Arctic diel and circadian acoustic pattern of Orcas, Fin, and Humpback whales revealed by deep learning from two months of continuous recordings

In the northern Norwegian fjords, orcas, fin and humpback whales gather each winter to feed on herring, overlapping with intense human activities such as fishing and whale watching. To assess how anthropophony and geophony influence their acoustic behavior, we conducted two months of continuous passive acoustic monitoring in Kvænangen fjord during winter 2022–2023. Whale vocalizations were automatically detected using a deep learning framework based on YOLOv5, enabling quantification of species-specific acoustic presence and activity. Ambient noise was estimated from power spectral density. Low- and high-noise conditions were identified for geophony and anthropophony using a three-step filtering procedure. Model performances were evaluated under various noise conditions to ensure robust and consistent detection accuracy. Analyzes were then performed to characterize diel, circadian and daily patterns of acoustic activity. All three species were detected nearly continuously, with orcas activity peaking in November. Acoustic patterns were strongly influenced by noise: orcas and fin whales were less vocally active with increasing anthropophony ( ρ < −0.31, p < 0.05), while humpback whales showed a time-dependent response, increasing vocal activity on short timescales (p < 0.01) but decreasing over longer periods ( ρ = −0.33, p = 0.008). Geophony was associated with reduced acoustic presence for all three species on a daily basis ( ρ < −0.34, p < 0.01), suggesting changes in spatial distribution or vocal behavior. Positive correlations between orcas and humpback whales vocal behavior indicated potential concurrent feeding. These findings revealed species- and timescale-specific acoustic responses to noise and illustrate how deep-learning can enhance ecoacoustic monitoring. • Orcas vocalized more at night, humpback whales reduced vocal activity at dawn. • Humpback whales showed opposite responses to anthropophony across timescales. • Orcas and fin whales vocal activity declined under increasing anthropophony. • Geophony lowered acoustic presence of all three cetacean species.

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Publication Details

Journal
Ecological Informatics
Published
2025-12-22
DOI
https://doi.org/10.1016/j.ecoinf.2025.103564
Primary Topic
Marine animal studies overview
Type
article
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article

Arctic diel and circadian acoustic pattern of Orcas, Fin, and Humpback whales revealed by deep learning from two months of continuous recordings

Justine Girardet, M. Poupard, Véronique Sarano, Stéphane Chavin et al.
Ecological Informatics
Marine animal studies overview
article

Arctic diel and circadian acoustic pattern of Orcas, Fin, and Humpback whales revealed by deep learning from two months of continuous recordings

Justine Girardet, M. Poupard, Véronique Sarano, Stéphane Chavin, Julie Guiderdoni, Hervé Glotin
article en

Abstract

In the northern Norwegian fjords, orcas, fin and humpback whales gather each winter to feed on herring, overlapping with intense human activities such as fishing and whale watching. To assess how anthropophony and geophony influence their acoustic behavior, we conducted two months of continuous passive acoustic monitoring in Kvænangen fjord during winter 2022–2023. Whale vocalizations were automatically detected using a deep learning framework based on YOLOv5, enabling quantification of species-specific acoustic presence and activity. Ambient noise was estimated from power spectral density. Low- and high-noise conditions were identified for geophony and anthropophony using a three-step filtering procedure. Model performances were evaluated under various noise conditions to ensure robust and consistent detection accuracy. Analyzes were then performed to characterize diel, circadian and daily patterns of acoustic activity. All three species were detected nearly continuously, with orcas activity peaking in November. Acoustic patterns were strongly influenced by noise: orcas and fin whales were less vocally active with increasing anthropophony ( ρ < −0.31, p < 0.05), while humpback whales showed a time-dependent response, increasing vocal activity on short timescales (p < 0.01) but decreasing over longer periods ( ρ = −0.33, p = 0.008). Geophony was associated with reduced acoustic presence for all three species on a daily basis ( ρ < −0.34, p < 0.01), suggesting changes in spatial distribution or vocal behavior. Positive correlations between orcas and humpback whales vocal behavior indicated potential concurrent feeding. These findings revealed species- and timescale-specific acoustic responses to noise and illustrate how deep-learning can enhance ecoacoustic monitoring. • Orcas vocalized more at night, humpback whales reduced vocal activity at dawn. • Humpback whales showed opposite responses to anthropophony across timescales. • Orcas and fin whales vocal activity declined under increasing anthropophony. • Geophony lowered acoustic presence of all three cetacean species.

Ecological InformaticsVol. 93
Centre National de la Recherche Scientifique (FR), Université de Toulon (FR), University of Pavia (IT), Toulon Var Technologies (France) (FR), Vallvi (Norway) (NO), Laboratoire d’Informatique et Systèmes (FR)
Life below water
Openalex Percentile: Top 31%
Marine animal studies overview
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