Human avoidance, selection for darkness and prey activity explain wolf diel activity in a highly cultivated landscape

Wildlife that share habitats with humans with limited options for spatial avoidance must either tolerate frequent human encounters or concentrate their activity on those periods with the least risk of encountering people. Based on 5,259 camera trap images of adult wolves from eight territories, we analyzed the extent to which diel activity patterns in a highly cultivated landscape with extensive public access (Denmark) could be explained by diel variation in darkness, human activity, and prey (deer) activity. A resource selection function that contrasted every camera observation (use) with 24 alternative hourly observations from the same day (availability), revealed that diel activity correlated with all three factors simultaneously with human activity having the strongest effect (negative), followed by darkness (positive) and deer activity (positive). A model incorporating these three effects had lower parsimony and classified use and availability observations just as well as a ‘circad..., Population monitoring and data collection
Since 2017, the Natural History Museum Aarhus and Aarhus University have monitored all wolves in Denmark for the Danish Environmental Protection Agency. The occurrence and turnover of individuals are registered from genetic markers obtained from scat, hair, saliva, or urine samples collected by systematic patrolling of forest roads and by snow tracking (active monitoring) as well as saliva samples from livestock kills obtained by the Danish Nature Agency.
A territory was defined as the area patrolled by a single wolf, pair, or pack for a minimum of six months. The core areas and approximate territory extensions were estimated from the distribution of wolf signs (scats, tracks, kills, photos, etc.) within the landscape. With permission from the landowners, we placed wildlife cameras in places known (from the appearance of footprints, scats, or other signs) or suspected (leading lines in the landscape which from experience are known to be used by ..., , # Data from: Human avoidance, selection for darkness and prey activity explain wolf diel activity in a highly cultivated landscape

### \"Hour\_count.csv\"

A data set containing hourly observations from camera traps. The file contains the following variables:

* Hour = hour of the day (0-23)
* Species = Observation category (Deer, Human, Wolf_adult, Wolf_pup)
* Season = Season of the year, quaternary (1:Feb-Apr, 2:May-Jul, 3:Aug-Oct, 4:Nov-Jan)
* Location = Name of the sampling region
* Count = number of observations within the given hour

### \"rsf\_5min.csv\"

A data set containing wolf observations filtered for 5-minute independent observations together with corresponding 24 hourly pseudo-observations.
The file contains the following variables:

* MuseumsID = Observation id
* Dato = date of observation in the format DD-MM-YYYY
* SurveyFixPoint = name of camera location
* CamType = camera model
* SampleAge = If observable, the age group of the observed wolf. \"Voksen\"...