A Review of Applications Evaluating Fisheries Management Scenarios through Marine Ecosystem Models. Issue 4 (2nd October 2021)

• Record Type: Journal Article
• Authors: Perryman, H. A.; Hansen, C.; Howell, D.; Olsen, E.
• Is Part Of: Reviews in fisheries science & aquaculture. Volume 29:Issue 4(2021)
• Page Start: 800
• Page End: 835
• Publication Date: 2021-10-02
• Subjects: Management strategy evaluation -- management procedure -- ecosystem models -- multispecies models -- ecosystem-based fisheries management; Fisheries -- Research -- Periodicals; 639.205
• DOI: 10.1080/23308249.2021.1884642
• Languages: English
• ISSNs: 2330-8249

Abstract

Abstract: Management Strategy Evaluation (MSE) is a framework to explore the tradeoffs amongst fishing strategies and assess the consequences for achieving management goals provided sources of uncertainty by means of simulation models (referred to as operating models). Single-species stock assessment often implements simulations for MSE, but the operating models often omit the dynamics of key biological interactions. This could be a disadvantage for the evaluation of tradeoffs as species interactions could have an impact on the performance of harvesting strategies. Tools for conducting ecosystem-based fisheries management (EBFM), such as integrated ecosystem assessments, include executing MSEs with ecosystem models, many of which explicitly include biological interactions. Although the support for EBFM has grown over the years, the amount of information provided by MSEs based on ecosystem models appears to be limited. A clear summary of such efforts would provide beneficial information for future efforts for EBFM. Herein, an inventory of applications simulating MSEs with ecosystem models that explicitly include biological interactions was developed based on findings from a literature review. First, the methodologies and foci across all identified applications are analyzed. Next, summaries of each application are provided. Lastly, general observations are provided along with recommendations for future applications.