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dc.contributor.authorBradter, Ute
dc.contributor.authorOzgul, Arpat
dc.contributor.authorGriesser, Michael
dc.contributor.authorLayton-Matthews, Kate
dc.contributor.authorEggers, Jeannette
dc.contributor.authorSinger, Alexander
dc.contributor.authorSandercock, Brett K.
dc.contributor.authorHaverkamp, Paul J.
dc.contributor.authorSnäll, Tord
dc.coverage.spatialSweden, Sverigeen_US
dc.description.abstractAim: To evaluate the utility of opportunistic data from citizen science programmes for forecasting species distributions against forecasts with a model of individualbased population dynamics. Location: Sweden. Methods: We evaluated whether alternative methods for building habitat suitability models (HSMs) based on opportunistic data from citizen science programmes produced forecasts that were consistent with forecasts from two benchmark models: (1) a HSM based on data from systematic monitoring and (2) an individual-based model for spatially explicit population dynamics based on empirical demographic and movement data. We forecasted population numbers and habitat suitability for three realistic, future forest landscapes for a forest bird, the Siberian jay (Perisoreus infaustus). We ranked simulated forest landscapes with respect to their benefits to Siberian jays for each modelling method and compared the agreement of the rankings among methods. Results: Forecasts based on our two benchmark models were consistent with each other and with expectations based on the species’ ecology. Forecasts from logistic regression models based on opportunistic data were consistent with the benchmark models if species detections were combined with high-quality inferred absences derived via retrospective interviews with experienced “super-reporters.” In contrast, forecasts with three other widely used methods were inconsistent with the benchmark models, sometimes with misleading rankings of future scenarios. Main conclusions: Our critical evaluation of alternative HSMs against a spatially explicit IBM demonstrates that information on species absences critically improves forecasts of species distributions using opportunistic data from citizen science programmes. Moreover, high-quality information on species absences can be retrospectively inferred from surveys of the consistency of reporti the identification skills of participating reporters. We recommend that citizen science projects incorporate procedures to evaluate reporting behaviour. Inferred absences may be especially useful for improving forecasts for species and regions poorly covered by s ystematic monitoring schemes.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectcitizen scienceen_US
dc.subjecthabitat suitabilityen_US
dc.subjectindividual-based modelen_US
dc.subjectinferred absenceen_US
dc.subjectopportunistically collecteden_US
dc.subjectSiberian jayen_US
dc.titleHabitat suitability models based on opportunistic citizen science data: Evaluating forecasts from alternative methods versus an individual-based modelen_US
dc.typePeer revieweden_US
dc.typeJournal article
dc.rights.holder© 2021 The Authorsen_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Basale biofag: 470en_US
dc.source.journalDiversity and Distributionsen_US
dc.relation.projectNorges forskningsråd: 295767en_US
dc.relation.projectSvenska Forskningsrådet Formas, Grant/Award Number: 2016- 00557 and 2016-01949en_US
dc.relation.projectUniversität Zürichen_US
dc.relation.projectSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Grant/Award Number: 31BD30_172465, PP00P3_150752 and PPOOP3_123520en_US

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Navngivelse 4.0 Internasjonal
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