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

A glimpse into the world of marine biological research

By Abby Tomita, undergraduate student, OSU College of Earth, Ocean, and Atmospheric Sciences

From long days in Newport performing the patience-testing task of bomb calorimetry, to spending hours transfixed by the microscopic world that exists in our oceans, I recently got an amazing glimpse into the world of marine biological research working with PhD student Rachel Kaplan. She has been an amazing teacher to my fellow intern Hadley and I, showing us the basics of the research process and introducing us to so many wonderful people at NOAA and the GEMM Lab. I am in my third year studying oceanography here at OSU and had no real lab experience before this, so I was eager to explore this area of research, and not only learn new information about our oceans, but also to see the research process up close and personal.

After being trained by Jennifer Fisher, a NOAA Research Fisheries Biologist, I sorted through zooplankton samples collected on the R/V Bell M. Shimada from the Northern California Current region. This data will be used to get an idea of where krill are found throughout the year, and in what abundances. Though my focus was mainly on two species of krill, I also found an assortment of other organisms, such as larval fish, squid, copepods, crabs, and tons of jellies, which were super interesting to see.

A small group of larval squid and other unknown species (photo by Abby Tomita).

I also studied krill through a technique called bomb calorimetry, which is not for the faint of heart! It takes a tough soul to be able to put these complex little creatures into a mortar and pestle and grind them into a dust that hits your nose like pepper. They then take their final resting place into the bomb calorimetry machine (which can and will find something to fuss over) until it finally manages to ignite and dispose of the krill’s remains. The light that guided me through this dark tunnel was the knowledge that these sacrificial krill were taken in the name of science, with the aim of eventually decreasing whale entanglements.

Abby placing the pellet within the coil for the bomb.

That, and Rachel’s contagious positivity. In the early stages, we would spend the majority of our time troubleshooting after constant “misfires”, in which the machine fails to combust the sample properly. Bomb calorimetry involves many tedious steps, and working with such small quantities of tissue – a single krill could weigh 0.01 grams or even less – poses a plethora of its own challenges. One of my biggest takeaways from this experience was to have patience with this kind of work and know when to take a much-needed dance break. Things often do not work out according to plan, and getting to see first-hand how to adapt to confounding variables and hitches in the procedure was an invaluable lesson.

I also got to see how collaborative the research process is. We received helpful advice from other members of the GEMM Lab at lunch, as well as constant help from our esteemed Resident Bomb Cal Expert, Elizabeth Daly. It was comforting for me to see that even when you are doing independent research, you are not expected to only work alone, and there can be so much community in higher level research.

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The final chapter: “The effects of vessel traffic and ocean noise on gray whale stress hormones”

By Leila S. Lemos, Ph.D., Postdoctoral Associate at Florida International University, former member of the GEMM Lab (Defended PhD. March 2020)

It’s been a long time since I wrote a blog post for the GEMM Lab (more than two years ago!). You may remember me as a former Ph.D. student working with gray whale body condition and hormone variation in association with ambient noise… and so much has happened since then!

After my graduation, since I have tropical blood running in my veins, I literally crossed the entire country in search of blue and sunny skies, warm weather and ocean, and of course different opportunities to continue doing research involving stressors and physiological responses in marine mammals and other marine organisms. It didn’t take me long to start a position as a postdoctoral associate with the Institute of Environment at Florida International University. I have learned so much in these past two years while mainly working with toxicology and stress biomarkers in a wide range of marine individuals including corals, oysters, fish, dolphins, and now manatees. I have started a new chapter in my life, and I am very eager to see where it takes me.

Talking about chapters… my Ph.D. thesis comprised four different chapters and I had published only the first one when I left Oregon: “Intra- and inter-annual variation in gray whale body condition on a foraging ground”. In this study we used drone-based photogrammetry to measure and compare gray whale body condition along the Oregon coast over three consecutive foraging seasons (June to October, 2016-2018). We described variations across the different demographic units, improved body condition with the progression of feeding seasons, and variations across years, with a better condition in 2016 compared to the following two years. Then in 2020, I was able to publish my second chapter entitled “Assessment of fecal steroid and thyroid hormone metabolites in eastern North Pacific gray whales”. In this study, we used gray whale fecal samples to validate and quantify four different hormone metabolite concentrations (progestins, androgens, glucocorticoids, and thyroid hormone). We reported variation in progestins and androgens by demographic unit and by year. Almost a year later, my third chapter “Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability” was published. In this chapter, we documented a negative correlation between body condition and glucocorticoids, meaning that slim whales were more stressed than the chubby ones.

