Author: Clara Bird

Reflecting on a solitary journey surrounded by an incredible team

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

Graduate school is an odd phase of life, at least in my experience. You spend years hyperfocused on a project, learning countless new skills – and the journey is completely unique to you. Unlike high school or undergrad, you are on your own timeline. While you may have peers on similar timelines, at the end of day your major deadlines and milestone dates are your own. This has struck me throughout my time in grad school, and I’ve been thinking about it a lot lately as I approach my biggest, and final milestone – defending my PhD! 

I defend in just about two months, and to be honest, it’s very odd approaching a milestone like this alone. In high school and college, you count down to the end together. The feelings of anticipation, stress, excitement, and anticipatory grief that can accompany the lead-up to graduation are typically shared. This time, as I’m in an intense final push to the end while processing these emotions, most of the people around me are on their own unique timeline. At times grad school can feel quite lonely, but this journey would have been impossible without an incredible community of people.

A central contradiction of being a grad student is that your research is your own, but you need a variety of communities to successfully complete it. Your community of formal advisors, including your advisor and committee members, guide you along the way and provide feedback. Professors help you fill specific knowledge and skill gaps, while lab mates provide invaluable peer mentorship. Finally, fellow grad students share the experience and can celebrate and commiserate with you. I’ve also had the incredible fortune of having the community of the GRANITE team, and I’ve recently been reflecting on how special the experience has been.

To briefly recap, GRANITE stands for Gray whale Response to Ambient Noise Informed by Technology and Ecology (read this blog to learn more). This project is one of the GEMM lab’s long-running gray whale projects focused on studying gray whale behavior, physiology, and health to understand how whales respond to ocean noise. Given the many questions under this project, it takes a team of researchers to accomplish our goals. I have learned so much from being on the team. While we spend most of the year working on our own components, we have annual meetings that are always a highlight of the year. Our team is made up of ecologists, physiologists, and statisticians with backgrounds across a range of taxa and methodologies. These meetings are an incredible time to watch, and participate in, scientific collaboration in action. I have learned so much from watching experts critically think about questions and draw inspiration from their knowledge bases. It’s been a multi-year masterclass and a critically important piece of my PhD. 

The GRANITE team during our first in person meeting

These annual meetings have also served as markers of the passage of time. It’s been fascinating to observe how our discussions, questions, and ideas have evolved as the project progressed. In the early years, our presentations shared proposed research and our conversations focused on working out how on earth we were going to tackle the big questions we were posing. In parallel, it was so helpful to work out how I was going to accomplish my proposed PhD questions as part of this larger group effort. During the middle years, it was fun to hear progress updates and to learn from watching others go through their process too. In grad school, it’s easy to feel like your setbacks and stumbles are failures that reflect your own incompetence, but working alongside and learning from these scientists has helped remind me that setbacks and stumbles are just part of the process. Now, in the final phase, as results abound, it feels extra exciting to celebrate with this team that has watched the work, and me grow, from the beginning. 

The GRANITE team taking a beach walk after our second in person meeting.

We just wrapped up our last team meeting of the GRANITE project, and this year provided a learning experience in a phase of science that isn’t often emphasized in grad school. For graduate students, our work tends to end when we graduate. While we certainly think about follow-up questions to our studies, we rarely get the opportunity to follow through. In our final exams, we are often asked to think of next steps outside the constraints of funding or practicality, as a critical thinking exercise. But it’s a different skillset to dream up follow-up questions, and to then assess which of those questions are feasible and could come together to form a proposal. This last meeting felt like a cool full-story moment. From our earliest meetings determining how to answer our new questions, to now deciding what the next new questions are, I have learned countless lessons from watching this team operate. 

The GRANITE team after our third in person meeting.

There are a few overarching lessons I’ll take with me. First and foremost, the value of patience and kindness. As a young scientist stumbling up the learning curve of many skills all at once, I am so grateful for the patience and kindness I’ve been shown. Second, to keep an open mind and to draw inspiration from anything and everything. Studying whales is hard, and we often need to take ideas from studies on other animals. Which brings me to my third takeaway, to collaborate with scientists from a wide range of backgrounds who can combine their knowledges bases with yours, to generate better research questions and approaches to answering them.

I am so grateful to have worked with this team during my final sprint to the finish. Despite the pressure of the end nearing, I’m enjoying moments to reflect and be grateful. I am grateful for my teachers and peers and friends. And I can’t wait to share this project with everyone.

