Exploring the potential impacts of machine learning on trust in fishery management

Abstract

The problem of sustainable fishery governance is well known, with common-pool resource management concerns highlighted by scholars globally (Defries & Nagendra, 2017;Jentoft & Chuenpagdee, 2009;Ostrom, 1990). Managing national and transboundary fisheries is challenging, as regulatory boundaries and species distributions are often not aligned (Kadagi et al., 2020; Palacios-Abrantes et al., 2020; Palacios-Abrantes et al., 2020). For example, in the Northwest Atlantic certain fish stocks off the coast of Canada straddle the jurisdictional boundary with the U.S., and in the Pacific Northwest, highly migratory species move freely between U.S. and Canadian waters (Engler, 2020). As fish species cross political boundaries, management and conservation policies become less effective due to differing regulatory and institutional contexts, and enforcement and monitoring agencies (Hickey et al., 2021). Climate change will