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Habitat suitability models based on opportunistic citizen science data : Evaluating forecasts from alternative methods versus an individual-based model

Habitat suitability models based on opportunistic citizen science data : Evaluating forecasts from alternative methods versus an individual-based model

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BRADTER, Ute, Arpat OZGUL, Michael GRIESSER, Kate LAYTON‐MATTHEWS, Jeannette EGGERS, Alexander SINGER, Brett K. SANDERCOCK, Paul J. HAVERKAMP, Tord SNÄLL, 2021. Habitat suitability models based on opportunistic citizen science data : Evaluating forecasts from alternative methods versus an individual-based model. In: Diversity and Distributions. Wiley. 27(12), pp. 2397-2411. ISSN 1366-9516. eISSN 1472-4642. Available under: doi: 10.1111/ddi.13409

@article{Bradter2021-12Habit-55152, title={Habitat suitability models based on opportunistic citizen science data : Evaluating forecasts from alternative methods versus an individual-based model}, year={2021}, doi={10.1111/ddi.13409}, number={12}, volume={27}, issn={1366-9516}, journal={Diversity and Distributions}, pages={2397--2411}, author={Bradter, Ute and Ozgul, Arpat and Griesser, Michael and Layton‐Matthews, Kate and Eggers, Jeannette and Singer, Alexander and Sandercock, Brett K. and Haverkamp, Paul J. and Snäll, Tord} }

Haverkamp, Paul J. 2021-10-05T10:54:06Z Snäll, Tord Griesser, Michael Bradter, Ute Layton‐Matthews, Kate Ozgul, Arpat Sandercock, Brett K. 2021-10-05T10:54:06Z Singer, Alexander Sandercock, Brett K. Eggers, Jeannette Ozgul, Arpat eng Attribution 4.0 International Griesser, Michael Haverkamp, Paul J. Bradter, Ute Singer, Alexander Eggers, Jeannette Snäll, Tord Layton‐Matthews, Kate Habitat suitability models based on opportunistic citizen science data : Evaluating forecasts from alternative methods versus an individual-based model Aim<br />To evaluate the utility of opportunistic data from citizen science programmes for forecasting species distributions against forecasts with a model of individual-based population dynamics.<br /><br />Location<br />Sweden.<br /><br />Methods<br />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.<br /><br />Results<br />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.<br /><br />Main conclusions<br />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 reporting of individual species and 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 systematic monitoring schemes. 2021-12

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