P.S. Interested in tuning into my defense seminar? Keep an eye on the GEMM lab Instagram (@gemm_lab) for the details and zoom link.

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Every breath [a whale] takes: How and why we study cetacean respiration

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

We need energy to function and survive. For animals in the wild who may have limited food availability, knowing how they spend their energy is a critical question for many scientists because it fundamentally informs how we understand their decisions about where they go and what they do. The entire field of foraging theory is founded on the concept that animals optimize their ratio of energy in and energy out so that they have enough energy to survive, reproduce (pass on their genes), watch out for threats, if need be, and rest. And, if we understand an animal’s ‘typical’ energy budget, we can then try to predict how disturbance or environmental change will affect their actual energy budgets as a consequence of that change. But how do we measure energy expenditure?

The most commonly measured energy currency is oxygen. Since our cells use oxygen to produce energy (this is why we need oxygen to live), we can measure oxygen consumption as a metric of energy expenditure. The more oxygen we consume, the more energy we’re expending. In ideal lab settings, oxygen consumption can be accurately measured by placing the subject in a chamber where the oxygen flow can be controlled (Speakman, 1999). However, you can probably see how that approach is problematic for measuring oxygen consumption in most large free-living animals, especially cetaceans. It isn’t exactly feasible to put a whale in a box.

Image 1. A great tit in a metabolic chamber. Figure 1 from Broggi et al., 2009

Fortunately, a tool called a spirometer was developed to measure oxygen consumption in restrained cetaceans. A spirometer is a device that can be placed over the blowhole(s) of an individual to accurately measure the amount of air that is exhaled and inhaled (Figure 1).  For trained cetaceans in captivity (e.g., dolphins), spirometers can be used to quantify how respiration changes after the animal performs certain behaviors (Fahlman et al., 2019). The breathing patterns of diving mammals are particularly interesting because they cannot breathe during most of their exercise (energy expenditure) as they are underwater. So, their breathing patterns after a dive tell us a lot about how much energy they spent during that dive. For example, Fahlman et al. (2019) used spirometer data from dolphins in captivity to study how their breathing patterns changed while recovering from dives of different durations. Interestingly, they found that after longer dives, dolphins took larger breaths (i.e., inhaled more air) while recovering but did not change the number of breaths. This finding is particularly relevant to the work we are conducting in the GEMM lab, where we utilize breathing patterns to quantify the energy expenditure of cetaceans in the wild, where spirometers cannot be used.

Figure 1. Figure 1 from Sumich et al. (2023). Left: a spirometer being held over the blow holes of JJ, a gray whale calf at sea world in 1997; one of the rare times that a large baleen whale was in captivity and available for these measurements. Right: example of a plot created using the data from a spirometer over JJ’s blow holes. The duration of a “blow” (exhale followed by immediate inhale) is on the x-axis, the flow rate (in liters per second) is on the y-axis. The positive curve during the exhale shows that the whale strongly exhales a lot of air very quickly, then the negative curve shows the whale inhaling a lot of air very quickly.

In a previous blog, I described how inter-breath intervals (the time between consecutive blows) are useful for estimating energy expenditure in free-living cetaceans. Essentially, a shorter interval indicates that the whale was just engaged in an energetically demanding activity. When you’re recovering from a sprint, you breathe faster (i.e., with shorter inter-breath intervals), than when you’re recovering from a walk. However, a big assumption in using inter-breath intervals as a proxy for energy expenditure is that every breath is equal. But as Fahlman et al. emphasize in their 2016 paper, every blow is not equal (Fahlman et al., 2016). In addition to varying the time between breaths, an animal can vary the intensity of each breath (e.g., Fahlman et al., 2019), the duration of each breath (Sumich et al., 2023), the number of breaths, and even the expansion of their nostrils (Nazario et al., 2022; check out this blog for more).

