Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/47887
DC FieldValueLanguage
dc.contributor.authorRossini, Lucait
dc.contributor.authorBono-Rossello, Nicolasit
dc.contributor.authorContarini, Marioit
dc.contributor.authorSperanza, Stefanoit
dc.contributor.authorGarone, Emanueleit
dc.date.accessioned2022-06-19T09:18:14Z-
dc.date.available2022-06-19T09:18:14Z-
dc.date.issued2022it
dc.identifier.issn1574-9541it
dc.identifier.urihttp://hdl.handle.net/2067/47887-
dc.description.abstractThe mathematical description of the dynamics of ectotherm populations has a crucial role in understanding and forecasting insect pests outbreaks, as well as to support the formulation of low-impact control strategies. Even though in recent years a considerable step forward has been made in modelling the biological life cycle of many ectotherms, the modelling of spatial aspects still remain partially unexplored. This work aims to fill a gap in knowledge by modelling the environmental factors-driven spatial motion of insects considering populations which develop over time, and through their life stages. Accordingly, spatial motion is proposed by modifying an existing physiologically based model by adding a subdivision of the area of interest in interconnected subareas represented by means of graph. Besides the mathematical framework, the model behavior is theoretically analyzed by considering three common cases of spatial motion occurring in cultivated fields: trap attraction, wind transportation, and a combination of the both cases. Simulations focus on the case of Drosophila suzukii, chosen due to the large amount of biological data available in the current literature. The behavior analysis shows the great potential of the model presented in this paper in the field of pest management, agriculture, and ecology.it
dc.format.mediumELETTRONICOit
dc.language.isoengit
dc.titleModelling ectotherms' populations considering physiological age structure and spatial motion: A novel approachit
dc.typearticle*
dc.identifier.doi10.1016/j.ecoinf.2022.101703it
dc.identifier.urlhttps://doi.org/10.1016/j.ecoinf.2022.101703it
dc.relation.journalECOLOGICAL INFORMATICSit
dc.relation.firstpage101703it
dc.relation.volume70it
dc.subject.scientificsectorAGR/11it
dc.subject.keywordsSpatial modelsit
dc.subject.keywordsDecision support systemsit
dc.subject.keywordsPrecision agricultureit
dc.subject.keywordsLinear systemsit
dc.subject.keywordsPhysiologically based modelsit
dc.subject.ercsectorLSit
dc.description.numberofauthors5it
dc.description.internationalit
dc.contributor.countryITAit
dc.contributor.countryBELit
dc.type.refereeREF_1it
dc.type.miur262*
item.fulltextWith Fulltext-
item.openairetypearticle-
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.journal.journalissn1574-9541-
crisitem.journal.anceE192045-
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