SSDM: Stacked Species Distribution Modelling

Allows to map species richness and endemism based on stacked species distribution models (SSDM). Individuals SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between- algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernoulli distribution. The SSDM package also provides a user-friendly interface.

AuthorSylvain Schmitt, Robin Pouteau, Dimitri Justeau, Philippe Birnbaum
Date of publication2016-02-17 09:00:40
MaintainerSylvain Schmitt <>
LicenseGPL (>= 3) | file LICENSE

View on CRAN


Algorithm.SDM-class Man page
ensemble Man page
ensemble,Algorithm.SDM-method Man page
ensemble_modelling Man page
Ensemble.SDM-class Man page
Env Man page
gui Man page
load_enm Man page
load.model Man page
load_occ Man page
load_stack Man page
load_var Man page
modelling Man page
Occurrences Man page
plot.model Man page
plot,SDM,ANY-method Man page
plot,Stacked.SDM,ANY-method Man page
save.enm Man page
save.enm,Ensemble.SDM-method Man page
save.model Man page
save.stack Man page
save.stack,Stacked.SDM-method Man page
SSDM Man page
SSDM-package Man page
Stacked.SDM-class Man page
stacking Man page
stacking,Ensemble.SDM-method Man page
stack_modelling Man page
sum,Algorithm.SDM-method Man page
update,Stacked.SDM-method Man page

Questions? Problems? Suggestions? or email at

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.