Unraveling Scale-Dependent Relationships Among Ecological Services Using a Machine Learning and PLS-SEM Approach in Northeast China

Unraveling Scale-Dependent Relationships Among Ecological Services Using a Machine Learning and PLS-SEM Approach in Northeast China | 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 Unraveling Scale-Dependent Relationships Among Ecological Services Using a Machine Learning and PLS-SEM Approach in Northeast China Eslam Rashad, Yujie Liu, Da Lü, Ahmed Refaee, Tao Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8164308/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 Context Interactions among ecosystem services (ESs) and their multiscale biophysical drivers are central to understanding landscape functioning and guiding ecological management. Despite extensive research, the scale-dependent behavior of ES relationships in highly dynamic agricultural regions remains insufficiently resolved. Objectives This study examines the spatiotemporal dynamics of five key ESs in Northeast China—grain production (GP), carbon storage (CS), soil conservation (SC), water yield (WY), and habitat quality (HQ)—and evaluates how their interactions and driving forces vary across pixel, county, and sub-basin scales between 2000 and 2020. Methods: We integrate partial least squares structural equation modelling (PLS-SEM), XGBoost, and SHAP analysis to identify dominant drivers, nonlinear responses, and causal pathways among ESs. ES trends and interactions were assessed across multiple spatial scales to reveal scale-sensitive patterns. Results WY, GP, and SC increased over the study period, while HQ and CS declined. Strong spatial heterogeneity was evident: mountainous regions were characterized by synergies among ESs, whereas lowland agricultural areas exhibited pronounced trade-offs. XGBoost identified DEM as the dominant driver of HQ, NDVI for CS and GP, precipitation for WY, and slope for SC, with driver influence varying across scales and years. Cross-scale causal analysis showed that WY exerted a negative direct effect on GP at the sub-basin scale but shifted to a positive effect at the pixel scale. SC imposed a negative indirect impact on GP at broader spatial scales. Conclusions These findings highlight the strong scale dependence of ES interactions and the need for spatially differentiated strategies when integrating ES relationships into land-use planning, ecological restoration, and regional policy. Scale dependence PLS-SEM Northeast China Ecosystem services Trade-off and synergy XGBoost-SHAP model Full Text Additional Declarations No competing interests reported. Supplementary Files AppendexD.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-8164308","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555191304,"identity":"e724ff50-3170-425d-b102-2208f581789e","order_by":0,"name":"Eslam 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China","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":"Scale dependence, PLS-SEM, Northeast China, Ecosystem services, Trade-off and synergy, XGBoost-SHAP model","lastPublishedDoi":"10.21203/rs.3.rs-8164308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8164308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eContext\u003c/em\u003e Interactions among ecosystem services (ESs) and their multiscale biophysical drivers are central to understanding landscape functioning and guiding ecological management. Despite extensive research, the scale-dependent behavior of ES relationships in highly dynamic agricultural regions remains insufficiently resolved.\u003c/p\u003e\u003cp\u003e\u003cem\u003eObjectives\u003c/em\u003e This study examines the spatiotemporal dynamics of five key ESs in Northeast China\u0026mdash;grain production (GP), carbon storage (CS), soil conservation (SC), water yield (WY), and habitat quality (HQ)\u0026mdash;and evaluates how their interactions and driving forces vary across pixel, county, and sub-basin scales between 2000 and 2020.\u003c/p\u003e\u003cp\u003eMethods: We integrate partial least squares structural equation modelling (PLS-SEM), XGBoost, and SHAP analysis to identify dominant drivers, nonlinear responses, and causal pathways among ESs. ES trends and interactions were assessed across multiple spatial scales to reveal scale-sensitive patterns.\u003c/p\u003e\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e WY, GP, and SC increased over the study period, while HQ and CS declined. Strong spatial heterogeneity was evident: mountainous regions were characterized by synergies among ESs, whereas lowland agricultural areas exhibited pronounced trade-offs. XGBoost identified DEM as the dominant driver of HQ, NDVI for CS and GP, precipitation for WY, and slope for SC, with driver influence varying across scales and years. Cross-scale causal analysis showed that WY exerted a negative direct effect on GP at the sub-basin scale but shifted to a positive effect at the pixel scale. SC imposed a negative indirect impact on GP at broader spatial scales.\u003c/p\u003e\u003cp\u003e\u003cem\u003eConclusions\u003c/em\u003e These findings highlight the strong scale dependence of ES interactions and the need for spatially differentiated strategies when integrating ES relationships into land-use planning, ecological restoration, and regional policy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Unraveling Scale-Dependent Relationships Among Ecological Services Using a Machine Learning and PLS-SEM Approach in Northeast China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 13:10:58","doi":"10.21203/rs.3.rs-8164308/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"8b599af7-f41e-4639-9abb-f65800f17578","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-28T21:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 13:10:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8164308","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8164308","identity":"rs-8164308","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}