Canadian Journal of Fisheries and Aquatic Sciences, Ahead of Print.
There is a need to improve fishing methods to select for certain sizes and species while excluding others. Experiments are conducted to quantify selectivity of fishing gears and how variables such as gear design (e.g., mesh size, mesh shape), environmental parameters (e.g., light, turbidity, substrate) or biological parameters (e.g., fish condition) alter selectivity; the resulting data need to be analyzed using specialized statistical methods in many cases. Here, we present a new tool for analyzing this type of data: an R package named “selfisher”. It allows estimating multiple fixed effects (e.g., fish length, total catch weight, environmental variables) and random effects (e.g., haul). A bootstrapping procedure is also provided. We demonstrate its use via four case studies, including (A) covered codend analyses of four gears, (B) a paired gear study with numerous covariates, (C) a catch comparison study of unpaired hauls of gillnets and (D) a catch comparison study of paired hauls using polynomials and splines. This software will make it easier to model selectivity, teach statistical methods, and make analyses more repeatable.

中文翻译：

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介绍 selfisher：用于渔具选择性统计分析的开源软件

加拿大渔业和水生科学杂志，印刷前。
有必要改进捕捞方法以选择某些大小和物种，同时排除其他的。进行实验以量化渔具的选择性以及诸如渔具设计（例如网孔尺寸、网孔形状）、环境参数（例如光、浊度、底物）或生物参数（例如鱼的条件）等变量如何改变选择性；在许多情况下，需要使用专门的统计方法分析生成的数据。在这里，我们提出了一个分析这类数据的新工具：一个名为“selfisher”的 R 包。它允许估计多个固定效应（例如，鱼的长度、总捕获重量、环境变量）和随机效应（例如，拖运）。还提供了一个引导程序。我们通过四个案例研究展示了它的用途，包括 (A) 涵盖的四个齿轮的密码分析，(B) 具有许多协变量的成对齿轮研究，(C) 未成对的刺网渔获量比较研究和 (D) 使用多项式和样条线对成对渔获物的渔获量比较研究。该软件将使选择性建模、教授统计方法和使分析更具可重复性变得更加容易。