These three chapters were “relatively easy” to publish compared to my fourth chapter, which had a long and somewhat stressful process (which is funny as I am trying to report stress responses in gray whales). Changes between journals, titles, analyses, content, and focus had to be made over the past year and a half for it to be accepted for publication. However, I believe that it was worth the extra work and invested time as our research definitely became more robust after all of the feedback provided by the reviewers. This chapter, now entitled “Effects of vessel traffic and ocean noise on gray whale stress hormones” was finally published earlier this month at the Nature Scientific Reports journal, and I’ll describe it further below.

Increased human activities in the last decades have altered the marine ecosystem, leaving us with a noisier, warmer, and more contaminated ocean. The noise caused by the dramatic increase in commercial and recreational shipping and vessel traffic^1-3has been associated with negative impacts on marine wildlife populations^4,5. This is especially true for baleen whales, whose frequencies primarily used for communication, navigation, and foraging^6,7 are “masked” by the noise generated by this watercraft. Several studies have reported alterations in marine mammal behavioral states^8-11, increased group cohesion^12-14, and displacement^8,15due to this disturbance, however, just a few studies have considered their physiological responses. Examples of physiological responses reported in marine mammals include altered metabolic rate^15,16and variations in stress-related hormone (i.e., glucocorticoids) concentrations relative to vessel abundance and ambient noise^17,18. Based on this context and on the scarcity of such assessments, we attempted to determine the effects of vessel traffic and associated ambient noise, as well as potential confounding variables (i.e., body condition, age, sex, time), on gray whale fecal glucocorticoid concentrations.

In addition to the data used in my previous three chapters collected from gray whales foraging off the Oregon coast, we also collected ambient noise levels using hydrophones, vessel count data from the Oregon Department of Fish and Wildlife (ODFW), and wind data from NOAA National Data Buoy Center (NDBC). Our first finding was a positive correlation between vessel counts and underwater noise levels (Fig. 1A), likely indicating that vessel traffic is the dominant source of noise in the area. To confirm this, we also compared underwater noise levels with wind speed (Fig. 1B), but no correlations were found.

**Figure 1:** Linear correlations between noise levels (daily median root mean square [rms] sound pressure level [SPL] in dB [re 1 μPa]; 50–1000 Hz) recorded on a hydrophone deployed outside the Newport harbor entrance during June to October of 2017 and 2018 and **(A)** vessel counts in Newport and Depoe Bay, Oregon, USA, and **(B)** daily median wind speed (m/s) from an anemometer station located on South Beach, Newport, Oregon, USA (station NWPO3). Asterisk indicates significant correlations between SPL and vessel counts in both years.

We also investigated noise levels by the hour of the day (Fig. 2), and we found that noise levels peaked between 6 and 8 am most days, coinciding with the peak of vessels leaving the harbor to get to fishing grounds. Another smaller peak is seen at 12 pm, which may represent “half-day fishing charter” vessels returning to the harbor. In contrast, wind speeds (in the lower graph) peaked between 3 and 4 pm, thus confirming the absence of correlation between noise and wind and providing more evidence that noise levels are dominated by the vessel activity in the area.

**Figure 2:** Median noise levels (root mean square sound pressure levels—SPLrms) for each hour of each day recorded on a hydrophone (50–10,000 Hz) deployed outside the Newport harbor entrance during June to October of 2017 (middle plot) and 2018 (upper plot), and hourly median noise level (SPL) against hourly median wind speed (lower plot) from an anemometer station located on South Beach, Newport, Oregon, USA (station NWPO3) over the same time period.

