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Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail
Modeling the biogeographic consequences of climate change requires confidence in model predictions under novel conditions. However, models often fail when extended to new locales, and such instances have been used as evidence of a change in physiological tolerance, that is, a fundamental niche shift...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Blackwell Publishing Ltd
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797481/ https://www.ncbi.nlm.nih.gov/pubmed/24223272 http://dx.doi.org/10.1002/ece3.680 |
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author | Woodin, Sarah A Hilbish, Thomas J Helmuth, Brian Jones, Sierra J Wethey, David S |
author_facet | Woodin, Sarah A Hilbish, Thomas J Helmuth, Brian Jones, Sierra J Wethey, David S |
author_sort | Woodin, Sarah A |
collection | PubMed |
description | Modeling the biogeographic consequences of climate change requires confidence in model predictions under novel conditions. However, models often fail when extended to new locales, and such instances have been used as evidence of a change in physiological tolerance, that is, a fundamental niche shift. We explore an alternative explanation and propose a method for predicting the likelihood of failure based on physiological performance curves and environmental variance in the original and new environments. We define the transient event margin (TEM) as the gap between energetic performance failure, defined as CT(max), and the upper lethal limit, defined as LT(max). If TEM is large relative to environmental fluctuations, models will likely fail in new locales. If TEM is small relative to environmental fluctuations, models are likely to be robust for new locales, even when mechanism is unknown. Using temperature, we predict when biogeographic models are likely to fail and illustrate this with a case study. We suggest that failure is predictable from an understanding of how climate drives nonlethal physiological responses, but for many species such data have not been collected. Successful biogeographic forecasting thus depends on understanding when the mechanisms limiting distribution of a species will differ among geographic regions, or at different times, resulting in realized niche shifts. TEM allows prediction of the likelihood of such model failure. |
format | Online Article Text |
id | pubmed-3797481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Blackwell Publishing Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-37974812013-11-12 Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail Woodin, Sarah A Hilbish, Thomas J Helmuth, Brian Jones, Sierra J Wethey, David S Ecol Evol Original Research Modeling the biogeographic consequences of climate change requires confidence in model predictions under novel conditions. However, models often fail when extended to new locales, and such instances have been used as evidence of a change in physiological tolerance, that is, a fundamental niche shift. We explore an alternative explanation and propose a method for predicting the likelihood of failure based on physiological performance curves and environmental variance in the original and new environments. We define the transient event margin (TEM) as the gap between energetic performance failure, defined as CT(max), and the upper lethal limit, defined as LT(max). If TEM is large relative to environmental fluctuations, models will likely fail in new locales. If TEM is small relative to environmental fluctuations, models are likely to be robust for new locales, even when mechanism is unknown. Using temperature, we predict when biogeographic models are likely to fail and illustrate this with a case study. We suggest that failure is predictable from an understanding of how climate drives nonlethal physiological responses, but for many species such data have not been collected. Successful biogeographic forecasting thus depends on understanding when the mechanisms limiting distribution of a species will differ among geographic regions, or at different times, resulting in realized niche shifts. TEM allows prediction of the likelihood of such model failure. Blackwell Publishing Ltd 2013-09 2013-08-22 /pmc/articles/PMC3797481/ /pubmed/24223272 http://dx.doi.org/10.1002/ece3.680 Text en © 2013 Published by John Wiley & Sons Ltd http://creativecommons.org/licenses/by/2.5/ Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation. |
spellingShingle | Original Research Woodin, Sarah A Hilbish, Thomas J Helmuth, Brian Jones, Sierra J Wethey, David S Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail |
title | Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail |
title_full | Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail |
title_fullStr | Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail |
title_full_unstemmed | Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail |
title_short | Climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail |
title_sort | climate change, species distribution models, and physiological performance metrics: predicting when biogeographic models are likely to fail |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3797481/ https://www.ncbi.nlm.nih.gov/pubmed/24223272 http://dx.doi.org/10.1002/ece3.680 |
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