Tag: Marine Mammals

Announcing our new project: SLATE – Scar-based Long-term Assessment of Trends in whale Entanglements

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

Filling the gaps

Reports of whale entanglements have been on the rise over the last decade on the US West Coast, with Dungeness crab fishing gear implicated in many cases (Feist et al., 2021; Samhouri et al., 2021; Santora et al., 2020). State agencies are responsible for managing this environmental issue that has implications both for the endangered whale sub-populations that are subject to entanglements, and for the fishing activities, which play an important social, cultural, and economic role for coastal communities. In Oregon, the Oregon Whale Entanglement Working Group (today the Oregon Entanglement Advisory Committee, facilitated by ODFW – Oregon Department of Fish and Wildlife) formed in 2017, tasked with developing options to reduce entanglement risk. The group members composed of managers, researchers and fishermen identified that a lack of information and understanding of whale distribution in Oregon waters was a significant knowledge gap of high priority.

In response, the GEMM Lab and its collaborators at ODFW developed the OPAL project (Overlap Predictions About Large whales, phase 1: 2018-2022). The first phase of the project (phase 1) was developed to 1) model and predict large whale distribution off the coast of Oregon in relation to dynamic environmental conditions, and 2) assess overlap with commercial crab fishing gear to inform conservation efforts. Although this first phase was extended up to June as a result of COVID, it is now coming to an end. As a postdoc in the GEMM Lab, I have been the main analyst working on this project. The habitat use models that I generated from several years of aerial and boat-based surveys provide improved knowledge about where and when rorqual whales (combining blue, humpback and fin) are most abundant (Derville et al., 2022). Moreover, we are about to publish an analysis of overlap between whale predicted densities and commercial Dungeness crab fishing effort. This analysis of co-occurrence over 10 years shows distinct spatio-temporal patterns in relation to climatic fluctuations affecting the northern California Current System (Derville et al., In review).

Although we are quite satisfied with the outputs of these four years of research, this is not the end of it! Project OPAL continues into a second phase (2022-2025; supported by NOAA Section 6 funding), during which models will be improved and refined via incorporation of new survey data (helicopter and boat-based) as well as prey data (krill and fish distribution). PhD student Rachel Kaplan is a key contributor to this research, and I will do my best to keep assisting her in this journey in the years to come.

Announcing SLATE!

As this newly acquired knowledge leads to potentially new management measures in Oregon, it becomes essential for managers to evaluate their impacts on the entanglement issue. But how do we know exactly how many entanglements occur during any year within Oregon waters? Is recording reports of entanglements or signs of entanglements in stranded whales enough? The simple answer is no. Entanglements are notoriously under-detected and under-reported (Tackaberry et al., 2022). Over the US West Coast, entanglements are also relatively rare events that can easily go unnoticed in the immensity of the ocean. Moreover, entangled large whales are often able to carry the fishing gear for some time away from the initial gearset location, which makes it hard to locate the origin of the gear causing problems (van der Hoop et al., 2017).

Figure 1: Graphical representation of the SLATE project representing the different tasks described below. Work in progress…

Our approach to the challenge of assessing humpback whale entanglement rates in Oregon waters is to use scar analysis. Our new “SLATE” (Scar-based Long-term Assessment of Trends in whale Entanglements, Figure 1) project will be using scar-based methods as a proxy to detect unobserved entanglement events (e.g., Basran et al., 2019; Bradford et al., 2009; George et al., 2017; Knowlton et al., 2012; Robbins, 2012). Indeed, this approach has been effective to detect potential interactions with fishing gear at a much higher frequency than entanglement reports in the Atlantic Ocean (e.g., only 10% of entanglements of humpback whales in the Gulf of Maine were estimated to be reported; Robbins, 2012). We will be examining hundreds of photographs of humpback whales observed in Oregon waters to try to detect wrapping scars and notches that result from entanglement events. Based on this scar pattern, we will assign each whale a qualitative probability of prior entanglement (i.e., uncertain, low, high). We will specifically be looking at the caudal peduncle (the attachment point of the whale’s fluke, see Figure 2) following a methodology developed in the Gulf of Maine by Robbins & Mattila, (2001).

Figure 2: Examples of unhealed injuries interpreted as entanglement related in 2010 in the Gulf of Maine. Figure reproduced from (Robbins, 2012).

Data please?

While this approach is to-date the most applicable way to assess otherwise undetected entanglements, it is sometimes limited by sample size. Although we plan to collect more photos in the field in summer 2023 and 2024, this long-term analysis of scarring patterns would not be possible without the contribution of the Cascadia Research Collective (CRC) led by John Calambokidis. The CRC humpback whale catalogue will be crucial to assessing entanglement rates at the individual level over the last decade.

Moreover, as we have been contemplating the task ahead of us, we realized that the data collected through traditional scientific surveys might not be sufficient to achieve our goal. We need the help of the people who live off the ocean and encounter whales on a day-to-day basis: fishermen. That is why we decided to solicit interested fishermen to take photographs of whales while at sea. Starting this year, we will work with at least three self-selected fishermen who are interested in supporting this program and collecting data to support the research efforts. Participants will be provided a stipend, equipped with a high-quality camera, and trained to photograph whales while following National Oceanic and Atmospheric Administration (NOAA) Marine Mammal Protection Act (MMPA) guidelines.

And here come the statistics…

If we have some of my previous blogs (e.g., May 2022, June 2018), you know that I usually participate in projects that have a significant statistical modeling component. As part of the SLATE project, I will be trying out some new approaches that I never had the opportunity to work with before, which makes me feels both super excited and slightly apprehensive!

First, I will analyze humpback whale scarring at the population level. That means I will be using all available photos of whales in Oregon waters without considering individual identification, and I will model the probability of entanglement scars in relation to space and time. This model will help us answer questions such as: did whales have a higher chance of becoming entangled in certain years over others? Did whales observed in a certain zone in Oregon waters have a higher risk of getting entangled?

