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Ecoacoustics is a recent ecological discipline focusing on the ecological role of sounds. Sounds from the geophysical, biological, and anthropic environment represent important cues used by animals to navigate, communicate, and transform unknown environments in well-known habitats. Sounds are utilized to evaluate relevant ecological parameters adopted as proxies for biodiversity, environmental health, and human wellbeing assessment due to the availability of autonomous audio recorders and of quantitative metrics. Ecoacoustics is an important ecological tool to establish an innovative biosemiotic narrative to ensure a strategic connection between nature and humanity, to help in-situ field and remote-sensing surveys, and to develop long-term monitoring programs. Acoustic entropy, acoustic richness, acoustic dissimilarity index, acoustic complexity indices (ACItf and ACIft and their evenness), normalized difference soundscape index, ecoacoustic event detection and identification routine, and their fractal structure are some of the most popular indices successfully applied in ecoacoustics. Ecoacoustics offers great opportunities to investigate ecological complexity across a full range of operational scales (from individual species to landscapes), but requires an implementation of its foundations and of quantitative metrics to ameliorate its competency on physical, biological, and anthropic sonic contexts.

The rise of passive acoustic monitoring and the rapid growth in large audio datasets is driving the development of analysis methods that allow ecological inferences to be drawn from acoustic data. Acoustic indices are currently one of the most widely applied tools in ecoacoustics. These numerical summaries of the sound energy contained in digital audio recordings are relatively straightforward and fast to calculate but can be challenging to interpret. Misapplication and misinterpretation have produced conflicting results and led some to question their value. To encourage better use of acoustic indices, we provide nine points of guidance to support good study design, analysis and interpretation. We offer practical recommendations for the use of acoustic indices in the study of both whole soundscapes and individual taxa and species, and point to emerging trends in ecoacoustic analysis. In particular, we highlight the critical importance of understanding the links between soundscape patterns and acoustic indices. Acoustic indices can offer insights into the state of organisms, populations, and ecosystems, complementing other ecological research techniques. Judicious selection, appropriate application and thorough interpretation of existing indices is vital to bolster robust developments in ecoacoustics for biodiversity monitoring, conservation and future research.

Current ecoacoustic analysis methods are unsuitable for exploring unknown sound‐types in large acoustic datasets. Ecoacoustic studies can collect considerable quantities of audio with minimal field effort; however, analysing these recordings effectively remains a challenge. Manual annotation is labour‐intensive, acoustic indices only summarise soundscape patterns and machine learning tools like BirdNet enable species‐level identification but are not optimised for non‐terrestrial taxa and cannot explore unknown sound‐types. This creates a clear need for exploratory methods that can efficiently identify unknown sound‐types, particularly in data‐deficient environments. We present a protocol to identify sound‐types in ecoacoustic recordings using beta acoustic indices and nested clustering (a multi‐level method where clusters contain sub‐clusters). Compared to existing methods, our protocol offers a more adaptable framework for identifying sound‐types in unsurveyed or data‐poor environments. It is suitable for large acoustic datasets and does not require advanced computational skills. To our knowledge, this is the first protocol to combine beta acoustic indices with nested clustering to identify sound‐types in ecoacoustic data. Limited evidence exists on how window length (WL) influences beta index calculations, so we tested 11 indices against six WLs and multiple cluster quantities. Our nested clustering approach addressed challenges including background noise, acoustic overlap and data imbalances (e.g. unequal quantities of reoccurring sound‐types). We tested our protocol in a stream soundscape as freshwater ecosystems are underexplored, lack a global underwater sound database and contain many unidentified sounds. We used a systematic testing framework to identify optimal combinations of beta indices and WL for sound‐type clustering. For the studied system, the Kolmogorov–Smirnov index with a 2048 WL produced the highest‐performing results. This combination scored ≥0.75 (normalised, 0–1) on all external validation metrics, a true positive rate of over 90% and identified almost 90% of sound‐types. This work presents a streamlined approach for identifying unknown sound events in large audio datasets using minimal manual effort and demonstrates a novel use‐case for beta indices. We anticipate this method will inspire new applications of beta indices and user‐friendly analysis tools for big data, advancing ecoacoustic analyses alongside technological advancements.

Ecoacoustics, the study of environmental sound, is a rapidly growing discipline offering ecological insights at scales ranging from individual organisms to whole ecosystems. Substantial methodological developments over the last 15 years have streamlined extraction of ecological information from audio recordings. One widely used set of methods are acoustic indices, which offer numerical summaries of the spectral, temporal and amplitude patterns in audio recordings. Currently, the specifics of each index's background, methodology and the soundscape patterns they are designed to summarise, are spread across multiple sources. Critically, details of index calculation are sometimes scarce, making it challenging for users to understand how index values are generated. Discrepancies in understanding can lead to misuse of acoustic indices or reporting of spurious results. This hinders ecological inference, replicability and discourages adoption of these tools for conservation and ecosystem monitoring, where they might otherwise provide useful insight. Here we present the Acoustic Index User's Guide—an interactive RShiny web app that defines and deconstructs eight of the most commonly used acoustic indices to facilitate consistent application across the discipline. We break the acoustic indices calculations down into easy‐to‐follow steps to better enable practical application and critical interpretation of acoustic indices. We demonstrate typical soundscape patterns using a suite of 91 example audio recordings: 66 real‐world soundscapes from terrestrial, aquatic and subterranean systems around the world, and 25 synthetic files demonstrating archetypal soundscape patterns. Our interpretation figures signpost specific soundscape patterns likely to be reflected in acoustic indices' values. This RShiny app is a living resource; additional acoustic indices will be added in the future through collaboration with authors of pre‐existing and new indices. The app also serves as a best‐practice template for the information required when publishing new acoustic indices, so that authors can facilitate the widest possible understanding and uptake of their indices. In turn, improved understanding of acoustic indices will aid effective hypothesis generation, application and interpretation in ecological research, ecosystem monitoring and conservation management.

