Using aerial surveys and citizen science to create species distribution models for an imperiled grouse

Estimating species distributions requires species presence data of sufficient quantity from reputable sources that are geographically representative of the species’ space use. Collecting presence data that meets these standards can be costly and is often complicated by limited land access. Citizen science projects are an appealing alternative source of presence data as these data are freely available and collected globally. Websites such as eBird have become increasingly large repositories of citizen science data. The vulnerable lesser prairie-chicken (LPC; Tympanuchus pallidicinctus ) is a species well-represented in the eBird database, with presence observations from 140 unique locations from 2012 to 2014. During that same period, a distribution-wide, standardized aerial survey with state and federal support recorded 106 LPC detections. Our objective was to compare species distribution models (SDMs) made with eBird data to models made with aerial survey data to determine the potential for citizen science data to contribute to conservation planning. We used maximum entropy modeling to create SDMs based on eBird data, aerial survey data, and a combination of both data sets using variables of biological significance to LPCs. We obtained comparable model performance using aerial survey data only [standardized test omission rate (STO): 23.4%, test AUC: 0.76] and with eBird data only (STO: 23.8%, AUC: 0.76). The I statistic confirmed a very high degree of similarity between the outputs of the two model sets ( I = 0.929). However, a road bias existed within both data sets (positive and negative biases), potentially confounding some environmental correlates. Despite this bias, our combined model predicted an increase of 1,732,500 ha of unique area suitable for the LPC. Our results indicated that eBird data could be used as a low-cost source for species occurrence data to create species distribution models, though biases in these datasets should be assessed to guide interpretability of the predicted outputs.