Correction: Lagrois et al. Low-to-Mid-Frequency Monopole Source Levels of Underwater Noise from Small Recreational Vessels in the St. Lawrence Estuary Beluga Critical Habitat. Sensors 2023, 23, 1674.

There was an error in the original publication [...].

Low-to-Mid-Frequency Monopole Source Levels of Underwater Noise from Small Recreational Vessels in the St. Lawrence Estuary Beluga Critical Habitat.

Anthropogenic noise from navigation is a major contributor to the disturbance of the acoustic soundscape in underwater environments containing noise-sensitive life forms. While previous studies mostly developed protocols for the empirical determination of noise source levels associated with the world's commercial fleet, this work explores the radiated noise emitted by small recreational vessels that thrive in many coastal waters, such as in the St. Lawrence Estuary beluga population's summer habitat. Hydrophone-based measurements in the Saguenay River (QC, Canada) were carried out during the summers of 2021 and 2022. Shore-based observations identified 45 isolated transits of small, motorized vessels and were able to track their displacement during their passage near the hydrophone. Received noise levels at the hydrophone typically fell below the hearing audiogram of the endangered St. Lawrence Estuary beluga. Monopole source levels at low frequencies (0.1-≲2 kHz) held on average twice the acoustic power compared to their mid-frequency (≳2-30 kHz) counterparts. The speed over ground of recreational vessel showed a positive correlation with the back-propagated monopole source levels. Estimations of the mid-frequency noise levels based on low-frequency measurements should be used moderately.

Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures.

A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert's workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (Delphinapterus leucas) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset's annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall.

Extracting spatial networks from capture-recapture data reveals individual site fidelity patterns within a marine mammal's spatial range.

Estimating the impacts of anthropogenic disturbances requires an understanding of the habitat-use patterns of individuals within a population. This is especially the case when disturbances are localized within a population's spatial range, as variation in habitat use within a population can drastically alter the distribution of impacts.Here, we illustrate the potential for multilevel binomial models to generate spatial networks from capture-recapture data, a common data source used in wildlife studies to monitor population dynamics and habitat use. These spatial networks capture which regions of a population's spatial distribution share similar/dissimilar individual usage patterns, and can be especially useful for detecting structured habitat use within the population's spatial range.Using simulations and 18 years of capture-recapture data from St. Lawrence Estuary (SLE) beluga, we show that this approach can successfully estimate the magnitude of similarities/dissimilarities in individual usage patterns across sectors, and identify sectors that share similar individual usage patterns that differ from other sectors, that is, structured habitat use. In the case of SLE beluga, this method identified multiple clusters of individuals, each preferentially using restricted areas within their summer range of the SLE.Multilevel binomial models can be effective at estimating spatial structure in habitat use within wildlife populations sampled by capture-recapture of individuals, and can be especially useful when sampling effort is not evenly distributed. Our finding of a structured habitat use within the SLE beluga summer range has direct implications for estimating individual exposures to localized stressors, such as underwater noise from shipping or other activities.

Avoiding sharp accelerations can mitigate the impacts of a Ferry's radiated noise on the St. Lawrence whales.

Exposure to anthropogenic noise from the commercial fleet is one of the primary constituents of the acoustic pollution perturbing the environment of aquatic life. Merchant ships (e.g. bulkers, tankers) have been the focus of numerous studies for underwater noise source level determination and modeling. This work extends pre-existing studies to the ferry ship class. Hydrophone-based measurements of the N.M. Trans-Saint-Laurent ferry near the Rivière-du-Loup harbor (Rivière-du-Loup, QC CANADA) were obtained for 186 transits between 2020 July 22th and 2020 September 5th. For each transit, monopole source levels are estimated for two (2) different modes of operation i.e., the low-speed phases of acceleration/deceleration when the ferry launches/docks at Rivière-du-Loup and the passages at quasi-operational speed at the hydrophone's closest-point-of-approach. Relative differences between the two (2) modes of operation are presented here in the low-frequency domain between 141 and 707 Hz. An average excess of 8 to 11.5 dB indicates that the ferry is likely one order of magnitude noisier, within this frequency band, during acceleration/deceleration when compared to passages at operational speed. This highlights that, in terms of marine mammal conservation, a significant reduction of the noise pollution could be achieved, for instance, by avoiding sudden speed changes in the vicinity of whales.

