DOI: 10.5061/dryad.cz8w9gjbt
title: Data from: Home range and habitat selection of wolves recolonising Central European human-dominated landscapes

description: Decades of persecution has resulted in the long-term absence of grey
 wolves (Canis lupus) from most European countries. However, recent changes
 in both legislation and public attitudes toward wolves has eased the
 pressure, allowing wolves to rapidly re-establish territories in their
 previous Central European habitats over the last 20 years. Unfortunately,
 these habitats are now heavily altered by humans. Understanding the
 spatial ecology of wolves in such highly modified environments is crucial,
 given the high potential for conflict and the need to reconcile their
 return with multiple human concerns. We equipped 20 wolves, originating
 from seven packs in six Central European regions, with GPS collars,
 allowing us to calculate monthly average home range sizes for 14 of the
 animals of 213.3 km2 using Autocorrelated Kernel Density Estimation. We
 then used ESA WorldCover data to assess the mosaic of available habitats
 used within each home range. Our data confirmed a general seasonal pattern
 for breeding individuals, with smaller apparent home ranges during the
 reproduction phase, and no specific pattern for non-breeders. Predictably,
 our wolves showed a general preference for remote areas, and especially
 forests, though some wolves within military training areas also showed a
 broader preference for grassland, possibly influenced by local land use
 and high availability of prey. Our results provide a comprehensive insight
 into the ecology of wolves during their re-colonisation of Central Europe.
 Though wolves are spreading relatively quickly across Central European
 landscapes, their permanent reoccupation remains uncertain due to
 conflicts with the human population. To secure the restoration of European
 wolf populations, further robust biological data, including data on
 spatial ecology, will be needed to clearly identify any management
 implications.
description: Wolves were captured using Belisle 8″ or Victor soft-catch
 leg-hold traps set along trails or at marking places identified by trained
 dogs, camera traps or snow tracking. During trapping, each site was
 permanently controlled via a satellite transmitter (Telonics Inc., USA), a
 GSM Live Trap Alarm (UOVision, China) and a GPRS camera trap (Spromise,
 China) that instantly transmitted footage once triggered, allowing
 researchers to be on site within 30 minutes up to two hours of wolf
 capture. On arrival, the wolf was immobilised with a
 medetomidine-butorphanol-ketamine mixture mixture administered by a
 trained veterinarian and blood samples collected for subsequent mSAT DNA
 analysis to reveal relationships between the trapped animals (Szewczyk et
 al. 2021). After determining sex and approximate age based on tooth
 development, month of trapping and body mass, a GPS Plus collar (Vectronic
 Aerospace GmbH., Germany) was fitted that allowing telemetry data to be
 sent GSM service. After a short recovery period, the wolf was released at
 the site of capture. Three modes of wolf activity monitoring were usually
 employed, each changed remotely. Immediately following release, the GPS
 schedule was programmed to collect telemetry positions every 0.5 hours
 (mode 1), after which fixes were obtained every three hours throughout the
 regular monitoring period (mode 2). Finally, detailed documentation of
 feeding activity was obtained by taking fixes every 0.5 hours between
 18:00 and 08:00 (mode 3), i.e. overnight. This regime was used for one
 month in summer and one in winter to conserve collar battery
 lifespan.
description: # Home range and habitat selection of wolves recolonising Central European
 human-dominated landscapes
 [https://doi.org/10.5061/dryad.cz8w9gjbt](https://doi.org/10.5061/dryad.cz8w9gjbt) The dataset contains GPS telemetry data of 19 the Grey wolves (Canis Lupus) in Central Europe (Czechia, Germany, Austria, Slovakia) and was used to estimate home ranges using autocorrelated kernel estimation and habitat selection. ## Description of the data and file structure The data are appropriate for analysing the spatial and movement ecology of wolves in different software. The dataset is appropriate for the generation and analysis of trajectories and overall animal movement analysis (e.g. using the package "traj", "adehabitatLT" or "momentumHMM" in the program R), but also for estimating home ranges using different methods (it is possible to start with simpler methods such as minimum convex polygon or kernel density estimation, but for estimating home ranges we recommend autocorrelated kernel density estimation, since GPS data are inherently autocorrelated and this method is the most suitable for them at the moment).  **Before using the data to estimate home ranges, we recommend estimating the movement mode for the animals, e.g. using net squared displacement; the packages "amt" or "migrater" can be used for this.** ### Wolves telemetry Data | `/Telemetry_data.csv` The main dataset contains GPS telemetry data for 19 wolves. The dataset contains raw data and needs to be cleaned before it can be used; to clean the data, we recommend the methodology presented in our paper, which relies primarily on the DOP value. For example, we recommend cleaning the telemetry data in the following way: since the dataset also contains test position fixes before collar fitting, only the data when the wolf had a collar on should be used (you can find the collar fitting for each wolf in the table "trap_day.csv"). Then, animals with less than 30 days of observation should be excluded to ensure sufficient data for reliable estimates of home ranges and to study seasonal (monthly) home range dynamics. Furthermore, filter out months in which the animal was observed &lt; 10 days (if the data will thus be used to analyze the spatiotemporal dynamics of home ranges). Finally, fixes with missing values (NA) were removed. To ensure sufficient accuracy, we also removed fixes with a DOP (dilution of precision) value &gt; 6 (Langley 1999). All coordinates are in the WGS84 coordinate system (EPSG:4326), but we recommend converting the data to a more appropriate format, such as ETRS89 (EPSG:3035). **The uploaded data contains coordinates rounded to 1 degree due to the fact that it is data of a critically endangered animal** The dataset contains the following variables, where each row represents a unique sampling event: | Variable(s) | Description | | :-------------------- | :---------------------------------------------------------------------------------------- | | **`Collar_ID`** | Six-digit identification number for each **individual animal** | | **`Acq. Time [UTC]`** | **Time in Coordinated Universal Time (UTC).** Format: month.day. year hour:minute:seconds | | **`Latitude[deg]`** | **Latitude;** WGS84 coordinate system (EPSG:4326) | | **`Longitude[deg]`** | **Longitude;** WGS84 coordinate system (EPSG:4326) | | **`Altitude[m]`** | **Altitude in metres** | | **`DOP`** | **Dilution of precision** | Missing data code : NA ### Trap day Data | `/Trap_day.csv` Data containing the date on which a GPS collar was fitted on a wolf. The dataset contains the following variables, where each row represents a unique sampling event: | Variable(s) | Description | | :-------------- | :------------------------------------------------------------------------------------------- | | **`Collar_ID`** | Six-digit identification number for each **individual animal** | | **`Trap_day`** | **Time when the collar was fitted on the wolf**. Format: month.day. year hour:minute:seconds | ## Sharing/Access information The uploaded data are modified because they contain movement data of an animal that is critically endangered in most of the study area. If you are interested in the original data, you can ask Ivo Kadlec ([kadleci@fzp.czu.cz](mailto:kadleci@fzp.czu.cz)) or Ales Vorl ([vorel@fzp.czu.cz](mailto:vorel@fzp.czu.cz)) for more detailed information. In your request, please state the reason why you need unmodified data and for what purpose you will use it. ## Code/Software The scripts we use to analyse the data in R can be found at [https://github.com/kadleci/.](https://github.com/kadleci/.)