Harmful algal blooms expose southern right whales to domoic acid and can potentially cause endocrine alterations

Dr. Alejandro Fernández Ajó, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Rises in ocean temperatures can lead to multiple alterations in marine ecosystems, including the increase and the frequency of Harmful Algal Blooms (HABs). HABs are characterized by the rapid growth of toxin-producing species of algae that can be harmful to people, animals, and the local ecology, even causing death in severe cases. Species of marine diatom within the genus Pseudo-nitzschia and Nitzschia can form HABs when they produce domoic acid (DA), a potent neurotoxin responsible for amnesic shellfish poisoning (D’Agostino et al., 2018, 2017).

Figure 1. Southern right whale (E. australis) mother and calf swimming at the gulfs of Peninsula Valdes, Argentina, during a phytoplankton bloom. Photo: Mariano Sironi / Instituto de Conservacion de Ballenas de Argentina.

During HABs, DA is transferred to higher organisms through the pelagic food web and is accumulated by intermediate vectors, such as copepods, euphausiids (i.e., krill), shellfish, and fish. As this neurotoxin affects top predators, DA poisoning poses a risk to the safety and health of humans and wildlife. This neurotoxin has caused mortality in many marine mammal species, including both pinnipeds and cetaceans (Gulland 1999; Lefebvre et al. 1999; Fire et al. 2010, 2021; Broadwater et al. 2018). In addition, the exposure to DA constitutes a stressor that may affect glucocorticoids (hormones involved in the stress response) concentrations.

The glucocorticoids (GCs; cortisol and corticosterone) are adrenal steroid hormones that maintain the essential functions of metabolism and energy balance in mammals. GCs can increase sharply in response to environmental stressors to elicit physiological and behavioral adaptations by individuals to support survival (Sapolsky et al. 2000; Bornier et al. 2009). However, with the chronic exposure to a stressor, this relationship can reverse, with GCs sometimes declining below its baseline levels (Dickens and Romero, 2013; Fernández Ajó et al., 2018). Moreover, DA can interfere with the stress response in mammals, and cause alterations in their physiological response. DA is an excitatory amino acid analog of glutamate (Pulido 2008), a well-known brain neurotransmitter that play an important role in the activation of the adrenal axis (which in turn regulate the production and secretion of the GCs) and regulate many of the pituitary hormones involved in the stress response (Brann and Mahesh 1994; Johnson et al. 2001). Hence, monitoring GC levels in marine mammals can be a potential useful metric for assessing the physiological impacts of exposure to DA.

Glucocorticoids are traditionally measured in plasma, but given that plasma sampling from free-ranging large whales is currently impossible, alternative sample types such as fecal samples, among others, can be utilized to quantify GCs in large whales (Ajó et al., 2021; Burgess et al., 2018, 2016; Fernández Ajó et al., 2020, 2018; Hunt et al., 2019, 2014, 2006; Rolland et al., 2017, 2005)(Figure 2). The analyses of fecal glucocorticoid metabolites (fGCm) is particularly useful for endocrine assessments of free-swimming whales, with several studies showing that fGCm correlate in meaningful ways with presumed stressors. For example, high levels of fGCm in North Atlantic right whales (NARW, Eubalaena glacialis) and in gray whales (Eschrichtius robustus) correlate with poor body condition (Hunt et al., 2006; Lemos et al., 2021), and fGCm increases were associated with whale entanglements and ship strikes (i.e., Lemos et al., 2020; Rolland et al., 2017).

Figure 2. Alternative samples types can be used to study hormones in large whales. 1-2-3 are sample types that can be obtained from free-living whales and provide a more instantaneous and acute measurement of the whales´ physiology. 4-5 can be obtained at necropsy when the whale is found dead at the beach and provide an integrated measure of the whale physiology that can expand through years or even the lifespan of an individual.

In Península Valdés, Argentina, southern right whales (SRW, E. australis) gather in large numbers to mate and nurse their calves during the austral winter months (Bastida and Rodríguez, 2009). SRWs are capital breeders, largely fasting during the breeding season and instead relying on stored blubber fuel reserves. However, they can occasionally feed on calanoid copepods (D’Agostino et al., 2018, 2016), particularly during the phytoplankton blooms that are dominated by diatoms of the genus Pseudo-nitzschia (Sastre et al. 2007; D’Agostino et al. 2015, 2018). Therefore, feeding SRWs in Península Valdés temporally overlap with these Pseudo-nitzschia blooms (D’Agostino et al. 2018, 2015) and represents a test case for assessing the relationship of DA exposure with GC levels (Figure 3).

Figure 3. Southern right whale (E. australis) skim feeding at the Peninsula Valdes breeding ground. Photo: Lucas Beltranino.

In our recent scientific publication (D’Agostino et al. 2021), we investigate SRW exposure to DA at their breeding ground in Peninsula Valdes and assessed its effects on fecal glucocorticoid concentrations. Although the sample size of this study is unavoidably small due to the difficulties of obtaining fecal samples from whales at their calving grounds where defecation is infrequent, we observed significantly lower fGCm in samples from whales exposed to DA (Figure 4). Our results agree with findings from a previous study in California sea lions (Zalophus californianus) exposed to DA, where these authors found a significant association of DA exposure with reduced serum cortisol (Gulland et al., 2009), which can be tentatively attributed to abnormal function of the adrenal axis due to the exposure.

Figure 4. Fecal glucocorticoid metabolite levels in southern right whales exposed (YES, solid triangles) and not-exposed (NO, open circles) to DA. Left panel: immunoreactive fecal corticosterone metabolites. Right panel: immunoreactive fecal cortisol metabolites. Hormone concentrations are expressed in ng of immunoreactive hormone per gram of dry fecal sample. Significant differences between groups are denoted with an asterisk (P<0.05). The black solid line indicates the mean for each group, and in parenthesis is the sample size for each group. Adapted from D’Agostino et al. 2021.

If ingestion of toxins produced by phytoplankton can result in long-term suppression of baseline GCs, whales and marine mammals in general, could suffer reduced ability to cope with additional stressors. The adrenal function is essential to maintain circulating blood glucose and other aspects of metabolism within normal bounds. Additionally, the ability to elevate GCs facilitates energy mobilization to physiologically cope with a stressful event and to initiate appropriate behavioral responses (i.e., flee from predators, heal wounds). Various toxicants have been shown to reduce adrenal function across taxa (Romero and Wingfield, 2016) and could have negative consequences on the ability of cetaceans to respond and adapt to ongoing environmental and anthropogenic changes. Compounding this problem, whales are exposed to an increasing number of stressors from multiple sources and with cumulative effects and they need to be able to physiologically respond to continue to reproduce and survive.

To our knowledge, this study provides the first quantification of fGCm levels in whales exposed to DA; and we hope this effort starts a growing dataset to which other researchers can add. Sampling and analysis of non-traditional matrices, such as feces, blubber, baleen and others, would likely increase sample sizes and thus our understanding of the interrelationships among DA exposure and age, sex, and reproductive status of cetaceans. Given that chronic exposure to DA could alter the capacity of animals to respond to stress, and indications that HABs are becoming more frequent and intense world-wide (Van Dolah 2000; Masó et al. 2006; Erdner et al. 2008), we believe that research evaluating the health status of marine mammal populations should include the assessment of stress physiology relative to natural and anthropogenic stressors including exposure to toxicants.

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get weekly updates and more! Just add your name into the subscribe box on the left panel.  


Bastida R, Rodríguez D (2009) Ballena franca austral [Southern right whale]. In: Mazzini V (ed) Mamíferos marinos de Patagonia y Antártida [Marine mammals of Patagonia and Antarctica]. Zagier & Urruty Publications, Buenos Aires, pp 72–84.

Bonier F, Moore IT, Martin PR, Robertson RJ (2009) The relationship between fitness and baseline glucocorticoids in a passerine bird. Gen Comp Endocrinol 163:208–213. https:// doi. org/ 10. 1016/j.ygcen. 2008. 12. 013.

Brann DW, Mahesh VB (1994) Excitatory amino acids: function and significance in reproduction and neuroendocrine regulation. Front Neuroendocrinol 15:3–49. https:// doi. org/ 10. 1006/ frne. 1994. 1002.

Broadwater MH, Van Dolah FM, Fire SE (2018) Vulnerabilities of marine mammals to harmful algal blooms. Harmful Algal Blooms 2:191–222.

Burgess, E.A., Hunt, K.E., Kraus, S.D., Rolland, R.M., 2016. Get the most out of blow hormones: Validation of sampling materials, field storage and extraction techniques for whale respiratory vapour samples. Conserv. Physiol. 4, cow024. https://doi.org/10.1093/conphys/cow024

Burgess, E.A., Hunt, K.E., Kraus, S.D., Rolland, R.M., 2018. Quantifying hormones in exhaled breath for physiological assessment of large whales at sea. Sci. Rep. 8, 10031. https://doi.org/10.1038/s41598-018-28200-8

D’Agostino VC, Hoffmeyer MS, Almandoz GO, Sastre V, Degrati M., 2015. Potentially toxic Pseudo-nitzschia species in plankton and fecal samples of Eubalaena australis from Península Valdés calv-ing ground, Argentina. J Sea Res 106:39–43. https:// doi. org/ 10.1016/j. seares.

D’Agostino, V.C., Degrati, M., Santinelli, N., Sastre, V., Dans, S.L., Hoffmeyer, M.S., 2018. The seasonal dynamics of plankton communities relative to the foraging of the southern right whale (Eubalaena australis) in northern Patagonian gulfs, Península Valdés, Argentina. Cont. Shelf Res. 164, 45–57. https://doi.org/10.1016/j.csr.2018.06.003

D’Agostino, V.C., Degrati, M., Sastre, V., Santinelli, N., Krock, B., Krohn, T., Dans, S.L., Hoffmeyer, M.S., 2017. Domoic acid in a marine pelagic food web: Exposure of southern right whales Eubalaena australis to domoic acid on the Península Valdés calving ground, Argentina. Harmful Algae 68, 248–257. https://doi.org/10.1016/j.hal.2017.09.001

D’Agostino, V.C., Hoffmeyer, M.S., Degrati, M., 2016. Faecal analysis of southern right whales (Eubalaena australis) in Península Valdés calving ground, Argentina: Calanus australis, a key prey species. J. Mar. Biol. Assoc. United Kingdom 96, 859–868. https://doi.org/10.1017/S0025315415001897

Dickens, M.J., Romero, L.M., 2013. A consensus endocrine profile for chronically stressed wild animals does not exist. Gen. Comp. Endocrinol. 191, 177–189. https://doi.org/10.1016/j.ygcen.2013.06.014

Erdner DL, Dyble J, Parsons ML, Stevens RC, Hubbard KA, Wrabel ML, Moore SK, Lefebvre KA, Anderson DM, Bienfang P, Bidi-gare RR, Parker MS, Moeller P, Brand LE, Trainer VL (2008) Centers for Oceans and Human Health: a unified approach to the challenge of harmful algal blooms. Environ Health 7:S2. https://doi. org/ 10. 1186/ 1476- 069X-7- S2- S2.

