A new methodology to infer the singing activity of an avian community: The Acoustic Complexity Index (ACI)
Introduction
Animals acoustic cues are influenced by the physical and energetic context (see f. i. Richards and Wiley, 1980) in which organisms live and, consequently, probably reflect these circumstances. The possibility to measure the sounds produced by animals represents the obligatory route to approach the ecological dynamics of this informative structure (Derryberry, 2009, Farina et al., accepted for publication).
New tools for monitoring natural systems have been recently provided by technological advances such as the opportunity to automatically record sounds from animal communities (f. i. birds: Celis-Murillo et al., 2009, Haselmayer and Quinn, 2000, Rempel et al., 2005, Scott et al., 2005; mammals: Gedamke and Robinson, 2010; amphibia: Meek, 2010). Audio-recordings have many benefits, for instance the irrelevant disturbances caused by the operators during field surveys and the opportunity to remotely listen the recorded sounds and post-process the collected acoustic information. The approach can also minimize observer errors by using a single interpreter, thus providing a potentially permanent record of surveys and solving the logistical problems that are often encountered in field studies, including the limited availability of expert ornithologists (Hobson et al., 2002).
Audio recordings are the basis of acoustic ecology, a recently developed ecological field of research that focuses on the relationship between the sounds of the environment (the soundscape) and the listener. The soundscape, defined as any acoustic environment, whether natural, urban, or rural, can be composed of three fundamental elements: the biophony (non-human biological sounds such as the vocalizations of birds, amphibians and other animals), the geophony (physical features of the environment such as the wind blowing through a forest or the burbling of water in a stream), and the anthrophony (human-induced noise from whatever source) (Krause et al., 2003, Pijanowsky et al., accepted for publication).
According to Schafer (1977), who was its initial promoter, the soundscape approach suggests that we should try to hear the natural acoustic environment as a musical symphony and, further, that we own responsibility for its composition and preservation from its bigger enemy: the pollution produced by noise. In recent years in particular, the increase in human mechanical noise (the anthrophony) has tended to mask the fine textures of the natural acoustic environment, forcing vocal creatures to displace or activate unusual adaptations (Dooling and Popper, 2007, Nemeth and Brumm, 2009, Rheindt, 2003, Slabbekoorn and Peet, 2003).
Nowadays, the study of the animal soundscape represents a field of growing interest because of the implications it has for the assessment of human–landscape interactions. Unfortunately, it continues to be a difficult subject to investigate, due to the wide variety of information available in each acoustic environment, and the difficulty that there is in identifying indices with which to quickly interpret the wide range of audio-registration data. Indeed, besides all the new technologies and software developed in recent years, the analysis of the natural sounds often remains a time-consuming process and this reduces the possibility to extract useful ecological data from this widely rich informative context. Several authors have successfully produced techniques based on bio-acoustic cues of a single species (e.g. Bardeli et al., 2010, Frommolt et al., 2008, Klinck et al., 2008, Wolf, 2009), while indices have been rarely calibrated for the monitoring of entire animals communities (e.g. Sueur et al., 2008a, Sueur et al., 2008b).
In particular, an attempt to quickly measure the singing behaviour of a bird community was made by Farina and Morri (2008) who elaborated an index to rapidly quantify the typical complexity of the biotic songs of a soundscape, despite the presence of constant human-generated-noise. The aim of the present work is to further test this methodology by using the index, which we describe in this paper with the acronym ACI (Acoustic Complexity Index).
Section snippets
Acoustic Complexity Index (ACI)
The Acoustic Complexity Index, elaborated by Farina and Morri (2008), was created in order to produce a direct and quick quantification of the birds vocalizations by processing the intensities registered in audio-files.
The hypothesis on which the ACI formula is based lays on the observation that many biotic sounds, such as bird songs, are characterized by an intrinsic variability of intensities, while some types of human generated noise (such as car passing or airplane transit) present very
Acoustic environment at the study area
The soundscape of all of the 20 stations was dominated by bird song (biophony) and the anthropogenic noise from the airplane flight paths which crossed the study area (anthrophony). On average, 14 airplane transits for every 2 h of recording were registered. Fig. 3 sets out a spectrogram of an audio scene polluted by this kind of anthropogenic noise. Other typical audio-scenes are provided as Supplementary Content, together with their spectrogram representation (Wavesurfer v.1.8.5 Sjölander and
Discussion and conclusions
Human activities are often the cause of dramatic changes to populations of wild animals, and we therefore need basic information about the extent of this impact if we are to make decisions about the most efficient ways of ensuring their conservation. Although methods and techniques are still a work in progress, the analysis of biotic sounds and their interrelations could produce new interpretations of community coalescence mechanisms and represent a new tool with which to monitor the complexity
Acknowledgements
We are extremely grateful to Prof. Catia Grimani for her assistance with the mathematics’ terminology of the index and her encouragement. We also thank the Editor F. Muller and two anonymous referees for helpful comments on the manuscript which were much appreciated.
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