Improving Detectability of Illegal Fishing Activities Across Supply Chains

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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.
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Improving Detectability of Illegal Fishing Activities Across Supply Chains | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Improving Detectability of Illegal Fishing Activities Across Supply Chains Rodrigo Oyanedel, Stefan Gelcich, E. J. Milner-Gulland, Chris Wilcox This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4668139/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version 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. Earth and environmental sciences/Ocean sciences/Marine biology Earth and environmental sciences/Environmental social sciences/Sustainability Bayesian model fisheries non-compliance sustainability risk Chile illegal wildlife trade. Full Text Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Nov, 2024 Reviews received at journal 08 Oct, 2024 Reviews received at journal 02 Oct, 2024 Reviewers agreed at journal 09 Sep, 2024 Reviewers agreed at journal 08 Sep, 2024 Reviewers agreed at journal 07 Sep, 2024 Reviewers invited by journal 06 Sep, 2024 Editor assigned by journal 08 Jul, 2024 Submission checks completed at journal 08 Jul, 2024 First submitted to journal 01 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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