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Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree
Identifying and predicting how species ranges will shift in response to climate change is paramount for conservation and restoration. Ecological niche models are the most common method used to estimate potential distributions of species; however, they traditionally omit knowledge of intraspecific va...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9484645/ https://www.ncbi.nlm.nih.gov/pubmed/36121872 http://dx.doi.org/10.1371/journal.pone.0274892 |
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author | Bayliss, Shannon L. J. Papeş, Monica Schweitzer, Jennifer A. Bailey, Joseph K. |
author_facet | Bayliss, Shannon L. J. Papeş, Monica Schweitzer, Jennifer A. Bailey, Joseph K. |
author_sort | Bayliss, Shannon L. J. |
collection | PubMed |
description | Identifying and predicting how species ranges will shift in response to climate change is paramount for conservation and restoration. Ecological niche models are the most common method used to estimate potential distributions of species; however, they traditionally omit knowledge of intraspecific variation that can allow populations to respond uniquely to change. Here, we aim to test how population X environment relationships influence predicted suitable geographic distributions by comparing aggregated population-level models with species-level model predictions of suitable habitat within population ranges and across the species’ range. We also test the effect of two variable selection methods on these predictions–both addressing the possibility of local adaptation: Models were built with (a) a common set, and number, of predictors and, (b) a unique combination and number of predictors specific to each group’s training extent. Our study addresses the overarching hypothesis that populations have unique environmental niches, and specifically that (1) species-level models predict more suitable habitat within the ranges of genetic populations than individual models built from those groups, particularly when compared models are built with the same set of environmental predictors; and (2) aggregated genetic population models predict more suitable habitat across the species’ range than the species-level model, an = d this difference will increase when models are trained with individualized predictors. We found the species models predicted more habitat within population ranges for two of three genetic groups regardless of variable selection, and that aggregated population models predicted more habitat than species’ models, but that individualized predictors increased this difference. Our study emphasizes the extent to which changes to model predictions depend on the inclusion of genetic information and on the type and selection of predictors. Results from these modeling decisions can have broad implications for predicting population-level ecological and evolutionary responses to climate change. |
format | Online Article Text |
id | pubmed-9484645 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-94846452022-09-20 Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree Bayliss, Shannon L. J. Papeş, Monica Schweitzer, Jennifer A. Bailey, Joseph K. PLoS One Research Article Identifying and predicting how species ranges will shift in response to climate change is paramount for conservation and restoration. Ecological niche models are the most common method used to estimate potential distributions of species; however, they traditionally omit knowledge of intraspecific variation that can allow populations to respond uniquely to change. Here, we aim to test how population X environment relationships influence predicted suitable geographic distributions by comparing aggregated population-level models with species-level model predictions of suitable habitat within population ranges and across the species’ range. We also test the effect of two variable selection methods on these predictions–both addressing the possibility of local adaptation: Models were built with (a) a common set, and number, of predictors and, (b) a unique combination and number of predictors specific to each group’s training extent. Our study addresses the overarching hypothesis that populations have unique environmental niches, and specifically that (1) species-level models predict more suitable habitat within the ranges of genetic populations than individual models built from those groups, particularly when compared models are built with the same set of environmental predictors; and (2) aggregated genetic population models predict more suitable habitat across the species’ range than the species-level model, an = d this difference will increase when models are trained with individualized predictors. We found the species models predicted more habitat within population ranges for two of three genetic groups regardless of variable selection, and that aggregated population models predicted more habitat than species’ models, but that individualized predictors increased this difference. Our study emphasizes the extent to which changes to model predictions depend on the inclusion of genetic information and on the type and selection of predictors. Results from these modeling decisions can have broad implications for predicting population-level ecological and evolutionary responses to climate change. Public Library of Science 2022-09-19 /pmc/articles/PMC9484645/ /pubmed/36121872 http://dx.doi.org/10.1371/journal.pone.0274892 Text en © 2022 Bayliss et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Bayliss, Shannon L. J. Papeş, Monica Schweitzer, Jennifer A. Bailey, Joseph K. Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree |
title | Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree |
title_full | Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree |
title_fullStr | Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree |
title_full_unstemmed | Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree |
title_short | Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree |
title_sort | aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9484645/ https://www.ncbi.nlm.nih.gov/pubmed/36121872 http://dx.doi.org/10.1371/journal.pone.0274892 |
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