A quantitative inventory of global soniferous fish diversity

Reviews in Fish Biology and Fisheries, Journal Year: 2022, Volume and Issue: 32(2), P. 581 - 595

Published: Feb. 18, 2022

Language: Английский

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Sounding the Call for a Global Library of Underwater Biological Sounds

Frontiers in Ecology and Evolution, Journal Year: 2022, Volume and Issue: 10

Published: Feb. 8, 2022

Aquatic environments encompass the world’s most extensive habitats, rich with sounds produced by a diversity of animals. Passive acoustic monitoring (PAM) is an increasingly accessible remote sensing technology that uses hydrophones to listen underwater world and represents unprecedented, non-invasive method monitor environments. This information can assist in delineation biologically important areas via detection sound-producing species or characterization ecosystem type condition, inferred from properties local soundscape. At time when worldwide biodiversity significant decline soundscapes are being altered as result anthropogenic impacts, there need document, quantify, understand biotic sound sources–potentially before they disappear. A step toward these goals development web-based, open-access platform provides: (1) reference library known unknown biological sources (by integrating expanding existing libraries around world); (2) data repository portal for annotated unannotated audio recordings single soundscapes; (3) training artificial intelligence algorithms signal classification; (4) citizen science-based application public users. Although individually, resources often met on regional taxa-specific scales, many not sustained and, collectively, enduring global database integrated has been realized. We discuss benefits such program provide, previous calls data-sharing libraries, challenges be overcome bring together bio- ecoacousticians, bioinformaticians, propagation experts, web engineers, processing specialists (e.g., intelligence) necessary support funding build sustainable scalable could address needs all contributors stakeholders into future.

Language: Английский

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Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey

Reviews in Aquaculture, Journal Year: 2025, Volume and Issue: 17(1)

Published: Jan. 1, 2025

ABSTRACT Digital aquaculture leverages advanced technologies and data‐driven methods, providing substantial benefits over traditional practices. This article presents a comprehensive review of three interconnected digital tasks, namely, fish tracking, counting, behaviour analysis, using novel unified approach. Unlike previous reviews which focused on single modalities or individual we analyse vision‐based (i.e., image‐ video‐based), acoustic‐based, biosensor‐based methods across all tasks. We examine their advantages, limitations, applications, highlighting recent advancements identifying critical cross‐cutting research gaps. The also includes emerging ideas such as applying multitask learning large language models to address various aspects monitoring, an approach not previously explored in literature. identify the major obstacles hindering progress this field, including scarcity datasets lack evaluation standards. To overcome current explore potential multimodal data fusion deep improve accuracy, robustness, efficiency integrated monitoring systems. In addition, provide summary existing available for analysis. holistic perspective offers roadmap future research, emphasizing need standards facilitate meaningful comparisons between promote practical implementations real‐world settings.

Language: Английский

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Global inventory of species categorized by known underwater sonifery

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: Dec. 18, 2023

A working group from the Global Library of Underwater Biological Sounds effort collaborated with World Register Marine Species (WoRMS) to create an inventory species confirmed or expected produce sound underwater. We used several existing inventories and additional literature searches compile a dataset categorizing scientific knowledge sonifery for 33,462 subspecies across marine mammals, other tetrapods, fishes, invertebrates. found 729 documented as producing active and/or passive sounds under natural conditions, another 21,911 deemed likely based on evaluated taxonomic relationships. The is available both figshare WoRMS where it can be regularly updated new information becomes available. data also integrated databases (e.g., SeaLifeBase, Biodiversity Information Facility) advance future research distribution, evolution, ecology, management, conservation underwater soniferous worldwide.

Language: Английский

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Characterization of the vocal behavior of the miniature and transparent fish model, Danionella cerebrum

The Journal of the Acoustical Society of America, Journal Year: 2024, Volume and Issue: 155(1), P. 781 - 789

Published: Jan. 1, 2024

Danionella cerebrum has recently been proposed as a promising model to investigate the structure and function of adult vertebrate brain, including development vocal–auditory neural pathways. This genetically tractable transparent cypriniform is highly vocal, but limited information available on its acoustic behavior underlying biological function. Our main goal was characterize repertoire diel variation in sound production D. cerebrum, well relationship between vocal reproduction. Sound recordings demonstrated high activity, with sounds varying from short sequences pulses known “bursts” (comprising up 15 pulses) notably longer sounds, termed “long bursts”, which extended 349 over 2.7 s. Vocal activity peaked at midday it very low night only few bursts. While number higher during daytime, interpulse interval night. In addition, calling time positively associated viable eggs, suggesting that communication important for These preliminary findings reveal potential using plasticity implications sexual selection reproduction novel neuroscience.

