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首页> 外文期刊>ICES Journal of Marine Science >A Bayesian hierarchical approach to estimate growth parameters from length data of narrow spread
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A Bayesian hierarchical approach to estimate growth parameters from length data of narrow spread

机译：一种贝叶斯分层方法，可从窄范围的长度数据估计生长参数

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摘要

Estimating fish growth from length frequency data is challenging. There is often a lack of clearly separated modes and modal progression in the length samples due to a combination of factors, including gear selectivity, slowing growth with increasing age, and spatial segregation of different year classes. In this study, we present an innovative Bayesian hierarchical model (BHM) that enables growth to be estimated where there are few distinguishable length modes in the samples. We analyse and identify the modes in multiple length frequency strata using a multinormal mixture model and then integrate the modes and associated variances into the BHM to estimate von Bertalanffy growth parameters. The hierarchical approach allows the parameters to be estimated at regional levels, where they are assumed to represent subpopulations, as well as at species level for the whole stock. We carry out simulations to validate the method and then demonstrate its application to Indian Ocean longtail tuna (Thunnus tonggol). The results show that the estimates are generally consistent with the range of estimates reported in the literature, but with less uncertainty. The BHM can be useful for deriving growth parameters for other species even if the length data contain few age classes and do not exhibit modal progression.
机译：从长度频率数据估计鱼类的生长具有挑战性。由于多种因素的综合考虑，长度样本中通常缺乏清晰分离的模式和模式进展，这些因素包括齿轮选择性，随着年龄增长而增长缓慢以及不同年级的空间隔离。在这项研究中，我们提出了一种创新的贝叶斯层次模型（BHM），该模型能够在样品中几乎没有可区分的长度模式的情况下估算增长。我们使用多重正态混合模型分析和识别多长度频率层中的模式，然后将模式和相关方差整合到BHM中以估算von Bertalanffy生长参数。分层方法允许在区域级别（假设它们代表子种群）以及整个种群的物种级别估计参数。我们进行仿真以验证该方法，然后证明其在印度洋长尾金枪鱼（Thunnus tonggol）中的应用。结果表明，估计值通常与文献报道的估计值范围一致，但不确定性较小。即使长度数据包含很少的年龄类别并且没有表现出模态进展，BHM仍可用于推导其他物种的生长参数。

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