The effect of grey wolf (Canis lupus) and human disturbance on the activity of big game species in the Bükk Hills, Hungary

Abstract

The recent return of wolves to the Hungarian forests escalates conflicts among stakeholders. Hunting management agencies communicate that the presence of wolves may change the behaviour of big game species leading to difficulties for hunting organization and logistics. Here, we take a data driven approach to explore the activity of wolves and big game species. For this purpose we analysed camera trap data, collected in the Bükk National Park, Hungary. To estimate avoidance among wolves, humans and games we calculated a non-parametric activity overlap coefficient (Δ4) and used a machine learning (XGBoost) model. Our results show that game species have higher overlap coefficient with wolf (Δ4 = 0.83-0.89) than with human activity (Δ4 = 0.26-0.52), because predators and games are active in the same periods of the day, mainly night and dawn, and human activity mainly takes place during daytime. We could detect the refugee effects in the case of all game species. Our XGBoost analyses only found a moderate negative effect of wolf on red deer occurrence, while human activity had higher importance value and lowered the occurrence of all three game species investigated. Our results may thus indicate that human disturbance might be more important in shaping game activity than the presence of the grey wolf in Hungary.