Converting a standardized saugeye and Walleye gill-net survey to the North American standard gill-net method: A paired-gear comparison study in Ohio reservoirs

ABSTRACT Objective The Ohio Division of Wildlife transitioned standardized gill-net surveys to North American standard (hereafter, “standard”) methods in 2024. To prepare for this transition, we conducted a paired-gear comparison study between the Ohio Division of Wildlife Inland Management System gill-net method and the standard gill-net method for saugeye (female Walleye Sander vitreus × male Sauger S. canadensis) and Walleye in order to develop models that allow us to convert metrics from one gill-net type to the other and estimate the sample sizes needed to achieve sampling objectives. Methods We set equal numbers of Inland Management System gill nets and standard gill nets in 13 Ohio reservoirs. We developed contact selectivity curves for both gill-net types for saugeye and Walleye and applied those curves to the catch data. We used linear regression to develop correction models for total CPUE, the CPUE of stock-length fish, proportional size distribution, and the proportional size distribution of preferred-length fish. We also conducted a resampling analysis to estimate the number of gill-net sets needed to catch 100 stock-length saugeye and Walleye and achieve a relative standard error of 25 or less for the CPUE of stock-length fish. Results Our regression models indicated strong positive relationships between net types (r2 = 0.57–0.84) for the four metrics of interest. The median number of standard gill-net sets needed to catch 100 stock-length saugeye and Walleye was 21, and the median number of sets needed to achieve a relative standard error of 25 or less was 9. Conclusions Our results show that strong correction models can be developed between existing sampling methods and standard methods that will allow comparisons using data from both gears. Our study provides an example for those who are interested in adopting standard methods but who are hesitant due to the existence of long-term, historic data sets. This study also contributes to understanding the sample size requirements for the standard gill-net method.