The coupled PLUS-InVEST-XGBoost model explores the response of climate to ecosystem services in SSP scenarios

The coupled PLUS-InVEST-XGBoost model explores the response of climate to ecosystem services in SSP scenarios | 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 The coupled PLUS-InVEST-XGBoost model explores the response of climate to ecosystem services in SSP scenarios Duanqiang Zhai, Jian Zhuo, Rongyao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5407564/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 Climate change stands as one of the most significant challenges globally, affecting regional ecosystems with broad and unpredictable consequences. Incorporating climate change into ecosystem monitoring is essential for sustainability. However, the relationship between climate variables and ecosystem services remains underexplored, and the specific impacts of these variables are not well understood. This study introduces a comprehensive framework to investigate the effects of climate variables on regional ecology. Using CMIP6 climate data, alongside the PLUS and InVEST models, this research predicts the spatial distribution of ecosystem services (ESs). Additionally, Spearman correlation analysis assess the trade-offs and synergies among four ecological indicators, and the eXtreme Gradient Boosting (XGBoost) model is utilized to analyze the response of these indicators to climate variables. The results showed that the ecological land type was protected and restored under the SSP126 scenario. The SSP245 scenario recommends moderate land development; The SSP585 scenario is characterized by rapid economic growth and urban expansion eroding ecological land. In the future, ESs index changes dramatically, SSP126 shows an increase, and the other two scenarios are reduced. The four ecosystem service functions show synergistic effect. Among the climate variables, there are significant changes from year to year. SSP126 scenario is considered to be the best path for regional ESs under climate change. We suggest that managers continue to control regional carbon emissions. Precipitation is always a positive factor to promote the development of regional ecosystem, and future development can reduce the uncertainty of future development by taking advantage of climate. Land Use Change Land Type Simulation Ecosystem Services Quantification CMIP6 Climate Model Climatic Analysis 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-5407564","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376500292,"identity":"5b7f819a-3f7d-4aed-abb2-826516f78bed","order_by":0,"name":"Duanqiang Zhai","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Duanqiang","middleName":"","lastName":"Zhai","suffix":""},{"id":376500295,"identity":"78ebe5fd-cdaf-4c56-8eff-99dcf0ee9562","order_by":1,"name":"Jian Zhuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIie3QPQrCQBCG4V0GtpqYdiVFPMJCIFXIRWwigXQWdilSBIRYptVbBLzAwkCqqFeIeIGUdv6UFrJrZ7FPPW/xDWOO85f8ke7qkfoANFomyG+shHy+E4WyTSBiA/DuggtpFYTtqQ82jYCIkClWJUtjwutzERwanMXk6ZH1xbo2JcDrWHqNhJhmmeI1mRMB7J0oftyiklYJCowiHDLegW0iUayu+1Lnkl5Pzmy2hC1pPSmd+i3ROFWJOfmU/XbuOI7jfPMEcOY6wuZACR4AAAAASUVORK5CYII=","orcid":"","institution":"Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhuo","suffix":""},{"id":376500296,"identity":"858bc8cf-46fd-42ac-9802-06640c7fb9f7","order_by":2,"name":"Rongyao Wang","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Rongyao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-11-07 07:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5407564/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5407564/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70832649,"identity":"bea1339d-7443-472b-b657-5a6c3497d9a7","added_by":"auto","created_at":"2024-12-07 14:01:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1440817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5407564/v1_covered_d48e5b66-facc-4892-a636-e34a8d832bbd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The coupled PLUS-InVEST-XGBoost model explores the response of climate to ecosystem services in SSP scenarios","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":"Land Use Change, Land Type Simulation, Ecosystem Services Quantification, CMIP6 Climate Model, Climatic Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5407564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5407564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change stands as one of the most significant challenges globally, affecting regional ecosystems with broad and unpredictable consequences. 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