The stock assessment theory of relativity: deconstructing the term “data-limited” fisheries into components and guiding principles to support the science of fisheries management

• Published in: Reviews in fish biology and fisheries 2023-03, Vol.33 (1), p.241-263
• Main Authors: Cope, Jason M., Dowling, Natalie A., Hesp, Sybrand A., Omori, Kristen L., Bessell-Browne, Pia, Castello, Leandro, Chick, Rowan, Dougherty, Dawn, Holmes, Steven J., McGarvey, Richard, Ovando, Daniel, Nowlis, Josh, Prince, Jeremy
• Format: Article
• Language: English
• Subjects: Biomedical and Life Sciences; Communication; Fisheries; Fisheries management; Fishery data; Fishery management; Fishery sciences; Freshwater & Marine Ecology; Life Sciences; Original Research; Principles; Relativity; Stock assessment; Zoology
• ISSN: 0960-3166; 1573-5184
• DOI: 10.1007/s11160-022-09748-1

The term “data-limited fisheries” is a catch-all to generally describe situations lacking data to support a fully integrated stock assessment model. Data conditions range from data-void fisheries to those that reliably produce quantitative assessments. However, successful fishery assessment can also be limited by resources (e.g., time, money, capacity). The term “data-limited fisheries” is therefore too vague and incomplete to describe such wide-ranging conditions, and subsequent needs for management vary greatly according to each fishery’s context. Here, we acknowledge this relativity and identify a range of factors that can constrain the ability of analyses to inform management, by instead defining the state of being “data-limited” as a continuum along axes of data (e.g., type, quality, and quantity) and resources (e.g., time, funding, capacity). We introduce a tool (the DLMapper) to apply this approach and define where a fishery lies on this relativity spectrum of limitations (i.e. from no data and no resources to no constraints on data and resources). We also provide a ranking of guiding principles, as a function of the limiting conditions. This high-level guidance is meant to identify current actions to consider for overcoming issues associated with data and resource constraints given a specific “data-limited” condition. We apply this method to 20 different fisheries to demonstrate the approach. By more explicitly outlining the various conditions that create “data-limited situations” and linking these to broad guidance, we aim to contextualize and improve the communication of conditions, and identify effective opportunities to continue to develop and progress the science of “limited” stock assessment in support of fisheries management.