Stock assessment using Bayesian state-space surplus production models: the case of bonga shad, Ethmalosa fimbriata (Bowdich, 1825), in the Eastern Central Atlantic Ocean

Stock assessment using Bayesian state-space surplus production models: the case of bonga shad, Ethmalosa fimbriata (Bowdich, 1825), in the Eastern Central Atlantic Ocean | 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 Research Article Stock assessment using Bayesian state-space surplus production models: the case of bonga shad, Ethmalosa fimbriata (Bowdich, 1825), in the Eastern Central Atlantic Ocean Komba Jossie Konoyima, Amidu Mansaray, Jiangfeng Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3976513/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Ethmalosa fimbriata is a crucial fish classified as either fully exploited or overfished, requiring closer monitoring. The Committee for Eastern Central Atlantic Fisheries (CECAF) manages shared stocks in the Eastern Central Atlantic Ocean (ECAO). CECAF’s reported CPUE (1995–2017) and archived regional catch data (1970–2021) in the FAO Fishstatj database were used in this study. The objectives were to evaluate the northern E. fimbriata stock south of ECAO and conduct convergence diagnostics of input data using Bayesian state-space surplus production models (SPMs). The applied SPMs CMSY++ (catch-only), AMSY (CPUE-only), BSM, and JABBA (comparing Schaefer, Fox, and Pella-Tomlison functions). The results may help dispel uncertainty about its state in the region. All models produced robust diagnostic results and a reasonable fit to the data, with significant improvement in posterior perceptions from prior knowledge at very low variance ratios (PPVR < 0.3). The Schaefer model in JABBA showed a better prediction effect on the data (RMSE = 16.9%, DIC = -382.7) than the Fox (RMSE = 17%, DIC = -382.4) and Pella-Tomlinson (RMSE = 17%, DIC = -382) models. All the SPMs suggested that the current fishing mortality rate of the assessed stock has either reached or exceeded the F MSY and that the biomass level has either fallen below or approached the B MSY . The Schaefer model in JABBA predicted a catch limit of 100,000 tons, effective in 2024. By implication, the assessed stock may be unsustainable, requiring catch limits. The Schaefer model proved most effective for evaluating the study species. Biomass overfished overfishing sensitivity stock surplus production Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3976513","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274489108,"identity":"a1daef18-b9d7-4e42-9748-c14e2f46dc5a","order_by":0,"name":"Komba Jossie Konoyima","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACCTBpAGSwN4AYFqRo4TkAYkgQqwXEkEhA4eMGku29Bx/zFGyTl5z5/OqGHwUSDPzt3Ql4tUjznEs25jG4bThbOqfsZg/QYRJnzm7Aq0VOIsdMGqiFcZ50TtoNHqAWA4lc4rTYz5M8k3bzDzFapKFaEmdLsB+7TZQtkj1njA3nGNxOntmTw3ZbxkCCh6BfJI73GD548+e27Yzjx5/dfPPHRo6/vRe/FhBg4gFTPAZgkqByEGD8AabYHxClehSMglEwCkYeAAAlPUNjNs6ygQAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Ocean University","correspondingAuthor":true,"prefix":"","firstName":"Komba","middleName":"Jossie","lastName":"Konoyima","suffix":""},{"id":274489109,"identity":"fd0795ad-c7d1-4925-92d7-b375433ec5e1","order_by":1,"name":"Amidu Mansaray","email":"","orcid":"","institution":"Shanghai Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Amidu","middleName":"","lastName":"Mansaray","suffix":""},{"id":274489110,"identity":"73a40955-90e6-4775-a9cf-5fa9aa720d46","order_by":2,"name":"Jiangfeng Zhu","email":"","orcid":"","institution":"Shanghai Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Jiangfeng","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-02-21 18:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3976513/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3976513/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51654141,"identity":"833cb51e-d072-4fcc-a214-457ffc7185c2","added_by":"auto","created_at":"2024-02-26 16:28:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1012920,"visible":true,"origin":"","legend":"","description":"","filename":"Mainmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3976513/v1_covered_670d05c1-9e45-46bd-90d1-4bd66caf7f91.pdf"},{"id":51653237,"identity":"02f05259-80c4-4c93-9828-8ca3849b1843","added_by":"auto","created_at":"2024-02-26 16:12:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":131349,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-3976513/v1/6a24d3219f75792783ec7c62.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stock assessment using Bayesian state-space surplus production models: the case of bonga shad, Ethmalosa fimbriata (Bowdich, 1825), in the Eastern Central Atlantic Ocean","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biomass, overfished, overfishing, sensitivity, stock, surplus production","lastPublishedDoi":"10.21203/rs.3.rs-3976513/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3976513/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eEthmalosa fimbriata\u003c/em\u003e is a crucial fish classified as either fully exploited or overfished, requiring closer monitoring. The Committee for Eastern Central Atlantic Fisheries (CECAF) manages shared stocks in the Eastern Central Atlantic Ocean (ECAO). CECAF\u0026rsquo;s reported CPUE (1995\u0026ndash;2017) and archived regional catch data (1970\u0026ndash;2021) in the FAO Fishstatj database were used in this study. The objectives were to evaluate the northern \u003cem\u003eE. fimbriata\u003c/em\u003e stock south of ECAO and conduct convergence diagnostics of input data using Bayesian state-space surplus production models (SPMs). The applied SPMs CMSY++ (catch-only), AMSY (CPUE-only), BSM, and JABBA (comparing Schaefer, Fox, and Pella-Tomlison functions). The results may help dispel uncertainty about its state in the region. All models produced robust diagnostic results and a reasonable fit to the data, with significant improvement in posterior perceptions from prior knowledge at very low variance ratios (PPVR\u0026thinsp;\u0026lt;\u0026thinsp;0.3). The Schaefer model in JABBA showed a better prediction effect on the data (RMSE\u0026thinsp;=\u0026thinsp;16.9%, DIC = -382.7) than the Fox (RMSE\u0026thinsp;=\u0026thinsp;17%, DIC = -382.4) and Pella-Tomlinson (RMSE\u0026thinsp;=\u0026thinsp;17%, DIC = -382) models. All the SPMs suggested that the current fishing mortality rate of the assessed stock has either reached or exceeded the F\u003csub\u003eMSY\u003c/sub\u003e and that the biomass level has either fallen below or approached the B\u003csub\u003eMSY\u003c/sub\u003e. The Schaefer model in JABBA predicted a catch limit of 100,000 tons, effective in 2024. By implication, the assessed stock may be unsustainable, requiring catch limits. 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