Deer density drives habitat use of establishing wolves in the Western European Alps

Ungulate data were collected by means of snow-tracking along 218 1km long transects, during four subsequent winters starting in 2012/13. In order to distribute transects in a stratified manner, the study area was first divided into 34 10x10km squares. In each square, an average of six transects (range: 1-10) were then placed so as to cover the elevational and environmental gradients present in the study area as representatively as possible, while accounting for accessibility, topography and safety (avalanches). The transects were surveyed twice per winter (December to March). To reduce observer effects, each transect was visited by the same person during the whole study period, and the entire fieldwork was conducted by two experienced wildlife-biologists, who trained and standardized their methods for one full season (2011/12) prior to the start of this study. Tracks found in the snow were recorded for the main potential wolf prey: roe deer, red deer, chamois, ibex, mouflon and wild boar. Based on imprint size and track distribution, we estimated the minimal number of individuals present at each visit to a given transect. Multiple individuals were counted if tracks of different size (e.g. from different sexes or age classes) or individuals travelling together in a group could be distinguished. We used a conservative approach: when in doubt we always recorded the lower number. To model the detection probability of ungulates in relation to the conditions during sampling, we recorded covariates of snow conditions during the transect surveys: average snow height, quality and percentage of snow cover. In addition, the daily amount of fresh snow recorded at all 56 weather stations across the study area was obtained , assigning each transect to the nearest weather station. For each transect walk, we calculated the number of days since the last snowfall. We also transformed this continuous variable into a categorical variable describing snow age. Finally, we recorded the amount of fresh snow that had fallen on the last day with snowfall previous to each survey. The environmental variables used for predicting relative ungulate densities and wolf occurrence in a spatially explicit way were extracted from existing digital information, from which we produced basic raster layers with a 25x25m cell size. We obtained data on topography (altitude, slope, terrain roughness and topographic position) from the digital elevation model (DEM) of Switzerland swissALTI3D. Roughness was calculated as the standard deviation of elevation of all cells within a predefined radius (564m), which corresponds to an area of 1km2. The topographic position index (TPI) represents the position of a focal cell relative to the surrounding terrain and is calculated as the sum of angles to the ground measured in all eight cardinal directions. Land use and land cover (forest, rock, scree, water bodies, anthropogenic areas, roads and railways, ski lifts) were derived from vector25, a digital vector-map produced and regularly updated by the Swiss Federal Office for Topography swisstopo (https://www.swisstopo.admin.ch), with the exception of grassland cover and forest type information, which were derived from the area statistics dataset of Switzerland and Landsat5 data respectively, both provided by the Swiss Federal office of statistics (https://www.bfs.admin.ch). Winter temperature and precipitation stemmed from the Worldclim dataset (http://www.worldclim.org), which was downscaled from a 1km2 raster to a resolution of 100x100m based on the SRTM-V4 digital elevation model and the method described in Zimmermann and Roberts (2001). Estimates of livestock (sheep and goat) densities were obtained by relating the number of livestock per community (as obtained from the Agricultural statistics of Switzerland) to the amount of pastures present in that community. Finally, spatial data on the location of game reserves was obtained from the Federal Office for the Environment (FOEN).