Dermacentor nuttalli

Wei, Hua, Xiong, Tao, Wang, Shan-Shan, Wang, Bai-Hui, Du, Li-Feng, Xu, Qing, Zheng, Jia-Jing, Cui, Xiao-Ming, Jia, Na, Jiang, Jia-Fu, Shi, Wenqiang, Zhao, Lin & Cao, Wu-Chun, 2024, Investigating the pathogens associated with Dermacentor nuttalli and its global distribution: A study integrating metagenomic sequencing, meta-analysis and niche modeling, International Journal for Parasitology: Parasites and Wildlife 23, pp. 100907-100907 : 100907-

3.4. Potential distribution of D. nuttalli

To systematically identify suitable habitats for D. nuttalli , ecological niche modeling was used to predict its global distribution. After trimming duplicate occurrences, there were 301 known distribution points, of which 266 points were randomly selected as the training set for Maxent model training, and the remaining 75 points were used as the

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test set for validation. The model input features included the standard 19 WorldClim Bioclimatic variables (BIO1–BIO19), land cover, elevation, slope degree, and slope aspect. Eleven independent variables were finally selected to train the model, including BIO1, BIO2, BIO7, BIO12, BIO17, BIO18, BIO19, land cover, elevation, slope aspect, and slope degree ( Table 2). The best model corresponded to the combination of quadratic (Q), product (P), and threshold (T) features and a regularization multiplier of 1.8, with the smallest Akaike information criterion value. The model’ s performance was further evaluated in terms of Area Under the Curve (AUC), which stood at 0.96 ± 0.02 across 25 replicates. This high AUC value signified a robust model fitting, reflecting the model’ s accuracy in predicting the suitable habitats for D. nuttalli .

Based on the variable contributions, temperature and precipitation were the most important factors influencing the distribution of D. nuttalli (Supplementary Fig. S2 ). Specifically, the most suitable habitat for D. nuttalli was characterized by an average annual temperature of 3.9 ◦ C, a temperature annual range of 58.5 ◦ C, and a mean diurnal range of 11.9 ◦ C In addition, the precipitation of the coldest quarter was 12 mm, and the precipitation of the driest quarter was seven mm.

The model suggested that D. nuttalli could have a wider distribution than previous records, including areas where it had never been recorded ( Fig. 5 ). The most suitable areas for D. nuttalli were primarily China,

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Mongolia, Russia, and North Korea. Notably, North Korea had a probability of suitability greater than 0.8. However, the occurrence of D. nuttalli had not been reported in the investigated databases of this study. Furthermore, five countries on the Eurasian continent processed potential habitats with a predicted probability above 0.5, including Kyrgyzstan, Nepal, Bhutan, Pakistan, and India. Notably, in both Canada and the United States, there were also potentially suitable areas with a predicted probability exceeding 0.4.

Figures

Fig. 2. Study design and data sources of the meta-analysis. A comprehensive meta-analysis was performed to evaluate D. nuttalli’s potential threats based on detected pathogens and geographical distribution positions. The database of D. nuttalli was constructed from four sources, including field surveys, literature review, a reference book, and an online biodiversity database (Global Biodiversity Information Facility, GBIF, https://www.gbif.org).

Fig. 5. Global potential distribution of D. nuttalli. The red area indicates greater possibilities of suitability for D. nuttalli, while the blue area is less likely to be suitable for D. nuttalli.