A new algorithm for estimating gillnet selectivity

Yanli Tang; Liuyi Huang; Changzi Ge; Zhenlin Liang; Peng Sun

doi:10.1007/s00343-010-9256-2

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A new algorithm for estimating gillnet selectivity

OriginalPaper | Updated：2023-05-16

- A new algorithm for estimating gillnet selectivity
- A new algorithm for estimating gillnet selectivity
- 海洋湖沼学报（英文） 2010年28卷第2期页码：274-279
- Affiliations：
  
  1. Fisheries College Ocean University of China
  2. Marine College Shandong University at Weihai
- Author bio：
- Funds：
  
  Supported by National Key Technology R&D Program of China (No.2006BAD09A05)
- DOI：10.1007/s00343-010-9256-2
  中图分类号：
- 网络首发：2010-03-20，
  
  纸质出版：2010
- Accepted：
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A new algorithm for estimating gillnet selectivity[J]. 海洋湖沼学报（英文）, 2010,28(2):274-279.

Tang, Y., Huang, L., Ge, C. et al. A new algorithm for estimating gillnet selectivity., Chin. J. Ocean. Limnol. 28, 274–279 (2010). https://doi.org/10.1007/s00343-010-9256-2
A new algorithm for estimating gillnet selectivity[J]. 海洋湖沼学报（英文）, 2010,28(2):274-279. DOI： 10.1007/s00343-010-9256-2.

Tang, Y., Huang, L., Ge, C. et al. A new algorithm for estimating gillnet selectivity., Chin. J. Ocean. Limnol. 28, 274–279 (2010). https://doi.org/10.1007/s00343-010-9256-2 DOI：

摘要

Abstract

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.

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references

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Fujimori Y, Tokai T. 1999. Estimation of gillnet selectivity curve by Ishida’s and Kitahara’s methods with MS-Excel. Bull. Jpn. Soc. Fish. Oceanogr., 63: 14–25.

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Madsen N, Holst R, Wileman D, Moth-Poulsen T. 1999. Size selectivity of sol gillnets fished in the North Sea. Fish. Res., 44: 589–573.

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