Cooperative Fish and Wildlife Research Units Program: New York
Education, Research and Technical Assistance for Managing Our Natural Resources

Sethi SA, Bradley C. (2016) Statistical arrival models to estimate missed passage counts at fish weirs. Canadian Journal of Fisheries and Aquatic Sciences 73:1251-1260. DOI:


Missed counts are commonplace when enumerating fish passing a weir. Typically connect-the-dots linear interpolation is used to impute missed passage; however, this method fails to characterize uncertainty about estimates, and cannot be implemented when the tails of a run are missed. Here, we present a statistical approach to imputing missing passage at weirs which addresses these shortcomings, consisting of a parametric run curve model to describe the smoothed arrival dynamics of a fish population and a process variation model to describe the likelihood of observed data. Statistical arrival models are fit in a Bayesian framework and tested with a suite of missing data simulation trials and against a selection of Pacific Salmon (Oncorhynchus spp.) case studies from the Yukon River drainage, Alaska, U.S.A. When compared against linear interpolation, statistical arrival models produced equivalent or better expected accuracy and a narrower range of bias outcomes. Statistical arrival models also successfully imputed missing passage counts for scenarios where the tails of a run were missed.