Statistical arrival models to estimate missed passage counts at fish weirs
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 presen...
| Published in: | Canadian Journal of Fisheries and Aquatic Sciences |
|---|---|
| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Canadian Science Publishing
2016
|
| Subjects: | |
| 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 |
| Summary: | 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. |
|---|