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Describing and understanding behavioral responses to multiple stressors and multiple stimuli

Understanding the effects of environmental change on natural ecosystems is a major challenge, particularly when multiple stressors interact to produce unexpected “ecological surprises” in the form of complex, nonadditive effects that can amplify or reduce their individual effects. Animals often resp...

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Detalles Bibliográficos
Autores principales: Hale, Robin, Piggott, Jeremy J., Swearer, Stephen E.
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/PMC5214703/
https://www.ncbi.nlm.nih.gov/pubmed/28070273
http://dx.doi.org/10.1002/ece3.2609
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author Hale, Robin
Piggott, Jeremy J.
Swearer, Stephen E.
author_facet Hale, Robin
Piggott, Jeremy J.
Swearer, Stephen E.
author_sort Hale, Robin
collection PubMed
description Understanding the effects of environmental change on natural ecosystems is a major challenge, particularly when multiple stressors interact to produce unexpected “ecological surprises” in the form of complex, nonadditive effects that can amplify or reduce their individual effects. Animals often respond behaviorally to environmental change, and multiple stressors can have both population‐level and community‐level effects. However, the individual, not combined, effects of stressors on animal behavior are commonly studied. There is a need to understand how animals respond to the more complex combinations of stressors that occur in nature, which requires a systematic and rigorous approach to quantify the various potential behavioral responses to the independent and interactive effects of stressors. We illustrate a robust, systematic approach for understanding behavioral responses to multiple stressors based on integrating schemes used to quantitatively classify interactions in multiple‐stressor research and to qualitatively view interactions between multiple stimuli in behavioral experiments. We introduce and unify the two frameworks, highlighting their conceptual and methodological similarities, and use four case studies to demonstrate how this unification could improve our interpretation of interactions in behavioral experiments and guide efforts to manage the effects of multiple stressors. Our unified approach: (1) provides behavioral ecologists with a more rigorous and systematic way to quantify how animals respond to interactions between multiple stimuli, an important theoretical advance, (2) helps us better understand how animals behave when they encounter multiple, potentially interacting stressors, and (3) contributes more generally to the understanding of “ecological surprises” in multiple stressors research.
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spelling pubmed-52147032017-01-09 Describing and understanding behavioral responses to multiple stressors and multiple stimuli Hale, Robin Piggott, Jeremy J. Swearer, Stephen E. Ecol Evol Review Understanding the effects of environmental change on natural ecosystems is a major challenge, particularly when multiple stressors interact to produce unexpected “ecological surprises” in the form of complex, nonadditive effects that can amplify or reduce their individual effects. Animals often respond behaviorally to environmental change, and multiple stressors can have both population‐level and community‐level effects. However, the individual, not combined, effects of stressors on animal behavior are commonly studied. There is a need to understand how animals respond to the more complex combinations of stressors that occur in nature, which requires a systematic and rigorous approach to quantify the various potential behavioral responses to the independent and interactive effects of stressors. We illustrate a robust, systematic approach for understanding behavioral responses to multiple stressors based on integrating schemes used to quantitatively classify interactions in multiple‐stressor research and to qualitatively view interactions between multiple stimuli in behavioral experiments. We introduce and unify the two frameworks, highlighting their conceptual and methodological similarities, and use four case studies to demonstrate how this unification could improve our interpretation of interactions in behavioral experiments and guide efforts to manage the effects of multiple stressors. Our unified approach: (1) provides behavioral ecologists with a more rigorous and systematic way to quantify how animals respond to interactions between multiple stimuli, an important theoretical advance, (2) helps us better understand how animals behave when they encounter multiple, potentially interacting stressors, and (3) contributes more generally to the understanding of “ecological surprises” in multiple stressors research. John Wiley and Sons Inc. 2016-11-29 /pmc/articles/PMC5214703/ /pubmed/28070273 http://dx.doi.org/10.1002/ece3.2609 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 Review
Hale, Robin
Piggott, Jeremy J.
Swearer, Stephen E.
Describing and understanding behavioral responses to multiple stressors and multiple stimuli
title Describing and understanding behavioral responses to multiple stressors and multiple stimuli
title_full Describing and understanding behavioral responses to multiple stressors and multiple stimuli
title_fullStr Describing and understanding behavioral responses to multiple stressors and multiple stimuli
title_full_unstemmed Describing and understanding behavioral responses to multiple stressors and multiple stimuli
title_short Describing and understanding behavioral responses to multiple stressors and multiple stimuli
title_sort describing and understanding behavioral responses to multiple stressors and multiple stimuli
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5214703/
https://www.ncbi.nlm.nih.gov/pubmed/28070273
http://dx.doi.org/10.1002/ece3.2609
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