The maximum-entropy (Maxent) methods is one of the most widely used approaches for species habitat modelling. It has its own dedicated software, the Maxent software (written in java and therefore cross-platform). The software is easy to use and includes fairly a complete help file and tutorial. But things get better…

There is also the option to run Maxent via R, using the dismo package. Dismo basically provides a R command line interface to the Maxent software. This makes it possible to directly ‘capture’ the maxent result model in R. You can use these to evaluate the results and combine them with other R functionality.

Dismo uses the raster package in R to deal with (potentially large) raster layers. This package has greatly enhanced the spatial capabilities of R. However, in my experience it is still a bit slow when handling very large raster layers (much will of course depend on your hardware, especially the amount of RAM). Therefore, when dealing with very large raster layers, I prefer to use GRASS GIS.

Although it isn’t very difficult to create in- and output files for use in Maxent, it takes time. But it has become much easier with the GRASS add-ons by Stefan Blumentrath from the Norwegian Institute for Nature Research (NINA). He created two packages, r.out.maxent and r.maxent.lambdas, that makes it much easier to work with Maxent. You can find both on the GRASS add-on wiki.

The first add-on, r.out.maxent, allows you to produce a set of SWD files as input to Maxent (background SWD and optionally a species SWD file). The other function, r.maxent.lambdas, allows you to compute raw and/or logistic prediction maps directly in GRASS GIS, using the MaxEnt lambdas files as input.

You can of course combine the different tools into a work flow that works best for you. For example you can use GRASS to deal with the raster layers and create input data, Maxent to create the models (possibly via R), GRASS GIS again to create probability distribution maps, and R and GRASS GIS to analyze the results.