  var_occ = model_data %>% select(-occ),
  variables = env_vars,
  target_var = "bio12",
  bins = 20,
  var_future = -200,
  pfun = pfun)

Contributor

1. David Cortes, helps to improve the flexibility of calling isotree.

We are welcome any helps! Please make a pull request or reach out to lsong@clarku.edu if you want to make any contribution.

Funding

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).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.