Toward a Monte Carlo approach to selecting climate variables in MaxEnt

Abstract

MaxEnt is an important aid in understanding the influence of climate change on species distributions. 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 by the large size of climate model output collections and the fact that MaxEnt can only operate on a relatively small set of predictors stored in a computer’s main memory. In this study, we demonstrate the feasibility of a Monte Carlo method that overcomes this limitation by finding a useful subset of predictors in a larger, externally-stored 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. In preliminary tests, the Monte Carlo approach selected a consistent set of top six variables within 540 runs, with the four most contributory variables of the top six accounting for approximately 93% of overall permutation importance in a final model. These results suggest that a Monte Carlo approach could offer a viable means of screening environmental predictors prior to final model construction that is amenable to parallelization and scalable to very large data sets. 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.

Automatic variable selection in ecological niche modeling: A case study using Cassin’s Sparrow (Peucaea cassinii)

“…We adopted a standard MERRA/Max screening configuration that we used as the default in all our timing trials and use cases. This included a MaxEnt feature class (FC) setting of LQHP (linear, quadratic, hinge, and product), a regularization multiplier (RM) setting of 1.0, 10 replicate cross-validation, and ten thousand background points from across the study area [13]. We used V = 2 random variables in all the independent MaxEnt sampling runs.…”

“…In previous work, we demonstrated the potential of a MaxEnt-based Monte Carlo method that addresses this issue by screening large data collections for viable predictors [13]. Based on a machine learning approach to maximum entropy modeling, MaxEnt is one of the most popular software packages in use today by the ENM community [14–16].…”

“…In our earlier, proof-of-concept work, we implemented the Monte Carlo selection algorithm as a single-threaded program running on a MacBook Pro laptop computer [13]. In the current study, we have implemented a parallel version of the Monte Carlo method in a high-performance cloud computing environment.…”

Automatic variable selection in ecological niche modeling: A case study using Cassin’s Sparrow (Peucaea cassinii)

“…Part of the problem lies in the fact that most ENM software tools require predictors and observations to be memory-resident in order for the programs to work [13,14]. This results in run-times and space requirements that have linear or higher-order scaling properties with respect to the size of a model's inputs.…”

“…In previous work, we demonstrated the potential of a MaxEnt-based Monte Carlo method that addresses this issue by screening large data collections for viable predictors [14]. Based on a machine learning approach to maximum entropy modeling, MaxEnt is one of the most popular software packages in use today by the ENM community [20][21][22].…”