Image 2. Gray whale blow. Source: https://www.lajollalight.com/sdljl-natural-la-jolla-winter-wildlife-2015jan08-story.html

Altogether, this means that it’s important to measure every breath and that no one metric tells the complete story. This also means my research question focused on comparing the energetic costs of different tactics is more complicated than I originally thought. If we go back to the first blog I wrote on this topic, I was planning ons only using inter-breath intervals to estimate energy expenditure. Fast forward four years, with all my new knowledge gained on respiration variability, I’ve modified my plan and now I’m working to first understand how all these different metrics of breathing relate to each other. Then, I’ll compare how breathing varies between different foraging tactics, which is an important follow up to my questions around individual specialization of foraging tactics. If different whales are using different foraging behaviors, does that mean they’re spending different amounts of energy? If so, are certain behaviors more advantageous than others? Of course, these answers are incomplete without understanding the prey the whales are eating, but that’s something that PhD student Nat Chazal is working to understand (check out her recent blog here).  For now, I’m working on bringing integrating all the measures of breathing, then we will start putting the story together and finding some answers to our pressing questions. 

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References

Broggi, J., Hohtola, E., Koivula, K., Orell, M., & Nilsson, J. (2009). Long‐term repeatability of winter basal metabolic rate and mass in a wild passerine. Functional Ecology, 23(4), 768–773. https://doi.org/10.1111/j.1365-2435.2009.01561.x

Fahlman, A., Brodsky, M., Miedler, S., Dennison, S., Ivančić, M., Levine, G., Rocho-Levine, J., Manley, M., Rocabert, J., & Borque-Espinosa, A. (2019). Ventilation and gas exchange before and after voluntary static surface breath-holds in clinically healthy bottlenose dolphins, Tursiops truncatus. Journal of Experimental Biology, 222(5), jeb192211. https://doi.org/10.1242/jeb.192211

Fahlman, A., van der Hoop, J., Moore, M. J., Levine, G., Rocho-Levine, J., & Brodsky, M. (2016). Estimating energetics in cetaceans from respiratory frequency: Why we need to understand physiology. Biology Open,5(4), 436–442. https://doi.org/10.1242/bio.017251

Nazario, E. C., Cade, D. E., Bierlich, K. C., Czapanskiy, M. F., Goldbogen, J. A., Kahane-Rapport, S. R., Hoop, J. M. van der, Luis, M. T. S., & Friedlaender, A. S. (2022). Baleen whale inhalation variability revealed using animal-borne video tags. PeerJ, 10, e13724. https://doi.org/10.7717/peerj.13724

Speakman, J. R. (1999). The Cost of Living: Field Metabolic Rates of Small Mammals. In A. H. Fitter & D. G. Raffaelli (Eds.), Advances in Ecological Research (Vol. 30, pp. 177–297). Academic Press. https://doi.org/10.1016/S0065-2504(08)60019-7

Sumich, J. L., Albertson, R., Torres, L. G., Bird, C. N., Bierlich, K. C., & Harris, C. (2023). Using audio and UAS-based video for estimating tidal lung volumes of resting and active adult gray whales (Eschrichtius robustus). Marine Mammal Science, 1(8). https://doi.org/10.1111/mms.13081

Wandering whales: what are Pacific gray whales doing in Atlantic?

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

Happy 2024 everyone! The holiday season usually involves a lot of travelling to visit friends and family, but we’re not the only ones. While most gray whales migrate long distances to their wintering grounds in the Pacific Ocean along the Baja Mexico peninsula, a few whales have made even longer journeys. In the past 13 years, there have been four reported observations of gray whales in the Atlantic and Mediterranean. Most recently, a gray whale was seen off south Florida in December 2023. While these reports always inspire some awe for the ability of a whale to travel such an incredible distance, they also inspire questions as to why and how these whales end up so far from home.

While there used to be a population of gray whales in the Atlantic, it was eradicated by whaling in the mid-nineteenth century (Alter et al., 2015), which made the first observation of a gray whale in the Mediterranean in 2010 especially incredible. This whale was first observed in May off the coast of Israel and then Spain (Scheinin et al., 2011). It was estimated to be about 13 m long (a rough visual estimate made through comparison with a boat) and in poor, but not critical, body condition. Scheinin et al. (2011) proposed that the whale likely crossed from the Bering Sea to the North Atlantic and followed the coasts of either North America or Eurasia (Figure 1).

Figure 1. Figure from Schenin et al. (2011) showing the possible routes the 2010 whale took to reach the Mediterranean and the path it took within.

A few years later, another gray whale was spotted in the Southern Atlantic, in Namibia’s Walvis Bay in May 2013. The observation report from the Namibian Dolphin Project proposes that the whale could have crossed through the Arctic or swum around the southern tip of South America (Peterson 2013).  While they did not estimate the size or condition of whale, the photos in the report indicate that the whale was not in good condition (Figure 2).

