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首页> 外文期刊>ICES Journal of Marine Science >Properties of age compositions and mortality estimates derived from cohort slicing of length data
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Properties of age compositions and mortality estimates derived from cohort slicing of length data

机译：年龄组成的属性和死亡率估计值（来自队列数据的队列切片）

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Cohort slicing can be used to obtain catch-at-age data from length frequency distributions when directly measured age data are unavailable. The procedure systematically underestimates the relative abundance of the youngest age groups and overestimates abundance at older ages. Cohort-sliced catch-at-age data can be used to estimate total mortality rate (Z) using a regression estimator or the Chapman-Robson estimator for right truncated data. However, the effect of cohort slicing on accuracy and precision of resulting Z estimates remains to be determined. We used Monte Carlo simulation to estimate the per cent bias and per cent root mean square error of the unweighted regression, weighted regression, and Chapman-Robson mortality estimators applied to cohort-sliced data. Incompletely recruited age groups were truncated from the cohort-sliced catch-at-age data using previously established recommendations and a variety of plus groups was used to combine older age groups. The sensitivity of the results to a range of plausible biological combinations of Z, growth parameters, recruitment variability, and length-at-age error was tested. Our simulation shows that cohort slicing can work well in some cases and poorly in others. Overall, plus group selection was more important in high K scenarios than it was in low K scenarios. Surprisingly, defining the plus group to start at a high age worked well in some cases, although length and age are poorly correlated for old ages. No one estimator was uniformly superior; we therefore provide recommendations concerning the appropriate estimator and plus group to use, depending on the parameters characterizing the stock. We further recommend that simulations be performed to determine exactly which plus group would be most appropriate given the scenario at hand.
机译：当直接测量的年龄数据不可用时，可以使用队列切片从长度频率分布中获取年龄捕获数据。该程序系统性地低估了最年轻年龄组的相对丰度，而高估了年龄较大的人群。可以使用回归估计量或Chapman-Robson估计量用于正确截断的数据，使用队列划分的年龄捕获数据来估计总死亡率（Z）。但是，队列划分对所得Z估计的准确性和精确性的影响尚待确定。我们使用蒙特卡洛模拟法来估算未加权回归，加权回归以及应用于队列数据的Chapman-Robson死亡率估计量的偏差和均方根误差。使用先前建立的建议，从队列划分的年龄捕获数据中截去了未完全征募的年龄组，并使用了多种加法组来组合年龄较大的年龄组。测试了结果对一系列可能的Z生物学组合，生长参数，募集变异性和年龄长度误差的敏感性。我们的模拟显示，同类群组切片在某些情况下效果很好，而在其他情况下效果不佳。总体而言，在高K场景中，加上组选择比在低K场景中更为重要。令人惊讶的是，在某些情况下，将加号组定义为从高龄开始的效果很好，尽管长度和年龄与老年之间的相关性很差。没有人能一概而论。因此，根据存量的参数，我们提供了有关要使用的适当估计量和加群的建议。我们进一步建议进行仿真以确定在给定情况下哪个加组最合适。

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