So big, but so small: why the smallest of the largest whales are not smaller

Dr. KC Bierlich, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab

Baleen whales are known for their gigantism and encompass a wide range in body sizes extending from blue whales that are the largest animals to live on earth (max length ~30 m) to minke whales (max length ~10 m) that are the smallest of baleen whales (Fig. 1). While all baleen whales are filter feeders, a group called the rorquals use a feeding strategy known as lunge feeding (or intermittent engulfment filtration), which involves engulfing large volumes of prey-laden water at high speeds and then filtering the water out of their mouth using their baleen as a “sieve”. There is positive allometry associated with this feeding technique and body size, meaning that as whales are larger, this feeding strategy becomes more efficient due to increased engulfment of water volume per each lunge feeding event. In other words, a bigger body size equates to a much larger mouthful of food. For example, a minke whale (body length ~7-10 m) will engulf water volume equivalent to ~42% of its body mass, while a blue whale (~21-24 m) engulfs ~135%. Thus, filter feeding enables gigantism through efficient exploitation of large, dense patches of prey. An interesting question then arises: what is the minimum body size at which filter feeding is still efficient? Or in other words, why are the smallest of the baleen whales, minke whales, not smaller? For this blog, I will highlight a study published today in Nature Ecology and Evolution titled “Minke whale feeding rate limitations suggest constraints on the minimum body size for engulfment filtration feeding” led by friend and collaborator of the GEMM Lab Dr. Dave Cade and included myself and other collaborators as co-authors from Stanford University, UC Santa Cruz, Cascadia Research Collective, Duke University, and University of Queensland.

Figure 1. Aerial imagery collected using drones of several baleen whales of various sizes. Each species shown is considered a rorqual whale, except for gray whales. Figure from Segre et al. (2022)

The largest animals of today are marine filter feeders, such as whale sharks, manta rays, and baleen whales, which all share parallel evolutionary histories in which their large body sizes and filter-feeding morphologies are derived from smaller-bodied ancestors that targeted single prey items. Changes in ocean productivity increased the concentrations of smaller prey in the oceans around 5 million years ago, enabling filter feeding as an efficient feeding strategy through capture of abundant aggregations of prey by filtering large volumes of water. It is interesting to note, that within these filter feeding lineages of animals, there are groups of animals that are single-prey foragers with smaller body sizes. For example, the whale shark is the only filter feeder amongst the carpet sharks and the manta ray is much larger than other rays that feed on single prey items. Amongst cetaceans, the smallest single-prey foragers, dolphins (~2-3 m) and porpoises (~1.4-1.9 m), are much smaller than the smallest of the filter feeding cetaceans, minke whales (~7-10 m). These common differences in body sizes and feeding strategies within lineages suggest that there may be minimum body size requirements for this filter feeding strategy to be efficient.

To investigate the limits on minimum body size for filter feeding, our study explored the foraging behavior of Antarctic minke whales, the smallest of the rorqual baleen whales, along the Western Antarctic Peninsula. Our team tagged a total of 23 individuals using non-invasive suction cup tags, like the ones we use for our tagging component in the GEMM Lab’s GRANITE project (see this blog for more details). One of my roles on the project was to obtain aerial imagery of the minke whales using drones to obtain body length measurements (sound familiar?) (Figs. 2-4). Flying drones in Antarctica over minke whales was an amazing experience. The minke whales were often found deep within the bays amongst ice floes and brash ice where they can be very tricky to spot, as they’ll often surface and then quickly disappear, hence their nickname “sneaky minkes”. They also appear “playful” and “athletic” as they are incredibly quick and maneuverable, doing barrel rolls and quick bank turns while they swim. Check out my past blog to read more on accounts of flying over these amazing whales.

Figure 2. Drone image of our team about to place a noninvasive suction cup biologging tag on an Antarctic minke whale. Photo credit: Duke University Marine Robotics and Remote Sensing Lab.

Figure 3. A drone image of a newly tagged and curious Antarctic minke whale approaching our research team. Photo credit: Duke University Marine Robotics and Remote Sensing Lab.

Figure 4. A drone image of a group of Antarctic minke whales swimming through the icy waters along the Antarctic Peninsula. Photo credit: Duke University Marine Robotics and Remote Sensing Lab.

In total, our team collected 437 hours of tag data consisting of day- and night-time foraging behaviors. While the proportion of time spent foraging and the number of lunges per dive (~3-4) was similar between day- and night-time foraging, daytime foraging was much deeper (~72 m) compared to nighttime foraging (~28 m) due to vertical migration of Antarctic krill, their main food source. Overall, nighttime foraging was much more intense than daytime foraging, with an average of 165 lunges per hour during the night compared to 53 lunges per hour during the day. These shallower nighttime dives enabled quicker surface sequences for replenishing oxygen reserves to then return to foraging, whereas the deeper dives during the day required longer surface recovery times before beginning another foraging dive. Thus, nighttime dives are a more efficient and critical component of minke whale foraging.

When it comes to body size, there was no relationship between dive depth and dive duration with body length, except for daytime deep dives, where longer minke whales dove for longer periods than smaller whales. These longer dive times also require longer surface times to replenish oxygen reserves. Longer minke whales can gulp larger amounts of food and thus need longer filtration times to process water from each engulfment. For example, a 9 m minke whale will take 50% longer to filter water through its baleen compared to a 5 m minke whale. In turn, smaller minke whales would need to feed more frequently than larger minke whales in order to maintain efficient foraging. This decreasing efficiency with smaller body size shines light on a broader trend for filter feeders that we refer to in our study as the minimum-size constraint (MSC) hypothesis: “while the maximum size of a filter-feeding body plan will be restricted by physical properties, the minimum size is restricted by the energetic efficiency of filter feeding and the time required to extract sufficient particles from the water” (Cade et al. 2023). When we examined the scaling of maximum feeding rates of minke whales, we found evidence of a minimum size constraint on efficiency at lengths around 5 m. Interestingly, the weaning length of minke whales is reported to be 4.5 – 5.5 m. Before weaning, newborn/yearling minke whales that are smaller than 4.5 – 5.5 m have a different foraging strategy where they are dependent on maternal milk. Thus, it is likely that the body size at weaning is influenced by the minimum size at which this specialized foraging technique of lunge feeding becomes efficient.

This study helps inform the evolutionary pathway for filter feeding whales and suggests that efficient filter feeding and gigantism likely co-evolved within the last 5 million years when ocean conditions changed to support larger prey patches suitable for lunge feeding. It is interesting to think about the MSC hypothesis for other baleen whale species that employ alternative filter feeding techniques, such as gray whales that generally use a form of filter feeding called suction feeding. Gray whales are estimated to have a birth length of ~4.6 m (Agbayani et al., 2020), and the body length of newly weaned calves that we have observed along the Oregon Coast from drone imagery seem to be ~8 – 9 m. Perhaps this is the minimum size of when suction feeding becomes efficient for a gray whale? This is something the GEMM Lab hopes to further explore as we continue to collect foraging data from suction cup tags and behavior and body size measurements from drone imagery.

References

Agbayani, S., Fortune, S. M., & Trites, A. W. (2020). Growth and development of North Pacific gray whales (Eschrichtius robustus). Journal of Mammalogy, 101(3), 742-754.