Finally, we assessed the effects of vessel counts, month, year, sex, whale body condition, and other hormone metabolites on glucocorticoid metabolite (GCm; “stress”) concentrations. Since we are working with fecal samples, we needed to consider the whale gut transit time and go back in time to link time of exposure (vessel counts) to response (glucocorticoid concentrations). However, due to uncertainty regarding gut transit time in baleen whales, we compared different time lags between vessel counts and fecal collection. The gut transit time in large mammals is ~12 hours to 4 days^3,19,20, so we investigated the influence of vessel counts on whale “stress hormone levels” from the previous 1 to 7 days. The model with the most influential temporal scale included vessel counts from previous day, which showed a significant positive relationship with GCm (the “stress hormone level”) (Fig. 3).

**Figure 3:** The effect of vessel counts in Newport and Depoe Bay (Oregon, USA) on the day before fecal sample collection on gray whale fecal glucocorticoid metabolite (GCm) concentrations.

Thus, the “take home messages” of our study are:

The soundscape in our study area is dominated by vessel noise.
Vessel counts are strongly correlated with ambient noise levels in our study area.
Gray whale glucocorticoid levels are positively correlated with vessel counts from previous day meaning that gray whale gut transit time may occur within ~ 24 hours of the disturbance event.

These four chapters were all very important studies not only to advance the knowledge of gray whale and overall baleen whale physiology (as this group is one of the most poorly understood of all mammals given the difficulties in sample collection²¹), but also to investigate potential sources for the unusual mortality event that is currently happening (2019-present) to the Eastern North Pacific population of gray whales. Such studies can be used to guide future research and to inform population management and conservation efforts regarding minimizing the impact of anthropogenic stressors on whales.

I am very glad to be part of this project, to see such great fruits from our gray whale research, and to know that this project is still at full steam. The GEMM Lab continues to collect and analyze data for determining gray whale body condition and physiological responses in association with ambient noise (Granite, Amber and Diamond projects). The gray whales thank you for this!

Cited Literature

1. McDonald, M. A., Hildebrand, J. A. & Wiggins, S. M. Increases in deep ocean ambient noise in the Northeast Pacific west of San Nicolas Island, California. J. Acoust. Soc. Am. 120, 711–718 (2006).

2. Kaplan, M. B. & Solomon, S. A coming boom in commercial shipping? The potential for rapid growth of noise from commercial ships by 2030. Mar. Policy 73, 119–121 (2016).

3. McCarthy, E. International regulation of underwater sound: establishing rules and standards to address ocean noise pollution (Kluwer Academic Publishers, 2004).

4. Weilgart, L. S. The impacts of anthropogenic ocean noise on cetaceans and implications for management. Can. J. Zool. 85, 1091–1116 (2007).

5. Bas, A. A. et al. Marine vessels alter the behaviour of bottlenose dolphins Tursiops truncatus in the Istanbul Strait, Turkey. Endanger. Species Res. 34, 1–14 (2017).

6. Erbe, C., Reichmuth, C., Cunningham, K., Lucke, K. & Dooling, R. Communication masking in marine mammals: a review and research strategy. Mar. Pollut. Bull. 103, 15–38 (2016).

7. Erbe, C. et al. The effects of ship noise on marine mammals: a review. Front. Mar. Sci. 6 (2019).

8. Sullivan, F. A. & Torres, L. G. Assessment of vessel disturbance to gray whales to inform sustainable ecotourism. J. Wildl. Manag. 82, 896–905 (2018).

9. Pirotta, E., Merchant, N. D., Thompson, P. M., Barton, T. R. & Lusseau, D. Quantifying the effect of boat disturbance on bottlenose dolphin foraging activity. Biol. Conserv. 181, 82–89 (2015).

10. Dans, S. L., Degrati, M., Pedraza, S. N. & Crespo, E. A. Effects of tour boats on dolphin activity examined with sensitivity analysis of Markov chains. Conserv. Biol. 26, 708–716 (2012).

11. Christiansen, F., Rasmussen, M. & Lusseau, D. Whale watching disrupts feeding activities of minke whales on a feeding ground. Mar. Ecol. Prog. Ser. 478, 239–251 (2013).

