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”


What if Google decides to discontinue Google Scholar?

Since Google discontinued Google reader I have always been wondering; what if they decide to stop with Google Scholar? If you are lucky enough to have access to an university library, you should be fine. But there are also a number of freely available alternatives. Just checking my bookmarks gave me gave me the list below. None of these tools have been able to convince me to abandon Google Scholar (to be completely fair, I haven’t tried them all out extensively), but at least if Google decides to kill of Scholar, I have somewhere else to go: Continue reading “What if Google decides to discontinue Google Scholar?”

Calculating the raster cell area of an unprojected raster layer

What if you get a raster layer with number of people per raster cell, like for example the population layer from Afripop,  and you want to convert it to a population density layer?

Well, obviously, you need to divide the number of people by the surface area of the raster cells. However, the surface area of the raster cells of an unprojected (lat/lon) are not constant; they decrease with increasing latitude. So what you need is a raster layer with the surface areas of the cells.

I thought I had seen a function in GRASS GIS to do this, but that might have been a typical case of the wish being the father to the thought. But anyway, it isn’t terribly difficult to calculate it yourself using the map calculator. Continue reading “Calculating the raster cell area of an unprojected raster layer”

Check out the new Landcover Polygon overlay function in the LecoS QGIS plugin

The author of the QGIS plugin LecoS came up with  a new feature which might come in handy for some of you: a polygon overlay tool. This tool can extract raster values and save them directly to the vector layers attribute table. See his post ‘LecoS update – Landcover Polygon overlay‘ for more information and an example how this feature can be used to calculate how much of the protected area is still covered by mature forest trees.

Stratified random sampling in GRASS GIS

There are various options to create a vector or raster layers with random sample points, including v.random, r.random and r.random.cells. The first two generate random points within the defined region. However, only r.random will respect the mask if set. The r.random.cells creates random points with spatial dependency.

But how to carry out stratified random sample points with e.g. an equal sample sizes per strata? You would need to divide your sample area in the desired strata, and sample each stratum separately. Below are two possible approaches Continue reading “Stratified random sampling in GRASS GIS”

Integrating Maxent, R and GRASS GIS

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 “Integrating Maxent, R and GRASS GIS”