Agent-based modelling reveals a disproportionate exposure of females and calves to a local increase in shipping and associated noise in an endangered beluga population.

Vessel underwater noise (VUN) is one of the main threats to the recovery of the endangered St. Lawrence Estuary Beluga population (SLEB). The 1% yearly population decline indicates that the cumulative threats are already beyond sustainable limits for the SLEB. However, a potential threefold increase in shipping traffic is expected within its critical habitat in the coming years resulting from proposed port-industrial projects in the Saguenay River. Current data indicate that SLEB typically use multiple sectors within their summer range, likely leading to differential VUN exposure among individuals. The degree of displacement and spatial mixing among habitats are not yet well understood but can be simulated under different assumptions about movement patterns at the individual and population levels. Here, we propose using an agent-based model (ABM) to explore the biases introduced when estimating exposure to stressors such as VUN, where individual-centric movement patterns and habitat use are derived from different spatial behaviour assumptions. Simulations of the ABM revealed that alternative behavioural assumptions for individual belugas can significantly alter the estimation of instantaneous and cumulative exposure of SLEB to VUN. Our simulations also predicted that with the projected traffic increase in the Saguenay River, the characteristics making it a quiet zone for SLEB within its critical habitat would be nullified. Whereas spending more time in the Saguenay than in the Estuary allows belugas to be exposed to less noise under the current traffic regime, this relationship is reversed under the increased traffic scenario. Considering the importance of the Saguenay for SLEB females and calves, our results support the need to understand its role as a possible acoustic refuge for this endangered population. This underlines the need to understand and describe individual and collective beluga behaviours using the best available data to conduct a thorough acoustic impact assessment concerning future increased traffic.

A voluntary conservation agreement reduces the risks of lethal collisions between ships and whales in the St. Lawrence Estuary (Québec, Canada): From co-construction to monitoring compliance and assessing effectiveness.

Lethal collisions with ships are limiting the recovery of several at-risk whale species worldwide. In the St. Lawrence Estuary (Quebec, Canada), the endangered blue whale and of special concern fin whale are among the migratory species subject to collisions with large ships. In 2011, a working group composed of representatives from the maritime industry, the government, non-governmental organizations, and academia was created to explore solutions to mitigate ship-whale collisions in the St. Lawrence Estuary. Adopting an adaptive risk management framework, the working group took advantage of the best available scientific data and tools to co-construct realistic collision mitigation options and evaluate their likely benefits for whale conservation and costs for the industry. In 2013, the working group recommended the implementation of voluntary measures to mitigate collision risks, consisting of a slow-down area, a no-go area, and a caution area; a recommended route was added in 2014. Along with the voluntary framework, the working group agreed to continuously monitor compliance with and assess effectiveness of these mitigation measures. After the fourth year of implementation, voluntary measures showed encouraging results, with a reduction of up to 40% of lethal collision risks with fin whales in the highest density area. This reduction in risk is mainly related to ship speed reduction in the slow-down area from 14.1 ± 2.6 knots in 2012 to 11.3 ± 1.7 knots since 2014. The presence of a mandatory pilotage area overlapping with the slow-down area was instrumental to facilitate communication about the mitigation measures, with the pilotage corporation sitting as a regular member of the working group. This resulted in significantly slower speeds in the slow-down area for ships with a pilot from the pilotage corporation onboard compared to those without (-0.8 knots, p-value < 0.001). It is also likely to explain the weaker compliance of the maritime industry with the no-go area located outside of the mandatory pilotage area. Other factors of success include: the continuous dedication of the government to a voluntary and transparent participatory process; the use of available data, tools and institutions; the presence of an environmental certification program representative in the working group; and the adoption by consensus of an adaptive risk management approach. The traditional regulatory approach to conservation is often blamed for its focus on deterring negative behaviors, doing nothing to encourage and reward positive ones. In agreement with other case studies, the benefits of the voluntary measures implemented in the St. Lawrence Estuary include the pro-active commitment from the industry (which is likely to reduce conflicts with regulators), the greater flexibility and freedom that allowed to come up with cost-effective and tailored-made mitigation measures, and the fast achievement of conservation gains. More importantly perhaps, the human and working capital built throughout the concertation process have the potential to be a fundamental cornerstone in dealing with more complex issues such as the chronically increasing level of underwater noise in whale habitats.