12. Bejder, L., Samuels, A., Whitehead, H. & Gales, N. Interpreting short-term behavioural responses to disturbance within a longitudinal perspective. Anim. Behav. 72, 1149–1158 (2006).

13. Nowacek, S. M., Wells, R. S. & Solow, A. R. Short-term effects of boat traffic on Bottlenose dolphins, Tursiops truncatus, in Sarasota Bay, Florida. Mar. Mammal. Sci. 17, 673–688 (2001).

14. Bejder, L., Dawson, S. M. & Harraway, J. A. Responses by Hector’s dolphins to boats and swimmers in Porpoise Bay, New Zealand. Mar. Mammal Sci. 15, 738–750 (1999).

15. Lusseau, D. Male and female bottlenose dolphins Tursiops spp. have different strategies to avoid interactions with tour boats in Doubtful Sound. New Zealand. Mar. Ecol. Prog. Ser. 257, 267–274 (2003).

16. Sprogis, K. R., Videsen, S. & Madsen, P. T. Vessel noise levels drive behavioural responses of humpback whales with implications for whale-watching. Elife 9, e56760 (2020).

17. Ayres, K. L. et al. Distinguishing the impacts of inadequate prey and vessel traffic on an endangered killer whale (Orcinus orca) population. PLoS ONE 7, e36842 (2012).

18. Rolland, R. M. et al. Evidence that ship noise increases stress in right whales. Proc. R. Soc. B Biol. Sci. 279, 2363–2368 (2012).

19. Wasser, S. K. et al. A generalized fecal glucocorticoid assay for use in a diverse array of nondomestic mammalian and avian species. Gen. Comp. Endocrinol. 120, 260–275 (2000).

20. Hunt, K. E., Trites, A. W. & Wasser, S. K. Validation of a fecal glucocorticoid assay for Steller sea lions (Eumetopias jubatus). Physiol. Behav. 80, 595–601 (2004).

21. Hunt, K. E. et al. Overcoming the challenges of studying conservation physiology in large whales: a review of available methods. Conserv. Physiol. 1, cot006–cot006 (2013).

How will upwelling ecosystems fare in a changing climate?

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

Global climate change is affecting all aspects of life on earth. The oceans are not exempt from these impacts. On the contrary, marine species and ecosystems are experiencing significant impacts of climate change at faster rates and greater magnitudes than on land^1,2, with cascading effects across trophic levels, impacting human communities that depend on healthy ocean ecosystems³.

In the lobby of the Gladys Valley Marine Studies building that we are privileged to work in here at the Hatfield Marine Science Center, a poem hangs on the wall: “The North Pacific Is Misbehaving”, by Duncan Berry. I read it often, each time moved by how he articulates both the scientific curiosity and the personal emotion that are intertwined in researchers whose work is dedicated to understanding the oceans on a rapidly changing planet. We seek to uncover truths about the watery places we love that capture our fascination; truths that are sometimes beautiful, sometimes puzzling, sometimes heartbreaking. Observations conducted with scientific rigor do not preclude complex human feelings of helplessness, determination, and hope.

Figure 1. Poem by Duncan Berry, entitled, “The North Pacific is Misbehaving”.

Here on the Oregon Coast, we are perched on the edge of a bountiful upwelling ecosystem. Upwelling is the process by which winds drive a net movement of surface water offshore, which is replaced by cold, nutrient-rich water. When this water full of nutrients meets the sunlight of the photic zone, large phytoplankton blooms occur that sustain high densities of forage species like zooplankton and fish, and yielding important feeding opportunities for predators such as marine mammals. Upwelling ecosystems, like the California Current system in our back yard that features in Duncan Berry’s poem, support over 20% of global fisheries catches despite covering an area less than 5% of the global oceans^4–6. These narrow bands of ocean on the eastern boundaries of the major oceans are characterized by strong winds, cool sea surface temperatures, and high primary productivity that ultimately support thriving and productive ecosystems (Fig. 2)⁷.

