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Data from: Can opportunistically-collected Citizen Science data fill a data gap for habitat suitability models of less common species?


Bradter, Ute et al. (2018), Data from: Can opportunistically-collected Citizen Science data fill a data gap for habitat suitability models of less common species?, Dryad, Dataset,


1. Opportunistically-collected species observations contributed by volunteer reporters are increasingly available for species and regions for which systematically collected data are not available. However, it is unclear if they are suitable to produce reliable habitat suitability models (HSMs), and hence if the species-habitat relationships found and habitat suitability maps produced can be used with confidence to advice conservation management and address basic and applied research questions. 2. We evaluated HSMs with opportunistically-collected observations against HSMs with systematically collected observations. We enhanced the opportunistically-collected presence-only data by adding inferred species absences. To obtain inferred absences, we asked individual reporters about their identification skills and if they reported certain species consistently and combined this information with their observations. We evaluated several HSM methods using a forest bird species, Siberian jay (Perisoreus infaustus), in Sweden: 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. 3. All HSM methods produced nationwide habitat suitability maps of Siberian jay that agreed well with systematically collected observations (AUC: 086-0.88) and were very similar to a habitat suitability map produced from the HSM with systematically-collected observations (Spearman rho: 0.94-0.98). At finer geographical scales there were differences among methods. 4. At finer scale, the resulting habitat suitability maps from logistic regression with inferred absences agreed better with results from systematically collected observations than other methods. The species-habitat relationships found with logistic regression also agreed well with those found from systematically collected data and with prior expectations based on the species ecology. 5. Synthesis and application: For many regions and species, systematically-collected data are not available. By using inferred absences from high-quality opportunistically-collected contributions of few very active reporters in logistic regression we obtained HSMs that produced results similar to those from a systematic survey. Adding high-quality inferred absences to opportunistically-collected data is likely possible for many less common species across various organism groups. Well performing HSMs are important to facilitate applications such as spatial conservation planning and prioritization, monitoring of invasive species, understanding species habitat requirements or climate change studies.

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