Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean

Detalhes bibliográficos
Autor(a) principal: Ramos Martins, Manuel
Data de Publicação: 2021
Outros Autores: Assis, Jorge, Abecasis, David
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.1/15638
Resumo: Aim: With climate change challenging marine biodiversity and resource management, it is crucial to anticipate future latitudinal and depth shifts under contrasting global change scenarios to support policy-relevant biodiversity impact assessments [e.g., Intergovernmental Panel on Climate Change (IPCC)]. We aim to demonstrate the benefits of complying with the Paris Agreement (United Nations Framework Convention on Climate Change) and limiting environmental changes, by assessing future distributional shifts of 10 commercially important demersal fish species. Location: Northern Atlantic Ocean. Time period: Analyses of distributional shifts compared near present-day conditions (2000–2017) with two Representative Concentration Pathway (RCP) scenarios of future climate changes (2090–2100): one following the Paris Agreement climate forcing (RCP2.6) and another without stringent mitigation measures (RCP8.5). Major taxa studied: Demersal fish. Methods: We use machine learning distribution models coupled with biologically meaningful predictors to project future latitudinal and depth shifts. Structuring projections with information beyond temperature-based predictors allowed us to encompass the physiological limitations of species better. Results: Our models highlighted the additional roles of temperature, primary productivity and dissolved oxygen in shaping fish distributions (average relative contribution to the models of 32.12 ± 10.24, 15.6 ± 7.5 and 12.1 ± 6.1%, respectively). We anticipated a generalized trend of poleward shifts in both future scenarios, with aggravated changes in suitable area with RCP8.5 (average area loss with RCP2.6 = 13.3 ± 4.1%; RCP8.5 = 40.9 ± 13.3%). Shifts to deeper waters were also predicted to be of greater magnitude with RCP8.5 (average depth gain = 25.4 ± 21.5 m) than with RCP2.6 (average depth gain = 10.4 ± 7.9 m). Habitat losses were projected mostly in the Mediterranean, Celtic and Irish Seas, the southern areas of the North Sea and along the NE coast of North America. Main conclusions: Inclusion of biologically meaningful predictors beyond temperature in species distribution modelling can improve predictive performances. Limiting future climate changes by complying with the Paris Agreement can translate into reduced distributional shifts, supporting biodiversity conservation and resource management.
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spelling Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic OceanBiologically meaningful predictorsClimate changeDemersal fishNorth Atlantic fisheriesParis agreementSpecies distribution modellingAim: With climate change challenging marine biodiversity and resource management, it is crucial to anticipate future latitudinal and depth shifts under contrasting global change scenarios to support policy-relevant biodiversity impact assessments [e.g., Intergovernmental Panel on Climate Change (IPCC)]. We aim to demonstrate the benefits of complying with the Paris Agreement (United Nations Framework Convention on Climate Change) and limiting environmental changes, by assessing future distributional shifts of 10 commercially important demersal fish species. Location: Northern Atlantic Ocean. Time period: Analyses of distributional shifts compared near present-day conditions (2000–2017) with two Representative Concentration Pathway (RCP) scenarios of future climate changes (2090–2100): one following the Paris Agreement climate forcing (RCP2.6) and another without stringent mitigation measures (RCP8.5). Major taxa studied: Demersal fish. Methods: We use machine learning distribution models coupled with biologically meaningful predictors to project future latitudinal and depth shifts. Structuring projections with information beyond temperature-based predictors allowed us to encompass the physiological limitations of species better. Results: Our models highlighted the additional roles of temperature, primary productivity and dissolved oxygen in shaping fish distributions (average relative contribution to the models of 32.12 ± 10.24, 15.6 ± 7.5 and 12.1 ± 6.1%, respectively). We anticipated a generalized trend of poleward shifts in both future scenarios, with aggravated changes in suitable area with RCP8.5 (average area loss with RCP2.6 = 13.3 ± 4.1%; RCP8.5 = 40.9 ± 13.3%). Shifts to deeper waters were also predicted to be of greater magnitude with RCP8.5 (average depth gain = 25.4 ± 21.5 m) than with RCP2.6 (average depth gain = 10.4 ± 7.9 m). Habitat losses were projected mostly in the Mediterranean, Celtic and Irish Seas, the southern areas of the North Sea and along the NE coast of North America. Main conclusions: