#Rarify data software

WE = ∑ 1/C (C is the number of grid cells each endemic occurs in)ģ. A WE emphasizes areas that have a high proportion of animals with restricted ranges. Weighted Endemism (WE), which is the sum of the reciprocal of the total number of cells each species in a grid cell is found in. SR = K (the total number of species in a grid cell)Ģ. Species Richness (SR) is sum of unique species per cell. As follows are the three diversity metrics: 1. Analyses that utilize point occurrence data and analyses that use binary SDMs. These tools estimate three common biodiversity metrics: species richness, weighted endemism and corrected weighted endemism.

#Rarify data software

Because these tools are well documented within the software and are not novel, they are not discussed in detail here, see ESRI. For legacy ArcGIS users, this familiar grouping of functions is almost identical to the tools contained in the Spatial Analysis Toolbar (now absent from ArcMap 10). This is a convenient grouping of 23 existing Spatial Analyst tools within ArcGIS 10 that are commonly used for geospatial analyses in ecology and evolution studies ( i.e. Multiband NetCDF to Separate Rasters (Folder).Raster Calculator: Standardize 0-1 (Folder).Increase Raster Extent/Snap All Raster to Same Extent (folder).Sample raster values at input localities (folder).

Project Shapefiles to User Specified Projection (folder).Zonal statistics of many rasters to single table.Export images of all color permutations of RGB raster.Apply same color ramp to all open rasters.Batch sum rasters- all same extent (by folder).Batch sum rasters – any extent (by folder).Batch project raster to any projection (by folder).Batch project raster to equal-area projection (by folder).Batch project shapefile to any projection (by folder).Sample raster or feature values to hexagon shapefile.Over-prediction correction: clip models by buffered minimum convex polygons.Create Microclim Bioclim variable – two factors.Create Microclim Bioclim variable – single factor.Spatial Rarefy Occurrence Data (a.k.a spatial filter occurrence data).Spatially Rarefy Occurrence Data for SDMs (reduce spatial autocorrelation).Distribution changes between binary SDMs.Split binary SDM by input clade relationship.Over-prediction correction: clip models by buffered minimum convex polygon s.Sample by distance from observation points.Sample by buffered local adaptive convex-hull.Gaussian kernel density of sampling localities.Area of range contraction, range expansion and no change in the species’ distribution.Distribution changes between binary SDMs:.Project climate and distribution data into equal-areas projection.Correcting latitudinal background selection bias:.Hacking the Spatial Jackknife batch scripts.Split SDM by input clade relationship- Inverse Distance Weighting.Least-cost paths and least-cost corridors among all sites.Least-cost paths and least-cost corridors among shared haplotypes.Quickly reclassify significance from randomizations.Biodiversity estimates: species richness, weighted endemism and corrected weighted endemism.