Estimating observer error and steelhead redd abundance using a modified Gaussian area-under-the-curve framework

Abstract

This study examined how a suite of habitat and environmental variables relate to the ability of a stream surveyor to identify (observer efficiency) and distinguish (observer accuracy) steelhead (Oncorhynchus mykiss) redds from other stream features. Two existing spawning survey protocols that included one or two redd observers were used to develop models to estimate redd observer error. In most cases, steelhead redd abundances using raw redd counts were underestimated. Mean annual rates of observer efficiency ranged from 0.44 to 0.57, and observer accuracy ranged from 0.67 to 0.83. Regardless of the observer error model used, adjusted annual redd abundance estimates were generally unbiased (range 1.6–0.6 redds). A Gaussian area-under-the-curve methodology that incorporates redd count data and observer error rates was used to generate unbiased estimates of steelhead redd abundance in the Wenatchee (170 redds, coefficient of variation (CV) = 44%) and Methow (106 redds, CV = 41%) rivers. Unbiased estimates of redd abundance will help inform new population viability analyses to better prioritize those populations with the greatest conservation need.

Estimating population size and observation bias for spring Chinook Salmon

“…Covariates that were important in influencing spring Chinook error rates were also important for steelhead redd survey error rates (Murdoch et al, ). However, important differences in these studies are worth noting.…”

“…We defined the commission error rate or the proportion of features identified that were not spring Chinook Salmon redds (e.g., hydraulic scour and depositional areas, test redds, or redds of other species) as: where C i is the error rate of commission, E i is the number of features erroneously identified as redds, and F i is the total number of redd‐like features found by the naïve redd observer i . This corresponds to one minus the accuracy rate described in (Murdoch et al, ). We defined the omission error rate or the proportion of actual redds missed by naïve surveyors as: where O i is the omission rate, M i is the number of redds found by the census surveyors but missed by the naïve surveyor i , and V cen is the number of visible redds identified by the census surveyors.…”

“…We defined the omission error rate or the proportion of actual redds missed by naïve surveyors as: where O i is the omission rate, M i is the number of redds found by the census surveyors but missed by the naïve surveyor i , and V cen is the number of visible redds identified by the census surveyors. This corresponds to one minus the efficiency rate described in (Murdoch et al, ). Ideally, commission and omission rates would be zero.…”

“…where C i is the error rate of commission, E i is the number of features erroneously identified as redds, and F i is the total number of redd-like features found by the naïve redd observer i. This corresponds to one minus the accuracy rate described in (Murdoch et al, 2018). We defined the omission error rate or the proportion of actual redds missed by naïve surveyors as:…”

“…In that study, census surveys were only conducted 3–4 times per season and the authors concluded that multi‐pass census redds surveys may provide adequate estimate of true population size, at a third of the cost, provided additional information about the population is known (i.e., proportion of females and prespawn mortality). More recently, models were recently developed to predict steelhead ( Oncorhynchus mykiss ) redd observer error rates using river reach specific covariates that included surveyor experience, habitat complexity, observed redd density, river condition, and size (Murdoch, Herring, Frady, See, & Jordan, ). In that study, steelhead redd observers found only between 48 and 58% of the known redds.…”