Second, I will analyze humpback whale scarring at the individual level. This time, we will only use encounters of a selected number of individuals that have a long recapture history, meaning that they were photo-identified and resighted several times throughout the last decade. Using a genetic database produced by the Cetacean Conservation and Genomic Laboratory (CCGL, Marine Mammal Institute), we will also be able to tell to which “Distinct Population Segment” (DPS) some of these individual whales belong. Down the line, this is an important piece of information because humpback whale DPS do not breed in the same areas, and these groups have different levels of population health. Then, we will use what is known as a “multi-event mark-recapture model” to estimate the probability of entanglement as a function of time and spatial residency or DPS assignment, while accounting for detection probability and survival.

Through these analyses, our goal is to produce a single indicator to help managers assess the effects of mandatory or voluntary changes in Oregon fishing practices. In the end, we hope that these models will provide a measurable and robust way of monitoring whale entanglements in fishing gear off the coast of Oregon.

References

Basran, C. J., Bertulli, C. G., Cecchetti, A., Rasmussen, M. H., Whittaker, M., & Robbins, J. (2019). First estimates of entanglement rate of humpback whales Megaptera novaeangliae observed in coastal Icelandic waters. Endangered Species Research, 38(February), 67–77. https://doi.org/10.3354/ESR00936

Bradford, A. L., Weller, D. W., Ivashchenko, Y. v., Burdin, A. M., & Brownell, R. L. (2009). Anthropogenic scarring of western gray whales (Eschrichtius robustus). Marine Mammal Science, 25(1), 161–175. https://doi.org/10.1111/j.1748-7692.2008.00253.x

Derville, S., Barlow, D. R., Hayslip, C. E., & Torres, L. G. (2022). Seasonal, Annual, and Decadal Distribution of Three Rorqual Whale Species Relative to Dynamic Ocean Conditions Off Oregon, USA. Frontiers in Marine Science, 9, 1–19. https://doi.org/10.3389/fmars.2022.868566

Derville, S., Buell, T., Corbett, K., Hayslip, C., & Torres, L. G. (n.d.). Exposure of whales to entanglement risk in Dungeness crab fish-ing gear in Oregon, USA, reveals distinctive spatio-temporal and climatic patterns. Biological Conservation.

Feist, B. E., Samhouri, J. F., Forney, K. A., & Saez, L. E. (2021). Footprints of fixed-gear fisheries in relation to rising whale entanglements on the U.S. West Coast. Fisheries Management and Ecology, 28(3), 283–294. https://doi.org/10.1111/fme.12478

George, J. C., Sheffield, G., Reed, D. J., Tudor, B., Stimmelmayr, R., Person, B. T., Sformo, T., & Suydam, R. (2017). Frequency of injuries from line entanglements, killer whales, and ship strikes on bering-chukchi-beaufort seas bowhead whales. Arctic, 70(1), 37–46. https://doi.org/10.14430/arctic4631

Knowlton, A. R., Hamilton, P. K., Marx, M. K., Pettis, H. M., & Kraus, S. D. (2012). Monitoring North Atlantic right whale Eubalaena glacialis entanglement rates: A 30 yr retrospective. Marine Ecology Progress Series, 466(Kraus 1990), 293–302. https://doi.org/10.3354/meps09923

Robbins, J. (2012). Scar-Based Inference Into Gulf of Maine Humpback Whale Entanglement : 2010 (Issue January). Report to the Northeast Fisheries Science Center National Marine Fisheries Service, EA133F09CN0253 Item 0003AB, Task 3.

Robbins, J., & Mattila, D. K. (2001). Monitoring entanglements of humpback whales ( Megaptera novaeangliae ) in the Gulf of Maine on the basis of caudal peduncle scarring. SC/53/NAH25. Report to the Scientific Committee of the International Whaling Commission, 14, 1–12. http://www.ccbaymonitor.org/pdf/scarring.pdf

Samhouri, J. F., Feist, B. E., Fisher, M. C., Liu, O., Woodman, S. M., Abrahms, B., Forney, K. A., Hazen, E. L., Lawson, D., Redfern, J., & Saez, L. E. (2021). Marine heatwave challenges solutions to human-wildlife conflict. Proceedings of the Royal Society B: Biological Sciences, 288, 20211607. https://doi.org/10.1098/rspb.2021.1607

Santora, J. A., Mantua, N. J., Schroeder, I. D., Field, J. C., Hazen, E. L., Bograd, S. J., Sydeman, W. J., Wells, B. K., Calambokidis, J., Saez, L., Lawson, D., & Forney, K. A. (2020). Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nature Communications, 11, 536. https://doi.org/10.1038/s41467-019-14215-w

Tackaberry, J., Dobson, E., Flynn, K., Cheeseman, T., Calambokidis, J., & Wade, P. R. (2022). Low Resighting Rate of Entangled Humpback Whales Within the California , Oregon , and Washington Region Based on Photo-Identification and Long-Term Life History Data. Frontiers in Marine Science, 8(January), 1–13. https://doi.org/10.3389/fmars.2021.779448

van der Hoop, J., Corkeron, P., & Moore, M. (2017). Entanglement is a costly life-history stage in large whales. Ecology and Evolution, 7(1), 92–106. https://doi.org/10.1002/ece3.2615

A Matter of Time: Adaptively Managing the Timescales of Ocean Change and Human Response

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 

Ocean ecosystems are complex and dynamic, shaped by the interconnected physical and biogeochemical processes that operate across a variety of timescales. A trip on the “ocean conveyer belt”, which transports water from the North Atlantic across the global ocean and back in a process called thermohaline circulation, takes about a thousand years to complete. Phytoplankton blooms, which cycle nutrients through the surface ocean and feed marine animals, often occur at the crucial, food-poor moment of spring, and last for weeks or months. The entanglement of a whale in fishing gear, a major anthropogenic threat to ocean life that drives the GEMM Lab’s Project OPAL, can happen in seconds.

