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Hankin and Reeves' (1988) approach to estimating fish abundance in small streams has been applied in stream-fish studies across North America. However, as with any method of population estimation, there are important assumptions that must be met for estimates to be minimally biased and reasonably precise. Consequently, I investigated effects of various levels of departure from these assumptions via simulation based on results from an example application in Hankin and Reeves (1988) and a spatially clustered population. Coverage of 95% confidence intervals averaged about 5% less than nominal when removal estimates equaled true numbers within sampling units, but averaged 62% - 86% less than nominal when they did not, with the exception where detection probabilities of individuals were >0.85 and constant across sampling units (95% confidence interval coverage = 90%). True total abundances averaged far (20% - 41%) below the lower confidence limit when not included within intervals, which implies large negative bias. Further, average coefficient of variation was about 1.5 times higher when removal estimates did not equal true numbers within sampling units (C{bar V} = 0.27 [SE = 0.0004]) than when they did (C{bar V} = 0.19 [SE = 0.0002]). A potential modification to Hankin and Reeves' approach is to include environmental covariates that affect detection rates of fish into the removal model or other mark-recapture model. A potential alternative is to use snorkeling in combination with line transect sampling to estimate fish densities. Regardless of the method of population estimation, a pilot study should be conducted to validate the enumeration method, which requires a known (or nearly so) population of fish to serve as a benchmark to evaluate bias and precision of population estimates

Hankin and Reeves' Approach to Estimating Fish Abundance in Small Streams : Limitations and Potential Options.