A new methodology to infer the singing activity of an avian community: The Acoustic Complexity Index (ACI)

Abstract The animal soundscape is a field of growing interest because of the implications it has for human–landscape interactions. Yet, it continues to be a difficult subject to investigate, due to the huge amount of information which it contains. In this contribution, the suitability of the Acoustic Complexity Index (ACI) is examined. It is an algorithm created to produce a direct quantification of the complex biotic songs by computing the variability of the intensities registered in audio-recordings, despite the presence of constant human-generated-noise. Twenty audio-recordings were made at equally spaced locations in a beech mountain forest in the Tuscan-Emilian Apennine National Park (Italy) between June and July 2008. The study area is characterized by the absence of recent human disturbance to forest assets but the presence of airplane routes does bring engine noise that overlaps and mixes with the natural soundscape, which resulted entirely composed by bird songs. The intensity values and frequency bin occurrences of soundscapes, the total number of bird vocalizations and the ACI were processed by using the Songscope v2.1 and Avisoft v4.40 software. The Spearman's rho calculation highlighted a significant correlation between the ACI values and the number of bird vocalizations, while the frequency bin occurrence and acoustic intensity were weaker correlated to bird singing activity because of the inclusion of all of the other geo/anthro-phonies composing the soundscape. The ACI tends to be efficient in filtering out anthrophonies (such as airplane engine noise), and demonstrates the capacity to synthetically and efficiently describe the complexity of bird soundscapes. Finally, this index offers new opportunities for the monitoring of songbird communities faced with the challenge of human-induced disturbances and other proxies like climate and land use changes.