Marine Science Pride: The Significance of Representation in the Workplace

Morgan O’Rourke-Liggett, Graduate Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

October is LGBTQIA2S+ (Lesbian, Gay, Bisexual, Transgender, Intersex, Asexual, Aromatic, Agender, Two-Spirit, plus) History Month in the United States. As a marine biologist and member of the LGBTQIA2S+ community, I publicly came out in 2016. Since then, I have been navigating coming out in the workplace. As a graduate student, I’m using this time to practice being an “out” marine biologist.

OutInSTEM, a student organization at Oregon State University (OSU), supports LGBTQIA2S+ students in science, technology, engineering, and mathematics (STEM). It provides mentorship and connection with faculty and other students in the LGBTQIA2S+ community. Another goal is to increase visibility in the profession and foster confidence in students as they continue their professional careers. Other initiatives like OutInSTEM exist in many forms across agencies and countries.

Within the National Oceanographic and Atmospheric Administration (NOAA), the National Marine Sanctuary System created the initiative #PrideInTheOcean to celebrate both Ocean Month and LGBTQIA2S+ Pride Month, which both occur in June in the United States. This program partners with Pride Outside, a group connecting the LGBTQIA2S+ community through outdoor activities.

Some notable LGBTQIA2S+ scientists in marine studies are members and alumni of the Marine Mammal Institute at OSU. One is Dominique Kone (He/Him) who is now a marine ecologist and science officer at the California Ocean Science Trust. He is a graduate of OSU’s Marine Mammal Institute and the GEMM laboratory. Dominique wrote about his story here on Ocean Wise. Another is Dr. Daniel Palacios (He/Him), Endowed Associate Professor in Whale Habitats and lead of the Whale Habitat, Ecology, and Telemetry laboratory (WHET Lab) at OSU’s Marine Mammal Institute. Read Daniel’s story here on 500 Queer Scientists.

Visibility and representation are critical for multiple reasons. One is creating an atmosphere where LGBTQIA2S+ members feel validated in their experiences, allowing them to express their opinions, and recognize their contributions. Without the stress of facing potential harassment in the workplace, we can be our genuine selves leading to a healthier work environment, increased engagement, and better results.

Not everyone can be “out” in all aspects of their life. Some may be out publicly, but not at work; only out to select friends, etc. If it’s not safe (financially, physically, etc.), some people are never able to come out. Personal safety usually drives this decision. Some don’t want to expose aspects of their personal life in the workplace. Others hide it until after they have been hired or passed the probation period. Some never share due to fear of reprisal, such as being passed over for a promotion.

Despite the presence of state and federal anti-discrimination policies, micro and macro-aggressions occur in the workplace, such as transgender people having to fight for appropriate housing assignments. As a fisheries biological technician in Alaska, I was moved around several times as they had never dealt with a non-binary, transmasculine professional in their dorm rooms. I was forced to move three times and was frequently misgendered and deadnamed (deadnaming is calling a transgender person by an incorrect name, often their birth name and no longer use upon transitioning). It was a difficult situation and negatively affected my personal and work experience. I felt demoralized, disheartened, and depressed. I lost my respect for the agency and my long-standing dream of working in Alaska. 

To avoid repeating my experience in Alaska, perhaps we can think critically about our labs and workspaces. The following is a non-exhaustive list of things to consider when including and thinking about LGBTQIA2S+ co-workers:

  • How are transgender and other gender-diverse co-workers treated?
  • Does your place of work have gender-inclusive restrooms on every floor of the building?
  • Are dorms or berths separated by binary gender?
  • Do the men’s restrooms have menstruation products and baby changing station(s)?
  • Does your field gear include sizing options for people who have non-conforming bodies?
  • If your lab does events including significant others, is the environment welcoming of same-gender spouses? How do you treat singles?
  • Are your field locations in places that could be dangerous for LGBTQIA2S+ and other marginalized identities threatened by extremists?
  • Do you have intake forms with gender or sex on them? Is it necessary?
  • Do you use gendered language when non-gendered language can be used? (Examples from Grammarly)
  • Have you examined your own preconceptions and possible role in microaggressions? (What is a microaggression? Common LGBTQIA2S+ microaggressions)

We work in an incredible profession with smart, kind, and fun co-workers. Let’s take action to ensure it is also safe and inclusive for all members.

