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: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=456848
• 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)是加拿大和许多其他国家用来估计鱼类种群大小的常用模型。SPA是队列分析的一个版本，其中使用了商业渔获物、其他死亡来源和渔业调查结果。这些是经过误差测量的纵向数据。此外，SPA中使用的假设通常是站不住脚的。这项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到随机效果模型的使用。我们将使用具有簇和长度自相关随机效应的广义线性混合模型(GLMM‘s)来估计一艘测量船相对于另一艘测量船的相对捕捞效率。在估计SPA时使用调查渔获量，如果调查船只或采样协议(例如净网)发生变化，则必须在SPA中说明这一点。我们将检查基于长度的流程在SPA等年龄结构模型中所扮演的角色，并对此类流程进行调整。我们还将使用GLMM改进股票到期日的估计，在GLMM中，一些随机回归参数是自相关的。成熟度与SPA结果相结合，以估计产卵种群生物量(SSB)--一个重要的种群数量。问题是，未完成的(例如最近的)队列的到期数据每年都会更新，这可能导致估计的重大变化，以及SSB估计的重大回溯差异。标记数据还可用于改进和改进SPA估计。在分析长度选择性渔业的标记收益时，一个难题是如何计算从放生到捕捞之间的鱼类生长。最近，随机效应模型被用于这一目的，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以检测何时SPA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，这可能会在库存估计中产生很大的偏差。我们将调查表明SPA何时严重指定错误的方法，以及错误指定的来源是什么。