Christine Erbe

Zones of impact around icebreakers affecting beluga whales in the Beaufort Sea.

A software model estimating zones of impact on marine mammals around man-made noise [C. Erbe and D. M. Farmer, J. Acoust. Soc. Am. 108, 1327-1331 (2000)] is applied to the case of icebreakers affecting beluga whales in the Beaufort Sea. Two types of noise emitted by the Canadian Coast Guard icebreaker Henry Larsen are analyzed: bubbler system noise and propeller cavitation noise. Effects on beluga whales are modeled both in a deep-water environment and a near-shore environment. The model estimates that the Henry Larsen is audible to beluga whales over ranges of 35-78 km, depending on location. The zone of behavioral disturbance is only slightly smaller. Masking of beluga communication signals is predicted within 14-71-km range. Temporary hearing damage can occur if a beluga stays within 1-4 km of the Henry Larsen for at least 20 min. Bubbler noise impacts over the short ranges quoted; propeller cavitation noise accounts for all the long-range effects. Serious problems can arise in heavily industrialized areas where animals are exposed to ongoing noise and where anthropogenic noise from a variety of sources adds up.

Communication masking in marine mammals: A review and research strategy

Underwater noise, whether of natural or anthropogenic origin, has the ability to interfere with the way in which marine mammals receive acoustic signals (i.e., for communication, social interaction, foraging, navigation, etc.). This phenomenon, termed auditory masking, has been well studied in humans and terrestrial vertebrates (in particular birds), but less so in marine mammals. Anthropogenic underwater noise seems to be increasing in parts of the worlds oceans and concerns about associated bioacoustic effects, including masking, are growing. In this article, we review our understanding of masking in marine mammals, summarise data on marine mammal hearing as they relate to masking (including audiograms, critical ratios, critical bandwidths, and auditory integration times), discuss masking release processes of receivers (including comodulation masking release and spatial release from masking) and anti-masking strategies of signalers (e.g. Lombard effect), and set a research framework for improved assessment of potential masking in marine mammals.

A software model to estimate zones of impact on marine mammals around anthropogenic noise

Anthropogenic noise impacts marine mammals in a variety of ways. In order to estimate over which ranges this happens, we first need to understand the propagation of noise through the ocean away from the noise source, and, second, understand the relationship between received noise levels and impact thresholds. A software package combining both aspects is presented. (1) A sound propagation model based on ray theory was developed to calculate received noise levels as a function of range, depth, and frequency. (2) Current knowledge of noise impact thresholds for marine mammals was gathered and included in software routines predicting zones of impact on marine mammals around industrial underwater noise sources. As input parameters, this software package requires the source level and spectrum of the noise of interest; physical oceanography data about the local ocean environment such as bathymetry, bottom and surface loss data, and sound speed profiles; and bioacoustical information about the target species in the form of an audiogram, critical auditory bandwidths, spectra of typical animal vocalizations, reported sound levels of disturbance, and criteria for hearing damage. As output, the software produces data files and plots of the zones of audibility, masking, disturbance, and potential hearing damage around a noise source.

Identifying modeled ship noise hotspots for marine mammals of Canada's Pacific region

The inshore, continental shelf waters of British Columbia (BC), Canada are busy with ship traffic. South coast waters are heavily trafficked by ships using the ports of Vancouver and Seattle. North coast waters are less busy, but expected to get busier based on proposals for container port and liquefied natural gas development and expansion. Abundance estimates and density surface maps are available for 10 commonly seen marine mammals, including northern resident killer whales, fin whales, humpback whales, and other species with at-risk status under Canadian legislation. Ship noise is the dominant anthropogenic contributor to the marine soundscape of BC, and it is chronic. Underwater noise is now being considered in habitat quality assessments in some countries and in marine spatial planning. We modeled the propagation of underwater noise from ships and weighted the received levels by species-specific audiograms. We overlaid the audiogram-weighted maps of ship audibility with animal density maps. The result is a series of so-called “hotspot” maps of ship noise for all 10 marine mammal species, based on cumulative ship noise energy and average distribution in the boreal summer. South coast waters (Juan de Fuca and Haro Straits) are hotspots for all species that use the area, irrespective of their hearing sensitivity, simply due to ubiquitous ship traffic. Secondary hotspots were found on the central and north coasts (Johnstone Strait and the region around Prince Rupert). These maps can identify where anthropogenic noise is predicted to have above-average impact on species-specific habitat, and where mitigation measures may be most effective. This approach can guide effective mitigation without requiring fleet-wide modification in sites where no animals are present or where the area is used by species that are relatively insensitive to ship noise.

