spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models

TLDR

Spatial thinning reduces sampling bias by removing the fewest records necessary while preserving information, but manual thinning is impractical for large datasets. The spThin R package’s thin function uses a randomization algorithm that, after sufficient iterations, returns the largest possible dataset that satisfies a specified thinning distance. In a worked example with the Caribbean spiny pocket mouse, the automated thinning produced results identical to manual thinning.

Abstract

Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.