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dc.contributor.authorBradter, Ute
dc.contributor.authorMair, Louise
dc.contributor.authorJönsson, Mari
dc.contributor.authorKnape, Jonas
dc.contributor.authorSnäll, Tord
dc.date.accessioned2019-01-09T21:41:25Z
dc.date.available2019-01-09T21:41:25Z
dc.date.issued2018
dc.identifier.citationBradter, U., Mair, L., Jönsson, M., Knape, J. and Snäll, T. (2018). Habitat suitability models for the Siberian jay (Perisoreus infaustus) from Citizen Science and systematic monitoring data: incorporating information about the reporting process. 5th European Congress of Conservation Biology. doi: 10.17011/conference/eccb2018/107740
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/62106
dc.description.abstractOpportunistically collected presence-only data contributed by volunteer reporters, so called Citizen Science data, are increasingly available for species and regions that lack systematic surveys. However, it is unclear if or how much the biases in opportunistically collected data influence different habitat suitability modelling methods and hence if they can be used with confidence to address different conservation questions. We evaluated habitat suitability models with opportunistically collected observations against models with systematically collected observations for a forest bird species, the Siberian jay (Perisoreus infaustus) in Sweden. Citizen Science data were obtained from the Swedish Species Observation System and systematically collected data from the Swedish Bird Survey. The opportunistically collected presence-only data were enhanced by adding high-quality inferred species absences. We obtained these by combining information on reporting behaviour and species identification skills of a few, very active reporters with their observations. We evaluated logistic regression with inferred absences, two versions of MaxEnt, a model combining presence-absence with presence-only observations and a Bayesian site-occupancy-detection model. All modelling methods produced nationwide habitat suitability maps of Siberian jay that agreed well with results from systematically collected data. At finer geographic scales there were differences among methods. Logistic regression with inferred absences produced results most similar to those from the systematic survey. Adding high-quality inferred absences to opportunistically collected data is likely possible for many less common species. They may facilitate the modelling and prediction of species distributions in areas or for species for which systematically collected data are not available.
dc.format.mimetypetext/html
dc.language.isoeng
dc.publisherOpen Science Centre, University of Jyväskylä
dc.relation.urihttps://peerageofscience.org/conference/eccb2018/107740/
dc.rightsCC BY 4.0
dc.titleHabitat suitability models for the Siberian jay (Perisoreus infaustus) from Citizen Science and systematic monitoring data: incorporating information about the reporting process
dc.typeconference paper not in proceedings
dc.type.urihttp://purl.org/eprint/type/ConferenceItem
dc.identifier.doi10.17011/conference/eccb2018/107740
dc.type.coarhttp://purl.org/coar/resource_type/c_18cp
dc.description.reviewstatuspeerReviewed
dc.type.versionpublishedVersion
dc.rights.copyright© the Authors, 2018
dc.rights.accesslevelopenAccess
dc.type.publicationconferenceObject
dc.relation.conferenceECCB2018: 5th European Congress of Conservation Biology. 12th - 15th of June 2018, Jyväskylä, Finland
dc.format.contentfulltext
dc.rights.urlhttp://creativecommons.org/licenses/by/4.0/


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    5th European Congress of Conservation Biology. 12th - 15th of June 2018, Jyväskylä, Finland

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CC BY 4.0
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