multiScaleR: A generalizable approach for multiscale ecological modeling and scale of effect estimation

multiScaleR: A generalizable approach for multiscale ecological modeling and scale of effect estimation | 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 multiScaleR: A generalizable approach for multiscale ecological modeling and scale of effect estimation William E. Peterman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7246115/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Nov, 2025 Read the published version in Landscape Ecology → Version 1 posted 9 You are reading this latest preprint version Abstract Context Analyses in landscape ecology often seek to understand how the landscape surrounding field survey locations relates to ecological responses measured at those sites. A central challenge in these studies is defining the spatial scale at which landscape variables matter. While the limitations of standard approaches to estimating this scale are well known, practical alternatives remain limited and often difficult to apply. Objectives Using simulation and the newly developed R package `mulitScaleR`, this paper describes the performance of scale optimization in relation to data type, sample size, effect size, sample independence, raster surface correlation, spatial autocorrelation, and habitat aggregation. I demonstrate how `multiScaleR` is a significant and accessible advancement for estimating scales of effect. Methods and results The package builds upon existing methods that apply kernel weighting functions to landscape variables but is more general and versatile than existing methods. Functions have been optimized for computational speed and efficiency, including parallelization, use of sparse matrices, and C++, facilitating efficient analyses of large data sets. Maximum likelihood-based regression frameworks commonly used in landscape ecology, including models from `unmarked`, `spaMM`, and `glmmTMB` can be seamlessly integrated with `multiScaleR`. The package provides a complete workflow for fitting models, conducting model selection, and spatially projecting models. Two critical insights emerge from simulations and analyses with `multiScaleR`: (1) scales of effect can be estimated with high accuracy and precision alongside regression parameters, but (2) achieving reliable estimates requires large sample sizes. Conclusions `multiScaleR` is a purpose-built R package to estimate scales of effect of landscape variables in regression analyses. The accessibility and flexibility of this package make it a powerful new resource in the toolbox of spatial ecologists. Distance weighting Kernel smoothing Scale optimization Spatial scale Spatial smoothing Zone of influence Full Text Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Published Journal Publication published 24 Nov, 2025 Read the published version in Landscape Ecology → Version 1 posted Editorial decision: Revision requested 03 Oct, 2025 Reviews received at journal 03 Oct, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers invited by journal 09 Aug, 2025 Editor assigned by journal 29 Jul, 2025 Submission checks completed at journal 29 Jul, 2025 First submitted to journal 29 Jul, 2025 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-7246115","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499390587,"identity":"0bace16c-150b-4ff9-9f49-7cc6000a6b29","order_by":0,"name":"William E. 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A central challenge in these studies is defining the spatial scale at which landscape variables matter. While the limitations of standard approaches to estimating this scale are well known, practical alternatives remain limited and often difficult to apply.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eUsing simulation and the newly developed R package `mulitScaleR`, this paper describes the performance of scale optimization in relation to data type, sample size, effect size, sample independence, raster surface correlation, spatial autocorrelation, and habitat aggregation. I demonstrate how `multiScaleR` is a significant and accessible advancement for estimating scales of effect.\u003c/p\u003e\u003ch2\u003eMethods and results\u003c/h2\u003e\u003cp\u003eThe package builds upon existing methods that apply kernel weighting functions to landscape variables but is more general and versatile than existing methods. 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