A Novel Hybrid Machine Learning Framework for Species Influence from Minimal Data

A Novel Hybrid Machine Learning Framework for Species Influence from Minimal Data | 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 Novel Hybrid Machine Learning Framework for Species Influence from Minimal Data MOUMITA GHOSH, Arnab Banerjee, Anirban Roy, Anirban Mukhopadhyay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8530925/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 Identifying ecologically influential species is crucial for biodiversity conservation. Yet, classical keystone estimation approaches such as Ecopath-derived KS1, KS2, and KS3 require numerous ecological parameters that are often difficult to obtain and sensitive to uncertainty. To overcome these limitations, we introduce a hybrid machine learning framework that infers species influence using only two widely accessible inputs: diet composition matrices and biomass. The methodology integrates mechanistic descriptors (such as Relative Total Impact) with graph-based topological features (such as PageRank, extended degree centrality) and employs three core learning strategies: Random Forest for supervised prediction, Label Propagation for semi-supervised inference, and GraphSAGE for inductive graph representation learning. These complementary models are combined through an ensemble strategy to generate robust and generalizable species-influence predictions. The framework is evaluated across multiple Ecopath ecosystems containing diet matrices, biomass data, and expert-validated keystoneness rankings. Results demonstrate that the ensemble effectively approximates Ecopath-derived ranking behaviour while requiring far fewer ecological inputs. Importantly, the objective is not to introduce a new keystone metric but to evaluate whether hybrid machine learning models can reliably reproduce expert-derived species rankings. By reducing data requirements and improving cross-ecosystem generalizability, the proposed approach offers a scalable, evidence-driven tool for ecological assessment and conservation planning in data-scarce environments-thereby advancing conservation and supporting SDG-aligned ecosystem management. Ecologically influential species Hybrid machine learning framework Species prioritisation Biodiversity conservation 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-8530925","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594015795,"identity":"23b98336-b713-4e90-8155-eef2099ee9c8","order_by":0,"name":"MOUMITA GHOSH","email":"","orcid":"","institution":"Adamas University","correspondingAuthor":false,"prefix":"","firstName":"MOUMITA","middleName":"","lastName":"GHOSH","suffix":""},{"id":594015796,"identity":"d88e5622-7c63-4750-b1f5-ded7b738416f","order_by":1,"name":"Arnab Banerjee","email":"","orcid":"","institution":"Sikkim University","correspondingAuthor":false,"prefix":"","firstName":"Arnab","middleName":"","lastName":"Banerjee","suffix":""},{"id":594015797,"identity":"1d6fab8d-78b0-47fb-a519-efacd6310b49","order_by":2,"name":"Anirban Roy","email":"","orcid":"","institution":"West Bengal Biodiversity Board","correspondingAuthor":false,"prefix":"","firstName":"Anirban","middleName":"","lastName":"Roy","suffix":""},{"id":594015798,"identity":"51fa9478-2e86-43c3-abac-9253669d9168","order_by":3,"name":"Anirban Mukhopadhyay","email":"data:image/png;base64,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","orcid":"","institution":"University of Kalyani","correspondingAuthor":true,"prefix":"","firstName":"Anirban","middleName":"","lastName":"Mukhopadhyay","suffix":""}],"badges":[],"createdAt":"2026-01-06 11:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8530925/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8530925/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106586746,"identity":"914d4515-8019-4115-b412-f3d820e69b37","added_by":"auto","created_at":"2026-04-10 07:43:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":717152,"visible":true,"origin":"","legend":"","description":"","filename":"SpeciesRankingManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8530925/v1_covered_eac746b3-d53c-4811-a444-ee85147861fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Hybrid Machine Learning Framework for Species Influence from Minimal Data","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":"Ecologically influential species, Hybrid machine learning framework, Species prioritisation, Biodiversity conservation","lastPublishedDoi":"10.21203/rs.3.rs-8530925/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8530925/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Identifying ecologically influential species is crucial for biodiversity conservation. Yet, classical keystone estimation approaches such as Ecopath-derived KS1, KS2, and KS3 require numerous ecological parameters that are often difficult to obtain and sensitive to uncertainty. To overcome these limitations, we introduce a hybrid machine learning framework that infers species influence using only two widely accessible inputs: diet composition matrices and biomass. The methodology integrates mechanistic descriptors (such as Relative Total Impact) with graph-based topological features (such as PageRank, extended degree centrality) and employs three core learning strategies: Random Forest for supervised prediction, Label Propagation for semi-supervised inference, and GraphSAGE for inductive graph representation learning. These complementary models are combined through an ensemble strategy to generate robust and generalizable species-influence predictions. 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