Fernández Ajó, A., Hunt, K.E., Dillon, D., Uhart, M., Sironi, M., Rowntree, V., Buck, C.L., 2021. Optimizing hormone extraction protocols for whale baleen: tackling questions of solvent:sample ratio and variation. Gen. Comp. Endocrinol. 113828. https://doi.org/10.1016/j.ygcen.2021.113828

Fernández Ajó, A.A., Hunt, K.E., Giese, A.C., Sironi, M., Uhart, M., Rowntree, V.J., Marón, C.F., Dillon, D., DiMartino, M., Buck, C.L., 2020. Retrospective analysis of the lifetime endocrine response of southern right whale calves to gull wounding and harassment: A baleen hormone approach. Gen. Comp. Endocrinol. 296, 113536. https://doi.org/10.1016/j.ygcen.2020.113536

Fernández Ajó, A.A., Hunt, K.E., Uhart, M., Rowntree, V., Sironi, M., Marón, C.F., Di Martino, M., Buck, C.L., 2018. Lifetime glucocorticoid profiles in baleen of right whale calves: potential relationships to chronic stress of repeated wounding by Kelp Gulls. Conserv. Physiol. 6, 1–12. https://doi.org/10.1093/conphys/coy045

Fire SE, Bogomolni A, DiGiovanni RA Jr, Early G, Leighfield TA, Matassa K, Miller GA, Moore KM, Moore M, Niemeyer M, Pugliares K (2021) An assessment of temporal, spatial and taxonomic trends in harmful algal toxin exposure in stranded marine mammals from the US New England coast. PLoS ONE 16(1):e0243570. https:// doi. org/ 10. 1371/ journ al. pone. 02435 70

Fire SE, Wang Z, Berman M, Langlois GW, Morton SL, Sekula-Wood E, Benitez-Nelson CR (2010) Trophic transfer of the harmful algal toxin domoic acid as a cause of death in a minke whale (Balaenoptera acutorostrata) stranding in southern California. Aquat Mamm 36(4):342–350. https:// doi. org/ 10. 1578/ AM. 36.4.2010. 342.

Gulland F.M., 1999. Domoic acid toxicity in California sea lions stranded along the central California Coast, May-October 1998. NOAA Tech. Memo. NMFS-OPR-8. USA National Marine Fisheries Service, US Department of Commerce.

Gulland FMD, Hall AJ, Greig DJ, Fram ER, Colegrove KM, Booth RKN, Wasser SK, S.-M.C., 2009. Gulland, Hall – 2012 – Evaluation of circulating eosinophil count and adrenal gland function in California sea lions naturally exposed t. J. Am. Vet. Med. Assoc. 241, 943–949.

Gulland, F., Pérez-Cortés, H., Urbán, J.R., Rojas-Bracho, L., Ylitalo, G., Weir, J., Norman, S., Muto, M., Rugh, D., Kreuder, C., Rowles, T., 2005. Eastern North Pacific gray whale (Eschrichtius robustus) unusual mortality event, 1999-2000. U.S. Dep. Commer. NOAA Tech. Memo. NMFS-AFSC-150. 33 pp.

Hunt, K.E., Robbins, J., Buck, C.L., Bérubé, M., Rolland, R.M., 2019. Evaluation of fecal hormones for noninvasive research on reproduction and stress in humpback whales (Megaptera novaeangliae). Gen. Comp. Endocrinol. 280, 24–34. https://doi.org/10.1016/j.ygcen.2019.04.004

Hunt, K.E., Rolland, R.M., Kraus, S.D., Wasser, S.K., 2006. Analysis of fecal glucocorticoids in the North Atlantic right whale (Eubalaena glacialis). Gen. Comp. Endocrinol. 148, 260–272. https://doi.org/10.1016/j.ygcen.2006.03.012

Hunt, K.E., Stimmelmayr, R., George, C., Hanns, C., Suydam, R., Brower, H., Rolland, R.M., 2014. Baleen hormones: a novel tool for retrospective assessment of stress and reproduction in bowhead whales (Balaena mysticetus). Conserv. Physiol. 2, cou030–cou030. https://doi.org/10.1093/conphys/cou030

Johnson MP, Kelly G, Chamberlain M (2001) Changes in rat serum corticosterone after treatment with metabotropic glutamate receptor agonists or antagonists. J Neuroendocrinol 13:670–677. https:// doi. org/ 10. 1046/j. 1365- 2826. 2001. 00678.x.

Lefebvre KA, Powell CL, Busman M, Doucette GJ, Moeller PDR, Sliver JB, Miller PE, Hughes MP, Singaram S, Silver MW, Tjeer-dema RS (1999) Detection of domoic acid in northern anchovies and California sea lions associated with an unusual mortality event. Nat Toxins 7(3):85–92. https:// doi. org/ 10. 1002/ (SICI) 1522-7189(199905/ 06)7: 3% 3C85:: AID- NT39% 3E3.0. CO;2-Q.

Lefebvre, K.A., Kendrick, P.S., Ladiges, W., Hiolski, E.M., Ferriss, B.E., Smith, D.R., Marcinek, D.J., 2017. Chronic low-level exposure to the common seafood toxin domoic acid causes cognitive deficits in mice. Harmful Algae 64, 20–29. https://doi.org/10.1016/j.hal.2017.03.003

Lemos, L.S., Olsen, A., Smith, A., Burnett, J.D., Chandler, T.E., Larson, S., Hunt, K.E., Torres, L.G., 2021. Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Mar. Mammal Sci. 1–11. https://doi.org/10.1111/mms.12877

Lemos, L.S., Olsen, A., Smith, A., Chandler, T.E., Larson, S., Hunt, K., Torres, L.G., 2020. Assessment of fecal steroid and thyroid hormone metabolites in eastern North Pacific gray whales. Conserv. Physiol. 8. https://doi.org/10.1093/conphys/coaa110

Masó M, Garcés E., 2006. Harmful microalgae blooms (HAB); prob-lematic and conditions that induce them. Mar Pollut Bull 53:620–630. https:// doi. org/ 10. 1016/j. marpo lbul. 2006. 08. 006.

Pulido O.M., 2008. Domoic acid toxicologic pathology: a review. Mar Drugs 6(2):180–219. https:// doi. org/ 10. 3390/ md602 0180.

Rolland, R., McLellan, W., Moore, M., Harms, C., Burgess, E., Hunt, K., 2017. Fecal glucocorticoids and anthropogenic injury and mortality in North Atlantic right whales Eubalaena glacialis. Endanger. Species Res. 34, 417–429. https://doi.org/10.3354/esr00866

Rolland, R.M., Hunt, K.E., Kraus, S.D., Wasser, S.K., 2005. Assessing reproductive status of right whales (Eubalaena glacialis) using fecal hormone metabolites. Gen. Comp. Endocrinol. 142, 308–317. https://doi.org/10.1016/j.ygcen.2005.02.002

Romero, M.L., Wingfield, J.C., 2016. Oxford series in behavioral neuroendocrinology. Tempests, poxes, predators, and people: stress in wild animals and how they cope 1–2.

Sapolsky RM, Romero LM, Munck AU, 2000. How do glucocorti-coids influence stress response? Intergrating permissive, suppres-sive, stimulatory, and preparative actions. Endocr Rev 21:55–89. https:// doi. org/ 10. 1210/ edrv. 21.1. 0389.

Sastre V, Santinelli N, Marino G, Solís M, Pujato L, Ferrario M., 2007. First detection of domoic acid produced by Pseudo-nitzschia spe-cies, Chubut coastal waters, Patagonia, Argentina. Harmful Algae News 34:12–14.

Van Dolah FM., 2000. Marine algal toxins: origins, health effects, and their increased occurrence. Environ Health Perspect 108:133–141. https:// doi. org/ 10. 1289/ ehp. 00108 s1133

Weighing-in on scale

Allison Dawn, GEMM Lab Master’s student, OSU Department of Fisheries, Wildlife and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab 

As the first term of my master’s program comes to an end and we head toward winter break, I am excited by the course material that has already helped direct my research and development as a scientist. There have been new, challenging topics to tackle, and each assignment has fostered deeper thinking into the formation of my thesis. While I learned new methods and analysis approaches this term, a single phrase pervades throughout my studies of ecology – “it depends!”. Ecologists work to uncover patterns driven by natural processes, and this single phrase seems to answer many questions about whether the pattern always exists. A reasonable follow up to that frequently used phrase is, “depends on what?” or “when or where would this pattern change?” In the context of foraging ecology, predator-prey patterns are frequently driven by environmental processes that depend on the scale you choose for your study. 

What do we mean by scale? Simply stated, scale is a graduation from one level of measurement to another. You can imagine a ruler, for example. You can measure how tall you are in inches with a ruler or in yards with a yard stick. When we think about scale in ecology, the “ruler” can have traditional units of space (meters, kilometers, etc.), units of time (minutes, days, hours, months, years, etc.), or sometimes both!  

The ocean is dynamic and heterogeneous, which simply means there is a lot going on at once. Oceanographic processes influence predator-prey interactions but due to the inherent variability in the system, it is important to explore which factors drive processes that influence patterns at different spatial and temporal scales.  

In marine ecology, the “explanatory power” of a factors’ influence on a given process depends on which scale you choose to build your research upon. Ocean ecosystems are hierarchical, with patterns happening at many temporal and spatial scales all at once. So, we could choose to study the same predator-prey interactions at the scale of meters and minutes or 100s of km and months, and we would likely find very different drivers of patterns. The topic of scale is particularly relevant in regard to whale foraging, as marine mammals employ different sensory methods to locate prey at different spatial scales (Torres 2017). 

Among the first papers to conduct multi-scale research on whale foraging was Jaquet and Whitehead, 1996. Here, they studied sperm whale distribution in relation to various physical and environmental variables. Analysis showed that the main drivers of sperm whale distribution were secondary productivity (e.g., bacteria and zooplankton), underwater topography, and the gradient between deep water and surface water productivity. However, these drivers had a different impact depending on the spatial scale. There was no correlation between the drivers and sperm whale distribution at small scales < 320 nautical miles. However, at large scales >= 320 nautical miles, female sperm whale distribution was correlated with high secondary productivity and steep underwater topography. These important findings demonstrate that small scale distribution of prey alone does not drive the distribution of sperm whale predators in this study region, while other factors contribute to predator movement.  

Figure 1. Figure reproduced from Jaquet & Whitehead, 1996. Plots show how the Spearman correlation results between sperm whale density and environmental variables change across multiple spatial scales. (A) Prey distribution, (B) distance to shore and bathymetric contour, and (C) the three main environmental drivers (secondary productivity, topography, and the deep water productivity gradient). 

Ten years later, a study on Mediterranean fin whales tackled a similar question of how interactions between prey and predator change at multiple scales. However, their work investigated responses to both spatial and temporal scale changes. Through spatial modeling relative to oceanographic factors, Cotté et al. 2009 found that at a large-scale (year and ocean basin-wide), fin whales demonstrated two distinct distribution patterns: in the summer they were aggregated, and in the winter they were more dispersed. However, at the meso-scale (weeks -months, and 20-100 km) fin whale fidelity switched to colder, saltier waters with steeper topography and temperature gradients. Based on these results, the authors concluded that at the large scale, whale movement was driven by annually persistent prey abundance. At smaller scales, prey aggregations are less predictable, thus the authors suggest that whale movement at the meso-scale is driven by physical processes, such as frontal zones and strong currents.  

