Statistical arrival models to estimate missed passage counts at fish weirs

• Published in: Canadian Journal of Fisheries and Aquatic Sciences
• Main Authors: Sethi, Suresh Andrew, Bradley, Catherine
• Format: Article in Journal/Newspaper
• Language: English
• Published: Canadian Science Publishing 2016
• Subjects: Yukon river; Alaska; Yukon
• Online Access: http://dx.doi.org/10.1139/cjfas-2015-0318; http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfas-2015-0318; http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfas-2015-0318

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 that 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, USA. 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.