{"paper_id":"32decbdc-50ea-45da-9ee1-8d4316f9d0aa","body_text":"A workflow to optimize spatial sampling in ecoacoustic studies | 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 A workflow to optimize spatial sampling in ecoacoustic studies Victor M. Martinez-Arias, Carolina Paniagua-Villada, Maria José Guerrero, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7207339/v2 This work is licensed under a CC BY 4.0 License Status: Under Review Version 2 posted 9 You are reading this latest preprint version Show more versions Abstract Context Landscape monitoring through sounds relies on spatial sampling to characterize soundscape variation across a heterogeneous space. This non-invasive approach complements traditional landscape metrics by capturing ecological dynamics not visible in spatial structure alone. However, the effectiveness of different sampling strategies remains poorly understood. Objectives We examined how spatial sampling design affects the representativeness of soundscape information across heterogeneous ecosystems. Our goal was to identify efficient acoustic Sub-sampling strategies that integrate landscape heterogeneity while balancing effort and performance. Specifically, we (1) assessed how sampling structure and effort affect interpolated soundscape representativeness; (2) compared random, thematic designs based on land-cover classes, and tessellation-based designs; (3) examined interactions among landscape, acoustic, and temporal variables; (4) evaluated the influence of landscape heterogeneity on sampling performance; and (5) developed a workflow to guide ecoacoustic Sub-sampling. Methods Our study was conducted across three Colombian ecosystems. We constructed discrete and continuous landscape proxies using Sentinel-2 imagery, calculated acoustic indices from field recordings, and then implemented various spatial sampling designs from sub samples based on image tessellation techniques. Using kriging, a geostatistical method for interpolation,we compared sub samples to full spatial scenarios and assessed their performance using structural and distributional metrics. We took representativeness as the spatial fidelity of interpolated soundscape surfaces relative to full sampling, capturing both acoustic patterns and underlying landscape structure. Results We found that tessellation-based methods, especially those based on watershed segmentation, significantly outperformed random and thematic designs, showing higher sample performance, stronger correlations with sample size, and greater representativeness of landscape heterogeneity. Multivariate analyses showed that tessellation-based designs exhibited consistent patterns across acoustic, temporal, and landscape variables. Our results demonstrate that the spatial arrangement of sampling locations, rather than sample size alone, critically determines sampling performance. We propose a landscape-based workflow to support the design of efficient ecoacoustic sampling strategies. This approach advances scalable and spatially informed biodiversity monitoring using ecoacoustics methods. Full Text Additional Declarations No competing interests reported. Supplementary Files OnlineResouresSUPPLEMENTARYINFORMATIONrevised.zip Cite Share Download PDF Status: Under Review Version 2 posted Editorial decision: Revision requested 16 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 30 Dec, 2025 Reviewers invited by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 27 Dec, 2025 You are reading this latest preprint version Show more versions 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-7207339\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":573557894,\"identity\":\"71373aac-f013-11f0-907b-06cc9d20a69f\",\"order_by\":0,\"name\":\"Victor M. 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Ecology](https://www.springer.com/journal/10980)\",\"snPcode\":\"10980\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10980/3\",\"title\":\"Landscape Ecology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7207339/v2\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7207339/v2\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eContext\\u003c/h2\\u003e \\u003cp\\u003eLandscape monitoring through sounds relies on spatial sampling to characterize soundscape variation across a heterogeneous space. This non-invasive approach complements traditional landscape metrics by capturing ecological dynamics not visible in spatial structure alone. However, the effectiveness of different sampling strategies remains poorly understood.\\u003c/p\\u003e\\u003ch2\\u003eObjectives\\u003c/h2\\u003e \\u003cp\\u003eWe examined how spatial sampling design affects the representativeness of soundscape information across heterogeneous ecosystems. Our goal was to identify efficient acoustic Sub-sampling strategies that integrate landscape heterogeneity while balancing effort and performance. Specifically, we (1) assessed how sampling structure and effort affect interpolated soundscape representativeness; (2) compared random, thematic designs based on land-cover classes, and tessellation-based designs; (3) examined interactions among landscape, acoustic, and temporal variables; (4) evaluated the influence of landscape heterogeneity on sampling performance; and (5) developed a workflow to guide ecoacoustic Sub-sampling.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eOur study was conducted across three Colombian ecosystems. We constructed discrete and continuous landscape proxies using Sentinel-2 imagery, calculated acoustic indices from field recordings, and then implemented various spatial sampling designs from sub samples based on image tessellation techniques. Using kriging, a geostatistical method for interpolation,we compared sub samples to full spatial scenarios and assessed their performance using structural and distributional metrics. We took representativeness as the spatial fidelity of interpolated soundscape surfaces relative to full sampling, capturing both acoustic patterns and underlying landscape structure.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eWe found that tessellation-based methods, especially those based on watershed segmentation, significantly outperformed random and thematic designs, showing higher sample performance, stronger correlations with sample size, and greater representativeness of landscape heterogeneity. Multivariate analyses showed that tessellation-based designs exhibited consistent patterns across acoustic, temporal, and landscape variables. Our results demonstrate that the spatial arrangement of sampling locations, rather than sample size alone, critically determines sampling performance. We propose a landscape-based workflow to support the design of efficient ecoacoustic sampling strategies. 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