A comprehensive method to integrate unbiased fisheries data in spatially-explicit population dynamics models

A comprehensive method to integrate unbiased fisheries data in spatially-explicit population dynamics models | 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 A comprehensive method to integrate unbiased fisheries data in spatially-explicit population dynamics models Romain Forestier, Lucas Bonnin, Inna Senina, Tiffany Vidal, Marc Ghergariu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8888848/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 Stock assessment models and other quantitative models rely heavily on fishery-dependent data, particularly in regions where fishery-independent data (e.g., scientific surveys) are unavailable. However, the relationship between catch-per-unit-effort (CPUE) and fish abundance is impacted by variations in catchability and selectivity across different fishing operations. We present a comprehensive methodology for preparing unbiased fisheries data for use in spatially-explicit population dynamics models such as SEAPODYM, a spatiotemporal model of population dynamics with age structure. Our approach addresses two key challenges: first, by systematically grouping fishing data into distinct fisheries with consistent catchability and selectivity patterns, and second, by leveraging high-resolution spatial data to maintain linear relationships between catch and biomass density at the grid cell level. We demonstrate this methodology using operational longline data from Pacific Island countries and distant-water fishing nations targeting yellowfin tuna in the Pacific Ocean. The approach incorporates covariates such as hooks between floats and target species to account for fishing-driven changes in catchability, while assuming remaining variability is driven by environmental factors and the heterogeneity in population density that SEAPODYM explicitly accounts for. By combining these operational data with coarse resolution aggregated data from all gears, we ensure comprehensive coverage of fishing mortality while integrating fine-scale spatial resolution data needed for parameter estimation. This approach improves how fisheries data are structured for spatially-explicit stock assessment models, improving the accuracy of population dynamics estimates. seapodym fisheries cpue yellowfin tuna Full Text Additional Declarations No competing interests reported. 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-8888848","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616121032,"identity":"cf5e4269-6b93-48c8-8697-e0c999fc12aa","order_by":0,"name":"Romain Forestier","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYLCCBAY5OSiDeC3GxlCGAdGajBMbIAwitJi3dyd+eLjDIH3+7OYHDA93/CGsRebM2c0SiWcMcjfcOWbAAGQQ1iIhkbtBIrHtD5DMYWBIbCNOy+YfQJXp8jNI0LINaItBAsMNorXwnN1mAVRpCPLLgcQ2YyK0sPduvvmzzUBefnbzw4c/2+QIa0HSzMBwgBT1EC2jYBSMglEwCrACAPwyOA4lvJ6zAAAAAElFTkSuQmCC","orcid":"","institution":"Secretariat of the Pacific Community","correspondingAuthor":true,"prefix":"","firstName":"Romain","middleName":"","lastName":"Forestier","suffix":""},{"id":616121033,"identity":"3f4819e5-0e60-4581-bd82-a6eb071a9a6f","order_by":1,"name":"Lucas Bonnin","email":"","orcid":"","institution":"Secretariat of the Pacific Community","correspondingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Bonnin","suffix":""},{"id":616121034,"identity":"a84042f9-fc69-4cae-b298-95438fa7ebea","order_by":2,"name":"Inna Senina","email":"","orcid":"","institution":"Secretariat of the Pacific Community","correspondingAuthor":false,"prefix":"","firstName":"Inna","middleName":"","lastName":"Senina","suffix":""},{"id":616121035,"identity":"9757771e-2332-4adc-977f-4670388af0f4","order_by":3,"name":"Tiffany Vidal","email":"","orcid":"","institution":"Secretariat of the Pacific Community","correspondingAuthor":false,"prefix":"","firstName":"Tiffany","middleName":"","lastName":"Vidal","suffix":""},{"id":616121036,"identity":"f75f03ea-3a2f-4339-9cfe-17aa36a21e46","order_by":4,"name":"Marc Ghergariu","email":"","orcid":"","institution":"Secretariat of the Pacific Community","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Ghergariu","suffix":""},{"id":616121037,"identity":"bc822be8-7fe8-461e-bb80-8702e5ec958a","order_by":5,"name":"Simon Nicol","email":"","orcid":"","institution":"Secretariat of the Pacific Community","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Nicol","suffix":""}],"badges":[],"createdAt":"2026-02-16 00:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8888848/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8888848/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108682494,"identity":"e4931e59-8386-462b-9921-d838ee316a3e","added_by":"auto","created_at":"2026-05-07 09:28:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2193940,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerSubmission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8888848/v1_covered_00c8862f-bf28-400c-ab73-1282c4119f99.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A comprehensive method to integrate unbiased fisheries data in spatially-explicit population dynamics models","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":"seapodym, fisheries, cpue, yellowfin tuna","lastPublishedDoi":"10.21203/rs.3.rs-8888848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8888848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStock assessment models and other quantitative models rely heavily on fishery-dependent data, particularly in regions where fishery-independent data (e.g., scientific surveys) are unavailable. 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