The Generalized Plant Allometry that Advances Metabolic Ecology Theory

The Generalized Plant Allometry that Advances Metabolic Ecology Theory | 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 Generalized Plant Allometry that Advances Metabolic Ecology Theory Stuart Bryce Dixon Sopp, Ruben Valbuena This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-871867/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 Plant allometry is key for determining the role of forests in global carbon cycles, through the calculation of tree biomass using proxy measurements such as tree diameters or heights. Metabolic ecology theory (MET) considers the general principles that underpin allometry, but MET scaling relationships have been challenged on their lack of fit to empirical data and global applicability. We postulated that MET scaling is applicable only for plant tissues combining conductive and supportive functionality (tracheids), but as plants evolved tissues of specialized conductive functionality (vessels) their allometry progressed into more complex relationships. According to this principle, we deducted generalized MET (gMET) relationships with mechanistically deducted coefficients. Our gMET models proved to have exceptional empirical support against global datasets, achieving unbiased predictions across biomes worldwide. These results prove gMET models to be a crucial improvement to MET-based allometry, providing a universally applicable theoretical framework for worldwide estimations of forest carbon. Forestry Ecological Modeling Metabolic Ecology Theory Allometry Tissue functionality Height-diameter scaling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Full Text Declarations The authors declare no competing interests. Supplementary Files suppFig1BiogeographicZones.jpg Supplementary Fig. 1 | Temperate mixed forest stratified by biome. 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-871867","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":49826172,"identity":"ff8cec27-4de6-4b8a-a06b-24a1ef964cdd","order_by":0,"name":"Stuart Bryce Dixon Sopp","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYPACmwQkDhtRWtJI13KYBC3yDewPHxfuOJ/HP7v94uMKBjt5BgkUKzGBwQEeY+OZZ24XS9w5U2x4hiHZsEEi7QB+LQw8bNK8bbcTG27kpEk2MDAnMEikNxBy2DOglnOJ8yFa6glrYTjAYAbUciBxw430Y0Ath4FaCDnsMNgvycWGN3KYDRsMjhu28TxLwO+w9nZQiNnlyd1If/iwoaJanp89zQC/w5iBiBHseB4DUGgQF5FQLewPiFE8CkbBKBgFIxAAAE4+QdXUpUF1AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0777-4493","institution":"Bangor University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Stuart","middleName":"Bryce Dixon","lastName":"Sopp","suffix":""},{"id":49826173,"identity":"df6b9bb6-1d0b-4ce5-9d76-325f5f896f51","order_by":1,"name":"Ruben Valbuena","email":"","orcid":"https://orcid.org/0000-0003-0493-7581","institution":"Bangor University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruben","middleName":"","lastName":"Valbuena","suffix":""}],"badges":[],"createdAt":"2021-09-03 13:37:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-871867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-871867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13229795,"identity":"589830e0-e198-441e-9f52-70d9c877e3e7","added_by":"auto","created_at":"2021-09-09 19:04:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48307,"visible":true,"origin":"","legend":"Tree architecture illustrating the differentiation of supportive and conductive systems within a branching network. This differentiation is set out by Eq. 9.","description":"","filename":"fig1TreeArchitecture.jpg","url":"https://assets-eu.researchsquare.com/files/rs-871867/v1/385b22bbf98dac1457eed126.jpg"},{"id":13230064,"identity":"679b0f93-a89d-4342-9062-afef1b3127e2","added_by":"auto","created_at":"2021-09-09 19:07:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170176,"visible":true,"origin":"","legend":"Truncated Cone \u0026 Cylinder approximation of tree volumes. This visualization illustrates the modelization of aggregated volumes of conductive tissues across branching generations (blue cone) within the total volume (cylinder).","description":"","filename":"fig2ConeDiagram.jpg","url":"https://assets-eu.researchsquare.com/files/rs-871867/v1/ca88068aac023c06658b9521.jpg"},{"id":13230096,"identity":"194600a0-749a-4486-a4b9-6b40805c1862","added_by":"auto","created_at":"2021-09-09 19:10:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":368229,"visible":true,"origin":"","legend":"Height-diameter model results. Comparisons of height-diameter ℎ-𝑑 models against the 2/3 scaling rule (in red). (A) Our generalized metabolic ecology theory (gMET)\nmodel for ℎ (Eq. 1) adjusted to empirical data from either Chave et al.\n27 (grey triangles) or Jucker et al. 19 (black dots), also comparing against Chave et al.’s ℎ-𝑑 quadratic log-log model27. (B) gMET ℎ-𝑑 models adjusted by functional group (sensu Jucker et al. 19 ): gymnosperms versus angiosperms. (C) gMET ℎ-𝑑 models adjusted by functional group / biome combination19. 