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Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales

Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization (genes, population, species, ecological communities and ecosyste...

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Autores principales: Gaggiotti, Oscar E., Chao, Anne, Peres‐Neto, Pedro, Chiu, Chun‐Huo, Edwards, Christine, Fortin, Marie‐Josée, Jost, Lou, Richards, Christopher M., Selkoe, Kimberly A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6050189/
https://www.ncbi.nlm.nih.gov/pubmed/30026805
http://dx.doi.org/10.1111/eva.12593
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author Gaggiotti, Oscar E.
Chao, Anne
Peres‐Neto, Pedro
Chiu, Chun‐Huo
Edwards, Christine
Fortin, Marie‐Josée
Jost, Lou
Richards, Christopher M.
Selkoe, Kimberly A.
author_facet Gaggiotti, Oscar E.
Chao, Anne
Peres‐Neto, Pedro
Chiu, Chun‐Huo
Edwards, Christine
Fortin, Marie‐Josée
Jost, Lou
Richards, Christopher M.
Selkoe, Kimberly A.
author_sort Gaggiotti, Oscar E.
collection PubMed
description Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization (genes, population, species, ecological communities and ecosystems), a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap, we present a unifying framework for the measurement of biodiversity across hierarchical levels of organization. Our weighted, information‐based decomposition framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon's entropy. We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components (alleles, species, etc.), we applied the framework to a real data set on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics and eco‐evolutionary dynamics.
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spelling pubmed-60501892018-07-19 Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales Gaggiotti, Oscar E. Chao, Anne Peres‐Neto, Pedro Chiu, Chun‐Huo Edwards, Christine Fortin, Marie‐Josée Jost, Lou Richards, Christopher M. Selkoe, Kimberly A. Evol Appl Original Articles Biological diversity is a key concept in the life sciences and plays a fundamental role in many ecological and evolutionary processes. Although biodiversity is inherently a hierarchical concept covering different levels of organization (genes, population, species, ecological communities and ecosystems), a diversity index that behaves consistently across these different levels has so far been lacking, hindering the development of truly integrative biodiversity studies. To fill this important knowledge gap, we present a unifying framework for the measurement of biodiversity across hierarchical levels of organization. Our weighted, information‐based decomposition framework is based on a Hill number of order q = 1, which weights all elements in proportion to their frequency and leads to diversity measures based on Shannon's entropy. We investigated the numerical behaviour of our approach with simulations and showed that it can accurately describe complex spatial hierarchical structures. To demonstrate the intuitive and straightforward interpretation of our diversity measures in terms of effective number of components (alleles, species, etc.), we applied the framework to a real data set on coral reef biodiversity. We expect our framework will have multiple applications covering the fields of conservation biology, community genetics and eco‐evolutionary dynamics. John Wiley and Sons Inc. 2018-02-20 /pmc/articles/PMC6050189/ /pubmed/30026805 http://dx.doi.org/10.1111/eva.12593 Text en © 2018 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd This is an open access article under the terms of the 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 Original Articles
Gaggiotti, Oscar E.
Chao, Anne
Peres‐Neto, Pedro
Chiu, Chun‐Huo
Edwards, Christine
Fortin, Marie‐Josée
Jost, Lou
Richards, Christopher M.
Selkoe, Kimberly A.
Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales
title Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales
title_full Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales
title_fullStr Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales
title_full_unstemmed Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales
title_short Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales
title_sort diversity from genes to ecosystems: a unifying framework to study variation across biological metrics and scales
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6050189/
https://www.ncbi.nlm.nih.gov/pubmed/30026805
http://dx.doi.org/10.1111/eva.12593
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