Update of r.mess to work in GRASS 7

A while back I wrote a GRASS GIS addon to calculate the Multivariate Environmental Similarity index (MES; see below for a short description). The addon was written as a shell script and R script and only runs on GRASS GIS 6.

I finally got around rewriting the addon in Python. This should make it easier to install (using the g.extension function) and it does not depend on R any more. You can install the addon from the g.extension menu or you can go here. Continue reading “Update of r.mess to work in GRASS 7”

A new method and tool (ExDet) to evaluate novelty environmental conditions

A common objective of correlative species distribution model is to be able to project the potential distribution of the target species into a new environmental space. This can be a new geographic space (e.g., invasive species) or projected future conditions.

One should be very careful in interpreting results if extrapolating to areas with conditions that fall outside the range of reference environmental variation. There are several methods to visualize this uncertainty. On this blog I have for example mentioned the multi-environmental similarity tool in Maxent (also implemented in R in amongst others the dismo package and as an addon for GRASS GIS), which allows you to create maps that provides the similarity of each point to a set of reference points (Elith et al. 2010) and thus provide a quick overview of areas with ‘novel’ conditions.

A disadvantage of this and other methods is that they only consider the ranges of the individual predictors, and ignore the correlation structure of the covariates used to build the model. In reality, it is not unlikely that at a given locations values of each univariate factor fall within the original range of values, but the combination of environmental conditions is new. Continue reading “A new method and tool (ExDet) to evaluate novelty environmental conditions”

Multivariate Environmental Similarity Surfaces (MESS) index in GRASS GIS

The Multivariate Environmental Similarity Surfaces (MESS) is an index that represents how similar a point in space is to a reference set of points, with respect to a set of predictor variables (Elith et al 2010). The function was first implemented as part of the Maxent software package, and is also available in the dismo package for R (see also here and here).

The latter works well on small and medium sized data sets. However, they take a long time to run on larger data sets, e.g., when working with 1km² raster grids covering eastern Africa. I therefore wrote a small R script to compute MESS in GRASS GIS. Continue reading “Multivariate Environmental Similarity Surfaces (MESS) index in GRASS GIS”