Mapping the Potential Distribution of Major Tick Species in China

Abstract

Ticks are known as the vectors of various zoonotic diseases such as Lyme borreliosis and tick-borne encephalitis. Though their occurrences are increasingly reported in some parts of China, our understanding of the pattern and determinants of ticks’ potential distribution over the country remain limited. In this study, we took advantage of the recently compiled spatial dataset of distribution and diversity of ticks in China, analyzed the environmental determinants of ten frequently reported tick species and mapped the spatial distribution of these species over the country using the MaxEnt model. We found that presence of urban fabric, cropland, and forest in a place are key determents of tick occurrence, suggesting ticks were likely inhabited close to where people live. Besides, precipitation in the driest month was found to have a relatively high contribution in mapping tick distribution. The model projected that theses ticks could be widely distributed in the Northwest, Central North, Northeast, and South China. Our results added new evidence on the potential distribution of a variety of major tick species in China and pinpointed areas with a high potential risk of tick bites and tick-borne diseases for raising public health awareness and prevention responses.

Current and future predicting habitat suitability map of Cunninghamia konishii Hayata using MaxEnt model under climate change in Northern Vietnam

“…The area under the response curve (AUC) was used under application of the receiver operating characteristic (ROC) model to determine the precision of the species potential distribution model. The AUC value ranges from 0 to 1, where AUC value >0.7 is adequate; >0.8 means good; and > 0.9 indicates excellent performance (Yang et al 2020).…”

Mapping the Potential Distribution of Ticks in the Western Kanto Region, Japan: Predictions Based on Land-Use, Climate, and Wildlife

“…Thus, tick distributions may be affected by vegetation and anthropogenic activities such as agriculture and food waste, which attract wildlife hosts. Additionally, the land-use pattern may reflect microclimatic factors, which are formed by understory vegetation, and litter layer, which provide habitats for ticks [20,22,23]. We used a High-Resolution Land Use Land Cover Map Ver.…”

“…The six species-Amblyomma testudinarium Koch, 1844; Haemaphysalis flava Neumann, 1897; Haemaphysalis kitaokai Hoogstraal, 1969; Haemaphysalis longicornis Neumann, 1901; Haemaphysalis megaspinosa Saito, 1969; and Ixodes ovatus Neumann, 1899-were included in the prediction model using MaxEnt 3.4.4. We used 75% of the distribution data as a training set and the other 25% as a test set [20,38].…”

“…The following setting was used for feature classes: "all feature classes", 1 for the regularization multiplier. Additionally, 10,000 random points were created as pseudo-absence data [19,20,38]. We repeated the test 10 times with a 25% random test percentage.…”

“…Here, we conducted a tick survey and estimated the potential distributions of major tick species in the western Kanto region of Japan using maximum entropy (MaxEnt) modeling [19], a machine-learning technique for species distribution models (SDMs). MaxEnt modeling estimates species distributions based on "presence" data for species and background factors, such as wildlife fauna, forest distribution, climatic patterns, and land-use patterns [20][21][22][23][24]. We estimated the potential distribution of major tick species based on our survey findings combined with land-use information and abiotic factors obtained by satellite images and government surveys.…”