Cade, D.E., Kahane-Rapport, S.R., Gough, W.T., Bierlich, K.C., Linksy, J.M.J., Johnston, D.W., Goldbogen, J.A., Friedlaender, A.S. (2023). Ultra-high feeding rates of Antarctic minke whales imply a lower limit for body size in engulfment filtration feeders. Nature Ecology and Evolution. https://www.nature.com/articles/s41559-023-01993-2

Paolo S. Segre, William T. Gough, Edward A. Roualdes, David E. Cade, Max F. Czapanskiy, James Fahlbusch, Shirel R. Kahane-Rapport, William K. Oestreich, Lars Bejder, K. C. Bierlich, Julia A. Burrows, John Calambokidis, Ellen M. Chenoweth, Jacopo di Clemente, John W. Durban, Holly Fearnbach, Frank E. Fish, Ari S. Friedlaender, Peter Hegelund, David W. Johnston, Douglas P. Nowacek, Machiel G. Oudejans, Gwenith S. Penry, Jean Potvin, Malene Simon, Andrew Stanworth, Janice M. Straley, Andrew Szabo, Simone K. A. Videsen, Fleur Visser, Caroline R. Weir, David N. Wiley, Jeremy A. Goldbogen; Scaling of maneuvering performance in baleen whales: larger whales outperform expectations. J Exp Biol 1 March 2022; 225 (5): jeb243224. doi: https://doi.org/10.1242/jeb.243224

How do we study the impact of whale watching?

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

Since its start, the GEMM Lab has been interested in the effect of vessel disturbance on whales. From former student Florence’s masters project to Leila’s PhD work, this research has shown that gray whales on their foraging grounds have a behavioral response to vessel presence (Sullivan & Torres, 2018) and a physiological response to vessel noise (Lemos et al., 2022). Presently, our GRANITE project is continuing to investigate the effect of ambient noise on gray whales, with an emphasis on understanding how these effects might scale up to impact the population as a whole (Image 1).

To date, all this work has been focused on gray whales feeding off the coast of Oregon, but I’m excited to share that this is about to change! In just a few weeks, Leigh and I will be heading south for a pilot study looking at the effects of whale watching vessels on gray whale mom/calf pairs in the nursing lagoons of Baja California, Mexico.

Image 1. Infographic for the GRANITE project. Credit: Carrie Ekeroth

We are collaborating with a Fernanda Urrutia Osorio, a PhD candidate at Scripps Institute of Oceanography, to spend a week conducting fieldwork in one of the nursing lagoons. For this project we will be collecting drone footage of mom/calf pairs in both the presence and absence of whale watching vessels. Our goal is to see if we detect any differences in behavior when there are vessels around versus when there are not. Tourism regulations only allow the whale watching vessels to be on the water during specific hours, so we are hoping to use this regulated pattern of vessel presence and absence as a sort of experiment.

Image 2. A mom and calf pair. *NOAA/NMFS permit #21678*.

The lagoons are a crucial place for mom/calf pairs, this is where calves nurse and grow before migration, and nursing is energetically costly for moms. So, it is important to study disturbance responses in this habitat since any change in behavior caused by vessels could affect both the calf’s energy intake and the mom’s energy expenditure. While this hasn’t yet been investigated for gray whales in the lagoons, similar studies have been carried out on other species in their nursing grounds.

Video 1. Footage of “likely nursing” behavior. NOAA/NMFS permit #21678.

We can use these past studies as blueprints for both data collection and processing. Disturbance studies such as these look for a wide variety of behavioral responses. These include (1) changes in activity budgets, meaning a change in the proportion of time spent in a behavior state, (2) changes in respiration rate, which would reflect a change in energy expenditure, (3) changes in path, which would indicate avoidance, (4) changes in inter-individual distance, and (5) changes in vocalizations. While it’s not necessarily possible to record all of these responses, a meta-analysis of research on the impact of whale watching vessels found that the most common responses were increases in the proportion of time spent travelling (a change in activity budget) and increased deviation in path, indicating an avoidance response (Senigaglia et al., 2016).

One of the key phrases in all these possible behavioral responses is “change in ___”. Without control data collected in the absence of whale watching vessels, it impossible to detect a difference. Some studies have conducted controlled exposures, using approaches with the research vessel as proxies for the whale watchers (Arranz et al., 2021; Sprogis et al., 2020), while others use the whale watching operators’ daily schedule and plan their data collection schedule around that (Sprogis et al., 2023). Just as ours will, all these studies collected data using drones to record whale behavior and made sure to collect footage before, during, and after exposure to the vessel(s).

One study focused on humpback mom/calf pairs found a decrease in the proportion of time spent resting and an increase in both respiration rate and swim speed during the exposure (Sprogis et al., 2020). Similarly, a study focused on short-finned pilot whale mom/calf pairs found a decrease in the mom’s resting time and the calf’s nursing time (Arranz et al., 2021). And, Sprogis et al.’s study of Southern right whales found a decrease in resting behavior after the exposure, suggesting that the vessels’ affect lasted past their departure (Sprogis et al., 2023, Image 3). It is interesting that while these studies found changes in different response metrics, a common trend is that all these changes suggest an increase in energy expenditure caused by the disturbance.

However, it is important to note that these studies focused on short term responses. Long term impacts have not been thoroughly estimated yet. These studies provide many valuable insights, not only into the response of whales to whale watching, but also a look at the various methods used. As we prepare for our fieldwork, it’s useful to learn how other researchers have approached similar projects.

Image 3. Visual ethogram from Sprogis et al. 2023. This shows all the behaviors they identified from the footage.

I want to note that I don’t write this blog intending to condemn whale watching. I fully appreciate that offering the opportunity to view and interact with these incredible creatures is valuable. After all, it is one of the best parts of my job. But hopefully these disturbance studies can inform better regulations, such as minimum approach distances or maximum engine noise levels.

As these studies have done, our first step will be to establish an ethogram of behaviors (our list of defined behaviors that we will identify in the footage) using our pilot data. We can also record respiration and track line data. An additional response that I’m excited to add is the distance between the mom and her calf. Former GEMM Lab NSF REU intern Celest will be rejoining us to process the footage using the AI method she developed last summer (Image 4). As described in her blog, this method tracks a mom and calf pair across the video frames, and allows us to extract the distance between them. We look forward to adding this metric to the list and seeing what we can glean from the results.

Image 4. Example of a labelled frame from SLEAP, highlighting labels: rostrum, blowhole, dorsal, dorsal-knuckle, and tail. This labels are drawn to train the software to recognize the whales in unlabelled frames.

While we are just getting started, I am excited to see what we can learn about these whales and how best to study them. Stay tuned for updates from Baja!

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References

Arranz, P., Glarou, M., & Sprogis, K. R. (2021). Decreased resting and nursing in short-finned pilot whales when exposed to louder petrol engine noise of a hybrid whale-watch vessel. Scientific Reports, 11(1), 21195. https://doi.org/10.1038/s41598-021-00487-0

Lemos, L. S., Haxel, J. H., Olsen, A., Burnett, J. D., Smith, A., Chandler, T. E., Nieukirk, S. L., Larson, S. E., Hunt, K. E., & Torres, L. G. (2022). Effects of vessel traffic and ocean noise on gray whale stress hormones. Scientific Reports, 12(1), Article 1. https://doi.org/10.1038/s41598-022-14510-5

Senigaglia, V., Christiansen, F., Bejder, L., Gendron, D., Lundquist, D., Noren, D., Schaffar, A., Smith, J., Williams, R., Martinez, E., Stockin, K., & Lusseau, D. (2016). Meta-analyses of whale-watching impact studies: Comparisons of cetacean responses to disturbance. Marine Ecology Progress Series, 542, 251–263. https://doi.org/10.3354/meps11497

Sprogis, K. R., Holman, D., Arranz, P., & Christiansen, F. (2023). Effects of whale-watching activities on southern right whales in Encounter Bay, South Australia. Marine Policy, 150, 105525. https://doi.org/10.1016/j.marpol.2023.105525

Sprogis, K. R., Videsen, S., & Madsen, P. T. (2020). Vessel noise levels drive behavioural responses of humpback whales with implications for whale-watching. ELife, 9, e56760. https://doi.org/10.7554/eLife.56760

Sullivan, F. A., & Torres, L. G. (2018). Assessment of vessel disturbance to gray whales to inform sustainable ecotourism. Journal of Wildlife Management, 82(5), 896–905. https://doi.org/10.1002/jwmg.21462

Keeping it simple: A lesson in model construction

By: Kate Colson, MSc Student, University of British Columbia, Institute for the Oceans and Fisheries, Marine Mammal Research Unit

Models can be extremely useful tools to describe biological systems and answer ecological questions, but they are often tricky to construct. If I have learned anything in my statistics classes, it is the importance of resisting the urge to throw everything but the kitchen sink into a model. However, this is usually much easier said than done, and model construction takes a lot of practice. The principle of simplicity is currently at the forefront of my thesis work, as I try to embody the famous quote by Albert Einstein:

“Everything should be made as simple as possible, but no simpler.”

