ABSTRACT
Humpback whales are a cosmopolitan, highly vocal species. Investigated here are their vocalizations recorded at the Vema Seamount (31°38′S, 08°20′E) from moored hydrophones in the austral spring of 2019. During the 11-d recording period over 600 non-song calls were detected. Calls were predominantly detected at night over three consecutive days. The most common calls were low, frequency-modulated sounds (whups). An impulsive sound (gunshot) previously unknown in humpback whales was also detected. The location and timing of the calls suggests that humpback whales may be using the Vema Seamount as a temporary stop on their migration to their polar feeding grounds.
1. Introduction
Section:
Humpback whales (Megaptera novaeangliae) undertake long annual migrations between their breeding and feeding grounds, sometimes covering over 8000 km each way (Stone et al., 1990). During migration, individuals may use shallow areas, such as seamounts, as regular stopping places to feed or rest (Best et al., 1998), or as navigation beacons (Derville et al., 2020; Garrigue et al., 2015; Rogers, 2018). Seamounts are isolated elevations in the open ocean with summits of over 1000 m above the sea floor (Rogers, 2018) that can provide important habitats for cetaceans [sperm whales: Hann et al. (2016), beaked whales: Johnston et al. (2008), and dolphins: Morato et al. (2008)] as they are often points of high primary productivity surrounded by deep waters, providing potential food sources for predators (Clark et al., 2010; Yesson et al., 2011). Data on humpback whale behavior (e.g., feeding calls) at seamounts will help to determine how important these features are during migration and potentially reveal important foraging locations, that will help improve international marine management objectives.
Two separate humpback whale sub-stocks are found in the eastern South Atlantic Ocean, breeding stock B1 (BSB1)—which breed off Angola and equatorial west Africa—and BSB2—which feed in the Benguela Ecosystem off western South Africa but whose breeding area is unknown (Fig. 1) (Best and Allison, 2010; Findlay and Best, 1995). The southwards migration of both sub-stocks from their breeding grounds towards their Antarctic feeding grounds takes place between September and November (Rosenbaum et al., 2009). Evidence from photographic identification, genetic recaptures (Barendse et al., 2011), and satellite tracking (Rosenbaum et al., 2014), shows that humpback whales move south from the Gulf of Guinea using two main routes: (1) along the continental shelf or (2) following the Walvis Ridge (Fig. 1). This second route may possibly bring them close to the Vema Seamount (“Vema” from here on).
Fig. 1. The location of the Vema Seamount (red star, panel 1) in relation to South Africa and the BSB1 breeding grounds and BSB2 feeding grounds (see panel 2). Also shown: the approximate migration route of the BSB humpback whale population along the Walvis Ridge (gray line) (Rosenbaum et al., 2014). The eastern and western locations of the hydrophones are indicated with red stars (panels 2 and 3).
Vema (31°38′S, 08°20′E) is in the eastern South Atlantic Ocean, roughly 1000 km west of Cape Town, South Africa. It boasts a high level of biodiversity of both shallow and deep-water species (Bergstad et al., 2019) making it the target of intensive fishing, which began five years after its discovery in 1959 (Simpson and Heydorn, 1965). After heavy exploitation, Vema was closed to fisheries in 2007 and is now classified as a vulnerable marine ecosystem according to the United Nations Food and Agriculture Organization criteria (FAO, 2019).
The importance of Vema for cetaceans is unknown due to its remoteness and lack of survey effort. Passive acoustic monitoring (PAM), however, is a useful tool to monitor remote areas such as this and is a well-established method to detect stereotypical sounds of marine megafauna (Kowarski and Moors-Murphy, 2021; Warren et al., 2021; Stanistreetet al., 2016; Varga et al., 2017), and humpback whale vocalizations specifically (Helble et al., 2013; Ross-Marsh et al., 2020; Stimpert et al., 2011; van Opzeeland et al., 2013).
Humpback whale vocalizations are divided into song and non-song calls (Dunlop et al., 2008; Payne and McVay, 1971). In general, they are highly characteristic and easily distinguishable from background noise in the marine environment. PAM is, therefore, useful to determine humpback whale presence (Dunlop et al., 2008; Rekdahl et al., 2015) on their breeding (Kobayashi et al., 2021) and feeding (Cerchio and Dahlheim, 2001) grounds as well as migration routes (Clapham and Mattila, 1990). Here, we investigate the humpback whale vocalizations detected at Vema and the potential significance of this area for the species.
