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SSDM: Stacked species distribution modelling

Travis-CI Build Status AppVeyor Build Status CRAN Downloads Coverage Status Research software impact

SSDM is a package to map species richness and endemism based on stacked species distribution models (SSDM). Individual 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 Bernouilli distribution. The SSDM package also provides a user-friendly interface gui.

For a full list of changes see NEWS.

Installation

Please be aware that SSDM package use a lot of dependencies (see DESCRIPTION)

Install from Github

You can install the latest version of SSDM from Github using the devtools package:

if (!requireNamespace("devtools", quietly = TRUE))
  install.packages("devtools")

devtools::install_github("sylvainschmitt/SSDM")

Install from CRAN

The stable version of SSDM, is available on CRAN:

install.packages("SSDM")

We advise users to install from github. Due to CRAN policies and the development of SSDM, many new features and bugfixes may be available on CRAN later.

Usage

After installing, SSDM package, you can launch the graphical user interface by typing gui() in the console.

[**Click to enlarge**](https://raw.githubusercontent.com/sylvainschmitt/SSDM/master/examples/SSDM.gif) ![Screenshot](https://raw.githubusercontent.com/sylvainschmitt/SSDM/master/examples/SSDM.gif)

Functionnalities

SSDM provides five categories of functions (that you can find in details below): Data preparation, Modelling main functions, Model main methods, Model classes, and Miscellaneous.

Data preparation

  • load_occ: Load occurrence data
  • load_var: Load environmental variables

Modelling main functions

  • modelling: Build an SDM using a single algorithm
  • ensemble_modelling: Build an SDM that assembles multiple algorithms
  • stack_modelling: Build an SSDMs that assembles multiple algorithms and species

Model main methods

  • ensemble,Algorithm.SDM-method: Build an ensemble SDM
  • stacking,Ensemble.SDM-method: Build an SSDM
  • update,Stacked.SDM-method: Update a previous SSDM with new occurrence data

Model classes

  • Algorithm.SDM: S4 class to represent SDMs
  • Ensemble.SDM: S4 class to represent ensemble SDMs
  • Stacked.SDM: S4 class to represent SSDMs

Miscellanous

  • gui: user-friendly interface for SSDM package
  • plot.model: Plot SDMs
  • save.model: Save SDMs
  • load.model: Load SDMs