Elsevier

Ecological Informatics

Volume 6, Issue 6, November 2011, Pages 354-363
Ecological Informatics

The soundscape methodology for long-term bird monitoring: A Mediterranean Europe case-study

https://doi.org/10.1016/j.ecoinf.2011.07.004Get rights and content

Abstract

The soundscape represents the acoustic footprint of a landscape, and may well be a source of a vast amount of information that could be used efficiently in, for example, long-term bird aggregation monitoring schemes. To depict such soundscape footprint, specific indexes are requested. In particular, the aim of this paper was to extensively describe the Acoustic Complexity Index (ACI) and to successively apply it to process the sound files recorded in an ecologically fragile area in a Mediterranean maqui (Eastern Liguria, Italy). Daily acoustic animal activity was sampled in 90 one-minute files between the end of May and the end of July, 2010, using a pre-programmed recording procedure (Songmeter, Wildlife Acoustic). The WaveSurfer software, powered by the Soundscape Metric plug-in, was then utilized to quickly process these data.

This approach allows the identification of the compositional changes and acoustic fluctuations activity of a local community (in the proposed case prevalently composed by birds and cicadas). In particular, two distinct patterns emerged during the investigation. From 20 May to 4 July, the soundscape was dominated by birds but, after that period, the onset of the cicadas' songs completely changed the sound dynamics. The proposed methodology has been demonstrated to be a powerful tool to identify the complex patterns of the soundscape across different temporal scales (hours, days and intraseason). This approach could also be adopted in long-term studies to monitor animal dynamics under different environmental scenarios.

Highlights

► Daily and seasonal patterns of the bird soundscape in a Mediterranean maqui is investigated from May to July 2010. ► The Acoustic Complexity Index (ACI) is applied on a WaveSurfer platform, using an appositively developed plug-in. ► The onset of cicada songs in July extensively masks bird acoustic behavior. ► This methodology allows to quickly track soundscape fluctuations both in frequency and time. ► ACI is an efficient tool for animal long-term monitoring.

Introduction

Over recent decades, the growing human intrusion into the Earth's ecosystems has led to the massive destruction and fragmentation of natural habitats (Vitousek et al., 1997). This change, along with an alteration of climatic dynamics, has accelerated the extinction of several species (Chapin et al., 2000) and caused the endangerment of many ecological processes (Fearn and Redford, 2008, Wilcove et al., 1998).

The complexity and increasing fragility of the linked human and natural system require new types of investigation if we are to be able to face the challenge of environmental surprises and take into account legacy effects (Liu et al., 2007).

Despite a tremendous effort to investigate the causes and effects of this unprecedented impact, especially when it comes to maintaining the well-being of man (MEA (Millennium Ecosystem Assessment), 2005a, MEA (Millennium Ecosystem Assessment), 2005b) and associated ecosystem services (Mooney et al., 2009), some processes, like the disruption of the communication systems between organisms (Carson, 1962, Harris-Jones, 2009), or chronic anthropogenic noise exposure (Barber et al., 2009), continue to be poorly understood.

For this reason, given the global threats that we face today, the flux of information in the communication network should be the primary component of environmental complexity to be investigated and monitored.

Of the ecological disciplines in existence, one of the more recent approaches to deal with this challenge seems to be Acoustic Ecology (Pijanowski et al., 2011). The focus of this recent area of research is the study of the soundscape, which is defined as the product of the relationships between the sounds of the environment and the listener (Schafer, 1977). The soundscape is thus an important epistemological tool, because it reflects both the physical and informative properties of environmental acoustic cues and the communication mechanisms of organisms and different species (Krause, 1987, Krause, 2002, Kull, 2010, Schafer, 1977, Schafer, 1994).

Such studies are now possible thanks to technological advances in the quality and efficiency of recording devices and the availability of new metrics and computation tools which can be applied to sound analysis (Pieretti et al., 2011, Pijanowski et al., in press, Qi et al., 2008, Sueur et al., 2008, Villanueva-Rivera et al., in press).

