Abstract
Recently-developed methods that integrate multiple data sources arising from the same ecological processes have typically utilized structured data from well-defined sampling protocols (e.g., capture-recapture and telemetry). Despite this new methodological focus, the value of opportunistic data for improving inference about spatial ecological processes is unclear and, perhaps more importantly, no procedures are available to formally test whether parameter estimates are consistent across data sources and whether they are suitable for integration. Using data collected on the reintroduced brown bear population in the Italian Alps, a population of conservation importance, we combined data from three sources: traditional spatial capture-recapture data, telemetry data, and opportunistic data. We developed a fully integrated spatial capture-recapture (SCR) model that included a model-based test for data consistency to first compare model estimates using different combinations of data, and then, by acknowledging data-type differences, evaluate parameter consistency. We demonstrate that opportunistic data lend itself naturally to integration within the SCR framework and highlight the value of opportunistic data for improving inference about space use and population size. This is particularly relevant in studies of rare or elusive species, where the number of spatial encounters is usually small and where additional observations are of high value. In addition, our results highlight the importance of testing and accounting for inconsistencies in spatial information from structured and unstructured data so as to avoid the risk of spurious or averaged estimates of space use and consequently, of population size. Our work supports the use of a single modeling framework to combine spatially-referenced data while also accounting for parameter consistency.
Highlights
Obtaining precise estimates of population density and space use can lead to a better understanding of the processes governing spatiotemporal ecological dynamics and, in turn, improve wildlife management and conservation practices
Despite this new methodological focus, the value of opportunistic data for improving inference about spatial ecological processes is unclear and, perhaps more importantly, no procedures are available to formally test whether parameter estimates are consistent across data sources and whether they are suitable for integration
Estimates of the parameter relating density to distance to the reintroduction point (β1) were negative under all models, and there was some variation in the strength of the effect, this result supports the hypothesis that density decreased with distance from the point were founders were released (S1 Table)
Summary
Obtaining precise estimates of population density and space use can lead to a better understanding of the processes governing spatiotemporal ecological dynamics and, in turn, improve wildlife management and conservation practices. Regardless of methodology, the quality of model-based inference is directly related to data quality, which can be an issue for elusive species, especially when resources for monitoring are limited. This has led to an emphasis on developing methods that integrate multiple data sources [2, 3] and, importantly, to a realization that the vast amounts of data regularly collected outside of formal scientific studies, unstructured or opportunistic data, are a potentially valuable data source [4, 5]. The majority of data integration methods have focused on improving estimates of species distribution and temporal population trends, opportunistic data has great potential to improve inferences about spatial ecological processes. Regardless, with the rapid increase in citizen science initiatives, finding innovative ways to utilize opportunistic data will broaden the scope of ecological enquiry that can be addressed within a single analytical framework [3]
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