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

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.