Gajendra N. Bhangale Babasaheb W. Narladkar


Rhipicephalus microplus is an important tick infesting livestock, particularly ruminants, and also transmits various economic diseases, viz. babesiosis and anaplasmosis. Considering the economic impact of those diseases and losses incurred due to tick infestations, it is pertinent to appraise the distribution of these tick species regarding climatic backgrounds. The present study aimed to employ a species distribution model for studying distribution patterns of Rhipicepaus microplus in India. Important bioclimatic variables viz Bio1 (Annual Mean temperature), Bio2 (Mean diurnal range of temperature), Bio12 (Annual precipitation) and Bio15 (Precipitation seasonality) were used for building a model with the help of ‘dismo’ R package. The results showed that temperature and precipitation significantly impact the distribution pattern of R. microplus. The resultant model indicated that bio 1, i.e. annual mean temperature, has significantly highest influence (0.0212 ± 0.0017; p<0.00001) on the occurrence of these ticks than the effects of bio12 (0.0007±0.0001; p<0.0001) and bio15 (-0.0153±0.0031; p<0.0001); however effects of bio2 (-0.0033±0.0042; p=0.427) was non-significant on its occurrence. The accuracy of this model is adjudged well by its AUC value being 0.874. On visual interpretation of the model maps, it was found that the drier regions comprising parts of Rajasthan, Gujarat and Madhya Pradesh of the country have low suitability as against those with sufficient precipitation. The temperature effects on the survivability of ticks and eggs are linked with the soil types of the country's various regions. The present study is the first attempt to present a distribution model of an important vector of livestock diseases.




Distribution pattern, Ecological niche model, Generalized linear model, Rhipicephalus microplus, Ruminant tick

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Research Articles

How to Cite

Study of distribution of Rhipicephalus microplus ticks in India based on Worldclim temperature and rainfall data through an ecological niche modeling approach. (2023). Journal of Applied and Natural Science, 15(2), 777-782. https://doi.org/10.31018/jans.v15i2.4561