The use of passive acoustic monitoring (PAM) in marine mammal research has become more widespread over the past decade. Increasingly, the functional use of calls by baleen whales under various social and behavioral contexts has been examined, as has the relevance of calling to population dynamics (Küsel et al., 2011; Marques et al., 2012). Evaluating population-specific trends in call or cue rates from passively collected acoustic data has, for example, been used to estimate population densities (Mellinger et al., 2007; Küsel et al., 2011; Marques et al., 2012; Stevenson et al., 2020). However, using passively collected acoustic data to monitor populations more comprehensively than noting presence and relative abundance requires a greater understanding of the contextual underpinnings of that population’s vocal activity, as well as the temporal and geospatial trends in their acoustic behavior (Stimpert et al., 2015; Lewis et al., 2018). A functional understanding of the acoustic behavior of a given population can yield insight into how individuals and groups interact on spatiotemporal scales (Stimpert et al., 2015; Lewis et al., 2018).

Technological advances in multi-sensor animal-borne tag capabilities over the past decade, such as Digital Acoustic Recording Tags (DTAGs) and those developed by Custom Animal Tracking Solutions (CATS), have increased our capacity to collect high-resolution and fine-scale video, audio, and kinematic data on baleen whales (Rutz & Hays, 2009; Cade et al., 2021). This synchronous collection of acoustic and accelerometer data has the potential to uncover contextual or functional aspects of acoustic activity in whales and add context to studies utilizing remotely collected acoustic data only. A number of studies have used a tag’s high-resolution accelerometer to differentiate between tagged and non-tagged animal call production in fin (Balaenoptera physalus), blue (Balaenoptera musculus), and humpback whale (Megaptera novaeangliae) acoustic tag data. (Goldbogen et al., 2014; Stimpert et al., 2015; Saddler et al., 2017; Oestreich et al., 2020; Stimpert et al., 2020). These studies found that calls produced by tagged animals were synchronously detected on both the tag’s hydrophone and accelerometer, thought to be the product of active vibrating of the tagged animal’s body as it produces sound (Goldbogen et al., 2014; Stimpert et al., 2015; Oestreich et al., 2020; Stimpert et al., 2020). Saddler et al. (2017), however, concluded that a proportion of the calls identified from blue whale tag data could have originated from a non-tagged individual. As such, further exploration into the context of call occurrence in tagged animals relative to trends in call type use, diel patterns, and call rates, is warranted to better understand the application of accelerometers in identifying individual-level patterns in calling or call function across various species.

Studies on the acoustic behavior of Eastern North Pacific (ENP) gray whales (Eschrichtius robustus) have been primarily focused on breeding grounds and during migration (e.g. Cummings et al., 1968; Dahlheim, 1987; Crane & Lashkari, 1996; Charles, 2011; Ponce et al., 2012; Guazzo et al., 2017; Burnham et al., 2018; Burnham & Duffus, 2020). Little is currently known about gray whale acoustic activity, calling function, and the relevance of both to population dynamics in feeding areas (Stafford et al., 2007; Burnham et al., 2018; Frouin-Mouy et al., 2020; Burnham & Duffus, 2022). Gray whales produce several call types, initially categorized by Dahlheim (1987) into six core classes from recordings made principally in breeding lagoons. These categories included pulsive knock-like sounds (Class 1), sweeping tones (Class 2), moans (Class 3), ‘rumbles’ (Class 4) and two variations of subsurface exhalations (Classes 5 and 6) (Dahlheim, 1987). Class 1 knock calls have been found to be the most prevalent call when gray whales are in breeding/calving lagoons (Dahlheim, 1987; Ollervides, 2001; Charles, 2011); seeming to suggest social or reproductive function (Youngson and Darling, 2016), but have also been reported present during migration (Crane & Lashkari, 1996; Burnham & Duffus, 2020) and in association with feeding (Dahlheim, 1987; Burnham & Duffus, 2022)

