Analysis of habitat quality characteristics andinfluencing factors in Anshun City based ongeographic detector

Analysis of habitat quality characteristics andinfluencing factors in Anshun City based ongeographic detector | 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 Analysis of habitat quality characteristics andinfluencing factors in Anshun City based ongeographic detector Hongyi YANG, jiegang Liu, shuang Li, Shixue MEI, Hongkang DENG, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8504051/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Revealing spatial heterogeneity in regional habitat quality and its potential drivers is important for ecosystem management. Based on land-use data for Anshun City in 2020, this study combined the Habitat Quality module of the InVEST model with a parameter-optimized Geodetector approach to characterize habitat quality patterns and to quantify the explanatory power of candidate drivers. Climatic, edaphic, topographic, and anthropogenic variables were discretized into six classes using the natural breaks (Jenks) method in ArcGIS prior to Geodetector analysis. The results show that the mean habitat quality index for Anshun in 2020 was 0.665, corresponding to a “relatively high–high” level; high-quality habitats were mainly distributed in mountainous areas with dense vegetation. Among the examined drivers, annual temperature range showed the highest explanatory power (q = 0.033), followed by annual precipitation (q = 0.0275) and precipitation in the driest period (q = 0.0251). Slope also exhibited a moderate association with habitat quality heterogeneity (q = 0.0169), whereas the tested soil variables and population density yielded relatively lower explanatory power. Notably, the absolute q-values were low overall (max q = 0.033), indicating that habitat quality patterns likely reflect the combined effects of multiple interacting factors and scale-dependent processes that are not fully captured by the selected predictors and discretization scheme. These findings suggest that climate variability is an important correlate of habitat quality heterogeneity in Anshun, while land-use optimization and targeted ecological restoration in low-quality areas (e.g., urban centers and intensively cultivated lands) may help improve regional ecological resilience under ongoing environmental change. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences habitat quality Geodetector driving factors ArcGIS InVEST Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 09 Mar, 2026 Reviews received at journal 24 Feb, 2026 Reviews received at journal 23 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor invited by journal 05 Jan, 2026 Editor assigned by journal 03 Jan, 2026 Submission checks completed at journal 03 Jan, 2026 First submitted to journal 02 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. 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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-8504051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":572773744,"identity":"0ab4ecae-f758-4917-b58c-09a3a0f24ade","order_by":0,"name":"Hongyi YANG","email":"","orcid":"","institution":"Guizhou Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Hongyi","middleName":"","lastName":"YANG","suffix":""},{"id":572773745,"identity":"6e8f344a-0abd-4705-8dd2-d7ea6cfbe897","order_by":1,"name":"jiegang 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Slope also exhibited a moderate association with habitat quality heterogeneity (q = 0.0169), whereas the tested soil variables and population density yielded relatively lower explanatory power. Notably, the absolute q-values were low overall (max q = 0.033), indicating that habitat quality patterns likely reflect the combined effects of multiple interacting factors and scale-dependent processes that are not fully captured by the selected predictors and discretization scheme. 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