Figure 2. Images of the gray whale seen in Namibia. Source: http://namibiandolphinproject.blogspot.com/2013/05/a-rare-and-mysterious-visitor-in-walvis.html

The most covered sighting was in 2021, when a gray whale was repeatedly seen in Mediterranean in May of 2021. This whale was estimated to be about two years old and skinny. Furthermore, it’s body condition continued to decline with each sighting (“Lost in the Mediterranean, a Starving Grey Whale Must Find His Way Home Soon,” 2021). The whale was first spotted off the coast of Morocco, then it appears to have crossed the Mediterranean to the coast of Italy and then traveled to the coast of France. Like the 2010 sighting, it is hypothesized that this whale crossed through the Arctic and then crossed the North Atlantic to the enter the Mediterranean through the Gibraltar Strait.

Image of the 2021 whale in the Mediterranean. Source: REUTERS/Alexandre Minguez, https://www.reuters.com/business/environment/lost-mediterranean-starving-grey-whale-must-find-his-way-home-soon-2021-05-07/

Most recently, a gray whale was seen off the coast of Miami in December 2023 (Rodriguez, 2023). While there is no information on its estimated size or condition, it does not appear to be in critical condition from the video (Video 1). This sighting is interesting because it breaks from the pattern that was forming with all the previous sightings occurring in late spring on the western side of the Atlantic. This recent gray whale was seen in winter on the eastern side of the Atlantic. The May timing suggests that those whales crossed into the Atlantic during the spring migration when leaving the wintering grounds and heading to summer foraging grounds. However, this December sighting indicates that this whale ‘got lost’ on its way to the wintering grounds after a foraging season. Another interesting pattern is the body condition, while condition was not always reported, the spring whales all seemed to be in poor condition, likely due to the long journey and/or the lack of suitable food. The Miami whale is the only one that appeared to be in decent condition, but this arrived just after the foraging season and travelled a shorter distance. Finally, it’s also interesting that there is no clear pattern of age, these sightings are of a mixture of adult (2010), juvenile (2021), and unknown (2013, 2023) age classes.

Video 1: NBC6 news report on the sighting

Another common theme across these sightings, is the proposed passage of the whale across the Arctic. Prior to dramatic declines in ice cover in the Arctic due to climate change which made this  an unfeasible route, reduced ice cover in the Arctic over the past couple of decades means that this is now possible (Alter et al., 2015). While these recent sightings could be random, they could also indicate that Pacific gray whales may be exploring the Atlantic more, prey availability in the arctic has been declining (Stewart et al., 2023) in recent years meaning that gray whales may be exploring new areas to find alternative food sources. Interestingly, a study by Alter et al. (2015) used genetic analysis to compare the DNA from Atlantic gray whale fossils and Pacific gray whale samples and found evidence that gray whales have moved between the Atlantic and Pacific several times in the last 1000 years when sea level and climate conditions (including ice cover) allowed them to. Meaning, that we could be seeing a pattern of mixing of whale populations between the two oceans repeating itself.

The possibility that we are observing the very early stages of a new population or group forming is particularly interesting to me in the context of how we think about the Pacific Coast Feeding Group (PCFG) of gray whales. If you’ve read our previous blogs, you know that the GEMM lab spends a lot of time studying this sub-group of the Eastern North Pacific (ENP) population. The PCFG feeds along the coast of the Pacific Northwest, which is different from the typical foraging habitat of the ENP in the Bering Sea. We in the GEMM lab often wonder how this subgroup formed (listen to postdoc KC Bierlich’s recent podcast here to learn more). Did it start like these recent observations? With a few whales leaving the typical feeding grounds in the Arctic in search for alternative prey sources and ending up in the Pacific Northwest? Did those whales also struggle to successfully feed at first but then develop new strategies to target new prey items? While whales may be making it through the Arctic now, there is no evidence that these whales have successfully found enough food to thrive. So, these sightings could be random or failed attempts at finding better foraging areas. Afterall, there have only been four reported gray whale sightings in the Atlantic in 13 years. But these are only the observed sightings, and maybe it’s only a matter of time and multiple tries before enough gray whales find each other and an alternative foraging ground in the Atlantic so that a new population is established. Nonetheless, it’s exciting and fun to think about the parallels between these sightings and the PCFG. As we start our ninth year of PCFG research, we hope to continue learning about the origins of this unique and special group. Stay tuned!

