Improving the foundations of sequential population analysis

序贯种群分析（SPA）是一种用于估计加拿大和许多其他国家鱼类种群规模的常用模型。SPA是群组分析的一个版本，其中使用商业渔获量、其他死亡来源和渔业调查结果。这些是有误差的纵向数据。 此外，SPA中使用的假设往往是脆弱的。本研究的目的是改进数据输入和建模方法。一个共同的主题涉及随机效应模型的使用。我们将使用具有聚类和长度自相关随机效应的广义线性混合模型（GLMM）来估计一艘调查船与另一艘调查船相比的相对捕捞效率。在估计SPA时使用调查渔获量，如果调查船或采样协议（例如网）发生变化，则必须在SPA中说明。我们将在SPA这样的年龄结构模型中研究基于角色长度的过程，并对这些过程进行调整。我们还将使用GLMM改进股票到期日的估计，其中一些随机回归参数是自相关的。成熟度与SPA结果相结合，以估计产卵群生物量（SSB）-一个重要的股票数量。问题是，未完成（例如最近）队列的到期数据每年更新一次，这可能导致估计值发生重大变化，以及SSB估计值的重大回顾性差异。标记数据也可用于细化和改进SPA估计。在分析选择性捕捞的标签回报时，一个困难的问题是在释放和捕获之间的鱼类生长。最近，随机效应模型已被用于此目的，我们希望适应这些方法的一个广泛的数据集，涉及鳕鱼标记的纽芬兰海岸自1997年以来。最后一个领域涉及开发诊断程序，以检测SPA何时可能出现严重错误。高度参数化的SPA可以掩盖模型假设中的严重违规行为，这可能会在股票估计中产生很大的偏差。我们将研究指示SPA何时被严重错误指定的方法，以及错误指定的来源是什么。

Sequential Population Analysis (SPA) is a common model used to estimate the size of fish stocks in Canada and many other countries. SPA is a version of cohort analysis in which commercial catches, other sources of mortality, and the results from fisheries surveys are used. These are longitudinal data measured with error. In addition, the assumptions used in SPA are often tenuous. The objective of this research is to improve the data inputs and the modelling method. A common theme involves the use of random effects models. We will use generalized linear mixed models (GLMM's) with cluster and length-autocorrelated random effects to estimate the relative fishing efficiency of one survey vessel compared to another. Survey catches are used when estimating an SPA, and if there is a change in survey vessels or sampling protocols (e.g. net) then this has to be accounted for in the SPA. We will examine the role length-based processes have in an age-structured model like SPA, and make adjustments for such processes. We will also improve estimates of stock maturities using GLMM's in which some random regression parameters are autocorrelated. Maturities are combined with SPA results to estimate spawning stock biomass (SSB) - an important stock quantity. The problem is that maturity data are updated annually for unfinished (e.g. recent) cohorts and this can result in substantial changes in estimates, and substantial retrospective differences in SSB estimates. Tagging data can also be used to refine and improve SPA estimates. A difficult problem when analyzing tag-returns from length-selective fisheries is accounting for fish growth between the time of release and capture. Recently, random-effects models have been used for this purpose, and we wish to adapt these approaches for an extensive data set involving cod tagged off the coast of Newfoundland since 1997. The final area involves developing diagnostics to detect when SPA may be in serious error. Highly parameterized SPA's can mask serious violations in model assumptions, and this can create large biases in stock estimates. We will investigate methods that indicate when an SPA is seriously mis-specified, and what the source of the mis-specification is.