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Combining information from active and passive sampling of mobile animals is challenging because active‐sampling data are affected by limited detection of rare or sparse taxa, while passive‐sampling data reflect both density and movement. We propose that a model‐based analysis allows information to be combined between these methods to interpret variation in the relationship between active estimates of density and passive measurements of catch per unit effort to yield novel information on activity rates (distance/time). We illustrate where discrepancies arise between active and passive methods and demonstrate the model‐based approach with seasonal surveys of fish assemblages in the Florida Everglades, where data are derived from concurrent sampling with throw traps, an enclosure‐type sampler producing point estimates of density, and drift fences with unbaited minnow traps that measure catch per unit effort (CPUE). We compared incidence patterns generated by active and passive sampling, used hierarchical Bayesian modeling to quantify the detection ability of each method, characterized interspecific and seasonal variation in the relationship between density and passively measured CPUE, and used a predator encounter‐rate model to convert variable CPUE–density relationships into ecological information on activity rates. Activity rate information was used to compare interspecific responses to seasonal hydrology and to quantify spatial variation in non‐native fish activity. Drift fences had higher detection probabilities for rare and sparse species than throw traps, causing discrepancies in the estimated spatial distribution of non‐native species from passively measured CPUE and actively measured density. Detection probability of the passive sampler, but not the active sampler, varied seasonally with changes in water depth. The relationship between CPUE and density was sensitive to fluctuating depth, with most species not having a proportional relationship between CPUE and density until seasonal declines in depth. Activity rate estimates revealed interspecific differences in response to declining depths and identified locations and species with high rates of activity. We propose that variation in catchability from methods that passively measure CPUE can be sources of ecological information on activity. We also suggest that model‐based combining of data types could be a productive approach for analyzing correspondence of incidence and abundance patterns in other applications.
Parkos, Joseph III; Kline, Jeffrey L.; and Trexler, Joel C., "Signal from the noise: model‐based interpretation of variable correspondence between active and passive samplers" (2019). HWCOM Faculty Publications. 157.
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