2. Methods
Section:
2.1 Data collection
Acoustic data were collected as part of a multi-objective, ship-based cruise from Cape Town, South Africa to Vema (Fig. 1) conducted by Greenpeace on board the RV Arctic Sunrise (23 October 2019 to 7 November 2019). Vema is roughly conical in shape, rising approximately 3000 m above the sea floor. The summit measures 11 km by 8 km, averaging 90–100 m in depth, with its most shallow point at 20 m deep (Bergstad et al. 2019). Two moored autonomous hydrophones (SoundTrap 300HF, Ocean Instruments Inc., NZ) were deployed on both the eastern (31.6371ºS, 8.40481ºE; deployed: October 25th, 2019) and western slopes (31.6338ºS, 8.27811ºE; deployed: October 27th, 2019) at water depths of 70 m and 55 m, respectively, roughly 12 km apart. The hydrophones were mounted approximately mid-water column (east: 35 m; west: 25 m) to limit the amount of background noise from the surface of the reef. The devices were deployed mid-afternoon, and recordings were timed to start at 18:00 h. Both moorings were fitted with acoustic release mechanisms and surface buoys to minimize the risk of instrument loss and successfully retrieved on November 4th, 2019.
2.2 Acoustics analysis
Acoustic data were recorded continuously during deployment, with a 96 kHz sample rate. Recordings were viewed in raven pro v1.6 (Center for Conservation Bioacoustics, 2019) with spectrograms created using a Hann window (overlap of 75%) with a fast Fourier transform (FFT) of 8096, viewed in 60 s windows with a frequency range of 0 to 8000 Hz. East and west recordings were investigated separately, both visually and aurally (E.C.R-M) in their entirety for song and non-song calls. Hydrophone recordings were treated independently, as we could not confidently determine whether calls were detected simultaneously on both devices. Detections from both hydrophones were pooled during analysis. Song was found only once, lasting <30 s and was therefore excluded from analysis. Non-song calls (“calls” from here on) were classified as “whups,” “grumbles,” “grumble-whups,” “tonals,” and “impulsive sounds” (Table 1) by authors E.C.R-M and T.G. as per previous studies for South Africa (authors' unpublished data) and other locations (Dunlop et al., 2008; Fournet et al., 2015; Rekdahl et al., 2017). For further validation, representative call type examples from Vema were compared with those recorded near (<100 m) feeding whales encountered in coastal South Africa (authors' unpublished data).
Table 1. Outcome of the random forest call classification test for humpback whale call types grumble, impulsive sound, tonal, and whup recorded at the Vema Seamount. True call types are shown in the rows and the predicted classification from the random forest model are shown in the columns. Number represented show the classification percentage (%) of each call type.
| Grumble | Impulsive sound | Tonal | Whup | |
|---|---|---|---|---|
| Grumble | 67 | 0 | 0 | 33 |
| Impulsive sound | 0 | 84 | 0 | 16 |
| Tonal | 0 | 0 | 100 | 0 |
| Whup | 8 | 2 | 0 | 90 |
Once calls were identified and grouped into recognized call types, call classification accuracy was quantitatively assessed using a supervised random forest analysis conducted in r 4.0.2 (R Core Team, 2020) and rstudio (RStudio Team, 2020) [“randomForest” package—Liaw and Wiener (2002)]. The random forest analysis was conducted on 11 robust signal measurements derived from raven pro, namely, Center Frequency, Bandwidth 50%, Bandwidth 90%, Frequency 5%, 25%, 75%, and 95%, Maximum Frequency, Duration 90%, Peak Frequency, and Delta Time. These measurements are based on signal power distributions and, therefore, do not rely heavily on the exact bounds of the selection created in raven (Charif et al., 2010). Calls were randomly assigned to a 75% training (n = 414 calls) and a 25% test dataset (n = 138 calls) with the maximum number of trials set to 1000. Classification of calls to four types was assessed through out of bag (OOB) error rates and the Cohens Kappa test was used to assess classification against chance in the unbalanced dataset. Parameter importance was assessed through mean decrease in accuracy and the Gini index.
Diel trends in call production were investigated. Data from SunCalc (2022) for Vema over the recording period were used to divide a 24 h day into five sections: Dawn (nautical dawn to sunrise), Morning (sunrise to solar noon), Afternoon (solar noon to sunset), Dusk (sunset to nautical dusk), and Night (nautical dusk to nautical dawn, when sun altitude is between 0° and 12°). The times chosen for these periods shifted each day, therefore, time intervals were averaged to determine a generalized start and end time for an average day at Vema resulting in Dawn = ∼1.5 h, Morning ≃ 6.5 h, Afternoon ≃ 6.5 h, Dusk ≃ 1.5 h and Night ≃ 8 h. Presence-absence of non-song calls were determined for each period in each day and plotted in rstudio [using the “mgcv” package—Wood (2017) and “nlme” pacakge—Pinheiro et al. (2019)] to create a binomial generalized additive model (GAM) to determine potential diel patterns.
