County-Level Distribution, Relative Abundance and Environmental Associations of Haemaphysalis longicornis (Acari: Ixodidae) in the Changbai Mountain Forest Area, Northeastern China.
- 1Key Lab of Preventive Veterinary Medicine in Jilin Province, Jilin 132101, China.
- 2College of Animal Science and Technology, Jilin Agricultural Science and Technology College, Jilin 132101, China.
Abstract
Haemaphysalis longicornis is a three-host tick of cattle and other livestock in East Asia. We surveyed ticks in 19 counties of the Changbai Mountain forest area, northeastern China, from March to October 2023-2025. Ticks were collected by flagging vegetation and from cattle at 103 sampling localities, identified morphologically to species and counted per county; no molecular confirmation of the identifications was performed. County-level counts were analyzed with global and local spatial statistics, a standard deviational ellipse, and rank correlations with eight environmental variables. An exploratory maximum entropy (MaxEnt) model was built on the eight counties in which the species was recorded. Of 1653 ticks belonging to five species, 246 (14.88%) were H. longicornis, recorded in 8 of the 19 counties, with the highest relative abundance in Hunchun (81.82%), Tumen (72.84%) and Ji'an (70.63%). Global Moran's I was 0.0416 (z = 0.84, p = 0.402) and local Getis-Ord Gi* identified no significant hot or cold spots, so no statistically significant clustering was detected at the county level; the sampling localities were arranged along an east-northeast to west-southwest axis (SDE azimuth 74.1 degrees). No environmental variable was significantly correlated with county-level relative abundance (all |rho| ≤ 0.33, p ≥ 0.16); with 19 counties the test has limited power, so this indicates that no association was detected rather than that none exists. In the exploratory MaxEnt model, four-fold spatial block cross-validation gave an AUC of 0.558 ± 0.113. At this sampling intensity the data support a county-level description of where the species was caught and a preliminary surveillance priority for the three border counties, but not a predictive habitat model.
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BibTeX
@article{li2026countylevel,
title = {{County-Level Distribution, Relative Abundance and Environmental Associations of Haemaphysalis longicornis (Acari: Ixodidae) in the Changbai Mountain Forest Area, Northeastern China.}},
author = {Miao Li and Xiu Xin and Qichao Tang and Xiujing Feng and Hongyu Qin},
journal = {Veterinary sciences},
year = {2026},
month = oct,
doi = {10.3390/vetsci13101118},
url = {https://europepmc.org/article/MED/42944212},
note = {CC BY},
}
RIS
TY - JOUR
TI - County-Level Distribution, Relative Abundance and Environmental Associations of Haemaphysalis longicornis (Acari: Ixodidae) in the Changbai Mountain Forest Area, Northeastern China.
AU - Miao Li
AU - Xiu Xin
AU - Qichao Tang
AU - Xiujing Feng
AU - Hongyu Qin
PY - 2026
DA - 2026/10/20
JO - Veterinary sciences
DO - 10.3390/vetsci13101118
UR - https://europepmc.org/article/MED/42944212
AB - Haemaphysalis longicornis is a three-host tick of cattle and other livestock in East Asia. We surveyed ticks in 19 counties of the Changbai Mountain forest area, northeastern China, from March to October 2023-2025. Ticks were collected by flagging vegetation and from cattle at 103 sampling localities, identified morphologically to species and counted per county; no molecular confirmation of the identifications was performed. County-level counts were analyzed with global and local spatial statistics, a standard deviational ellipse, and rank correlations with eight environmental variables. An exploratory maximum entropy (MaxEnt) model was built on the eight counties in which the species was recorded. Of 1653 ticks belonging to five species, 246 (14.88%) were H. longicornis, recorded in 8 of the 19 counties, with the highest relative abundance in Hunchun (81.82%), Tumen (72.84%) and Ji'an (70.63%). Global Moran's I was 0.0416 (z = 0.84, p = 0.402) and local Getis-Ord Gi* identified no significant hot or cold spots, so no statistically significant clustering was detected at the county level; the sampling localities were arranged along an east-northeast to west-southwest axis (SDE azimuth 74.1 degrees). No environmental variable was significantly correlated with county-level relative abundance (all |rho| ≤ 0.33, p ≥ 0.16); with 19 counties the test has limited power, so this indicates that no association was detected rather than that none exists. In the exploratory MaxEnt model, four-fold spatial block cross-validation gave an AUC of 0.558 ± 0.113. At this sampling intensity the data support a county-level description of where the species was caught and a preliminary surveillance priority for the three border counties, but not a predictive habitat model.
SN - 2306-7381
ER -