Pantropical and continental stand-level biomass models

Pantropical and continental stand-level biomass 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 Pantropical and continental stand-level biomass models Hassan C. David, Ronald E. McRoberts, Erik Naesset, Terje Gobakken, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6256560/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background : This study utilized a pantropical tree database to simulate forest ground plots across the tropical zone. The aims were to (i) generate pantropical and continental stand-level aboveground biomass (AGB) models, (ii) compare performances of stand- and tree-level models in AGB prediction per unit area, and (iii) quantify gains in accuracy and precision by adding Lorey’s height as a model predictor. Forest stand variables such as basal area and tree density were calculated and then used as predictors in the AGB models. An additional predictor, Lorey’s height, was included and its contribution to the model performance was quantified. We estimated models at two scales: pantropical and continental [Tropical (T) Africa, T. Asia, T. Central America and T. South America]. Our models were compared with respect to accuracy and precision of predictions to the pantropical biomass model – referred to as ‘Model0’. Results: Our continental models reduced the mean error of AGB prediction from 51.9 to -0.1 Mg ha -1 in T. Central America, from -16.8 to -0.7 in Mg ha -1 T. Asia, and from 11.0 to -0.3 in Mg ha -1 in T. South America, relative to ‘Model0’. Globally, our pantropical model predicted AGB per unit area 3.7 times more accurately than ‘Model0’, but a bit less precisely. Most models improved in performance when adding Lorey’s height as a predictor. Conclusions: We recommend our models to predict forest AGB at large scales, highlighting that our models independent of Lorey’s height are an option that ensures accurate AGB predictions. Forest simulation Pantropical biomass database Stand-level modeling Lorey’s height Global forest carbon. Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1.docx Supplementaryfile2.txt Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Jun, 2025 Reviews received at journal 06 May, 2025 Reviews received at journal 21 Apr, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviewers agreed at journal 18 Apr, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers invited by journal 23 Mar, 2025 Editor assigned by journal 22 Mar, 2025 Submission checks completed at journal 22 Mar, 2025 First submitted to journal 18 Mar, 2025 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-6256560","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435166351,"identity":"0291232f-1eb7-4547-bf1c-8971d90dc4c0","order_by":0,"name":"Hassan C. 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The aims were to (i) generate pantropical and continental stand-level aboveground biomass (AGB) models, (ii) compare performances of stand- and tree-level models in AGB prediction per unit area, and (iii) quantify gains in accuracy and precision by adding Lorey’s height as a model predictor. Forest stand variables such as basal area and tree density were calculated and then used as predictors in the AGB models. An additional predictor, Lorey’s height, was included and its contribution to the model performance was quantified. We estimated models at two scales: pantropical and continental [Tropical (T) Africa, T. Asia, T. Central America and T. South America]. Our models were compared with respect to accuracy and precision of predictions to the pantropical biomass model – referred to as ‘Model0’.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our continental models reduced the mean error of AGB prediction from 51.9 to -0.1 Mg ha\u003csup\u003e-1\u003c/sup\u003e in T. Central America, from -16.8 to -0.7 in Mg ha\u003csup\u003e-1\u003c/sup\u003e T. Asia, and from 11.0 to -0.3 in Mg ha\u003csup\u003e-1\u003c/sup\u003e in T. South America, relative to ‘Model0’. Globally, our pantropical model predicted AGB per unit area 3.7 times more accurately than ‘Model0’, but a bit less precisely. 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