Figure 2. Figure reproduced from Cotté et. al 2009. Map shows Mediterranean fin whale distribution against oceanographic conditions. Color gradient indicates sea surface temperature (SST), fin whale observations shown in white and red circles, black arrows show current direction, with inset temperature/salinity diagram for September 28-30th 2006. 

A key takeaway from these papers is that it is important to investigate how processes and responses can vary at different scales, because results can sometimes depend on the time and space measurement applied in the analysis. For my thesis, I will explore which drivers take a front seat role in gray whale foraging at both fine and meso-scales. I am interested to compare my results on the relationships between PCFG gray whales and their zooplankton prey to the results from the above described studies. Stay tuned for more updates! 

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get weekly updates and more! Just add your name into the subscribe box on the left panel. 


Cotté, C., Guinet, C., Taupier-Letage, I., Mate, B., & Petiau, E. (2009). Scale-dependent habitat use by a large free-ranging predator, the Mediterranean fin whale. Deep Sea Research Part I: Oceanographic Research Papers, 56(5), 801-811. 

Jaquet, N., & Whitehead, H. (1996). Scale-dependent correlation of sperm whale distribution with environmental features and productivity in the South Pacific. Marine ecology progress series, 135, 1-9. 

​​Torres, L. G. (2017). A sense of scale: Foraging cetaceans’ use of scale‐dependent multimodal sensory systems. Marine Mammal Science, 33(4), 1170-1193.

Different blue whale populations sing different songs

By Dawn Barlow, PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

In human cultures, how you sound is often an indicator of where you are from. Have you ever taken a linguistics quiz that tries to guess what part of the United States you grew up in? Questions about whether you pronounce the sugary sweet treat caramel as “carr-mul” or “care-a-mel”, whether you say “soda” or “pop”, or whether a certain type of intersection is called a “roundabout”, “rotary”, or “traffic circle” are used to make a guess at where in the country you were raised. I have spent time in the United States, Australia, and New Zealand, I was amused to learn that the shoes you might wear in summertime can be called flip flops, slippers, thongs, or jandals, depending on which English-speaking country you are in. We know that listening to how someone speaks can tell us about their heritage or culture. As it turns out, the same is true for blue whales. We can learn a lot about blue whales by listening to them.

A blue whale comes up for air in the South Taranaki Bight, New Zealand. We catch only a short glimpse of these ocean giants when they are at the surface. By listening to their vocalizations using acoustic recordings, we can gain a whole new perspective on their lives. Photo by D. Barlow.

Sound is an incredibly important sense to marine mammals, particularly since sound waves can efficiently transmit over long distances in the ocean where other senses, such as vision or smell, are limited. Therefore, passive acoustic monitoring—placing hydrophones underwater to listen for an extended period of time and record the sounds of animals and their environment—is a highly effective tool for studying marine mammals, including blue whales. Throughout the world, blue whales sing. In this case, “song” is defined as a limited number of sound types that are produced in succession to form a recognizable pattern (McDonald et al. 2006). These songs are presumed to be produced by males only, most likely used to maintain associations and mediate social interactions, and seem to play a role in reproduction (Oleson et al. 2007, Lewis et al. 2018). Furthermore, these songs are highly stereotyped, and stable over decadal scales (McDonald et al. 2006).

Figure reproduced from McDonald et al. (2006), illustrating the variation and in blue whale songs from different geographic regions, and their stability over time: Recordings from New Zealand (A), the Central North Pacific (B), Australia (C), the Northeast Pacific (D) and North Indian Ocean (E) illustrate the stable character of the blue whale song over long time periods. All song types for which long time spans of recordings are available show some frequency drift through time, but only minor change in character. These examples were chosen because recordings over a significant time span were available to the authors in raw form, and not because these song types are more stable than the others.

Fascinatingly, blue whale songs have acoustic characteristics that are distinct between geographic regions. A blue whale in the northeast Pacific sings a different song than a blue whale in the north Atlantic; the song heard around Australia is distinct from the one sung off the coast of Chile, and so on. Therefore, differences in blue whale songs between areas can be used as a provisional hypothesis about population structure (McDonald et al. 2006, Samaran et al. 2013, Balcazar et al. 2015). Vocalizations may evolve more rapidly than traditional markers such as genetics or morphology that are often used to delineate populations, particularly in long-lived mammalian species such as blue whales (McDonald et al. 2006).

Figure reproduced from McDonald et al. (2006): Blue whale residence and population divisions suggested from their song types. Arrows indicate the direction of seasonal movements.

Despite the general rule of thumb that population-specific blue whale songs occur in separate geographic regions, there are examples throughout the southern hemisphere where songs from different populations overlap and are recorded in the same location (Samaran et al. 2010, 2013, Tripovich et al. 2015, McCauley et al. 2018, Buchan et al. 2020, Leroy et al. 2021). However, these examples may be instances where the populations temporally or ecologically partition their use of the area. For example, there may be differences in the timing of peak occurrence so that overlap is minimized by alternating which population is predominantly present in different seasons (Leroy et al. 2018). Alternatively, whales from different populations may overlap in space and time, but occupy different ecological niches at the same site. In this case, an area may simultaneously be a migratory corridor for one population and a foraging ground for another (Tripovich et al. 2015).

Figure reproduced from Leroy et al. (2021): Distribution of the five blue whale acoustic populations of the Indian Ocean: the Sri Lankan—NIO (yellow); Madagascan—SWIO (orange); Australian—SEIO (blue); and Arabian Sea—NWIO (red) pygmy blue whales; the hypothesized Chagos pygmy blue whale (green); and the Antarctic blue whale (black dashed line). These distributions have been inferred from the acoustic recordings conducted in the area. The long-term recording sites used to infer these distribution areas are indicated by red stars. Blue whale illustration by Alicia Guerrero.

In the South Taranaki Bight (STB) region of New Zealand, where the GEMM lab has been studying blue whales for the past decade (Torres 2013), the New Zealand song type is recorded year-round (Barlow et al. 2018). New Zealand blue whales rely on a productive upwelling system in the STB that supports an important foraging ground (Barlow et al. 2020, 2021). Antarctic blue whales also seasonally pass through New Zealand waters, likely along their migratory pathway between polar feeding grounds and lower latitude areas (Warren et al. 2021). What does it mean in terms of population connectivity or separation when two different populations occasionally share the same waters? How do these different populations ecologically partition the space they occupy? What drives their differing occurrence patterns? These are the sorts of questions I am diving into as we continue to explore the depths of our acoustic recordings from the STB region. We still have a lot to learn about these blue whales, and there is a lot to be learned through listening.


Balcazar NE, Tripovich JS, Klinck H, Nieukirk SL, Mellinger DK, Dziak RP, Rogers TL (2015) Calls reveal population structure of blue whales across the Southeast Indian Ocean and the Southwest Pacific Ocean. J Mammal 96:1184–1193.

Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG (2020) Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar Ecol Prog Ser 642:207–225.

Barlow DR, Klinck H, Ponirakis D, Garvey C, Torres LG (2021) Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep 11:1–10.

Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.

Buchan SJ, Balcazar-Cabrera N, Stafford KM (2020) Seasonal acoustic presence of blue, fin, and minke whales off the Juan Fernández Archipelago, Chile (2007–2016). Mar Biodivers 50:1–10.

Leroy EC, Royer JY, Alling A, Maslen B, Rogers TL (2021) Multiple pygmy blue whale acoustic populations in the Indian Ocean: whale song identifies a possible new population. Sci Rep 11:8762.

Leroy EC, Samaran F, Stafford KM, Bonnel J, Royer JY (2018) Broad-scale study of the seasonal and geographic occurrence of blue and fin whales in the Southern Indian Ocean. Endanger Species Res 37:289–300.

Lewis LA, Calambokidis J, Stimpert AK, Fahlbusch J, Friedlaender AS, Mckenna MF, Mesnick SL, Oleson EM, Southall BL, Szesciorka AR, Širović A (2018) Context-dependent variability in blue whale acoustic behaviour. R Soc Open Sci 5.

McCauley RD, Gavrilov AN, Jolli CD, Ward R, Gill PC (2018) Pygmy blue and Antarctic blue whale presence , distribution and population parameters in southern Australia based on passive acoustics. Deep Res Part II 158:154–168.

McDonald MA, Mesnick SL, Hildebrand JA (2006) Biogeographic characterisation of blue whale song worldwide: using song to identify populations. J Cetacean Res Manag 8:55–65.

Oleson EM, Wiggins SM, Hildebrand JA (2007) Temporal separation of blue whale call types on a southern California feeding ground. Anim Behav 74:881–894.

Samaran F, Adam O, Guinet C (2010) Discovery of a mid-latitude sympatric area for two Southern Hemisphere blue whale subspecies. Endanger Species Res 12:157–165.

Samaran F, Stafford KM, Branch TA, Gedamke J, Royer J, Dziak RP, Guinet C (2013) Seasonal and Geographic Variation of Southern Blue Whale Subspecies in the Indian Ocean. PLoS One 8:e71561.

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.

Tripovich JS, Klinck H, Nieukirk SL, Adams T, Mellinger DK, Balcazar NE, Klinck K, Hall EJS, Rogers TL (2015) Temporal Segregation of the Australian and Antarctic Blue Whale Call Types (Balaenoptera musculus spp.). J Mammal 96:603–610.

Warren VE, Širović A, McPherson C, Goetz KT, Radford CA, Constantine R (2021) Passive Acoustic Monitoring Reveals Spatio-Temporal Distributions of Antarctic and Pygmy Blue Whales Around Central New Zealand. Front Mar Sci 7:1–14.

Learning to Listen for Animals in the Sea

By Rachel Kaplan, PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Part of what makes being a graduate student so exciting is the way that learning can flip the world around: you learn a new framework or method, and suddenly everything looks a little different. I am experiencing this fabulous phenomenon lately as I learn to collect and process active acoustic data, which can reveal the distribution and biomass of animals in the ocean – including those favored by foraging whales off of Oregon, like the tiny shrimp-like krill.

Krill, like this Thysanoessa spinifera, play a key role in California Current ecosystems. Photo credit: Scripps Institution of Oceanography.

We know that whales seek out the dense, energy-rich swarms that krill form, and that knowing where to expect krill can give us a leg up in anticipating whale distributions. Project OPAL (Overlap Predictions About Large whales) seeks to model and provide robust predictions of whale distributions off the coast of Oregon, so that managers can make spatially discrete decisions about potential fishery closures, minimizing burdens to fishermen while also maximizing protection of whales. We hope that including prey in our ecosystem models will help this effort, and working on this aim is one of the big tasks of my PhD.

So, how do we know where to expect krill to be off the coast of Oregon? Acoustic tools give us the opportunity to flip the world upside down: we use a tool called an echosounder to eavesdrop on the ocean, yielding visual outputs like the ones below that let us “see” and interpret sound.

Echograms like these reveal features in the ocean that scatter “pings” of sound, and interpreting these signals can show life in the water column.

This is how it works. The echosounder emits pulses of sound at a known frequency, and then it listens for their return after it bounces of the sea floor or things in the water column. Based on sound experiments in the laboratory, we know to expect our krill species, Euphausia pacifica and Thysanoessa spinifera, to return those echoes at a characteristic decibel level. By constantly “pinging” the water column with this sound, we can record a continuous soundscape along the cruise track of a vessel, and analyze it to identify the animals and features recorded.

