Robert P. Anderson

TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.

TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.

TL;DR: In this paper, the authors provide a first synthetic view of an emerging area of ecology and biogeography, linking individual and population-level processes to geographic distributions and biodiversity patterns.

TL;DR: A new open-source release of the Maxent software for modeling species distributions from occurrence records and environmental data is announced, and a new R package for fitting Maxent models using the glmnet package for regularized generalized linear models is described.

TL;DR: ENMeval as mentioned in this paper is an R package that creates data sets for k-fold cross-validation using one of several methods for partitioning occurrence data (including options for spatially independent partitions), builds a series of candidate models using Maxent with a variety of user-defined settings and provides multiple evaluation metrics to aid in selecting optimal model settings.