DOI: 10.5061/dryad.2v6wwpzhx
title: Deer density drives habitat use of establishing wolves in the Western European Alps

description: 1. The return of top carnivores to their historical range triggers
 conflicts with the interests of different stakeholder groups. Anticipating
 such conflicts is key to appropriate conservation management, which calls
 for reliable spatial predictions of future carnivore occurrence. Previous
 models have assessed general habitat suitability for wolves, but the
 factors driving the settlement of dispersing individuals remain
 ill-understood. In particular, little attention has been paid to the role
 of prey availability in the recolonization process. 2.
 High-spatial-resolution, area-wide relative densities of the wolf’s main
 ungulate prey species (red deer, roe deer and chamois) were assessed from
 snow-track surveys and modelled along with wolf presence data and other
 environmental descriptors to identify the main drivers of habitat
 selection of re-establishing wolves in the Western European Alps. 3. Prey
 species abundance was estimated from the minimum number of individuals
 recorded from snow-tracks along 218 1km transects surveyed twice a year
 during four successive winters (2012/13–2015/16). Abundance estimates per
 transect, corrected for species-specific detection probabilities and
 averaged across winters, were used to model area-wide relative prey
 density and biomass. 4. Confirmed wolf observations during the same four
 winters were used to develop a spatially-explicit habitat selection model
 for establishing wolves, based on our estimates of prey supply and other
 environmental descriptors of topography, land-use and climate. 5.
 Detection-corrected ungulate prey abundances and modelled relative
 densities varied considerably in space (0–2.8, 1.3–4.5 and 0–6.3 per 50ha
 in red deer, roe deer and chamois, respectively; 1.3–11.65 pooled), while
 total predicted prey biomass ranged from 23–304kg per 50ha. 6. Red deer
 density was the most important factor explaining wolf occurrence (31%
 contribution), followed by roe deer density (22%), winter precipitation
 (19%) and presence of game reserves (16%), showing that food supply,
 especially red deer as the most profitable prey in the Western Alps, was
 the main driver of winter habitat selection during the settlement phase.
 7. Synthesis and applications. We demonstrate the crucial importance of
 including accurate, fine-grained information about prey supply for
 predicting recolonization patterns of carnivores and thus anticipating
 areas with potential human-wildlife conflicts where preventive measures
 should be prioritized.
description: 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.<br> <br> 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
 <i>swissALTI<sup>3D</sup></i>. Roughness was
 calculated as the standard deviation of elevation of all cells within a
 predefined radius (564m), which corresponds to an area of
 1km<sup>2</sup>. 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 <i>vector25</i>, 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 <i>Landsat5</i>
 data respectively, both provided by the Swiss Federal office of statistics
 (https://www.bfs.admin.ch). Winter temperature and precipitation stemmed
 from the <i>Worldclim</i> dataset<i>
 </i>(http://www.worldclim.org),<i> </i>which was
 downscaled from a 1km<sup>2</sup> raster to a resolution of
 100x100m based on the <i>SRTM-V4</i> 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)<i> </i>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).
description: Ungulate data is stored in the files
 "transects_20xx.csv" for every survey season (2012 means the
 season 2012/13, 2013 means 2013/14,...) These are the
 attributes: DATE: The date of visiting the transect.
 Both visits per season were entered separately. QUAD:
 Number of the 10x10km square used to distribute transects in a stratified
 manner TRANS: Number of transect (orignially in each
 square many transects were considered, but only few chosen)
 TR_ID: The unique ID of each transect (formed from square number
 and transect number) CC: Number of observed minimal
 number of roe deer (Capreolus capreolus) CE: Number of
 observed minimal number of red deer (Cervus elaphus)
 RR: Number of observed minimal number of chamoix (Rupicapra
 rupicapra) SS: Number of observed minimal number of
 wild boar (Sus scrofa) CI: Number of observed minimal
 number of ibex (Capra ibex) SNOW_H: recorded snow
 height (in cm) SNOW_C: recorded snow quality
 (a&gt;= 5 days old, f&lt;5 days old) SNOW_FO:
 snow cover in forested areas (%) SNOW_OP: snow cover in
 open areas (%) RUN: first or second time this transect
 was visited in the season STAT: abbreviation of the
 name of the closest weather station DSLSF: days since
 last snow fall NWSN: height of fresh snow
 S_C_C: Snow age (1: 0-1 day old; 2: 2-4 days old; 3: &gt;5
 days old)   Environmental data is
 stored in "buffer_data_vs_X00.csv", where 200 or 350 represents
 the radius of the buffer around the transect for which the data was
 extracted. For red deer, we used 350m, for roe deer and chamoix
 200m. The attributes are: TR_ID: the
 unique ID of each transect AREA: the area represented
 by the buffer (different because transects were not straight but
 curvy) ALTI: average altitude in the buffer
 ROUGH: roughness SLOPE: average
 slope NORTH: average northness EAST:
 average eastness TPI: topographical position
 index FOR: percentage of forest cover
 ROCK: percentage of rock cover SCREE:
 percentage of scree cover OT_SOIL: percentage of
 grassland cover RIV_CR: percentage of rivers and
 creeks LAKE_D: average distance to next lake
 RIV_CR_D: average distance to next river or creek
 WAT_DIS: average distance to next water body
 DENSE_FO: percentage of dense forest cover
 OPEN_FO: percentage of open forest cover
 CONI_FO: percentage of coniferous forest cover
 CONIMI_FO: percentage of coniferous-mixed forest cover
 DECMI_FO: percentage of decidious-mixed forest cover
 DECI_FO: percentage of decidious forest cover
 FO_EDG_AL: length of all forest edge
 FO_EDG_IN: length of inner forest edge
 FO_EDG_OU: length of outer forest edge
 RO_RA_D: average distance to roads/rails
 SETTLE: percentage cover of settlements
 SETTLE_D: average distance to nearest settlement
 SKI_CAB_D: average distance to skilifts and cableways
 STR_BIG_L: length of main roads (category 1-3)
 STR_BIG_S: length of secondary roads (category 4-6)
 HUN_RES: percentage cover of hunting ban areas
 HUN_RES_PR: presence of hunting ban areas
 SHE_GOA: Number of sheep and goats PREC_S:
 average precipitation in summer months PREC_W: average
 precipitation in winter months TEMP_S: average
 temperature in summer months TEMP_W: average
 temperature in winter months DAYS_NO_SNOW: average
 annual number of days without snowcover