More recently, studies have been successful in documenting gray whale calling behavior during periods of migration, showing that Class 3 moans dominate the acoustic repertoire in this context (Crane & Lashkari, 1996; Guazzo et al., 2017; Burnham et al., 2018; Rannankari et al., 2018; Burnham & Duffus, 2020). Comparatively less work has been conducted on foraging grounds (Stafford et al., 2007), though a recent study showed that gray whales may alter their vocal behavior on feeding grounds in relation to social setting and prey abundance (Burnham & Duffus, 2022). Understanding a population’s acoustic activity across the full range of its behavioral states is important in call categorization attempts, as modifications to calling may occur in response to differing social or physiological contexts.

To broaden our knowledge of ENP gray whale call use on foraging grounds, and better understand the potential application of an accelerometer-based caller identification method for the vocally complex gray whale, we performed an acoustically focused analysis of multi-sensor biologging data. Our focal whales were a small group (n = 8 to 12) of known individuals, commonly referred to as the “Sounders”, that return annually to northern Puget Sound (NPS) in Washington State (Weitkamp et al., 1992; Calambokidis et al., 2015; Pruitt and Donoghue, 2016, Calambokidis, 2017; Carretta et al., 2022). While the majority of ENP gray whales migrate directly from their calving and breeding grounds in Baja California Sur to highly productive foraging areas in the Chukchi and Bering Sea from November to February, the Sounders make migratory detours to NPS to feed nearly exclusively on ghost shrimp living in intertidal zones before continuing to the Arctic (Weitkamp et al., 1992; Pruitt & Donoghue, 2016; Calambokidis, 2017; Carretta et al., 2022). The well documented life histories of the Sounders and their predictable return to NPS made them ideal test-candidates for our study. Here, we evaluate the acoustic behavior of gray whales on feeding grounds as well as the application of a new method in caller identification via tag data for this species.

Approximately 185 hours of acoustic data were gathered over the course of the study period via CATS tag accelerometers (n = 128.4 hr), tag cameras (n = 26.6 hr), tag hydrophones (n = 17 hr). Information concerning by-deployment variation in data collected and calls identified can be found in Table 2 of the Supplementary Materials . A total of 107 calls were identified in tag hydrophone or camera acoustic recordings between all tag deployments and were classified to one of the six core call types (Dahlheim, 1987; Wisdom et al., 2001; Burnham et al., 2018; Burnham & Duffus, 2020). The dominant call type recorded (n = 37) was higher frequency pulsive Class 4 ‘rumble’ calls. Class 3 tonal calls, encompassing single or multiple-octave tonal calls, often referred to as moans, occurring primarily under 300 Hz, were also heard at relatively high densities (n = 31). Several Class 2 (n = 23) multi-harmonic tonal calls, occurring primarily under 300 Hz and with down or up-swept frequency modulation, and a few (n = 4) Class 1 knock calls were also present (see Figure 1 ; Supplemental Materials Table 3 for call type examples and descriptive statistics, respectively). Finally, an additional 12 calls were identified and were too variable in call structure to be reliably categorized using current cataloguing systems and were therefore considered “uncategorized”.

Forty-nine calls were classified as high SNR. Of these, 24 showed evidence of an associated accelerometer call signature matching the acoustic detection, with the remaining 25 high SNR calls not found to be associated with an accelerometry call signature. None of the low SNR calls examined (n = 32) were found to have an associated call signature on the accelerometer at the time calls were acoustically detected. Therefore, accelerometry detection was determined to be indicative of tagged animal call production, allowing the categorization of calls as originating from the tagged whale or a conspecific even if the call was categorized to have high SNR. Of the 24 high SNR calls attributed to tagged animals, most were Class 3 (n = 16) or Class 2 (n = 7) calls, with a single (n = 1) Class 4 call. Using accelerometry call signatures verified by acoustic recordings as reference, we identified an additional 34 calls during the approximately 81 hours of accelerometer recordings only (no acoustic data) likely produced by the tagged animal. Combining detection of calls via accelerometry from periods both with and without synchronous acoustic recording resulted in a cumulative total of 58 calls considered to be produced by tagged whales from all deployments. All accelerometry-based call signatures varied in intensity between accelerometer axes on an individual level, with most signatures displaying the highest intensity in the accelerometer’s x (n = 28) or z (n = 25) channel or axes ( Figure 2 ). Lastly, all high SNR calls with accelerometer calls signatures showed structural (frequency range, harmonic count, overall duration) differences when comparing accelerometer data to the full-frequency calls in the hydrophone data ( Figure 2 ). Examination of the data manually did not find any acoustic signals similar in parameters to calls that were not vocalisations (false positive results), confirming that all the signals included in this analysis were indeed calls.

