Predicting the potential global distribution of Ixodes pacificus under climate change

software (version 10.8) was purchased by the Vector Control Department of the Institute of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Preven- tion. R software (version 4.0.3, http://www.r-project.org/, accessed on 31 May 2021) and DIVA-GIS software (version 7.5.0, https://www.diva-gis. org/, accessed on 31 May 2021) were used to adjust model parameters. ActivePrel (version 5.26) was downloaded from https:// www.activestate.com (accessed on 12 May 2023). The base map of the world was derived from the 1:110 Natural Earth I (http://www.naturalearthdata.com, accessed on 11 October 2021) drawn by the North American Cartographic Information Society (NACIS). The scale of the map in this article represents mid-latitude distances. Environmental data Download from WorldClim website (https://worldclim.org/) Including historical climate and environmental monitoring data (1970–2000) and future climate and environmental prediction data, a total of 56 climate variables (at a 2.5 min spatial resolution): 19 bioclimatic variables (bio 1–19) (Table 1), 36 climate variables (monthly maximum temperature, monthly mini- mum temperature, and monthly precipitation), and 1 elevation data. The future climate data includes climate variables from 2021 to 2100 using the BCC-CSM2-MR medium resolution climate system model of the China (Beijing) Climate Center climate system in the 6th Interna- tional Coupled Model Comparison Program (CMIP6), which includes four shared social eco-

nomic paths (SSP) from 1–2.6 (sustainability and radiative forcing of 2.6 W/m2 to 2100), 2–4.5 (middle of the road and forcing of 4.5 W/m2 to 2100), 3–7.0 (regional rivalry and forcing of 7.0 W/m2 to 2100), and 5–8.5 (fossil-fuel development and forcing of 8.5 W/m2 to 2100). Use the ArcGIS 10.8 software conversion tool to convert it to ASCII format for software use. To avoid overfitting of environmental data, it is necessary to filter the environmental data. Related studies have shown that environmental variables such as bio8 (average temperature of the wettest season), bio9 (average temperature of the driest season), bio18 (precipitation of the Table 1. Descriptions of each of the 19 bioclimatic variables. Variable Description Unit Bio1 Annual Mean Temperature ˚C Bio2 Mean Diurnal Range (Mean of monthly (max temp—min temp)) ˚C Bio3 Isothermality (BIO2/BIO7) (×100) % Bio4 Temperature Seasonality (standard deviation ×100) ˚C Bio5 Max Temperature of Warmest Month ˚C Bio6 Min Temperature of Coldest Month ˚C Bio7 Temperature Annual Range (BIO5-BIO6) ˚C Bio8 Mean Temperature of Wettest Quarter ˚C Bio9 Mean Temperature of Driest Quarter ˚C Bio10 Mean Temperature of Warmest Quarter ˚C Bio11 Mean Temperature of Coldest Quarter ˚C Bio12 Annual Precipitation mm Bio13 Precipitation of Wettest Month mm Bio14 Precipitation of Driest Month mm Bio15 Precipitation Seasonality (Coefficient of Variation) % Bio16 Precipitation of Wettest Quarter mm Bio17 Precipitation of Driest Quarter mm Bio18 Precipitation of Warmest Quarter mm

Bio19 Precipitation of Coldest Quarter mm https://doi.org/10.1371/journal.pone.0309367.t001 PLOS ONE Prediction of the distribution of Ixodes pacificus PLOS ONE | https://doi.org/10.1371/journal.pone.0309367 August 27, 2024 3 / 18