Implementation of the United Nations Sustainable Development Goals requires assessments of the global state of fish populations. While we have reliable estimates of stock status for fish populations accounting for approximately half of recent global catch, our knowledge of the state of the majority of the world's “unassessed” fish stocks remains highly uncertain. Numerous publications have produced estimates of the global status of these unassessed fisheries, but limited quantity and quality of data along with methodological differences have produced counterintuitive and conflicting results. Here, we show that despite numerous efforts, our understanding of the status of global fish stocks remains incomplete, even when new sources of broadly available data are added. Estimates of fish populations based primarily on catch histories on average performed 25% better than a random guess. But, on average, these methods assigned fisheries to the wrong FAO status category 57% of the time. Within these broad summaries, the performance of models trained on our tested data sources varied widely across regions. Substantial improvements to estimates of the state of the world's exploited fish populations depend more on expanded collection of new information and efficient use of existing data than development of new modelling methods.

中文翻译：

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改进对全球渔业状况的估计取决于更好的数据

联合国可持续发展目标的实施需要对全球鱼类种群状况进行评估。虽然我们对占最近全球捕捞量约一半的鱼类种群状况有可靠估计，但我们对世界上大多数“未评估”鱼类种群状况的了解仍然高度不确定。许多出版物对这些未经评估的渔业的全球状况进行了估计，但数据的数量和质量有限以及方法上的差异产生了违反直觉和相互矛盾的结果。在这里，我们表明，尽管做出了很多努力，我们对全球鱼类种群状况的理解仍然不完整，即使添加了新的广泛可用的数据来源。主要基于捕捞历史的鱼类种群估计平均比随机猜测好 25%。但是，平均而言，这些方法有 57% 的时间将渔业归入错误的粮农组织状态类别。在这些广泛的摘要中，在我们测试的数据源上训练的模型的性能在不同地区差异很大。对世界开发鱼类种群状况估计的实质性改进更多地依赖于新信息的扩大收集和现有数据的有效利用，而不是新建模方法的开发。