Compounding this complexity, even the timescales that research has clarified are changing. Many processes in the ocean are shifting – and often accelerating – due to global climate change. Images of melting sea ice, calving glaciers, and coastal erosion all exemplify our natural world’s rapid reorganization, and even discrete events can have dramatic repercussions and leave their mark for years. For example, a marine heatwave that occurred in 2014-2015 raised temperatures up to 2.5° C warmer than usual, redistributed species northward along the United States’ West Coast, spurred harmful algal blooms, and shut down fisheries. The toxic blooms also caused marine mammal strandings, domoic acid poisoning in California sea lions, and seabird mass death events (McCabe et al., 2016).

Figure 1. Figures like this Stommel diagram reveal the broad temporal and spatial scales over which ocean phenomena occur. Source: Sloyan et al., 2019

As humans seek to manage ocean ecosystems and mitigate the effects of climate change, our political processes have their own time scales, interconnected cycles, and stochasticity, just like the ocean. At the federal level in the United States, the legislative process takes place over months to decades, sometimes punctuated by relatively quicker actions enacted through Executive Orders. In addition, just as plankton have their turnover times, so do governmental branches. Both the legislative branch and the executive branch change frequently, with new members of Congress coming in every two years, and the president and administration changing every four or eight years. Turnover in both of these branches may constitute a total regime shift, with new members seeking to redirect science policy efforts.

The friction between oceanic and political timescales has historically made crafting effective ocean conservation policy difficult. In recent years, the policy approach of “adaptive management” has sought to respond to the challenges at the tricky intersection of politics, climate change, and ocean ecosystems. The U.S. Department of the Interior’s Technical Guide to Adaptive Management highlights its capacity to deal with the uncertainty inherent to changing ecosystems, and its ability to accommodate progress made through research: “Adaptive management [is a decision process that] promotes flexible decision making that can be adjusted in the face of uncertainties as outcomes from management actions and other events become better understood. Careful monitoring of these outcomes both advances scientific understanding and helps adjust policies or operations as part of an iterative learning process” (Williams et al, 2009).

Over the last several years, adaptive management policy approaches have been key as resource managers along the West Coast have responded to the problem of whale entanglement in fishing gear. When the 2014-2015 marine heatwave event caused anomalously low krill abundance in the central California Current region, humpback whales used a tactic called “prey-switching”, and fed on inshore anchovy schools rather than offshore krill patches. The resulting habitat compression fueled an increase in humpback whale entanglement events in Dungeness crab fishing gear (Santora et al, 2020). 

This sudden uptick in whale entanglements necessitated strategic management responses along the West Coast. In 2017, the California Dungeness Crab Fishing Gear Working Group developed the Risk Assessment and Mitigation Program (RAMP) to analyze real-time whale distribution and ocean condition data during the fishing season, and provide contemporaneous assessments of entanglement risk to the state’s Department of Fish and Wildlife. The Oregon Whale Entanglement Working Group (OWEWG) formed in 2017, tasked with developing options to reduce risk. Oregon Department of Fish and Wildlife (ODFW) has guided whale entanglement reduction efforts by identifying four areas of ongoing work: accountability, risk reduction, best management practices, and research – with regular, scheduled reviews of the regulations and opportunities to update and adjust them.

Figure 2. Entanglement in fishing gear can occur in seconds and may negatively impact whales for years. Source Scott Benson/NOAA

The need for research to support the best possible policy is where the GEMM Lab comes in. ODFW has established partnerships with Oregon State University and Oregon Sea Grant in order to improve understanding of whale distributions along the coast that can inform management efforts. Being involved in this cooperative “iterative learning process” is exactly why I’m so glad to be part of Project OPAL. Initial results from this work have already shaped ODFW’s regulations, and the framework of adaptive management and assessment means that regulations can continue being updated as we learn more through our research.

Ecosystem management will always be complex, just like ecosystems themselves. Today, the pace at which the climate is changing causes many people concern and even despair (Bryndum-Buchholz, 2022). Building adaptive approaches into marine policymaking, like the ones in use off the West Coast, introduces a new timescale into the U.S. policy cycle – one more in line with the rapid changes that are occurring within our dynamic ocean.

References

Williams, B. L., Szaro, R. C., and Shapiro, C. D. 2009. Adaptive management: the U.S. Department of the Interior Technical Guide. Adaptive Management Working Group, v pp.

Bryndum-Buchholz, A. (2022). Keeping up hope as an early career climate-impact scientist. ICES Journal of Marine Science, 79(9), 2345–2350. https://doi.org/10.1093/icesjms/fsac180

McCabe, R. M., Hickey, B. M., Kudela, R. M., Lefebvre, K. A., Adams, N. G., Bill, B. D., Gulland, F. M., Thomson, R. E., Cochlan, W. P., & Trainer, V. L. (2016). An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys Res Lett, 43(19), 10366–10376. https://doi.org/10.1002/2016GL070023

Santora, J. A., Sydeman, W. J., Schroeder, I. D., Wells, B. K., & Field, J. C. (2011). Mesoscale structure and oceanographic determinants of krill hotspots in the California Current: Implications for trophic transfer and conservation. Progress in Oceanography, 91(4), 397–409. https://doi.org/10.1016/j.pocean.2011.04.002

Sloyan, B. M., Wilkin, J., Hill, K. L., Chidichimo, M. P., Cronin, M. F., Johannessen, J. A., Karstensen, J., Krug, M., Lee, T., Oka, E., Palmer, M. D., Rabe, B., Speich, S., von Schuckmann, K., Weller, R. A., & Yu, W. (2019). Evolving the Physical Global Ocean Observing System for Research and Application Services Through International Coordination. Frontiers in Marine Science, 6, 449. https://doi.org/10.3389/fmars.2019.00449

Clicks, buzzes, and rasps: How the MMPA has spurred what we know about beaked whale acoustic repertoire

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

In October 1972, the tides turned for U.S. environmental politics: the Marine Mammal Protection Act (MMPA) was passed. Its creation ushered in a new flavor of conservation and management. With phrases like “optimum sustainable population” baked into its statutory language, it marked among the first times that ecosystem-based management — an approach which directly calls upon knowledge of ecology to inform action — was required by law (Ray and Potter 2022). Transitioning from reductionist, species-siloed policies, the MMPA instead placed the interdependency of species at the core of ecosystem function and management. 