Language: Английский

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Unidentified fish sounds as indicators of coral reef health and comparison to other acoustic methods

Frontiers in Remote Sensing, Journal Year: 2024, Volume and Issue: 5

Published: March 1, 2024

The global decline of coral reefs is a major contributor to the biodiversity crisis and requires improved monitoring at these critically important habitats. Non-invasive passive acoustic assessments may address this need, leveraging rich variety spatiotemporal variability biological sounds present in reef environments offering near-continuous temporal coverage. Despite this, metrics that reliably represent health are still debated, ground-truthing methods limited. Here we investigated how prevalence low frequency biotic (without species information) relates health, providing foundation from which one can compare assessment methods. We first quantified call rates for three exhibiting different community assemblages around St. John, U.S. Virgin Islands, by manually annotating presumed fish noises 1 min every 30 across 8 days each site. Annotated were selected key points lunar cycles. These then compared with traditional visual surveys, several indices commonly used underwater soundscape research. found that, overall, detected successfully differentiated between reefs, capturing variation crepuscular activity levels–a pattern consistent previous work highlights importance diel choruses. Moreover, vocal predictors hard cover, abundance, richness, while most failed parse out fine distinctions among sites. Some, such as Acoustic Complexity Index, reveal any expected differences sites or times day, Bioacoustic Index could only identify acoustically active reef, otherwise having weak correlations metrics. Of tested, root-mean-squared sound pressure level Entropy, both calculated band (50–1,200 Hz), showed strongest association measures. findings an step toward using cues assessments. limited generalizability locations emphasizes need caution their application. Therefore, it crucial improve utilizing sounds, automatic detectors able generalize well new soundscapes.

Language: Английский

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FishSounds Version 1.0: A website for the compilation of fish sound production information and recordings

Ecological Informatics, Journal Year: 2022, Volume and Issue: 74, P. 101953 - 101953

Published: Dec. 13, 2022

Language: Английский

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Applications of machine learning to identify and characterize the sounds produced by fish

ICES Journal of Marine Science, Journal Year: 2023, Volume and Issue: 80(7), P. 1854 - 1867

Published: Aug. 11, 2023

Abstract Aquatic ecosystems are constantly changing due to anthropic stressors, which can lead biodiversity loss. Ocean sound is considered an essential ocean variable, with the potential improve our understanding of its impact on marine life. Fish produce a variety sounds and their choruses often dominate underwater soundscapes. These have been used assess communication, behaviour, spawning location, biodiversity. Artificial intelligence provide robust solution detect classify fish sounds. However, main challenge in applying artificial recognize lack validated data for individual species. This review provides overview recent publications use machine learning, including deep detection, classification, identification. Key challenges limitations discussed, some points guide future studies also provided.

Language: Английский

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Midbrain node for context-specific vocalisation in fish

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 2, 2024

Abstract Vocalizations communicate information indicative of behavioural state across divergent social contexts. Yet, how brain regions actively pattern the acoustic features context-specific vocal signals remains largely unexplored. The midbrain periaqueductal gray (PAG) is a major site for initiating vocalization among mammals, including primates. We show that PAG neurons in highly fish species ( Porichthys notatus ) are activated distinct patterns during agonistic versus courtship calling by males, with few co-activated non-vocal behaviour, foraging. Pharmacological manipulations within vocally active PAG, but not hindbrain, sites evoke network output to sonic muscles matching temporal and calls, showing balance inhibitory excitatory dynamics likely necessary patterning different call types. Collectively, these findings support hypothesis mammals share functionally comparable nodes some can influence structure signals.

Language: Английский

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Automatic detection of unidentified fish sounds: a comparison of traditional machine learning with deep learning

Frontiers in Remote Sensing, Journal Year: 2024, Volume and Issue: 5

Published: Aug. 22, 2024

Many species of fishes around the world are soniferous. The types sounds produce vary among and regions but consist typically low-frequency ( < 1.5 kHz) pulses grunts. These can potentially be used to monitor non-intrusively could complement traditional monitoring techniques. However, significant time required for human analysts manually label fish in acoustic recordings does not yet allow passive acoustics as a viable tool fishes. In this paper, we compare two different approaches automatically detect sounds. One is more machine learning technique based on detection transients spectrogram classification using Random Forest (RF). other deep approach overlapping segments (0.2 s) ResNet18 Convolutional Neural Network (CNN). Both algorithms were trained 21,950 annotated non-fish collected from 2014 2019 at five locations Strait Georgia, British Columbia, Canada. performance detectors was tested part data Georgia that withheld training phase, Barkley Sound, Port Miami, Florida, United States. CNN performed up 1.9 times better than RF id="m2">F1 score: 0.82 vs. 0.43). some cases, able find faint analyst well environments one it (Miami id="m3">F1 0.88). Noise analysis 20–1,000 Hz frequency band shows still reliable noise levels greater 130 dB re 1 id="m4">μ Pa Miami becomes less Sound past 100 id="m5">μ due mooring noise. proposed efficiently (unidentified) variety also facilitate development species-specific detectors. We provide software FishSound Finder, an easy-to-use open-source implementation detector with detailed documentation.

Language: Английский

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