2.2. Data collection and processing
We obtained the C. imicola presence points (n = 1045) from
literature (Leta, Fetene, Mulatu, Amenu, Jaleta, Beyene, Negussie,
Kriticos, et al., 2019; Ye, Liu, Li, Qiu, & Liu, 2019) and the Global
Biodiversity Information Facility database
(https://www.gbif.org/). The
reported AHS outbreaks were extracted from the archive of Food and
Agriculture Organization (FAO) of the United Nations from December 22,
2005 to September 1, 2020 (n = 203).
To determine the influence of the environmental variables on the AHS
distribution and C.imicola, we considered 19 bioclimate
variables, 12 land cover
variables, and global horse distribution density as risk factors in the
models (Table 1). Bioclimate variables were obtained from the
WorldClim database
(http://worldclim.org/version1).
Global land cover characterizations were obtained from the
archives of the United States
Geological Survey Earth Resources Observation and Science center
(https://www.usgs.gov/). The global horse distribution density was
downloaded from the FAO database
(http://www.fao.org/livestock-systems/).
All the occurrence data were rarefied at 10 km2(Radosavljevic & Anderson, 2014) to minimize the spatial
autocorrelation using the SDM Toolbox (Brown, Bennett, & French, 2017).
Thus, 728 spatially rarefied occurrence records of C. imicola and
110 locations of AHS were used in the MaxEnt modelling of this study
(Figure 1).
To avoid the multi-collinearity of environmental variables, Pearson
correlation analyses were performed using the SPSS 22.0 software. Each
pair of variables had a correlation value |r| ≥ 0.80,
and one of the variables was considered to remove from the final model
(Ma et al., 2019; Wu, Sharp, Zhao, Shirato, & Jiang, 2007). Finally,
two sets of variables were included in the AHS model and theC.imicola model (see Table 1). In ArcGIS 10.2, all the
environmental variables were resampled to the ASCII raster grids at a
resolution of 2.5 arcmin.