Quantification of Variability, Abundance, and Mortality of Maumee River Larval Walleye (Sander vitreus) Using Bayesian Hierarchical Models
type: CreativeWork
description: The estimation of abundance is complicated by factors contributing to spatial and temporal variability. Many organisms are highly variable across both of these scales, thereby violating assumptions of conventional abundance estimation methods. Larval walleye in the Maumee River are extremely variable; however estimates of abundance and mortality are important in understanding anthropogenic impacts on this spawning group and their role in Lake Erie walleye recruitment. Bayesian hierarchical models were used to quantify spatial and temporal variability, and estimate abundance and mortality within the river while accounting for spatial and temporal uncertainty. We sampled larval walleye at the river mouth and in the intake canal of a water-cooled power plant in 2010 and at an additional upstream site near the spawning grounds in 2011. Temporal variability and uncertainty was greater than spatial variability at all sites and years during the study. Daily abundance at each site and year was related to patterns in river discharge and temperature. Larval walleye abundance decreased in a downstream fashion, with an estimated annual natural mortality rate of 63.7% in 2011. Downstream (B) and power plant abundance (C) varied between years leading to a decrease in power plant entrainment mortality from 2010 to 2011, 11.1 to 2.8% respectively. Total in-river mortality was estimated at 64.8% when entrainment mortality was included. Quantifying sources of variability lead to an adjustment in sampling protocol, which increased precision in estimated values. Bayesian hierarchical models provided an optimal framework for understanding sources of variability and estimating larval fish abundance and mortality in this large river system.
abstract: The estimation of abundance is complicated by factors contributing to spatial and temporal variability. Many organisms are highly variable across both of these scales, thereby violating assumptions of conventional abundance estimation methods. Larval walleye in the Maumee River are extremely variable; however estimates of abundance and mortality are important in understanding anthropogenic impacts on this spawning group and their role in Lake Erie walleye recruitment. Bayesian hierarchical models were used to quantify spatial and temporal variability, and estimate abundance and mortality within the river while accounting for spatial and temporal uncertainty. We sampled larval walleye at the river mouth and in the intake canal of a water-cooled power plant in 2010 and at an additional upstream site near the spawning grounds in 2011. Temporal variability and uncertainty was greater than spatial variability at all sites and years during the study. Daily abundance at each site and year was related to patterns in river discharge and temperature. Larval walleye abundance decreased in a downstream fashion, with an estimated annual natural mortality rate of 63.7% in 2011. Downstream (B) and power plant abundance (C) varied between years leading to a decrease in power plant entrainment mortality from 2010 to 2011, 11.1 to 2.8% respectively. Total in-river mortality was estimated at 64.8% when entrainment mortality was included. Quantifying sources of variability lead to an adjustment in sampling protocol, which increased precision in estimated values. Bayesian hierarchical models provided an optimal framework for understanding sources of variability and estimating larval fish abundance and mortality in this large river system.
author: DuFour, Mark R.
language: English
keywords: Applied Mathematics, Aquatic Sciences, Biology, Ecology, Environmental Science, Freshwater Ecology, Statistics, larval drift, walleye, Maumee River, variability, abundance, mortality, Bayesian, temporal, spatial
date published: 2012
date modified: 2026-07-24T03:36:08Z
provider: OhioLINK
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Toledo