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References

Alter, S. E., Meyer, M., Post, K., Czechowski, P., Gravlund, P., Gaines, C., Rosenbaum, H. C., Kaschner, K., Turvey, S. T., van der Plicht, J., Shapiro, B., & Hofreiter, M. (2015). Climate impacts on transocean dispersal and habitat in gray whales from the Pleistocene to 2100. Molecular Ecology, 24(7), 1510–1522. https://doi.org/10.1111/mec.13121

Lost in the Mediterranean, a starving grey whale must find his way home soon. (2021, May 7). Reuters. https://www.reuters.com/business/environment/lost-mediterranean-starving-grey-whale-must-find-his-way-home-soon-2021-05-07/

Rodriguez, G. (2023, December 19). Extremely rare and ‘special’ whale sighting near South Florida coast. NBC 6 South Florida. https://www.nbcmiami.com/news/local/extremely-rare-and-special-whale-sighting-near-south-florida-coast/3187746/

Scheinin, A. P., Kerem, D., MacLeod, C. D., Gazo, M., Chicote, C. A., & Castellote, M. (2011). Gray whale ( Eschrichtius robustus) in the Mediterranean Sea: Anomalous event or early sign of climate-driven distribution change? Marine Biodiversity Records, 4, e28. https://doi.org/10.1017/S1755267211000042

Stewart, J. D., Joyce, T. W., Durban, J. W., Calambokidis, J., Fauquier, D., Fearnbach, H., Grebmeier, J. M., Lynn, M., Manizza, M., Perryman, W. L., Tinker, M. T., & Weller, D. W. (2023). Boom-bust cycles in gray whales associated with dynamic and changing Arctic conditions. Science, 382(6667), 207–211. https://doi.org/10.1126/science.adi1847

Migrating back east

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

With the changing of the season, gray whales are starting their southbound migration that will end in the lagoons off the Baja California Mexico. The migration of the gray whale is the longest migration of any mammal—the round trip totals ~10,000 miles (Pike, 1962)! 

Map of the migration route taken by gray whales along the west coast of North America. (Image credit: Angle, Asplund, and Ostrander, 2017 https://www.slocoe.org/resources/parent-and-public-resources/what-is-a-california-gray-whale/california-gray-whale-migration/)

Like these gray whales, I am also undertaking my own “migration” as I leave Newport to start my post-Master’s journey. However, my migration will be a little shorter than the gray whale’s journey—only ~3,000 miles—as I head back to the east coast. As I talked about in my previous blog, I have finished my thesis studying the energetics of gray whale foraging behaviors and I attended my commencement ceremony at the University of British Columbia last Wednesday. As my time with the GEMM Lab comes to a close, I want to take some time to reflect on my time in Newport. 

Me in my graduation regalia (right) and my co-supervisor Andrew Trites holding the university mace (left) after my commencement ceremony at the University of British Columbia rose garden. 

Many depictions of scientists show them working in isolation but in my time with the GEMM Lab I got to fully experience the collaborative nature of science. My thesis was a part of the GEMM Lab’s Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project and I worked closely with the GRANITE team to help achieve the project’s research goals. The GRANITE team has annual meetings where team members give updates on their contributions to the project and flush out ideas in a series of very busy days. I found these collaborative meetings very helpful to ensure that I was keeping the big picture of the gray whale study system in mind while working with the energetics data I explored for my thesis. The collaborative nature of the GRANITE project provided the opportunity to learn from people that have a different skill set from my own and expose me to many different types of analysis. 

GRANITE team members hard at work thinking about gray whales and their physiological response to noise. 

This summer I also was able to participate in outreach with the partnership of the Oregon State University Marine Mammal Institute and the Eugene Exploding Whales (the alternate identity of the Eugene Emeralds) minor league baseball team to promote the Oregon Gray Whale License plates. It was exciting to talk to baseball fans about marine mammals and be able to demonstrate that the Gray Whale License plate sales are truly making a difference for the gray whales off the Oregon coast. In fact, the minimally invasive suction cup tags used in to collect the data I analyzed in my thesis were funded by the OSU Gray Whale License plate fund!

Photo of the GEMM Lab promoting Oregon Gray Whale License plates at the Eugene Exploding Whales baseball game. If you haven’t already, be sure to “Put a whale on your tail!” to help support marine mammal research off the Oregon Coast. 

