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Utilising data from ecosystem monitoring for managing fisheries: development of statistical summaries of indices arising from the CCAMLR Ecosystem Monitoring Program

机译:利用来自生态系统监测的数据来管理渔业:开发由CCAMLR生态系统监测计划产生的指标的统计摘要

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A potential method is presented for combining data collected as part of the CCAMLR ecosystem monitoring program (CEMP) into a single index for each of predator, prey and environmental parameters. The paper is divided into four main parts. The first part develops the proposed method of forming summary indices, which is based on the usual theory of multivariate statistics and takes into account the covariance between parameters. The second part reports on a Monte Carlo simulation study that examines the robustness of the indices to missing data and the degree of correlation between parameters. These trials show that missing values were unlikely to be a problem for time series of parameters that are highly correlated (>0.6). Criteria for inclusion of parameters in the indices are discussed when parameters are moderately or poorly correlated. The third part uses further simulation tests to examine the power of the statistical procedure adopted by WG-EMM in 1996 for identifying anomalies in CEMP parameters. The power of the procedure to detect anomalies was found to fall to low levels once more than a few anomalous values have appeared in the data. An alternative procedure, using estimates of the mean and variance of baseline time series, was found to have consistently better statistical power regardless of the accumulation of anomalies. The last section outlines an approach to the further development of CEMP indices for application in CCAMLR.
机译:提出了一种潜在的方法,可以将作为CCAMLR生态系统监视程序(CEMP)的一部分收集的数据组合到一个针对食肉动物,猎物和环境参数的单个索引中。本文分为四个主要部分。第一部分基于常规的多元统计理论,并考虑了参数之间的协方差,提出了一种建议的汇总索引形成方法。第二部分报告有关蒙特卡洛模拟研究的内容,该研究检查了缺失数据索引的稳健性以及参数之间的相关程度。这些试验表明,对于高度相关(> 0.6)的参数时间序列,缺失值不太可能成为问题。当参数相关性中等或较弱时,讨论将参数包含在索引中的标准。第三部分使用进一步的模拟测试来检验WG-EMM在1996年采用的统计程序的功能,以识别CEMP参数中的异常。一旦数据中出现了多个异常值,发现异常检测程序的能力就会下降到较低水平。发现使用基线时间序列的均值和方差的估计值的替代方法,无论异常情况如何累积,始终具有更好的统计能力。最后一部分概述了进一步开发在CCAMLR中使用的CEMP指数的方法。

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