Research area
The study area is located between N 29°54’-44°32’, E 64°0’-85°17’, namely the Pamir Plateau and its huge mountain range that extended to the surrounding area (Pan Pamir Plateau)(Fig.1). Located in the southeast of Central Asia and the western most end of China, Pamir Plateau straddles the south of Tajikistan and the north of Afghanistan. It is a huge junction of Kunlun Mountains, Karakoram mountains, Hindu Kush mountains and Tianshan Mountains, covering an area of about 1×105 square kilometers. The Pamir Plateau is composed of several groups of mountains and wide valleys and basins between mountains, with an average altitude of more than 5000 meters. It is a strong continental alpine climate with severe cold, especially in the eastern Pamirs, with a long winter (October to April of the next year). The difference between the East and the west of Pamir Plateau is significant. The west Pamir Plateau is a typical high mountain plateau with very large absolute and relative height and the complex terrain. The abundant rainfall helps to develop large dense net of rivers and prospective vegetation. The absolute and relative height of the East Pamir Plateau are small compare with its west part. The significant features of east are wide valleys and widespread 1085 glaciers covering an area of 8041 square kilometers. It is the breeding ground for many small wild ruminants. Since the hostile nature of habitat on the high, huge mountains in Tashkurgan in Pamirs Plateau, it drives the migration of large populations of ruminants, such as yak, camel and ovis ammon rugularly and shed a great risk to countries on the both sides of the boarder.

Date processing

67 environmental  factors from 1979 to 2013 were extracted from CHELSA database (V.1.2) with a resolution of 30 arc-seconds. The monthly precipitation (n=12), monthly mean/minimum/maximum temperature (n=36), derived bio climatic variants (n=19), slope, vegetation cover layer (ESA CCI), population density (Asia Continental Population Data sets (2000-2020) were used. Slope and Euclidean distances to rivers are extracted from DEM (http://www.gscloud.cn/)(Table1). 396 geographical locations of PPR case from OIE report and published articles were used for model production. The rarefying of spatial auto-correlation refers to Joka(Joka et al., 2019). The multi-collinearity among variants is identified and limited by Principal component analysis (PCA) by SPSS 22.0(Moriguchi et al., 2016). The VIF value <10 was set as the threshold of multi-collinearity(Duque-Lazo et al., 2016, Leanne et al., 2018).

Prediction for the Spatial distribution of PPR

Due to the large height difference in study area, the areas were analyzed separately as ≧1500m group and ˂1500m group(Himeidan et al., 2009). The disease distribution points were screen by spatial rarefying for the model. The environmental variables and non environmental variables are diagnosed to exclude multiple collinearity. Then MaxEnt model analysis is carried out to eliminate the factors with low contribution rate. The final factor obtained is used for the prediction of the final MaxEnt model(Gils et al., 2014). The prediction map obtained by overlaying of results of the two groups by spatial analysis fuzzy overlay in ArcGIS. The setting and self-test of MaxEnt refer to Joka (Joka et al., 2019).

Prediction of the maximum available transboundary paths

Corresponding to least cost path (LCP) and according to the research purpose, all transboundary channels available to ruminants are named as maximum available paths (MAP)(Balbi et al., 2019). Cluster analysis by ArcGIS 10.2 were performed for the outbreak points of PPR both in China and abroad(Zulu et al., 2014). The LCP model of ArcGIS 10.2 was supplied for the results of cluster by pair-wised calculation (China versus abroad) to obtain transboundary paths(Ray, 2004). By excluding those that with a starting point far away from the border and those finally incorporated into other most convenient channels, the maximum available transboundary paths were reached. For this research, the resistance coefficient is defined by land cover and altitude. Further, the land cover (land cover data was extracted from ESA CCI Land Cover project database, containing 9 categories 21 subcategories) and altitude are categories as available and unavailable according to expert experience and the ethology of small ruminants; the available land cover and altitude were reclassified into 1 and 9 scales to construct the resistance layer(Sawyer et al., 2011). 1 for low resistance and 9 for high resistance.