PT  - JOURNAL ARTICLE
AU  - John L. Schnase
AU  - Mark L. Carroll
AU  - Roger L. Gill
AU  - Glenn S. Tamkin
AU  - Jian Li
AU  - Savannah L. Strong
AU  - Thomas P. Maxwell
AU  - Mary E. Aronne
TI  - Toward a Monte Carlo approach to selecting climate variables in MaxEnt: A case study using Cassin’s Sparrow (<em>Peucaea cassinii</em>)
AID  - 10.1101/2020.07.15.202945
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.15.202945
4099  - http://biorxiv.org/content/early/2020/08/01/2020.07.15.202945.short
4100  - http://biorxiv.org/content/early/2020/08/01/2020.07.15.202945.full
AB  - MaxEnt is an important aid in understanding the influence of climate change on species distributions and abundance. There is growing interest in using IPCC-class global climate model outputs as environmental predictors in this work. These models provide realistic, global representations of the climate system, projections for hundreds of variables (including Essential Climate Variables), and combine observations from an array of satellite, airborne, and in-situ sensors. Unfortunately, direct use of this important class of data in MaxEnt modeling has been limited due to the large size of climate model output collections. In this study, we investigated the potential of a Monte Carlo method to find a useful subset of predictors in a larger collection of environmental variables in a reasonable amount of time. Our proposed solution takes an ensemble approach wherein many MaxEnt runs, each drawing on a small random subset of variables, converges on a global estimate of the top contributing subset of variables in the larger collection. The Monte Carlo approach resulted in a consistent set of top six variables within 540 runs, and the four most contributory variables of the top six accounted for approximately 93% of overall permutation importance in the final model. These preliminary results suggest that a Monte Carlo approach could offer a viable means of selecting environmental predictors for MaxEnt models that is amenable to parallelization and scalable to large data sets, including externally-stored collections. This points to the possibility of near-real-time multiprocessor implementations that could enable broader and more exploratory use of global climate model outputs in environmental niche modeling and aid in the discovery of viable predictors.Competing Interest StatementThe authors have declared no competing interest.