Inferring biodiversity from indicator species using co-occurrence network structure

Inferring biodiversity from indicator species using co-occurrence network structure | 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 Inferring biodiversity from indicator species using co-occurrence network structure Ilhem Bouderbala, Daniel Fortin, Junior A Tremblay, Christian Hébert, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8919967/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Biodiversity loss is accelerating worldwide, yet comprehensive assessment of species assemblages across environmental gradients remains impractical for ecological forecasting and conservation. Indicator species are therefore widely used as proxies for community composition, but existing approaches struggle to reconstruct full assemblages, particularly for rare or poorly sampled species. Moreover, most indicator-based studies focus on predicting aggregate biodiversity metrics such as species richness, rather than inferring the occurrences of the species that compose assemblages. Here we present an integrated framework combining indicator species analysis, ecological network theory, and machine learning to infer the occurrence of non-indicator species from assemblage data. By leveraging structural properties of species co-occurrence networks, the approach captures latent community structure without explicit environmental modeling, enabling species-level inference under sparse sampling. Across species abundance gradients, we identify three distinct regimes of predictability: high predictability for abundant species, reduced performance for species of intermediate prevalence, and unexpectedly strong predictability for rare species driven by strong co-occurrence structure with indicator species. By exploiting complementary information captured by multiple network metrics, the framework recovers species with diverse connectivity profiles and consistently outperforms richness-based or random indicator selection in both accuracy and coverage. Overall, this data-efficient approach provides a transferable pathway for biodiversity monitoring and forecasting, while offering new insights into the network organization of ecological communities. Biological sciences/Ecology Biological sciences/Ecology/Biodiversity Biological sciences/Ecology/Ecological networks Biological sciences/Ecology/Ecological modelling Biodiversity reconstruction indicator species species co-occurrence networks ecological net works centrality metrics species associations latitudinal climate gradient. Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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. 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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-8919967","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":598906753,"identity":"25d15284-33d1-4f38-8f01-ea53d7561a42","order_by":0,"name":"Ilhem 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Indicator species are therefore widely used as proxies for community composition, but existing approaches struggle to reconstruct full assemblages, particularly for rare or poorly sampled species. Moreover, most indicator-based studies focus on predicting aggregate biodiversity metrics such as species richness, rather than inferring the occurrences of the species that compose assemblages. Here we present an integrated framework combining indicator species analysis, ecological network theory, and machine learning to infer the occurrence of non-indicator species from assemblage data. By leveraging structural properties of species co-occurrence networks, the approach captures latent community structure without explicit environmental modeling, enabling species-level inference under sparse sampling.\r\n\r\nAcross species abundance gradients, we identify three distinct regimes of predictability: high predictability for abundant species, reduced performance for species of intermediate prevalence, and unexpectedly strong predictability for rare species driven by strong co-occurrence structure with indicator species. By exploiting complementary information captured by multiple network metrics, the framework recovers species with diverse connectivity profiles and consistently outperforms richness-based or random indicator selection in both accuracy and coverage. 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