As you might remember from my earlier blog, the goal of my thesis is to use biologging data to define different foraging behaviors of Pacific Coast Feeding Group (PCFG) gray whales, and then calculate the energetic cost of those behaviors. I am defining PCFG foraging behaviors at two scales: (1) dives that represent different behavior states (e.g., travelling vs foraging), and (2) roll events, which are periods during dives where the whale is rolled onto their side, that represent different foraging tactics (e.g., headstanding vs side-swimming).

Initially, I was planning to use a clustering analysis to define these different foraging behaviors at both the dive and roll event scale, as this method has been used to successfully classify different foraging strategies for Galapagos sea lions (Schwarz et al., 2021). In short, this clustering analysis uses summary variables from events of interest to group events based on their similarity. These can be any metric that describes the event such as duration and depth, or body positioning variables like median pitch or roll. The output of the clustering analysis method results in groups of events that can each be used to define a different behavior.

However, while this method works for defining the foraging tactics of PCFG gray whales, my discussions with other scientists have suggested that there is a better method available for defining foraging behavior at the dive scale: Hidden Markov Models (HMMs). HMMs are similar to the clustering method described above in that they use summary variables at discrete time scales to define behavior states, but HMMs take into account the bias inherent to time series data – events that occur closer together in time are more likely to be more similar. This bias of time can confound clustering analyses, making HMMs a better tool for classifying a series of dives into different behavior states.

Like many analytical methods, the HMM framework was first proposed in a terrestrial system where it was used to classify the movement of translocated elk (Morales et al., 2004). The initial framework proposed using the step length, or the spatial distance between the animal’s locations at the start of subsequent time intervals, and the corresponding turning angle, to isolate “encamped” from “exploratory” behaviors in each elk’s movement path (Figure 1, from Morales et al., 2004). “Encamped” behaviors are those with short step lengths and high turning angles that show the individual is moving within a small area, and they can be associated with foraging behavior. On the other hand, “exploratory” behaviors are those with long step lengths and low turning angles that show the individual is moving in a relatively straight path and covering a lot of ground, which is likely associated with travelling behavior.

*Figure 1.* The difference between “encamped” and “exploratory” behavior states from a simple Hidden Markov Model (HMM) in a translocated elk equipped with a GPS collar (Fig. 1 in Morales et al., 2004). The top rose plots show the turning angles while the bottom histograms show the step lengths as a daily movement rate. The “encamped” state has short step lengths (low daily movement rate) and high turning angles while the “exploratory” state has long step lengths (high daily movement rate) and low turning angle. These behavior states from the HMM can then be interpollated to elk behavior, as the low daily movement and tight turns of the “encamped” behavior state likely indicates foraging while the high daily movement and direct path of the “exploratory” behavior state likely indicates traveling. Thus, it is important to keep the biological relevance of the study system in mind while constructing and interpreting the model.

In the two decades following this initial framework proposed by Morales et al. (2004), the use of HMMs in anlaysis has been greatly expanded. One example of this expansion has been the development of mutlivariate HMMs that include additional data streams to supplement the step length and turning angle classification of “encamped” vs “exploratory” states in order to define more behaviors in movement data. For instance, a multivariate HMM was used to determine the impact of acoustic disturbance on blue whales (DeRuiter et al., 2017). In addition to step length and turning angle, dive duration and maximum depth, the duration of time spent at the surface following the dive, the number of feeding lunges in the dive, and the variability of the compass direction the whale was facing during the dive were all used to classify behavior states of the whales. This not only allowed for more behavior states to be identified (three instead of two as determined in the elk model), but also the differences in behavior states between individual animals included in the study, and the differences in the occurrence of behavior states due to changes in environmental noise.

The mutlivariate HMM used by DeRuiter et al. (2017) is a model I would ideally like to emulate with the biologging data from the PCFG gray whales. However, incorporating more variables invites more questions during the model construction process. For example, how many variables should be incorporated in the HMM? How should these variables be modeled? How many behavior states can be identified when including additional variables? These questions illustrate how easy it is to unnecessarily overcomplicate models and violate the principle of simiplicity toted by Albert Einstein, or to be overwhelmed by the complexity of these analytical tools.

*Figure 2.* Example of expected output of Hidden Markov Model (HMM) for the PCFG gray whale biologging data (GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678). The figure shows the movement track the whale swam during the deployment of the biologger, with each point representing the start of a dive. The axes show “Easting” and “Northing” rather than map coordinates because this is the relative path the whale took rather than GPS coordinates of the whale’s location. Each color represents a different behavior state—blue has short step lengths and high turning angles (likely foraging), red has intermediate step lengths and turning angles (likely searching), and black has long step lengths and low turning angles (likely transiting). These results will be refined as I construct the multivariate HMM that will be used in my thesis.

Luckily, I can draw on the support of Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project collaborators Dr. Leslie New and Dr. Enrico Pirotta to guide my HMM model construction and assist in interpreting the outputs (Figure 2). With their help, I have been learning the importance of always asking if the change I am making to my model is biologically relevent to the PCFG gray whales, and if it will help give me more insight into the whales’ behavior. Even though using complex tools, such as Hidden Markov Models, has a steep learning curve, I know that this approach is not only placing this data analysis at the cutting edge of the field, but helping me practice fundamental skills, like model construction, that will pay off down the line in my career.

Sources

DeRuiter, S. L., Langrock, R., Skirbutas, T., Goldbogen, J. A., Calambokidis, J., Friedlaender, A. S., & Southall, B. L. (2017). A multivariate mixed Hidden Markov Model for blue whale behaviour and responses to sound exposure. Annals of Applied Statistics, 11(1), 362–392. https://doi.org/10.1214/16-AOAS1008

Morales, J. M., Haydon, D. T., Frair, J., Holsinger, K. E., & Fryxell, J. M. (2004). Extracting more out of relocation data: Building movement models as mixtures of random walks. Ecology, 85(9), 2436–2445. https://doi.org/10.1890/03-0269

Schwarz, J. F. L., Mews, S., DeRango, E. J., Langrock, R., Piedrahita, P., Páez-Rosas, D., & Krüger, O. (2021). Individuality counts: A new comprehensive approach to foraging strategies of a tropical marine predator. Oecologia, 195(2), 313–325. https://doi.org/10.1007/s00442-021-04850-w

Return of the whales: The GRANITE 2022 field season comes to a close

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

It’s hard to believe that it’s already been four and half months since we started the field season (check out Lisa’s blog for a recap of where we began), but as of this weekend the GRANITE project’s 8^th field season has officially ended! As the gray whales wrap up their foraging season and start heading south for the winter, it’s time for us to put our gear into storage, settle into a new academic year, and start processing the data we spent so much time collecting.