Outside of the amazing science opportunities, I have thoroughly enjoyed the privilege of exploring Newport and the Oregon coast. I was lucky enough to find lots of agates and enjoyed consistently spotting gray whale blows on my many beach walks. I experienced so many breathtaking views from hikes (God’s thumb was my personal favorite). I got to attend an Oregon State Beavers football game where we crushed Stanford! And most of all, I am so thankful for all the friends I’ve made in my time here. These warm memories, and the knowledge that I can always come back, will help make it a little easier to start my migration away from Newport. 

Me and my friends outside of Reser Stadium for the Oregon State Beavers football game vs Stanford this season. Go Beavs!!!
Me and my friends celebrating after my defense. 

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conservation? Subscribe to our blog and get a weekly message when we post a new
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References

Pike, G. C. (1962). Migration and feeding of the gray whale (Eschrichtius gibbosus). Journal of the Fisheries Research Board of Canada, 19(5), 815–838. https://doi.org/10.1139/f62-051

The whales keep coming and we keep learning: a wrap up of the eighth GRANITE field season.

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

As you may remember, last year’s field season was a remarkable summer for our team. We were pleasantly surprised to find an increased number of whales in our study area compared to previous years and were even more excited that many of them were old friends. As we started this field season, we were all curious to know if this year would be a repeat. And it’s my pleasure to report that this season was even better!

Team selfies on the water

We started the season with an exciting day (6 known whales! see Lisa’s blog) and the excitement (and whales) just kept coming. This season we saw 71 individual whales across 215 sightings! Of those 71, 44 were whales we saw last year, and 10 were new to our catalog, meaning that we saw 17 whales this season that we had not seen in at least two years! There is something extra special about seeing a whale we have not seen in a while because it means that they are still alive, and the sighting gives us valuable data to continue studying health and survival. Another cool note is that 7 of our 12 new whales from last year came back this year, indicating recruitment to our study region.

Included in that group of 7 whales are the two calves from last year! Again, indicating good recruitment of new whales to our study area. We saw both Lunita and Manta (previously nick-named ‘Roly-poly’) throughout this season and we were always happy to see them back in our area and feeding on their own.

Drone image of Lunita from 2023
Drone image of Manta from 2023

We had an especially remarkable encounter with Lunita at the end of this season when we found this whale surface feeding on porcelain crab larvae (video 1)! This is a behavior that we rarely observe, and we’ve never seen a juvenile whale use this behavior before, inspiring questions around how Lunita knew how to perform this behavior.

Not only did we resight our one-year-old friends, but we found two new calves born to well-known mature females (Clouds and Spotlight). We had previously documented Clouds with a calf (Cheetah) in 2016 so it was exciting to see her with a new calf and to meet Cheetah’s sibling! Cheetah has become one of our regulars so we’re curious to see if this new calf joins the regular crew as well. We’re also hoping that Spotlight’s calf will stick around; and we’re optimistic since we observed it feeding alone later in the season.

Collage of new calves from 2023! Left: Clouds and her calf, Center: Spotlight and her calf, Right: Spotlight’s calf independently foraging

Of course, 71 whales means heaps of data! We spent 226 hours on the water, conducted 132 drone flights (a record!), and collected 61 fecal samples! Those 132 flights were over 64 individual whales, with Casper and Pacman tying for “best whale to fly over” with 10 flights each. We collected 61 fecal samples from 26 individual whales with a three-way tie for “best pooper” between Hummingbird, Scarlett, and Zorro with 6 fecal samples each. And we continued to collect valuable prey and habitat data through 80 GoPro drops and 79 zooplankton net tows.

Above: (left) drone image of the team on our boat next to Pacman, (right) KC piloting the drone. Below: the team celebrating good fecal samples!

And if you were about to ask, “but what about tagging?!”, fear not! We continued our suction cup tagging effort with a successful window in July where we were joined by collaborators John Calambokidis from Cascadia Research Collective and Dave Cade from Hopkins Marine Station and deployed four suction-cup tags.

The team during our tagging effort this season.

