کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5765619 | 1626779 | 2017 | 11 صفحه PDF | دانلود رایگان |
Selectivity studies have found applications in a wide range of topics within fishery science, such as fishery management, stock assessment, and ecological process studies. However, obtaining selectivity functions can often be a difficult and costly endeavor. Because of this difficulty, many studies are limited to the comparison of catch from two fishing gears, where relative differences in catch efficiency are often presented in the form of catch comparison rate or catch ratio. Studies of these rates are well known to often improve commercial fisheries, which benefit from highly selective gears. However, utility of these statistics for the purposes of fisheries surveys and stock assessment is not very well understood. In this study we adapted methods previously used for catch ratio to obtain length-dependent selectivity ratio function for two survey gears. Selectivity ratio can be obtained when area-swept or volume-swept fish density estimates are available from both gears. In other cases it is possible to obtain relative selectivity ratio. We present a general approach to obtain selectivity ratio in survey gear comparison studies and model it using three alternative techniques (linear and smooth mixed effect, and beta-regression). We use crossvalidation to choose between alternative models. We present examples of practical application of selectivity ratio with three case studies: a comparison of fine-and large mesh bottom trawls used in Arctic surveys, a study testing an assumption of non-selectivity of the Nephrops bottom trawl for snow crab in the Bering Sea, and a comparison of two survey midwater trawls for pollock in the Bering Sea. We show that selectivity ratio statistics can be used as a generalization of selectivity studies, where one gear is non-selective, as well as in catch comparison studies where selectivity of both gears is unknown.
Journal: Fisheries Research - Volume 191, July 2017, Pages 76-86