The use of indicators, triage, and clinical decision rules to assess data-limited fish populations

2010年，近3850万人从野生捕捞渔业中获得经济利益，30亿人从这些渔业中获得20%的动物蛋白。尽管这种全球性的重要性，保护状态是未知的近85%的收获类群。缺乏状况评估的主要原因是缺乏关于这些人口的数据。为了评估鱼类种群的状况，科学家们试图将复杂的统计方法应用于渔获量、科学调查结果、渔获量和调查的大小和年龄分布等数据。这些评估的目标是估计种群的开发率以及目前的丰度，通常以生物量（B）为单位，相对于种群长期最具生产力的丰度，这被称为将产生最大可持续产量的生物量（Bmsy）。渔业中的许多管理养护触发因素和种群状态确定都是基于比率B/Bmsy。然而，对于未评估的人口，许多所需的数据并不存在，因此无法应用传统的评估方法。从一个种群捕获的渔获量数据往往是这些数据有限的种群的唯一可用数据。因此，已经作出了大量努力，以制定方法，仅根据这一渔获量数据评估种群，这反过来又产生了相当多的争议。这些方法的目标是产生一个值的B/BMSY或开采率相对于最佳率，这些方法的质量已确定比较其输出的已知值从模拟数据或估计来自更复杂的评估模型。然而，当涉及到这些数据有限的群体时，管理人员最关心的是群体是否高于或低于某个指定的阈值（例如，B/Bmsy= 1.0）或群体是否增加或减少;对于这些数据有限的群体，很少关注B/Bmsy的具体值。自世纪初以来，医学一直在处理非常相似的问题，最初是以战场分类的形式，最近则是以临床决策规则的形式。在分流范例中，一套诊断（例如，呼吸、出血、失去肢体等）基本上同时进行评估，并生成称为风险评分的最终评分。风险评分假设所有诊断的结果都可用;没有缺失数据。然后使用风险评分将患者分为不同的严重程度类别。临床决策规则更多地使用逐步的层次结构，以帮助医生确定何时可能需要进行额外的测试，并帮助他们平衡不同类型的错误决策的总体概率（假阴性和假阳性）这项研究计划建议采用医疗分诊和临床决策的技术，以开发多个在假阳性和假阴性之间的权衡不对称的情况下，确定鱼类种群状况和状况趋势的指标框架。发展这种框架及其评估与这些框架中的决定有关的假阳性和假阴性率的能力，将有助于渔业科学家和管理人员就采取行动的必要性和增加数据采集的价值作出更明智的决定。虽然这些方法的重点是数据有限的鱼类种群，但所吸取的经验教训将适用于海洋领域以外数据有限的情况，特别是濒危物种领域。

In 2010, nearly 38.5 million people derived economic benefits from wild-capture fisheries, with 3 billion people obtaining 20% of their animal protein from these fisheries. Despite this global importance, the conservation status is unknown nearly 85% of the harvested taxa. This lack of status assessment is largely due to the lack of data on these populations. In order to assess the status of fish populations, scientists attempt to apply sophisticated statistical methods to data on the volume of catch, results of scientific surveys, size and age distributions from the catch and the surveys, etc. The goals of these assessments are to estimate the exploitation rate for the population as well as the current abundance, often in terms of biomass (B), relative to the abundance at which the stock is most productive in the long term, which is referred to as the biomass that will produce the maximum sustainable yield (Bmsy). Many of the management conservation triggers and population status determinations in fisheries are based on the ratio, B/Bmsy. For the unassessed populations, however, much of the required data does not exist so traditional assessment methods cannot be applied. Data on the volume of catch taken from a population is often the only data available for these data-limited populations. As such, there has been a great deal of effort to develop methods to assess populations solely on this catch data, which has in turn generated quite a bit of controversy. The goal of these methods is to produce a value for either B/BMSY or the exploitation rate relative to the optimal rate, and the quality of these methods has been determined by comparing their output to either known values from simulated data or estimates derived from more complicated assessment models. However, when it comes to these data-limited populations, managers are mostly concerned about whether the population is above or below some specified threshold value (e.g., B/Bmsy= 1.0) or whether the population is increasing or decreasing; the specific value for B/Bmsy is rarely of concern for these data-limited populations. Medical science has been dealing with very similar questions since the early 19th century, first in the form of battlefield triage and more recently in the form of clinical decision rules. In the triage paradigm, a suite of diagnostics (e.g., breathing, bleeding, loss of limbs, etc.) is assessed essentially simultaneously, and a final score, called the risk score, is generated. The risk score assumes that the results of all the diagnostics are available; there is no missing data. The risk score is then used to place patients into different categories of severity. Clinical decisions rules use more of a step-wise hierarchical structure to help doctors determine when additional tests may be necessary and help them balance the overall probability of different types of incorrect decisions (false negatives and false positives) This research program proposes to adapt techniques from medical triage and clinical decision-making to develop multiple-indicator frameworks to determine the status and trend in status for fish populations when trade-offs between false positives and false negatives are asymmetric. The development such frameworks and their ability to assess the false positive and false negative rates associated with the decisions in these frameworks, will help fisheries scientists and managers make more informed decisions about the need for action and the value of increased data acquisition. While the focus of these methods is on data-limited fish populations, the lessons learned will be applicable to data-limited situations beyond the marine realm, particularly in the area of endangered species.