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Fitting growth models to length frequency data

机译：使增长模型适合长度频率数据

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摘要

A novel two-stage procedure for fitting growth curves to length frequency data collected from commercial fisheries is described. The method is suitable for species in which cohorts are spawned over a limited time period, and samples of length frequency data are collected regularly (e.g. in weekly, fortnightly, or monthly time intervals) over an extended time period. In the first stage of analysis, Gaussian mixtures are fitted separately to the data for each time interval, and summary statistics (component means and standard errors) are extracted. In the second stage, parametric growth models, such as the von Bertalanffy seasonal growth curve, are fitted to the summary data. The error structure in this second stage of analysis incorporates random between-year effects, random within-year age-group effects, random within-year time-interval effects, random within-year age-group and time-interval interactions, and sampling errors. This complex error structure incorporating unbalanced crossed and nested random effects acknowledges that commercial fishing is not an exercise in random sampling, and allows for the inevitable additional sources of random variation in such an enterprise. The method is applied to South Australian southern bluefin tuna length frequency data collected from 1964 to 1989, and leads to the conclusion that juvenile tuna grew faster in the 1980s than in the 1960s, with the 1970s being a decade of highly variable growth.
机译：描述了一种新颖的两阶段程序，用于将生长曲线拟合到从商业渔业中收集的长度频率数据。该方法适用于在有限的时间段内产生队列的物种，并且在较长的时间段内定期（例如，每周，每两周或每月一次）收集长度频率数据的样本。在分析的第一阶段，将高斯混合分别拟合到每个时间间隔的数据，并提取汇总统计信息（成分均值和标准误）。在第二阶段，将参数增长模型（例如von​​ Bertalanffy季节性增长曲线）拟合到摘要数据。在第二阶段的分析中，误差结构包括随机的年际效应，年内的随机年龄组效应，年内的随机时间间隔效应，年内的随机年龄组和时间间隔相互作用以及抽样误差。这种复杂的错误结构结合了不平衡的交叉和嵌套随机效应，这承认商业捕鱼不是随机抽样中的一项工作，并且在这种企业中不可避免地会产生额外的随机变化源。该方法应用于从1964年至1989年收集的南澳大利亚南部蓝鳍金枪鱼长度频率数据，得出的结论是，少年金枪鱼在1980年代比1960年代增长更快，而1970年代是高度变化的十年。

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