SUMMARY Fin whales are among the largest predators on earth, yet little is known about their foraging behavior at depth. These whales obtain their prey by lunge-feeding, an extraordinary biomechanical event where large amounts of water and prey are engulfed and filtered. This process entails a high energetic cost that effectively decreases dive duration and increases post-dive recovery time. To examine the body mechanics of fin whales during foraging dives we attached high-resolution digital tags, equipped with a hydrophone, a depth gauge and a dual-axis accelerometer, to the backs of surfacing fin whales in the Southern California Bight. Body pitch and roll were estimated by changes in static gravitational acceleration detected by orthogonal axes of the accelerometer, while higher frequency, smaller amplitude oscillations in the accelerometer signals were interpreted as bouts of active fluking. Instantaneous velocity of the whale was determined from the magnitude of turbulent flow noise measured by the hydrophone and confirmed by kinematic analysis. Fin whales employed gliding gaits during descent, executed a series of lunges at depth and ascended to the surface by steady fluking. Our examination of body kinematics at depth reveals variable lunge-feeding behavior in the context of distinct kinematic modes, which exhibit temporal coordination of rotational torques with translational accelerations. Maximum swimming speeds during lunges match previous estimates of the flow-induced pressure needed to completely expand the buccal cavity during feeding.

We assessed the behavioral context of calls produced by blue whales Balaenoptera musculus off the California coast based on acoustic, behavioral, and dive data obtained through acoustic recording tags, sex determination from tissue sampling, and coordinated visual and acoustic observations. Approximately one-third of 38 monitored blue whales vocalized, with sounds categorized into 3 types: (1) low-frequency pulsed A and tonal B calls, in either rhythmic repetitive song sequences or as intermittent, singular calls; (2) downswept D calls; and (3) highly variable amplitudeor frequency-modulated calls. Clear patterns of behavior, sex, and group size are evident for some call types. Only males were documented producing AB calls, with song produced by lone, traveling blue whales, and singular AB calls were more typically produced by whales in pairs; D calls were heard from both sexes during foraging, commonly from individuals within groups. The sex bias evident in AB callers suggests that these calls probably play a role in reproduction, even though the calls are produced year-round. All calls are produced at shallow depth, and calling whales spend more time at shallow depths than non-calling whales, suggesting that a cost may be incurred during D calling, as less time is spent feeding at deeper depths. This relationship between calling and depth may predict the traveling behavior of singing blue whales, as traveling whales do not typically dive to deep depths and therefore would experience little extra energetic cost related to the production of long repetitive song bouts while moving between foraging areas.

Locomotor activity by diving marine mammals is accomplished while breath-holding and often exceeds predicted aerobic capacities. Video sequences of freely diving seals and whales wearing submersible cameras reveal a behavioral strategy that improves energetic efficiency in these animals. Prolonged gliding (greater than 78% descent duration) occurred during dives exceeding 80 meters in depth. Gliding was attributed to buoyancy changes with lung compression at depth. By modifying locomotor patterns to take advantage of these physical changes, Weddell seals realized a 9.2 to 59.6% reduction in diving energetic costs. This energy-conserving strategy allows marine mammals to increase aerobic dive duration and achieve remarkable depths despite limited oxygen availability when submerged.

There were several errors published in J. Exp. Biol. 214,[131][132][133][134][135][136][137][138][139][140][141][142][143][144][145][146] In the first line of the 'Kinematics of diving and lunge feeding' section of the Results (p. 134), the number of blue whales that were tagged was incorrectly given as 265 -the correct number is 25.In Fig.A1 (p. 142), two mistakes were introduced. In the 'Energy in' column, krill energy density should have been given as 4600kJkg -1 (rather than 4600kJg -1 ). Also in the 'Energy in' column, the units were missing from the 'Energy obtained from ingested krill'; this should have read 'Energy obtained from ingested krill  4,868,640 kJ'.The correct version of the figure is shown below. Energy in Energy outShape and engulfment drag = 569 kJ Pre-engulfment acceleration = 376 kJ Efficiency = 77 699 ErratumIn Table 3, the data from the 'Net energy gain' column were inadvertently repeated in the 'Energy loss, total' column. The correct version of Table 3, with the original data for the 'Energy loss, total' column, is shown below.We apologise sincerely to authors and readers for any inconvenience these errors may have caused.

