A new algorithm for estimating gillnet selectivity  

作　　者：唐衍力 黄六一 葛长字 梁振林 孙鹏 

机构地区：[1]Fisheries College,Ocean University of China [2]Marine College,Shandong University at Weihai

出　　处：《Chinese Journal of Oceanology and Limnology》2010年第2期274-279,共6页中国海洋湖沼学报（英文版）

基　　金：Supported by National Key Technology R&D Program of China(No.2006BAD09A05)

摘　　要：The estimation of gear selectivity is a critical issue in fishery stock assessment and management.Several methods have been developed for estimating gillnet selectivity,but they all have their limitations,such as inappropriate objective function in data fitting,lack of unique estimates due to the difficulty in finding global minima in minimization,biased estimates due to outliers,and estimations of selectivity being influenced by the predetermined selectivity functions.In this study,we develop a new algorithm that can overcome the above-mentioned problems in estimating the gillnet selectivity.The proposed algorithms include minimizing the sum of squared vertical distances between two adjacent points and minimizing the weighted sum of squared vertical distances between two adjacent points in the presence of outliers.According to the estimated gillnet selectivity curve,the selectivity function can also be determined.This study suggests that the proposed algorithm is not sensitive to outliers in selectivity data and improves on the previous methods in estimating gillnet selectivity and relative population density of fish when a gillnet is used as a sampling tool.We suggest the proposed approach be used in estimating gillnet selectivity.The estimation of gear selectivity is a critical issue in fishery stock assessment and management. Several methods have been developed for estimating gillnet selectivity, but they all have their limitations, such as inappropriate objective function in data fitting, lack of unique estimates due to the difficulty in finding global minima in minimization, biased estimates due to outliers, and estimations of selectivity being influenced by the predetermined selectivity functions. In this study, we develop a new algorithm that can overcome the above-mentioned problems in estimating the gillnet selectivity. The proposed algorithms include minimizing the sum of squared vertical distances between two adjacent points and minimizing the weighted sum of squared vertical distances between two adjacent points in the presence of outliers. According to the estimated gillnet selectivity curve, the selectivity function can also be determined. This study suggests that the proposed algorithm is not sensitive to outliers in selectivity data and improves on the previous methods in estimating gillnet selectivity and relative population density of fish when a gillnet is used as a sampling tool. We suggest the proposed approach be used in estimating gillnet selectivity.

关 键 词：ALGORITHM gillnet selectivity Kitahara method 

分 类 号：S932[农业科学—渔业资源]