Bine Bedjanič, Ana Mia Bedjanič, Jernej Polajnar, Al Vrezec, Matjaž Bedjanič

Owls of the Dobrovlje Plateau in the foothills of the Kamnik–Savinja Alps (central Slovenia): faunistics and comparison of different survey methodologies

Introduction

Owls of the dobrovlje plateau in the foothills of the kamnik–savinja alps (central slovenia): faunistics and comparison of different survey methodologies. Discover 5 owl species on Slovenia's Dobrovlje Plateau. This study compares playback and passive acoustic monitoring, finding PAM more efficient for detecting tawny, Ural, boreal, pygmy, and eagle owls.

Abstract

Between February and April 2024, a survey of the owl assemblage of the karstified forested plateau of Dobrovlje was carried out on the western edge of the Savinja Valley in central Slovenia. At a total of 17 sites, we conducted conventional species surveys using the playback method and passive acoustic monitoring (PAM), with autonomous AudioMoth recorders, aiming to assess the occurrence of target species. Using both methods, we recorded five owl species in the study area: tawny owl Strix aluco, Ural owl Strix uralensis, boreal owl Aegolius funereus, Eurasian pygmy owl Glaucidium passerinum, and Eurasian eagle-owl Bubo bubo. Complete faunistic data from our survey are presented. The most frequent species were the tawny owl, detected at all 17 localities, followed by the Ural owl at 11 sites, the boreal owl at 6 sites, the Eurasian pygmy owl at 5 sites, and the Eurasian eagle-owl at a single site. In general, we found higher vocal activity in larger owl species, whereas smaller species (the boreal owl and the Eurasian pygmy owl) vocalised with lower frequency. Comparison of survey methods indicates higher efficiency of passive acoustic monitoring, within the framework of which around 150 hours of soundscape recordings obtained at each site in mid-March 2024 were analysed. Deploying autonomous sound recorders is technically relatively straightforward, and the method is substantially more intensive than conventional surveys given the length of the observation period at each individual census point. However, processing and listening to recordings, manual annotation, and subsequent analysis require considerable effort and time. In optimising the PAM approach, particularly in light of the many additional analytical possibilities contained in soundscape data from a given area, the use of artificial intelligence and applications such as BirdNET holds great potential.

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