Estimating the Size Selectivity of Trap Nets using a Gill-Net Selectivity Experiment: Method Development and Application to Lake Whitefish in Lake Huron

Abstract

Fisheries management requires information about the size selectivity of the fishing gears, but often the experimental data to estimate size selectivity are absent. We developed a two-stage method to use data from an experimental survey for one type of gear (gill nets) to estimate the size selectivity of another type of gear (trap nets) based on the commercial catch data from both gear types, and we applied this method to Lake Whitefish Coregonus clupeaformis in Lake Huron. In stage I, an information-theoretic approach (Akaike’s information criterion) was used to select among five candidate models for the size selectivity of experimental gill nets. A double logistic function (a dome-shaped curve skewed to the right) was chosen as the best model to describe the size selectivity of gill nets targeting Lake Whitefish. In stage II, we estimated the size selectivity of trap nets by applying the model of size selectivity from experimental gill nets to commercial catch data from trap-net and gill-net fisheries operating in a similar region and temporal period. Trap-net selectivity was fitted by using a symmetric logistic equation. This two-stage method predicted that 513-mm FL Lake Whitefish are fully selected by gill nets with a mesh size of 114 mm, whereas ≥615-mm FL fish are fully selected by commercial trap nets.

Received April 12, 2017; accepted September 8, 2017 Published online November 9, 2017

ACKNOWLEDGMENTS

We are grateful to Adam Cottrill, Steve Gile, and Lloyd Mohr for access to data and for sharing their knowledge of OMNRF sampling programs and the Lake Huron commercial fisheries. We also thank Dave McLeod for his involvement in data management and Erin Dunlop for comments on an earlier draft of the manuscript. Financial support was provided by the Great Lakes Fishery Commission (Fisheries Research Program). Three anonymous reviewers provided constructive comments and suggestions that significantly improved the quality of the manuscript.