Snowmobile noise alters bird vocalization patterns during winter and pre‐breeding season

• Published in: The Journal of applied ecology 2024-02, Vol.61 (2), p.340-350
• Main Authors: Cretois, Benjamin, Bick, Ian Avery, Balantic, Cathleen, Gelderblom, Femke B., Pávon‐Jordán, Diego, Wiel, Julia, Sethi, Sarab S., Betchkal, Davyd H., Banet, Ben, Rosten, Carolyn M., Reinen, Tor Arne
• Format: Article
• Language: English
• Subjects: Acoustic tracking; Animal breeding; Animal communication; anthropogenic noise; Biodiversity; biodiversity conservation; bird vocalizations; Birds; Breeding seasons; Deep learning; Foraging behavior; Monitoring; National parks; Natural resource management; Natural resources; Noise pollution; Pollution; Seasons; Snowmobiles; soundscape ecology; Surveillance; Vocalization behavior; Winter
• ISSN: 0021-8901; 1365-2664
• DOI: 10.1111/1365-2664.14564

Noise pollution poses a significant threat to ecosystems worldwide, disrupting animal communication and causing cascading effects on biodiversity. In this study, we focus on the impact of snowmobile noise on avian vocalizations during the non‐breeding winter season, a less‐studied area in soundscape ecology. We developed a pipeline relying on deep learning methods to detect snowmobile noise and applied it to a large acoustic monitoring dataset collected in Yellowstone National Park. Our results demonstrate the effectiveness of the snowmobile detection model in identifying snowmobile noise and reveal an association between snowmobile passage and changes in avian vocalization patterns. Snowmobile noise led to a decrease in the frequency of bird vocalizations during mornings and evenings, potentially affecting winter and pre‐breeding behaviours such as foraging, predator avoidance and successfully finding a mate. However, we observed a recovery in avian vocalizations after detection of snowmobiles during mornings and afternoons, indicating some resilience to sporadic noise events. Synthesis and applications: Our findings emphasize the need to consider noise impacts in the non‐breeding season and provide valuable insights for natural resource managers to minimize disturbance and protect critical avian habitats. The deep learning approach presented in this study offers an efficient and accurate means of analysing large‐scale acoustic monitoring data and contributes to a comprehensive understanding of the cumulative impacts of multiple stressors on avian communities. Résumé La pollution sonore représente une menace significative pour les écosystèmes dans le monde entier, perturbant la communication animale et causant des effets en cascade sur la biodiversité. Dans cette étude, nous nous concentrons sur l'impact du bruit des motoneiges sur les vocalizations des oiseaux pendant la saison hivernale non‐reproductive, un domaine moins étudié dans l'écologie des paysages sonores. Nous avons développé une chaîne de traitement basée sur des méthodes d'apprentissage profond pour détecter le bruit des motoneiges et l'avons appliquée à un large ensemble de données de surveillance acoustique collectées dans le Parc National de Yellowstone. Nos résultats démontrent l'efficacité du modèle de détection des motoneiges pour identifier le bruit de ces véhicules et révèlent une association entre le passage des motoneiges et les changements dans les modèles de vocalization des oise