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Confronting species distribution model predictions with species functional traits

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development o...

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Autores principales: Wittmann, Marion E., Barnes, Matthew A., Jerde, Christopher L., Jones, Lisa A., Lodge, David M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761765/
https://www.ncbi.nlm.nih.gov/pubmed/26941933
http://dx.doi.org/10.1002/ece3.1898
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author Wittmann, Marion E.
Barnes, Matthew A.
Jerde, Christopher L.
Jones, Lisa A.
Lodge, David M.
author_facet Wittmann, Marion E.
Barnes, Matthew A.
Jerde, Christopher L.
Jones, Lisa A.
Lodge, David M.
author_sort Wittmann, Marion E.
collection PubMed
description Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(−1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = −0.26, 95% CI (−0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.
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spelling pubmed-47617652016-03-03 Confronting species distribution model predictions with species functional traits Wittmann, Marion E. Barnes, Matthew A. Jerde, Christopher L. Jones, Lisa A. Lodge, David M. Ecol Evol British Ecological Society Special Feature: Demography Beyond the Population Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day(−1). Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = −0.26, 95% CI (−0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms. John Wiley and Sons Inc. 2016-02-22 /pmc/articles/PMC4761765/ /pubmed/26941933 http://dx.doi.org/10.1002/ece3.1898 Text en © 2016 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle British Ecological Society Special Feature: Demography Beyond the Population
Wittmann, Marion E.
Barnes, Matthew A.
Jerde, Christopher L.
Jones, Lisa A.
Lodge, David M.
Confronting species distribution model predictions with species functional traits
title Confronting species distribution model predictions with species functional traits
title_full Confronting species distribution model predictions with species functional traits
title_fullStr Confronting species distribution model predictions with species functional traits
title_full_unstemmed Confronting species distribution model predictions with species functional traits
title_short Confronting species distribution model predictions with species functional traits
title_sort confronting species distribution model predictions with species functional traits
topic British Ecological Society Special Feature: Demography Beyond the Population
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761765/
https://www.ncbi.nlm.nih.gov/pubmed/26941933
http://dx.doi.org/10.1002/ece3.1898
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