Statistical arrival models to estimate missed passage counts at fish weirs

• Main Authors: Sethi, Suresh A., Bradley, Catherine
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: NRC Research Press (a division of Canadian Science Publishing) 2015
• Subjects: Pacific; Weir; Yukon; Yukon river; Alaska
• Online Access: http://hdl.handle.net/1807/72318; http://www.nrcresearchpress.com/doi/abs/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 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. The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author.