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Patterns of underwater human-generated noise events and durations of noise-free intervals (NFIs) are soundscape metrics that can potentially affect animal communication and behavior. Due to the arduous task of manual analysis, these metrics have not been described in Glacier Bay National Park and Preserve (GBNP). To surmount this challenge, we created a machine-learning (ML) model trained on 18 hr of labeled audio samples from a hydrophone operating in GBNP since 2000. The validated convolutional neural net transfer-learning model (GBNP-CNN) was used to classify several categories of sound sources in nearly 9,000 hours of data from the same hydrophone, enabling our study of vessel noise between 2017 and 2020. We focused on the occurrence and duration of NFI and the hourly proportion (HP) of vessel noise. As expected, shorter NFI and higher HP were found during daytime hours. The GBNP-CNN F1 score was 75%, largely due to the model’s confusion of vessel noise with harbor seal roars. Therefore, NFI lengths should be considered minimum estimates, but the errors do not qualitatively affect diurnal or seasonal patterns. In 2018, mean daytime NFI during peak tourism months (June–August) was less than half the duration compared to May and September (1.3 min vs. 2.9 min). In 2020, when large-vessel tourism was substantially reduced but small-craft activity continued, we found that HP decreased in June–August. In conjunction with other soundscape metrics, monitoring NFI trends using ML models such as GBNP-CNN will provide crucial information for management and conservation of acoustic habitats and sensitive species in GBNP. [ABSTRACT FROM AUTHOR]

Patterns of underwater human-generated noise events and durations of noise-free intervals (NFIs) are soundscape metrics that can potentially affect animal communication and behavior. Due to the arduous task of manual analysis, these metrics have not been described in Glacier Bay National Park and Preserve (GBNP). To surmount this challenge, we created a machine-learning (ML) model trained on 18 hr of labeled audio samples from a hydrophone operating in GBNP since 2000. The validated convolutional neural net transfer-learning model (GBNP-CNN) was used to classify several categories of sound sources in nearly 9,000 hours of data from the same hydrophone, enabling our study of vessel noise between 2017 and 2020. We focused on the occurrence and duration of NFI and the hourly proportion (HP) of vessel noise. As expected, shorter NFI and higher HP were found during daytime hours. The GBNP-CNN F1 score was 75%, largely due to the model’s confusion of vessel noise with harbor seal roars. Therefore, NFI lengths should be considered minimum estimates, but the errors do not qualitatively affect diurnal or seasonal patterns. In 2018, mean daytime NFI during peak tourism months (June–August) was less than half the duration compared to May and September (1.3 min vs. 2.9 min). In 2020, when large-vessel tourism was substantially reduced but small-craft activity continued, we found that HP decreased in June–August. In conjunction with other soundscape metrics, monitoring NFI trends using ML models such as GBNP-CNN will provide crucial information for management and conservation of acoustic habitats and sensitive species in GBNP.

This study investigated the emission of subaquatic noise from recreational tourism motorboats, schooners and a sea-bottom mounted water pump. Analyses demonstrated alterations in several whistle (IF: t = 2.42, P = 0.015; FF: t = −2.22, P = 0.025) and calls patterns (MIF: t = −3.13, P = 0.001; MAF: t = −3.49, P = 0.0005; FD: t = −2.21, P = 0.027; D: t = 2.89, P = 0.004), caused primarily by motorboats. Duration of clicks was also modified (D: t = −3.85, P = 0.0001), mainly by the water pump. The frequency range of all noises (0.43–35.8 kHz) overlaps that used by dolphins (1–48 kHz), causing sound emissions changes, with a considerable increase in number of whistles and a reduction in clicks trains. These changes may be a strategy developed by these dolphins to overcome the noise band. Mitigation measures, such as boating regulations and environmental education for the local community, boaters and tourists are needed to conserve the species. The Guiana dolphin population is apparently already suffering, evidenced by diminished residence time and reduced number of individuals entering the inlet during the presence of pleasure craft. [ABSTRACT FROM PUBLISHER]