Estimating Riverwide Abundance of Juvenile Fish Populations: How Much Sampling is Enough?

Estimating riverwide abundance of juvenile fish populations is challenging because detection probability is typically low and juveniles can be patchily distributed over large areas. We used a hierarchical Bayesian model to estimate the abundance of juvenile steelhead Oncorhynchus mykiss in two rivers in British Columbia over 3 years based on a multigear, two‐phase sampling design. These estimates were used to drive a simulation model to evaluate how the precision of abundance estimates varied with the number of single‐pass index and mark–recapture sites that were sampled, the proportion of shoreline sampled, and the mean and variation of detection probability and fish density across sites. The extent of variation in fish densities across index sites was the most important factor influencing the precision of river‐wide abundance estimates, and increasing the number of index sites was the best approach to reduce variability in abundance estimates. River size, which controls the proportion of habitat sampled for a given level of sampling effort, had a moderate effect on precision, but only when the extent of site‐to‐site variation in fish density was high. Factors affecting detection probability, such as the number of mark–recapture sites, the mean detection probability, or the extent of variation in detection probability across sites, had much less influence on precision of abundance estimates unless the proportion of river sampled was high. Hierarchical Bayesian models are no substitute for collecting informative data, but they improve our understanding of variance structure, which is critical for providing realistic estimates of uncertainty and designing informative and efficient sampling programs. Received July 16, 2015; accepted October 7, 2015 Published online March 8, 2016