Multi-Indicator Harvest Strategies for Data-Limited Fisheries: A Practitioner Guide to Learning and Design

• DOI: 10.3389/fmars.2021.757877
• Publication Date: 2021-12-08T06:12:58Z
• Authors: William J. Harford; Ricardo Amoroso; Richard J. Bell; Matias Caillaux; Jason Marc Cope; Dawn Dougherty; Natalie Anne Dowling; Frank Hurd; Serena Lomonico; Josh Nowlis; Dan Ovando; Ana M. Parma; Jeremy D. Prince; Jono R. Wilson
• Keywords: 0106 biological sciences; management strategy; indicator; Science; Q; General. Including nature conservation, geographical distribution; 006; MANAGEMENT STRATEGY; QH1-199.5; FRAMEWORK; 01 natural sciences; STOCK ASSESSMENT; FISHERY MANAGEMENT; framework; fishery management; https://purl.org/becyt/ford/4.1; https://purl.org/becyt/ford/4; 14. Life underwater; INDICATOR; stock assessment

As the world population grows, fisheries practitioners will be under increased pressure to address global challenges in data-limited fisheries management. With a focus on addressing localized and case-specific management needs, we provide a practical guide to the design and development of multi-indicator frameworks for fishery management. In a data-limited context, indicators are observations or estimates of the state of the fishery resource that are typically proxies for variables of interest, rather than quantities such as stock biomass estimated from data-rich stock assessments. Indicator frameworks structure the integration and interpretation of indicators to guide tactical fishery decision-making, often when the application of more formal analytical assessments is not feasible, yet where indicators in combination provide insight into stock status. With a focus on multi-indicator frameworks, we describe a pragmatic approach for their development via a set of organizational steps, considering a wide spectrum of types and severity of information limitations. We highlight where multi-indicator frameworks can be insightful and informative in relation to single indicator approaches but also point to potential pitfalls, with emphasis on critical evaluation and detection of performance flaws during the design phase using methods such as management strategy evaluation.