The field season can be quite an intense time (40 days equaling over 255 hours on the water!), so we often don’t take a moment to reflect until the end. But this season has been nothing short of remarkable. As you may remember from past blogs, the past couple years (2020-21) have been a bit concerning, with lower whale numbers than previously observed. Since many of us started working on the project during this time, most of us were expecting another similar season. But we were wrong in the best way. From the very first day, we saw more whales than in previous years and we identified whales from our catalog that we hadn’t seen in several years.

Image 1: Collage of photos from our field season.

We identified friends – old and new!

This season we had 224 sightings of 63 individual whales. Of those 63, 51 were whales from our catalog (meaning we have seen them in a previous season). Of these 51 known whales, we only saw 20 of them last year! This observation brings up interesting questions such as, where did most of these whales forage last year? Why did they return to this area this year? And, the classic end of season question, what’s going to happen next year?

We also identified 12 whales that were not in our catalog, making them new to the GEMM lab. Two of our new whales are extra exciting because they are not just new to us but new to the population; we saw two calves this year! We were fortunate enough to observe two mom-calf pairs in July. One pair was of a “new” mom in our catalog and her calf. We nicknamed this calf “Roly-poly” because when we found this mom-calf pair, we recorded some incredible drone footage of “roly-poly” continuously performing body rolls while their mom was feeding nearby (video 1).

Video 1: “Roly-poly” body rolling while their mom headstands. NOAA/NMFS permit #21678.

The other pair includes a known GEMM lab whale, Luna, and her calf (currently nicknamed “Lunita”). We recently found “Lunita” feeding on their own in early October (Image 2), meaning that they are now independent from its mom (for more on mom-calf behavior check out Celest’s recent blog). We’ll definitely be on the lookout for Roly-Poly and Lunita next year!

Image 2: (left) drone image of Luna and Lunita together in July and (right) drone image of Lunita on their own in October. *NOAA/NMFS permit #21678*.

We flew, we scooped, we collected heaps of data!

From our previous blogs you probably know that in addition to photo-ID images, our other two most important forms of data collection are drone flights (for body condition and behavior data) and fecal samples (for hormone analysis). And this season was a success for both!

We conducted 124 flights over 49 individual whales. The star of these flights was a local favorite Scarlett who we flew over 18 different times. These repeat samples are crucial data for us because we use them to gain insight into how an individual’s body condition changes throughout the season. We also recorded loads of behavior data, collecting footage of different foraging tactics like headstanding, side-swimming, and surfacing feeding on porcelain crab larvae (video 2)!

Video 2: Two whales surface feeding on porcelain crab larvae. NOAA/NMFS permit #21678.

We also collected 61 fecal samples from 26 individual whales (Image 3). The stars of that dataset were Soléand Peak who tied with 7 samples each. These hard-earned samples provide invaluable insight into the physiology and stress levels of these individuals and are a crucial dataset for the project.

Image 3: Photos of fecal sample collection. Left – a very heavy sample, center: Lisa and Enrico after collecting the first fecal sample of the season, right: Clara and Lisa celebrating a good fecal sample collection.

On top of all that amazing data collection we also collected acoustic data with our hydrophones, prey data from net tows, and biologging data from our tagging efforts. Our hydrophones were in the water all summer recording the sounds that the whales are exposed to, and they were successfully recovered just a few weeks ago (Image 4)! We also conducted 69 net tows to sample the prey near where the whales were feeding and identify which prey the whales might be eating (Image 5). Lastly, we had two very successful tagging weeks during which we deployed (and recovered!) a total of 9 tags, which collected over 30 hours of data (Image 6; check out Kate’s blog for more on that).

Image 4 – Photos from hydrophone recovery.

Image 5: Photos from zooplankton sampling.

Image 6: Collage of photos from our two tagging efforts this season.

Final thoughts

All in all, it’s been an incredible season. We’ve seen the return of old friends, collected lots of awesome data, and had some record-breaking days (28 whales in one day!). As we look toward the analysis phase of the year, we’re excited to dig into our eight-year dataset and work to understand what might explain the increase in whales this year.

To end on a personal note, looking through photos to put in this blog was the loveliest trip down memory lane (even though it only ended a few days ago) – I am so honored and proud to be a part of this team. The work we do is hard; we spend long hours on a small boat together and it can be a bit grueling at times. But, when I think back on this season, my first thoughts are not of the times I felt exhausted or grumpy, but of all the joy we felt together. From the incredible whale encounters to the revitalizing snacks to the off-key sing alongs, there is no other team I would rather do this work with, and I so look forward to seeing what next season brings. Stay tuned for more updates from team GRANITE!

Surprises at Sea

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

By Renee Albertson, Senior Instructor and Research Associate, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Marine Mammal Institute

Going to sea is always full of surprises, and the most recent Northern California Current (NCC) cruise was no different. We had surprises both logistical and scientific, disappointing and delightful. By the end, what stood out clearly is that with a great team of people like the one aboard the R/V Bell M. Shimada, any challenging situation is made the best of, and any exciting moment is only more so.

Our great science party enjoys the Seattle skyline at the end of the September 2022 NCC cruise.

A few days into the cruise, engine trouble caused the Commanding Officer to decide that we needed to cut the trip short, halt instrument deployment operations, and head in to port. Lucky for us, this new plan included 30 hours of transit to Seattle, and long transits are exactly when we collect marine mammal observations. We were able to keep surveying as we moved up the coast and through the Strait of Juan de Fuca into Seattle. There were many surprises here too – we did not find whales in areas where we have previously sighted many, and overall made fewer sightings than is typical.

For example, we expected to see many whales on the Heceta Head Line (south of Newport), whose shallow depth makes the region a rich underwater garden that supports prey and attracts whales. Instead, we saw hardly any whales in this area. Perhaps they simply weren’t present, or perhaps we missed spotting some whales due to the heavy fog, which makes sighting animals that are not near the ship difficult to impossible. This dearth of animals led us to have to interesting conversations with other researchers as we speculated about what might be going on. The scientists on board these NCC cruises collectively research a wide range of oceanographic fields, including ocean chemistry, phytoplankton, zooplankton, fish, seabirds, and marine mammals. Bringing these data together can provide a better understanding of how the ecosystem is changing over time and help contextualize observations in the moment.

Though we often think about how the distributions of prey structure those of foraging whales, we started to wonder whether a lower trophic level could be at play here. Interestingly, in situ phytoplankton analyses showed a type of diatom called Pseudo-nitzchia along much of our cruise track, with the highest concentration off Cape Meares. In stressful conditions, these diatoms sometimes produce the toxin domoic acid, and we wondered whether this could possibly be related to the low whale counts.

Cells of *Pseudo-nitzschia*, a genus of microalgae that includes several species that make the neurotoxin domoic acid. NOAA photo courtesy of Vera Trainer.

Along the northern Oregon coast and near the Columbia River, the number of whales we observed increased dramatically. The vast majority were humpbacks, some of which were quite active, breaching and tail slapping the surface of the water. On our best day, we turned into the Strait of Juan de Fuca and sighted about 20 whales in quick succession, as well as a sea otter, and both Steller and California sea lions.

Simultaneously as we surveyed for whales, we were able to continue collecting concurrent echosounder data, which reveals the presence of nearby prey like krill and forage fish. Early in the trip, other researchers also collected krill samples that we could bring back to shore and analyze for their caloric content. Even with a shorter time at sea, we felt lucky to be able to fulfill these scientific goals.

