Habitat suitability models for the Siberian jay (Perisoreus infaustus) from Citizen Science and systematic monitoring data: incorporating information about the reporting process
Bradter, 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
© the Authors, 2018
Opportunistically 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. ...
PublisherOpen Science Centre, University of Jyväskylä
ConferenceECCB2018: 5th European Congress of Conservation Biology. 12th - 15th of June 2018, Jyväskylä, Finland
MetadataShow full item record
- ECCB 2018 
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