Failure to estimate capture efficiency, defined as the probability of capturing individual fish, can introduce a systematic error or bias into estimates of fish abundance. We evaluated the efficacy of multipass electrofishing removal methods for estimating fish abundance by comparing estimates of capture efficiency from multipass removal estimates to capture efficiencies measured by the recapture of known numbers of marked individuals for bull trout Salvelinus confluentus and westslope cutthroat trout Oncorhynchus clarki lewisi. Electrofishing capture efficiency measured by the recapture of marked fish was greatest for westslope cutthroat trout and for the largest size‐classes of both species. Capture efficiency measured by the recapture of marked fish also was low for the first electrofishing pass (mean, 28%) and decreased considerably (mean, 1.71 times lower) with successive passes, which suggested that fish were responding to the electrofishing procedures. On average, the removal methods overestimated three‐pass capture efficiency by 39% and underestimated fish abundance by 88%, across both species and all size‐classes. The overestimates of efficiency were positively related to the cross‐sectional area of the stream and the amount of undercut banks and negatively related to the number of removal passes for bull trout, whereas for westslope cutthroat trout, the overestimates were positively related to the amount of cobble substrate. Three‐pass capture efficiency measured by the recapture of marked fish was related to the same stream habitat characteristics that influenced (biased) the removal estimates and did not appear to be influenced by our sampling procedures, including fish marking. Simulation modeling confirmed our field observations and indicated that underestimates of fish abundance by the removal method were negatively related to first‐pass sampling efficiency and the magnitude of the decrease in capture efficiency with successive passes. Our results, and those of other researchers, suggest that most electrofishing‐removal‐based estimates of fish abundance are likely to be biased and that these biases are related to stream characteristics, fish species, and size. We suggest that biologists regard electrofishing‐removal‐based estimates as biased indices and encourage them to measure and model the efficiency of their sampling methods to avoid introducing systematic errors into their data.

Despite the widespread use of underwater observation to census stream‐dwelling fishes, the accuracy of snorkeling methods has rarely been validated. We evaluated the efficiency of day and night snorkel counts for estimating the abundance of bull trout Salvelinus confluentus in 215 sites within first‐ to third‐order streams. We used a dual‐gear approach that applied multiple‐pass electrofishing catch data adjusted for capture efficiency to estimate true or baseline fish abundance. Our multiple‐pass electrofishing capture efficiency models were based on a prior study and used recapture data for known numbers of individually marked fish. Snorkeling efficiency was estimated by comparing day and night snorkel counts with the baseline. We also evaluated the influence of fish size and stream habitat features on snorkeling efficiency. Bull trout snorkeling efficiency was higher at night (mean = 33.2%) than during the day (mean = 12.5%). Beta‐binomial regression indicated that bull trout day and night snorkeling efficiencies were positively related to fish size and negatively related to stream width and habitat characteristics. Day snorkeling efficiency also was positively influenced by water temperature and nonlinearly related to underwater visibility, whereas night snorkeling efficiency was nonlinearly related to water temperature and pool abundance. Although bull trout were our target species, day and night snorkeling efficiencies combined for rainbow trout Oncorhynchus mykiss and subspecies of cutthroat trout O. clarkii averaged 32.3% and 18.0%, respectively. Our ability to detect and accurately count fish underwater was influenced by fish size, species, time of day, and stream habitat characteristics. Although snorkeling is versatile and has many advantages over other sampling methods, the use of raw snorkel counts unadjusted for the effects of these biases will result in biased conclusions. We recommend that biologists adjust underwater count data to minimize the effect of such biases. We illustrate how to apply sampling efficiency models to validate snorkel counts.

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