Research cruises always center around two things: science and people. Discussing the scientific surprises we observed with other researchers aboard was inspirational, and left us with interesting questions to pursue. Navigating changes to the cruise plan highlighted the importance of the people aboard even more. Everyone worked together to refine our plans with cooperation and positivity, and we all marveled at what a great group it was, often saying, “Good thing we like each other!”

The cruise ended by transiting under the Fremont Bridge into Lake Union.

On the last day of the cruise, we transited into Seattle, moving through the Ballard Locks and into Lake Union. It was an incredible experience to see the city from the water, and an amazing way to cap off the trip. With the next NCC cruise ahead in a few months, we are excited to get back out to sea together soon and tackle whatever surprises come our way.

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Keeping up with the HALO project: Recovering Rockhopper acoustic recording units and eavesdropping on Northern right whale dolphins

Marissa Garcia, PhD Student, Cornell University, Department of Natural Resources and the Environment, K. Lisa Yang Center for Conservation Bioacoustics

It was a June morning on the Pacific Ocean, and the R/V Pacific Storm had come to a halt on its journey back to shore. The night before, the Holistic Assessment of Living marine resources off Oregon (HALO) project team had disembarked from Newport and began the long transit to NH 65, a site 65 nautical miles offshore along the Newport Hydrographic line (NH line). Ever since the 1960s, researchers have been conducting oceanographic studies along the NH line; the HALO project seeks to add the biological dimension to these historical data collections.

We were on a mission to recover our first set of Rockhoppers that we had deployed in October 2021, just nine months earlier. The Rockhopper is an underwater passive acoustic recording unit developed by K. Lisa Yang Center for Conservation Bioacoustics at Cornell University. Earlier versions of underwater recorders were optimized to record baleen whales. By contrast, the Rockhopper is designed to record both baleen whales and dolphins on longer and deeper deployments, making it apt for research endeavors such as the HALO project. Three units, deployed at NH 25, 45, and 65, continuously recorded the soundscape of the Oregon waters for six months. In June, we were headed out to sea to recover these three units, collect the acoustic data, and deploy three new units.

**Figure 1:** *The HALO project routinely surveys the trackline spanning between NH 25 and NH 65 on the NH line. Credit: Leigh Torres.*

With the ship paused, our first task was to recover the Rockhopper we had deployed at NH 65. This Rockhopper deployment at NH 65 was our deepest successful deployment to date, moored at nearly 3,000 m.

So, how does one recover an underwater recording unit that is nearly 3,000 m below the surface? When the Rockhopper was deployed, it was anchored to the seafloor with a 60 kg cast iron anchor. It seems improbable that an underwater recording unit — anchored by such heavy weights — can eventually rise to the surface, but this capability is made possible through a piece of attached equipment called the acoustic release. By sending a signal of a numbered code from a box on the boat deck through the water column to the Rockhopper, the bottom of the acoustic release will begin to spin and detach from the weights. The weights are then left on the seafloor, as the Rockhopper slowly rises to the surface, now unhindered by the weights. Since these weights are composed of iron, they will naturally erode, without additional pollution contributed to the ecosystem. At NH 65, it took approximately an hour for the Rockhopper to reach the surface.

**Figure 2:** *A diagram of the Rockhopper mooring. Of particular importance to this blog post is the acoustic release (Edgtech PORT MFE release) and the 60 kg anchor (Source: Klinck et al., 2020).*

The next challenge is finding the Rockhopper bobbing amongst the waves in the vast ocean — much like searching for a needle in a haystack. The color of the Rockhopper helps aid in this quest. It’s imperative anyone out on the boat deck wears a life jacket; if someone goes overboard while wearing a life-jacket, on-board passengers can more easily spot a bright orange spot in an otherwise blue-green ocean with white caps. The design of the Rockhopper functions similarly; the unit is contained in a bright orange hard hat, helping researchers on-board to more easily spot the device, especially in an ocean often characterized by high sea state.

We also use a Yagi antenna to listen for the VHF (Very High Frequency) signal of the recovery gear, a signal the Rockhopper emits once it’s surfaced above the waterline. Pointing the antenna toward the ocean, we can detect the signal, which will become stronger when we point antenna in the direction of the Rockhopper; once we hear that strong signal, we can recommend to the boat captain to start moving the vessel in that direction.

**Figure 3:** Derek Jaskula, a member of the field operations team at the K. Lisa Yang Center for Conservation Bioacoustics, points the Yagi antenna to detect the signal from the surfaced Rockhopper. Credit: Marissa Garcia.

At that point, all eyes are on the water, binoculars scanning the horizon for the orange. All ears are eager for the exciting news: “I see the Rockhopper!”

Once that announcement is made, the vessel carefully inches toward the Rockhopper until it is just next to the vessel’s side. Using a hook, the Rockhopper is pulled upward and back onto the deck.

What we weren’t expecting, however, during this recovery was to have our boat surrounded by two dolphin species: Pacific white-sided dolphins (Lagenorhynchus obliquidens) and Northern right whale dolphins (Lissodelphis borealis).

One HALO team member shouted, “I see Northern right whale dolphins!”

Charged with excitement, I quickly climbed up the crow’s nest to get a birds-eye look at the ocean bubbling around us with surfacing dolphins. Surely enough, I spotted the characteristic stripe of the Pacific-white sided dolphins zooming beneath the surface, in streaks of white. But what I was even more eager to see were the Northern right whale dolphins, flipping themselves out of the water, unveiling their bright white undersides. Because they lack dorsal fins, we on-board colloquially refer to Northern right whale dolphins as “sea slugs” to describe their appearance as they surface.

**Figure 4:** *The Northern right whale dolphin (*Lissodelphis borealis) surfaces during a HALO cruise. Source: HALO Project Team Member. Permit: NOAA/NMFS permit #21678.

In my analysis of the HALO project data for my PhD, I am interested in using acoustics to describe how the distribution of dolphins and toothed whales in Oregon waters varies across space and time. One species I am especially fascinated to study in-depth is the Northern right whale dolphin. To my knowledge, only three papers to date have attempted to describe their acoustics — two of which were published in the 1970s, and the most recent of which was published fifteen years ago (Fish & Turl, 1976; Leatherwood & Walker, 1979; Rankin et al., 2007).

Leatherwood & Walker (1979) proposed that Northern right whale dolphins produced two categories of whistles: a high frequency whistle that turned into burst-pulse vocalizations, and low frequency whistles. However, Rankin et al. (2007) proposed that Northern right whale dolphins may not actually produce whistles, based on two lines of evidence. First, Rankin et al. (2007) combined visual and acoustic survey, and all vocalizations recorded were localized via beamforming methods to verify that recorded vocalizations were produced by the visually observed dolphins. The visual surveying component is key to validating the vocalizations of the species, which also hints that the HALO project’s multi-surveying approach (acoustic and visual) could help arrive at similar results. Second, the Rankin et al. (2007) explored the taxonomy of the Northern right whale dolphin to verify which vocalizations the species is likely to produce based on the vocal repertoire of its close relatives. The right whale dolphin is closely related to dolphins in the genus Lagenorhynchus — which includes white-sided dolphins — and Cephalorhynchus — which includes Hector’s dolphin. The vocal repertoire of these relatives don’t produce whistles, and instead predominantly produced pulsed sounds or clicks (Dawson, 1991; Herman & Tavolga, 1980). Northern right whale dolphins primarily produce echolocation clicks trains and burst-pulses. Although Rankin et al. (2007) claims that the Northern right whale dolphin does not produce whistles, stereotyped burst-pulse series may be unique to individuals, just as dolphin species use stereotyped signature whistles, or they may be relationally shared just as discrete calls of killer whales are.

**Figure 5:** *The Northern right whale dolphin (*Lissodelphis borealis) produces burst-pulses. There exists variation in series of burst-pulses. The units marked by (a) and (b) ultimately get replaced by the unit marked by (c). (Source: Rankin et al., 2007).

