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Amidst rapidly changing ocean soundscapes, research is still unraveling how marine animals use sound to communicate, detect predators, seek prey, and find suitable habitat. These vital behaviors may also be impacted by anthropogenic noise. Here, we describe a new tool, a Reef Acoustic Playback System, or RAPS, designed to be a cost-effective, extended-duration device that allows researchers to remotely and replay sound cues, manipulate soundscapes, and introduce “noise” into field-based experiments to address key questions regarding sound use or noise impacts within ocean ecology and conservation. The RAPS, outlined herein, has been deployed in the field for days to weeks, powered by renewable solar energy. The tool has been proven to be flexible in applications and robust to a range of ocean conditions. We outline the tool and describe several use cases, including use of the RAPS to replay healthy soundscapes to enhance the settlement of coral larvae, a fundamental ecological process sustaining coral reefs. Fundamentally, the RAPS is a new, potentially scalable means of supporting both healthy and imperiled reefs undergoing restoration, enhancing settlement of reef larvae, and broadening our ability to conduct a range of acoustic behavior studies. 

Acoustic enrichment can facilitate coral and fish larval settlement, offering a promising method to rebuild degraded reefs. Yet it is critical to understand sound propagation in complex shallow-water coral reefs to effectively apply this method over large restoration-scale areas. In this field-based study, we quantified propagation features of multiple sound types emitted through a custom playback system over varying coral reef habitat. Sound levels were computed at different distances from the source in both pressure and particle motion, the latter being detected by marine invertebrates. Detection distances were primarily determined by source levels, and depth-dependent transmission losses. Transmission losses and detection distances were similar for sound pressure and particle acceleration measurements. Importantly, broadband particle acceleration levels could be closely estimated at distances >10 m using a single hydrophone and a plane wave approximation. Using empirically determined coral larvae sound detection thresholds, we found that low frequency sounds (<1 kHz) such as fish calls from healthy coral reef soundscapes may be detectable by larvae hundreds of meters away. These results provide key data to help design standardized methods and protocols for scientists, managers and restoration practitioners aiming to rebuild coral reef ecosystems over reasonably large spatial scales using acoustic enrichment. 

The quantity of passive acoustic data collected in marine environments is rapidly expanding; however, the software developments required to meaningfully process large volumes of soundscape data have lagged behind. A significant bottleneck in the analysis of biological patterns in soundscape datasets is the human effort required to identify and annotate individual acoustic events, such as diverse and abundant fish sounds. This paper addresses this problem by training a YOLOv5 convolutional neural network (CNN) to automate the detection of tonal and pulsed fish calls in spectrogram data from five tropical coral reefs in the U.S. Virgin Islands, building from over 22 h of annotated data with 55 015 fish calls. The network identified fish calls with a mean average precision of up to 0.633, while processing data over 25× faster than it is recorded. We compare the CNN to human annotators on five datasets, including three used for training and two untrained reefs. CNN-detected call rates reflected baseline reef fish and coral cover observations; and both expected biological (e.g., crepuscular choruses) and novel call patterns were identified. Given the importance of reef-fish communities, their bioacoustic patterns, and the impending biodiversity crisis, these results provide a vital and scalable means to assess reef community health. 

Coral reefs are biodiverse marine ecosystems that are undergoing rapid changes, making monitoring vital as we seek to manage and mitigate stressors. Healthy reef soundscapes are rich with sounds, enabling passive acoustic recording and soundscape analyses to emerge as cost-effective, long-term methods for monitoring reef communities. Yet most biological reef sounds have not been identified or described, limiting the effectiveness of acoustic monitoring for diversity assessments. Machine learning offers a solution to scale such analyses but has yet to be successfully applied to characterize the diversity of reef fish sounds. Here we sought to characterize and categorize coral reef fish sounds using unsupervised machine learning methods. Pulsed fish and invertebrate sounds from 480 min of data sampled across 10 days over a 2-month period on a US Virgin Islands reef were manually identified and extracted, then grouped into acoustically similar clusters using unsupervised clustering based on acoustic features. The defining characteristics of these clusters were described and compared to determine the extent of acoustic diversity detected on these reefs. Approximately 55 distinct calls were identified, ranging in centroid frequency from 50 Hz to 1,300 Hz. Within this range, two main sub-bands containing multiple signal types were identified from 100 Hz to 400 Hz and 300 Hz–700 Hz, with a variety of signals outside these two main bands. These methods may be used to seek out acoustic diversity across additional marine habitats. The signals described here, though taken from a limited dataset, speak to the diversity of sounds produced on coral reefs and suggest that there might be more acoustic niche differentiation within soniferous fish communities than has been previously recognized. 

Resources for passive acoustic monitoring (PAM) are continuously expanding and being developed, yet a major challenge for users is staying up-to-date and finding the best software or application for their acoustics investigation. We expand on previous efforts (Rhinehart & Nicholson, 2022; Felgate, 2023) with the aim of providing a current, comprehensive list of 1) underwater sound repositories of raw sound data without significant processing, 2) biological sound reference libraries, with species or taxa identification, and 3) sound processing tools for visualization, annotation, or analysis. This spreadsheet contains three pages, one dedicated to each of the aforementioned items, along with some descriptive information to help users identify the best resources for their needs. This work was done to support the Global Library of Underwater Biological Sounds (GLUBS) project and funded in part by the Richard Lounsbery Foundation and from funding to SCOR WG #169 (GLUBS) provided by national committees of the Scientific Committee on Oceanic Research (SCOR) and from a grant to SCOR from the US National Science Foundation (OCE--2140395), with support from the International Quiet Ocean Experiment.