If you wish to read other LGBTQIA2S+ scientists’ stories you can find them at https://500queerscientists.com/, https://ocean.org/blog/international-lgbtqia-stem-day-role-models-in-ocean-science/, and follow #PrideInSTEM , #LGBTQSTEMDay , and #PrideInTheOcean on social media. The first four articles in the reference section for this blog contain other peer-reviewed studies and testimonials about the importance of LGBTQIA2S+ representation in the workplace and fields ranging from geosciences to sports media.

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References

Fisher, Kathleen Quardokus, et al. “Developing scientists as champions of diversity to transform the geosciences.” Journal of Geoscience Education 67.4 (2019): 459-471.

Johns, Nikara. “Pride Month: Nike’s Jarvis Sam on the Importance of Queer & Black Representation in the Workplace.” 18 June 2021. Footwear News.

Kilicaslan, Jan and Melissa Petrakis. “Heteronormative models of health-care delivery: investigating staff knowledge and confidence to meet the needs of LGBTIQ+ people.” Social Work in Health Care 58.6 (2019): 612-632.

Magrath, Rory. “”Progress…Slowly, but Surely”: The Sports Media Workplace, Gay Sports Journalists, and LGBT Media Representation in Sport.” Journalism Studies 21.2 (2020): 2545-270.

Palacios, Daniel. Daniel Palacios. 2022. https://500queerscientists.com/daniel-palacios/

Robinson, Chloe. International LGBTQIA2S+ STEM Day: Role Models in Ocean Science. 18 November 2021. Webpage. https://ocean.org/blog/international-lgbtqia-stem-day-role-models-in-ocean-science/

The Who’s Who of the fin whale seas: Defining specific large whale populations by their acoustic call rates.

Imogen Lucciano, Graduate student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab.

Is the Fin Whale endangered? | Scientific Approach
Fin whales. Photo credit: https://www.futurismo.pt/blog/wildlife/is-the-fin-whale-endangered/.

One year ago, I packed up my 11-year-old daughter, Mavis (for the purposes of this blog, I’ll refer to her as “my sidekick”), our two dogs, and all our books and we moved to Oregon. I was thrilled to arrive and begin my graduate studies in cetacean ecology and bioacoutics with the GEMM lab and the Marine Mammal Institute. It has not been an easy set of tasks to achieve high standards in graduate school while maintaining a constant presence as a single mother, but I am honestly having the time of my life. I am involved in an amazing graduate program and I get to do it with my sidekick cheering me on and making my life feel very whole. This is why I am excited to write this blog reporting on the progression of my thesis and the incredible animals that I have the pleasure of studying: the fin whale.  

Fin whales (Balaenoptera physalus) are the second largest cetacean on the planet and are present in nearly all temperate and polar oceanic regions of the world (1). For my master’s thesis, I will focus solely on the fin whales within a detectable range of our team’s research area off the Oregon coast. In the Northern Hemisphere, fin whales are known to grow up to 23 meters in length and weigh up to 40-50 metric tons (2). They have a slender profile and can be further identified by their hook-shaped dorsal fin in addition to a V-shape on their back referred to as a “chevron” (Fig. 1). Fin whales are a baleen whale in the rorqual family, which have adapted lunge feeding as their primary foraging method (3). This species of whales is also classified as endangered (1), making them a key focal species for research in our modern times of shifting conditions in ocean environments.

Figure 1. Fin whale denoting a clear depiction of the V-shaped chevron. Photo credit: https://www.adrianabasques.com/water/ocean-giants/43

Although I am working to correlate the acoustic detections of fin whales across space and time with environmental drivers (like temperature and chlorophyll concentration), as an aspiring cetacean bioacoustician, one of my other burning related questions is: How can fin whale vocalizations be utilized to differentiate populations across the oceans? Perhaps my analysis of fin whales off the Oregon coast can contribute to the pool of researchers studying this species worldwide to help understand drivers of fin whale vocalization variability.