3. Results
Section:
Hydrophones deployed at Vema were active for 11 days, totaling 423 h 8 min of recordings when pooled between both devices. Of the 652 calls identified, 558 were classified as either whups (n = 38, 68%), grumbles (n = 76, 14%), grumble-whups (n = 6, 1%), tonals (n = 19, 3%), or impulsive sounds (n = 77, 14%). These visually matched sound types previously documented during focal group recordings of humpback whales in coastal South Africa (Fig. 2). Ninety-four calls remained “unclassified,” and were excluded from analysis, as they were mostly recorded with low signal-to-noise ratio. Unclassified calls did not conform to any known or described call type, nor did they show any clear groupings amongst themselves to justify a new call type.
Fig. 2. A comparison of call types recorded at the Vema Seamount (two examples; blue block) to those recorded off the west coast of South Africa (one example; red block). Call types represented are whup, grumble, grumble-whup, tonal, and impulsive sound, with a brief description of each (black block).
Results from the random forest analysis (Table 1) showed strong support for the visual and aural categorization of call types. Low sample sizes prevented reliable inclusion of the call category “grumble-whup,” but random forest classification of the remaining call types had an overall classification success rate of 86%. Call classification success ranged from 67% to 100% depending on call type (see supplementary material for a table detailing the call classification success1). A Cohen's kappa statistic of 0.7 indicates moderate classification above chance in this unbalanced data set. The greatest error in classification was for the grumble category, as one third of cases (n = 6 of 18) were misclassified into the whup category. In contrast, confidence of whup classification, the dominant call recorded, was high with 90% correct classification for this call type. Classification was based mostly on the bandwidth 90% and 95% frequency parameters.
Most of the calls included in the analysis (n = 485, 87%) were detected on only three days of the deployment period, the 27th to the 29th of October (Fig. 3), with minimal to no detections on the other 8 days. No visual observation effort was made during the time of peak call detection. Grumble-whups were only detected on one day (29th October). Although rare, these calls were detected very clearly both visually and aurally (SNR of ∼17.2 dB), and easily matched to representative calls recorded off the coast of South Africa supporting identification of this novel combination call type.
Fig. 3. Count of non-song calls detected over the deployment period from 25 October 2019 to 11 April 2019 at the Vema Seamount. Call detections from both hydrophones were pooled.
The stereotyped impulsive sound recorded were broadband (50 to 16 000 Hz). Peak frequency was 122 ± 170 Hz (duration = ∼1.2 s), (see supplementary material for a recording example of the impulsive sound1). Half of the impulsive sounds (n = 39) detected were recorded mid-morning, between 06:00 and 08:30. The impulsive sounds were structurally similar to the right whale “gunshot” (Parks et al., 2005; Shabangu et al., 2021) but were detected simultaneously (within ± 10 s) with other known humpback whale calls (whups and grumbles) strongly indicating that they are produced by humpback whales.
All call types were more prevalent in the early morning and late evening (Fig. 4). Only five calls of any type were detected between 14:00 and 21:00 during the recording period (Fig. 4). This result was supported by the GAM analysis. Hour of the day [approximate significance of smooth terms: s(Hour) edf = 3, χ2 = 10,52, P < 0.05] was a significant predictor of call presence, showing a decreased probability of detecting calls during the afternoon and dusk hours. Day period, however, was not a significant predictor of call presence (P > 0.05). Whup calls were most prevalent in the late evening and early morning with roughly 48% (n = 269) of all whup calls occurring between 23:00 and 03:00. Grumble-whups only occurred during the 08:00 h on one day during the recording period.
Fig. 4. Number of call types detected at the Vema Seamount per hour of the day from 00:00 (00) to 23:00 (23) during the recording period. Call detections from both hydrophones were pooled.
4. Discussion
Section:
This is the first description of humpback whale vocalizations near a seamount in the southeast Atlantic. Overall, calling showed a strong diel pattern and when detected, humpback whale calls were found predominantly at night during the recording period and peaked between 27 and 29 October. Nighttime calling is consistent with calling behavior in other areas [Gulf of Maine, USA: Huang et al. (2016) and Gully MPA, Canada: Kowarski et al. (2018)] and singing behavior off the coast of southern Africa (Ross-Marsh et al., 2020). In contrast to coastal South Africa, song was not clearly detected at Vema during the recording period. This was unexpected as humpback whales are generally common in the South Atlantic Ocean (Wedekin et al., 2017), especially during their southward migration between September and November (Barendse et al. 2011) when they are known to sing (Ross Marsh et al., 2020; Gridley et al., 2018; Hawkey et al., 2020).
