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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. [ABSTRACT FROM AUTHOR]

Freshwater turtles exhibit temporary threshold shifts (TTS) when exposed to broadband sound, but whether frequency-restricted narrowband noise induces TTS was unknown. Underwater TTS was investigated in two freshwater turtle species (Emydidae) following exposures to 1 6 -octave narrowband noise (155–172 dB re 1 μPa2 s). While shifts occurred in all turtles at the noise center frequency (400 Hz), there were more instances of TTS and greater shift magnitudes at 1 2 octave above the center frequency, despite considerably lower received levels. These frequency-specific data provide new insight into how TTS manifests in turtles and expand empirical models to predict freshwater turtle TTS. [ABSTRACT FROM AUTHOR]

Coral reef ecosystems are now commonly affected by major climate and disease disturbances. Disturbance impacts are typically recorded using reef benthic cover, but this may be less reflective of other ecosystem processes. To explore the potential for reef water‐based disturbance indicators, we conducted a 7‐year time series on US Virgin Island reefs where we examined benthic cover and reef water nutrients and microorganisms from 2016 to 2022, which included two major disturbances: hurricanes Irma and Maria in 2017 and the stony coral tissue loss disease outbreak starting in 2020. The disease outbreak coincided with the largest changes in the benthic habitat, with increases in the percent cover of turf algae and Ramicrusta, an invasive alga. While sampling timepoint contributed most to changes in reef water nutrient composition and microbial community beta diversity, both disturbances led to increases in ammonium concentration, a mechanism likely contributing to observed microbial community shifts. We identified 10 microbial taxa that were sensitive and predictive of increasing ammonium concentration. This included the decline of the oligotrophic and photoautotrophic Prochlorococcus and the enrichment of heterotrophic taxa. As disturbances impact reefs, the changing nutrient and microbial regimes may foster a type of microbialization, a process that hastens reef degradation. [ABSTRACT FROM AUTHOR]