Biodiversity hotspot assessment in the Altai Mountains transboundary region based on Mammals and Aves

density data is selected from the 2000 and 2020 data of OAK RIDGE NATIONAL NABORA- TORY (https://landscan.ornl.gov/). The resolution is about 1km. (7) Protected area data comes from Protected Areas (WDPA, https://www.protectedplanet.net/), updated in 2023. By resampling all datasets to a uniform resolution of about 1.3 km2, we ensured that all data layers were compatible for integration and analysis in the MaxEnt model. This approach minimizes potential biases and inconsistencies that could arise from using data at different resolutions. Model and operation We used the MaxEnt model [32] to predict the potential distribution area of species and gener- ate biodiversity hotspots maps. This method has been widely used and verified [33, 34]. The MaxEnt model not only has high accuracy in predicting the distribution of species’ suitable areas, but can also effectively explain the mechanism of various environmental variables on species distribution, and make scientific predictions for timely intervention in future changes of species [34]. The species occurrence data in this study come from the Global Biodiversity Information Facility (GBIF; http://www.gbif.org). Considering species representation, data availability, and species diversity, we chose mammals and birds for our study. The selected species include those classified as CR, EN, VU, and NT on the IUCN Red List, with NT included to monitor species that may soon be at higher risk if not given timely attention. Detailed IUCN classifica- tion criteria are shown in S Table 4 in S1 File.

In terms of time, we simulated the current scenario. For environmental variables, we con- sidered the impact of climate, topography, landscape, and human activities. Climate variables, including temperature and precipitation (Table 2), were chosen because they are fundamental drivers of species distributions. These variables define the bioclimatic niches of species and influence their physiological processes, phenology, and habitat suitability. We selected seven environmental climate variables and 19 bioclimatic variables from the WorldClim dataset to capture the seasonal and annual climatic variations that affect the Altai Mountains region. Topography variables such as elevation and slope were included to account for the micro- Table 2. The MaxEnt model uses data. Driving force Driving factors Unit Resolution Factorcode Climate BIO1~BIO 19 ˚ C, mm, etc. 1km bio1~19 Topography Elevation m 30m elev Slope ˚ 30m slo Landscape Land use and land cover - 30m lulc Water resource distance m 30m distowater Contagion Index - 30m contag Shannon’s Diversity Index - 30m shdi Human activity Night light - 450m ntl Population density km-2 1km peo Road distance m 30m distoroad Note: BIO1: Annual Mean Temperature; BIO2: Mean Diurnal Range (Mean of monthly (max temp—min temp)); BIO3: Isothermality (BIO2/BIO7) (×100); BIO4: Temperature Seasonality (standard deviation ×100); BIO5: Max Temperature of Warmest Month; BIO6: Min Temperature of Coldest Month; BIO7: Temperature

Annual Range (BIO5-BIO6); BIO8: Mean Temperature of Wettest Quarter; BIO9 = Mean Temperature of Driest Quarter; BIO10: Mean Temperature of Warmest Quarter; BIO11: Mean Temperature of Coldest Quarter; BIO12 = Annual Precipitation; BIO13: Precipitation of Wettest Month; BIO14: Precipitation of Driest Month; BIO15: Precipitation Seasonality (Coefficient of Variation); BIO16: Precipitation of Wettest Quarter; BIO17: Precipitation of Driest Quarter; BIO18: Precipitation of Warmest Quarter; BIO19: Precipitation of Coldest Quarter. https://doi.org/10.1371/journal.pone.0314075.t002 PLOS ONE Biodiversity hotspot assessment in the Altai Mountains transboundary region PLOS ONE | https://doi.org/10.1371/journal.pone.0314075 December 4, 2024 5 / 20