Predicting the potential geographic distribution of cotton mealybug Phenacoccus solenopsis in India based on MAXENT ecological niche model.

Mealybug, Phenacoccus solenopsis Tinsley has recently emerged as a serious insect pest of cotton in India. This study demonstrates the use of Maxent algorithm for modeling the potential geographic distribution of P. solenopsis in India with presence-only data. Predictions were made based on the analysis of the relationship between 111 occurrence records for P. solenopsis and the corresponding current and future climate data defined on the study area. The climate data from worldclim database for current (1950-2000) and future (SRES A2 emission scenario for 2050) conditions were used. DIVA-GIS, an open source software for conducting spatial analysis was used for mapping the predictions from Maxent. The algorithm provided reasonable estimates of the species range indicating better discrimination of suitable and unsuitable areas for its occurrence in India under both present and future climatic conditions. The fit for the model as measured by AUC was high, with value of 0.930 for the training data and 0.895 for the test data, indicating the high level of discriminatory power for the Maxent. A Jackknife test for variable importance indicated that mean temperature of coldest quarter with highest gain value was the most important environmental variable determining the potential geographic distribution of P. solenopsis. The approaches used for delineating the ecological niche and prediction of potential geographic distribution are described briefly. Possible applications and limitations of the present modeling approach in future research and as a decision making tool in integrated pest management are discussed.