Figure 2. Reproduced from Bograd et al. 2023. Maps showing global means in several key properties during the warm season (June through August in the Northern Hemisphere and January through March in the Southern Hemisphere). The locations of the four eastern boundary current upwelling systems (EBUSs) are shown by black outlines in each panel. (a) 10-m wind speed (*colors*) and vectors. (b) SST. (c) Dissolved oxygen concentrations at 200-m depth. (d) Concentration of ocean chlorophyll a. Abbreviations: BenCS, Benguela Current System; CalCS, California Current System; CanCS, Canary Current System; HumCS, Humboldt Current System; SST, sea surface temperature.

Because of their importance to human societies, eastern boundary current upwelling systems (EBUSs) have been well-studied over time. Now, scientists around the world who have dedicated their careers to understanding and describing the dynamics of upwelling systems are forced to reckon with the looming question of what will happen to these systems under climate change. The state of available information was recently synthesized in a forthcoming paper by Bograd et al. (2023). These authors find that the future of upwelling systems is uncertain, as climate change is anticipated to drive conflicting physical changes in their oceanography. Namely, alongshore winds could increase, which would yield increased upwelling. However, a poleward shift in these upwelling systems will likely lead to long-term changes in the intensity, location, and seasonality of upwelling-favorable winds, with intensification in poleward regions but weakening in equatorward areas. Another projected change is stronger temperature gradients between inshore and offshore areas, and vertically within the water column. What these various opposing forces will mean for primary productivity and species community structure remains to be seen.

While most of my prior research has centered around the importance of productive upwelling systems for supporting marine mammal feeding grounds^8–10, my recent focus has shifted closer to home, to the nearshore waters less than 5 km from the coastline. Despite their ecological and economic importance, nearshore habitats remain understudied, particularly in the context of climate change. Through the recently launched EMERALD project, we are investigating spatial and temporal distribution patterns of harbor porpoises and gray whales between San Francisco Bay and the Columbia River in relation to fluctuations in key environmental drivers over the past 30 years. On a scientific level, I am thrilled to have such a rich dataset that enables asking broad questions relating to how changing environmental conditions have impacted these nearshore sentinel species. On a more personal level, I must admit some apprehension of what we will find. The excitement of detecting statistically significant northward shift in harbor porpoise distribution stands at odds with my own grappling with what that means for our planet. The oceans are changing, and sensitive species must move or adapt to persist. What does the future hold for this “wild edge of a continent of ours” that I love, as Duncan Berry describes?

Figure 4. The view from Cape Foulweather, showing the complex mosaic of nearshore habitat features. Photo: D. Barlow.

Evidence exists that the nearshore realm of the Northeast Pacific is actually decoupled from coastal upwelling processes¹¹. Rather, these areas may be a “sweet spot” in the coastal boundary layer where headlands and rocky reefs provide more stable retention areas of productivity, distinct from the strong upwelling currents just slightly further from shore (Fig. 4). As the oceans continue to shift under the impacts of climate change, what will it mean for these critically important nearshore habitats? While they are adjacent to prominent upwelling systems, they are also physically, biologically, and ecologically distinct. Will nearshore habitats act as a refuge alongside a more rapidly changing upwelling environment, or will they be impacted in some different way? Many unanswered questions remain. I am eager to continue seeking out truth in the data, with my drive for scientific inquiry fueled by my underlying connection to this wild edge of a continent that I call home.

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References:

1. Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Chang. 3, (2013).

2. Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).

3. Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science (2010). doi:10.1126/science.1189930

4. Mann, K. H. & Lazier, J. R. N. Dynamics of Marine Ecosystems: Biological-physical interactions in the oceans. Blackwell Scientific Publications (1996). doi:10.2307/2960585

5. Ryther, J. Photosynthesis and fish production in the sea. Science (80-. ). 166, 72–76 (1969).

6. Cushing, D. H. Plankton production and year-class strength in fish populations: An update of the match/mismatch hypothesis. Adv. Mar. Biol. 9, 255–334 (1990).

7. Bograd, S. J. et al. Climate Change Impacts on Eastern Boundary Upwelling Systems. Ann. Rev. Mar. Sci. 15, 1–26 (2023).

8. Barlow, D. R., Bernard, K. S., Escobar-Flores, P., Palacios, D. M. & Torres, L. G. 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 (2020).

