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.

顺序人口分析（SPA）是一种通用模型，用于估计加拿大和许多其他国家的鱼类种群的规模。水疗中心是一种队列分析的版本，其中使用了商业捕获，其他死亡率以及渔业调查的结果。这些是用错误测量的纵向数据。 此外，水疗中使用的假设通常很脆弱。这项研究的目的是改善数据输入和建模方法。一个共同的主题涉及使用随机效应模型。我们将使用具有群集和长度自动相关的随机效应的广义线性混合模型（GLMM），以估计与另一个调查容器相比的相对捕捞效率。估计水疗中心时使用调查捕获量，如果调查容器或采样方案发生变化（例如净），则必须在水疗中心考虑这一点。我们将研究基于长度的过程在像水疗中心这样的年龄结构化模型中所具有的，并对此类过程进行调整。我们还将使用GLMM提高对库存到期的估计值，其中一些随机回归参数是自相关的。将成熟度与水疗结果结合在一起，以估计产卵库存生物量（SSB） - 重要的库存数量。问题在于，对于未完成的（例如最近）的同伙，每年都会更新成熟度数据，这可能会导致估计值发生很大变化，并在SSB估计中发生实质性的回顾性差异。标记数据也可用于完善和改进水疗估计。在分析长度选择性渔业的标签回归时，一个困难的问题是在释放和捕获时间之间考虑鱼类的生长。最近，随机效应模型已用于此目的，我们希望将这些方法适应涉及自1997年以来在纽芬兰海岸上标记的COD的广泛数据集。最终区域涉及开发诊断以检测SPA何时可能处于严重错误。高度参数化的水疗中心可以在模型假设中掩盖严重的违规行为，这可能会在库存估计中产生巨大的偏见。我们将调查指示何时严重错误指定的方法，以及错误指定的来源是什么。