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.