9. Barlow, D. R., Klinck, H., Ponirakis, D., Garvey, C. & Torres, L. G. Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci. Rep. 11, 1–10 (2021).

10. Derville, S., Barlow, D. R., Hayslip, C. & Torres, L. G. Seasonal, Annual, and Decadal Distribution of Three Rorqual Whale Species Relative to Dynamic Ocean Conditions Off Oregon, USA. Front. Mar. Sci. 9, 1–19 (2022).

11. Shanks, A. L. & Shearman, R. K. Paradigm lost? Cross-shelf distributions of intertidal invertebrate larvae are unaffected by upwelling or downwelling. Mar. Ecol. Prog. Ser. 385, 189–204 (2009).

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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Bombs Away! A Summer of Bomb Calorimetry

By Hadley Robinson, undergraduate student, OSU College of Earth, Ocean, and Atmospheric Sciences and School of Language, Culture, and Society

My name is Hadley Robinson and I am a sophomore undergraduate at OSU, double majoring in Environmental Science and Spanish. This summer, I had the privilege of working with Rachel on her PhD research project involving bomb calorimetry, a technique that allows you to quantify the caloric content of organisms like the zooplankton krill.

Hadley preparing the bomb calorimetry machine to run a sample (photo by Rachel Kaplan).

Prior to this internship, I had never worked in a lab before, and as an environmental science major, I had no previous exposure to oceanography. The connection that Rachel made between our labwork and the broader goal of helping decrease whale entanglement events sparked my interest in this project. Our work this summer aimed to process a set of krill samples collected off the coast of Oregon and Washington, so that we could find the number of calories in single krill, and then look at patterns in krill caloric content based on their species, sex, and other characteristics.

We first identified the krill by species and sex (this was my favorite part of the experiment!). I not only loved looking at them under the microscope, but I also loved how it became a collaborative process. We quickly began getting each other’s opinions on whether or not a krill was Euphausia pacifica, Thysanoessa spinifera, male, female, sexless, gravid (carrying eggs), and much more.

Female *Thysanoessa spinifera* krill (photo by Abby Tomita).

After identification, we weighed and dried the krill, and finally turned them into small pellets that could fit in an instrument called a bomb calorimeter. These pellets were placed individually into in a “bomb cell” that could then be filled with oxygen and receive a shock from a metal wire. When the machine sent an electric pulse through the wire and combusted the krill pellet, the water surrounding the bomb cell warmed very slightly. The instrument measures this minute temperature change and uses it to calculate the amount of energy in the combusted material. With this information, we were able to quantify how many calories each krill sample contained. Eventually, this data could be used to create a seasonal caloric map of the ocean. Assuming that foraging whales seek out regions with calorically dense prey, such a map could play a crucial role in predicting whale distributions.

Working with Rachel taught me how dynamic the world of research really is. There were many variables that we had to control and factor into our process, such as the possibility of high-calorie lipids being lost if the samples became too warm during the identification process, the risk of a dried krill becoming rehumidified if it sat out in the open air, and even the tiny amount of krill powder inevitably lost in the pelletization process. This made me realize that we cannot control everything! Grappling with these realities taught me to think quickly, adapt, and most importantly, realize that it is okay to refine the process of research as it is being conducted.

Intern Abby (left) pressing the krill powder into a pellet and Hadley (right) prepping the bomb (photo by Rachel Kaplan).

Decisions, decisions: New GEMM Lab publication reveals trade-offs in prey quantity and quality in gray whale foraging

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

Obtaining enough food is crucial for predators to ensure adequate energy gain for maintenance of vital functions and support for energetically costly life history events (e.g., reproduction). Foraging involves decisions at every step of the process, including prey selection, capture, and consumption, all of which should be as efficient as possible. Making poor foraging decisions can have long-term repercussions on reproductive success and population dynamics (Harris et al. 2007, 2008, Grémillet et al. 2008), and for marine predators that rely on prey that is spatially and temporally dynamic and notoriously patchy (Hyrenbach et al. 2000), these decisions can be especially challenging. Prey abundance and density are frequently used as predictors of marine predator distribution, movement, and foraging effort, with predators often selecting highly abundant or dense prey patches (e.g., Goldbogen et al. 2011, Torres et al. 2020). However, there is increased recognition that prey quality is also an important factor to consider when assessing a predator’s ecology and habitat use (Spitz et al. 2012), and marine predators do show a preference for higher quality prey items (e.g., Haug et al. 2002, Cade et al. 2022). Moreover, negative impacts of low-quality prey on the health and breeding success of some marine mammals (Rosen & Trites 2000, Trites & Donnelly 2003) have been documented. Therefore, examining multiple prey metrics, such as prey quantity and quality, in predator ecology studies is critical.

