Nonparametric quantile regression captures regional variability and scaling deviations in Atlantic surfclam length–weight relationships

Nonparametric quantile regression captures regional variability and scaling deviations in Atlantic surfclam length–weight relationships | 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 Nonparametric quantile regression captures regional variability and scaling deviations in Atlantic surfclam length–weight relationships Gorka Bidegain, Marta Sestelo, Patricia L Luque, Eric N Powell, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7040551/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The universality of the allometric model for describing the length–weight relationship in marine species has been questioned, particularly for some marine invertebrates such as sea urchins, clams, and barnacles. In such cases, nonparametric regression models may offer improved flexibility and capture specific patterns—such as inflection points in growth curves—not identified by standard parametric models. These features can support the identification of biologically meaningful thresholds relevant to fisheries, including size-dependent yield. Nonparametric quantile regression further enhances inference by characterizing variability across the entire distribution of body condition. Here, we assess the comparative performance of parametric and nonparametric regression models for the Atlantic surfclam, Spisula solidissima, using data collected from three regions along the U.S. Atlantic coast (Virginia, Delaware/Maryland, and New Jersey). First, we compare two mean regression approaches—a classic allometric model and a kernel-based nonparametric alternative—using a bootstrap-based procedure. Second, we apply a boosting-based quantile regression technique to both parametric and nonparametric frameworks to investigate size-dependent variation in growth patterns. Model selection for mean regressions was based on a hypothesis test contrasting the allometric model with a general nonparametric alternative, while the quantile regressions were evaluated using a goodness-of-fit test derived from the cumulative sum of the gradient vector. Our results indicate that the allometric model provides a better fit in the mean regression context, while the nonparametric model proves more effective for quantile regression, particularly in detecting distributional heterogeneity. These findings support the complementary application of both modeling strategies to better characterize growth dynamics in marine invertebrate populations. Other long-lived marine bivalves, such as Arctica islandica and Mercenaria mercenaria, which show environmentally driven variation in growth and condition, may similarly benefit from modeling approaches that distinguish central from marginal populations. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Ocean sciences Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviewers invited by journal 11 Jul, 2025 Editor assigned by journal 11 Jul, 2025 Editor invited by journal 08 Jul, 2025 Submission checks completed at journal 07 Jul, 2025 First submitted to journal 07 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. 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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-7040551","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":482372631,"identity":"d62fa8ee-47d0-4116-be0d-cf874092665d","order_by":0,"name":"Gorka 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