Cargando…
Toward a Monte Carlo approach to selecting climate variables in MaxEnt
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, pro...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928495/ https://www.ncbi.nlm.nih.gov/pubmed/33657125 http://dx.doi.org/10.1371/journal.pone.0237208 |
_version_ | 1783659868757950464 |
---|---|
author | Schnase, John L. Carroll, Mark L. Gill, Roger L. Tamkin, Glenn S. Li, Jian Strong, Savannah L. Maxwell, Thomas P. Aronne, Mary E. Spradlin, Caleb S. |
author_facet | Schnase, John L. Carroll, Mark L. Gill, Roger L. Tamkin, Glenn S. Li, Jian Strong, Savannah L. Maxwell, Thomas P. Aronne, Mary E. Spradlin, Caleb S. |
author_sort | Schnase, John L. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7928495 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-79284952021-03-10 Toward a Monte Carlo approach to selecting climate variables in MaxEnt Schnase, John L. Carroll, Mark L. Gill, Roger L. Tamkin, Glenn S. Li, Jian Strong, Savannah L. Maxwell, Thomas P. Aronne, Mary E. Spradlin, Caleb S. PLoS One Research Article 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. Public Library of Science 2021-03-03 /pmc/articles/PMC7928495/ /pubmed/33657125 http://dx.doi.org/10.1371/journal.pone.0237208 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Schnase, John L. Carroll, Mark L. Gill, Roger L. Tamkin, Glenn S. Li, Jian Strong, Savannah L. Maxwell, Thomas P. Aronne, Mary E. Spradlin, Caleb S. Toward a Monte Carlo approach to selecting climate variables in MaxEnt |
title | Toward a Monte Carlo approach to selecting climate variables in MaxEnt |
title_full | Toward a Monte Carlo approach to selecting climate variables in MaxEnt |
title_fullStr | Toward a Monte Carlo approach to selecting climate variables in MaxEnt |
title_full_unstemmed | Toward a Monte Carlo approach to selecting climate variables in MaxEnt |
title_short | Toward a Monte Carlo approach to selecting climate variables in MaxEnt |
title_sort | toward a monte carlo approach to selecting climate variables in maxent |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928495/ https://www.ncbi.nlm.nih.gov/pubmed/33657125 http://dx.doi.org/10.1371/journal.pone.0237208 |
work_keys_str_mv | AT schnasejohnl towardamontecarloapproachtoselectingclimatevariablesinmaxent AT carrollmarkl towardamontecarloapproachtoselectingclimatevariablesinmaxent AT gillrogerl towardamontecarloapproachtoselectingclimatevariablesinmaxent AT tamkinglenns towardamontecarloapproachtoselectingclimatevariablesinmaxent AT lijian towardamontecarloapproachtoselectingclimatevariablesinmaxent AT strongsavannahl towardamontecarloapproachtoselectingclimatevariablesinmaxent AT maxwellthomasp towardamontecarloapproachtoselectingclimatevariablesinmaxent AT aronnemarye towardamontecarloapproachtoselectingclimatevariablesinmaxent AT spradlincalebs towardamontecarloapproachtoselectingclimatevariablesinmaxent |