High rates of biodiversity loss caused by human-induced changes in the environment require new methods for large scale fauna monitoring and data analysis. While ecoacoustic monitoring is increasingly being used and shows promise, analysis and interpretation of the big data produced remains a challenge. Computer-generated acoustic indices potentially provide a biologically meaningful summary of sound, however, temporal autocorrelation, difficulties in statistical analysis of multi-index data and lack of consistency or transferability in different terrestrial environments have hindered the application of those indices in different contexts. To address these issues we investigate the use of time-series motif discovery and random forest classification of multi-indices through two case studies. We use a semi-automated workflow combining time-series motif discovery and random forest classification of multi-index (acoustic complexity, temporal entropy, and events per second) data to categorize sounds in unfiltered recordings according to the main source of sound present (birds, insects, geophony). Our approach showed more than 70% accuracy in label assignment in both datasets. The categories assigned were broad, but we believe this is a great improvement on traditional single index analysis of environmental recordings as we can now give ecological meaning to recordings in a semi-automated way that does not require expert knowledge and manual validation is only necessary for a small subset of the data. Furthermore, temporal autocorrelation, which is largely ignored by researchers, has been effectively eliminated through the time-series motif discovery technique applied here for the first time to ecoacoustic data. We expect that our approach will greatly assist researchers in the future as it will allow large datasets to be rapidly processed and labeled, enabling the screening of recordings for undesired sounds, such as wind, or target biophony (insects and birds) for biodiversity monitoring or bioacoustics research.
Anthropogenic noise is a global pollutant and several studies have identified its impact on wildlife. This research shows how the noise produced by mining affects crickets' acoustic communication. Two passive acoustic monitoring devices (SMII) were installed in a forest fragment located at 500 m from the Brucutu Mine in Brazil. Another two SMII were installed distant 2500 from the mine. The equipment was configured to record from 17:00 to 05:00 h during seven days in April 2013. The authors analyzed the spectral characteristics of acoustic activity of three species of crickets (Anaxipha sp., Gryllus sp., and a Podoscirtinae species) before, during, and after the passing of mine trucks. For comparison the authors analyzed the acoustic characteristics for Anaxipha sp. and Gryllus sp. found in the distant site. Results showed a calling interruption for all the species during truck transit. Gryllus sp. emitted calls with higher maximum frequencies, average power, and larger bandwidth in the site close to the mine. Podoscirtinae species emitted calls with lower minimum frequencies, higher average power, and large bandwidth in the close site. The authors show that insect acoustic behavior varies between areas with different levels of noise. The disruption of this behavior may have negative consequences for their reproductive success.
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