Beyond deepening the role of science on Capitol Hill, the MMPA’s greatest influence may have been spurred by the language that prohibited “the taking and importation of marine mammals” (16 U.S.C. 1361). Because the word “taking” is multivalent, it carries on its back many interpretations. “Taking” a marine mammal is not limited to intentionally hunting or killing them, or even accidental bycatch. “Taking” also includes carelessly operating a boat when a marine mammal is present, feeding a marine mammal in the wild, or tagging a marine mammal without the appropriate scientific permit. “Taking” a marine mammal can also extend to the fatal consequences caused by noise pollution — not intent, but incident (16 U.S.C. 1362).

The latter circumstances remain reverberant for the U.S. Navy. To comply with the MMPA, they are granted “incidental, but not intentional, taking of small numbers of marine mammals….[when] engag[ing] in a specified activity (other than commercial fishing)” (87 FR 33113). So, if the sonar activities required for national security exercises adversely impact marine mammals, the Navy has a bit of leeway but is still expected to minimize this impact. To further mitigate this potential harm, the Navy thus invests heavily in marine mammal research. (If you are interested in learning more about how the Navy has influenced the trajectory of oceanographic research more broadly, you may find this book interesting.) 

Beaked whales are an example of a marine mammal we know much about due to the MMPA’s call for research when incidental take occurs. Three decades ago, many beaked whales stranded ashore following a series of U.S. Navy sonar exercises. Since then, the Navy has flooded research dollars toward better understanding beaked whale hearing, vocal behavior, and movements (e.g., Klinck et al. 2012). Through these efforts, a deluge of research charged with developing effective tools to acoustically monitor and conserve beaked whales has emerged.  

These studies have laid the foundation for my Ph.D. research, which is dedicated to the Holistic Assessment of Living marine resources off Oregon (HALO) project. Through both visual and acoustic surveys, the HALO project’s mission is to understand how changes in ocean conditions — driven by global climate change — influence living marine resources in Oregon waters. 

In my research specifically, I aim to learn more about beaked whales off the Oregon coast. Beaked whales represent nearly a fourth of cetacean species alive today, with at least 21 species recorded to date (Roman et al. 2013). Even so, 90% of beaked whales are considered data deficient: we lack enough information about them to confidently describe the state of their populations or decide upon effective conservation action. 

Much remains to be learned about beaked whales, and I aim to do so by eavesdropping on them. By referring to the “acoustic repertoire” of beaked whales — that is, their vocalizations and corresponding behaviors — I aim to tease out their vocalizations from the broader ocean soundscape and understand how their presence in Oregon waters varies over time. 

Beaked whales are notoriously cryptic, elusive to many visual survey efforts like those aboard HALO cruises. In fact, some species have only been identified via carcasses that have washed ashore (Moore and Barlow 2013). Acoustic studies have elucidated ecological information (beaked whales forage at night at seamounts summits; Johnston et al. 2008) and have also introduced promising population-level monitoring efforts (beaked whales have been acoustically detected in areas with a historical scarcity of sightings; Kowarski et al. 2018). Their deep-diving nature often renders them inconspicuous, and they forage at depths between 1,000 and 2,000 m, on dives as long as 90 minutes (Moore and Barlow 2013; Klinck et al. 2012). Their echolocation clicks are produced at frequencies within the hearing range of killer whales, and previous studies have suggested that Blainville’s beaked whales are only vocally active during deep foraging dives and not at the surface, possibly to prevent being acoustically detected by predatory killer whales. Researchers refer to this phenomenon as “acoustic crypsis,” or when vocally-active marine mammals are strategically silent to avoid being found by potential predators (Aguilar de Soto et al. 2012).

We expect to see evidence of Blainville’s beaked whales in Oregon waters, as well as Baird’s, Cuvier’s, Stejneger’s, Hubb’s, and other beaked whale species. Species-specific echolocation clicks were comprehensively described a decade ago in Baumann-Pickering et al. 2013 (Figure 1). While this study laid the groundwork for species-level beaked whale acoustic detection, much more work is still needed to describe their acoustic repertoire with higher resolution detail. For example, though Hubb’s beaked whales live in Oregon waters, their vocal behavior remains scantly defined.

Figure 1: Baird’s, Blainville’s, Cuvier’s, and Stejneger’s beaked whales are among the most comprehensively acoustically described beaked whales inhabiting central Oregon waters, though more work would improve accuracy in species-specific acoustic detection. Credit: Marissa Garcia. Infographic draws upon beaked whale imagery from NOAA Fisheries and spectrograms and acoustical statistics published in Baumann-Pickering et al. 2013.

The HALO project seeks to add a biological dimension to the historical oceanographic studies conducted along the Newport Hydrographic (NH) line ever since the 1960s (Figure 2). Rockhopper acoustic recording units are deployed at sites NH 25, NH 45, and NH 65. The Rockhopper located at site NH 65 is actively recording on the seafloor about 2,800 m below the surface. Because beaked whales tend to be most vocally active at these deep depths, we will first dive into the acoustic data on NH 65, our deepest unit, in hopes of finding beaked whale recordings there.

Figure 2: The HALO project team conducts quarterly visual surveys along the NH line, spanning between NH 25 and NH 65. Rockhopper acoustic recording units continuously record at the NH 25, NH 45, and NH 65 sites. Credit: Leigh Torres.

Beaked whales’ acoustic repertoire can be broadly split into four primary categories: burst pulses (aka “search clicks”), whistles, buzz clicks, and rasps. Beaked whale search clicks, which are regarded as burst pulses when produced in succession, have distinct qualities: their upswept frequency modulation (meaning the frequency gets higher within the click), their long duration especially when compared to other delphinid clicks, and a consistent interpulse interval  which is the time of silence between signals (Baumann-Pickering et al. 2013). Acoustic analysts can identify different species based on how the frequency changes in different burst pulse sequences (Baumann-Pickering et al. 2013; Figure 1). For this reason, when I conduct my HALO analyses, I intend to automatically detect beaked whale species using burst pulses, as they are the best documented beaked whale signal, with unique signatures for each species. 

