Abstract
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.
Highlights
Biodiversity loss is a global environmental issue (Cardinale et al, 2012), and it is imperative to develop methods to efficiently monitor wildlife, accounting for spatial and temporal coverage (Joppa et al, 2016)
Remote sensing techniques include a range of technologies, like satellite imaging (Bonthoux et al, 2018), camera traps (Fontúrbel et al, 2021), Unmanned Aerial Vehicles (UAVs) (Nowak et al, 2019), and passive acoustic monitoring (PAM) (Froidevaux et al, 2014; Wrege et al, 2017)
Passive acoustic monitoring is routinely used in terrestrial environments to monitor biodiversity (Gibb et al, 2019) with several purposes, such as understanding acoustic community composition of frog choruses (Ulloa et al, 2019), investigating acoustic species diversity of different taxonomic groups (Aide et al, 2017), and bird species recognition based on syllable recognition (Petrusková et al, 2016)
Summary
Biodiversity loss is a global environmental issue (Cardinale et al, 2012), and it is imperative to develop methods to efficiently monitor wildlife, accounting for spatial and temporal coverage (Joppa et al, 2016). Remote sensing techniques are being used to fill this gap, as they can be applied over large geographic areas where access may be difficult, allowing for some degree of unattended monitoring (Kerr and Ostrovsky, 2003). Subsampling is one way of dealing with these constraints, but it can limit the temporal and/or spatial scale of monitoring, methods to analyze and filter recordings are necessary
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