Fin whales can travel great distances, yet their unique vocal renditions of repetitive pulse calls at either a 20 Hz or 40 Hz frequency have geographic patterns (4). These renditions are stereotyped by inter-pulse interval (IPI), which is the rate at which the pulses are detected (5). What’s even more interesting is that unlike many other large baleen whale species, there is little evidence of seasonal behavior and vocalization patterns (5) (Figs. 2 & 3). This suggests that fin whales might not make repetitive annual migrations to accommodate foraging and reproduction. Are these animals prey driven with exemplary senses for finding prey over incredibly large distances in the ocean? Are fin whales consistently present off the Oregon coast? What are their names? Bob, Lucinda, Frederick? There is much to ponder here.

Figure 2. Fin whale 20 Hz calls patterns off the coast of Hawaii, showing a unique A and B call rendition with an IPI of ~ “`25 seconds (6).
Figure 3. Fin whale 20 Hz calls identified in the Northeastern Pacific with varying observable patterns and IPI between the years 2003 – 2013 (7).

This past summer the Holistic Assessment of Living marine resources off Oregon (HALO) team recovered its first six months of continuously collected acoustic data from three hydrophones moored at designated source locations off the Newport coast. Around the same time, I transplanted my sidekick and myself in Ithaca, New York for the summer, so I could spend my summer days learning to identify and log baleen whale calls among other acousticians at the K. Lisa Yang Center for Conservation Bioacoustics at Cornell University. This work would contribute to my preparation for the analysis of the HALO acoustic data.

I was less than a month into this work when my sidekick ended up spending an entire week with us in the lab because the counselors at her summer camp all caught COVID-19. My sidekick is a dedicated book worm and had no problem keeping herself busy while we all worked, however, she is young and vivacious and so she would often share her music and jokes with the group. I recall (with an uncontrollable smirk on my face) one of her songs called the “Oof” song (Video 1), that is literally a repetitive beat with the onomatopoeia, “oof” being played over and over again. When it started playing I looked up from my computer to see a row of researchers sitting next to Mavis all bobbing their heads to the repetitive tone of “oof”, a tone that hilariously reminded us of a sped-up version of the repetitive pulse of fin whale song. From that point on, “oof” has involuntarily become a part of our language among this group of acousticians.

Video 1. The “oof song”, that was played by Mavis in the lab this past summer. The tones resemble a sped-up version of fin whale song.

The summer blazed by, Fall is here, and my sidekick and I are back in Oregon. I am in the process of organizing our collected HALO data to accommodate analysis of baleen whales, including fin whales. At this point I am already able to see fin whale calls in our data (Fig. 4). Subsequently, I will spend the next few months analyzing these data to determine the patterns of fin whale calls over time at our three observation sites (on the shelf, the shelf edge, and off the shelf). Within this analysis I will also look to define the vocal repertoire of fin whales over our six-month study period, which will allow me to report on the frequency where they are primarily detected and the IPI with which the pulses occur.

Figure 4. Spectrograms of fin whale calls in the October 2021 – June 2022 HALO acoustic dataset.

Moving forward, the HALO team will continuously retrieve and replace the three hydrophones to collect our acoustic data, returning a rich long-term dataset of the study area. I am eager to learn whether the fin whale IPI will remain the same in this location or show changes according to shifts in upwelling or seasonally, assuming they remain in the Northern California Current and do not migrate away. I will continue to assess the acoustic patterns of fin whales over the next year to describe their distribution patterns. All the while with the “oof” song stuck in my head and with my vivacious book worm head banging in the background.

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References

(1) Fin Whale. NOAA Fisheries. https://www.fisheries.noaa.gov/species/fin-whale.

(2) Aguilar, A. & Garcia-Vernet, R. 2018. Encyclopedia of Marine Mammals, Third Edition: Fin Whale, Balaenoptera physalus, Pg 369-371. Academic Press, ISBN 978-0-12-804327-1.

(3) Shadwick, R. et al. 2019. Lunge feeding in rorqual whales. Physiology, 34: 409-418. https://journals.physiology.org/doi/epdf/10.1152/physiol.00010.2019.  

(4) Oleson, E. et al. 2014. Synchronous seasonal change in fin whale song in the North Pacific. Plos ONE, 9 (12). https://doi.org/10.1371/journal.pone.0115678.

(5) Morano, J. et al. 2012. Seasonal and geographical patterns of fin whale song in the western North Atlantic Ocean. The Journal of the Acoustical Society of America, 132 (1207): 1207-1212. https://doi.org/10.1121/1.4730890.

