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-

2.5. Distribution status and potential distribution of D. nuttalli

The exact locations (longitude and latitude coordinates) of the collection points and the collection time of D. nuttalli were extracted from the database mentioned above. In instances where specific coordinates were not provided in the literature, the centroid of the administrative region was utilized as a proxy. ArcMap v10.7 was used to visualize the geographical distribution of D. nuttalli and unify the layer format.

To predict potential distributions of D. nuttalli , environmental and meteorological factors were obtained from the WorldClim database (htt ps://worldclim.org). This included the average minimum temperature (◦ C), average maximum temperature (◦ C), and total precipitation (mm) post-2005. These parameters were chosen as over 97% of the samples were collected after 2005. Using R (version 3.6.3, dismo package) (Hijmans et al., 2022), these data were employed to generate the standard 19 WorldClim Bioclimatic variables (BIO1–BIO19). The elevation data were also obtained and used by the Spatial Analyst Tool of ArcMap v10.7 to generate the slope degree and slope aspect. Besides, the global land cover data were collected from The Global Land Cover by National

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Mapping Organizations (GLCNMO) (version 1) ( Kobayashi et al., 2017).

Then an ecological niche model was applied to predict potential distributions of D. nuttalli , and the maximum entropy approach was applied to optimize models through Maxent v3.4.4 ( Phillips et al., 2017). For predicting the distribution of D. nuttalli , the environmental and meteorological factors mentioned above were used to fit the model. To avoid model overfitting, duplicated distribution points were removed using the trimming duplicate occurrence function, and highly correlated factors were screened out by the correlation function in ENMTools v1.4.4 ( Warren et al., 2010). Parameters calibration, evaluation, and selection of candidate models were made by R (version 3.6.3, kuenm package) to select the best model ( Cobos et al., 2019).