Inclusion of biologically meaningful predictors beyond temperature in species distribution modelling can improve predictive performances. Limiting future climate changes by complying with the Paris Agreement can translate into reduced distributional shifts, supporting biodiversity conservation and resource management.MAR2020; European Maritime and Fisheries Fund; Fundação para a Ciência e a TecnologiaWileySapientiaRamos Martins, ManuelAssis, JorgeAbecasis, David2022-06-02T00:30:15Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/15638eng1466-822X10.1111/geb.13327info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:28:04Zoai:sapientia.ualg.pt:10400.1/15638Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:06:27.796252Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean
title Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean
spellingShingle Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean
Ramos Martins, Manuel
Biologically meaningful predictors
Climate change
Demersal fish
North Atlantic fisheries
Paris agreement
Species distribution modelling
title_short Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean
title_full Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean
title_fullStr Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean
title_full_unstemmed Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean
title_sort Biologically meaningful distribution models highlight the benefits of the Paris Agreement for demersal fishing targets in the North Atlantic Ocean
author Ramos Martins, Manuel
author_facet Ramos Martins, Manuel
Assis, Jorge
Abecasis, David
author_role author
author2 Assis, Jorge
Abecasis, David
author2_role author
author
dc.contributor.none.fl_str_mv Sapientia
dc.contributor.author.fl_str_mv Ramos Martins, Manuel
Assis, Jorge
Abecasis, David
dc.subject.por.fl_str_mv Biologically meaningful predictors
Climate change
Demersal fish
North Atlantic fisheries
Paris agreement
Species distribution modelling
topic Biologically meaningful predictors
Climate change
Demersal fish
North Atlantic fisheries
Paris agreement
Species distribution modelling
description Aim: With climate change challenging marine biodiversity and resource management, it is crucial to anticipate future latitudinal and depth shifts under contrasting global change scenarios to support policy-relevant biodiversity impact assessments [e.g., Intergovernmental Panel on Climate Change (IPCC)]. We aim to demonstrate the benefits of complying with the Paris Agreement (United Nations Framework Convention on Climate Change) and limiting environmental changes, by assessing future distributional shifts of 10 commercially important demersal fish species. Location: Northern Atlantic Ocean. Time period: Analyses of distributional shifts compared near present-day conditions (2000–2017) with two Representative Concentration Pathway (RCP) scenarios of future climate changes (2090–2100): one following the Paris Agreement climate forcing (RCP2.6) and another without stringent mitigation measures (RCP8.5). Major taxa studied: Demersal fish. Methods: We use machine learning distribution models coupled with biologically meaningful predictors to project future latitudinal and depth shifts. Structuring projections with information beyond temperature-based predictors allowed us to encompass the physiological limitations of species better. Results: Our models highlighted the additional roles of temperature, primary productivity and dissolved oxygen in shaping fish distributions (average relative contribution to the models of 32.12 ± 10.24, 15.6 ± 7.5 and 12.1 ± 6.1%, respectively). We anticipated a generalized trend of poleward shifts in both future scenarios, with aggravated changes in suitable area with RCP8.5 (average area loss with RCP2.6 = 13.3 ± 4.1%; RCP8.5 = 40.9 ± 13.3%). Shifts to deeper waters were also predicted to be of greater magnitude with RCP8.5 (average depth gain = 25.4 ± 21.5 m) than with RCP2.6 (average depth gain = 10.4 ± 7.9 m). Habitat losses were projected mostly in the Mediterranean, Celtic and Irish Seas, the southern areas of the North Sea and along the NE coast of North America. Main conclusions: Inclusion of biologically meaningful predictors beyond temperature in species distribution modelling can improve predictive performances. Limiting future climate changes by complying with the Paris Agreement can translate into reduced distributional shifts, supporting biodiversity conservation and resource management.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
2022-06-02T00:30:15Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.1/15638
url http://hdl.handle.net/10400.1/15638
dc.language.iso.fl_str_mv eng
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10.1111/geb.13327
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dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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