Establishing Move on CRAN

On friday, December 14th, we successfully published our software package ‘move’ at CRAN, the popular R repository. This was a huge step for me and my group. We were working on the package, that allows to import, analyze and visualize animal movement data, almost a year. We already published stable versions on r-forge prior to the publication on CRAN.

Thanks to colleagues and users from around the world we received useful feedback to make the package even better. We fixed problems and added further functionality. We now decided that it is time to make the package available to a broader audience. We published at CRAN since it is the primary repository for R packages.

Now, it is possible to download and install the ‘move’ package from within R with:

install.packages("move")

We aim to add further functionalities to the package in the near future. We hope that the package will be useful for biologists in general and movement ecologists in specific.

Transforming UD rasters into contour rasters

Calculating utilization distributions for animals, e.g. with Kernel Density or Dynamic Brownian Bridge Movement Model, result in rasters with probabilities of presence. To calculate areas with equal levels of presence (contours) it is necessary to transform the raster, which is now done by the getVolumeUD  function from the move package.

The function works with objects of the Raster class. This is an advantage over a similar function from the adehabitatHR package (getvolumeUD), which works with objects of the class estUD and estUDm, that are created for example by the kernelUD function from the same package. Allowing objects from the Raster class (raster package) makes the getVolumeUD more flexible.

Using:

new <- getVolumeUD(raster)
contour95 <- new<=0.95

where new is the raster that is returned from getVolumeUD, you calculate a new raster that has only 0 for cells that are outside the 95% contour and 1 for cells that are inside the 95% contour. It represents the 95% contour of the UD.

[A great thank you to Bart Kranstauber that provided the majority of the functions code.]

UD raster to contour raster
Two rasters with UD from the same record. Left: raw output from a DBBMM calculation (probabilities are between 0 and 0.03). Right: transformed raster (cells have now the values of the contour they belong to; values are between 0 and 1).