Tick species diversity and potential distribution alternation of dominant ticks under different climate scenarios in Xinjiang, China.

Abstract

Ticks are a hematophagous parasite and a vector of pathogens for numerous human and animal diseases of significant importance. The expansion of tick distribution and the increased risk of tick-borne diseases due to global climate change necessitates further study of the spatial distribution trend of ticks and their potential influencing factors. This study constructed a dataset of tick species distribution in Xinjiang for 60 years based on literature database retrieval and historical data collection (January 1963-January 2023). The distribution data were extracted, corrected, and deduplicated. The dominant tick species were selected for analysis using the MaxEnt model to assess their potential distribution in different periods under the current and BCC-CSM2.MR mode scenarios. The results indicated that there are eight genera and 48 species of ticks in 108 cities and counties of Xinjiang, with Hyalomma asiaticum, Rhipicephalus turanicus, Dermacentor marginatus, and Haemaphysalis punctatus being the top four dominant species. The MaxEnt model analysis revealed that the suitability areas of the four dominant ticks were mainly distributed in the north of Xinjiang, in areas such as Altay and Tacheng Prefecture. Over the next four periods, the medium and high suitable areas within the potential distribution range of the four tick species will expand towards the northwest. Additionally, new suitability areas will emerge in Altay, Changji Hui Autonomous Prefecture, and other local areas. The 60-year tick dataset in this study provides a map of preliminary tick distribution in Xinjiang, with a diverse array of tick species and distribution patterns throughout the area. In addition, the MaxEnt model revealed the spatial change characteristics and future distribution trend of ticks in Xinjiang, which can provide an instrumental data reference for tick monitoring and tick-borne disease risk prediction not only in the region but also in other countries participating in the Belt and Road Initiative.