Fisheries stock assessments have traditionally modeled age and size composition data using the multinomial likelihood, however the multinomial cannot appropriately account for the correlations and overdispersion that exist in the observed data or in the model residuals. Not accounting for these phenomena can affect assessment performance. Methods to remedy this have included down-weighting composition data within assessments either arbitrarily or by using iterative re-weighting algorithms, and by using alternative likelihoods to the multinomial that can be weighted within the assessment. Iteratively re-weighting composition data in stock assessments is inefficient and does not ultimately account for correlations in the residuals, and alternative likelihoods for composition data have not all been evaluated using stock assessment simulations. To evaluate the performance of alternative likelihoods in fitting composition data, we first developed a spatially explicit age-structured operating model to simulate correlation structure observed in real composition data. We then fit spatially aggregated assessment models to the simulated data and assessed the performance of various formulations of composition likelihoods (Multinomial, Robust Multinomial, Dirichlet, Dirichlet-multinomial, and Logistic-normal) in estimating stock dynamics and quantities of management interest. Results suggest that the degree of process error (combining both process variation and model misspecification) and the sample size of the composition data have a larger effect on the relative performance of different likelihoods than the degree of overdispersion and correlations in composition data. When the composition sample size was moderate to large and there existed at least a moderate amount of process error, the Logistic-normal likelihood performed best. When the sample size was small, or when process error was non-existent or negligible, the Dirichlet-multinomial likelihood performed best.

渔业资源评估传统上使用多项似然对年龄和规模组成数据进行建模，但是多项式无法恰当地说明观测数据或模型残差中存在的相关性和过度分散。不考虑这些现象会影响评估绩效。解决这个问题的方法包括任意地或通过使用迭代重新加权算法来降低评估中的成分数据的权重，以及通过使用可以在评估中加权的多项式的替代可能性。在股票评估中反复重新加权成分数据是低效的，并且最终不能解释残差中的相关性，而且成分数据的替代可能性还没有全部使用股票评估模拟进行评估。为了评估替代可能性在拟合成分数据中的性能，我们首先开发了一个空间明确的年龄结构操作模型来模拟在真实成分数据中观察到的相关结构。然后，我们将空间聚合评估模型拟合到模拟数据中，并评估了各种组合可能性公式（多项式、稳健多项式、狄利克雷、狄利克雷多项式和逻辑正态）在估计股票动态和管理兴趣数量方面的性能。结果表明，过程误差的程度（结合过程变异和模型错误指定）和成分数据的样本大小对不同可能性的相对性能的影响比成分数据中的过度分散和相关性的程度更大。当组成样本量中等到大且至少存在中等程度的过程误差时，Logistic-正态似然表现最好。当样本量较小，或过程误差不存在或可忽略不计时，狄利克雷多项似然表现最佳。