I had the opportunity to use an echosounder for the first time recently, on the first HALO cruise. We deployed the echosounder soon after sunrise, 65 miles offshore from Newport. After a little fiddling and troubleshooting, I was thrilled to start “listening” to the water; I was able to see the frothy noise at its surface, the contours of the seafloor, and the pixelated patches that indicate prey in between. Although it’s difficult to definitively identify animals only based on the raw output, we saw swarms that looked like our beloved krill, and other aggregations that suggested hake. Sometimes, at the same time that the team of visual observers on the flying bridge of the vessel sighted whales, I also saw potential prey on the echogram.

 I spent much of the HALO cruise monitoring incoming data from the transducer on the SIMRAD EK60. Photo: Marissa Garcia.

I’m excited to keep collecting these data, and grateful that I can also access acoustic data collected by others. Many research vessels use echosounders while they are underway, including the NOAA Ship Bell M. Shimada, which conducts cruises in the Northern California Current several times a year. Starting in 2018, GEMM Lab members have joined these cruises to conduct marine mammal surveys.

This awesome pairing of data types means that we can analyze the prey that was available at the time of marine mammal sightings. I’ve been starting to process acoustic data from past Northern California Current cruises, eavesdropping on the preyscape in places that were jam-packed with whales, such as this echogram from the September 2020 cruise, below.

An echogram from the September 2020 NCC cruise shows a great deal of prey at different depths.

Like a lot of science, listening to animals in the sea comes down to occasional bursts of fieldwork followed by a lot of clicking on a computer screen during data analysis. This analysis can be some pretty fun clicking, though – it’s amazing to watch the echogram unfurl, revealing the preyscape in a swath of ocean. I’m excited to keep clicking, and learn what it can tell us about whale distributions off of Oregon.

Memoirs from above: drone observations of blue, humpback, Antarctic minke, and gray whales

By KC Bierlich, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

With the GRANITE field season officially over, we are now processing all of the data we collected this summer. For me, I am starting to go through all the drone videos to take snapshots of each whale to measure their body condition. As I go through these videos, I am reflecting on the different experiences I am fortunate enough to have with flying different drones, in different environments, over different species of baleen whales: blue, humpback, Antarctic minke, and now gray whales. Each of these species have a different morphological design and body shape (Woodward et al., 2006), which leads to different behaviors that are noticeable from the drone. Drones create immense opportunity to learn how whales thrive in their natural environments [see previous blog for a quick history], and below are some of my memories from above. 

I first learned how drones could be used to study the morphology and behavior of large marine mammals during my master’s degree at Duke University, and was inspired by the early works of John Durban (Durban et al., 2015, 2016) Fredrick Christiansen (Christiansen et al., 2016) and Leigh Torres (Torres et al., 2018). I immediately recognized the value and utility of this technology as a new tool to better monitor the health of marine mammals. This revelation led me to pursue a PhD with the Duke University Marine Robotics and Remote Sensing (MaRRS) Lab led by Dr. Dave Johnston where I helped further develop tools and methods for collecting drone-based imagery on a range of species in different habitats. 

When flying drones over whales, there are a lot of moving parts; you’re on a boat that is moving, flying something that is moving, following something that is moving. These moving elements are a lot to think about, so I trained hard, so I did not have to think about each step and flying felt intuitive and natural. I did not grow up playing video games, so reaching this level of comfort with the controls took a lot of practice. I practiced for hours over the course of months before my first field excursion and received some excellent mentorship and training from Julian Dale, the lead engineer in the MaRRS Lab. Working with Julian and the many hours of training helped me establish a solid foundation in my piloting skills and feel confident working in various environments on different species. 

Blue whales offshore of Monterey, California. 

In 2017 and 2018 I was involved in collaborative project with the MaRRS Lab and Goldbogen Lab at Stanford University, where we tagged and flew drones over blue whales offshore of Monterey, California. We traveled about an hour offshore and reliably found groups of blue whales actively feeding. Working offshore typically brought a large swell, which can often make landing the drone back into your field partner’s hands tricky as everything is bobbing up and down with the oscillations of the swell. Fortunately, we worked from a larger research vessel (~56 ft) and quickly learned that landing the drone in the stern helped dampen the effects of bobbing up and down. The blue whales we encountered often dove to a depth of around 200 m for about 20-minute intervals, then come to the surface for only a few minutes. This short surface period provided only a brief window to locate the whale once it surfaced and quickly fly over it to collect the imagery needed before it repeated its dive cycle. We learned to be patient and get a sense of the animal’s dive cycle before launch in order to time our flights so the drone would be in the air a couple of minutes before the whale surfaced. 

Once over the whales, the streamlined body of the blue whales was noticeable, with their small, high aspect ratio flippers and fluke that make them so well adapted for fast swimming in the open ocean (Fig. 1) (Woodward et al., 2006). I also noticed that because these whales are so large (often 21 – 24 m), I often flew at higher altitudes to be able fit them within the field of view of the camera. It was also always shocking to see how small the tagging boat (~8 m) looked when next to Earth’s largest creatures. 

Figure 1. Two blue whales surface after a deep dive offshore of Monterey, Ca. (Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03)

Antarctic minke whales and humpback whales along the Western Antarctic PeninsulaA lot of the data included in my dissertation came from work along the Western Antarctic Peninsula (WAP), which had a huge range of weather conditions, from warm and sunny days to cold and snowy/foggy/rainy/windy/icy days. A big focus was often trying to keep my hands warm, as it was often easier to fly without gloves in order to better feel the controls. One of the coldest days I remember was late in the season in mid-June (almost winter!) in Wilhemina Bay where ice completely covered the bay in just a couple hours, pushing the whales out into the Gerlache Strait; I suspect this was the last ice-free day of the season. Surprisingly though, the WAP also brought some of the best conditions I have ever flown in. Humpback and Antarctic minke whales are often found deep within the bays along the peninsula, which provided protection from the wind. So, there were times where it would be blowing 40 mph in the Gerlache Strait, but calm and still in the bays, such as Andvord Bay, which allowed for some incredible conditions for flying. Working from small zodiacs (~7 m) allowed us more maneuverability for navigating around or through the ice deep in the bays (Fig. 2) 

Figure 2. Navigating through ice-flows along the Western Antarctic Peninsula. (Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)

Flying over Antarctic minke whale was always rewarding, as they are very sneaky and can quickly disappear under ice flows or in the deep, dark water. Flying over them often felt like a high-speed chase, as their small streamlined bodies makes them incredibly quick and maneuverable, doing barrel rolls, quick banked turns, and swimming under and around ice flows (Fig. 3). There would often be a group between 3-7 individuals and it felt like they were playing tag with each other – or perhaps with me!  

Figure 3. Two Antarctic minke whales swimming together along the Western Antarctic Peninsula. (Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)

Humpbacks displayed a wide range of behaviors along the WAP. Early in the season they continuously fed throughout the entire day, often bubble net feeding in groups typically of 2-5 animals (Fig. 4). For as large as they are, it was truly amazing to see how they use their pectoral fins to perform quick accelerations and high-speed maneuvering for tight synchronized turns to form bubble nets, which corral and trap their krill, their main food source (Fig. 4) (Woodward et al., 2006). Later in the season, humpbacks switched to more resting behavior in the day and mostly fed at night, taking advantage of the diel vertical migration of krill. This behavior meant we often found humpbacks snoozing at the surface after a short dive, as if they were in a food coma. They also seemed to be more curious and playful with each other and with us later in the season (Fig. 5).

We also encountered a lot of mom and calf pairs along the WAP. Moms were noticeably skinny compared to their plump calf in the beginning of the season due to the high energetic cost of lactation (Fig. 6). It is important for moms to regain this lost energy throughout the feeding season and begin to wean their calves. I often saw moms refusing to give milk to their nudging calf and instead led teaching lessons for feeding on their own.

Figure 4. Two humpback whales bubble-net feeding early in the feeding season (December) along the Western Antarctic Peninsula. (Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)
Figure 5. A curious humpback whale dives behind our Zodiac along the Western Antarctic Peninsula. (Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)
Figure 6. A mom and her calf rest at the surface along the Western Antarctic Peninsula. Note how the mom looks skinnier compared to her plump calf, as lactation is the most energetically costly phase of the reproductive cycle. (Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)

Gray whales off Newport, Oregon

All of these past experiences helped me quickly get up to speed and jump into action with the GRANITE field team when I officially joined the GEMM Lab this year in June. I had never flown a DJI Inspire quadcopter before (the drone used by the GEMM Lab), but with my foundation piloting different drones, some excellent guidance from Todd and Clara, and several hours of practice to get comfortable with the new setup, I was flying over my first gray whale by day three of the job. 

The Oregon coast brings all sorts of weather, and some days I strangely found myself wearing a similar number of layers as I did in Antarctica. Fog, wind, and swell could all change within the hour, so I learned to make the most of weather breaks when they came. I was most surprised by how noticeably different gray whales behave compared to the blue, Antarctic minke, and humpback whales I had grown familiar with watching from above. For one, it is absolutely incredible to see how these huge whales use their low-aspect ratio flippers and flukes (Woodward et al., 2006) to perform low-speed, highly dynamic maneuvers to swim in very shallow water (5-10 m) so close to shore (<1m sometimes!) and through kelp forest or surf zones close to the beach. They have amazing proprioception, or the body’s ability to sense its movement, action, and position, as gray whales often use their pectoral fins and fluke to stay in a head standing position (see Clara Bird’s blog) to feed in the bottom sediment layer, all while staying in the same position and resisting the surge of waves that could smash them against the rocks (Video 1) . It is also remarkable how the GEMM Lab knows each individual whale based on natural skin marks, and I started to get a better sense of each whale’s behavior, including where certain individuals typically like to feed, or what their dive cycle might be depending on their feeding behavior. 

Video 1. Two Pacific Coast Feeding Group (PCFG) gray whales “head-standing” in shallow waters off the coast of Newport, Oregon. NOAA/NMFS permit #21678

I feel very fortunate to be a part of the GRANITE field team and to contribute to data collection efforts. I look forward to the data analysis phase to see what we learn about how the morphology and behavior of these gray whales to help them thrive in their environment. 


Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. Y., and Bejder, L. (2016).Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7, e01468–18.

Durban, J. W., Fearnbach, H., Barrett-Lennard, L. G., Perryman, W. L., & Leroi, D. J. (2015). Photogrammetry of killer whales using a small hexacopter launched at sea. Journal of Unmanned Vehicle Systems3(3), 131-135.

Durban, J. W., Moore, M. J., Chiang, G., Hickmott, L. S., Bocconcelli, A., Howes, G., et al.(2016). Photogrammetry of blue whales with an unmanned hexacopter. Mar. Mammal Sci. 32, 1510–1515.

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science5, 319.

Woodward, B. L., Winn, J. P., and Fish, F. E. (2006). Morphological specializations of baleen whales associated with hydrodynamic performance and ecological niche. J. Morphol. 267, 1284–1294.

How much energy does that mouthful cost?