Five call types were identified and visual classification of four of these was supported by random forest analysis. Visual identification of a combination call type—the grumble-whup—was straightforward and matched regional data from coastal South Africa. The most common call detected was the whup, which is widely considered as a contact call (Wild and Gabriele, 2014). This could indicate interactions taking place between individuals in the area (Dunlop, 2017), such as between mother-calf pairs moving southward after the breeding season (Dunlop, 2017; Dunlop et al., 2008; Indeck et al., 2021). Whup and grumble calls are also produced by foraging whales and commonly heard on feeding grounds (Cerchio and Dahlheim, 2001; Fournet et al., 2018; Rekdahl et al., 2013; Wild and Gabriele, 2014). Feeding is consistent with behavioral observations (JF) of two humpback whales at Vema from the RV Arctic Sunrise on 4 November 2019, which overlapped with call detections. The individuals engaged in repeated long dives (estimated 10 min in duration) in the same location, interspersed with surface activity (including pectoral slapping).
Impulsive sounds (gunshot-like calls) were also detected regularly at Vema. Until now, impulsive gunshots had only been associated with other baleen whale species—notably bowhead whales, who do not inhabit the region (Best, 2007), and right whales (Shabangu et al., 2021; Stafford et al., 2008). Right whale calls are readily detected in South African waters (Hofmeyr-Juritz and Best, 2011), and gunshot sounds in offshore waters between Vema and South Africa have been attributed to southern right whales (Shabangu et al., 2021). However, no right whales were seen during the survey (JF), nor were any southern right whale calls detected on the moored hydrophones (this study), therefore, it seems unlikely that the sounds reported here were made by right whales. The regular detection of these impulsive sounds at Vema, which were temporally associated with well-defined humpback whale calls, along with the support of several detections of impulsive sounds by author S.H.E. in coastal South Africa near feeding super-groups of humpback whales, indicates that these impulsive call types might be generated by humpback whales. Additionally, a similar sound type, the “bop,” was recorded near Peregain Beach, Brisbane during the southward migration and reported by Rekdahl et al. (2015). This may further indicate the ability of humpback whales to produce impulsive sounds such as those reported here.
The detection of non-song calls from humpback whales at Vema indicates that the area is used as more than just a migration “marker” by humpback whales, although conclusions are limited by the short duration of the recording period. The presence of non-song calling in conjunction with observed feeding behavior may indicate that Vema could provide humpback whales with the opportunity to feed. Globally, whale populations are beginning to struggle as food resources shift and decline (Schleimer et al., 2019; Simmonds and Isaac, 2007; van den Berg et al., 2021). It is possible, therefore, that areas like Vema and the Benguela feeding ground may provide important supplementary feeding opportunities along migration routes. Although we were not able to determine the population origin of the humpback whales recorded using the current data, telemetry data reported by Rosenbaum et al. (2014) and the South African Department of Environmental Affairs (Department of Environmental Affairs, 2016) shows that individuals from both breeding stock B and breeding stock C migrate past this area. In conjunction with acoustic monitoring and repertoire matching between recordings at the seamount and known populations, satellite telemetry could help to determine the residence time for humpback whales in the region as well as their origin. Further, fine scale telemetry devices (for example, acoustic tags) could potentially elucidate behavior and habitat use for this area (Bejder et al., 2019; Derville et al., 2020). Integrating these complementary streams of data, as well as extending the duration of acoustic monitoring at the seamount, would enable us to better understand the function of the calls made during their time at Vema, and potentially other seamounts in the area. This could be an important first step in providing a clearer picture on routes and timings of migrations for whales that transit the South Atlantic Ocean. Knowledge of migration routes, timings, and the associated behaviours of pelagic cetaceans is an important step towards the understanding of the health and drivers of deep ocean ecosystems.
Acknowledgments
Thank you to the crew of the Greenpeace vessel RV Arctic Sunrise for practical help during the research. We also thank the logistics staff from Greenpeace International Ships Unit. Most importantly, we thank Grant Oakes, David Santillo, Paul Johnston, and Sandra Schoettner for helping to make this research possible. This research was funded by the Umweltstiftung Greenpeace Foundation, Hamburg, and supported by the Greenpeace Research Laboratories, Biosciences, University of Exeter, United Kingdom.
1 See the supplementary material at https://www.scitation.org/doi/suppl/10.1121/10.0010072 for a table detailing the call classification success and a recording example of the impulsive sound.
