Aligning Decision-making and Key Behaviors with Effective Fisheries Management

At least two-thirds of global fish stocks are overfished or fully exploited (FAO, 2014). As a result, fisheries are not producing nearly as much food, profit, or livelihood opportunities as they could be. Well implemented and effective Rights Based Management (RBM) can reverse these trends, but designing and implementing such systems is challenging. There are good design principles based on research and experience for designing RBM systems, focused on ensuring that stakeholders buy into management measures and that fishermen can capture the benefits of their own conservation efforts. However, there are many other decisions that must be made and behaviors that must be exhibited by fishery scientists, resource managers, fishermen, and others to make the entire RBM system effective. Because managing a fishery is a human enterprise, understanding the decisions and behaviors of fishermen and managers is imperative for achieving sustainability. The fishery management process is complex, involving multiple decisions and behaviors by several actors. Fishery managers, scientists, and fishermen are motivated and affected by a number of internal and external variables. Economic, social, political, cultural, psychological, or other personal factors influence decision-making and can induce undesired or unintended behavioral responses. Therefore, understanding human decision-making processes and their drivers is vital in ensuring the success of effective fishery management strategies. The purpose of this report is to describe specific behaviors and decisions that have large impacts on the efficacy of fishery management, and generate ideas for interventions that may influence those behaviors such that they become more aligned with effective management. This report does not discredit top-down regulations nor advocate for an entirely behavioral approach. Rather, it seeks to establish a broader context for discussion regarding challenges in fishery management that may be amenable to behavioral interventions. Behavioral interventions deployed as part of a comprehensive management strategy would be anticipated to enhance the efficacy of fishery management, just as they have in other sectors such as health, education, and energy use (Thaler & Sunstein, 2009). Generic interventions suggested in this assessment are for illustrative purposes only, and are neither prescriptive nor a panacea for all fishery management problems. Every fishery is unique and interventions need to be specific to local needs and contexts. The methodology for this research is a desktop analysis, an extensive literature review of the major challenges and drivers impeding effective fishery management. We begin with a background discussion of human behavior and how behavioral interventions may influence better decision-making. We then outline the fishery management process to describe the stakeholders involved in managing a fishery and the types of decisions that must be taken for its success. We examine three key groups of actors in fisheries management: the fishery management authority, fisheries scientists, and fishermen. Each group is analyzed, including their roles, level of influence within the decision-making process, and currently exhibited behaviors. There are six challenges addressed in this report that appear consistently throughout fisheries management literature and that have a major impact on fishery efficiency and sustainability: (1) resistance to data-limited assessment (2) translating science to management action (3) communicating uncertainty and risk to stakeholders (4) catch misreporting (5) bycatch and discarding and (6) destructive fishing (Peterman, 2004; Hilborn et al., 2005; Daw and Gray, 2005; OECD, 2010; OECD; 2013; Government of Canada, 2011). Drawing on theories from psychology, behavioral economics, and social sciences literature, we investigate the drivers of each challenge and craft illustrative behavioral interventions. (exerpt from Introduction, download PDF for full introduction.)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.