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Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling

Individuals along the periphery of a species distribution regularly encounter more challenging environmental and climatic conditions than conspecifics near the center of the distribution. Due to these potential constraints, individuals in peripheral margins are expected to change their habitat and b...

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Autores principales: Barnhart, Paul R., Gillam, Erin H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5147799/
https://www.ncbi.nlm.nih.gov/pubmed/27935936
http://dx.doi.org/10.1371/journal.pone.0152508
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author Barnhart, Paul R.
Gillam, Erin H.
author_facet Barnhart, Paul R.
Gillam, Erin H.
author_sort Barnhart, Paul R.
collection PubMed
description Individuals along the periphery of a species distribution regularly encounter more challenging environmental and climatic conditions than conspecifics near the center of the distribution. Due to these potential constraints, individuals in peripheral margins are expected to change their habitat and behavioral characteristics. Managers typically rely on species distribution maps when developing adequate management practices. However, these range maps are often too simplistic and do not provide adequate information as to what fine-scale biotic and abiotic factors are driving a species occurrence. In the last decade, habitat suitability modelling has become widely used as a substitute for simplistic distribution mapping which allows regional managers the ability to fine-tune management resources. The objectives of this study were to use maximum-entropy modeling to produce habitat suitability models for seven species that have a peripheral margin intersecting the state of North Dakota, according to current IUCN distributions, and determine the vegetative and climatic characteristics driving these models. Mistnetting resulted in the documentation of five species outside the IUCN distribution in North Dakota, indicating that current range maps for North Dakota, and potentially the northern Great Plains, are in need of update. Maximum-entropy modeling showed that temperature and not precipitation were the variables most important for model production. This fine-scale result highlights the importance of habitat suitability modelling as this information cannot be extracted from distribution maps. Our results provide baseline information needed for future research about how and why individuals residing in the peripheral margins of a species’ distribution may show marked differences in habitat use as a result of urban expansion, habitat loss, and climate change compared to more centralized populations.
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spelling pubmed-51477992016-12-28 Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling Barnhart, Paul R. Gillam, Erin H. PLoS One Research Article Individuals along the periphery of a species distribution regularly encounter more challenging environmental and climatic conditions than conspecifics near the center of the distribution. Due to these potential constraints, individuals in peripheral margins are expected to change their habitat and behavioral characteristics. Managers typically rely on species distribution maps when developing adequate management practices. However, these range maps are often too simplistic and do not provide adequate information as to what fine-scale biotic and abiotic factors are driving a species occurrence. In the last decade, habitat suitability modelling has become widely used as a substitute for simplistic distribution mapping which allows regional managers the ability to fine-tune management resources. The objectives of this study were to use maximum-entropy modeling to produce habitat suitability models for seven species that have a peripheral margin intersecting the state of North Dakota, according to current IUCN distributions, and determine the vegetative and climatic characteristics driving these models. Mistnetting resulted in the documentation of five species outside the IUCN distribution in North Dakota, indicating that current range maps for North Dakota, and potentially the northern Great Plains, are in need of update. Maximum-entropy modeling showed that temperature and not precipitation were the variables most important for model production. This fine-scale result highlights the importance of habitat suitability modelling as this information cannot be extracted from distribution maps. Our results provide baseline information needed for future research about how and why individuals residing in the peripheral margins of a species’ distribution may show marked differences in habitat use as a result of urban expansion, habitat loss, and climate change compared to more centralized populations. Public Library of Science 2016-12-09 /pmc/articles/PMC5147799/ /pubmed/27935936 http://dx.doi.org/10.1371/journal.pone.0152508 Text en © 2016 Barnhart, Gillam http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Barnhart, Paul R.
Gillam, Erin H.
Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling
title Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling
title_full Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling
title_fullStr Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling
title_full_unstemmed Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling
title_short Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling
title_sort understanding peripheral bat populations using maximum-entropy suitability modeling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5147799/
https://www.ncbi.nlm.nih.gov/pubmed/27935936
http://dx.doi.org/10.1371/journal.pone.0152508
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