We have just finished processing the first round of acoustic data for the HALO project, and it is ready now for analysis. Already previewing an hour of data on the Rockhopper by NH 25, we identified potential Northern right whale dolphin recordings . So far, we have only visually observed Northern right whale dolphins nearby Rockhopper units placed at sites NH 65 and NH 45, so it was surprising to acoustically detect this species on the most inshore unit at NH 25. I look forward to demystifying the mystery of Northern right whale dolphin vocalizations as our research on the HALO project continues!

**Figure** 6: *Potential Northern right whale dolphin vocalizations recorded at the Rockhopper deployed at NH 25.*

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References

Dawson, S. (1991). Clicks and Communication: The Behavioural and Social Contexts of Hector’s Dolphin Vocalizations. Ethology, 88(4), 265–276. https://doi.org/10.1111/j.1439-0310.1991.tb00281.x

Fish, J. F. & Turl, C. W. (1976). Acoustic Source Levels of Four Species of Small Whales.

Herman, L. M., and Tavolga, W. N. (1980). “The communication systems of cetaceans,” in Cetacean behavior: Mechanisms and functions, edited by L. M. Herman (Wiley, New York), 149–209.

Klinck, H., Winiarski, D., Mack, R. C., Tessaglia-Hymes, C. T., Ponirakis, D. W., Dugan, P. J., Jones, C., & Matsumoto, H. (2020). The Rockhopper: a compact and extensible marine autonomous passive acoustic recording system. Global Oceans 2020: Singapore – U.S. Gulf Coast, 1–7. https://doi.org/10.1109/IEEECONF38699.2020.9388970

Leatherwood, S., and Walker, W. A. (1979). “The northern right whale dolphin Lissodelphis borealis Peale in the eastern North Pacific,” in Behavior of marine animals, Vol. 3: Cetaceans, edited by H. E. Winn and B. L. Olla (Plenum, New York), 85–141.

Rankin, S., Oswald, J., Barlow, J., & Lammers, M. (2007). Patterned burst-pulse vocalizations of the northern right whale dolphin, Lissodelphis borealis. The Journal of the Acoustical Society of America, 121(2), 1213–1218. https://doi.org/10.1121/1.2404919

Putting Fitbits on whales: How tag data allows for estimating calories burned by foraging PCFG gray whales

By: Kate Colson, MSc Student, University of British Columbia, Institute for the Oceans and Fisheries, Marine Mammal Research Unit

Hello! My name is Kate Colson and I am a master’s student at the University of British Columbia, co-supervised by Dr. Andrew Trites of the Marine Mammal Research Unit and Dr. Leigh Torres of the GEMM Lab. As part of my thesis work, I have had the opportunity to spend the summer field season with Leigh and the GEMM Lab team.

For my master’s I am studying the foraging energetics of Pacific Coast Feeding Group (PCFG) gray whales as part of the much larger Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project. Quantifying the energy expenditure of PCFG gray whales during foraging can help establish a baseline for how disturbance impacts the ability of this unique population to meet their energy needs. Additionally, determining how many calories are burned during different PCFG foraging behaviors might help explain why some gray whales are in better body condition than others.

To understand how much energy different PCFG foraging behaviors cost, I am using data from suction cup tags we have temporarily applied on PCFG gray whales (Figure 1). You can read more about the why the GEMM Lab started using these tags in an earlier blog here. What I want to talk about in this blog is how exactly we can use this tag data to estimate energy expenditure of PCFG gray whales.

***Figure 1.*** *The famous “Scarlett*” with a suction cup tag just attached using a carbon fiber pole (seen on far right). This minimally invasive tag has many data sensors, all of which sample at high frequencies, that can allow for an estimation of energy expenditure for different gray whale behaviors. **Source:** GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678

The suction cups tags used in this project have many data sensors that are useful for describing the movement of the tagged whale including accelerometers, magnetometers, gyroscopes, and pressure sensors, and all are sampling at high frequencies. For example, the accelerometer is taking 400 measurements per second! The accelerometer, magnetometer, and gyroscope take measurements in 3 dimensions along the X, Y, and Z-axes. The whale’s movement around the X-axis indicates roll (if the whale is swimming on its side), while movement around the Y-axis indicates pitch (if the whales head is oriented towards the surface or the sea floor). Changes in the whale’s movement around the Z-axis indicates if the whale is changing its swimming direction. Together, all of these sensors can describe the dive profile, body orientation, fluking behavior, and fine-scale body movements of the animal down to the second (Figure 2). This allows for the behavior of the tagged whale to be specifically described for the entirety of the tag deployment.

***Figure 2.*** An example of what the tag sensor data looks like. The **top panels** show the depth of the animal and can be used to determine the diving behavior of the whale. The **middle panels** show the body roll of the whale (the X axis) —a roll value close to 0 means the whale is swimming “normally” with no rotation to either side, while a higher roll value means the whale is positioned on its side. The **bottom panels** show the fluking behavior of the animal: each spike is the whale using its tail to propel itself through the water, with higher spikes indicating a stronger fluke stroke. **Source:** GEMM Lab, NMFS permit no. 21678

Although these suction cup tags are a great advancement in collecting fine-scale data, they do not have a sensor that actually measures the whale’s metabolism, or rate of calories burned by the whale. Thus, to use this fine-scale tag data as an estimate for energy expenditure, a summary metric must be calculated from the data and used as a proxy. The most common metric found in the literature is Overall Dynamic Body Acceleration (ODBA) and many papers have been published discussing the pros and cons of using ODBA as a proxy for energy expenditure (Brown et al., 2013; Gleiss et al., 2011; Halsey, 2017; Halsey et al., 2011; Wilson et al., 2020). The theory behind ODBA is that because an animal’s metabolic rate is primarily comprised of movement costs, then measuring the acceleration of the body is an effective way of determining energy expenditure. This theory might seem very abstract, but if you have ever worn a Fitbit or similar fitness tracking device to estimate how many calories you’ve burned during a workout, the same principle applies. Those fitness devices use accelerometers and other sensors, to measure the movement of your limbs and produce estimates of energy used.

So now that we’ve established that the goal of my research is to essentially use these suction cup tags as Fitbits for PCFG gray whales, let’s look at how accelerometry data has been used to detect foraging behavior in large whales so far. Many accelerometry tagging studies have used rorquals as a focal species (see Shadwick et al. (2019) for a review). Well-known rorqual species include humpback, fin, and blue whales. These species forage by using lunges to bulk feed on dense prey patches in the water column. Foraging lunges are indicated by isolated periods of high acceleration that are easily detectable in the tag data (Figure 3; Cade et al., 2016; Izadi et al., 2022).

***Figure 3.*** **Top image:** A foraging blue whale performing a surface lunge (Photo credit: GEMM Lab). Note the dense aggregation of krill in the whale’s mouth. **Bottom image:** The signature acceleration signal for lunge feeding (adapted from Izadi et al., 2022)*. Each color represents one of the 3D axes of whale movement. The discrete periods of high acceleration represent lunges*.

However, gray whales feed very differently from rorquals. Gray whales primarily suction feed on the benthos, using their head to dig into the sediment and filter prey out of the mud using their baleen. Yet, PCFG gray whales often perform many other foraging behaviors such as headstanding and side-swimming (Torres et al., 2018). Additionally, PCFG gray whales tend to feed in water depths that are often shallower than their body length. This shallow depth makes it difficult to isolate signals of foraging in the accelerometry data from random variation in the data and separate the tag data into periods of foraging behaviors (Figure 4).

