Hierarchical Bayesian estimation of unobserved salmon passage through weirs

We developed a hierarchical Bayesian model (HBM) to estimate missing counts of Chinook salmon (Oncorhynchus tshawytscha (Walbaum in Artedi, 1792)) at a weir on the Kogrukluk River, Alaska, between 1976 and 2015. The model assumed that fish passage during a breach of the weir was typical of passage d...

Full description

Saved in:
Bibliographic Details
Published in:Canadian journal of fisheries and aquatic sciences 2018-07, Vol.75 (7), p.1151-1159
Main Authors: Jasper, James R, Short, Margaret, Shelden, Chris, Grant, W. Stewart
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian theory
Counters
Dams
Environmental aspects
Escapement
Estimates
Fish
Fish counters
Fish ladders
Fishways
Freshwater
Methods
Migration
Missing data
Multilevel analysis
Oncorhynchus tshawytscha
Parameter estimation
Rivers
Salmon
Spawning migrations
Spawning populations
Stock assessment (Wildlife management)
Weirs
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We developed a hierarchical Bayesian model (HBM) to estimate missing counts of Chinook salmon (Oncorhynchus tshawytscha (Walbaum in Artedi, 1792)) at a weir on the Kogrukluk River, Alaska, between 1976 and 2015. The model assumed that fish passage during a breach of the weir was typical of passage during normal operations. Counts of fish passing the weir were missing for some days during the runs, or only partial counts for a given 24-hour period were available. The HBM approach provided more defensible estimates of missing data and total escapement than ad hoc or year-by-year model estimates, because estimates of passage for a given year were informed not only by counts for the current year, but also by counts for all previous years. The results of the HBM yielded less variable estimates of escapement than did ad hoc or year-by-year model estimates. The HBM represents a standardized approach to estimate missing counts and total escapement and eliminates the need for ad hoc estimates of missing counts. The model also provides managers with a measure of uncertainty around estimates of escapement and around estimates of hyper-parameters to define run curves in subsequent years with incomplete fish counts.
ISSN:0706-652X
1205-7533
DOI:10.1139/cjfas-2016-0398