In the landscape of beaked whale acoustics, the acoustic repertoire of Blainville’s beaked whales (Mesoplodon densirostris) — a species of focus in my HALO analyses — is especially well defined. Blainville’s beaked whale whistles have been recorded up to 900 m deep, representing the deepest whistle recorded for any marine mammal to date in the literature (Aguilar de Soto et al. 2012). While Blainville’s beaked whales only spend 40% of their time at depths below 170 m, two key vocalizations occur at these depths: whistles and rasps. While they remain surprisingly silent near the surface, beaked whales produce whistles and rasps at depths up to 900 m. The beaked whales dive together in synchrony, and right before they separate from each other, they produce the most whistles and rasps, further indicating that these vocalizations are used to enhance foraging success (Aguilar de Soto et al. 2006). As beaked whales transition to foraging on their own, they predominantly produce frequently modulated clicks and buzzes. Beaked whales produce buzzes in the final stages of prey capture to receive up-to-date information about their prey’s location. The buzzes’ high repetition enables the whale to achieve 300+ updates on their intended prey’s location in the last 3 m before seizing their feast (Johnson et al. 2006; Figure 3). 

Figure 3: Blainville’s beaked whales generally have four categories within their acoustic repertoire, including burst pulses, whistles, buzz clicks, and rasps. Credit: Marissa Garcia.

All of this knowledge about beaked whale acoustics can be linked back to the MMPA, which has also achieved broader success. Since the MMPA’s implementation, marine mammal population numbers have risen across the board. For marine mammal populations with sufficient data, approximately 65% of these stocks are increasing and 17% are stable (Roman et al. 2013). 

Nevertheless, perhaps much of the MMPA’s true success lies in the research it has indirectly fueled, by virtue of the required compliance of governmental bodies such as the U.S. Navy. And the response has proven to be a boon to knowledge: if the U.S. Navy has been the benefactor of marine mammal research, beaked whale acoustics has certainly been the beneficiary. We hope the beaked whale acoustic analyses stemming from the HALO Project can further this expanse of what we know.

References

Aguilar de Soto, N., Madsen, P. T., Tyack, P., Arranz, P., Marrero, J., Fais, A., Revelli, E., & Johnson, M. (2012). No shallow talk: Cryptic strategy in the vocal communication of Blainville’s beaked whales. Marine Mammal Science, 28(2), E75–E92. https://doi.org/10.1111/j.1748-7692.2011.00495.x

Baumann-Pickering, S., McDonald, M. A., Simonis, A. E., Solsona Berga, A., Merkens, K. P. B., Oleson, E. M., Roch, M. A., Wiggins, S. M., Rankin, S., Yack, T. M., & Hildebrand, J. A. (2013). Species-specific beaked whale echolocation signals. The Journal of the Acoustical Society of America, 134(3), 2293–2301. https://doi.org/10.1121/1.4817832

Dawson, S., Barlow, J., & Ljungblad, D. (1998). SOUNDS RECORDED FROM BAIRD’S BEAKED WHALE, BERARDIUS BAIRDIL. Marine Mammal Science, 14(2), 335–344. https://doi.org/10.1111/j.1748-7692.1998.tb00724.x

Johnston, D. W., McDonald, M., Polovina, J., Domokos, R., Wiggins, S., & Hildebrand, J. (2008). Temporal patterns in the acoustic signals of beaked whales at Cross Seamount. Biology Letters (2005), 4(2), 208–211. https://doi.org/10.1098/rsbl.2007.0614

Johnson, M., Madsen, P. T., Zimmer, W. M. X., de Soto, N. A., & Tyack, P. L. (2004). Beaked whales echolocate on prey. Proceedings of the Royal Society. B, Biological Sciences, 271(Suppl 6), S383–S386. https://doi.org/10.1098/rsbl.2004.0208

Johnson, M., Madsen, P. T., Zimmer, W. M. X., de Soto, N. A., & Tyack, P. L. (2006). Foraging Blainville’s beaked whales (Mesoplodon densirostris) produce distinct click types matched to different phases of echolocation. Journal of Experimental Biology, 209(Pt 24), 5038–5050. https://doi.org/10.1242/jeb.02596

Klinck, H., Mellinger, D. K., Klinck, K., Bogue, N. M., Luby, J. C., Jump, W. A., Shilling, G. B., Litchendorf, T., Wood, A. S., Schorr, G. S., & Baird, R. W. (2012). Near-real-time acoustic monitoring of beaked whales and other cetaceans using a Seaglider. PloS One, 7(5), e36128. https://doi.org/10.1371/annotation/57ad0b82-87c4-472d-b90b-b9c6f84947f8

Kowarski, K., Delarue, J., Martin, B., O’Brien, J., Meade, R., Ó Cadhla, O., & Berrow, S. (2018). Signals from the deep: Spatial and temporal acoustic occurrence of beaked whales off western Ireland. PloS One, 13(6), e0199431–e0199431. https://doi.org/10.1371/journal.pone.0199431

Madsen, P. T.,  Johnson, M., de Soto, N. A., Zimmer, W. M. X., & Tyack, P. (2005). Biosonar performance of foraging beaked whales (Mesoplodon densirostris). Journal of Experimental Biology, 208(Pt 2), 181–194. https://doi.org/10.1242/jeb.01327

McCullough, J. L. K., Wren, J. L. K., Oleson, E. M., Allen, A. N., Siders, Z. A., & Norris, E. S. (2021). An Acoustic Survey of Beaked Whales and Kogia spp. in the Mariana Archipelago Using Drifting Recorders. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.664292

Moore, J. E. & Barlow, J. P. (2013). Declining abundance of beaked whales (family Ziphiidae) in the California Current large marine ecosystem. PloS One, 8(1), e52770–e52770. https://doi.org/10.1371/journal.pone.0052770

Ray, G. C. & Potter, F. M. (2011). The Making of the Marine Mammal Protection Act of 1972. Aquatic Mammals, 37(4), 522.