References and links
Section:
- 1. Barendse, J., Best, P. B., Thornton, M., Elwen, S. H., Rosenbaum, H. C., Carvalho, I., Pomilla, C., Collins, T. J. Q., Meÿer, M. A., and Leeney, R. H. (2011). “ Transit station or destination? Attendance patterns, movements and abundance estimate of humpback whales off west South Africa from photographic and genotypic matching,” Afr. J. Mar. Sci. 33, 353–373. https://doi.org/10.2989/1814232X.2011.637343, Google ScholarCrossref
- 2. Bejder, L., Videsen, S., Hermannsen, L., Simon, M., Hanf, D., and Madsen, P. T. (2019). “ Low energy expenditure and resting behaviour of humpback whale mother-calf pairs highlights conservation importance of sheltered breeding areas,” Sci. Rep. 9, 771. https://doi.org/10.1038/s41598-018-36870-7, Google ScholarCrossref
- 3. Bergstad, O. A., Gil, M., Høines, Å. S., Sarralde, R., Maletzky, E., Mostarda, E., Singh, L., António, M. A., Ramil, F., Clerkin, P., and Campanis, G. (2019). “ Megabenthos and benthopelagic fishes on Southeast Atlantic seamounts,” Afr. J. Mar. Sci. 41(1), 29–50. https://doi.org/10.2989/1814232X.2019.1571439, Google ScholarCrossref
- 4. Best, P. B. (2007). Whales and Dolphins of the Southern African Subregion ( Cambridge University Press, Cambridge). Google Scholar
- 5. Best, P. B., and Allison, C. (2010). “ Catch history, seasonal and temporal trends in the migrations of humpback whales along the west coast of southern Africa,” Paper No. SC/62/SH5 submitted to IWC Scientific Committee. Google Scholar
- 6. Best, P. B., Findlay, K. P., Sekiguchi, K., Peddemors, V. M., Rakotonirina, B., Rossouw, A., and Gove, D. (1998). “ Winter distribution and possible migration routes of humpback whales Megaptera novaeangliae in the southwest Indian Ocean,” Mar. Ecol.: Prog. Ser. 162, 287–299. https://doi.org/10.3354/meps162287, Google ScholarCrossref
- 7. Centre for Conservation Bioacoustics (2019). “Raven Pro: Interactive sound analysis software (version 1.6.1) [computer program],” The Cornell Lab of Ornithology, Ithaca, NY, http://www.birds.cornell.edu/raven (Last viewed 10/5/2021). Google Scholar
- 8. Cerchio, S., and Dahlheim, M. (2001). “ Variation in feeding vocalizations of humpback whales Megaptera novaeangliae from southeast Alaska,” Bioacoustics 11(4), 277–295. https://doi.org/10.1080/09524622.2001.9753468, Google ScholarCrossref
- 9. Charif, R. A., Waack, A. M., and Strickman, L. M. (2010). Raven Pro 1.4 User's Manual ( Cornell Lab of Ornithology, Ithaca, NY). Google Scholar
- 10. Clapham, P. J., and Mattila, D. K. (1990). “ Humpback whale songs as indicators of migration routes,” Marine Mammal Sci. 6, 155–160. https://doi.org/10.1111/j.1748-7692.1990.tb00238.x, Google ScholarCrossref
- 11. Clark, M. R., Rowden, A. A., Schlacher, T., Williams, A., Consalvey, M., Stocks, K. I., Rogers, A. D., O'Hara, T. D., White, M., Shank, T. M., and Hall-Spencer, J. M. (2010). “ The ecology of seamounts: Structure, function, and human impacts,” Ann. Rev. Mar. Sci. 2, 253–278. https://doi.org/10.1146/annurev-marine-120308-081109, Google ScholarCrossref
- 12. Department of Environmental Affairs (2016). “ State of the oceans and coasts around South Africa 2017 report card based on research and monitoring observations,” edited by H. Verheye , J. Huggett , and R. Crawford, Report No. 15-RP348/2016, Department of Environmental Affairs (RSA), Pretoria. Google Scholar
- 13. Derville, S., Torres, L. G., Zerbini, A. N., Oremus, M., and Garrigue, C. (2020). “ Horizontal and vertical movements of humpback whales inform the use of critical pelagic habitats in the western South Pacific,” Sci. Rep. 10, 4871. https://doi.org/10.1038/s41598-020-61771-z, Google ScholarCrossref
- 14. Dunlop, R. A. (2017). “ Potential motivational information encoded within humpback whale non-song vocal sounds,” J. Acoust. Soc. Am. 141, 2204–2213. https://doi.org/10.1121/1.4978615, Google ScholarScitation
- 15. Dunlop, R. A., Cato, D. H., and Noad, M. J. (2008). “ Non-song acoustic communication in migrating humpback whales (Megaptera novaeangliae),” Mar. Mam. Sci. 24, 613–629. https://doi.org/10.1111/j.1748-7692.2008.00208.x, Google ScholarCrossref
