Passive acoustic monitoring (PAM, i.e., the remote detection of underwater sounds) provides a unique data stream for evaluating the distribution, abundance, spatiotemporal patterns, and behaviours of numerous soniferous (i.e., sound-producing) taxa (Parsons et al., 2022). Sounds may be actively produced for communication (e.g., the calls of marine mammals or fishes) or passively produced as the result of other activities (e.g., the grinding of teeth while eating or the digging of nests for reproduction; Looby et al., 2022). Whether detected as individual sounds or mass phenomena (e.g., fish choruses), acoustic signals provide valuable information about sound-producers, their associated populations, and evolutionary histories (e.g., Rice et al., 2022; Almagro and Barría, 2024). Biological sounds may also serve as proxies for biodiversity and biotic-abiotic interactions—even if the sound sources are yet to be identified (Desiderà et al., 2019; Hawkins et al., 2025).

Despite the growing recognition of the importance and utility of underwater sounds, many soniferous species and their sounds remain to be identified. Only 4% of fish species and even fewer underwater invertebrates and plants have been tested for sound production (Looby et al., 2022; Looby et al., 2023; Desjonquères et al., 2024; Lee et al., 2025). New reports of sonifery for previously untested species are being documented every year (e.g., Almagro and Barría, 2024), aided in part by new technological advances (e.g., Mouy et al., 2023; Dantzker et al., 2025), but the rate of discovery remains woefully inadequate to document the many thousands of soniferous species thought to exist (Desjonquères et al., 2024; Looby et al., 2022; Looby et al., 2023). Similarly, even for relatively well-documented sounds, their sources may take years to identify (e.g., Bolgan et al., 2019; Allen et al., 2024). Consequently, except for marine mammals, the majority of communicative sounds recorded in the biological component of a soundscape remain of unknown origin. This is particularly true in coastal biodiversity-hotspots such as coral reefs and seagrass meadows dominated by sounds from invertebrates and fishes (Tricas and Boyle, 2014; Di Iorio et al., 2021; Bolgan et al., 2022), as well as in understudied deep-sea ecosystems (Lin and Kawagucci, 2024).

While research and monitoring applications can be maximized if sound sources are identified to species, even sounds from these unknown sources can still provide ecologically relevant information. Many fishes, for instance, produce a variety of sounds that are components of acoustic communities whose diversity and richness are related to taxonomic richness and diversity (Desiderà et al., 2019). These acoustic communities of primarily unknown sounds are also habitat-specific, reflect habitat quality, and respond to management actions (Di Iorio et al., 2021; La Manna et al., 2021; Raick et al., 2023). Changes in the composition of these acoustic communities likely reflect changes in behaviours or underlying assemblages related to environmental variations or human impact. As unidentified sounds present unique challenges, however, they are not commonly prioritized and are often underreported—serving as an elusive yet valuable source of ecological and evolutionary knowledge. As the identification of new unknown sounds is constantly increasing—at a much higher pace than the identification of their sources—their description and characterization is critical to establish reference catalogues as well as adequate detection and classification algorithms to capture regime shift and biodiversity dynamics that may have implications for ecological processes and ecosystems.

The Research Topic presented here provides an exploration of sound types, patterns, detection advances, and pitfalls, encompassing various habitats, regions, and taxonomic groups. The articles are centered around four primary themes: advancements in automated detection and classification; global baselines and regional catalogues; spatiotemporal and behavioral dynamics; and acoustic indicators of biodiversity and habitat health. Collectively, these papers highlight the value of studying unidentified underwater biological sounds as well as important developments in their documentation.

Collectively, these contributions present conceptual and methodological shifts in the effort to detect, categorise, and document unidentified underwater biological sounds. They highlight the diversity of research topics and identify gaps. For example, a major common thread was the integration of advanced automated detection and classification tools to bridge the gap between massive acoustic datasets and human analytical capacity. Compared to computationally simpler acoustic indices (e.g., Acoustic Complexity Index) that may have inconsistent results, the analysis of individual signals and their patterns is increasingly providing an alternative and potentially complementary pathway for deriving ecological insights, offering a promising direction for sound diversity assessment. Many analytical approaches—such as active learning and unsupervised clustering—are emerging to reduce the ‘bottleneck’ of manual annotation. The transition from ‘hand-crafted’ features to deep learning embeddings (e.g., convolutional neural networks) represents another foundational methodological advance. However, to more effectively move beyond detection and towards exploring relationships between acoustic diversity and ecology, manual processing and artificial intelligence tools should work ‘hand in code’, which would be aided through the establishment of global baselines and regional catalogues. The diversity of unidentified sound types documented in the compiled contributions underscores the necessity for comprehensive, shared catalogues.

The diversity of unidentified sounds also reflects the wealth of biological and ecological information that can be derived from biophonies. For example, analyzing spatiotemporal and behavioral dynamics of biological sounds enables researchers to estimate and predict animal abundance and distributions, track the impacts of anthropogenic and environmental changes in the future, and determine the biological or ecological context in which sounds are produced. Evaluating biophonic diversity, comparing sound types across bioregions, and examining the composition of acoustic spaces can yield valuable insights into habitats that are otherwise challenging to study—such as mesophotic reefs—as well as into biogeographical patterns, interspecies interactions within animal communities, and relationships between acoustic communities and their habitats. Integrating such information into analyses may lead to more consistent acoustic indicators of biodiversity and health and more robust interpretation of their ecological relevance, a critical factor if these indicators are to be used for environmental monitoring and management.

Despite these advances, several significant gaps remain. The world’s waters are vast and overwhelmingly acoustically unexplored, and the application of PAM in the aquatic environment is expanding rapidly across the globe. In terms of unidentified sounds, PAM remains in a time of discovery. New sounds are being uncovered far more quickly than sources are being identified, and the opportunities for natural observations to glean ecological meaning of each sound are much smaller than the volumes of data being remotely collected. Thus, the primary challenge continues to be the lack of ‘ground-truthing’ to link specific sounds to their biological sources and associated functions. Some sounds, however, may remain difficult to assign to species based on their acoustic characteristics. Even if sources remain obscure, evaluating the ecological relevance of sound types, signal rates, and the effect of environmental and anthropogenic context on the detected signals remains vital to the generalized application of any emerging artificial intelligence techniques and is unlikely to be achieved with artificial intelligence methods alone. Without identification or ecoacoustic contexts, results may remain open to misinterpretation. Additionally, the need for a unifying Global Library of Underwater Biological Sounds (GLUBS; Parsons et al., 2022) remains paramount for future progress. Such a library would allow comparisons between recorded sound types and species-specific or unknown sounds, as well as the establishment of links between sound types across the globe, identifying shared or specific sound types.

The ability to describe spatiotemporal changes in biodiversity, detect regime shifts, and assess ecosystem condition, all by simply listening under water—even when the individual callers remain anonymous—is a vital step for non-invasive monitoring, management, and conservation. Documenting unidentified calls, establishing inventories of biological sounds, and developing generalizable machine learning models contained within this Research Topic provide insights into the emerging tools and baselines that advance our ability to monitor aquatic ecosystems during an unprecedented global biodiversity crisis. As we move forward, the continued collaboration and open sharing of data through repositories and global libraries will be the cornerstone of our ability to listen to, and ultimately protect, the symphony of life beneath the waves.