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Modelling maritime pine (Pinus pinaster Aiton) spatial distribution and productivity in Portugal : tools for forest management.

2021Journal article Open access
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dc.contributor.authorAlegria, C.M.M.
dc.contributor.authorRoque, Natália
dc.contributor.authorAlbuquerque, M.T.D.
dc.contributor.authorFernandez, Paulo
dc.contributor.authorRibeiro, M.M.A.
dc.date.accessioned2021-04-06T10:25:20Z
dc.date.available2021-04-06T10:25:20Z
dc.date.issued2021
dc.description.abstractResearch Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk. Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAlegria, C.[et al.] (2021). Modelling maritime pine (Pinus pinaster Aiton) spatial distribution and productivity in Portugal : tools for forest management. Forests Forests. ISSN 1999-4907. 12:3, 368.pt_PT
dc.identifier.doi10.3390/f12030368pt_PT
dc.identifier.issn1999-4907
dc.identifier.urihttp://hdl.handle.net/10400.11/7497
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/1999-4907/11/8/880pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectEnvironmental datapt_PT
dc.subjectMachine learning modellingpt_PT
dc.subjectSequential Gaussian Simulationpt_PT
dc.subjectWildfirespt_PT
dc.subjectNatural regenerationpt_PT
dc.titleModelling maritime pine (Pinus pinaster Aiton) spatial distribution and productivity in Portugal : tools for forest management.pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceBaselpt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage368pt_PT
oaire.citation.titleForestspt_PT
oaire.citation.volume12pt_PT
person.familyNameAlegria
person.familyNameMartins Roque
person.familyNameAlbuquerque
person.familyNameFernandez
person.familyNameRibeiro
person.givenNameCristina Maria
person.givenNameNatália
person.givenNameMaria Teresa
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-id5A1C-8956-4C0A
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.orcid0000-0002-8782-6133
person.identifier.orcid0000-0001-7252-8320
person.identifier.orcid0000-0003-4684-1262
person.identifier.ridB-1536-2013
person.identifier.ridM-4235-2013
person.identifier.scopus-author-id36952993700
person.identifier.scopus-author-id57200227653
person.identifier.scopus-author-id55507421600
person.identifier.scopus-author-id56675313500
person.identifier.scopus-author-id7201715611
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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