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Insights from the study of complex systems for the ecology and evolution of animal populations

Populations of animals comprise many individuals, interacting in multiple contexts, and displaying heterogeneous behaviors. The interactions among individuals can often create population dynamics that are fundamentally deterministic yet display unpredictable dynamics. Animal populations can, therefo...

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Detalles Bibliográficos
Autores principales: Fisher, David N, Pruitt, Jonathan N
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7245006/
https://www.ncbi.nlm.nih.gov/pubmed/32467699
http://dx.doi.org/10.1093/cz/zoz016
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author Fisher, David N
Pruitt, Jonathan N
author_facet Fisher, David N
Pruitt, Jonathan N
author_sort Fisher, David N
collection PubMed
description Populations of animals comprise many individuals, interacting in multiple contexts, and displaying heterogeneous behaviors. The interactions among individuals can often create population dynamics that are fundamentally deterministic yet display unpredictable dynamics. Animal populations can, therefore, be thought of as complex systems. Complex systems display properties such as nonlinearity and uncertainty and show emergent properties that cannot be explained by a simple sum of the interacting components. Any system where entities compete, cooperate, or interfere with one another may possess such qualities, making animal populations similar on many levels to complex systems. Some fields are already embracing elements of complexity to help understand the dynamics of animal populations, but a wider application of complexity science in ecology and evolution has not occurred. We review here how approaches from complexity science could be applied to the study of the interactions and behavior of individuals within animal populations and highlight how this way of thinking can enhance our understanding of population dynamics in animals. We focus on 8 key characteristics of complex systems: hierarchy, heterogeneity, self-organization, openness, adaptation, memory, nonlinearity, and uncertainty. For each topic we discuss how concepts from complexity theory are applicable in animal populations and emphasize the unique insights they provide. We finish by outlining outstanding questions or predictions to be evaluated using behavioral and ecological data. Our goal throughout this article is to familiarize animal ecologists with the basics of each of these concepts and highlight the new perspectives that they could bring to variety of subfields.
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spelling pubmed-72450062020-05-27 Insights from the study of complex systems for the ecology and evolution of animal populations Fisher, David N Pruitt, Jonathan N Curr Zool Articles Populations of animals comprise many individuals, interacting in multiple contexts, and displaying heterogeneous behaviors. The interactions among individuals can often create population dynamics that are fundamentally deterministic yet display unpredictable dynamics. Animal populations can, therefore, be thought of as complex systems. Complex systems display properties such as nonlinearity and uncertainty and show emergent properties that cannot be explained by a simple sum of the interacting components. Any system where entities compete, cooperate, or interfere with one another may possess such qualities, making animal populations similar on many levels to complex systems. Some fields are already embracing elements of complexity to help understand the dynamics of animal populations, but a wider application of complexity science in ecology and evolution has not occurred. We review here how approaches from complexity science could be applied to the study of the interactions and behavior of individuals within animal populations and highlight how this way of thinking can enhance our understanding of population dynamics in animals. We focus on 8 key characteristics of complex systems: hierarchy, heterogeneity, self-organization, openness, adaptation, memory, nonlinearity, and uncertainty. For each topic we discuss how concepts from complexity theory are applicable in animal populations and emphasize the unique insights they provide. We finish by outlining outstanding questions or predictions to be evaluated using behavioral and ecological data. Our goal throughout this article is to familiarize animal ecologists with the basics of each of these concepts and highlight the new perspectives that they could bring to variety of subfields. Oxford University Press 2020-02 2019-04-23 /pmc/articles/PMC7245006/ /pubmed/32467699 http://dx.doi.org/10.1093/cz/zoz016 Text en © The Author(s) (2019). Published by Oxford University Press on behalf of Editorial Office, Current Zoology. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Articles
Fisher, David N
Pruitt, Jonathan N
Insights from the study of complex systems for the ecology and evolution of animal populations
title Insights from the study of complex systems for the ecology and evolution of animal populations
title_full Insights from the study of complex systems for the ecology and evolution of animal populations
title_fullStr Insights from the study of complex systems for the ecology and evolution of animal populations
title_full_unstemmed Insights from the study of complex systems for the ecology and evolution of animal populations
title_short Insights from the study of complex systems for the ecology and evolution of animal populations
title_sort insights from the study of complex systems for the ecology and evolution of animal populations
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7245006/
https://www.ncbi.nlm.nih.gov/pubmed/32467699
http://dx.doi.org/10.1093/cz/zoz016
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