At the present time, the application of soundscape analysis could enable us to efficiently investigate the dynamics of animal behavior, particularly when habitats are modified, fragmented, or destroyed. Birds are good bioindicators of such changes (f.i. Furness et al., 1993, Hill, 1995), and many studies have indeed focused on the monitoring of bird species' richness and distribution in an attempt to highlight differences in environmental health (Andren, 1994, MacArthur et al., 1962). The reason for this is the intrinsic characteristics of birds: they are distributed over a wide range of landscapes; their presence is an indicator of the state of the structural complexity of the vegetation (Bradbury et al., 2005, MacArthur and MacArthur, 1961); and, finally, they are easy to detect in comparison to other animal groups (Furness et al., 1993). In addition, we have acquired a good knowledge of the biology of most bird species over the years, meaning that the results obtained from monitoring them are both meaningful and able to be more easily interpreted (Bardeli et al., 2010).

The aim of this contribution is to further illustrate a recently developed metric, the Acoustic Complexity Index (ACI), (Pieretti et al., 2011), in order to investigate the avian soundscape as an indicator of the informative and communication properties of a bird community. This is possible because this new tool may quickly highlight changes in behavior and the composition of a community, both in time and space.

The effective suitability of this new form of soundscape analysis for avian monitoring, a detailed explanation of the different, potential approaches, and the possibility of its applicability to a long-term avian monitoring scheme in fragile areas are all extensively discussed in this paper.

Section snippets

Study area

The study area, which is located on a westerly exposed gentle slope at 250 masl along the coastal range of the Eastern Liguria Region (Italy) (44°13′30″ N, 9°30′23″ E), is now covered by a dense, luxuriant Mediterranean maqui. Until a complete abandonment in the 1950s, it was previously intensively cultivated, as demonstrated by the remnants of terraces that are still visible in some parts.

There are no paved roads in the site, and urban development seems to be a very remote prospect, but the

Acoustic Complexity Index (ACI) metrics

The Acoustic Complexity Index (ACI) (Farina and Morri, 2008, Pieretti et al., 2011) has been designed to measure the complexity of sound spectrograms obtained from a linear scale of sound intensity and, specifically, to analyze the bird soundscape.

The ACI measures the absolute difference between two adjacent values of intensity, Ik  Ik + 1, where k is the kth position in the intensity values recorded along a single frequency bin (i) and in a single temporal subset (j) on the original matrix

Results

The ACI(loc)d results revealed that the bird community has two main acoustic peaks, one on about 30 May and a second on 16 June. Thereafter, a general decrease in the ACI values was noted, and after July 9 the cicadas' songs dominated the soundscape (Fig. 5).

In order to distinguish bird song activity from that of the cicadas, we split the ACI(loc)h into two separate periods (May 20–July 8, July 9–26). The first portrays a typical bird community shape, with the majority of activity being early

Some comments on the methods

On the basis of the stated aims of this contribution, we applied the ACI metric, showing the potentialities, and the manipulations of the data according to the different temporal frames (hours, days and intra-seasons) and the long, different groups of frequency bands. This approach enables us to investigate behavior and community complexity and to obtain answers to some questions relating to the multifaceted way in which birds interact acoustically.

The choice of recording sessions of 1 min every

Conclusions

Until recently, the processing of sound files frequently required a long computation time, discouraging their extensive use in monitoring schemes. Today, however, the SoundscapeMeter plug-in has proved to be a tool that is able to process a large amount of sound data in a very short period of time; for instance, a one minute sound file is processed in 5 s. This development enables data to be processed in real time, immediately after its collection from the field. Accordingly, it is in this

Acknowledgments

We thank Emilio Padoa-Schioppa, Bryan Pijanowksi, Jerome Sueur and Haven Wiley for helpful comments on the manuscript.

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