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Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal

2023Journal article Open access
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dc.contributor.authorAlegria, C.M.M.
dc.contributor.authorAlmeida, Alice
dc.contributor.authorRoque, Natália
dc.contributor.authorFernandez, Paulo
dc.contributor.authorRibeiro, M.M.A.
dc.date.accessioned2023-03-17T10:52:07Z
dc.date.available2023-03-17T10:52:07Z
dc.date.issued2023
dc.description.abstractTo 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationALEGRIA, 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/f14030591pt_PT
dc.identifier.doi14, 591. https://doi.org/10.3390/f14030591pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/8412
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationFEDER-01-0145-FEDER-000020pt_PT
dc.relationResearch Center in Natural Resources, Environment and Society
dc.relationForest Research Centre
dc.relationMediterranean Institute for Agriculture, Environment and Development
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectSpecies distribution modellingpt_PT
dc.subjectSpecies ecological envelopept_PT
dc.subjectMaxEnt softwarept_PT
dc.subjectRCP 4.5 to RCP 8.5 climate change scenariospt_PT
dc.subjectConcordancept_PT
dc.titleSpecies distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugalpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Center in Natural Resources, Environment and Society
oaire.awardTitleForest Research Centre
oaire.awardTitleMediterranean Institute for Agriculture, Environment and Development
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00681%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00239%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05183%2F2020/PT
oaire.citation.titleForestspt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAlegria
person.familyNameMartins Roque
person.familyNameFernandez
person.familyNameRibeiro
person.givenNameCristina Maria
person.givenNameNatália
person.givenNamePaulo
person.givenNameMaria Margarida
person.identifieruser=YnBEv3oAAAAJ&hl=en
person.identifierNatália Martins Roque
person.identifier349097
person.identifier.ciencia-id9311-1EE5-AB03
person.identifier.ciencia-id451A-332D-0798
person.identifier.ciencia-id9E1E-1C0B-0DCB
person.identifier.ciencia-idAD12-4D32-7A48
person.identifier.orcid0000-0002-6906-6660
person.identifier.orcid0000-0001-8859-4365
person.identifier.orcid0000-0001-7252-8320
person.identifier.orcid0000-0003-4684-1262
person.identifier.ridM-4235-2013
person.identifier.scopus-author-id36952993700
person.identifier.scopus-author-id57200227653
person.identifier.scopus-author-id56675313500
person.identifier.scopus-author-id7201715611
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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