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Proof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera)

dc.contributor.authorSequeira, João G. N.
dc.contributor.authorNobre, Tânia
dc.contributor.authorDuarte, Sónia
dc.contributor.authorJones, Dennis
dc.contributor.authorEsteves, Bruno
dc.contributor.authorNunes, Lina
dc.date.accessioned2023-01-09T11:01:32Z
dc.date.available2023-01-09T11:01:32Z
dc.date.issued2022-02-03
dc.date.updated2023-01-05T09:57:28Z
dc.description.abstractOver the past few decades, species distribution modelling has been increasingly used to monitor invasive species. Studies herein propose to use Cellular Automata (CA), not only to model the distribution of a potentially invasive species but also to infer the potential of the method in risk prediction of Reticulitermes grassei infestation. The test area was mainland Portugal, for which an available presence-only dataset was used. This is a typical dataset type, resulting from either distribution studies or infestation reports. Subterranean termite urban distributions in Portugal from 1970 to 2001 were simulated, and the results were compared with known records from both 2001 (the publication date of the distribution models for R. grassei in Portugal) and 2020. The reported model was able to predict the widespread presence of R. grassei, showing its potential as a viable prediction tool for R. grassei infestation risk in wooden structures, providing the collection of appropriate variables. Such a robust simulation tool can prove to be highly valuable in the decisionmaking process concerning pest management.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/f13020237pt_PT
dc.identifier.slugcv-prod-2904832
dc.identifier.urihttp://hdl.handle.net/10400.19/7515
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectSubterranean termitespt_PT
dc.subjectinfestation riskpt_PT
dc.subjectcellullar automatapt_PT
dc.subjectmodelpt_PT
dc.titleProof-of-Principle That Cellular Automata Can Be Used to Predict Infestation Risk by Reticulitermes grassei (Blattodea: Isoptera)pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage237pt_PT
oaire.citation.issue2pt_PT
oaire.citation.startPage237pt_PT
oaire.citation.titleForestspt_PT
oaire.citation.volume13pt_PT
person.familyNameEsteves
person.givenNameBruno
person.identifier2119255
person.identifier.ciencia-id521B-63A7-B248
person.identifier.orcid0000-0001-6660-3128
person.identifier.ridC-2173-2012
person.identifier.scopus-author-id57202554441
rcaap.cv.cienciaid521B-63A7-B248 | Bruno Miguel Morais Lemos Esteves
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationa3351493-41e8-4758-acae-39e3382d0b04
relation.isAuthorOfPublication.latestForDiscoverya3351493-41e8-4758-acae-39e3382d0b04

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