By Lisa Hildebrand, PhD student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Tagging a whale is no easy feat, nor is it without some impact to the whale – no matter how minimized through the use of non-penetrating suction cup tags. Yet, in August 2021 the GEMM Lab initiated a new phase in our research on gray whales, aimed at obtaining a better understanding of the underwater lives and energetics of a gray whale (Figure 1, top image). We captured some amazing data through these specialized, non-invasive tags that provide a brief window into their world and physiology. The video recordings from the tags showed us whales digging their heads into the benthos generating billowing clouds of sediment, likely exploiting desirable prey patches (Figure 1, middle images). We also saw foraging whales undertake dizzying spins and headstands for hours, demonstrating the fascinating maneuverability and flexibility of gray whales (Figure 1, bottom image). But what is motivating us to capture this information?

The GEMM Lab has researched the ecology and physiology of Pacific Coast Feeding Group (PCFG) gray whales since 2015. Our efforts have filled crucial knowledge gaps to better understand this sub-group of the Eastern North Pacific (ENP) gray whale population. We now know that gray whale body condition increases throughout a foraging season and can fluctuate considerably between years (Soledade Lemos et al. 2020). Additionally, body condition varies significantly by reproductive state, with calves and pregnant females displaying higher body conditions (Soledade Lemos et al. 2020). We have also validated and quantified fecal steroid and thyroid hormone metabolite concentrations, providing us with thresholds to identify a stressed vs. a not stressed whale based on its hormone levels (Lemos et al. 2020). These validations have allowed us to make correlations between poor body condition and the steroid hormone cortisol which confirm that slim whales are stressed, while chubby whales are relaxed (Lemos et al. 2021). These physiological results are particularly salient in the light of our recent findings that PCFG gray whales select prey quality over prey quantity when foraging (Hildebrand et al. in review) and that the caloric content of available prey species in the PCFG range vary significantly (Hildebrand et al. 2021).

While we have addressed several fundamental questions about the PCFG in the last 7 years, answering one question has led to asking 10 more questions – a common pattern in science. Given that we know (1) PCFG whales improve their body condition over the course of the foraging season (Soledade Lemos et al. 2020), (2) PCFG females are able to successfully give birth to and wean calves (Calambokidis & Perez 2017), and (3) certain prey in the PCFG region are of higher caloric value than prey in the ENP Arctic foraging grounds (Hildebrand et al. 2021), a big question that we continue to scratch our heads about is why does the PCFG sub-group have such a small abundance (~250 individuals; Calambokidis et al. 2017) in comparison to the much larger ENP population (~21,000 individuals; Stewart & Weller 2021). Several hypotheses have been suggested including that the energetic costs of feeding may differ between ENP and PCFG whales, with the latter having to expend more energy to obtain prey due to the different foraging behaviors employed (Torres et al. 2018) to obtain diverse prey types, thus justifying the larger abundance of the ENP (Hildebrand et al. 2021). 

Quantifying the energetic cost of baleen whale behaviors is not simple. However, the development of animal-borne tags has allowed scientists to make big strides regarding behavioral cost quantification. The majority of this work has focused on rorqual whales (i.e., blue, humpback, fin whales; e.g., Goldbogen et al. 2013; Cade et al. 2016) as their characteristic lunge-feeding strategy produces a distinct signal in the accelerometer sensors integrated within the tags, making feeding events easier to identify. Gray whales, unlike rorquals, do not lunge-feed. ENP gray whales predominantly feed benthically; diving down to the benthos where they turn onto their side and suction mouthfuls of soft sediment (mud) that contains amphipods that they filter out of the mud (Nerini & Oliver 1983). PCFG whales feed benthically as well, but they also use a number of other feeding behaviors to obtain a variety of prey in a variety of benthic habitats, including headstands, bubble blasts, and sharking (Torres et al. 2018). The above-mentioned gray whale feeding behaviors involve much subtler movements than the powerful, distinctive lunges displayed by rorquals, yet they undoubtedly still incur some energetic cost to the whales. However, exactly how energetically costly the various gray whale feeding behaviors are remains unknown.

One of the three suction cup tags we deployed on gray whales. Dr. Cade printed special “kelp shields” (blue part of the tag) to prevent kelp from potentially getting caught underneath the tag since PCFG whales often forage on reefs with a lot of kelp. This tag includes a video camera (the lens can be seen in the center of the tag) to record video of the whale’s underwater behavior. Source: L. Torres.

This knowledge gap is one of the reasons why the GEMM Lab initiated a new project in close collaboration with Dr. Dave Cade from Stanford University and John Calambokidis from Cascadia Research Collective to quantify and understand the energetics and underwater behavior of gray whales using suction-cup tags. The project was kick-started with a very successful pilot effort the week of August 16th this year. Tags were placed on the backs of three different PCFG gray whales with a long carbon fiber pole and attached to the whales with four suction cups. The tags recorded video, position, accelerometry, and magnetometry data, which we will use to recreate the animal’s movements (pitch, roll), heading, trackline, and environment. Although the weather forecast did not look promising for most of the week, we lucked out with perfect conditions for one day during which we managed to deploy three tags on three different gray whales that are well-known, long-term study animals of the GEMM Lab. The tags stayed on the whales for 1-6 hours and were all recovered (including an adventurous trip up the Alsea River which involved a kayak deployment!). 

Dr. Cade spent the rest of the week teaching GEMM Lab PI Leigh Torres, University of British Columbia Master’s student Kate Colson (who is co-advised by Leigh and Dr. Andrew Trites), and myself the intricacies of data download, processing, and preliminary analysis of the tag data. For her Master’s research, Kate will develop a bioenergetics model for the PCFG sub-group that includes data on foraging energetics (estimated from the tag data) and prey availability in the PCFG foraging range. I plan on using the tag data to assess behavior patterns of PCFG whales relative to habitat as part of my PhD research. All together analysis of the data from these short-term tag deployments will help us get closer to understanding the behavioral choices, habitat needs, and energetic trade-offs of whales living in a rapidly changing ocean. With the success of this pilot effort, we plan to conduct another suction-cup tagging effort next summer to hopefully capture and explore more mysterious underwater behaviors of the PCFG.

An ecstatic team at the end of a very long yet successful day of suction cup tagging. Bottom (from left): Leigh Torres, Lisa Hildebrand, Clara Bird, Dave Cade, KC Bierlich. Top: John Calambokidis.

This project was funded by sales and renewals of the special Oregon whale license plate, which benefits MMI. We gratefully thank all the gray whale license plate holders, who made this research effort possible.

Literature cited

Cade, D. E., Friedlaender, A. S., Calambokidis, J., & Goldbogen, J. A. 2016. Kinematic diversity in rorqual whale feeding mechanisms. Current Biology 26(19):2617-2624. doi:10.1016/j.cub.2016.07.037.

Calambokidis, J., & Perez, A. 2017. Sightings and follow-up of mothers and calves in the PCFG and implications for internal recruitment. IWC Report SC/A17/GW/04 for the Workshop on the Status of North Pacific Gray Whales (La Jolla: IWC). 

Calambokidis, J., Laake, J., & Perez, A. 2017. Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2015. IWC Report SC/A17/GW/05 for the Workshop on the Status of North Pacific Gray Whales (La Jolla: IWC).

Goldbogen, J. A., Friedlaender, A. S., Calambokidis, J., McKenna, M. F., Simon, M., & Nowacek, D. P. 2013. Integrative approaches to the study of baleen whale diving behavior, feeding performance, and foraging ecology. BioScience 63(2):90-100. doi:10.1525/bio.2013.63.2.5.

Hildebrand, L., Bernard, K. S., & Torres, L. G. 2021. Do gray whales count calories? Comparing energetic values of gray whale prey across two different feeding grounds in the eastern North Pacific. Frontiers in Marine Science 1008. doi:10.3389/fmars.2021.683634.

Lemos, L. S., Olsen, A., Smith, A., Burnett, J. D., Chandler, T. E., Larson, S., Hunt, K. E., & Torres, L. G. 2021. Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Marine Mammal Science. doi:10.111/mms.12877.

Lemos, L.S., Olsen, A., Smith, A., Chandler, T.E., Larson, S., Hunt, K., and L.G. Torres. 2020. Assessment of fecal steroid and thyroid hormone metabolites in eastern North Pacific gray whales. Conservation Physiology 8:coaa110.

Nerini, M. K., & Oliver, J. S. 1983. Gray whales and the structure of the Bering Sea benthos. Oecologia 59:224-225. doi:10.1007/bf00378840.

Soledade Lemos, L., Burnett, J. D., Chandler, T. E., Sumich, J. L., & Torres, L. G. 2020. Intra- and inter-annual variation in gray whale body condition on a foraging ground. Ecosphere 11(4):e03094.

Stewart, J. D., & Weller, D. W. 2021. Abundance of eastern North Pacific gray whales 2019/2020. Department of Commerce, NOAA Technical Memorandum NMFS-SWFSC-639. United States: NOAA. doi:10.25923/bmam-pe91.

Torres, L.G., Nieukirk, S.L., Lemos, L., and T.E. Chandler. 2018. Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Frontiers in Marine Science: https://doi.org/10.3389/fmars.2018.00319.


The first voyage of the HALO project

Miranda Mayhall, Graduate Student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab. Marissa Garcia, Graduate Student, Cornell Department of Ornithology Center for Conservation Bioacoustics.

There is nothing quite like the excitement of starting a fresh project, and the newly organized Holistic Assessment of Living marine resources off the Oregon coast (HALO) project team was alive with it on 8 October as we prepared our various elements of research gear aboard the R/V Pacific Storm in the Newport bayfront (Fig 1). The weather was predicted suitable enough for our 24-hour trip out along the Newport Hydrographic line (NHL; Fig 2), and so we focused on the questions of whether we had remembered to pack each necessary piece of equipment, whether we had sufficiently charged and calibrated each bit of gear, whether we had enough snacks, and the most looming question of all, what would we see and hear when we get out there? The species guessing game only enhanced the thrill of our departure.

Figure 1. The R/V Pacific Storm docked at the Newport bayfront. Photo: Rachel Kaplan.

The HALO project aims to fill gaps in knowledge on the abundance and distribution of cetaceans off the Oregon coast, and relative to ongoing climate change and marine renewable energy development projects along the Oregon coast. The core of the HALO project is deployment of three hydrophones to record year-round cetacean vocalizations in the same area where we will conduct visual line surveys for cetaceans monthly in addition to mapping prey. Needless to say, we (the grad student authors of this blog) feel humbled and grateful to be on the project – not to mention, eager to gain our sea legs like the rest of the pros on the team and boat crew (much sea sickness meds were at the ready!).

This HALO team is well stacked, engaging the expertise and specialties of researchers from three different schools of science. Leigh Torres of the Marine Mammal Institute (MMI)’s GEMM lab (assisted by newcomer graduate student, Miranda Mayhall/coauthor of this post) brings to the project the knowledge of visual survey distance sampling data collection and analysis and will work alongside Craig Hayslip of MMI who will serve as lead visual observer. The visual sightings will inform us on cetacean occurrence patterns in the region. Since cetaceans also spend a great deal of time underwater, Holger Klinck, an expert bioacoustician from Cornell University and affiliate MMI professor (with graduate student Marissa Garcia, also a coauthor of this blog) will oversee the deployment of specialized hydrophones along our research line to record acoustic data. After the hydrophones are deployed, and while we are on-survey looking for cetaceans, we will also run a EK60 transducer (A.K. echosounder) to record backscatter data on prey in the area. This aspect of HALO brings in the third element of research from OSU’s College of Earth, Ocean, and Atmospheric Sciences (CEOAS) Zooplankton Ecology Lab, Kim Bernard who is leading the effort to collect and analyze prey data. During this first voyage, Rachel Kaplan, a grad student of both the GEMM lab and Zooplankton Ecology Labs, came along to run the echosounder and ensure data quality.

