Do co-occurrence matrices suggest more than usual when comparing vegetation types? A test based on data from Picea abies and Abies alba forests in the Friulian Alps

Do co-occurrence matrices suggest more than usual when comparing vegetation types? A test based on data from Picea abies and Abies alba forests in the Friulian Alps | 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 Do co-occurrence matrices suggest more than usual when comparing vegetation types? A test based on data from Picea abies and Abies alba forests in the Friulian Alps Bressan Enrico, Burba Nicoletta, Feoli Enrico, Ganis Paola, Malaroda Massimo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8617058/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 In this study, we introduce a framework for comparing vegetation types based on species co-occurrence matrices. The comparison relies on two complementary approaches: (1) quantifying the negentropy of the co-occurrence matrix for each vegetation type, and (2) evaluating the evenness of eigenvalues derived from pairwise co-occurrence matrices extracted from the overall species co-occurrence matrix of the vegetation system. The method is illustrated using phytosociological relevés from Picea abies and Abies alba forests of the Friulian Alps (NE Italy). Our results show that vegetation types associated with more mesophilic environmental conditions—according to Landolt’s ecological indicators—exhibit co-occurrence matrices with higher species connectance, as captured by information-based metrics. Furthermore, for the dataset considered, similarity matrices derived from species co-occurrence patterns outperform traditional approaches (frequency vectors, mean cover, presence/absence data, and standard similarity indices) in predicting ecological indicator patterns. Classification Ecological indicator values Homogeneity Interactions Information theory Neg-entropy Operational Sampling Units Vegetation descriptors 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. 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A test based on data from Picea abies and Abies alba forests in the Friulian Alps","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":"Classification, Ecological indicator values, Homogeneity, Interactions, Information theory, Neg-entropy, Operational Sampling Units, Vegetation descriptors","lastPublishedDoi":"10.21203/rs.3.rs-8617058/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8617058/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, we introduce a framework for comparing vegetation types based on species co-occurrence matrices. The comparison relies on two complementary approaches: (1) quantifying the negentropy of the co-occurrence matrix for each vegetation type, and (2) evaluating the evenness of eigenvalues derived from pairwise co-occurrence matrices extracted from the overall species co-occurrence matrix of the vegetation system. The method is illustrated using phytosociological relev\u0026eacute;s from \u003cem\u003ePicea abies\u003c/em\u003e and \u003cem\u003eAbies alba\u003c/em\u003e forests of the Friulian Alps (NE Italy).\u003c/p\u003e \u003cp\u003eOur results show that vegetation types associated with more mesophilic environmental conditions\u0026mdash;according to Landolt\u0026rsquo;s ecological indicators\u0026mdash;exhibit co-occurrence matrices with higher species connectance, as captured by information-based metrics. Furthermore, for the dataset considered, similarity matrices derived from species co-occurrence patterns outperform traditional approaches (frequency vectors, mean cover, presence/absence data, and standard similarity indices) in predicting ecological indicator patterns.\u003c/p\u003e","manuscriptTitle":"Do co-occurrence matrices suggest more than usual when comparing vegetation types? 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