Published May 16, 2024 | Version v1
Dataset Open

Data from: Home range and habitat selection of wolves recolonising Central European human-dominated landscapes

  • 1. Czech University of Life Sciences Prague
  • 2. University of Veterinary Medicine Vienna
  • 3. Šumava National Park
  • 4. ,
  • 5. Mendel University Brno

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.

Notes

Funding provided by: Interreg Czech-Saxony*
Crossref Funder Registry ID:
Award Number: 100400831

Funding provided by: Interreg Czech-Saxony*
Crossref Funder Registry ID:
Award Number: 100322836

Funding provided by: Interreg Czech-Bayern*
Crossref Funder Registry ID:
Award Number: BYCZ01-001

Funding provided by: Operational Programme Environment*
Crossref Funder Registry ID:
Award Number: CZ.05.4.27/0.0/0.0/20_139/0012815

Funding provided by: Government of Lower Austria
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100015053
Award Number:

Funding provided by: Interreg Central Europe LECA*
Crossref Funder Registry ID:
Award Number: CE0100170

Methods

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.

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Additional details

Related works

Is supplemented by
https://github.com/kadleci/ (URL)