When Do Mountain Whitefish (Prosopium williamsoni) Spawn? A Comparison of Estimates Based on Gonadosomatic Indices and Spawner and Egg Counts
Robyn L. Irvine1, *, Joseph L. Thorley1, Louise Porto2
Identifiers and Pagination:Year: 2017
First Page: 12
Last Page: 22
Publisher Id: TOFISHSJ-10-12
Article History:Received Date: 10/09/2016
Revision Received Date: 22/11/2016
Acceptance Date: 30/11/2016
Electronic publication date: 31/03/2017
Collection year: 2017
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Determining when fish spawn has major implications for effective fisheries management, particularly in dam-controlled rivers where reproductive potential may be affected by an altered hydrograph. Three methods for estimating spawn timing in riverine broadcast spawners were compared for their precision, effort and potential impact on a population of Mountain Whitefish in the regulated Lower Duncan River, Canada. The first method is based on the Gonadosomatic Index (GSI), which is a measure of the relative mass of an individual’s gonads. The second method is based on counts of aggregating adults, while the third method is based on passive egg collection using egg mats. Analysis of the GSI data provided the most precise estimates. It estimated that spawning occurred between October 30th and November 26th in 2010 and between November 8th and November 27th in 2011. Collection of GSI data required moderate effort and had some impact due to the need for lethal harvest. Analysis of the spawner counts using a simple Bayesian Area-Under-the-Curve model provided less precise estimates of spawn timing but the method likely had negligible impact on the population and required only moderate effort. Deployment of egg mats required high effort and collected insufficient information to derive statistical estimates of spawn timing. We discuss how information from different methods could be combined together into a single integrated model to maximize the precision while minimizing the effort and impact.