AUTHOR=Xu Luyao , Guo Yunhai , Yang Limin , Li Zengkui , Kang Ming , Han Xiaoling , Chen Changjiang , He Shunfu , Hu Xiaoyu , He Yongcai , Wang Yuezhong , Li Zhongyu , Chen Jiyong , Geng Pengcheng , Chen Qiang , Jiang Shuo , Ma Jinghua , Zhang Xiao , Tai Ximei , Li Ying 

TITLE=Projected distribution and dispersal patterns of prevalent ticks and tick-borne pathogens in the Sanjiangyuan area of Qinghai province, China, under intense climatic conditions

JOURNAL=Frontiers in Environmental Science

VOLUME=12

YEAR=2024

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2024.1429718

DOI=10.3389/fenvs.2024.1429718

ISSN=2296-665X

ABSTRACT=<p><bold>Introduction:</bold> The Maximum Entropy models (MaxEnt) is commonly employed for early detection of disease transmission, particularly effective in assessing the risk zones and intensity of tick-borne disease transmission based on climatic factors.</p><p><bold>Methods:</bold> Utilizing the diversity data of common ticks in Qinghai, along with surveillance statistics tick-borne pathogens of ticks and epidemiological information, we have charted and predicted the prevalence of tick and tick-borne pathogens across Qinghai province.</p><p><bold>Results and Discussion:</bold> The findings indicate that the pivotal environmental factors influencing the distribution of ticks and tick-borne pathogens include land use and land cover (Lucc), elevation (Elev), annual precipitation (Bio12) and annual mean temperature (Bio1). Notably, for <italic>Dermacentor nuttalli</italic>, the significant environmental factors accounted for 35.2% for Lucc, 24.7% for Elev and 18.6% for Bio12. In the case of <italic>Dermacentor silvaru</italic>, the pivotal factors contributed 33.2% for Lucc, 23.7% for Elev and 22.6% for Bio1. For <italic>Haemaphysalis qinghaiensis</italic>, the key environmental variables were 45% for Elev, 30.9% for Lucc and 18.4% for Bio12. Regarding the pathogens, the environmental factors influencing <italic>Borrelia burgdorferi</italic> contributed 61.5% for Lucc, 13.3% for Elev and 11.9% for Bio1. For <italic>Piroplasmida</italic>, the contribution was 62.1% for Lucc, 16.7% for Bio1 and 9.9% for Bio12. <italic>Rickettsia</italic> was influenced by factors accounting for 34.2% for Lucc, 29.7% for Elev and 17.3% for Bio12, while <italic>Anaplasma</italic> variables contributed 38.2% for Bio1, 26.6% for Lucc and 18.9% for Bio12. The finding indicated that the three predominantly ticks species (<italic>D. nuttalli</italic>, <italic>D. silvaru,</italic> and <italic>H. qinghaiensis</italic>) and the primary tick-borne pathogens (<italic>B. burgdorferi</italic>, <italic>Piroplasmida</italic>, <italic>Rickettsia,</italic> and <italic>Anaplasma</italic>) are predominantly concentrated in the source regions of the Yellow River, the Yangtze River, the Lancang River, and the eastern part of the Haixi Autonomous Prefecture. Projected under the ssp245 scenario, there is a notable expansion trend in the risk areas for ticks and tick-borne pathogens These findings are consistent with previous epidemiological studies on major ticks and tick-borne diseases in Qinghai conducted by our laboratory, this suggests the feasibility of using the MaxEnt model to predict the distribution of tick-borne disease transmission and compensates for the paucity of research on the Maxent model in the Qinghai Province.</p>