𝐸: environmental stress parameter27 .","description":"","filename":"fig3HeightDiameter.jpg","url":"https://assets-eu.researchsquare.com/files/rs-871867/v1/8b8fe92165049a3c1c42b363.jpg"},{"id":13229796,"identity":"0c929e71-e224-435b-8d12-6eeb97d71390","added_by":"auto","created_at":"2021-09-09 19:04:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":495660,"visible":true,"origin":"","legend":"Monte Carlo results, and their confidence intervals propagated on height\ndiameter models. Monte Carlo simulations compared against estimated 𝛽̂\n1 and 𝛽̂ 2 values. (A-B) Density plots of 𝛽1-𝛽2 join distributions generated through Monte Carlo simulations,\nfor both pantropical27 (A) and global19 (B) datasets, with 95% confidence intervals given\nobtained through a 𝜒\n2 distribution. (C-D) Monte Carlo simulation-derived 95% confidence interval values for 𝛽1 and 𝛽2, propagated into the ℎ-𝑑 gMET (without 𝐸) model","description":"","filename":"fig4HeightDiameterMonteCarlo.jpg","url":"https://assets-eu.researchsquare.com/files/rs-871867/v1/aa7a4ed4b12fd3ae9134f620.jpg"},{"id":13229799,"identity":"05831483-7965-4c3b-a0b5-73a64967672f","added_by":"auto","created_at":"2021-09-09 19:04:20","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":406234,"visible":true,"origin":"","legend":"Above-ground biomass-diameter model results. Comparisons of biomass-diameter 𝑎𝑔𝑏-𝑑 models against the 8/3 scaling rule (in red). (A) Our generalized metabolic ecology\ntheory (gMET) model for 𝑎𝑔𝑏 (Eq. 3) adjusted to empirical data from Chave et al.27 is\npresented (with and without 𝐸), compared against Chave et al.’s27 𝑎𝑔𝑏-𝑑 quadratic log-log\nmodel. 𝐸: environmental stress parameter27. (B) Monte Carlo simulation-derived 95% confidence interval values for 𝛽1 and 𝛽2 (Fig. 2C), propagated into the 𝑎𝑔𝑏-𝑑 gMET (without 𝐸) model. (C-D) enhancements of panels A and B showing only the data for smaller trees. ","description":"","filename":"fig5agbDiameter.jpg","url":"https://assets-eu.researchsquare.com/files/rs-871867/v1/da4b222144b80c311bee14fb.jpg"},{"id":13678275,"identity":"94eabf66-b14c-48f2-9259-7bc2f770f0a2","added_by":"auto","created_at":"2021-09-17 11:38:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1555735,"visible":true,"origin":"","legend":"","description":"","filename":"SoppValbuenaMainTextcombined.pdf","url":"https://assets-eu.researchsquare.com/files/rs-871867/v1_covered.pdf"},{"id":13230097,"identity":"4dafa2a2-ba68-47df-8ce5-87337f3e4e73","added_by":"auto","created_at":"2021-09-09 19:10:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1552251,"visible":true,"origin":"","legend":"","description":"","filename":"SoppValbuenaMainTextcombined.pdf","url":"https://assets-eu.researchsquare.com/files/rs-871867/v1_covered.pdf"},{"id":13229800,"identity":"9e37eb9b-2376-4c13-b376-d4cc97635ab2","added_by":"auto","created_at":"2021-09-09 19:04:20","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":102245,"visible":true,"origin":"","legend":"Supplementary Fig. 1 | Temperate mixed forest stratified by biome. \n","description":"","filename":"suppFig1BiogeographicZones.jpg","url":"https://assets-eu.researchsquare.com/files/rs-871867/v1/4109bbf34be1f47915ccaea3.jpg"}],"financialInterests":"","formattedTitle":"\u003cp\u003eThe Generalized Plant Allometry that Advances Metabolic Ecology Theory\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-871867/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"b7999001-2486-4e35-ad78-6ac27f4e4589","identifier":"10.13039/501100000270","name":"Natural Environment Research Council","awardNumber":"Stuart Sopp's PhD funding","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Bangor University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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":"Metabolic Ecology Theory, Allometry, Tissue functionality, Height-diameter scaling","lastPublishedDoi":"10.21203/rs.3.rs-871867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-871867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\tPlant allometry is key for determining the role of forests in global carbon cycles, through the calculation of tree biomass using proxy measurements such as tree diameters or heights. Metabolic ecology theory (MET) considers the general principles that underpin allometry, but MET scaling relationships have been challenged on their lack of fit to empirical data and global applicability. We postulated that MET scaling is applicable only for plant tissues combining conductive and supportive functionality (tracheids), but as plants evolved tissues of specialized conductive functionality (vessels) their allometry progressed into more complex relationships. According to this principle, we deducted generalized MET (gMET) relationships with mechanistically deducted coefficients. Our gMET models proved to have exceptional empirical support against global datasets, achieving unbiased predictions across biomes worldwide. These results prove gMET models to be a crucial improvement to MET-based allometry, providing a universally applicable theoretical framework for worldwide estimations of forest carbon.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","manuscriptTitle":"The Generalized Plant Allometry that Advances Metabolic Ecology Theory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-09-09 19:04:18","doi":"10.21203/rs.3.rs-871867/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":"9b50612a-cc7b-4ac3-ab83-21a430316720","owner":[],"postedDate":"September 9th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":6943573,"name":"Forestry"},{"id":6943574,"name":"Ecological Modeling"}],"tags":[],"updatedAt":"2021-09-09T19:04:18+00:00","versionOfRecord":[],"versionCreatedAt":"2021-09-09 19:04:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-871867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-871867","identity":"rs-871867","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}