Robert J. Hijmans

TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).

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 created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km2), including monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970-2000, using data from between 9000 and 60,000 weather stations.

TL;DR: In this article, a broad suite of algorithms with independent presence-absence data from multiple species and regions were evaluated for 46 species (from six different regions of the world) at three sample sizes (100, 30 and 10 records).

TL;DR: This article evaluated the ability of CEMs to predict species distributions under different climates by comparing their predictions with those obtained with a mechanistic model (MM), in which the distribution of a species is modeled based on knowledge of the species' physiology and the potential distributions of 100 plant species were modeled with an MM for current conditions, a past climate reconstruction (21000 years before present) and a future climate projection (double preindustrial CO2 conditions).