Fish stock assessments using surveys and indicators
Fish stock assessments using surveys and indicators
复制标题
使用调查和指标评估鱼类种群
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
B. Mesnil
中科院分区：
文献类型：
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作者：
P. Petitgas;J. Cotter;V. Trenkel;B. Mesnil
传统的鱼类资源评估方法大多依赖于商业捕捞数据。但是，越来越多的情况需要替代的评估方法。例如，对于存量锐减的鱼类，渔业可能会关闭，从而无法获得或不可靠的渔获。误报和下落不明的丢弃物造成难以可靠地将登陆量转化为海上有效捕获量。此外，还需要对越来越多的鱼类提供咨询意见，而对其中许多鱼类来说，没有按年龄分类的渔业捕获量的历史序列，因此无法进行常规评估。在这方面，在海上以标准化方法和已知条件进行的基于研究调查的测量是一套宝贵的独立于渔业的数据，可作为评估的基础。这些数据会导致什么类型的评估，这些评估如何与现有方法一起或代替现有方法有用？本期特刊的论文集论述了这些问题。这些文件提出了单物种种群评估的方法和管理策略，这些方法仅使用来自研究调查的与渔业无关的信息。还提供了示例来说明方法的使用。一些论文的R语言脚本在网上有附件。这些方法是在欧盟渔业独立调查业务评估工具FISBOAT项目（FP6合同编号502572；www.ifremer.fr/drvecohal/fisboat）内开发的。它们分为三类：基于库存属性指标的监测程序、评估模型和模拟评估工具。前两类方法基本上是对存量变化的诊断。模拟工具允许我们调查管理方案的效果，并且是诊断工具的补充。第一篇论文提出了调查数据的一些局限性（Trenkel和Cotter）。第二篇论文探讨了从拖网调查收集的鱼类样本估计指标平均值时出现的一些统计问题（Cotter）。随后的论文是关于基于指标的评估。首先，提出了计算种群水平指标的方法，这些指标反映了种群属性。这些指标涉及生物指标（Cotter等）和空间分布指标（Woillez等）。由于许多指标可能被估计，因此如何构建不相关的多变量指标是两篇论文（Petitgas and Poulard; Woillez et al.）的主题。然后，在两篇文章中提出了一套分析指标时间序列和检测时间变化的方法。一（Cotter）回顾了非参数趋势分析方法。另一篇综述了统计过程控制方案（例如，累积总和：CUSUM），旨在监测随时间序列平均值的变化（Mesnil和Petitgas）。为了实现基于多个指标时间序列分析的所有信息的综合评估，提出了一种集成程序（Petitgas），该程序使用CUSUM构造与参考均值向量偏差的交通灯表。该组的最后一篇论文提出了各种仅调查数据的评估模型（Mesnil等人），并使用具有不同属性的模拟数据评估其性能。所有这些基于调查的方法都导致了基于对许多指标时间序列的显著变化进行检验的相对评估。由于使用了许多指标，因此评估具有广泛的生物学基础。模拟工具是监测和评估方案的补充，因为它们允许测试假设和/或估计参考点或极限。它们还允许测试管理选项。这需要人口模型和收获控制规则。基于长度的种群模拟模型ALADYM （Lembo等人）在非基于tac的管理环境中很有意义。Apostolaki和Hillary回顾了捕捞控制规则，并在Hillary的论文中介绍了仿真平台FLR （R中的渔业库）中可用的许多工具。最后，Cotter等人的论文总结了在使用基于调查的程序或与传统方法一起使用时如何进行全面评估，或者将其作为推动采用基础更广泛的生态系统方法来管理渔业的一部分。本期中提出的许多方法可以用于生态系统监测和评估以及鱼类种群，一次一个物种。它们有望有助于渔业的未来发展和生态系统评估，并最终促进更好、更可持续的管理。
Conventional fish stock assessment methods are mostly dependent on commercial catch data. But there are more and more situations in which alternative assessment methods are needed. For instance, for stocks that are collapsed, the fishery might be closed so that fishery catches are unavailable or unreliable. Misreporting and unaccounted discards generate difficulties reliably converting landings to effective catches at sea. Also, advice is required for an increasing number of stocks, and for many of them historical series of fishery catches disaggregated by age do not exist, making conventional assessments impossible. In that context, research survey-based measurements made at sea with standardised methods and in known conditions represent an invaluable set of fishery-independent data on which to base an assessment. What type of assessment do these data lead to and how could such assessments be useful alongside or instead of existing methods? The collection of papers in this special issue addresses these questions. The papers present methodology for single-species stock assessments and management strategies using only fishery-independent information from research surveys. Examples to illustrate the use of methods are also provided. For some papers scripts in the R language are attached on-line. The methods were developed within the EU project FISBOAT on Fishery Independent Survey Based Operational Assessment Tools (FP6 contract No. 502572; www.ifremer.fr/drvecohal/fisboat). They belong to three categories: monitoring procedures based on indicators of stock attributes, assessment models, and simulation evaluation tools. The two first categories of method are essentially diagnostic of stock changes. Simulation tools allow us to investigate the effects of management options and are complementary to the diagnostic tools. The first paper presents some of the limitations of survey data (Trenkel and Cotter). The second paper examines some statistical issues that arise when estimating mean values of indicators from samples of fish collected by a trawl survey (Cotter). Subsequent papers are about indicator-based assessments. First, methods to compute population-level indices that are indicative of stock attributes are presented. These concern biological indicators (Cotter et al.) and also indicators of spatial distributions (Woillez et al.). Because many indicators are likely to be estimated, how to construct uncorrelated multivariate indicators is the subject of two papers (Petitgas and Poulard; Woillez et al.). Then, a set of methods to analyse indicator time series and detect temporal changes are presented in two papers. One (Cotter) reviews non parametric trend analysis methods. The other reviews statistical process control schemes, (e.g., cumulative sums: CUSUM) designed to monitor changes in the mean value along time series (Mesnil and Petitgas). To achieve an integrated assessment based on all the information from the analysis of the many indicator time series, an integration procedure is proposed (Petitgas) that uses CUSUM to construct a traffic light table of the deviations from a reference mean vector. The last paper in this group presents various survey-data-only assessment models (Mesnil et al.) and evaluates their performance using simulated data with different properties. All these survey-based methods lead to relative assessments based on testing for significant changes in many indicators time series. Because many indicators are used, the assessments have a broad biological basis. Simulation tools are complementary to monitoring and assessment schemes as they allow testing of hypotheses and/or estimation of reference points or limits. They also allow testing of management options. This requires population models as well as harvest control rules. The length-based population simulation model ALADYM (Lembo et al.) is of interest in non-TAC based management contexts. Harvest control rules are reviewed by Apostolaki and Hillary and the many tools available in the simulation platform, FLR (Fisheries Library in R), are presented in the paper by Hillary. Finally, the paper by Cotter et al. summarises how comprehensive assessments might be carried out when using survey-based procedures either alongside conventional approaches, or instead of them as part of a drive towards a more broadly based, ecosystem approach to management of fisheries. Many of the methods presented in this issue could serve for ecosystem monitoring and assessment as well as for fish stocks, one species at a time. They will hopefully contribute to the future development of fisheries and ecosystem assessments and, ultimately, to better, more sustainable management.