Improving Detectability of Illegal Fishing Activities Across Supply Chains

Abstract Improving detectability (i.e., enforcers’ capacity to detect illegal fishing activities) is crucial for fisheries management, food security and livelihoods. Identifying factors associated with higher probabilities of illegal activities and their detection across supply chains are necessary for effective interventions. Here, we developed a Bayesian Hierarchical Model to assess the detectability of illegal fishing activities. We use a large fisheries enforcement dataset from Chile covering all supply chain actors (e.g., fishers, traders, restaurants). Our model allowed evaluation of determinants of detectability and probability of violation across supply chain actors, species, regulations and effort predictors. Our results show an overall detectability rate of illegal fishing activities at 7%, with this rate varying significantly across supply chain actors. Notably, those positioned higher in the supply chain, such as processors and restaurants, which are also those receiving less enforcement effort, exhibit markedly higher detection rates. This study provides relevant management insights to improve detectability of infringements of fisheries regulations in Chile and more broadly. Our approach complements recent technological advances (e.g., satellite monitoring), and can support improved targeting of enforcement across supply chains, particularly in situations where capacity exists, but budgets are constrained. Improving detectability of rules violations is a key component of reducing illegal fishing and promoting compliance.