Improving estimates of the state of global fisheries depends on better data. Issue 6 (19th July 2021)

• Record Type: Journal Article
• Authors: Ovando, Daniel; Hilborn, Ray; Monnahan, Cole; Rudd, Merrill; Sharma, Rishi; Thorson, James T.; Rousseau, Yannick; Ye, Yimin
• Is Part Of: Fish and fisheries. Volume 22:Issue 6(2021)
• Page Start: 1377
• Page End: 1391
• Publication Date: 2021-07-19
• Subjects: catch‐only models -- data‐limited assessment -- fisheries management -- global fisheries -- stock assessment -- United Nations sustainable development goals; Fisheries -- Periodicals; Fishes -- Periodicals; 639.2
• DOI: 10.1111/faf.12593
• Languages: English
• ISSNs: 1467-2960

Abstract

Abstract: 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.