Ecoacoustics: acoustic sensing for biodiversity monitoring at scale

Abstract

Nature sound recordings have been collected for over a hundred years, with an exponential increase since the 1950s (Ranft 2004). Most such recordings were taken in order to describe and decipher animal communication. However, the sounds of animals reveal more than behaviour: they also reflect the structure and functioning of the ecosystem of which the animals are a part. The practice of deploying remote acoustic sensors in natural environments has been systematized under the term 'passive acoustic monitoring' (PAM), a technical term mostly used in marine acoustics but then employed in terrestrial and aquatic acoustics (Gillespie et al. 2009; Marques et al. 2012). Acoustic sensing has distinct advantages which make it complementary to other sampling modalities. Like camera trapping, acoustic sensing can be used on land or under water, in all type of habitats. An acoustic sensor has the advantage that it can capture a wide spatial range (often 360° and about 100 m in terrestrial habitats), and is much less affected by occlusion than imagery. It can also record continuously or regularly over a long time period and can collect information of a full assemblage of species as it captures all the sounds in the surrounding environnment. These properties ensure a high sampling effort with a rather low technical investment (Ciira wa Maina 2016; Hill et al. 2018). In many studies, acoustic data are analysed manually or with simple energy-based detectors, and with the goal of targeted monitoring of a single species (Dawson and Efford 2009; Gillespie et al. 2009; Digby et al. 2013). However, ambient sound recordings such as those obtained with automatic devices contain evidence for a long list of ecological information, such as: species absence/presence, population density, population structure, community structure, landscape architecture, animal phenology, reproduction period, migration period, species interactions or ecosystem functions. Many of these only become evident through large-scale studies, with analysis methods tailored to acoustic data. Benefiting from growth in recent decades of the scale of data capture and processing, the focus in acoustic monitoring can shift to broader ecosystem-level questions, while using audio as a prime source of evidence. This is the main goal of ecoacoustics (Sueur and Farina 2015). Ecoacoustic methods can cover all types of environment from deep sea to tropical forest, and are complementary to other biodiversity monitoring techniques such as camera trapping, LIDAR, satellite-based remote sensing or environmental DNA. Research in ecoacoustic methods has grown massively over the past 15 years, developing methodology in hardware devices, signal processing, machine learning and visualization (see Sugai et al. 2019, this issue, for a review). Particularly important is the move from ecoacoustics as a fundamental to an applied science, with such methods being deployed in practical conservation and ecosystem monitoring (Ciira wa Maina 2016; Gordon et al. 2019; Sertlek et al. 2019; Znidersic et al. 2020). Within the context of the United Nations Sustainable Development Goals (UN SDGs), ecoacoustic methods have already been demonstrated to contribute useful evidence, which can complement other evidence sources. Within SDG 14 'Life below water' and SDG 15 'Life on land', these include monitoring threatened species (Braune et al. 2008; Hill et al. 2018), invasive species (Grant and Grant 2010), poaching (Hill et al. 2018), noise pollution (both on land and below water) (Fairbrass et al. 2018; Sertlek et al. 2019), land degradation and mountain ecosystems (Helbig-Bonitz et al. 2015). Much of the recent technical progress has been at the level of signal analysis and sound classification, in particular the development of acoustic indices on the one hand, and the use of deep learning tools on the other (Stowell et al. 2019; Joly et al. 2019), and from signal processing engineering work on representations and transformations of audio data (Sueur et al. 2014; Phillips et al. 2018). At the sensor level, recent progress is in low-cost innovation (Hill et al. 2018), and the main challenge is now to have connected sensors so that data streams can be integrated continuously (Roch et al. 2017; Sethi et al. 2018) or to have integrated systems being able to run signal analyses and classification directly on board. Large-scale acoustic methods should now be transferred to application, and used more widely for conservation and management. It is now time to use ecoacoustics as a tool. Acoustic sensors should be included in large scale (i.e. national and international) monitoring programmes, in complementary fashion to other standard methods and in particular to design acoustic monitoring into long term programs. As cited above, there are many documented case studies and methodological developments that support this move. We are pleased to introduce this special issue of Remote Sensing in Ecology and Conserva