Deer density drives habitat use of establishing wolves in the Western European Alps
Creators
- 1. University of Bern
- 2. KORA*
- 3. Swiss Ornithological Institute
- 4. University of Lausanne
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.
Notes
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>= 5 days old, f<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: >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
Files
buffer_data_vs_200.csv
Files
(247.3 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:01a6ca9da85f7578a0368ac9473d18a7
|
68.6 kB | Preview Download |
|
md5:94b8729f070ce416c3c48cc5e85b793a
|
76.2 kB | Preview Download |
|
md5:715e4b129092c89c3d35840457deac0b
|
25.7 kB | Preview Download |
|
md5:5611b32102af30e27a1661eb34beef80
|
25.8 kB | Preview Download |
|
md5:c76921b749b6b0356b4b0f2fe68cdfc4
|
25.5 kB | Preview Download |
|
md5:2b80666558025dfb8ffe7bc7fca1f241
|
25.5 kB | Preview Download |