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Modelling invasive alien plant distribution : A literature review of concepts and bibliometric analysis

2021-09Journal article Restricted access
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dc.contributor.authorSilva, Lara Dutra
dc.contributor.authorElias, Rui B.
dc.contributor.authorSilva, Luís
dc.date.accessioned2021-11-24T13:39:02Z
dc.date.available2021-11-24T13:39:02Z
dc.date.issued2021-09
dc.description.abstractIn the last decades, the number of publications dedicated to the application of species distribution models (SDMs) to invasive alien plants (IAPs) has constantly increased. Although recent reviews have addressed very relevant issues in the application of SDMs, the modelling approaches (i.e., algorithms) applied to IAPs have not been systematized. Therefore, we undertook a bibliographic review of articles devoted to SDMs and IAPs, from 1996 to 2019. Our results indicate that maximum entropy, generalized linear models, boosted regression trees and random forest were the four most frequent types of modelling approaches. It was clear that there was a variety of different approaches, regarding the type of algorithm to be used. We discuss the characteristics of the most cited algorithms, providing examples of their application in SDMs dedicated to IAPs. We advocate the use of a combination of different algorithms, an intensive evaluation of predictors, a thorough validation process, and a critical analysis of model predictions.en
dc.description.sponsorshipProject FOREST-ECO2 - Towards an Ecological and economic valorisation of the Azorean Forest ACORES-01- 0145-FEDER-000014-Azores 2020 PO, 2016–2019; FEDER (European Regional Development Fund) funds through the Operational Programme for Competitiveness Factors-COMPETE and by National Funds through FCT-Foundation for Science and Technology under the UID/BIA/50027/ 2019 and POCI-01-0145-FEDER-006821; DRCT-M1.1.a/005/Funcionamento-C-/2019 (CIBIO-A), Secretaria Regional do Mar, Ciência e Tecnologia, Governo dos Açores.en
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSilva, L.D., Elias, R.B. & Silva, L. (2021). Modelling invasive alien plant distribution: a literature review of concepts and bibliometric analysis. "Environmental Modelling & Software", 145, 105203. DOI:10.1016/j.envsoft.2021.105203en
dc.identifier.doi10.1016/j.envsoft.2021.105203pt_PT
dc.identifier.eissn1873-6726
dc.identifier.issn1364-8152
dc.identifier.urihttp://hdl.handle.net/10400.3/6116
dc.identifier.wos000704443500003
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationPOCI-01-0145-FEDER-006821pt_PT
dc.relationResearch Network in Biodiversity and Evolutionary Biology
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectBiotic Dataen
dc.subjectEcological Modellingen
dc.subjectInvasive Alien Plants (IAPs)en
dc.subjectModelling Approachesen
dc.subjectPredictionsen
dc.subjectSpecies Distribution Models (SDMs)en
dc.titleModelling invasive alien plant distribution : A literature review of concepts and bibliometric analysisen
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Network in Biodiversity and Evolutionary Biology
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FBIA%2F50027%2F2019/PT
oaire.citation.conferencePlaceNetherlandsen
oaire.citation.endPage23pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleEnvironmental Modelling & Softwareen
oaire.citation.volume145pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNamePires Bento da Silva Elias
person.givenNameRui Miguel
person.identifierB-9055-2018
person.identifier.ciencia-idB816-7471-C4B4
person.identifier.orcid0000-0003-2397-2438
person.identifier.scopus-author-id7006549169
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication33c216c2-97be-4807-9614-d3e42970e4da
relation.isAuthorOfPublication.latestForDiscovery33c216c2-97be-4807-9614-d3e42970e4da
relation.isProjectOfPublication343e00f2-5de8-418c-871c-4f3326d59e49
relation.isProjectOfPublication.latestForDiscovery343e00f2-5de8-418c-871c-4f3326d59e49

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