Calculate spatially marginal, independence, and SHAP-based response figures. They can help to diagnose both how and where the species responses to environmental variables.

  shap_nsim = 0,
  seed = 10L,
  visualize = FALSE



(isolation_forest). It could be the item model of POIsotree made by function isotree_po.


(data.frame, tibble) The data.frame style table that include values of environmental variables at occurrence locations.


(stars) The stars of environmental variables. It should have multiple attributes instead of dims. If you have raster object instead, you could use st_as_stars to convert it to stars or use read_stars directly read source data as a stars. You also could use item variables of POIsotree made by function isotree_po.


(integer) The number of Monte Carlo repetitions in SHAP method to use for estimating each Shapley value. See details in documentation of function explain in package fastshap. Set it to 0 if you don't want to make SHAP-based spatial dependence. When the number of variables is large, a smaller shap_nsim could be used. Be cautious that making SHAP-based spatial dependence will be slow because of Monte-Carlo computation for all pixels. But it is worth the time because it is much more informative. See details in documentation of function explain in package fastshap. The default is 0. Usually a value 10 - 20 is enough.


(integer) The seed for any random progress. The default is 10L.


(logical) if TRUE, plot the response curves. The default is FALSE.


(SpatialResponse) A list of

  • spatial_marginal_response (list) A list of stars object of spatially marginal response of all variables

  • spatial_independent_response (list) A list of stars object of spatially independent response of all variables

  • spatial_shap_dependence (list) A list of stars object of spatially SHAP-based response of all variables


The values show how each environmental variable affects the modeling prediction in space. These maps could help to answer questions of where in terms of environmental response. Compared to marginal dependence or independent dependence maps, SHAP-based maps are way more informative because SHAP-based dependence explain the contribution of each variable to final result.


# Using a pseudo presence-only occurrence dataset of
# virtual species provided in this package

# Prepare data
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"

# 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 = "presence_only")

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

# With imperfect_presence mode,
mod <- isotree_po(
  obs_mode = "imperfect_presence",
  obs = obs_train_eval$obs,
  obs_ind_eval = obs_train_eval$eval,
  variables = env_vars, ntrees = 20,
  sample_size = 0.8, ndim = 1L,
  seed = 123L, nthreads = 1,
  response = FALSE,
  spatial_response = FALSE,
  check_variable = FALSE)

spatial_responses <- spatial_response(
  model = mod$model,
  var_occ = mod$vars_train,
  variables = mod$variables,
  shap_nsim = 1)