This readme file was generated on 2022-09-12 by Giorgia Ausilio

GENERAL INFORMATION

Title of Dataset: Environmental and anthropogenic features in relation to human hunting and wolf predation risk


Author/Principal Investigator Information
Name: Giorgia Ausilio
ORCID: https://orcid.org/0000-0003-0927-8829
Institution: Inland Norway University of Applied Sciences
Address: Anne Evenstads vei 80, 2480 Koppang, Norway
Email: giorgia.ausilio@inn.no

Author/Associate or Co-investigator Information
Name: Barbara Zimmermann
ORCID: https://orcid.org/0000-0001-5133-9379
Institution: Inland Norway University of Applied Sciences
Address: Anne Evenstads vei 80, 2480 Koppang, Norway
Email: barbara.zimmermann@inn.no


Date of data collection: 2018-09-01 to 2020-04-30

Geographic location of data collection: South-central Scandinavia, in the Norwegian counties of Hedmark, Våler, Åsnes, Elverum and Trysil, and the Swedish county of Värmland

Information about funding sources that supported the collection of the data: INN, FORMAS


SHARING/ACCESS INFORMATION

Recommended citation for this dataset: Ausilio et al. (2022) "Environmental and anthropogenic features in relation to human hunting and wolf predation risk"


DATA & FILE OVERVIEW

File List: wolf_kills_ecosphere, hunter_kills_ecosphere, rug_wolf.tif, distm_wolf.tif, build_wolf.tif, distcl_wolf.tif, distfor_wolf.tif,
moose.tif, wolf1819.tif, distbog_wolf.tif

wolf_kills_ecosphere: Excel file containing the coordinates of wolf-killed moose during winter and fall of two consecutive years (2018 and 2019) 
hunter_kills_ecosphere: Excel file containing the coordinates of hunter-killed moose during the fall of two consecutive years (2018 and 2019)
rug_wolf.tif: Raster file of the terrain ruggedness within our study area (derived from DEM)
distm_wolf.tif: Raster file showing the euclidean distance to the nearest main road in our study area (meters)
distcl_wolf.tif: Raster file of the euclidean distance to the nearest clearcut or young forest within the study area (meters)
distfor_wolf.tif: Raster file of the euclidean distance to the nearest forest road within the study area (meters)
moose.tif: Raster file of the moose density within the study area (moose per squared km)
wolf1819.tif: Raster file of wolf space use for 2018 and 2019
distbog_wolf.tif: Raster file showing the euclidean distance to the nearest bog in the study area (meters)

av_build_wolf.tif: Raster file showing the average building density within the study area 
av_distbog_wolf.tif: Raster file showing the average distance to bog within the study area (meters)
av_distcl_wolf.tif: Raster file showing the average distance to clearcut and young forest in the study area (meters)
av_distf_wolf.tif: raster file showing the average distance to forest roads within the study area (meters)
av_distm_wolf.tif: Raster file showing the average distance to main roads within the study area (meters) 
av_moose19_wolf: Raster file showing the average moose density for 2019 in the study area
av_rug2_wolf.tif: raster file showing the average terrain ruggedness within the study area 
av_wolf_hunt19.tif: Raster file showing the average wolf space use for 2019 within the study area 
METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data: 
We used GPS positions from collared wolves to identify potential kill site locations using machine learning methods.
We surveyed each hunting team within our study area for the location of moose killed during the hunt. 

Methods for processing the data: For each wolf-killed and hunter-killed moose, we then extracted several environmental and anthropogenic
variables (within brackets the name they have in the file): distance to bogs (distb), distance to clearcuts and young forests (distcl), distance to forest roads (distf) and main roads (distm),
building density (build) and terrain ruggedness (rug). We also extracted moose density (derived from pellet counts; moose) and wolf
space use (derived from utilization distributions; wolf). 

Instrument- or software-specific information needed to interpret the data: R Software (Version 4.2.0), a full RMarkDown file is provided to replicate the analyses.

DATA-SPECIFIC INFORMATION FOR: wolf_kills_ecosphere

Number of variables: 11

Number of cases/rows: 162

Variable List: kill (1 = kill site); lat (latitude); long (longitude); territory (name of wolf territory); 
actual_date_point (date of kill site matched for each random point as well); isDaylight (whether the position or random point was 
during daylight hours or at night); month; year; day; mooseyear (corresponding to the year of the moose pellet count inventory, either 2018 or 2019);
season (either fall or winter). 

DATA-SPECIFIC INFORMATION FOR: hunter_kills_ecosphere

Number of variables: 6

Number of cases/rows: 881

Variable List: hunt_site (1=hunt kill site); lat (latitude); long (longitude); year; month; mooseyear (corresponding to the year of the moose
pellet count inventory, either 2018 or 2019).