It’s hard to believe all the work we’ve accomplished in the past five months, and I continue to be honored and proud to be on this incredible team. But as this season has come to a close, I have found myself reflecting on something else. Learning. Over the past several years we have learned so much about not only these whales in our study system but about how to conduct field work. And while learning is continuous, this season in particular has felt like an exciting time for both. In the past year our group has published work showing that we can detect pregnancy in gray whales using fecal samples and drone imagery (Fernandez Ajó et al., 2023), that PCFG gray whales are shorter and smaller than ENP whales (Bierlich et al., 2023), and that gray whales are consuming high levels of microplastics (Torres et al., 2023). We also have several manuscripts in review focused on our behavior work from drones and tags. While this information does not directly affect our field work, it does mean that while we’re observing these whales live, we better understand what we’re observing and we can come up with more specific, in-depth questions based on this foundation of knowledge that we’re building. I have enjoyed seeing our questions evolve each year based on our increasing knowledge and I know that our collaborative, inquisitive chats on the boat will only continue inspiring more exciting research.

On top of our gray whale knowledge, we have also learned so much about field work. When I think back to the early days compared to now, there is a stark difference in our knowledge and our confidence. We do a lot on our little boat! And so many steps that we once relied on written lists to remember to do are now just engrained in our minds and bodies. From loading the boat, to setting up at the dock, to the go pro drops, fecal collections, drone operations, photo taking, and photo ID, our team has become quite the well-oiled machine. We were also given the opportunity to reflect on everything we’ve learned over the past years when it was our turn to train our new team member, Nat! Nat is a new PhD student in the GEMM lab who is joining team GRANITE. Teaching her all the ins and outs of our fieldwork really emphasized how much we ourselves have learned.

Nat’s first days in the field!

On a personal note, this was my third season as a drone pilot, and honestly, I was pleasantly surprised by my experience this season. Since I started piloting, I have experienced pretty intense nerves every time I’ve flown the drone. From stress dreams, to mild nausea, and an elevated heart rate, flying the drone was something that I didn’t necessarily look forward to. Don’t get me wrong – it’s incredibly valuable data and a privilege to watch the whales from a bird’s eye view in real time. But the responsibility of collecting good data, while keeping the drone and my team members safe was something that I felt viscerally. And while I gained confidence with every flight, the nerves were still as present as ever and I was starting to accept that I would never be totally comfortable as a pilot. Until this season, when the nerves finally cleared, and piloting became as innate as all the other field work components. While there are still some stressful moments, the nerves don’t come roaring back. I have finally gone through enough stressful situations to not be fazed by new ones. And while I am fully aware that this is just how learning works, I write this reflection as a reminder to myself and anyone going through the process of learning any new skill to push through that fear. Remember there can be a disconnect between the time when you know how to do something well, or well-enough, and the time when you feel comfortable doing it. I am just as proud of myself for persevering as I am of the team for collecting so much incredible data. And as I look ahead to my next scary challenge (finishing my PhD!), this is a feeling that I am trying to hold on to. 

Stay tuned for updates from team GRANITE!

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References

Bierlich, K. C., Kane, A., Hildebrand, L., Bird, C. N., Fernandez Ajo, A., Stewart, J. D., Hewitt, J., Hildebrand, I., Sumich, J., & Torres, L. G. (2023). Downsized: Gray whales using an alternative foraging ground have smaller morphology. Biology Letters, 19(8), 20230043. https://doi.org/10.1098/rsbl.2023.0043

Fernandez Ajó, A., Pirotta, E., Bierlich, K. C., Hildebrand, L., Bird, C. N., Hunt, K. E., Buck, C. L., New, L., Dillon, D., & Torres, L. G. (2023). Assessment of a non-invasive approach to pregnancy diagnosis in gray whales through drone-based photogrammetry and faecal hormone analysis. Royal Society Open Science, 10(7), 230452. https://doi.org/10.1098/rsos.230452

Torres, L. G., Brander, S. M., Parker, J. I., Bloom, E. M., Norman, R., Van Brocklin, J. E., Lasdin, K. S., & Hildebrand, L. (2023). Zoop to poop: Assessment of microparticle loads in gray whale zooplankton prey and fecal matter reveal high daily consumption rates. Frontiers in Marine Science, 10. https://www.frontiersin.org/articles/10.3389/fmars.2023.1201078

Fantastic beasts and how to measure  them! 

Sagar Karki, Master’s student in the Computer Science Department at Oregon State University 

What beasts? Good question! We are talking about gray whales in this article but honestly we can tweak the system discussed in this blog a little and make it usable for other marine animals too.  

