Effects of tones associated with drilling activities on bowhead whale calling rates.

During summer 2012 Shell performed exploratory drilling at Sivulliq, a lease holding located in the autumn migration corridor of bowhead whales (Balaena mysticetus), northwest of Camden Bay in the Beaufort Sea. The drilling operation involved a number of vessels performing various activities, such as towing the drill rig, anchor handling, and drilling. Acoustic data were collected with six arrays of directional recorders (DASARs) deployed on the seafloor over ~7 weeks in Aug-Oct. Whale calls produced within 2 km of each DASAR were identified and localized using triangulation. A "tone index" was defined to quantify the presence and amplitude of tonal sounds from industrial machinery. The presence of airgun pulses originating from distant seismic operations was also quantified. For each 10-min period at each of the 40 recorders, the number of whale calls localized was matched with the "dose" of industrial sound received, and the relationship between calling rates and industrial sound was modeled using negative binomial regression. The analysis showed that with increasing tone levels, bowhead whale calling rates initially increased, peaked, and then decreased. This dual behavioral response is similar to that described for bowhead whales and airgun pulses in earlier work. Increasing call repetition rates can be a viable strategy for combating decreased detectability of signals arising from moderate increases in background noise. Meanwhile, as noise increases, the benefits of calling may decrease because information transfer becomes increasingly error-prone, and at some point calling may no longer be worth the effort.

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.

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.