Figure 1. Site map of the Port Orford TOPAZ/JASPER integrated projects. Blue squares represent the location of the 12 sampling stations within the 2 study sites (site boundaries demarcated with black lines). Brown dot represents the cliff-top observation site where theodolite tracking occurred.

Our integrated TOPAZ/JASPER projects in Port Orford do just this! We collect both prey quantity and quality data from a tandem research kayak, while we track Pacific Coast Feeding Group (PCFG) gray whales from shore. The prey and whale sampling overlap spatially (and often temporally within the same day). This kind of concurrent predator-prey sampling at similar scales is often logistically challenging to achieve, yet because PCFG gray whales have an affinity for nearshore, coastal habitats, it is something we have been able to achieve in Port Orford. Since 2016, a field team comprised of graduate, undergraduate, and high school students has collected data during the month of August to investigate gray whale foraging decisions relative to prey. Every day, a kayak team collects GoPro videos (to assess relative prey abundance; AKA: quantity) and zooplankton samples using a tow net (to assess prey community composition; AKA: quality through caloric content of different species) (Figure 1). At the same time, a cliff team surveys for gray whales from shore and tracks them using a theodolite, which provides us with tracklines of individual whales; We categorize each location of a whale into three broad behavior states (feeding, searching, transiting) based on movement patterns. Over the years, the various students who have participated in the TOPAZ/JASPER projects have written many blog posts, which I encourage you to read here (particularly to get more detailed information about the field methods).

Figure 2. An example daily layer of relative prey abundance (increasing color darkness corresponds with increasing abundance) in one study site with a whale theodolite trackline recorded on the same day overlaid and color-coded by behavioral state.

Several years of data are needed to conduct a robust analysis for our ecological questions about prey choice, but after seven years, we finally had the data and I am excited to share the results, which are due to the many years of hard work from many students! Our recent paper in Marine Ecology Progress Series aimed to determine whether PCFG gray whale foraging decisions are driven by prey quantity (abundance) or quality (caloric content of species) at a scale of 20 m (which is slightly less than 2 adult gray whale body lengths). In this study, we built upon results from my previous Master’s publication, which revealed that there are significant differences in the caloric content between the six common nearshore zooplankton prey species that PCFG gray whales feed on (Hildebrand et al. 2021). Therefore, in this study we addressed the hypothesis that individual whales will select areas where the prey community is dominated by the mysid shrimp Neomysis rayii, since it is significantly higher in caloric content than the other two prey species we identified, Holmesimysis sculpta (a medium quality mysid shrimp species) and Atylus tridens (a low quality amphipod species) (Hildebrand et al. 2021). We used spatial statistics and model to make daily maps of prey abundance and quality that we compared to our whale tracks and behavior from the same day. Please read our paper for the details on our novel methods that produced a dizzying amount of prey layers, which allowed us to tease apart whether gray whales target prey quantity, quality, or a mixture of both when they forage.

Figure 3. Figure shows the probability of gray whale foraging relative to prey abundance (color-coded by prey species). Dark grey vertical line represents the mean threshold for the *H. sculpta* curves (12.0); light grey vertical lines: minimum (7.2) and maximum (15.3) thresholds for the *H. sculpta* curves. Inflection points could not be calculated for the N. rayii curves