Most marine mammal­ strandings coincident with naval sonar exercises have involved Cuvier's beaked whales (Ziphius cavirostris). We recorded animal movement and acoustic data on two tagged Ziphius and obtained the first direct measurements of behavioural responses of this species to mid-frequency active (MFA) sonar signals. Each recording included a 30-min playback (one 1.6-s simulated MFA sonar signal repeated every 25 s); one whale was also incidentally exposed to MFA sonar from distant naval exercises. Whales responded strongly to playbacks at low received levels (RLs; 89–127 dB re 1 µPa): after ceasing normal fluking and echolocation, they swam rapidly, silently away, extending both dive duration and subsequent non-foraging interval. Distant sonar exercises (78–106 dB re 1 µPa) did not elicit such responses, suggesting that context may moderate reactions. The observed responses to playback occurred at RLs well below current regulatory thresholds; equivalent responses to operational sonars could elevate stranding risk and reduce foraging efficiency.

SUMMARYLunge feeding in rorqual whales is a drag-based feeding mechanism that is thought to entail a high energetic cost and consequently limit the maximum dive time of these extraordinarily large predators. Although the kinematics of lunge feeding in fin whales supports this hypothesis, it is unclear whether respiratory compensation occurs as a consequence of lunge-feeding activity. We used high-resolution digital tags on foraging humpback whales (Megaptera novaengliae) to determine the number of lunges executed per dive as well as respiratory frequency between dives. Data from two whales are reported, which together performed 58 foraging dives and 451 lunges. During one study, we tracked one tagged whale for approximately 2 h and examined the spatial distribution of prey using a digital echosounder. These data were integrated with the dive profile to reveal that lunges are directed toward the upper boundary of dense krill aggregations. Foraging dives were characterized by a gliding descent, up to 15 lunges at depth, and an ascent powered by steady swimming. Longer dives were required to perform more lunges at depth and these extended apneas were followed by an increase in the number of breaths taken after a dive. Maximum dive durations during foraging were approximately half of those previously reported for singing (i.e. non-feeding) humpback whales. At the highest lunge frequencies (10 to 15 lunges per dive), respiratory rate was at least threefold higher than that of singing humpback whales that underwent a similar degree of apnea. These data suggest that the high energetic cost associated with lunge feeding in blue and fin whales also occurs in intermediate sized rorquals.

Stable isotope analysis in mysticete skin and baleen plates has been repeatedly used to assess diet and movement patterns. Accurate interpretation of isotope data depends on understanding isotopic incorporation rates for metabolically active tissues and growth rates for metabolically inert tissues. The aim of this research was to estimate isotopic incorporation rates in blue whale skin and baleen growth rates by using natural gradients in baseline isotope values between oceanic regions. Nitrogen (δ15N) and carbon (δ13C) isotope values of blue whale skin and potential prey were analyzed from three foraging zones (Gulf of California, California Current System, and Costa Rica Dome) in the northeast Pacific from 1996–2015. We also measured δ15N and δ13C values along the lengths of baleen plates collected from six blue whales stranded in the 1980s and 2000s. Skin was separated into three strata: basale, externum, and sloughed skin. A mean (±SD) skin isotopic incorporation rate of 163±91 days was estimated by fitting a generalized additive model of the seasonal trend in δ15N values of skin strata collected in the Gulf of California and the California Current System. A mean (±SD) baleen growth rate of 15.5±2.2 cm y-1 was estimated by using seasonal oscillations in δ15N values from three whales. These oscillations also showed that individual whales have a high fidelity to distinct foraging zones in the northeast Pacific across years. The absence of oscillations in δ15N values of baleen sub-samples from three male whales suggests these individuals remained within a specific zone for several years prior to death. δ13C values of both whale tissues (skin and baleen) and potential prey were not distinct among foraging zones. Our results highlight the importance of considering tissue isotopic incorporation and growth rates when studying migratory mysticetes and provide new insights into the individual movement strategies of blue whales.

Characterization of multivariate time series of behaviour data from animal-borne sensors is challenging. Biologists require methods to objectively quantify baseline behaviour, then assess behaviour changes in response to environmental stimuli. Here, we apply hidden Markov models (HMMs) to characterize blue whale movement and diving behaviour, identifying latent states corresponding to three main underlying behaviour states: shallow feeding, travelling, and deep feeding. The model formulation accounts for inter-whale differences via a computationally efficient discrete random effect, and measures potential effects of experimental acoustic disturbance on between-state transition probabilities. We identify clear differences in blue whale disturbance response depending on the behavioural context during exposure, with whales less likely to initiate deep foraging behaviour during exposure. Findings are consistent with earlier studies using smaller samples, but the HMM approach provides more nuanced characterization of behaviour changes.