Figure 2. HALO’s research track-line: a 40-mile stretch along the Newport Hydrographic Line (NHL) from NH65 to NH25. The three points indicate the locations of the three deployed hydrophones.

With the sun nearly set, the R/V Pacific Storm left the dock at 7pm, pushing from Yaquina Bay out to the Pacific along the NHL hopping over swells that rocked the boat. Despite our strong-willed confidence, it was tough then to focus on anything but maintaining personal physiological equilibrium. Darkness surrounded the vessel, and we wouldn’t be able to see much of the Pacific Ocean until morning. It would take us nearly eleven hours to reach our first destination, 65 miles offshore (NH65) at which point all the activities would begin. All we could do was brace through the evening and hope that by dawn the dizziness would subside. We had field work adventures ahead! So, the focus went from extreme high energy to tucking in and allowing the Storm’s highly experienced crew to maintain watch and bring us to our first destination.   

Figure 3. The research lab room on the R/V Pacific Storm with four eager scientists just as team HALO departed Yaquina Bay; from the left Holger Klinck, Marissa Garcia, Rachel Kaplan & Leigh Torres. Photo: Miranda Mayhall.

At sunrise, the team rose to their feet, and we (the grad students) did what we could to muster the energy to crawl up the stairs, snap on lifejackets and ample out on the boat deck. Despite our condition, we looked out to a sight unlike anything we had ever seen before. The ocean was a deep purple, with flecks of orange bordering the horizon behind fluffy, indigo clouds. We were at NH65, and at this point it was time to deploy the first Rockhopper, a specialized hydrophone developed at Cornell lab of Ornithology, with the capability of recording at a high sampling rate (394 kHz), which allows it to detect and record most marine mammal species. In this case, we were recording at 197 kHz, only leaving our porpoises from the recordings.

Although the acoustic team has extensively prepared the hydrophones for deployment, nothing quite prepared us for focusing on the final connections and tests on the back deck while the boat rocked back and forth. The team initiated the Rockhopper for recording, and then we proceeded with setting up the mooring — connecting the Rockhopper to the acoustic release, float, and weights. We then slowly slid it off the edge of the boat, and there it went into the ocean, where it will record for six months, approximately 3,000 meters under the surface.

Figure 4. The HALO team prepared the Rockhopper (the orange orb-like device) for deployment; from the left, Craig Hayslip, Holger Klinck, and Marissa Garcia. Photo: Rachel Kaplan.

 Once the first Rockhopper was deployed, making its way to the ocean floor, the “transducer pole” was deployed off the side of the vessel to collect echosounder data and the long endeavor of conducting visual survey for the length of the research line began. Observers were glued to binoculars, scouring the sea for the presence of cetaceans, as the ocean swell rocked the boat on our journey eastward. Those with an appetite nibbled on Tony’s Chocoloney chocolate bars (Thanks, Leigh!), breaking off pieces and passing around the bar to each visual observer — an optimal fuel for remaining attentive.

Figure 5. HALO team up on the flying bridge; Observers clockwise from the lower left: Leigh Torres, Marissa Garcia, Craig Hayslip, Miranda Mayhall, Holger Klinck.

During visual survey effort, we observe from the flying bridge the entire front 180 degrees of the vessel trackline, all the while recording data on where we do and don’t see cetaceans (presence and absence data). During this survey effort we record the sighting conditions (visibility, sea state, glare), and when we see cetaceans we record the distance to the marine mammals from the boat, the species identification, and the number of animals in the sighting. We use a program called SeaScribe to collect our data. As we use the data collection protocols on each of the 12 planned monthly surveys, we will obtain a valuable, standardized dataset that can be analyzed relative to environmental conditions and in comparison, to the acoustic data to understand cetacean distribution patterns. The survey pressed on, and all the while the echosounder was actively recording prey availability data, with Rachel Kaplan at the control. 

Figure 6. Rachel Kaplan monitoring the incoming data from the transducer on the SIMRAD EK60. Photo: Marissa Garcia.

Over the course of the survey, the visual team spotted northern right whale dolphins, a fin whale, a small group of killer whales and many scattered humpback whales. All three Rockhoppers were deployed at their intended locations at NH65, NH45, and then NH25. The echosounder successfully collected backscatter data for the duration of the survey, and interestingly we noticed increased prey on the echosounder at the same time as we observed the humpbacks. Already we are detecting connections between the environment and cetaceans!           

Figure 8. Fin whale spotted while on our first HALO survey. Photo: Leigh Torres, NOAA/NMFS permit # 21678

After nearly twelve hours conducting field work, the shoreline was close in sight, and we stopped our survey effort. For the first time all day, we all collectively sat in the vessel’s laboratory, finally putting our feet up to rest. We pulled back into Newport harbor around 7:00 pm, with the first HALO cruise successfully in the books. And though we visually observed many cetaceans and collected prey data, we still couldn’t help wondering what the Rockhoppers were recording at the bottom of the ocean. The thought of getting back out there for more surveys and retrieving the sound data keeps our momentum in full swing. For the next 11 months (and hopefully longer!) we will conduct the same 24 hr. cruise. The future is exciting, and we can’t wait to report back on our future trips and research findings.

Figure 9. The HALO team walking along the dock to their cars in Newport, Oregon, heading home after cruise #1. Photo: Miranda Mayhall.

This project was funded by sales and renewals of the special Oregon whale license plate, which benefits MMI. We gratefully thank all the gray whale license plate holders, who made this research trip possible.

Scouting mission to Kodiak: Reconnaissance of potential gray whale research in Kodiak, Alaska.

Dr. KC Bierlich, Dr. Alejandro Fernández Ajó, and Dr. Leigh Torres, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Eastern North Pacific (ENP) gray whales (Eschrichtius robustus) undertake one of the longest annual migrations of any mammal, traveling from their winter breeding grounds in the warm waters of Baja California, Mexico to their summer feeding grounds in the icy waters of the Bering and Chukchi Seas1,2. Yet, a distinct subgroup of this population, called the Pacific Coast Feeding Group (PCFG), instead shorten their migration farther south to spend the summer foraging along waters from northern California, USA to northern British Columbia, Canada1 (Figure 1). On these summer feeding grounds gray whales will forage almost continuously to increase their energy reserves to support migration and reproduction during the rest of the year.

The GEMM Lab has been studying the ecology and physiology of the PCFG gray whales in Oregon waters since 2015, combining traditional photo-ID and behavioral observation methods with fecal sample collection, drone flights, and prey assessment to integrate data on individual whale behavior, nutritional status, prey consumption, and hormone variation. These multidisciplinary methods have proven effective to obtain an improved understanding of PCFG gray whale body condition and hormone variation by demographic unit and over time3,4,5, as well as prey energetics and foraging ecology6.

Figure 1. Left: ENP Gray whale´s range from the breeding grounds in Baja California, Mexico to the northernmost feeding grounds in the Arctic. Right: Overview of Kodiak Island; the red square shows a zoom in image of the study area, including the shore – and boat-based data collection sites in yellow.

Since the PCFG remains a small proportion (~230 individuals) of the larger eastern ENP population (~20,000 individuals), the GEMM Lab and multiple collaborators are interested in extending the research design implemented by the GEMM Lab in Oregon to study gray whale ecology and physiology of whales feeding on the more northern foraging grounds. The goal would be to fill some of the many critical knowledge gaps including gray whale resilience and response to climate change, connectivity between foraging grounds, population dynamics of the PCFG and ENP, and physiological variation (body condition, hormones) as a function of habitat, prey, demography, and time of year.

Kodiak Island, Alaska is a middle distance between PCFG foraging grounds in Newport, Oregon and the traditional ENP foraging grounds in Chukchi and Beaufort Seas (Figure 1). Two studies documented high gray whale encounter rates in Ugak Bay in Kodiak Island, including during summer months when foraging behavior was observed7,8. Evidence from photo-ID matches in these studies indicated that some PCFG whales might also extends their feeding grounds further north to Kodiak Island7,8.

During August-September of this year, GEMM Lab postdocs KC Bierlich and Alejandro Fernández Ajó traveled to Kodiak Island to assess opportunities for researching gray whales in the area. The mission objectives included determining gray whale presence, assessing behavioral states and foraging areas, determining feasibility of drone operations and fecal sample collection, collecting photo ID images, assessing feasibility of boat and shore-based operations in Ugak Bay (Figure 1), and connecting with local scientists and stakeholders interested in collaborating.

We landed in Kodiak the evening of August 28 (Figure 2), with a beautiful sunset. The next morning, we met our captain, Alexus Kwachka, over breakfast to discuss a plan for going offshore to look for gray whales later in the week. Alexus is a local fisherman in Kodiak with over 30 years of experience fishing in Alaska and incredible knowledge on local wildlife and navigating the rough Alaskan seas. It was particularly interesting to hear his stories on the local changes he has noticed over the years, not just in weather and fishing, but also in the seals, birds, and whales.

Figure 2. Arriving to Kodiak after a long day of travel.

Next, we met with Sun’aq Tribe’s biologist Matthew Van Daele, who coordinates the marine mammal stranding network on Kodiak Island and has a deep knowledge of the locations to find whales. Matt showed us several great spots to scout for gray whales along the shore in the Pasagshak area (Figure 1), which overlooks Ugak Bay and is about 1 hour drive from Kodiak (Figure 3). Along the way, Matt discussed the high mortality rate of gray whales he has observed over the past two years and his concerns about some skinny whales in the area he recently observed during aerial surveys. Since 2019, an Unusual Mortality Event (UME) of gray whales along the whole North Pacific west coast (Mexico, USA, Canada) has impacted the ENP gray whales and while the exact cause(s) of these mortalities is largely unknown, evidence suggests reduced nutritional status may be a likely cause of death9. We learned from Matt that while gray whale strandings are decreasing compared to the previous two years, the numbers are still concerningly high. It was an absolute pleasure spending the day with Matt, as being born and raised in Kodiak he has such great knowledge of the area and the local wildlife. Together we saw Kodiak’s beautiful landscape with lots of different wildlife, which included some huge Kodiak brown bears a few hundred meters away from the road (Figure 4).

Figure 3. The views from Pasagshak Point that are good observation locations for gray whales. The gray arrows represent the view looking left (A) and right (C) from Pasagshak Point (B). A panorama of the view from left to right on the point is also shown (D). Photo: KC Bierlich.
Figure 4. Sighting of a Kodiak brown bear (Ursus arctos) off the roadside on our way to Pasagshak. The Kodiak brown bear is the largest recognized subspecies (or population) of the brown bear, and one of the largest bears alive today. Photo: Alejandro Fernández Ajó.

