Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropy

The concept of resilience has become popular in many disciplines far beyond its original use in the field of ecology. Despite of its wide use, it has received different definitions not always coincident. Such ambiguity is still more evident in its quantitative characterization. Most of the available...

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Autores: Ginebreda, Antoni, Sabater Liesa, Laia, Barceló i Cullerés, Damià
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/17660
Acceso en línea:http://hdl.handle.net/10256/17660
Access Level:acceso abierto
Palabra clave:Resiliència (Ecologia)
Resilience (Ecology)
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spelling Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropyGinebreda, AntoniSabater Liesa, LaiaBarceló i Cullerés, DamiàResiliència (Ecologia)Resilience (Ecology)The concept of resilience has become popular in many disciplines far beyond its original use in the field of ecology. Despite of its wide use, it has received different definitions not always coincident. Such ambiguity is still more evident in its quantitative characterization. Most of the available methods are heavily context dependent and often difficult to apply in the practice. Here, we propose to define and calculate resilience starting from the data matrices resulting from multivariate measurements of different biological metrics. - The resilience between two field scenarios (each one characterized by their corresponding datasets) can be conveniently captured as the difference between its respective data complexities. - Complexity is quantified by means of the entropy associated to the spectral distribution of the singular values of each data matrix. - The method proposed has been illustrated with a case study in which the resilience of a river (Ebro River, NE Spain) is calculated comparing six biological metrics associated to the phytoplankton, upstream and downstream to a series of large reservoirs that alter the natural river flow regimeThis study has been financially supported by the EU FP7 project GLOBAQUA [Grant Agreement No. 603629] and by the Generalitat de Catalunya [Consolidated Research Groups: 2017 SGR 01404-Water and Soil Quality Unit]Elsevier2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/17660http://hdl.handle.net/10256/17660MethodsX, 2019, vol. 6, p. 1668-1676Articles publicats (ICRA)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2019.07.020info:eu-repo/semantics/altIdentifier/eissn/2215-0161info:eu-repo/grantAgreement/EC/FP7/603629Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/176602026-05-29T05:05:01Z
dc.title.none.fl_str_mv Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropy
title Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropy
spellingShingle Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropy
Ginebreda, Antoni
Resiliència (Ecologia)
Resilience (Ecology)
title_short Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropy
title_full Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropy
title_fullStr Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropy
title_full_unstemmed Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropy
title_sort Quantification of ecological complexity and resilience from multivariate biological metrics datasets using singular value decomposition entropy
dc.creator.none.fl_str_mv Ginebreda, Antoni
Sabater Liesa, Laia
Barceló i Cullerés, Damià
author Ginebreda, Antoni
author_facet Ginebreda, Antoni
Sabater Liesa, Laia
Barceló i Cullerés, Damià
author_role author
author2 Sabater Liesa, Laia
Barceló i Cullerés, Damià
author2_role author
author
dc.subject.none.fl_str_mv Resiliència (Ecologia)
Resilience (Ecology)
topic Resiliència (Ecologia)
Resilience (Ecology)
description The concept of resilience has become popular in many disciplines far beyond its original use in the field of ecology. Despite of its wide use, it has received different definitions not always coincident. Such ambiguity is still more evident in its quantitative characterization. Most of the available methods are heavily context dependent and often difficult to apply in the practice. Here, we propose to define and calculate resilience starting from the data matrices resulting from multivariate measurements of different biological metrics. - The resilience between two field scenarios (each one characterized by their corresponding datasets) can be conveniently captured as the difference between its respective data complexities. - Complexity is quantified by means of the entropy associated to the spectral distribution of the singular values of each data matrix. - The method proposed has been illustrated with a case study in which the resilience of a river (Ebro River, NE Spain) is calculated comparing six biological metrics associated to the phytoplankton, upstream and downstream to a series of large reservoirs that alter the natural river flow regime
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/17660
http://hdl.handle.net/10256/17660
url http://hdl.handle.net/10256/17660
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2019.07.020
info:eu-repo/semantics/altIdentifier/eissn/2215-0161
info:eu-repo/grantAgreement/EC/FP7/603629
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv MethodsX, 2019, vol. 6, p. 1668-1676
Articles publicats (ICRA)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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