Optimal sample trees diameter distribution in developing allometric biomass models

Optimal sample trees diameter distribution in developing allometric 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 Optimal sample trees diameter distribution in developing allometric biomass models Ioan Dutcă, Ronald E. McRoberts This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4222725/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 Background: Improving forest biomass and carbon estimates is essential for sustaining the mitigation of climate change efforts in the forestry sector. An important source of uncertainty in forest estimates originates in the allometric model predictions. When developing allometric biomass models, the tree selection process is an important step that affects the model’s predictive performance. Typically, the diameter at breast height (D) range of the species is divided into D-classes, followed by random selection of sample trees within the D-classes; the number of trees in each D-class defines the sample tree D-distribution. Here, using a simulation study, we compared six types of sample tree D-distributions with respect to the precision of estimates of mean population biomass that the models produced. Results: The results showed that randomly selecting from each D-class a number of trees that is proportional to the basal area in that specific D-class in the population (i.e., sample trees were selected to produce a distribution of basal area in the sample that matched the distribution in the population) was the optimal D-distribution of the sample trees for minimizing the standard errors of the estimates of the population mean for a given sample size. When a-priori information about the distribution of tree attributes in the population is unknown, a uniform D-distribution represents a good alternative to the optimal sample D-distribution. Although producing the greatest precision of the estimate, the optimal D-distribution of sample trees did not produce models with the most accurate model fit (greatest coefficient of determination), suggesting that model fit alone is not a sufficient indicator of model’s predictive performance. Conclusions: The sample tree D-distribution affected considerably the precision of estimates of mean population biomass that the models produced. Therefore, to optimize the tree selection, and, therefore, to develop the models that produce precise estimators of the population mean, we recommend using an optimal D-distribution of the sample trees. biomass tree selection diameter distribution allometric biomass models national forest inventory 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-4222725","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288144554,"identity":"c86272b9-d880-49af-a544-7fb25a582c20","order_by":0,"name":"Ioan Dutcă","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACNoYEEGXDwMAMZBOrhbGBgSENqIWZSC0MEC2HgQxitfCx55g/+FFzPnE7O/+xxzwM9+QIO4znjWFjz7HbiTubmdmNeRiKjQlrkcgxbGZgu5244TAzm+QMhoTEBuK0/DtHqhbGtgNgLRIfiNLC86xwZm9fsjFQi7nBB4MEwn6Rb0/e8OHHNzvZDecPPnuQUJFAOMTQgAGpGkbBKBgFo2AUYAUAAbE3CBQbywYAAAAASUVORK5CYII=","orcid":"","institution":"Transilvania University of Brasov","correspondingAuthor":true,"prefix":"","firstName":"Ioan","middleName":"","lastName":"Dutcă","suffix":""},{"id":288144555,"identity":"03a43357-7558-469c-ba21-ab111a3bb3b0","order_by":1,"name":"Ronald E. McRoberts","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Ronald","middleName":"E.","lastName":"McRoberts","suffix":""}],"badges":[],"createdAt":"2024-04-05 11:42:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4222725/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4222725/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84562542,"identity":"8a38ff79-4113-452b-8fd1-61cab5b93327","added_by":"auto","created_at":"2025-06-13 13:24:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":505528,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4222725/v1_covered_38b48f44-c8f9-4bc1-a0f2-6478bd56b254.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimal sample trees diameter distribution in developing allometric biomass models","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":"biomass, tree selection, diameter distribution, allometric biomass models, national forest inventory","lastPublishedDoi":"10.21203/rs.3.rs-4222725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4222725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eImproving forest biomass and carbon estimates is essential for sustaining the mitigation of climate change efforts in the forestry sector. An important source of uncertainty in forest estimates originates in the allometric model predictions. When developing allometric biomass models, the tree selection process is an important step that affects the model\u0026rsquo;s predictive performance. Typically, the diameter at breast height (D) range of the species is divided into D-classes, followed by random selection of sample trees within the D-classes; the number of trees in each D-class defines the sample tree D-distribution. Here, using a simulation study, we compared six types of sample tree D-distributions with respect to the precision of estimates of mean population biomass that the models produced.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe results showed that randomly selecting from each D-class a number of trees that is proportional to the basal area in that specific D-class in the population (i.e., sample trees were selected to produce a distribution of basal area in the sample that matched the distribution in the population) was the optimal D-distribution of the sample trees for minimizing the standard errors of the estimates of the population mean for a given sample size. When a-priori information about the distribution of tree attributes in the population is unknown, a uniform D-distribution represents a good alternative to the optimal sample D-distribution. Although producing the greatest precision of the estimate, the optimal D-distribution of sample trees did not produce models with the most accurate model fit (greatest coefficient of determination), suggesting that model fit alone is not a sufficient indicator of model\u0026rsquo;s predictive performance.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThe sample tree D-distribution affected considerably the precision of estimates of mean population biomass that the models produced. Therefore, to optimize the tree selection, and, therefore, to develop the models that produce precise estimators of the population mean, we recommend using an optimal D-distribution of the sample trees.\u003c/p\u003e","manuscriptTitle":"Optimal sample trees diameter distribution in developing allometric biomass models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 06:49:36","doi":"10.21203/rs.3.rs-4222725/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":"34ed91bc-c795-41ee-8076-114741501aa7","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-13T13:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 06:49:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4222725","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4222725","identity":"rs-4222725","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}