***Figure 4.*** **Top image:** A foraging PCFG gray whale rolls on its side to feed on mysid prey. **Bottom image:** The graph shows the accelerometry data from our suction cup tags that can be used to calculate Overall Dynamic Body Acceleration (ODBA) as a way to estimate energy expenditure. Each color represents a different axis in the 3D motion of the whale. The X-axis is the horizontal axis shows forward and backward movement of the whale, the Y-axis shows the side-to-side movement of the whale, and the Z-axis shows the up-down motion of the whale. Note how there are no clear periods of high acceleration in all 3 axes simultaneously to indicate different foraging behaviors like is apparent during lunges of rorqual whales. However, there is a pattern showing that when acceleration in the Z-axis (blue line) is positive, the X- and Y-axes (red and green lines) are negative. **Source:** GEMM Lab; NMSF permit no. 21678

But there is still hope! Thanks to the GEMM Lab’s previous work describing the foraging behavior of the PCFG sub-group using drone footage, and the video footage available from the suction cup tags deployed on PCFG gray whales, the body orientation calculated from the tag data can be a useful indication of foraging. Specifically, high body roll is apparent in many foraging behaviors known to be used by the PCFG, and when the tag data indicates that the PCFG gray whale is rolled onto its sides, lots of sediment (and sometimes even swarms of mysid prey) is seen in the tag video footage. Therefore, I am busy isolating these high roll events in the collected tag data to identify specific foraging events.

My next steps after isolating all the roll events will be to use other variables such as duration of the roll event and body pitch (i.e., if the whales head is angled down), to define different foraging behaviors present in the tag data. Then, I will use the accelerometry data to quantify the energetic cost of performing these behaviors, perhaps using ODBA. Hopefully when I visit the GEMM Lab again next summer, I will be ready to share which foraging behavior leads to PCFG gray whales burning the most calories!

References:

Brown, D. D., Kays, R., Wikelski, M., Wilson, R., & Klimley, A. P. (2013). Observing the unwatchable through acceleration logging of animal behavior. Animal Biotelemetry, 1(1), 1–16. https://doi.org/10.1186/2050-3385-1-20

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. https://doi.org/10.1016/j.cub.2016.07.037

Duley, P. n.d. Fin whales feeding [photograph]. NOAA Northeast Fisheries Science Center Photo Gallery. https://apps-nefsc.fisheries.noaa.gov/rcb/photogallery/finback-whales.html

Gleiss, A. C., Wilson, R. P., & Shepard, E. L. C. (2011). Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure. Methods in Ecology and Evolution, 2(1), 23–33. https://doi.org/10.1111/j.2041-210X.2010.00057.x

Halsey, L. G. (2017). Relationships grow with time: A note of caution about energy expenditure-proxy correlations, focussing on accelerometry as an example. Functional Ecology, 31(6), 1176–1183. https://doi.org/10.1111/1365-2435.12822

Halsey, L. G., Shepard, E. L. C., & Wilson, R. P. (2011). Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comparative Biochemistry and Physiology – A Molecular and Integrative Physiology, 158(3), 305–314. https://doi.org/10.1016/j.cbpa.2010.09.002

Izadi, S., Aguilar de Soto, N., Constantine, R., & Johnson, M. (2022). Feeding tactics of resident Bryde’s whales in New Zealand. Marine Mammal Science, 1–14. https://doi.org/10.1111/mms.12918

Shadwick, R. E., Potvin, J., & Goldbogen, J. A. (2019). Lunge feeding in rorqual whales. Physiology, 34, 409–418. https://doi.org/10.1152/physiol.00010.2019

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 Science, 5, 1–14. https://doi.org/10.3389/fmars.2018.00319

Wilson, R. P., Börger, L., Holton, M. D., Scantlebury, D. M., Gómez-Laich, A., Quintana, F., Rosell, F., Graf, P. M., Williams, H., Gunner, R., Hopkins, L., Marks, N., Geraldi, N. R., Duarte, C. M., Scott, R., Strano, M. S., Robotka, H., Eizaguirre, C., Fahlman, A., & Shepard, E. L. C. (2020). Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. Journal of Animal Ecology, 89(1), 161–172. https://doi.org/10.1111/1365-2656.13040

Reuniting with some old friends: The 8th GRANITE field season is underway

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

We are almost halfway through June which means summer has arrived! Although, here on the Oregon coast, it does not entirely feel like it. We have been swinging between hot, sunny days and cloudy, foggy, rainy days that are reminiscent of those in spring or even winter. Despite these weather pendulums, the GEMM Lab’s GRANITE project is off to a great start in its 8^th field season! The field team has already ventured out onto the Pacific Ocean in our trusty RHIB Ruby on four separate days looking for gray whales and in this blog post, I am going to share what we have seen so far.

*The core GRANITE field team before the May 24th “trial run”. From left to right: Leigh Torres, KC Bierlich, Clara Bird, Lisa Hildebrand, Alejandro Fernández Ajó*. *Source: L. Torres.*

PI Leigh, PhD candidate Clara and I headed out for a “trial run” on May 24^th. While the intention for the day was to make sure all our gear was running smoothly and we still remembered how to complete the many tasks associated with our field work (boat loading and trailering, drone flying and catching, poop scooping, data download, to name a few), we could not resist surveying our entire study range given the excellent conditions. It was a day that all marine field scientists hope for – low winds (< 5 kt all day) and a 3 ft swell over a long period. Despite surveying between Waldport and Depoe Bay, we only encountered one whale, but it was a whale that put a smile on each of our faces. After “just” 252 days, we reunited with Solé, the star of our GRANITE dataset, with record numbers of fecal samples and drone flights collected. This record is due to what seems to be a strong habitat or foraging tactic preference by Solé to remain in a relatively small spatial area off the Oregon coast for most of the summer, rather than traveling great swaths of the coast in search for food. Honest truth, on May 24^th we found her exactly where we expected to find her. While we did not collect a fecal sample from her on that day, we did perform a drone flight, allowing us to collect a critical early feeding season data point on body condition. We hope that Solé has a summer full of mysids on the Oregon coast and that we will be seeing her often, getting rounder each time!

Our superstar whale Solé. Her identifying features are a small white line on her left side (green box) and a white dot in front of her dorsal hump on the right side (red circle). Source: GEMM Lab. Photograph captured under NOAA/NMFS permit #21678

Just a week after this trial day, we had our official start to the field season with back-to-back days on the water. On our first day, postdoc Alejandro, Clara and I were joined by St. Andrews University Research Fellow Enrico Pirotta, who is another member of the GRANITE team. Enrico’s role in the GRANITE project is to implement our long-term, replicate dataset into a framework called Population consequences of disturbance (PCoD; you can read all about it in a previous blog). We were thrilled that Enrico was able to join us on the water to get a sense for the species and system that he has spent the last several months trying to understand and model quantitatively from a computer halfway across the world. Luckily, the whales sure showed up for Enrico, as we saw a total of seven whales, all of which were known individuals to us! Several of the whales were feeding in water about 20 m deep and surfacing quite erratically, making it hard to get photos of them at times. Our on-board fish finder suggested that there was a mid-water column prey layer that was between 5-7 m thick. Given the flat, sandy substrate the whales were in, we predicted that these layers were composed of porcelain crab larvae. Luckily, we were able to confirm our hypothesis immediately by dropping a zooplankton net to collect a sample of many porcelain crab larvae. Porcelain crab larvae have some of the lowest caloric values of the nearshore zooplankton species that gray whales likely feed on (Hildebrand et al. 2021). Yet, the density of larvae in these thick layers probably made them a very profitable meal, which is likely the reason that we saw another five whales the next day feeding on porcelain crab larvae once again.