The next day, the weather was great, so we returned to the Pasagshak lookout points to spend the day looking for whales. We spotted several gray whales from the cliffs and shore. At Burton Beach, we spotted a gray whale very close to shore that first appeared to be traveling, but then changed direction and started moving closer inshore –less than 10 m from where we were standing on the beach! The whale then swam back and forth along the shore, providing an opportunity to collect photos of its right and left side to use for photo ID. KC flew the drone over the whale and recorded some amazing behavior of lateral swimming and great images for photogrammetry. Our excitement was sky high as within two days on the trip we had documented the presence of gray whales, recorded the best places to work from land, and even captured some interesting behavior, photo ID, and photogrammetry data from shore! (Figure 5).

Figure 5. Gray whale feeding off Burton Beach, Kodiak Island. This photo was taken from the shore, as the whale swam back and forth amazingly close to the shoreline. In this picture you can see the whale´s head from a ventral perspective. Photo: Alejandro Fernández Ajó / GEMM Lab. Photograph captured under NOAA/NMFS permit #21678.

The weather deteriorated over the next couple days, bringing foggy and rainy conditions. We used this time to process data and meet with some of the local researchers. When the weather conditions improved, we met back up with Alexus and boarded his fishing vessel, “No Point”, and headed off to Ugak Bay to look for gray whales. During transit we encountered a humpback whale mother-calf pair lunge feeding and breaching (Figure 6). As we approached Pasagshak we sighted a gray whale diving and benthic feeding in 60 m water depth, and then 2-3 other individual whales exhibiting the same behavior close by. We collected photo ID data, but high wind conditions hindered drone operations, so we continued surveying further into Ugak Bay and turned around following the coast towards Gull Point (Figure 7).

Figure 6. A breaching humpback whale on the way to Ugak Bay from Kodiak. Photo: Alejandro Fernández Ajó / GEMM Lab. Photograph captured under NOAA/NMFS permit #21678.
Figure 7. Track line (shown in blue) of boat-based operations. White circles represent the locations for sightings of gray whales.

During our survey effort we spotted a gray whale foraging on a shallow rocky, kelp reef (12 m depth) along the northwest point of Ugak Bay. This sighting was similar to behavior we often observe in Oregon, with whales feeding in near shore shallow, reef habitats. Conditions for flying the drone were still too windy, but we observed the whale defecate and collected a fecal sample! For us, fecal samples are like “biological gold”, as we can study hormones (which include assessments of their reproductive status, nutritional condition, sex determination, and stress levels), genetics, prey, and much more! We were so excited to collect this sample because it provides the chance to start looking at the physiological parameters of these Alaskan whales and compare findings to what we observe in samples collected from whales in Oregon (Figure 8).

Figure 8. A gray whale fecal sample right after being scooped from the water using nets attached long aluminum poles. Photo: KC Bierlich.

After a beautiful night anchored in a sheltered bay near Gull Point (Figure 9) we continued west to scan for whales. Back in Ugak Bay, we found six more gray whales diving and feeding in 50-60 m depth near the same location as the previous day off Pasagshak point. Weather conditions had finally improved, allowing us to fly the drone. We flew over four whales and collected video for behavior and photogrammetry analysis, which allows us to measure the body condition of the whales to assess how healthy it is (Figure 10).

Figure 9. “Home sweet home” for the night where our vessel “No point” anchored in a sheltered bay. Photo: Alejandro Fernández Ajó.
Figure 10. Drone image of two gray whales feeding near each other. Note the trailing sediment plume from the whale’s mouth and body indicating it was bottom feeding in a muddy benthic habitat. Photo: KC Bierlich. Photograph captured under NOAA/NMFS permit #21678.

Another highlight of our field work was the collection of a benthic prey sample using a Ponar grab sampler at this location in Pasagshak Bay where the whales were foraging. The bottom was muddy and rich with invertebrates; the sample literally looked like it was boiling from the amount of prey in it (Figure 11). From this sample, we will determine the invertebrate species and caloric content of these prey for comparison to the prey found in Oregon waters.

Figure 11. The Ponar bottom grab sample, full of invertebrate prey, taken near whales feeding in ~50-60 m depth. Photo: CK Bierlich.

Overall, this scouting mission to Kodiak was a great success! Through boat surveys, shore-based observations, and the conversations with locals, we determined the best areas and timing to effectively work from boats and shore to expand our gray whale research to Kodiak. Moreover, our scouting mission resulted in the collection of relevant pilot data including fecal samples for hormonal analyses, drone images for body condition and behavioral assessments, prey samples, and photo-ID images. This scouting mission identified several knowledge gaps regarding gray whale ecology, physiology, and population connectivity that can be feasibly addressed through expansion of GEMM Lab research efforts to the Alaskan region. Importantly, the trip facilitated important networking with locals to establish potential collaborations for future work. We are optimistic and excited to grow our collaborative research in Kodiak.

This pilot project was funded by sales and renewals of the special Oregon whale license plate, which benefits MMI. We gratefully thank all the gray whale license plate holders, who made this scouting trip possible.


1 Calambokidis J, Darling JD, Deecke V, Gearin P, Gosho M, Megill W, et al. Abundance, range and movements of a feeding aggregation of gray whales (Eschrichtius robustus) from California to south- eastern Alaska in 1998. J Cetacean Res Manag 2002; 4:267–76.

2Stewart JD, Weller DW. Abundance of eastern North Pacific gray whales 2019/2020. 2021. U.S. Department of Commerce, NOAA Technical Memorandum NMFS-SWFSC-639. https://doi.org/10. 25923/bmam-pemorandum NMFS-SWFSC-639. https://doi.org/10. 25923/bmam-pe91.

3Lemos, L. S. et al. Assessment of fecal steroid and thyroid hormone metabolites in eastern North Pacific gray whales. Conserv. Physiol. 8, (2020).

4Lemos, L. S. et al. Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Mar. Mammal Sci. 1–11 (2021). doi:10.1111/mms.12877

5Soledade Lemos, L., Burnett, J. D., Chandler, T. E., Sumich, J. L. & Torres, L. G. Intra‐ and inter‐annual variation in gray whale body condition on a foraging ground. Ecosphere 11, (2020).

6Hildebrand, L., Bernard, K. S. & Torres, L. G. (2021). Do Gray Whales Count Calories? Comparing Energetic Values of Gray Whale Prey Across Two Different Feeding Grounds in the Eastern North Pacific. Frontiers in Marine Science, 8(July), 1–13. https://doi.org/10.3389/fmars.2021.683634

7Gosho Merrill, Patrick Gearin, Ryan Jenkinson, Jeff Laake, Lori Mazzuca, David Kubiak, John Calambokidis, Will Megill, Brian Gisborne, Dawn Goley, Christina Tombach, James Darling, V. D. gosho_et_al._2011_-_sc-m11-awmp2.pdf. (2011).

8Moore, S. E., Wynne, K. M., Kinney, J. C. & Grebmeier, J. M. GRAY WHALE OCCURRENCE AND FORAGE SOUTHEAST OF KODIAK, ISLAND, ALASKA. Mar. Mammal Sci. 23, 419–428 (2007).

9Christiansen F, Rodríguez-González F, Martínez-Aguilar S, Urbán J and others (2021) Poor body condition associated with an unusual mortality event in gray whales. Mar Ecol Prog Ser 658:237-252. https://doi.org/10.3354/meps13585

 [TL1]Add this link here:


Learning the right stuff – examining social transmission in humans, monkeys, and cetaceans

Clara Bird, PhD Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

The start of a new school year is always an exciting time. Like high school, it means seeing friends again and the anticipation of preparing to learn something new. Even now, as a grad student less focused on coursework, the start of the academic year involves setting project timelines and goals, most of which include learning. As I’ve been reflecting on these goals, one of my dad’s favorite sayings has been at the forefront of my mind. As an overachieving and perfectionist kid, I often got caught up in the pursuit of perfect grades, so the phrase “just learn the stuff” was my dad’s reminder to focus on what matters. Getting good grades didn’t matter if I wasn’t learning. While my younger self found the phrase rather frustrating, I have come to appreciate and find comfort in it. 

Given that my research is focused on behavioral ecology, I’ve also spent a lot of time thinking about how gray whales learn. Learning is important, but also costly. It involves an investment of energy (a physiological cost, Christie & Schrater, 2015; Jaumann et al., 2013), and an investment of time (an opportunity cost). Understanding the costs and benefits of learning can help inform conservation efforts because how an individual learns today affects the knowledge and tactics that the individual will use in the future. 

Like humans, individual animals can learn a variety of tactics in a variety of ways. In behavioral ecology we classify the different types of learning based on the teacher’s role (even though they may not be consciously teaching). For example, vertical transmission is a calf learning from its mom, and horizontal transmission is an individual learning from other conspecifics (individuals of the same species) (Sargeant & Mann, 2009). An individual must be careful when choosing who to learn from because not all strategies will be equally efficient. So, it stands to reason than an individual should choose to learn from a successful individual. Signals of success can include factors such as size and age. An individual’s parent is an example of success because they were able to reproduce (Barrett et al., 2017). Learning in a population can be studied by assessing which individuals are learning, who they are learning from, and which learned behaviors become the most common.

An example of such a study is Barrett et al. (2017) where researchers conducted an experiment on capuchin monkeys in Costa Rica. This study centered around the Panama ́fruit, which is extremely difficult to open and there are several documented capuchin foraging tactics for processing and consuming the fruit (Figure 1). For this study, the researchers worked with a group of monkeys who lived in a habitat where the fruit was not found, but the group included several older members who had learned Panamá fruit foraging tactics prior to joining this group. During a 75-day experiment, the researchers placed fruits near the group (while they weren’t looking) and then recorded the tactics used to process the fruit and who used each tactic. Their results showed that the most efficient tactic became the most common tactic over time, and that age-bias was a contributing factor, meaning that individuals were more like to copy older members of the group. 

Figure 1. Figure from Barrett et al. (2017) showing a capuchin monkey eating a Panamá fruit using the canine seam technique.

Social learning has also been documented in dolphin societies. A long-term study on wild bottlenose dolphins in Shark Bay, Australia assessed how habitat characteristics and the foraging behaviors used by moms and other conspecifics affected the foraging tactics used by calves (Sargeant & Mann, 2009). Interestingly, although various factors predicted what foraging tactic was used, the dominant factor was vertical transmission where the calf used the tactic learned from its mom (Figure 2). Overall, this study highlights the importance of considering a variety of factors because behavioral diversity and learning are context dependent.

Figure 2. Figure from Sargeant & Mann (2009) showing that the probability of a calf using a tactic was higher if the mother used that tactic.

Social learning is something that I am extremely interested in studying in our study population of gray whales in Oregon. While studies on social learning for such long-lived animals require a longer study period than of the span of our current dataset, I still find it important to consider the role learning may play. One day I would love to delve into the different factors of learning by these gray whales and answer questions such as those addressed in the studies I described above. Which foraging tactics are learned? How much of a factor is vertical transmission? Considering that gray whale calves spend the first few months of the foraging season with their mothers I would expect that there is at least some degree of vertical transmission present. Furthermore, how do environmental conditions affect learning? What tactics are learned in good vs. poor years of prey availability? Does it matter which tactic is learned first? While the chances that I’ll get to address these questions in the next few years are low, I do think that investigating how tactic diversity changes across age groups could be a good place to start. As I’ve discussed in a previous blog, my first dissertation chapter will focus on quantifying the degree of individual specialization present in my study group. After reading about age-biased learning, I am curious to see if older whales, as a group, use fewer tactics and if those tactics are the most energetically efficient.

