Abstract
Bycatch, the capture of non-target species during fishing operations, causes significant ecological, physical, and socio-economic impacts. Despite widespread Open Access policies worldwide, effective bycatch assessment using Open Access data remains obstructed by cultural barriers, data deficiencies, and insufficient data sharing practices. This study evaluated Open Access datasets in the context of estimated bycatch in Alaskan EEZ fisheries, an underutilized approach in fisheries policies aimed at improving transparency. We used Machine Learning and GIS data to evaluate longline fisheries’ impacts on marine populations by analyzing ten key species and producing replicable results. We reassessed accuracy and quality of existing bycatch estimation in Alaskan longline groundfish fisheries. Our findings revealed data aspects related to greater impacts on bycatch species than previously reported, with potential ecological effects extending beyond the Exclusive Economic Zone (EEZ). Spanning 1995–2001, we included projections for 2050, identifying systemic underestimations in current fisheries law and data policy. Our assessment raises concerns about governance and sustainable certifications within US fisheries, especially under the Magnuson-Stevens Act (lacking effective bycatch data/policies) and the United Nations Convention on the Law of the Sea (UNCLOS) without mandatory Open Access or software standards. The current data practices are outdated and require revision, they hinder professional performance, progress, trust, and accountability in validating sustainable fisheries governance in the US and its role as a global model. Our results favor adopting documented Open Access workflows explicit in space and time as best practice enhancing transparency and sustainability and improving fisheries management, addressing sustainability gaps in current practices.