Roman, J., Altman, I., Dunphy-Daly, M. M., Campbell, C., Jasny, M., & Read, A. J. (2013). The Marine Mammal Protection Act at 40: status, recovery, and future of U.S. marine mammals. Annals of the New York Academy of Sciences, 1286(1), 29–49. https://doi.org/10.1111/nyas.12040

How fat do baleen whales get? Recent publication shows how humpback whales increase their body condition over the foraging season. 

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

Traveling across oceans takes a lot of energy. Most baleen whales use stored energy acquired on their summer foraging grounds to support the costs of migration to and reproduction on their winter breeding grounds. Since little, if any, feeding takes place during the migration and winter season, it is essential that baleen whales obtain enough food to increase their fat reserves to support reproduction. As such, baleen whales are voracious feeders, and they typically depart the foraging grounds much fatter than when they had arrived. 

So, how fat do baleen whales typically get by the end of the foraging season, and how does this differ across reproductive classes, such as a juvenile female vs. a pregnant female? Understanding these questions is key for identifying what a typical “healthy” whale looks like, information which can then help scientists and managers monitor potential impacts from environmental and anthropogenic stressors. In this blog, I will discuss a recent publication in Frontiers in Marine Science (https://doi.org/10.3389/fmars.2022.1036860) that is from my PhD dissertation with the Duke University Marine Robotics and Remote Sensing (MaRRS) Lab, and also includes GEMM lab members Allison Dawn and Clara Bird. In this study, we analyzed how humpback whales (Megaptera novaeangliae) along the Western Antarctic Peninsula (WAP) increase their fat reserves throughout the austral summer foraging season (Bierlich et al., 2022). This work also helps provide insight to the GEMM Lab’s GRANITE project (Gray whale Response to Ambient Noise Informed by Technology and Ecology), where we are interested in how Pacific Coast Feeding Group (PCFG) gray whales increase their energy reserves in response to environmental variability and increasing human activities. 

Eastern South Pacific humpback whales, identified as Stock G by the International Whaling Commission, travel over 16,000 km between summer foraging grounds along the WAP and winter breeding grounds between Ecuador and Costa Rica (Fig. 1). Like most baleen whales, Stock G humpback whales were heavily exploited by 20th century commercial whaling. Recent evidence suggests that this population is recovering, with an estimated increase in population size of ~7,000 individuals in 2000 to ~19,107 in 2020 (Johannessen et al., 2022). 

However, there are long-term concerns for this population. The WAP is one of the fastest warming regions on the planet, and regional populations of krill, an important food source for humpback whales, have declined steeply over the past half-century. Additionally, the WAP has seen a rapid expansion of human activities, such as tourism and krill fishing. Specifically, the WAP has experienced an increase in tourism from a total of 6,700 visitors from 59 voyages in 1990 to 73,000 visitors from 408 voyages in 2020, which may be causing increased stress levels amongst Stock G (Pallin et al., 2022). Furthermore, the krill fishery has increased harvest activities in key foraging areas for humpback whales (Reisinger et al., 2022). Understanding how humpback whales increase their energy reserves over the course of the foraging season can help researchers establish a baseline to monitor future impacts from climate change and human activities. This work also provides an opportunity for comparisons to other baleen whale populations that are also exposed to multiple stressors, such as the PCFG gray whales off the Newport Coast who are constantly exposed to vessel traffic and at risk of entanglement from fishing gear. 

Figure 1. The migration route of the Stock G humpback whale population. Figure adapted from Whales of the Antarctic Peninsula Report, WWF 2018.

To understand how humpback whales increase their energy reserves throughout the foraging season, we collected drone imagery of whales along the WAP between November and June, 2017-2019 (Fig. 2). We used these images to measure the length and width of the whale to estimate body condition, which represents an animal’s relative energy reserve and can reflect foraging success (see previous blog). We collected drone imagery from a combination of research stations (Palmer Station), research vessels (Laurence M. Gould), and tour ships (One Ocean Expeditions). We used several different drones types and accounted for measurement uncertainty associated with the camera, focal length lens, altitude, and altimeter (barometer/LiDAR) from each drone (see previous blog and Bierlich et al., 2021a, 2021b). We also took biopsy samples to identify the sex of each individual and to determine if females were pregnant or not. 

Figure 2. Two humpbacks gracefully swimming in the chilly water along the Western Antarctic Peninsula. Photo taken by KC Bierlich & the Duke University Marine Robotics and Remote Sensing (MaRRS) Lab.

Our final dataset included body condition measurements for 228 total individuals. We found that body condition increased linearly between November and June for each reproductive class, which included calves, juvenile females, juvenile whales of unknown sex, lactating females, mature whales of unknown sex, and non-pregnant females (Fig. 3). This was an interesting finding because a recent publication analyzing tagged whales from the same population found that humpback whales have high foraging rates in early season that then significantly decrease by February and March (Nichols et al., 2022). So, despite these reduced foraging rates throughout the season, humpback whales continue to gain substantial mass into the late season. This continued increase in body condition implies a change in krill abundance and/or quality into the late season, which may compensate for the lower feeding rates. For example, krill density and biomass increases by over an order of magnitude across the season (Reiss et al., 2017) and their lipid content increases by ~4x (Hagen et al., 1996). Thus, humpback whales likely compensate for their lower feeding rates by feeding on denser and higher quality krill, ultimately increasing their efficiency in energy deposition. 

Figure 3. Body condition, here measured as Body Area Index (BAI), increases linearly for each reproductive class across the austral summer foraging season (Nov – June) for humpback whales along the Western Antarctic Peninsula. The shading represents the uncertainty around the estimated relationship. The colors represent the month of data collection.

We found that body condition increase varied amongst reproductive classes. For example, lactating females had the poorest measures of body condition across the season, reflecting the high energetic demands of nursing their calves (Fig. 3). Conversely, non-pregnant females had the highest body condition at the start of the season compared to all the other classes, likely reflecting the energy saved and recovered by skipping breeding that year.  Calves, juvenile whales, and mature whales all reached similar levels of body condition by the end of the season, though mature whales will likely invest most of their energy stores toward reproduction, whereas calves and juveniles likely invest toward growth. We also found a positive relationship between the total length of lactating females and their calves, suggesting that bigger moms have bigger calves (Fig. 4). A similar trend has also been observed in other baleen whale species including southern and North Atlantic right whales (Christiansen et al., 2018; Stewart et al., 2022).

