Predicting the climate niche of Puccinia abrupta var. partheniicola using maximum entropy for strategic biological control of Parthenium hysterophorus in India

Predicting the climate niche of Puccinia abrupta var. partheniicola using maximum entropy for strategic biological control of Parthenium hysterophorus in India | 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 Predicting the climate niche of Puccinia abrupta var. partheniicola using maximum entropy for strategic biological control of Parthenium hysterophorus in India Prakya Sreerama Kumar, Natarajan Sivaraj, Prasanna Holajjer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8277700/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Puccinia abrupta var. partheniicola is a rust fungus specific to Parthenium hysterophorus , a globally invasive weed causing ecological and health problems. As a potential classical biocontrol agent, understanding the rust’s ecological distribution is crucial. This study applies MaxEnt species distribution modelling to assess the current and potential distribution of P. abrupta var. partheniicola in India, using nineteen global occurrence records and bioclimatic variables from the WorldClim database. MaxEnt excels at modelling species presence under environmental constraints, making it ideal for rare species like P. abrupta var. partheniicola , where absence data are limited. The model revealed high habitat suitability in northwestern India (Punjab, Haryana, western Uttar Pradesh), moderate to high suitability in central, eastern and southern regions, and low suitability in the Thar Desert, Gujarat and Coastal Plains. Precipitation of the driest month (bio14) and isothermality (bio3) were key drivers, with bio3 showing the highest permutation importance, indicating sensitivity to thermal stability. Mean temperature of the driest quarter (bio9) was the most informative predictor in jackknife analysis. The model performed exceptionally well (AUC = 0.995), confirming high predictive accuracy. Omission rate and predicted area analyses further validated strong calibration. These findings highlight thermal and moisture factors as critical determinants of the rust’s distribution. The study underscores MaxEnt’s value in forecasting pathogen spread and guiding sustainable biological control of P. hysterophorus . Future work should integrate fine-scale climatic data and assess potential impacts under climate change scenarios. Ecological Modeling Bioclimatic variables DIVA-GIS MaxEnt rust fungi species distribution modelling weed biocontrol Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-8277700","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555158309,"identity":"bfcaefa9-096a-4cb2-8d27-78c1cae6ca97","order_by":0,"name":"Prakya Sreerama 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As a potential classical biocontrol agent, understanding the rust’s ecological distribution is crucial. This study applies MaxEnt species distribution modelling to assess the current and potential distribution of \u003cem\u003eP. abrupta\u003c/em\u003e var. \u003cem\u003epartheniicola\u003c/em\u003e in India, using nineteen global occurrence records and bioclimatic variables from the WorldClim database. MaxEnt excels at modelling species presence under environmental constraints, making it ideal for rare species like \u003cem\u003eP. abrupta\u003c/em\u003e var. \u003cem\u003epartheniicola\u003c/em\u003e, where absence data are limited. The model revealed high habitat suitability in northwestern India (Punjab, Haryana, western Uttar Pradesh), moderate to high suitability in central, eastern and southern regions, and low suitability in the Thar Desert, Gujarat and Coastal Plains. 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