Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.11/8412 |
Resumo: | To date, a variety of species potential distribution mapping approaches have been used, and the agreement in maps produced with different methodological approaches should be assessed. The aims of this study were: (1) to model Maritime pine potential distributions for the present and for the future under two climate change scenarios using the machine learning Maximum Entropy algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental data set and climate change scenarios; and (3) to perform an agreement analysis for the species distribution maps produced with both methodological approaches. The species distribution maps produced by each of the methodological approaches under study were reclassified into presence– absence binary maps of species to perform the agreement analysis. The results showed that the MaxEnt-predicted map for the present matched well the species’ current distribution, but the species ecological envelope map, also for the present, was closer to the species’ empiric potential distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in species ecological envelopes maps were higher and with greater future losses than the latter. The 76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance for the species occupancy in the present, decreasing around 30%–35% in the future under the climate change scenarios. Both methodologies proved to be complementary to set species’ best suitability areas, which are key as support decision tools for planning afforestation and forest management to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and productivity. |
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Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in PortugalSpecies distribution modellingSpecies ecological envelopeMaxEnt softwareRCP 4.5 to RCP 8.5 climate change scenariosConcordanceTo date, a variety of species potential distribution mapping approaches have been used, and the agreement in maps produced with different methodological approaches should be assessed. The aims of this study were: (1) to model Maritime pine potential distributions for the present and for the future under two climate change scenarios using the machine learning Maximum Entropy algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental data set and climate change scenarios; and (3) to perform an agreement analysis for the species distribution maps produced with both methodological approaches. The species distribution maps produced by each of the methodological approaches under study were reclassified into presence– absence binary maps of species to perform the agreement analysis. The results showed that the MaxEnt-predicted map for the present matched well the species’ current distribution, but the species ecological envelope map, also for the present, was closer to the species’ empiric potential distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in species ecological envelopes maps were higher and with greater future losses than the latter. The 76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance for the species occupancy in the present, decreasing around 30%–35% in the future under the climate change scenarios. Both methodologies proved to be complementary to set species’ best suitability areas, which are key as support decision tools for planning afforestation and forest management to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and productivity.MDPIRepositório Científico do Instituto Politécnico de Castelo BrancoAlegria, C.M.M.Almeida, Alice M.Roque, N.Fernandez, P.Ribeiro, M.M.A.2023-03-17T10:52:07Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/8412engALEGRIA, C.M.M. [et al.] (2023) - Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal. Forests. 14, 591. DOI 14, 591. https://doi.org/10.3390/f1403059114, 591. https://doi.org/10.3390/f14030591info: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:RCAAP2023-03-25T01:48:09ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal |
title |
Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal |
spellingShingle |
Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal Alegria, C.M.M. Species distribution modelling Species ecological envelope MaxEnt software RCP 4.5 to RCP 8.5 climate change scenarios Concordance |
title_short |
Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal |
title_full |
Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal |
title_fullStr |
Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal |
title_full_unstemmed |
Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal |
title_sort |
Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal |
author |
Alegria, C.M.M. |
author_facet |
Alegria, C.M.M. Almeida, Alice M. Roque, N. Fernandez, P. Ribeiro, M.M.A. |
author_role |
author |
author2 |
Almeida, Alice M. Roque, N. Fernandez, P. Ribeiro, M.M.A. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico de Castelo Branco |
dc.contributor.author.fl_str_mv |
Alegria, C.M.M. Almeida, Alice M. Roque, N. Fernandez, P. Ribeiro, M.M.A. |
dc.subject.por.fl_str_mv |
Species distribution modelling Species ecological envelope MaxEnt software RCP 4.5 to RCP 8.5 climate change scenarios Concordance |
topic |
Species distribution modelling Species ecological envelope MaxEnt software RCP 4.5 to RCP 8.5 climate change scenarios Concordance |
description |
To date, a variety of species potential distribution mapping approaches have been used, and the agreement in maps produced with different methodological approaches should be assessed. The aims of this study were: (1) to model Maritime pine potential distributions for the present and for the future under two climate change scenarios using the machine learning Maximum Entropy algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental data set and climate change scenarios; and (3) to perform an agreement analysis for the species distribution maps produced with both methodological approaches. The species distribution maps produced by each of the methodological approaches under study were reclassified into presence– absence binary maps of species to perform the agreement analysis. The results showed that the MaxEnt-predicted map for the present matched well the species’ current distribution, but the species ecological envelope map, also for the present, was closer to the species’ empiric potential distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in species ecological envelopes maps were higher and with greater future losses than the latter. The 76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance for the species occupancy in the present, decreasing around 30%–35% in the future under the climate change scenarios. Both methodologies proved to be complementary to set species’ best suitability areas, which are key as support decision tools for planning afforestation and forest management to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and productivity. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-03-17T10:52:07Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.11/8412 |
url |
http://hdl.handle.net/10400.11/8412 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ALEGRIA, C.M.M. [et al.] (2023) - Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal. Forests. 14, 591. DOI 14, 591. https://doi.org/10.3390/f14030591 14, 591. https://doi.org/10.3390/f14030591 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
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instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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