Convergence of emerging technologies: Development of a risk-based paradigm for marine mammal monitoring for offshore wind energy operations.

The ability to gather real-time and near real-time data on marine mammal distribution, movement, and habitat use has advanced significantly over the past two decades. These advances have outpaced their adoption into a meaningful, risk-based assessment framework so critically needed to support society's growing demands for a transition to increased reliance on renewable energy. Marine acoustics have the capacity to detect, identify, and locate vocalizations over broad areas. Photogrammetric and image processing increases the ability to visually detect animals from surface or aerial platforms. Ecological models based on long-term observational data coupled with static and remotely sensed oceanographic data are able to predict daily and seasonal habitat suitability. Extensive monitoring around anthropogenic activities, combined with controlled experiments of exposure parameters (i.e., sound), supports better informed decisions on reducing effects. Population models and potential consequence modeling provide the ability to estimate the significance of individual and population exposure. The collective capacities of these emerging technical approaches support a risk ranking and risk management approach to monitoring and mitigating effects on marine mammals related to development activities. The monitoring paradigm related to many offshore energy-related activities, however, has long been spatially limited, situationally myopic, and operationally uncertain. A case evaluation process is used to define and demonstrate the changing paradigm of effective monitoring aimed at protecting living resources and concurrently providing increased certainty that essential activities can proceed efficiently. Recent advances in both technologies and operational approaches are examined to delineate a risk-based paradigm, driven by a diversity of regional data inputs, that is capable of meeting the imperative for timely development of offshore wind energy. Integr Environ Assess Manag 2022;18:939-949. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Effects of airgun sounds on bowhead whale calling rates: evidence for two behavioral thresholds.

In proximity to seismic operations, bowhead whales (Balaena mysticetus) decrease their calling rates. Here, we investigate the transition from normal calling behavior to decreased calling and identify two threshold levels of received sound from airgun pulses at which calling behavior changes. Data were collected in August-October 2007-2010, during the westward autumn migration in the Alaskan Beaufort Sea. Up to 40 directional acoustic recorders (DASARs) were deployed at five sites offshore of the Alaskan North Slope. Using triangulation, whale calls localized within 2 km of each DASAR were identified and tallied every 10 minutes each season, so that the detected call rate could be interpreted as the actual call production rate. Moreover, airgun pulses were identified on each DASAR, analyzed, and a cumulative sound exposure level was computed for each 10-min period each season (CSEL10-min). A Poisson regression model was used to examine the relationship between the received CSEL10-min from airguns and the number of detected bowhead calls. Calling rates increased as soon as airgun pulses were detectable, compared to calling rates in the absence of airgun pulses. After the initial increase, calling rates leveled off at a received CSEL10-min of ~94 dB re 1 µPa2-s (the lower threshold). In contrast, once CSEL10-min exceeded ~127 dB re 1 µPa2-s (the upper threshold), whale calling rates began decreasing, and when CSEL10-min values were above ~160 dB re 1 µPa2-s, the whales were virtually silent.

Range estimation of bowhead whale (Balaena mysticetus) calls in the Arctic using a single hydrophone.

Bowhead whales generate low-frequency calls in shallow-water Arctic environments, whose dispersive propagation characteristics are well modeled by normal mode theory. As each mode propagates with a different group speed, a call's range can be inferred by the relative time-frequency dispersion of the modal arrivals. Traditionally, at close ranges modal arrivals are separated using synchronized hydrophone arrays. Here a nonlinear signal processing method called "warping" is used to filter the modes on just a single hydrophone. The filtering works even at relatively short source ranges, where distinct modal arrivals are not separable in a conventional spectrogram. However, this warping technique is limited to signals with monotonically increasing or decreasing frequency modulations, a relatively common situation for bowhead calls. Once modal arrivals have been separated, the source range can be estimated using conventional modal dispersion techniques, with the original source signal structure being recovered as a by-product. Twelve bowhead whale vocalizations recorded near Kaktovik (Alaska) in 2010, with signal-to-noise ratios between 6 and 23 dB, are analyzed, and the resulting single-receiver range estimates are consistent with those obtained independently via triangulation from widely-distributed vector sensor arrays. Geoacoustic inversions for each call are necessary in order to obtain the correct ranges.

Automated detection and localization of bowhead whale sounds in the presence of seismic airgun surveys.

An automated procedure has been developed for detecting and localizing frequency-modulated bowhead whale sounds in the presence of seismic airgun surveys. The procedure was applied to four years of data, collected from over 30 directional autonomous recording packages deployed over a 280 km span of continental shelf in the Alaskan Beaufort Sea. The procedure has six sequential stages that begin by extracting 25-element feature vectors from spectrograms of potential call candidates. Two cascaded neural networks then classify some feature vectors as bowhead calls, and the procedure then matches calls between recorders to triangulate locations. To train the networks, manual analysts flagged 219 471 bowhead call examples from 2008 and 2009. Manual analyses were also used to identify 1.17 million transient signals that were not whale calls. The network output thresholds were adjusted to reject 20% of whale calls in the training data. Validation runs using 2007 and 2010 data found that the procedure missed 30%-40% of manually detected calls. Furthermore, 20%-40% of the sounds flagged as calls are not present in the manual analyses; however, these extra detections incorporate legitimate whale calls overlooked by human analysts. Both manual and automated methods produce similar spatial and temporal call distributions.