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Comprehensive characterization of ecological features and spatiotemporal distribution patterns of ticks in Shandong Province, China (2021-2023).
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Han Yingnan, Yin Hao, Liu Yan, Cao Xinyue, Sun Qintong, Cai Tong, Yang Yingchun, Han Chenxin, Liu Wenjie, Liu Hongmei, Wang Xuejun
Аннотация
BACKGROUND: As a globally significant disease vector, ticks harbor diverse pathogens, occupy various ecological niches, and attach to a wide range of animal hosts. However, their distribution patterns in Shandong province remain poorly characterized.
METHODS: This study systematically investigated tick species composition, spatial-temporal distribution patterns, and environmental influencing factors, such as temperature, dewpoint temperature and precipitation, across 13 prefecture-level cities in Shandong province from 2021 to 2023.
RESULTS: Haemaphysalis longicornis was identified as the dominant species, accounting for over 90% of all collected ticks with widespread geographic distributions. Significant interannual variation in host-seeking tick densities was particularly, with 2021 recoding significantly higher values than 2022 and 2023. Densities were particularly elevated in suburban parks and unused grasslands compared with other habitat types. Host-specific analysis revealed that sheep exhibited significantly higher infestation rates and tick indices than dogs, cats, or cattle. Strong temporal aggregation patterns were observed in host-seeking and host collected tick counts from 2021 to 2023, with seasonal fluctuations peaking between March and June. While the host collected tick index remained stable across years, an unusual high tick density peak was recorded in Jinan in March 2021. Generalized Linear Mixed Model analysis indicated that mean air temperature was positively correlated with tick density, whereas mean dew point temperature showed a negative correlation.
CONCLUSIONS: This study represents the first comprehensive ecological assessment of ticks in Shandong province and provides the first dataset for risk stratification and targeted prevention strategies for tick-borne diseases. These findings highlight the need to prioritize surveillance in rural habitats, focus on sheep as sentinel hosts, target spring as a high-risk period, and incorporate meteorological factors into early warning and prevention strategies.
METHODS: This study systematically investigated tick species composition, spatial-temporal distribution patterns, and environmental influencing factors, such as temperature, dewpoint temperature and precipitation, across 13 prefecture-level cities in Shandong province from 2021 to 2023.
RESULTS: Haemaphysalis longicornis was identified as the dominant species, accounting for over 90% of all collected ticks with widespread geographic distributions. Significant interannual variation in host-seeking tick densities was particularly, with 2021 recoding significantly higher values than 2022 and 2023. Densities were particularly elevated in suburban parks and unused grasslands compared with other habitat types. Host-specific analysis revealed that sheep exhibited significantly higher infestation rates and tick indices than dogs, cats, or cattle. Strong temporal aggregation patterns were observed in host-seeking and host collected tick counts from 2021 to 2023, with seasonal fluctuations peaking between March and June. While the host collected tick index remained stable across years, an unusual high tick density peak was recorded in Jinan in March 2021. Generalized Linear Mixed Model analysis indicated that mean air temperature was positively correlated with tick density, whereas mean dew point temperature showed a negative correlation.
CONCLUSIONS: This study represents the first comprehensive ecological assessment of ticks in Shandong province and provides the first dataset for risk stratification and targeted prevention strategies for tick-borne diseases. These findings highlight the need to prioritize surveillance in rural habitats, focus on sheep as sentinel hosts, target spring as a high-risk period, and incorporate meteorological factors into early warning and prevention strategies.