Working Towards a Framework for Stock Evaluations in Data‐Limited Fisheries. (7th May 2018)

• Record Type: Journal Article
• Authors: Sagarese, Skyler R.; Rios, Adyan B.; Cass‐Calay, Shannon L.; Cummings, Nancie J.; Bryan, Meaghan D.; Stevens, Molly H.; Harford, William J.; McCarthy, Kevin J.; Matter, Vivian M.
• Is Part Of: North American journal of fisheries management. Volume 38:Number 3(2018)
• Page Start: 507
• Page End: 537
• Publication Date: 2018-05-07
• Subjects: Fishery management -- United States -- Periodicals; 333.956097305
• DOI: 10.1002/nafm.10047
• Languages: English
• ISSNs: 0275-5947

Abstract

Abstract: Data‐limited approaches to managing fisheries are widespread in regions where insufficient data prevent traditional stock assessments from determining stock status with sufficient certainty to be useful for management. Where severe data limitations persist, a catch‐only approach is commonly employed, such as in the U.S. Caribbean region. This approach, however, has not received the level of scrutiny required to determine the potential long‐term risks (e.g., probability of overfishing) to fish stocks. In this study, we present a framework for comparison and implementation of data‐limited methods, including the static Status Quo approach, which uses average catch landings. Candidate species for stock evaluation were identified through a data triage and included Yellowtail Snapper Ocyurus chrysurus (Puerto Rico), Queen Triggerfish Balistes vetula (St. Thomas and St. John), and Stoplight Parrotfish Sparisoma viride (St. Croix). Feasible data‐limited methods, based on data availability and quality, included empirical indicator approaches using relative abundance (i.e., catch per unit effort) or mean length. Results from the management strategy evaluation support the use of adaptive data‐limited methods, which incorporate feedback in contrast to the static Status Quo approach. The proposed framework can help guide the development of catch advice for dynamic fisheries management in data‐limited regions.