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dc.contributor.authorKarppinen, Santeri
dc.contributor.authorRajala, Tuomas
dc.contributor.authorMäntyniemi, Samu
dc.contributor.authorKojola, Ilpo
dc.contributor.authorVihola, Matti
dc.date.accessioned2022-09-01T11:10:31Z
dc.date.available2022-09-01T11:10:31Z
dc.date.issued2022
dc.identifier.citationKarppinen, S., Rajala, T., Mäntyniemi, S., Kojola, I., & Vihola, M. (2022). Identifying territories using presence-only citizen science data : An application to the Finnish wolf population. <i>Ecological Modelling</i>, <i>472</i>, Article 110101. <a href="https://doi.org/10.1016/j.ecolmodel.2022.110101" target="_blank">https://doi.org/10.1016/j.ecolmodel.2022.110101</a>
dc.identifier.otherCONVID_155777618
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/82904
dc.description.abstractCitizens, community groups and local institutions participate in voluntary biological monitoring of population status and trends by providing species data e.g. for regulations and conservation. Sophisticated statistical methods are required to unlock the potential of such data in the assessment of wildlife populations. We develop a statistical modelling framework for identifying territories based on presence-only citizen science data. The framework can be used to jointly estimate the number of active animal territories and their locations in time. Our approach is based on a data generating model which consists of a dynamic submodel for the appearance/removal of territories and an observation submodel that accounts for the varying observation intensity and links the data to the territories. We first estimate the observation intensity using past presence-only observations made by citizens, conditioning on previously known territories. We then infer the territories using a state-of-the-art sequential Monte Carlo method, which extends earlier approaches by allowing for spatial inhomogeneity in the observation process. We verify our data generating model and inference method successfully in synthetic scenarios. We apply our framework for estimating the locations and number of wolf territories in March 2020 in Finland using one year of confirmed citizen-made wolf observations. The observation intensity is estimated using wolf observation data collected in 2011–2019, conditioning on official territory estimates and data from GPS-collared wolves. Our experiments with synthetic data suggest that the estimation of territories can be feasible with presence-only data. Our location and territory count inferences for March 2020 based on past data are comparable to the official wolf population assessment of March 2020 by the Natural Resources Institute Finland. The results suggest that the framework can provide useful information for assessing populations of territorial animals. Furthermore, our methods and findings, such as the developed data generating model and the estimation of the spatio-temporal observation intensity can be relevant also beyond the strictly territorial setting.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesEcological Modelling
dc.rightsCC BY 4.0
dc.subject.othercitizen science data
dc.subject.otherBayesian statistics
dc.subject.othersequential Monte Carlo
dc.subject.otherspatio-temporal model
dc.subject.otherterritory identification
dc.subject.otherpresence-only data
dc.titleIdentifying territories using presence-only citizen science data : An application to the Finnish wolf population
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202209014438
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn0304-3800
dc.relation.volume472
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 the Authors
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber315619
dc.subject.ysokansalaistiede
dc.subject.ysosusi
dc.subject.ysokansalaishavainnot
dc.subject.ysobayesilainen menetelmä
dc.subject.ysopopulaatiot
dc.subject.ysopaikkatietoanalyysi
dc.subject.ysoMonte Carlo -menetelmät
dc.subject.ysoreviirit
dc.subject.ysoeläinkannat
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p28992
jyx.subject.urihttp://www.yso.fi/onto/yso/p16400
jyx.subject.urihttp://www.yso.fi/onto/yso/p29381
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p5038
jyx.subject.urihttp://www.yso.fi/onto/yso/p28516
jyx.subject.urihttp://www.yso.fi/onto/yso/p6361
jyx.subject.urihttp://www.yso.fi/onto/yso/p12299
jyx.subject.urihttp://www.yso.fi/onto/yso/p5037
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.ecolmodel.2022.110101
dc.relation.funderSuomen Akatemiafi
dc.relation.funderResearch Council of Finlanden
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundinginformationSK and MV were supported by the Academy of Finland research grant 315619.
dc.type.okmA1


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