Critical ratios of beluga whales (Delphinapterus leucas) and masked signal duration

This article examines the masking of a complex beluga vocalization by natural and anthropogenic noise. The call consisted of six 150 ms pulses exhibiting spectral peaks between 800 Hz and 8 kHz. Comparing the spectra and spectrograms of the call and noises at detection threshold showed that the animal did not hear the entire call at threshold. It only heard parts of the call in frequency and time. From the masked hearing thresholds in broadband continuous noises, critical ratios were computed. Fletcher critical bands were narrower than either 15 or 111 of an octave at the low frequencies of the call (<2 kHz), depending on which frequency the animal cued on. From the masked hearing thresholds in intermittent noises, the audible signal duration at detection threshold was computed. The intermittent noises differed in gap length, gap number, and masking, but the total audible signal duration at threshold was the same: 660 ms. This observation supports a multiple-looks model. The two amplitude modulated noises exhibited weaker masking than the unmodulated noises hinting at a comodulation masking release.

Detection of whale calls in noise: Performance comparison between a beluga whale, human listeners, and a neural network

This article examines the masking by anthropogenic noise of beluga whale calls. Results from human masking experiments and a software backpropagation neural network are compared to the performance of a trained beluga whale. The goal was to find an accurate, reliable, and fast model to replace lengthy and expensive animal experiments. A beluga call was masked by three types of noise, an icebreakers bubbler system and propeller noise, and ambient arctic ice-cracking noise. Both the human experiment and the neural network successfully modeled the beluga data in the sense that they classified the noises in the same order from strongest to weakest masking as the whale and with similar call-detection thresholds. The neural network slightly outperformed the humans. Both models were then used to predict the masking of a fourth type of noise, Gaussian white noise. Their prediction ability was judged by returning to the aquarium to measure masked-hearing thresholds of a beluga in white noise. Both models and the whale identified bubbler noise as the strongest masker, followed by ramming, then white noise. Natural ice-cracking noise masked the least. However, the humans and the neural network slightly overpredicted the amount of masking for white noise. This is neglecting individual variation in belugas, because only one animal could be trained. Comparing the human model to the neural network model, the latter has the advantage of objectivity, reproducibility of results, and efficiency, particularly if the interference of a large number of signals and noise is to be examined.

Automatic detection of marine mammals using information entropy

This article describes an automatic detector for marine mammal vocalizations. Even though there has been previous research on optimizing automatic detectors for specific calls or specific species, the detection of any type of call by a diversity of marine mammal species still poses quite a challenge--and one that is faced more frequently as the scope of passive acoustic monitoring studies and the amount of data collected increase. Information (Shannon) entropy measures the amount of information in a signal. A detector based on spectral entropy surpassed two commonly used detectors based on peak-energy detection. Receiver operating characteristic curves were computed for performance comparison. The entropy detector performed considerably faster than real time. It can be used as a first step in an automatic signal analysis yielding potential signals. It should be followed by automatic classification, recognition, and identification algorithms to group and identify signals. Examples are shown from underwater recordings in the Western Canadian Arctic. Calls of a variety of cetacean and pinniped species were detected.

Computer models for masked hearing experiments with beluga whales (Delphinapterus leucas)

Environmental assessments of manmade noise and its effects on marine mammals need to address the question of how noise interferes with animal vocalizations. Seeking the answer with animal experiments is very time consuming, costly, and often infeasible. This article examines the possibility of estimating results with software models. A matched filter, spectrogram cross-correlation, critical band cross-correlation, and a back-propagation neural network detected a beluga vocalization in three types of ocean noise. Performance was compared to masked hearing experiments with a beluga whale [C. Erbe and D. M. Farmer, Deep-Sea Res. II 45, 1373-1388 (1998)]. The artificial neural network simulated the animal data most closely and raised confidence in its ability to predict the interference of a variety of noise source with a variety of vocalizations.

Underwater noise of small personal watercraft (jet skis)

Personal watercraft (water scooters, jet skis) were recorded under water in Bramble Bay, Queensland, Australia. Underwater noise emissions consisted of broadband energy between 100 Hz and 10 kHz due to the vibrating bubble cloud generated by the jet stream, overlain with frequency-modulated tonals corresponding to impeller blade rates and harmonics. Broadband monopole source levels were 149, 137, and 122 dB re 1 μPa @ 1 m (5th, 50th, and 95th percentiles). Even though these are lower than those of small propeller-driven boats, it is not necessarily the broadband source level that correlates with the bioacoustic impact on marine fauna.

Effects of Underwater Noise on Marine Mammals

Public concern about the effects of underwater noise on marine mammals has steadily increased over the past few decades. Research programs have been developed around the globe to investigate noise impacts. Government departments in many countries regulate underwater noise emission. Industries, in particular the oil and gas industry, undertake environmental impact assessments of underwater noise expected from planned marine activities and submit these to regulatory agencies as part of a permit application process. Lawsuits have been brought against the Navy in an attempt to protect marine mammals from sonar testing. The number and diversity of stakeholders in the management of noise and marine animals is great. Marine Mammals and Noise (Richardson et al. 1995) was the first book to review and synthesize research on the noise effects on marine mammals. In the 15 years since then, a handful of review projects have been undertaken, with focus on specific aspects (e.g., Committee on Characterizing Biologically Significant Marine Mammal Behavior 2005; Committee on Potential Impacts of Ambient Noise in the Ocean on Marine Mammals 2003; National Research Council 2000; Nowacek et al. 2007; Southall et al. 2007).