Figure 4. Big mothers have big calves. Total length (TL) measurement between mother-calf pairs. The bars around each point represents the uncertainty (95% highest posterior density intervals). The colors represent the month of data collection. The blue line represents the best fit from a Deming regression, which incorporate measurement uncertainty in both the independent (mother’s TL) and dependent variable (calf’s TL).

The results from the humpback study provide insight for my current work exploring how PCFG gray whales increase their energy reserves in relation to environmental variability and increasing human activities. Over the past seven years, the GEMM Lab has been collecting drone images of PCFG gray whales off the coast of Oregon to measure their body condition (see this GRANITE Project blog). Many of the individuals we encounter are seen across years and throughout the foraging season, providing an opportunity to evaluate how an individual’s body condition is influenced by environmental variation, stress levels, maturity, and reproduction. For example, we had nine total body condition measurements of a female PCFG whale named “Sole”, who had a curvilinear increase in body condition throughout the summer foraging season – a rapid increase in early season that slowed as the season progressed (Fig. 5). This raises many questions for us: is this how most PCFG whales typically increase their body condition during the summer? Is this increase different for pregnant or lactating females? How is this increase impacted by environmental variability or anthropogenic stressors? Repeated measurements of individuals, in addition to Sole, in different reproductive classes across different years will help us determine what body condition is considered a healthy range for gray whales. This is particularly important for monitoring any potential health consequences from anthropogenic stressors, such as vessel noise and traffic (see recent blog by GEMM Lab alum Leila Lemos). We are currently analyzing body condition measurements between 2016 – 2022, so stay tuned for upcoming results!

Figure 6. Body condition, here measured as Body Area Index (BAI), increases curvilinearly for “Sole”, a mature female Pacific Coat Feeding Group gray whale, imaged nine times along the Oregon coast in 2021. The colors represent the month of data collection. 

References

Bierlich, K. C., Hewitt, J., Bird, C. N., Schick, R. S., Friedlaender, A., Torres, L. G., et al. (2021a). Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Front. Mar. Sci. 8, 1–16. doi:10.3389/fmars.2021.749943.

Bierlich, K. C., Hewitt, J., Schick, R. S., Pallin, L., Dale, J., Friedlaender, A. S., et al. (2022). Seasonal gain in body condition of foraging humpback whales along the Western Antarctic Peninsula. Front. Mar. Sci. 9, 1–16. doi:10.3389/fmars.2022.1036860.

Bierlich, K., Schick, R., Hewitt, J., Dale, J., Goldbogen, J., Friedlaender, A., et al. (2021b). Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones. Mar. Ecol. Prog. Ser. 673, 193–210. doi:10.3354/meps13814.

Christiansen, F., Vivier, F., Charlton, C., Ward, R., Amerson, A., Burnell, S., et al. (2018). Maternal body size and condition determine calf growth rates in southern right whales. Mar. Ecol. Prog. Ser. 592, 267–281.

Hagen, W., Van Vleet, E. S., and Kattner, G. (1996). Seasonal lipid storage as overwintering strategy of Antarctic krill. Mar. Ecol. Prog. Ser. 134, 85–89. doi:10.3354/meps134085.

Johannessen, J. E. D., Biuw, M., Lindstrøm, U., Ollus, V. M. S., Martín López, L. M., Gkikopoulou, K. C., et al. (2022). Intra-season variations in distribution and abundance of humpback whales in the West Antarctic Peninsula using cruise vessels as opportunistic platforms. Ecol. Evol. 12, 1–13. doi:10.1002/ece3.8571.

Nichols, R., Cade, D. E., Kahane-Rapport, S., Goldbogen, J., Simpert, A., Nowacek, D., et al. (2022). Intra-seasonal variation in feeding rates and diel foraging behavior in a seasonally fasting mammal, the humpback whale. Open Sci. 9, 211674.

Pallin, L. J., Botero-Acosta, N., Steel, D., Baker, C. S., Casey, C., Costa, D. P., et al. (2022). Variation in blubber cortisol levels in a recovering humpback whale population inhabiting a rapidly changing environment. Sci. Rep. 12, 1–13. doi:10.1038/s41598-022-24704-6.

Reisinger, R., Trathan, P. N., Johnson, C. M., Joyce, T. W., Durban, J. W., Pitman, R. L., et al. (2022). Spatiotemporal overlap of baleen whales and krill fisheries in the Antarctic Peninsula region. Front. Mar. Sci. doi:doi: 10.3389/fmars.2022.914726.

Reiss, C. S., Cossio, A., Santora, J. A., Dietrich, K. S., Murray, A., Greg Mitchell, B., et al. (2017). Overwinter habitat selection by Antarctic krill under varying sea-ice conditions: Implications for top predators and fishery management. Mar. Ecol. Prog. Ser. 568, 1–16. doi:10.3354/meps12099.

Stewart, J. D., Durban, J. W., Europe, H., Fearnbach, H., Hamilton, P. K., Knowlton, A. R., et al. (2022). Larger females have more calves : influence of maternal body length on fecundity in North Atlantic right whales. Mar. Ecol. Prog. Ser. 689, 179–189. doi:10.3354/meps14040.

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 land1,2, 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 oceans4–6. 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 grounds8–10, 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 8th 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!

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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

A Hundred and One Data Visualizations: What We Can Infer about Gray Whale Health Using Public Data

By Braden Adam Vigil, Oregon State University Undergraduate, GEMM Lab NSF REU Intern

Introduction

My name is Braden Vigil, and I am enjoying this summer with the company of Lisa Hildebrand and Dr. Leigh Torres as a National Science Foundation (NSF) Research Experience for Undergraduates (REU) intern. By slicing off a manageable chunk of the GRANITE project to focus on, I’ve had the chance to explore my passion for data visualization. My excitement for biological research was instilled in me by an impactful high school biology teacher (thank you Mr. Villalobos!) and was narrowed to marine biology research after a chance visit to Oregon State University’s Hatfield Marine Science Center. I’ve come all the way from Southern New Mexico to explore this passion of mine, and the REU program has been one of my first chances to get my feet wet. My advice for any students debating taking big leaps for the sake of passion is to do it – it’s scary, but I’d say there’s nothing better than living out what you want to do (and hopefully getting paid for it!). For this project, the GEMM Lab has saved me the trouble of collecting data – this summer, I’m all action. 

