Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.

Detalhes bibliográficos
Autor(a) principal: Almeida, Alice Maria
Data de Publicação: 2023
Outros Autores: Ribeiro, Maria Margarida, Ferreira, Miguel R., Roque, Natália, Quintela-Sabarís, Celestino, Fernandez, Paulo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.5/29158
Resumo: Climate change’s huge impact on Mediterranean species’ habitat suitability and spatial and temporal distribution in the coming decades is expected. The present work aimed to reconstruct rockrose (Cistus ladanifer L.) historical and future spatial distribution, a typically Mediterranean species with abundant occurrence in North Africa, Iberian Peninsula, and Southern France. The R ensemble modeling approach was made using the biomod2 package to assess changes in the spatial distribution of the species in the Last Interglacial (LIG), the Last Glacial Maximum (LGM), and the Middle Holocene (MH), in the present, and in the future (for the years 2050 and 2070), considering two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The current species potential distribution was modeled using 2,833 occurrences, six bioclimatic variables, and four algorithms, Generalized Linear Model (GLM), MaxEnt, Multivariate Adaptive Regression Splines (MARS), and Artificial Neural Networks (ANN). Two global climate models (GCMs), CCSM4 and MRI-CGCM3, were used to forecast past and future suitability. The potential area of occurrence of the species is equal to 15.8 and 14.1% of the study area for current and LIG conditions, while it decreased to 3.8% in the LGM. The species’ presence diaminished more than half in the RCP 4.5 (to 6.8% in 2050 and 7% in 2070), and a too low figure (2.2%) in the worst-case scenario (RCP 8.5) for 2070. The results suggested that the current climatic conditions are the most suitable for the species’ occurrence and that future changes in environmental conditions may lead to the loss of suitable habitats, especially in the worst-case scenario. The information unfolded by this study will help to understand future predictable desertification in the Mediterranean region and to help policymakers to implement possible measures for biodiversity maintenance and desertification avoidance.
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spelling Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.Rock rosespecies distribution modelingbiomod2ensemble modelingclimate changeClimate change’s huge impact on Mediterranean species’ habitat suitability and spatial and temporal distribution in the coming decades is expected. The present work aimed to reconstruct rockrose (Cistus ladanifer L.) historical and future spatial distribution, a typically Mediterranean species with abundant occurrence in North Africa, Iberian Peninsula, and Southern France. The R ensemble modeling approach was made using the biomod2 package to assess changes in the spatial distribution of the species in the Last Interglacial (LIG), the Last Glacial Maximum (LGM), and the Middle Holocene (MH), in the present, and in the future (for the years 2050 and 2070), considering two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The current species potential distribution was modeled using 2,833 occurrences, six bioclimatic variables, and four algorithms, Generalized Linear Model (GLM), MaxEnt, Multivariate Adaptive Regression Splines (MARS), and Artificial Neural Networks (ANN). Two global climate models (GCMs), CCSM4 and MRI-CGCM3, were used to forecast past and future suitability. The potential area of occurrence of the species is equal to 15.8 and 14.1% of the study area for current and LIG conditions, while it decreased to 3.8% in the LGM. The species’ presence diaminished more than half in the RCP 4.5 (to 6.8% in 2050 and 7% in 2070), and a too low figure (2.2%) in the worst-case scenario (RCP 8.5) for 2070. The results suggested that the current climatic conditions are the most suitable for the species’ occurrence and that future changes in environmental conditions may lead to the loss of suitable habitats, especially in the worst-case scenario. The information unfolded by this study will help to understand future predictable desertification in the Mediterranean region and to help policymakers to implement possible measures for biodiversity maintenance and desertification avoidance.FrontiersRepositório da Universidade de LisboaAlmeida, Alice MariaRibeiro, Maria MargaridaFerreira, Miguel R.Roque, NatáliaQuintela-Sabarís, CelestinoFernandez, Paulo2023-10-30T15:44:16Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/29158engAlmeida AM, Ribeiro MM, Ferreira MR, Roque N, Quintela-Sabarís C and Fernandez P (2023) Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L. Front. Ecol. Evol. 11:113622410.3389/fevo.2023.1136224info: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:RCAAP2023-11-05T01:31:48Zoai:www.repository.utl.pt:10400.5/29158Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:26:46.724542Repositório 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 Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.
title Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.
spellingShingle Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.
Almeida, Alice Maria
Rock rose
species distribution modeling
biomod2
ensemble modeling
climate change
title_short Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.
title_full Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.
title_fullStr Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.
title_full_unstemmed Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.
title_sort Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L.
author Almeida, Alice Maria
author_facet Almeida, Alice Maria
Ribeiro, Maria Margarida
Ferreira, Miguel R.
Roque, Natália
Quintela-Sabarís, Celestino
Fernandez, Paulo
author_role author
author2 Ribeiro, Maria Margarida
Ferreira, Miguel R.
Roque, Natália
Quintela-Sabarís, Celestino
Fernandez, Paulo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Almeida, Alice Maria
Ribeiro, Maria Margarida
Ferreira, Miguel R.
Roque, Natália
Quintela-Sabarís, Celestino
Fernandez, Paulo
dc.subject.por.fl_str_mv Rock rose
species distribution modeling
biomod2
ensemble modeling
climate change
topic Rock rose
species distribution modeling
biomod2
ensemble modeling
climate change
description Climate change’s huge impact on Mediterranean species’ habitat suitability and spatial and temporal distribution in the coming decades is expected. The present work aimed to reconstruct rockrose (Cistus ladanifer L.) historical and future spatial distribution, a typically Mediterranean species with abundant occurrence in North Africa, Iberian Peninsula, and Southern France. The R ensemble modeling approach was made using the biomod2 package to assess changes in the spatial distribution of the species in the Last Interglacial (LIG), the Last Glacial Maximum (LGM), and the Middle Holocene (MH), in the present, and in the future (for the years 2050 and 2070), considering two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The current species potential distribution was modeled using 2,833 occurrences, six bioclimatic variables, and four algorithms, Generalized Linear Model (GLM), MaxEnt, Multivariate Adaptive Regression Splines (MARS), and Artificial Neural Networks (ANN). Two global climate models (GCMs), CCSM4 and MRI-CGCM3, were used to forecast past and future suitability. The potential area of occurrence of the species is equal to 15.8 and 14.1% of the study area for current and LIG conditions, while it decreased to 3.8% in the LGM. The species’ presence diaminished more than half in the RCP 4.5 (to 6.8% in 2050 and 7% in 2070), and a too low figure (2.2%) in the worst-case scenario (RCP 8.5) for 2070. The results suggested that the current climatic conditions are the most suitable for the species’ occurrence and that future changes in environmental conditions may lead to the loss of suitable habitats, especially in the worst-case scenario. The information unfolded by this study will help to understand future predictable desertification in the Mediterranean region and to help policymakers to implement possible measures for biodiversity maintenance and desertification avoidance.
publishDate 2023
dc.date.none.fl_str_mv 2023-10-30T15:44:16Z
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/10400.5/29158
url http://hdl.handle.net/10400.5/29158
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Almeida AM, Ribeiro MM, Ferreira MR, Roque N, Quintela-Sabarís C and Fernandez P (2023) Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L. Front. Ecol. Evol. 11:1136224
10.3389/fevo.2023.1136224
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 Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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