(6) Helble, T. et al. 2020. Fin whale song patterns shift over time in the central North Pacific. Frontiers of Marine Science, 2 (Marine Megafauna). https://doi.org/10.3389/fmars.2020.587110.  

(7) Weirathmueller, M. et al. 2017. Spatial and temporal trends in fin whale vocalizations recorded in the NE Pacific Ocean between 2003-2013. Plos ONE, 12 (10): e0186127. https://doi.org/10.1371/journal.pone.0186127.

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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Surprises at Sea

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

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

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

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

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

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

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

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

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

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

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

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

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

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Bombs Away! A Summer of Bomb Calorimetry

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

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

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

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

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

Female Thysanoessa spinifera krill (photo by Abby Tomita).

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

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

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

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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

Do you lose SLEAP over video analysis of gray whale behavior? Not us in the GEMM Lab! 

Celest Sorrentino, University of California, Santa Barbara, Department of Ecological, Evolution, and Marine Biology, GEMM Lab NSF REU intern

Are you thinking “Did anyone proofread this blog beforehand? Don’t they know how to spell SLEEP?”  I completely understand this concern, but not to fear: the spelling of SLEAP is intentional! We’ll address that clickbait in just a moment. 

My name is Celest Sorrentino, a first-generation Latina undergrad who leaped at the opportunity to depart from the beaches of Santa Barbara, California to misty Newport, Oregon to learn and grow as a scientist under the influential guidance of Clara Bird, Dr. Leigh Torres and the powerhouse otherwise known as the GEMM lab. As a recent NSF REU (Research Experience for Undergraduates) intern in the GEMM Lab at Oregon State University, I am thrilled to have the chance to finally let you in on the project Clara, Leigh and I have been working on all summer. Ready for this?

Our project uses a deep-learning platform called SLEAP A.I. ( https://sleap.ai/) that can predict and track multiple animals in video to track gray whale mother calf pairs in drone footage. We also took this project a step further and explored how the distance between a gray whale mother and her calf, a proxy for calf independence, varied throughout the season and by different calf characteristics. 

In this story, we’ve got a little bit for everyone: the dynamic duo of computer vision and machine learning for my data scientist friends, and ecological inquest for my cetacean researcher friends. 

About the Author

Before we begin, I’d like to share that I am not a data scientist. I’ve only ever taken one coding class. I also do not have years of gray whale expertise under my belt (not yet at least!). I’m entering my 5th year at University of California, Santa Barbara as a double major in Ecology and Evolution (BS) as well as Italian Studies (BA). I am sharing this information to convey the feasibility of learning how to use machine-learning as a solution to streamline the laborious task of video analysis, which would permit more time towards answering your own ecological question, as we did here.

Essential Background

Hundreds of Hours of Drone footage

Since 2016, the GEMM Lab has been collecting drone footage of gray whales off the Oregon Coast to observe gray whale behavior in more detail (Torres et al. 2018). Drones have been shown to increase observational time of gray whales by a three-fold (Torres et al. 2018), including the opportunity to revisit the video with fresh eyes at any time one pleases. The GEMM Lab has flow over 500 flights in the past 6 years, including limited footage of gray whale mother-calf pairs. Little is known about gray whale mother-calf dynamics and even less about factors that influence calf development. As we cannot interview hundreds of gray-whale mother-calf pairs to develop a baseline for this information, we explore potential proxies for calf development instead (similar to how developmental benchmarks are used for human growth). 

Distance and Development

During our own life journey, each of us became less and less dependent on our parents to survive on our own. Formulating our first words so that we can talk for ourselves, cracking an egg for our parents so that we can one day cook for ourselves, or even letting go of their hand when crossing the street. For humans, we spend many years with our kin preparing for these moments, but gray whale mother-calf pairs only have a few months after birth until they separate. Gray whale calves are born on their wintering grounds in Baja California, Mexico (around February), migrate north with their mothers to the foraging grounds, and are then weaned during the foraging season (we think around August). This short time with their mother means that they have to become independent pretty quickly (about 6 months!).