*Porcelain crab larvae. Source: GEMM Lab.*

On our most recent field work day, we only encountered Solé, suggesting that the porcelain crab swarms had dissipated (or had been excessively munched on by gray whales), and many whales went in search for food elsewhere. We have done a number of zooplankton net tows across our study area and while we did collect a good amount of mysid shrimp already, they were all relatively small. My prediction is that once these mysids grow to a more profitable size in a few days or weeks, we will start seeing more whales again.

*The GRANITE team from above, waiting & watching for whales, as we will be doing for the rest of the summer! Source: GEMM Lab.*

So far we have seen nine unique individuals, flown the drone over eight of them, collected fecal samples from five individuals, conducted 10 zooplankton net tows and seven GoPro drops in just four days of field work! We are certainly off to a strong start and we are excited to continue collecting rock solid GRANITE data this summer to continue our efforts to understand gray whale ecology and physiology.

Literature cited

Hildebrand L, Bernard KS, Torres LGT. 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. doi: 10.3389/fmars.2021.683634

Experiencing a Physical Manifestation of my PhD at Sea in the NCC

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

I always have a small crisis before heading into the field, whether for a daytrip or a several-month stint. I’m always dying to go – up until the moment when it is actually time to leave, and I decide I’d rather stay home, keep working on whatever has my current focus, and not break my comfortable little routine.

Preparing to leave on the most recent Northern California Current (NCC) cruise was no different. And just as always, a few days into the cruise, I forgot about the rest of my life and normal routines, and became totally immersed in the world of the ship and the places we went. I learned an exponential amount while away. Being physically in the ecosystem that I’m studying immediately had me asking more, and better, questions to explore at sea and also bring back to land.

Many of these questions and realizations centered on predator-prey relationships between krill and whales at fine spatial scales. We know that distributions of prey species are a big factor in structuring whale distributions in the ocean, and one of our goals on this cruise was to observe these relationships more closely. The cruise offered an incredible opportunity to experience these relationships in real time: while my labmates Dawn and Clara were up on the flying bridge looking for whales, I was down in the acoustics lab, watching incoming echosounder data in order to identify krill aggregations.

*From left, Clara and Dawn survey for marine mammals on the flying bridge.*

We used radios to stay in touch with what we were each seeing in real time, and learned quickly that we tended to spot whales and krill almost simultaneously. Experiencing this coherence between predator and prey distributions felt like a physical manifestation of my PhD. It also affirmed my faith in one of our most basic modeling assumptions: that the backscatter signals captured in our active acoustic data are representative of the preyscape that nearby whales are experiencing.

Being at sea with my labmates also catalyzed an incredible synthesis of our different types of knowledge. Because of the way that I think about whale distributions, I usually just focus on whether a certain type of whale is present or not while surveying. But Clara, with her focus on cetacean behavior, thinks in a completely different way from me. She timed the length of dives and commented on the specific behaviors she noticed, bringing a new level of context to our observations. Dawn, who has been joining these cruises for five years now, shared her depth of knowledge built through returning to these places again and again, helping us understand how the system varies through time.

*Observing whale behavior, such as for these humpbacks, provides valuable information on how they are using a given area.*

One of the best experiences of the cruise for me was when we conducted a targeted net tow in an area of foraging humpbacks on the Heceta Head Line off the central Oregon coast. The combination of the krill signature I was seeing on the acoustics display, and the radio reports from Dawn and Clara of foraging dives, convinced me that this was an opportunity for a net tow, if possible, to see exactly what zooplankton was in the water near the whales. Our chief scientist, Jennifer Fisher, and the ship’s officers worked together to quickly turn the ship around and get a net in the water, in an effort to catch krill from the aggregation I had seen.

This unique opportunity gave me a chance to test my own interpretation of the acoustics data, and compare what we captured in the net with what I expected from the backscatter signal. It also prompted me to think more about the synchrony and differences between what is captured by net tows and echosounder data, two primary ways for looking at whale prey.

*Collecting tiny yet precious krill samples associated with foraging humpbacks!*

Throughout the entire cruise, the opportunity to build my intuition and notice ecological patterns was invaluable. Ecosystem modeling gives us the opportunity to untangle incredible complexity and put dynamic relationships in mathematical terms, but being out on the ocean provides the chance to develop a feel for these relationships. I’m so glad to bring this new perspective to my next round of models, and excited to continue trying to tease apart fine-scale dynamics between whales and krill.

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Yonder Whales and Nearby Prey: A New Look at a Familiar System

Rachel Kaplan¹, Dawn Barlow², Clara Bird³

¹PhD 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

²Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

³PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

What do peanut butter m&ms, killer whales, affogatos, tired eyes, and puffins all have in common? They were all major features of the recent Northern California Current (NCC) ecosystem survey cruise.

The science party of the May 2022 Northern California Current ecosystem cruise.

We spent May 6–17 aboard the NOAA vessel Bell M. Shimada in northern California, Oregon, and Washington waters. This fabulously interdisciplinary cruise studies multiple aspects of the NCC ecosystem three times per year, and the GEMM lab has put marine mammal observers aboard since 2018.

This cruise was a bit different than usual for the GEMM lab: we had eyes on both the whales and their prey. While Dawn Barlow and Clara Bird observed from sunrise to sunset to sight and identify whales, Rachel Kaplan collected krill data via an echosounder and samples from net tows in order to learn about the preyscape the whales were experiencing.

From left, Rachel, Dawn, and Clara after enjoying some beautiful sunset sightings.

We sailed out of Richmond, California and went north, sampling as far north as La Push, Washington and up to 200 miles offshore. Despite several days of challenging conditions due to wind, rain, fog, and swell, the team conducted a successful marine mammal survey. When poor weather prevented work, we turned to our favorite hobbies of coding and snacking.

Rachel attends “Clara’s Beanbag Coding Academy”.

Cruise highlights included several fin whales, sperm whales, killer whales, foraging gray whales, fluke slapping and breaching humpbacks, and a visit by 60 pacific white-sided dolphins. While being stopped at an oceanographic sampling station typically means that we take a break from observing, having more time to watch the whales around us turned out to be quite fortunate on this cruise. We were able to identify two unidentified whales as sei whales after watching them swim near us while paused on station.

Marine mammal observation segments (black lines) and the sighting locations of marine mammal species observed during the cruise.

On one of our first survey days we also observed humpbacks surface lunge feeding close to the ship, which provided a valuable opportunity for our team to think about how to best collect concurrent prey and whale data. The opportunity to hone in on this predator-prey relationship presented itself in a new way when Dawn and Clara observed many apparently foraging humpbacks on the edge of Heceta Bank. At the same time, Rachel started observing concurrent prey aggregations on the echosounder. After a quick conversation with the chief scientist and the officers on the bridge, the ship turned around so that we could conduct a net tow in order to get a closer look at what exactly the whales were eating.

Success! Rachel collects krill samples collected in an area of foraging humpback whales.

This cruise captured an interesting moment in time: southerly winds were surprisingly common for this time of year, and the composition of the phytoplankton and zooplankton communities indicated that the seasonal process of upwelling had not yet been initiated. Upwelling brings deep, cold, nutrient-rich waters to the surface, generating a jolt of productivity that brings the ecosystem from winter into spring. It was fascinating to talk to all the other researchers on the ship about what they were seeing, and learn about the ways in which it was different from what they expected to see in May.

Experiencing these different conditions in the Northern California Current has given us a new perspective on an ecosystem that we’ve been observing and studying for years. We’re looking forward to digging into the data and seeing how it can help us understand this ecosystem more deeply, especially during a period of continued climate change.

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We identified friends – old and new!

We flew, we scooped, we collected heaps of data!

Final thoughts

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