Assessing Species Distribution Models for Fine-scale Predictions of Ixodes Scapularis, what are we missing?

Assessing Species Distribution Models for Fine-scale Predictions of Ixodes Scapularis, what are we missing? | 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 Assessing Species Distribution Models for Fine-scale Predictions of Ixodes Scapularis, what are we missing? Grace F. Hummell, Matthew Gonnerman, Cody Kent, Frances Buderman, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8652659/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Ticks and tick-borne diseases are increasingly threatening public health in the United States, emphasizing the importance of accurately predicting their distribution to develop effective management strategies. However, modeling tick distributions can be challenging due to their three-host life cycle, clustered dispersion, and dependence on specific microhabitats. In this study, we compared three modeling methods for predicting the distribution of blacklegged ticks ( Ixodes scapularis ) across three urban parks in Maryland: presence-only Maximum Entropy (MaxEnt), presence-only Log Gaussian Cox Processes (LGCP) utilizing a latent stochastic partial differential equation (SPDE), and a presence-absence GLMM with an SPDE, based on site-specific, field-collected non-detections. We aimed to assess whether a spatially continuous presence-absence GLMM-SPDE could serve as an alternative or complement to the popular MaxEnt model, potentially offering better computational efficiency and predictive accuracy. Results indicated that both MaxEnt and LGCP models predicted tick distributions moderately well, although the MaxEnt model tended to overpredict presence in fragmented urban environments. The presence-absence model achieved the highest accuracy (mean AUC = 0.854 ± 0.04; CBI = 0.985), effectively identifying occupied sites while maintaining reasonable specificity, primarily when park-specific thresholds were used. These findings demonstrate that integrating a continuous spatial autocorrelation structure enables presence-only GLMMs to perform adequately. However, the most precise predictions in diverse urban areas come from field-collected presence-absence data. Therefore, we recommend using spatially explicit binomial SPDE-based GLMMs that require field ecologists to check drags or flags during tick sampling systematically and to record both absences and presences. Ixodes scapularis Species Distribution Models Maximum Entropy Generalized Linear Mixed Models Stochastic Partial Differential Equation Fine Scale Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 01 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviews received at journal 22 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 01 Feb, 2026 Submission checks completed at journal 28 Jan, 2026 First submitted to journal 20 Jan, 2026 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-8652659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589701073,"identity":"d7b3ab4b-f403-47cd-b156-8d3f2b36e0c1","order_by":0,"name":"Grace F. 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However, modeling tick distributions can be challenging due to their three-host life cycle, clustered dispersion, and dependence on specific microhabitats. In this study, we compared three modeling methods for predicting the distribution of blacklegged ticks (\u003cem\u003eIxodes scapularis\u003c/em\u003e) across three urban parks in Maryland: presence-only Maximum Entropy (MaxEnt), presence-only Log Gaussian Cox Processes (LGCP) utilizing a latent stochastic partial differential equation (SPDE), and a presence-absence GLMM with an SPDE, based on site-specific, field-collected non-detections. We aimed to assess whether a spatially continuous presence-absence GLMM-SPDE could serve as an alternative or complement to the popular MaxEnt model, potentially offering better computational efficiency and predictive accuracy. 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