Distance between mother and calf can be considered a measure of independence because we would expect increased distance between the pair as calf independence increases. In a study by Nielson et al (2019), distance between Southern Right Whale mother-calf pairs was found to increase as the calf grew, indicating that it can serve as a good proxy for independence. The moment a mother-calf pair separates has not been documented, but the GEMM lab has footage of calves during the foraging season pre-weaning that can be used to investigate this process.  However, video analysis is no easy feat: video analysis can range from post-processing, diligent evaluation, and video documentation (Torres et al. 2018). Although the use of UAS has become a popular method for many researchers, the extensive time required for video analysis is a limitation. As mentioned in Clara’s blog, the choice to pursue different avenues to streamline this process, such as automation through machine learning, is highly dependent on the purpose and the kind of questions a project intends to answer.

SLEAP A.I.

 In a world where modern technology is constantly evolving to cater towards making everyday tasks easier, machine learning leads the charge with its ability for a machine to perform human tasks. Deep learning is a subset of machine learning in which the model learns and adapts the ability to perform a task given a dataset. SLEAP (Social LEAP Estimation of Animal Poses) A.I. is an open-source deep-learning framework created to be able to track multiple subjects, specifically animals, throughout a variety of environmental conditions and social dynamics. In previous cases, SLEAP has tracked animals with distinct morphologies and conditions such as mice interactions, fruit flies engaging in courtship, and bee behavior in a petri dish (Pereira 2020). While these studies show that SLEAP could help make video analysis more efficient, these experiments were all conducted on small animals and in controlled environments. However, large megafauna, such as gray whales, cannot be cultivated and observed in a controlled Petri dish. Could SLEAP learn and adapt to predict and track gray whales in an uncontrolled environment, where conditions are never the same (ocean visibility, sunlight, obstructions)? 

Methods

In order to establish a model within SLEAP, we split our mother-calf drone video dataset into training (n=9) and unseen/testing (n=3) videos. Training involves teaching the model to recognize gray whales, and necessitated me to label every four frames using the following labels (anatomical features): rostrum, blowhole, dorsal, dorsal-knuckle, and tail (Fig. 1). Once SLEAP was trained and able to successfully detect gray whales, we ran the model on unseen video. The purpose of using unseen video was to evaluate whether the model could adapt and perform on video it had never seen before, eliminating the need for a labeler to retrain it. 

We then extracted the pixel coordinates for the mom and calf, calculated the distance between their respective dorsal knuckles, and converted the distance to meters using photogrammetry (see KC’s blog  for a great explanation of these methods).  The distance between each pair was then summarized on a daily scale as the average distance and the standard deviation. Standard deviation was explored to understand how variable the distance between mother-calf pair was throughout the day. We then looked at how distance and the standard deviation of distance varied by day of year, calf Total Length (TL), and calf Body Area Index (BAI; a measure of body condition). We hypothesized that these three metrics may be drivers of calf independence (i.e., as the calf gets longer or fatter it becomes more independent from its mother).  

Fig 1. Example of a labelled frame from SLEAP, highlighting labels: rostrum, blowhole, dorsal, dorsal-knuckle, and tail. 

Results

SLEAP A.I. was able to successfully detect and track gray whale mother-calf pairs across all videos (that’s a total of 1318 frames!). When evaluating how the average distance changed across Day of Year, calf Total length, and calf BAI, the plots did not demonstrate the positive relationship we anticipated (Fig 2A). However, when evaluating the standard deviation of distance across Day of Year, calf Total Length, and calf BAI, we did notice that there does appear to be an increase in variability of distance with an increase in Day of Year and calf Total length (Fig 2B)

Fig 2A: Relationship between average distance(m) between each mother and calf (colors represent different named calves) relative to Day of Year, Total length of calf, and BAI of calf. 
Fig 2B: Relationship between standard deviation of  distance(m) between each mother and calf (colors represent different named calves) relative to Day of Year, Total length of calf, and BAI of calf.

Concluding thoughts

These results are monumental! We demonstrated the feasibility to use AI to create a model that can track gray whale pairs in drone footage, which is a fantastic tool that can be applied to updated datasets in the future. As more footage of gray whale mother-calf pairs are collected, this video can be quickly uploaded to SLEAP for model evaluation, predictions can be exported, and results subsequently included in the distance analysis to update our plots and increase our understanding. Our data currently provide a preliminary understanding of how the distance between mother-calf pairs changes with Day of Year, Total length, and BAI, but we are now able to continue updating our dataset as we collect more drone footage. 

