December 13, 2017. The paper for SDMtoolbox 2.0 is finally out: Brown JL, Bennett JR, French CM (2017). SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. PeerJ PDF
Also SDMtoolbox 2.1 is finally available to the public. This release includes a complete re-writing of several tools, many bug fixes, and the inclusion of many new tools. Go now to downloads page and get it! Thanks so much for everyone’s patience.
SDMtoolbox now has a forum!
SDMtoolbox is a python-based ArcGIS toolbox for spatial studies of ecology, evolution and genetics. SDMtoolbox consists of a series python scripts (80 and growing) designed to automate complicated ArcMap (ESRI) analyses. A large set of the tools were created to complement MaxEnt species distribution models (SDMs) or to improve the predictive performance of MaxEnt models (for an overview, see chapter 5 in the user guide Running a SDM in MaxEnt: from Start to Finish). MaxEnt uses maximum entropy to model species’ geographic distributions using presence-only data (Phillips et al. 2006) and has become one of the most prevalent methods due to its high predictive performance, computational efficiency and ease of use. SDMtoolbox is not limited to analyses of MaxEnt models and many tools are also available for use on other data (i.e. haplotype networks) or the results of other SDM methods (see Universal SDM Analyses).
Brown JL, Bennett JR, French CM (2017). SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. PeerJ PDF
Brief Overview of Main Analyses:
- Calculation of species richness, weighted endemism and corrected weighted endemism
- Calculation of least-cost corridors and least-cost paths among shared haplotypes or among all sites (see image to right)
- Run spatial jackknifing in MaxEnt. Also automated independent evaluation of many feature classes and regularization multiplier values (see image to right)
- Creation of MaxEnt bias files for sampling biases associated with latitudinal changes in the area encompassed by decimal degree units
- Creation of MaxEnt bias files to limit background point selection to a maximum distance from presence points or within a buffered minimum-convex polygon (MCP) of a species’ distribution
- Spatially rarefy occurrence data (a.k.a. spatial filtering) to reduce spatial auto-correlation of occurrence points for use in species distribution modeling
- SDM over-prediction correction: clip by buffered MCP (see image to right)
- Limit dispersal in future SDMs
- Create a friction layer from a species distribution model (a.k.a. ecological niche model or environmental niche model)
- Calculate area of habitat contraction, expansion and other distribution changes between current and future SDMs (see image to right)
- Calculate vectors of core distributional changes between current and future SDMs
- Randomly select points
- Split shapefile by field attributes
- Explore summary statistics and correlations between environmental rasters before running a SDM
- Batch raster processing (i.e. preparing Worldclim data for MaxEnt): ASCII to raster files, raster to ASCII files, project to any projection, clip to a particular extent, re-sampling resolution, reclassifying and summing many rasters
- Create tessellated hexagon shapefiles