Cooperative Fish and Wildlife Research Units Program: Washington
Education, Research and Technical Assistance for Managing Our Natural Resources


Sipe, H.A., Keren, I.N., and S.J. Converse. 2022. Integrating community science and agency-collected monitoring data to expand monitoring capacity at large spatial scales. Ecology and Evolution 14: e4585

Abstract

Monitoring species to better understand their status, ecology, and management needs is a major expense for agencies tasked with biodiversity conservation. Community science data have the potential to improve monitoring for minimal cost, given appropriate analytical frameworks are established. We describe a framework for integrating data from the eBird community science platform with agency-collected monitoring data using a multi-state occupancy model. Our novel occupancy model accounts for the structural differences in eBird data and agency-collected monitoring data and allows for estimation of both occupancy and breeding probabilities. The framework was applied to Common Loons (Gavia immer) in Washington State. Common Loons are state listed in Washington as Sensitive and identified as a species of greatest conservation need, and little is known about their breeding distribution beyond the locations of known nesting pairs. Based on our model, we predicted that only 6.5% (95% Bayesian credible interval, BCI = 1.6%, 50.9%) of the 2324 sites in our sampling frame were occupied by Common Loons, though Common Loons were predicted to be breeding at 95% (95% BCI = 71.2%, 99.9%) of the lakes they occupied. We identified only 1 abiotic covariate – elevation – that was a useful predictor of occupancy or reproduction probabilities in Common Loons; improvements in the predictive power of our model might be achieved with information on difficult-to-monitor biotic covariates, such as fish community metrics. We found that state agency biologists were 16 times more likely to detect breeding Common Loons during a visit than were eBird users (94.2%, 95% BCI = 77.9%, 98.9% for agency biologists vs. 8.2%, 95% BCI = 6.3%, 10.4% for eBird users). However, the amount of effort expended by eBird users meant that they confirmed Common Loons at 94 sites while agency biologists confirmed them at just 24 sites. Importantly, though, certain information – namely, evidence of reproduction – was only contributed by agency biologists. Our results provide a better understanding of the distribution of Common Loons in Washington, while further demonstrating that community science data can be a valuable complement to agency-collected data, if appropriate frameworks are developed to integrate these data sources.