- 16. FAO (2019). Fisheries Management Reports 2019 (South East Atlantic Fisheries Organisation, Rome). Google Scholar
- 17. Findlay, K. P., and Best, P. B. (1995). “ Summer incidence of humpback whales on the west coast of South Africa,” Afr. J. Mar. Sci. 15, 279–282. https://doi.org/10.2989/02577619509504851, Google ScholarCrossref
- 18. Fournet, M. E., Gabriele, C. M., Culp, D. C., Sharpe, F., Mellinger, D. K., and Klinck, H. (2018). “ Some things never change: Multi-decadal stability in humpback whale calling repertoire on Southeast Alaskan foraging grounds,” Sci. Rep. 8(1), 1–13. Google ScholarCrossref
- 19. Fournet, M. E., Szabo, A., and Mellinger, D. K. (2015). “ Repertoire and classification of non- song calls in Southeast Alaskan humpback whales (Megaptera novaeangliae),” J. Acoust. Soc. Am. 137, 1–10. https://doi.org/10.1121/1.4904504, Google ScholarScitation
- 20. Garrigue, C., Clapham, P. J., Geyer, Y., Kennedy, A. S., and Zerbini, A. N. (2015). “ Satellite tracking reveals novel migratory patterns and the importance of seamounts for endangered South Pacific humpback whales,” R. Soc. Open Sci. 2, 150489. https://doi.org/10.1098/rsos.150489, Google ScholarCrossref
- 21. Gridley, T., Silva, M. F. P., Wilkinson, C., Seakamela, S. M., and Elwen, S. H. (2018). “ Song recorded near a super-group of humpback whales on a mid-latitude feeding ground off South Africa,” J. Acoust. Soc. Am. 143, EL298–EL304. https://doi.org/10.1121/1.5032126, Google ScholarScitation
- 22. Hann, C. H., Smith, T. D., and Torres, L. G. (2016). “ A sperm whale's perspective: The importance of seasonality and seamount depth,” Mar. Mam. Sci. 32, 1470–1481. https://doi.org/10.1111/mms.12320, Google ScholarCrossref
- 23. Hawkey, J. S., Elwen, S. H., James, B. S., Gridley, T., and Simone, A. P. (2020). “ First look at humpback whale (Megaptera novaeangliae) song structure from western South Africa,” Afr. Zool. 55, 224–232. https://doi.org/10.1080/15627020.2020.1796524, Google ScholarCrossref
- 24. Helble, T. A., D'Spain, G. L., Hildebrand, J. A., Campbell, G. S., Campbell, R. L., and Heaney, K. D. (2013). “ Site specific probability of passive acoustic detection of humpback whale calls from single fixed hydrophones,” J. Acoust. Soc. Am. 134(3), 2556–2570. https://doi.org/10.1121/1.4816581, Google ScholarScitation
- 25. Hofmeyr-Juritz, L. H., and Best, P. B. (2011). “ Acoustic behaviour of southern right whales in relation to numbers of whales present in Walker Bay, South Africa,” Afr. J. Mar. Sci. 33(3), 415–427. https://doi.org/10.2989/1814232X.2011.637616, Google ScholarCrossref
- 26. Huang, W., Wang, D., and Ratilal, P. (2016). “ Diel and spatial dependence of humpback song and non-song vocalizations in fish spawning ground,” Remote Sens. 8, 712. https://doi.org/10.3390/rs8090712, Google ScholarCrossref
- 27. Indeck, K. L., Girola, E., Torterotot, M., Noad, M. J., and Dunlop, R. A. (2021). “ Adult female-calf acoustic communication signals in migrating east Australian humpback whales,” Bioacoustics 30, 341–365. https://doi.org/10.1080/09524622.2020.1742204, Google ScholarCrossref
- 28. Johnston, D. W., McDonald, M., Polovina, J., Domokos, R., Wiggins, S., and Hildebrand, J. (2008). “ Temporal patterns in the acoustic signals of beaked whales at Cross Seamount,” Biol. Lett. 4, 208–211. https://doi.org/10.1098/rsbl.2007.0614, Google ScholarCrossref
- 29. Kobayashi, N., Okabe, H., Higashi, N., Miyahara, H., and Uchida, S. (2021). “ Diel patterns in singing activity of humpback whales in a winter breeding area in Okinawan (Ryukyuan) waters,” Mar. Mam. Sci. 37, 982–992. https://doi.org/10.1111/mms.12790, Google ScholarCrossref
- 30. Kowarski, K., Evers, C., Moors-Murphy, H., Martin, B., and Denes, S. L. (2018). “ Singing through winter nights: Seasonal and diel occurrence of humpback whale (Megaptera novaeangliae) calls in and around the Gully MPA, offshore eastern Canada,” Mar. Mam. Sci. 34, 169–189. https://doi.org/10.1111/mms.12447, Google ScholarCrossref
