Improving the foundations of sequential population analysis

改善序贯总体分析的基础

• 批准号: 298365-2007
• 负责人: Cadigan, Noel
• 金额: $ 1.24万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2009
• 资助国家: 加拿大
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=427544
• 关键词: Improving; foundations; sequential; population; analysis

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.

序贯种群分析 (SPA) 是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 是队列分析的一种形式，其中使用了商业捕捞量、其他死亡率来源以及渔业调查的结果。这些是有误差的纵向测量数据。 此外，SPA 中使用的假设通常很脆弱。本研究的目的是改进数据输入和建模方法。一个常见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。估算 SPA 时会使用调查渔获量，如果调查船或采样协议（例如净量）发生变化，则必须在 SPA 中予以考虑。我们将研究基于长度的流程在 SPA 等年龄结构模型中的作用，并对此类流程进行调整。我们还将使用 GLMM 改进对股票到期日的估计，其中一些随机回归参数是自相关的。成熟度与 SPA 结果相结合来估算产卵种群生物量 (SSB)——一个重要的种群数量。问题在于，未完成的（例如最近的）队列的成熟度数据每年都会更新，这可能会导致估计值发生重大变化，并导致 SSB 估计值出现重大回顾性差异。标记数据还可用于细化和改进 SPA 估计。分析长度选择性渔业的标签返回时的一个难题是考虑释放和捕获期间鱼类的生长。最近，随机效应模型已用于此目的，我们希望将这些方法应用于涉及自 1997 年以来在纽芬兰海岸标记的鳕鱼的广泛数据集。最后一个领域涉及开发诊断方法以检测 SPA 何时可能出现严重错误。高度参数化的 SPA 可能会掩盖模型假设的严重违规，这可能会在库存估计中产生很大的偏差。我们将研究一些方法来指示 SPA 何时被严重错误指定，以及错误指定的根源是什么。