2.3 Environmental variables
We selected the most relevant environmental factors to predict their
distribution and habitat selection, considering the ecological
requirements of grey wolf and golden jackal. The environmental variables
were classified into three categories including topography (elevation,
slope and topographic roughness), vegetation (vegetation cover, and
Normalized Difference Vegetation Index; NDVI), land use (distance to
agricultural land). Also, anthropogenic variables were classified into
one category including human disturbance (distance to roads, villages,
and dump sites).
A digital elevation model (DEM) from the 30m Shuttle Radar Topography
Mission (SRTM, downloaded from http://earthexplorer.usgs.gov), was used
to calculate slope (using Surface Tool in Spatial Analyst Tools) and
surface roughness variables (Geomorphometry and Gradient Metrics
toolkit) (Evans, Oakleaf, Cushman, & Theobald, 2014) in ArcGIS 10.2.
To calculate NDVI, we extracted red and near infrared bands of Landsat 8
OLI images for the year 2016 at 30m resolution and calculated the index
using the Image Analysis tool in ArcGIS v10.2. For vegetation cover,
vegetation types with density higher than 25% from the land cover map
of the study area (Markazi DoE,
2016) were extracted. Among land use classes, we extracted agricultural
lands from the land cover map of the study area .We calculated Euclidean
distance to human settlement, roads and dump sites using the Spatial
Analyst tool in ArcGIS 10.2. The degree of multicollinearity between the
predictors was tested by calculating the Pearson correlation coefficient
between pairs of the variables and based on the threshold value of 0.8
(Elith* et al., 2006). Accordingly, we only identified high degree of
collinearity between two variables of slope and topographic roughness.