Models for Predicting Tree Diameter at Breast Height from Over and Under Bark Diameter of Stump in Eucalyptus camaldulensis Plantations

Models for Predicting Tree Diameter at Breast Height from Over and Under Bark Diameter of Stump in Eucalyptus camaldulensis Plantations | 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 Article Models for Predicting Tree Diameter at Breast Height from Over and Under Bark Diameter of Stump in Eucalyptus camaldulensis Plantations Denis U.O. Austin, Eshetu Yirdaw This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5765573/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 Allometric functions that predict tree diameter at breast height (D) from stump diameter (DS), referred to as DS-D models, are essential for estimating forest metrics like stand volume and belowground carbon (C), especially when D cannot be measured, such as after inadequately recorded clearcutting or illegal logging of threatened species.Many available DS-D models are generic for diverse species and ecological condition, and arelargely based on DS measured over bark (DSoB). Studies show that bark thickness (BT), a factor of DSoB, varies intra-species and across ecosystemsas a response to fire history and other ecological factors. This raises concerns about the reliance on generic, DSoB-based modelsfor inventoryon regenerating clearcut plantation sites. We hypothesize that local DS-D models calibrated with DS under bark (DSuB) better account for in-situ ecological variability inBT. To test this, we gathered data through destructive and non-destructive sampling of clonally propagated (CP), post-fire recovery (FR), and coppice-regenerated (CR) stands of monoculture Eucalyptus camaldulensis plantations (ECPs) in East Africa. Using the data, we employed machine learning and traditional statistical methods to calibrate DS-D models, alternately based on DSoB and DSuB as predictor variables. Through error residuals and effect sizes analyses, we compared (1) the performance of previously published, ex-situ generic DS-D equations versus the study-derived local models, (2) the effectivenessDSoB versus DSuB for DSoBboth as the predictor and the input variables for DS-D models, and (3) assessed the statistical variation of DS-D models between post-fire recovery and non-fire impacted ECP stands. The results showed that (1) in-situ models outperformed ex-situ equations (2) substituting DSoB with DSuB as regressors improved model accuracy, (3) DSoB substitution with DSuB as input variable did not reduce the performance of DSoB-based models. and (4) modeling of DS-D allometry post-fire recovery stand was complicated by high heterogeneity in tree diameter classes. These findings show that recalibrating DSoB-based models with DSuB can better capture DS-D allometry due to the circumvention of local environmental effects on BT. The findings further confirm the utility of DS-D models even when the stump's bark layer is missing. Biological sciences/Plant sciences Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental social sciences/Sustainability Stump diameter bark thickness post-fire recovery stump diameter over bark stump diameter under bark Eucalyptus camaldulensisplantations relative root mean squared error (RRMSE) 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-5765573","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":402959885,"identity":"9a7491b1-08af-4b7c-a245-74a240f9ec21","order_by":0,"name":"Denis U.O. 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Studies show that bark thickness (BT), a factor of DSoB, varies intra-species and across ecosystemsas a response to fire history and other ecological factors. This raises concerns about the reliance on generic, DSoB-based modelsfor inventoryon regenerating clearcut plantation sites.\u003c/p\u003e\n\u003cp\u003eWe hypothesize that local DS-D models calibrated with DS under bark (DSuB) better account for \u003cem\u003ein-situ\u003c/em\u003eecological variability inBT. To test this, we gathered data through destructive and non-destructive sampling of clonally propagated (CP), post-fire recovery (FR), and coppice-regenerated (CR) stands of monoculture \u003cem\u003eEucalyptus camaldulensis\u003c/em\u003eplantations (ECPs) in East Africa. Using the data, we employed machine learning and traditional statistical methods to calibrate DS-D models, alternately based on DSoB and DSuB as predictor variables. Through error residuals and effect sizes analyses, we compared (1) the performance of previously published, \u003cem\u003eex-situ\u003c/em\u003egeneric DS-D equations versus the study-derived local models, (2) the effectivenessDSoB versus DSuB for DSoBboth as the predictor and the input variables for DS-D models, and (3) assessed the statistical variation of DS-D models between post-fire recovery and non-fire impacted ECP stands.\u003c/p\u003e\n\u003cp\u003eThe results showed that (1) in-situ models outperformed ex-situ equations (2) substituting DSoB with DSuB as regressors improved model accuracy, (3) DSoB substitution with DSuB as input variable did not reduce the performance of DSoB-based models. and (4) modeling of DS-D allometry post-fire recovery stand was complicated by high heterogeneity in tree diameter classes.\u003c/p\u003e\n\u003cp\u003eThese findings show that recalibrating DSoB-based models with DSuB can better capture DS-D allometry due to the circumvention of local environmental effects on BT. The findings further confirm the utility of DS-D models even when the stump's bark layer is missing.\u003c/p\u003e","manuscriptTitle":"Models for Predicting Tree Diameter at Breast Height from Over and Under Bark Diameter of Stump in Eucalyptus camaldulensis Plantations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 16:19:42","doi":"10.21203/rs.3.rs-5765573/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":"477d6d46-bada-4002-88ab-39a44ed8e4aa","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42958899,"name":"Biological sciences/Plant sciences"},{"id":42958900,"name":"Earth and environmental sciences/Climate sciences"},{"id":42958901,"name":"Earth and environmental sciences/Environmental social sciences/Sustainability"}],"tags":[],"updatedAt":"2025-07-24T13:08:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-20 16:19:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5765573","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5765573","identity":"rs-5765573","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}