Abstract
The maximum entropy (MaxEnt) algorithm was used to predict the potential geographic distribution of the mango stone weevil (MSW), Sternochetus mangiferae, because of its quarantine importance. Projections were made based on the relation between MSW presence data as well as current and projected climate data for the study region. The MaxEnt simulation results give an accurate estimation of the species' range in terms of bias of appropriate and inappropriate regions for its incidence adaptability in the present and projected climatic scenarios. The ecological niche model gives an excellent fit for MSW distribution with a high value of area under the curve (AUC) of 0.900. The jackknife test showed that the most important bioclimatic variable in determining the distribution of MSW is the mean diurnal range. South Asia (India, Myanmar, Bangladesh, Vietnam, Thailand), Australia, and Africa (Tanzania) were predicted to have high suitability areas for MSW distribution. In India, the model predicted higher pest suitability areas in Andhra Pradesh, Orissa's coastal regions, West Bengal's southern parts, and parts of Kerala and Karnataka. Projections of future climate scenarios reveal a relative increase in S. mangiferae distribution. The study identified high-suitability regions around the world, along with India, for the potential establishment of MSW. It advises developing biosecurity/quarantine measures to prevent it from expanding to new areas.
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Acknowledgements
The authors are thankful to the Director, IARI, New Delhi, for providing facilities to carry out the work. The authors thank Dr. Abraham Verghese, Former Director ICAR-National Bureau of Agricultural Insect Resources (NBAIR), Bangalore (India), for the critical comments and suggestions for the improvement of the manuscript. Thanks, are also due to the Indian Council of Agricultural Research, New Delhi, for ICAR- Postdoctoral fellowship for lead author.
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Baradevanal, G., Chander, S., Singh, H.S. et al. Mapping the risk of quarantine pest Sternochetus mangiferae under different climate change scenarios through species distribution modelling. Int J Trop Insect Sci 43, 919–932 (2023). https://doi.org/10.1007/s42690-023-01000-y
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DOI: https://doi.org/10.1007/s42690-023-01000-y