I suppose you can say I did mislead you a bit with my title, as I have lost some SLEEP recently. But, not over video analysis per say but rather in the form of inspiration. Inspiration toward expanding my understanding of machine learning so that it can be applied toward answering pressing ecological questions. This project has only propelled me to dig my heels in and investigate further the potential of machine learning to analyze dense datasets for huge knowledge gains.

Fig 3A: Snapshot of Celest working in SLEAP GUI.

Acknowledgements

This project was made possible in partnership by the continuous support by Clara Bird, Dr. Leigh Torres, KC Bierlich, and the entire GEMM Lab!

References

Nielsen, M., Sprogis, K., Bejder, L., Madsen, P., & Christiansen, F. (2019). Behavioural development in southern right whale calves. Marine Ecology Progress Series, 629, 219–234. https://doi.org/10.3354/meps13125

Pereira, Talmo D., Nathaniel Tabris, Junyu Li, Shruthi Ravindranath, Eleni S. Papadoyannis, Z. Yan Wang, David M. Turner, et al. “SLEAP: Multi-Animal Pose Tracking.” Preprint. Animal Behavior and Cognition, September 2, 2020. https://doi.org/10.1101/2020.08.31.276246.

Torres, 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. https://doi.org/10.3389/fmars.2018.00319

Port Orford Gray Whale Foraging Ecology Project 2022 Field Season Wrap-Up

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

The 8th year of Port Orford Gray Whale Foraging Ecology Project (TOPAZ/JASPER) has come to an end and it feels truly bittersweet. Last Friday, the team hosted our annual community presentation to close out the project and I was filled with pride to see them confidently convey all they learned over this summer to an audience of family, friends, and community members.

Figure 1: Team B.W.E poses for the annual team photo after the community presentation alongside Tom Calvanese (field station manager) and Lisa Hildebrand (previous project lead). 

I am amazed by all that you can accomplish in one summer, especially with an enthusiastic and adaptable team. I’ve compiled a “by the numbers” table (Fig. 2) that summarizes our hard work this season. 

Figure 2: Port Orford Gray Whale Forage Ecology (GWFE) field season 2022 by the numbers.

Every Spring, the GEMM lab works diligently to hire a solid team of students for this project, which just finished its 8th consecutive year. These students are initially total strangers who come together to live and work at the Port Orford field station on a project that is as physically and mentally tasking as it is rewarding. Although attention to all the daily details is critical, without a genuine desire to form strong connections and learn from each other – the real “glue” for teamwork – this project would not be as successful as it has been. Like the teams before them, team Big Whale Energy (B.W.E.) started off with little to no gray whale knowledge, sea kayaking experience, zooplankton ID, theodolite operation, or other skills that this project demands. The learning curve required of these students in such a short time is steep, but each year these bright, young scientists prove that with patience, determination, and a positive mindset you can gain not only valuable skills but lifelong connections. 

I also experienced a learning curve as this was my first year leading the project solo. While Leigh and Lisa trained me well last year, and were always a phone call away, there are certain skills that can only truly be honed with experience, many of which must be learned through the inevitable curve balls each new field season brings. During the six week project, Team B.W.E. grew as individuals and as a team as we encountered every challenge with a positive mindset and creative adaptation – from learning new knots to secure our downrigger line, to creating new songs while patiently watching for whales. I know I speak for all of us when I say we are so grateful for everything this 2022 field season experience has taught us about both the process of scientific research and ourselves.

During our community presentation, Leigh wonderfully conveyed how informative and exciting long term data sets can be, especially because 8 years is long enough for us to begin to observe cycles. We have been able to observe cycles in both the ecological changes in Port Orford and in the succession of students who have taken part in the project. Last year, the ecological habitat suitability seemed to have reached a new low, while this year we have seen more kelp and an uptick of whale activity as compared to 2021. We are hopeful this change is indicative of an ecosystem recovery. The cycle of returning project leads and previous interns (both virtual and in person) allows for a meaningful interchange of wisdom, memories, and excitement for the future of this project.

Figure 3: Mosaic of memories for Team B.W.E.

Thank you Team B.W.E. for helping me grow as a leader, contributing to the GEMM lab legacy, and making the 8th year of this project a great success. 

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