So, what did we find? The models proved our hypothesis wrong: foraging probability was significantly correlated with the quantity and quality of the mysid H. sculpta, which has significantly lower calories than N. rayii. This result puzzled us, until we started looking at the overall quantity of these two prey types in the study area and realized that the amount of calorically-rich N. rayii never reached a threshold where it was beneficial for gray whales to forage. But, there was a lot of H. sculpta, which likely made for an energetic gain for the whales despite not being as calorically rich as N. rayii. We determined a threshold of H. sculpta relative abundance that is required to initiate the gray whale foraging behavior, and the abundance of N. rayii never came close to this level (Figure 3). Despite not having the highest quality, H. sculpta did have the highest abundance and showed a significant positive relationship with foraging behavior, unlike the other prey items. Interestingly, whales never selected areas dominated by the low-calorie species A. tridens. These results demonstrate trade-off choices by whales for this abundant, medium-quality prey.

To our knowledge, individual baleen whale foraging decisions relative to available prey quantity and quality have not been addressed previously at this very fine-scale. Interestingly, this trade-off between prey quantity and quality has also been detected in humpback whales foraging in Antarctica at depths deeper than where the densest krill patches occur; while the whales are exploiting less dense krill patches, these krill composed of larger, gravid, higher-quality krill (Cade et al. 2022). While it is unclear how baleen whales differentiate between prey species or reproductive stages, several mechanisms have been suggested, including visual and auditory identification (Torres 2017). We assume here that gray whales, and other baleen whale species, can differentiate between prey species. Thus, our results showcase the importance of knowing the quality (such as caloric content) of prey items available to predators to understand their foraging ecology (Spitz et al. 2012).

References

Cade DE, Kahane-Rapport SR, Wallis B, Goldbogen JA, Friedlaender AS (2022) Evidence for size-selective pre- dation by Antarctic humpback whales. Front Mar Sci 9:747788

Goldbogen JA, Calambokidis J, Oleson E, Potvin J, Pyenson ND, Schorr G, Shadwick RE (2011) Mechanics, hydrody- namics and energetics of blue whale lunge feeding: effi- ciency dependence on krill density. J Exp Biol 214:131−146

Grémillet D, Pichegru L, Kuntz G, Woakes AG, Wilkinson S, Crawford RJM, Ryan PG (2008) A junk-food hypothesis for gannets feeding on fishery waste. Proc R Soc B 275: 1149−1156

Harris MP, Beare D, Toresen R, Nøttestad L, and others (2007) A major increase in snake pipefish (Entelurus aequoreus) in northern European seas since 2003: poten- tial implications for seabird breeding success. Mar Biol 151:973−983

Harris MP, Newell M, Daunt F, Speakman JR, Wanless S (2008) Snake pipefish Entelurus aequoreus are poor food for seabirds. Ibis 150:413−415

Haug T, Lindstrøm U, Nilssen KT (2002) Variations in minke whale (Balaenoptera acutorostrata) diet and body condi- tion in response to ecosystem changes in the Barents Sea. Sarsia 87:409−422

Hildebrand L, Bernard KS, Torres LG (2021) Do gray whales count calories? Comparing energetic values of gray whale prey across two different feeding grounds in the eastern North Pacific. Front Mar Sci 8:1008

Hyrenbach KD, Forney KA, Dayton PK (2000) Marine pro- tected areas and ocean basin management. Aquat Con- serv 10:437−458

Rosen DAS, Trites AW (2000) Pollock and the decline of Steller sea lions: testing the junk-food hypothesis. Can J Zool 78:1243−1250

Spitz J, Trites AW, Becquet V, Brind’Amour A, Cherel Y, Galois R, Ridoux V (2012) Cost of living dictates what whales, dolphins and porpoises eat: the importance of prey quality on predator foraging strategies. PLOS ONE 7:e50096

Torres LG, Barlow DR, Chandler TE, Burnett JD (2020) Insight into the kinematics of blue whale surface forag- ing through drone observations and prey data. PeerJ 8: e8906

Torres LG (2017) A sense of scale: foraging cetaceans’ use of scale-dependent multimodal sensory systems. Mar Mamm Sci 33:1170−1193

Trites AW, Donnelly CP (2003) The decline of Steller sea lions Eumetopias jubatus in Alaska: a review of the nutri- tional stress hypothesis. Mammal Rev 33:3−28

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

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We identified friends – old and new!

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

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