Where Gray Whales Are and Why It Matters

Just as you might find yourself at a grocery store to buy food or at a coffee shop catching up with an old friend, so too do whales have places to go and reasons for being there. Research concerning gray whale ecology – understanding the who, what, when, where, whys – should then have a lot to do with the “where?” and “why?” That’s what my project is about: investigating where the gray whales off the Oregon coast are, and what features of the environment are related to their presence and other aspects of the population. After all, distribution is considered the foundational unit for the biogeographical understanding of a population’s location and its interactions with other species. An example of an environmental driver may be phytoplankton and – subsequently – zooplankton abundance. It’s been shown that bottom-up trophic cascades based on primary productivity directly influence predator and prey populations in both terrestrial and marine ecosystems (Sinclair and Krebs 2002; Benoit-Bird and McManus 2012). This driver specifically could then inform something as significant as population abundance of a predator, though that’s out of the scope of my project. Instead, I’m studying how these environmental drivers, specifically sea water temperature, affects the variation of the thyroid hormone (tri-iodothyronine, T3) in gray whales, which the GEMM Lab quantifies from fecal samples that they non-invasively and opportunistically collect. In terrestrial mammals, T3 is believed to be associated with thermoregulation, yet it is unclear if T3 has the same function in baleen whales who use blubber insulation to thermoregulate. To estimate blubber insulation, we use a proxy, called body area index (BAI) collected via drone footage (Burnett et al. 2018), which you can read more about in Clara’s blog. Insights into variations in T3 hormone levels as related to changes in the environment may allow researchers to better understand thermoregulatory challenges whales face in warming oceans.

This Sounds Like a Lot of Data About the Environment, Where’s it Coming From?

Not only has the GEMM Lab relieved me of the hassle that data collection and fieldwork can be, so too has the Ocean Observatories Initiative (OOI). Starting in 2014, the OOI has set up several buoys off the U.S. West Coast, each equipped with numerous sensors and data-collecting devices. These have been extracting data from the nearby environment since then, including aspects such as dissolved oxygen, pH, and most important to this study, sea temperature. These buoys run deep too! Some devices reach as low as 25 m, which is where we often expect to see whales foraging during surveys. For our interest, there is one specific buoy that is within the GRANITE project’s survey region, the Oregon Inshore Surface Mooring.

Figure 1. Locations of OOI buoys. Blue dots represent buoys, while the yellow dot represents our buoy of interest, the Oregon Inshore Surface Mooring. 

Expectations

The OOI has published, and continues to publish, an unbelievable amount of data. There are many things that would be interesting to investigate, but until we know how much we can bite off versus how much we can chew, we’ve narrowed it down to a few hypotheses we’re currently investigating. 

Table 1. Hypotheses and Expected Results.

A Hundred and One Data Visualizations

As fun as I find testing correlations between variables and creating satisfying looking plots, I must admit that I’m not even halfway into this project and I’ve made a LOT of plots. Plots can be an easy way to understand big datasets and observations. Since not all of the data-collecting devices on the OOI data are continuously running, I first needed to get an idea of how much data we have to work with, and how much of that data overlaps in time with our annual gray whale survey period (June 1 – October 15). Some of these preliminary plots look like Figure 2. In addition, these plots grant us an idea of how variable sea surface temperatures have been in these past few years. Marine heatwaves have occurred recently in the Pacific Ocean and off the U.S. West Coast, and it is important to know if their effects continue to linger to the present. Other, unexplained peaks might also be worth investigating. 

Figure 2. Preliminary plot comparing sea surface temperature data over time, from around June 2016 to December 2021. Straight lines between December to June each year indicates no data, as we have removed these periods from our analysis. 

The goal here is to eventually compare the variables of sea temperature to the T3 hormone levels in gray whales foraging off the Oregon coast. Before this step, it is important to decide what depth of temperature readings are most appropriate to assess. I’ve made several correlation plots of sea  temperature between varying depths of 1 m, 7 m, and 25 m. One such plot is included below (Figure 3). This plot shows variation of temperature between different depths. If there is strong variation between the depths of 1 m and 25 m, then the water column may be well stratified, meaning that gray whale response to environmental temperature may be distinct between these distances, possibly even between 1 m and 7 m. 

Figure 3. Sea surface temperature at 1 m versus 25 m in degrees Celsius, with points color coded by year. 

Conclusion

As previously described, this study plays part into the larger GRANITE project with the goal to understand and make predictions about the ecology and physiology of the gray whale population off of the U.S. West Coast. This study will investigate the significance of sea temperature on aspects of whale health – so far including BAI and T3 hormone level. I will be pursuing a stronger grasp on the variation of these relationships through ongoing analysis. My results should be used to clarify nodes and the correlation between them in the web of dynamics encircling the population. This project has given me great insight into how raw data can be turned into meaningful understandings and subsequent impacts. The public OOI data is a scattershot of many different measurements using many different devices constantly. The answers/solutions to the conservation of species threatened by the Anthropocene are out there, all that’s required is that we harness them. 

References

Benoit-Bird, K. J., & McManus, M. A. (2012). Bottom-up regulation of a pelagic community through spatial aggregations. Biology Letters, 8(5), 813–816. https://doi.org/10.1098/rsbl.2012.0232

Burnett, J. D., & Wing, M. G. (2018). A low-cost near-infrared digital camera for fire detection and monitoring. International Journal of Remote Sensing, 39(3), 741–753. https://doi.org/10.1080/01431161.2017.1385109

Sinclair, A. R. E., & Krebs, C. J. (2002). Complex numerical responses to top–down and bottom–up processes in vertebrate populations. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 357(1425), 1221–1231.https://doi.org/10.1098/rstb.2002.1123.