Abstract
AbstractAimTo evaluate the utility of opportunistic data from citizen science programmes for forecasting species distributions against forecasts with a model of individual‐based population dynamics.LocationSweden.MethodsWe 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.ResultsForecasts 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.Main conclusionsOur 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.
Highlights
We asked if anticipatory forecasts with habitat suitability models (HSMs) using opportunistic data can be useful despite the challenges of modelling citizen science data
Our first major findings were that the conclusions from our two benchmark forecasts were consistent with each other and with existing knowledge of the species ecology for Siberian jays
Our second finding was that HSMs based on opportunistic data could recover the same rankings of management scenarios as our benchmark models, but only for a subset of models
Summary
We used life-history data from a long-term population study of Siberian jays at a field site in the boreal forest near Arvidsjaur, northern Sweden (Figure 1). Empirical life-history data were collected in a long-term study population in the boreal forest of northern Sweden (black square), 2000–2017 processes. We present a new individual-based model that integrated our long-term demographic data for Siberian jays with variation in environmental conditions by estimating response functions relating variation in environmental conditions with demographic processes determining population size, such as births, deaths, emigration and immigration (Schurr et al, 2012). The following processes were executed for each individual and at each time step in this order: reproduction (summer breeders only), survival, stage-to-stage transition, emigration (decision to leave a cell), dispersal (movement between cells), immigration (at cells occupied by an existing group) or colonization (of unoccupied cells). −3.83 + 0.28*PercMature + 0.44*MeanAge + 0.27*PercOther − 1.65*WinterTemp −0.51*SpringPrec + 1.55*Elevation − 0.46*Elevation2 − 0.49*(WinterTemp*SpringPrec)
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