Acoustic approaches have been recently proposed to investigate critical ecological issues, such as biodiversity loss and different typologies of impacts, including climate change. However, the extensive use of acoustic monitoring is hampered by the lack of algorithms enabling the discrimination among different sound sources (e.g. geophysical, anthropogenic and biological). Eco- and bioacoustic indexes have been applied to provide non-invasive information on the temporal and spatial patterns of marine biodiversity and on the anthropogenic impact on marine life. Here, we review the potential of acoustic tools in expanding the monitoring of marine ecosystems from a current three-dimensional perception to a four-dimensional dimension. We also explore the use of acoustic indexes, mostly developed in terrestrial ecology, for the investigation of different marine ecosystems. Their appraisal, strengths and limits, and potential for future investigations in the biological exploration of the oceans are also discussed. This article is part of the theme issue ‘Integrative research perspectives on marine conservation’.

Knowledge that can be gained from acoustic data collection in tropical ecosystems is low-hanging fruit. There is every reason to record and with every day, there are fewer excuses not to do it. In recent years, the cost of acoustic recorders has decreased substantially (some can be purchased for under US$50, e.g., Hill et al. 2018) and the technology needed to store and analyze acoustic data is continuously improving (e.g., Corrada Bravo et al. 2017, Xie et al. 2017). Soundscape recordings provide a permanent record of a site at a given time and contain a wealth of invaluable and irreplaceable information. Although challenges remain, failure to collect acoustic data now in tropical ecosystems would represent a failure to future generations of tropical researchers and the citizens that benefit from ecological research. In this commentary, we (1) argue for the need to increase acoustic monitoring in tropical systems; (2) describe the types of research questions and conservation issues that can be addressed with passive acoustic monitoring (PAM) using both shortand long-term data in terrestrial and freshwater habitats; and (3) present an initial plan for establishing a global repository of tropical recordings.

Biodiversity monitoring has entered an era of ‘big data’, exemplified by a near-continuous collection of sounds, images, chemical and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analysing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underlying, cyclic behaviour of the soundscape for each site, we show how one can identify circadian patterns in acoustic activity. Soundscapes in the dry season had a complex diel cycle, requiring multiple harmonics to represent daily variation, while in the wet season there was less variance attributable to the daily cyclic patterns. Our framework can be applied to most continuous, or near-continuous ecological data collected at a fine temporal resolution, allowing ecologists to explore patterns of temporal autocorrelation at multiple levels for biologically meaningful trends. Such methods will become indispensable as biological big data are used to understand the impact of anthropogenic pressures on biodiversity and to inform efforts to mitigate them.

Sound classification plays a crucial role in enhancing the interpretation, analysis, and use of acoustic data, leading to a wide range of practical applications, of which environmental sound analysis is one of the most important. In this paper, we explore the representation of audio data as graphs in the context of sound classification. We propose a methodology that leverages pre-trained audio models to extract deep features from audio files, which are then employed as node information to build graphs. Subsequently, we train various graph neural networks (GNNs), specifically graph convolutional networks (GCNs), GraphSAGE, and graph attention networks (GATs), to solve multi-class audio classification problems. Our findings underscore the effectiveness of employing graphs to represent audio data. Moreover, they highlight the competitive performance of GNNs in sound classification endeavors, with the GAT model emerging as the top performer, achieving a mean accuracy of 83% in classifying environmental sounds and 91% in identifying the land cover of a site based on its audio recording. In conclusion, this study provides novel insights into the potential of graph representation learning techniques for analyzing audio data.

The sounds produced by animals have been a topic of research into animal behaviour for a very long time. If acoustic signals are undoubtedly a vehicle for exchanging information between individuals, environmental sounds embed as well a significant level of data related to the ecology of populations, communities and landscapes. The consideration of environmental sounds for ecological investigations opens up a field of research that we define with the term ecoacoustics . In this paper, we draw the contours of ecoacoustics by detailing: the main theories, concepts and methods used in ecoacoustic research, and the numerous outcomes that can be expected from the ecological approach to sound. Ecoacoustics has several theoretical and practical challenges, but we firmly believe that this new approach to investigating ecological processes will generate abundant and exciting research programs.

Biosemiotics to date has focused on the exchange of signals between organisms, in line with bioacoustics; consideration of the wider acoustic environment as a semiotic medium is under-developed. The nascent discipline of ecoacoustics, that investigates the role of environmental sound in ecological processes and dynamics, fills this gap. In this paper we introduce key ecoacoustic terminology and concepts in order to highlight the value of ecoacoustics as a discipline in which to conceptualise and study intra- and interspecies semiosis. We stress the inherently subjective nature of all sensory scapes (vivo-, land-, vibro- and soundscapes) and propose that they should always bear an organismic attribution. Key terms to describe the sources ( geophony, biophony, anthropophony, technophony ) and scales ( sonotopes, soundtopes, sonotones ) of soundscapes are described. We introduce epithets for soundscapes to point to the degree to which the global environment is implicated in semiosis ( latent, sensed and interpreted soundscapes ); terms for describing key ecological structures and processes ( acoustic community, acoustic habitat, ecoacoustic events ) and examples of ecoacoustic events ( choruses and noise ) are described. The acoustic eco-field is recognized as the semiotic model that enables soniferous species to intercept core resources like food, safety and roosting places. We note that whilst ecoacoustics to date has focused on the critical task of the development of metrics for application in conservation and biodiversity assessment, these can be enriched by advancing conceptual and theoretical foundations. Finally, the mutual value of integrating ecoacoustic and biosemiotics perspectives is considered.