- 31. Kowarski, K. A., and Moors-Murphy, H. (2021). “ A review of big data analysis methods for baleen whale passive acoustic monitoring,” Mar. Mam. Sci. 37, 652–673. https://doi.org/10.1111/mms.12758, Google ScholarCrossref
- 32. Liaw, A., and Wiener, M. (2002). “ Classification and regression by randomForest,” R News 2, 18–22. Google Scholar
- 33. Morato, T., Varkey, D. A., Damaso, C., Machete, M., Santos, M., Prieto, R., Santos, R. S., and Pitcher, T. J. (2008). “ Evidence of a seamount effect on aggregating visitors,” Mar. Ecol. Prog. Ser. 357, 23–32. https://doi.org/10.3354/meps07269, Google ScholarCrossref
- 34. Parks, S. E., Hamilton, P. K., Kraus, S. D., and Tyack, P. L. (2005). “ The gunshot sound produced by male North Atlantic right whales (Eubalaena glacialis) and its potential function in reproductive advertisement,” Mar. Mammal Sci. 21, 458–475. https://doi.org/10.1111/j.1748-7692.2005.tb01244.x, Google ScholarCrossref
- 35. Payne, R. S., and McVay, S. (1971). “ Songs of humpback whales,” Science 173, 585–597. https://doi.org/10.1126/science.173.3997.585, Google ScholarCrossref
- 36. Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., R Core Team (2019). “ nlme: Linear and nonlinear mixed effects models,” R package version 3.1-140, https://CRAN.R-project.org/package=nlme (Last viewed 10/14/2019). Google Scholar
- 37. R Core Team (2020). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (Last viewed 28/7/2020). Google Scholar
- 38. Rekdahl, M., Tisch, C., Cerchio, S., and Rosenbaum, H. (2017). “ Common non-song social calls of humpback whales (Megaptera novaeangliae) recorded off northern Angola, southern Africa,” Mar. Mam. Sci. 33, 365–375. https://doi.org/10.1111/mms.12355, Google ScholarCrossref
- 39. Rekdahl, M. L., Dunlop, R. A., Goldizen, A. W., Garland, E. C., Biassoni, N., Miller, P., and Noad, M. J. (2015). “ Non-song social call bouts of migrating humpback whales,” J. Acoust. Soc. Am. 137, 3042–3053. https://doi.org/10.1121/1.4921280, Google ScholarScitation
- 40. Rekdahl, M. L., Dunlop, R. A., Noad, M. J., and Goldizen, A. W. (2013). “ Temporal stability and change in the social call repertoire of migrating humpback whales,” J. Acoust. Soc. Am. 133, 1785–1795. https://doi.org/10.1121/1.4789941, Google ScholarScitation
- 41. Rogers, A. D. (2018). “ The Biology of seamounts: 25 years on,” Adv. Mar. Biol. 79, 137–224. https://doi.org/10.1016/bs.amb.2018.06.001, Google ScholarCrossref
- 42. Rosenbaum, H. C., Maxwell, S. M., Kershaw, F., and Mate, B. (2014). “ Long-range movement of humpback whales and their overlap with anthropogenic activity in the South Atlantic Ocean,” Biology 28, 604–615. https://doi.org/10.1111/cobi.12225, Google ScholarCrossref
- 43. Rosenbaum, H. C., Pomilla, C., Mendez, M., Leslie, M. S., Best, P. B., Findlay, K. P., Minton, G., Ersts, P. J., Collins, T., Engel, M. H., Bonatto, S. L., Kotze, D. P. G. H., Meÿer, M., Barendse, J., Thornton, M., Razafindrakoto, Y., Ngouessono, S., Vely, M., and Kiszka, J. (2009). “ Population structure of humpback whales from their breeding grounds in the South Atlantic and Indian Oceans,” PLoS One 4, e7318. https://doi.org/10.1371/journal.pone.0007318, Google ScholarCrossref
- 44. Ross-Marsh, E. C., Elwen, S. H., Prinsloo, A. S., James, B. S., and Gridley, T. (2020). “ Singing in South Africa: Monitoring the occurrence of humpback whale (Megaptera novaeangliae) song near the Western Cape,” Bioacoustics 30, 163–179. https://doi.org/10.1080/09524622.2019.1710254, Google ScholarCrossref
- 45. RStudio Team (2020). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, http://www.rstudio.com/ (Last viewed 28/7/2020). Google Scholar
- 46. Schleimer, A., Ramp, C., Delarue, J., Carpentier, A., Bérubé, M., Palsbøll, P. J., Sears, R., and Hammond, P. S. (2019). “ Decline in abundance and apparent survival rates of fin whales (Balaenoptera physalus) in the northern Gulf of St. Lawrence,” Ecol. Evol. 9, 4231–4244. https://doi.org/10.1002/ece3.5055, Google ScholarCrossref