The importance of understanding learning is related to that of studying individual specialization, which can allows us to estimate how behavioral tactics might change in popularity over time and space. We could then combine this with knowledge of how tactics are related to morphology and habitat and the associated energetic costs of each tactic. This knowledge would allow us to estimate the impacts of environmental change on individuals and the population. While my dissertation research only aims to provide a few puzzle pieces in this very large and complicated gray whale ecology puzzle, I am excited to see what I find. Writing this blog has both inspired new questions and served as a good reminder to be more patient with myself because I am still, “just learning the stuff”.

Let me introduce you to… dugongs!

By Solène Derville, Postdoc, OSU Department of Fisheries, Wildlife, and Conservation Science, Geospatial Ecology of Marine Megafauna Lab

Today let me take you on a journey into the tropical waters of the Indo-Pacific Ocean, far from Oregon’s beautiful coasts. Although I have been working as a postdoc on the OPAL project for a year, the pandemic has prevented me from moving to the US as planned. Like so many around the globe, I have been working remotely from my study area (Oregon coastal waters), imagining my study species (blue, fin and humpback whales) gently swimming and feeding along the productive California Current system. One day, I’ll get to see these amazing animals for real, that’s for sure.

But in the meantime, I have taken this year as an opportunity to work with the GEMM lab, while continuing to enjoy the marvels of New Caledonia, a French overseas territory where I have lived for more than 6 years now. Among the animals that I get to approach and observe regularly in the coral reef lagoons that surround the island, the dugong (Dugon dugon) is perhaps the most emblematic and intriguing. This marine mammal is listed as vulnerable in the IUCN Red list of threatened species and has been the focus of important research and conservation efforts in New Caledonia over the last two decades1–3. During my previous post-doctoral position at the French Institute of Research for Sustainable Development, I contributed to some recent research involving satellite tracking of dugongs in the region. This work has led to a publication, now in review4, and will be the topic of my oral presentation at the 7th International Bio-Logging Science Symposium hosted in Hawaii in a couple weeks.

While I was analyzing dugong satellite tracks, writing this paper with my colleagues and preparing for the symposium, I learned a lot about these strange “sea cows”. Dugongs belong to the Sirenian marine mammal order, just like manatees (West Indian, Amazonian and West African species), which they are often mistaken for (watch out: Google Images will misleadingly suggest hundreds of manatee pictures if you make a “dugong” keyword search). The physiology and anatomy of dugongs is actually quite different from that of manatees (Figure 1). They also live in a different part of the world as they are broadly distributed in the Indo-Pacific coastal and island waters. Dugongs form separate populations, some of which are very isolated and at high risk of extirpation. They are found in 37 different countries, with Australia being home to the largest populations by far (exceeding 70,000 individuals5).

Figure 1: Manatee vs Dugong, can you tell them apart? Among other things, dugongs and manatees have a very different body shape. As the famous Sirenian specialist Helene Marsh said, a dugong essentially looks like “a manatee that goes to the gym”5! Illustration by S. Derville.

Sea cow or sea elephant?

Through the tree of evolution, the dugong and manatee’s closest relative is not the one you would think… other marine mammals like cetaceans or pinnipeds. Indeed, molecular genetic analyses have placed the Sirenians in the Afrotheria Superorder of mammals. Therefore, it appears that dugongs are more closely related to elephant and golden moles than to whales and dolphins!

As a memory aid to help remember this ancient origin, we may notice that both elephants and dugongs have tusks. Mature male and female dugongs have erupted tusks, although the females’ only erupt rarely and at a very old age. Interestingly, tusks are used by scientists to determine age. Analyses of growth layers in bisected dugong tusks have revealed that dugongs are long-lived, with a maximum longevity record of 73 years (estimated from a female individual found in Western Australia5).

An (almost) vegetarian marine mammal

Dugongs and manatees are the only predominantly herbivorous aquatic mammals. Given that manatees use both marine and fresh water ecosystems they tend to have a broader diet, eating many kinds of submerged, floating or emergent algae and seagrass (even bank growth!). On the other hand, dugongs are a strictly marine species and primarily feed on seagrass, which may look very similar to seaweeds, but are in fact marine flowering plants. Seagrass tend to form underwater shallow meadows that are among the most productive ecosystems in the world6. In fact, dugong grazing influences the biomass, species composition and nutritional quality of seagrass meadows7,8. Just like we take care of our gardens, dugongs regulate seagrass ecosystems. But there is more. Recent research conducted in the Great Barrier Reef indicates that seagrass seeds that have been digested by dugongs germinate at a faster rate9. As well as playing a role in dispersal10, it appears that dugongs are pooping seeds with enhanced germination potential, hence participating to seagrass meadow resilience.

Figure 2: Dugong mother and calf feeding on a dense seagrass bed (a) and solitary adult foraging in a very sparce seagrass bed (b). Seagrass grows in many different types of meadows, which may vary in density, species composition and substrate. For instance, seagrass species of the Halophila genus are among the preferred dugong’s meals although may be very thinly distributed (c). Photo credit: Serge Andréfouët, New Caledonia.

Unlike manatees, dugongs cannot feed over the whole water column and are strictly bottom feeders. They use their deflected snout (Figure 1) to search the seabed for their favorite food (Figure 2). The feeding trails left by dugongs in dense seagrass meadows are easily detectable from above, just like the sediment clouds that they generate when searching muddy bottoms. Although seagrass is undoubtedly the main component of the dugong’s diet, they may incidentally (or not) ingest algae and invertebrates5.

A legendary animal

The etymology for the word Sirenian comes from the mermaids, or “sirens” of the Greek mythology. These aquatic creatures with the upper body of a female human would sing to lure sailors towards the shore… and towards a certain death. The morphology of dugongs and manatees shares some resemblance with mermaids, at least enough for desperate and lonely sailors to think so!

In addition to having a scientific name rooted in legends, dugongs are also important to contemporary human cultures. In tropical islands and coastal communities, marine megafauna species such as dugongs are considered heritage, due to the strong bond that their people have forged with the ocean5. Dugongs may play an important cultural role because they can be part of the socio-symbolic organization of societies, associated with the imaginary world, or simply because they are seen as companions of the sea, which people frequently encounter. For New Caledonia’s indigenous people, the Kanaks, dugongs can be totem to tribes. Like other large marine species (whales, sharks), the dugong is also considered as an embodiment of ancestors11.

Dugongs have been hunted throughout their range since prehistoric times. Archaeological excavations such as those conducted on the island of Akab in the United Arab Emirates12, indicate that dugong hunting played a role in ancient rituals, in addition to providing a large quantity of meat. The cultural value of dugongs is recognized by multiple countries, which have therefore authorized indigenous dugong hunting, sometimes under quotas. For instance, in Australia, dugongs may be legally hunted by Aboriginal and Torres Strait Islander people (Figure 3) under section 211 of the Native Title Act 1993.

In New Caledonia, the dugong has been protected since 1962 and its hunting is only authorized in one province, with a dispensation for traditional Kanak celebrations13. However, in view of the critical situation in which the New Caledonian dugong population finds itself, estimated at around 700 individuals in 2008-201214, no hunting exemptions have been issued since 2004.

Figure 3: “Naath” (dugong hunting platform), hand colored linocut by Torres Strait Islander artist Dennis Nona. The art piece represents traditional dugong hunting where the hunter is guided by the phosphorescent glow the dugong would leave in the water at night.

What future for dugongs?

Despite legislations to forbid dugong meat consumption outside specific traditional permits, poaching persists, in New Caledonia and in many of the “low-income” countries that are home to dugongs. As climate change and demography intensifies risks to food security, scientists and stakeholders fear for dugongs. Moreover, dugongs entirely rely on seagrass ecosystems that are also disappearing at an alarming rate (7% per year6) as a result of coastal development, pollution and overfishing.

Can we preserve dugongs in regions of high climate vulnerability and where people still have low levels of access to basic needs? Can dugongs play the role of “umbrellas” for the conservation of the ecosystem they live in? I do not have the answer to these questions but I certainly believe that people’s well-being and environmental conservation are tightly intertwined. I hope that rising transdisciplinary approaches such as those supported by the “One Health” framework will help reconnect human populations to their environment, and achieve the goal of optimal health for everyone, humans and animals.


1.        Garrigue, C., Patenaude, N. & Marsh, H. Distribution and abundance of the dugong in New Caledonia, southwest Pacific. Mar. Mammal Sci. 24, 81–90 (2008).

2.        Cleguer, C., Grech, A., Garrigue, C. & Marsh, H. Spatial mismatch between marine protected areas and dugongs in New Caledonia. Biol. Conserv. 184, 154–162 (2015).

3.        Cleguer, C., Garrigue, C. & Marsh, H. Dugong (Dugong dugon) movements and habitat use in a coral reef lagoonal ecosystem. Endanger. Species Res. 43, 167–181 (2020).

4.        Derville, S., Cleguer, C. & Garrigue, C. Ecoregional and temporal dynamics of dugong habitat use in a complex coral reef lagoon ecosystem. Sci. Rep. (In review)

5.        Marsh, H., O’Shea, T. J. & Reynolds, J. E. I. Ecology and conservation of the Sirenia: dugongs and manatees, Vol 18. (Cambridge University Press, Cambridge, 2011).

6.        Unsworth, R. K. F. & Cullen-Unsworth, L. C. Seagrass meadows. Curr. Biol. 27, R443–R445 (2017).

7.        Aragones, L. V., Lawler, I. R., Foley, W. J. & Marsh, H. Dugong grazing and turtle cropping: Grazing optimization in tropical seagrass systems? Oecologia 149, 635–647 (2006).

8.        Preen, A. Impacts of dugong foraging on seagrass habitats: observational and experimental evidence for cultivation grazing. Mar. Ecol. Prog. Ser. 124, 201–213 (1995).

9.        Tol, S. J., Jarvis, J. C., York, P. H., Congdon, B. C. & Coles, R. G. Mutualistic relationships in marine angiosperms: Enhanced germination of seeds by mega-herbivores. Biotropica (2021) doi:10.1111/btp.13001.

10.      Tol, S. J. et al. Long distance biotic dispersal of tropical seagrass seeds by marine mega-herbivores. Sci. Rep. 7, 1–8 (2017).

11.      Dupont, A. Évaluation de la place du dugong dans la société néo-calédonienne. (Mémoire Master. Encadré par L. Gardes (Agence des Aires Marines Protégées) et C. Sabinot (IRD), 2015).

12.      Méry, S., Charpentier, V., Auxiette, G. & Pelle, E. A dugong bone mound: The Neolithic ritual site on Akab in Umm al-Quwain, United Arab Emirates. Antiquity 83, 696–708 (2009).

13.      Leblic, I. Vivre de la mer, vivre de la terre… en pays kanak. Savoirs et techniques des pêcheurs kanak du sud de la Nouvelle-Calédonie. (Société des Océanistes, 2008).

14.      Hagihara, R. et al. Compensating for geographic variation in detection probability with water depth improves abundance estimates of coastal marine megafauna. PLoS One 13, e0191476 (2018).