Context: One mainstay of soundscape ecology is to understand acoustic pattern changes, in particular the relative balance between biophony (biotic sounds), geophony (abiotic sounds), and anthropophony (human-related sounds). However, little research has been pursued to automatically track these three components.
Objectives: Here, we introduce a 15-year program that aims at estimating soundscape dynamics in relation to possible land use and climate change. We address the relative prevalence patterns of these components during the first year of recording.
Methods: Using four recorders, we monitored the soundscape of a large coniferous Alpine forest at the France-Switzerland border. We trained an artificial neural network (ANN) with mel frequency cepstral coefficients to systematically detect the occurrence of silence and sounds coming from birds, mammals, insects (biophony), rain (geophony), wind (geophony), and aircraft (anthropophony).
Results: The ANN satisfyingly classified each sound type. The soundscape was dominated by anthropophony (75% of all files), followed by geophony (57%), biophony (43%), and silence (14%). The classification revealed expected phenologies for biophony and geophony and a co-occurrence of biophony and anthropophony. Silence was rare and mostly limited to night time.
Conclusions: It was possible to track the main soundscape components in order to empirically estimate their relative prevalence across seasons. This analysis reveals that anthropogenic noise is a major component of the soundscape of protected habitats, which can dramatically impact local animal behavior and ecology.
Supplementary information: The online version contains supplementary material available at 10.1007/s10980-021-01360-1.
Keywords: Anthropophony; Automatic classification; Climate; Cold forest; Protected area; Soundscape.
© The Author(s), under exclusive licence to Springer Nature B.V. 2022.