- 47. Shabangu, F. W., Andrew, R. K., and Findlay, K. (2021). “ Acoustic occurrence, diel-vocalizing pattern, and detection ranges of southern right whale gunshot sounds off South Africa's west coast,” Mar. Mam. Sci. 37, 733–750. https://doi.org/10.1111/mms.12760, Google ScholarCrossref
- 48. Simmonds, M. P., and Isaac, S. J. (2007). “ The impacts of climate change on marine mammals: Early signs of significant problems,” Oryx 41, 19–26. https://doi.org/10.1017/S0030605307001524, Google ScholarCrossref
- 49. Simpson, E., and Heydorn, A. (1965). “ Vema Seamount,” Nature 207, 249–251. https://doi.org/10.1038/207249a0, Google ScholarCrossref
- 50. Stafford, K. M., Moore, S. E., Laidre, K. L., and Heide-Jørgensen, M. P. (2008). “ Bowhead whale springtime song off West Greenland,” J. Acoust. Soc. Am. 124, 3315–3323. https://doi.org/10.1121/1.2980443, Google ScholarScitation
- 51. Stanistreet, J. E., Nowacek, D. P., Read, A. J., Baumann-Pickering, S., Moors-Murphy, H. B., and Van Parijs, S. M. (2016). “ Effects of duty-cycled passive acoustic recordings on detecting the presence of beaked whales in the northwest Atlantic,” J. Acoust. Soc. Am. 140(1), EL31–EL37. https://doi.org/10.1121/1.4955009, Google ScholarScitation
- 52. Stimpert, A. K., Au, W. W., Parks, S. E., Hurst, T., and Wiley, D. N. (2011). “ Common humpback whale (Megaptera novaeangliae) sound types for passive acoustic monitoring,” J. Acoust. Soc. Am. 129(1), 476–482. https://doi.org/10.1121/1.3504708, Google ScholarScitation
- 53. Stone, G. S., Florez-Gonzalez, L., and Katona, S. (1990). “ Whale migration record,” Nature 346, 705. https://doi.org/10.1038/346705a0, Google ScholarCrossref
- 54. SunCalc (2022). www.suncalc.org (Last viewed 15/5/2021). Google Scholar
- 55. van den Berg, G. L., Vermeulen, E., Valenzuela, L. O., Bérubé, M., Ganswindt, A., Gröcke, D. R., Hall, G., Hulva, P., Neveceralova, P., Palsbøll, P. J., and Carroll, E. L. (2021). “ Decadal shift in foraging strategy of a migratory Southern Ocean predator,” Glob. Change Biol. 27, 1052–1067. https://doi.org/10.1111/gcb.15465, Google ScholarCrossref
- 56. van Opzeeland, I., van Parijs, S., Kindermann, L., Burkhardt, E., and Boebel, O. (2013). “ Calling in the cold: Pervasive acoustic presence of humpback whales (Megaptera novaeangliae) in Antarctic coastal waters,” PLoS One 8, e73007. https://doi.org/10.1371/journal.pone.0073007, Google ScholarCrossref
- 57. Varga, L. M., Frasier, K. E., Trickey, J. S., Debich, A. J., Hildebrand, J. A., Rice, A. C., Thayre, B. J., Rafter, M., Wiggins, S. M., Baumann-Pickering, S., and Hodge, L. E. (2017). “ Passive acoustic monitoring for marine mammals in the Jacksonville Range Complex July–November 2015.”, Google Scholar
- 58. Warren, V. E., Širović, A., McPherson, C., Goetz, K. T., Radford, C. A., and Constantine, R. (2021). “ Passive acoustic monitoring reveals spatio-temporal distributions of Antarctic and pygmy blue whales around central New Zealand,” Front. Mar. Sci. 7, 1162. https://doi.org/10.3389/fmars.2020.575257, Google ScholarCrossref
- 59. Wedekin, L. L., Engel, M. H., Andriolo, A., Prado, P. I., Zerbini, A. N., Marcondes, M. M. C., Kinas, P. G., and Simões-Lopes, P. C. (2017). “ Running fast in the slow lane: Rapid population growth of humpback whales after exploitation,” Mar. Ecol. Prog. Ser. 575, 195–206. https://doi.org/10.3354/meps12211, Google ScholarCrossref
- 60. Wild, L. A., and Gabriele, C. M. (2014). “ Putative contact calls made by humpback whales (Megaptera novaeangliae) in south-eastern Alaska,” Can. Acoust. 42(4), 23–31. Google Scholar
- 61. Wood, S. N. (2017). Generalized Additive Models: An Introduction with R, 2nd ed. ( CRC Press, Boca Raton, FL). Google ScholarCrossref
- 62. Yesson, C., Clark, M. R., Taylor, M. L., and Rogers, A. D. (2011). “ The global distribution of seamounts based on 30 arc seconds bathymetry data,” Deep-Sea Res. Part I: Oceanogr. Res. Pap. 58(4), 442–453. https://doi.org/10.1016/j.dsr.2011.02.004, Google ScholarCrossref
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