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 →
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… Continue reading →
GRASS GIS is a very powerfull GIS offering an extensive set of tools for geospatial data management and analysis, image processing, and spatial modeling. The possibility to directly interact with R further improves the geospatial analysis capabilities of GRASS (see R Spatial View). Continue reading →
I am currently working on the modeling of the distribution of different vegetation types and associate species in eastern Africa. In absence of more detailed climate data for the region, a great source of global climatic data is the WorldClim website. Besides the usual monthly temperature and rainfall data, it provides bioclimatic variables which are derived from these monthly temperature and rainfall values in order to generate more biologically meaningful variables. However, it only does this for the current conditions (interpolations of observed data, representative of 1950-2000) but not yet (?) for the future conditions (downscaled data from global climate model (GCM) output, IPPC 3rd assessment). Thus, I had to calculate them myself, which I did using GRASS GIS and R. Continue reading →