Comparisons of tagged whale call occurrence to behavioral and kinematic tag information found vocalizations occurred primarily while travelling at slow speeds (mean 1.03 ± .25 m/s^-1), while at shallow depths (mean 7.63 ± .99 m), and temporally proximate to surface behavior (mean 43.3 ± 9.7 seconds) ( Figure 3 ; Table 1 ). This was similar to overall trends in depth and speed while not actively engaged in foraging, with average speeds of 1.46 ± (11) m/s and depths of 5.78 ± (2) m. Despite tagged whales spending 44% of the time engaged in foraging behavior, a relatively small proportion (7.1%) of the calls originating from tagged whales occurred during these periods. A comparatively higher proportion (27%) of environmental calls coincided with periods of the tagged whale feeding. Calls produced by tagged whales typically occurred towards the conclusion of a feeding event rather than before the start of one, with a mean period of 87.7 ± ( 9.3) or 210 ± (1) minutes having elapsed between a tagged whale vocalization and the conclusion or initiation of a tagged whale foraging event, respectively ( Figure 3 ; Table 1 ). Significant inter-deployment variation was observed in both the speed of the animal at the time of calling and the time elapsed until the next foraging event (p< 0.0001) ( Table 1 ). However, depth at time of calling, the temporal proximity to surfacing behavior and the most recent foraging event were not found to have significant inter-deployment variation ( Table 1 ).

An overall mean hourly calling rate (number of calls produced by one whale per hour) of 0.67 ± ( 1) calls/hr/whale was calculated from all the deployments, while a weighted mean based on the respective tag’s deployment duration was 0.44 calls/hr/whale. Calling rates were compared to rates reported by relevant studies (see Table 4 in Supplemental Resources) and were generally found to be higher than previously reported.

The work presented here is a first look at gray whale acoustic behavior on foraging grounds using animal-borne tags and adds to the few recordings of gray whales during feeding (Stafford et al., 2007; Burnham & Duffus, 2022). It is also an additional application of caller identification via accelerometry in tag-based acoustic data that, with further testing and validation, could revolutionize how individual-level acoustic activity is related to broad-scale population dynamics. Finally, the behavioral context of individual-level call production evaluated in this study has implications regarding our current understanding of the functional use of calling by gray whales, as it provides call-production specific contexts unobservable via remote or static monitoring (i.e., surface-based visual observation and passive acoustic recording).

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://purl.stanford.edu/bn098hs8476.

Ethical review and approval was not required for the animal study because all operations involving tagging of animals were conducted under National Marine Fishery Service permit #16111. Tagging operations were previously reviewed and approved as required by the Animal Welfare Act by an Institutional Animal Care and Use Committee.

HC conceived the study with guidance from JC and DC, and conducted all data analyses and processing, with the exclusion of raw tag data processing, which was initially performed by DC. JC JG, and DC collected the data via tag deployments. Funding support developed by JC and JG. RB assisted HC in the categorization and review of calls based on previously reported call types. HC interpreted results and drafted all manuscript versions. All authors took part in reviewing and editing the manuscript draft prior to submission. All authors contributed to the article and approved the submitted version.