Understanding the morphology, such as body area and length, of wild animals and populations can provide important information on animal  behavior and health (check out postdoc Dr. KC Bierlich’s post on this topic). Since 2015, the GEMM Lab has been flying drones over whales to collect aerial imagery to allow for photogrammetric measurements to gain this important morphological data. This photogrammetry data has shed light on multiple important aspects of gray whale morphology, including the facts that the whales feeding off Oregon are skinnier [1] and shorter [2] than the gray whales that feed in the Arctic region.  But, these surprising conclusions overshadow the immense, time-consuming labor that takes place behind the scenes to move from aerial images to accurate measurements.  

To give you a sense of this laborious process, here is a quick run through of the methods: First the 10 to 15 minute videos must be carefully watched to select the perfect frames of a whale (flat and straight at the surface) for measurement. The selected frames from the drone imagery are then imported into MorphoMetriX, which is a custom software developed for photogrammetry measurement [1]. MorphoMetriX is an interactive application that allows an analyst to manually measure the length by clicking points along the centerline of the whale’s body. Based on this line, the whale is divided into a set of sections perpendicular to the centerline, these are used to then measure widths along the body. The analyst then clicks border points at the edge of the whale’s body to delineate the widths following the whale’s body curve. MorphoMetriX then generates a file containing the lengths and widths of the whale in pixels for each measured image. The length and widths of whales are converted from pixels to metric units using a software called CollatriX [4] and this software also calculates metrics of body condition from the length and width measurements. 

While MorphoMetriX [3] and CollatriX [4] are both excellent platforms to facilitate these photogrammetry measurements, each measurement takes time, a keen eye, and attention to detail. Plus, if you mess up one step, such as an incorrect length or width measurement, you have to start from the first step. This process is a bottleneck in the process of obtaining important morphology data on animals. Can we speed this process up and still obtain reliable data? 

What if we can apply automation using computer vision to extract the frames we need and automatically obtain measurements that are as accurate as humans can obtain? Sounds pretty nice, huh? This is where I come into the picture. I am a Master’s student in the Computer Science Department at OSU, so I lack a solid background in marine science, but bring to the table my skills as a computer programmer. For my master’s project, I have been working in the GEMM Lab for the past year to develop automated methods to obtain accurate photogrammetry measurements of whales.  

We are not the first group to attempt to use computers and AI to speed up and improve the identification and detection of whales and dolphins in imagery. Researchers have used deep learning networks to speed up the time-intensive and precise process of photo-identification of  individual whales and dolphins [5], allowing us to more quickly determine animal location, movements and abundance. Millions of satellite images of the earth’s surface are collected daily and scientists are attempting to utilize these images to  benefit marine life by studying patterns of species occurrence, including detection of gray whales in satellite images using deep learning [6]. There has also been success using computer vision to identify whale species and segment out the body area of the whales  from drone imagery [7]. This process involves extracting segmentation masks of the whale’s body followed by length extraction from the mask. All this previous research shows promise for the application of computer vision and AI to assist with animal research and conservation. As discussed earlier, the automation of image extraction and photogrammetric measurement  from drone videos will help researchers collect vital data more quickly so that decisions that impact  the health of whales can be more responsive and effective.For instance,  photogrammetry data extracted from drone images can diagnose pregnancy of the whales [8], thus automation of this information could speed up our ability to understand population trends. 

Computer vision and natural language processing fields are growing exponentially. There are new foundation models like ChatGPT that can do most of the natural language understanding and processing tasks. Foundational models are also emerging for computer vision tasks, such as “the segment anything model” from Meta. Using these foundation models along with other existing research work in computer vision, we have developed and deployed a system that automates the manual and computational tasks of MorphoMetriX and CollatriX systems.  

This system is currently in its testing and monitoring phase, but we are rapidly moving toward a publication to disseminate all the tools developed, so stay tuned for the research paper that will explain in detail the methodologies followed on data processing, model training and test results. The following images give a sneak peak of results. Each image  illustrates a frame from a drone video that was  identified and extracted through automation, followed by another automation process that identified important points along the whale’s body and curvature.  The user interface of the system aims to make the user experience intuitive and easy to follow. The deployment is carefully designed to run on different hardwares, with easy monitoring and update options using the latest open source frameworks. The user has to do just two things. First, select the videos for analysis. The system then generates potential frames for photogrammetric analysis (you don’t need to watch 15 mins of drone footage!). Second, the user selects the frame of choice for photogrammetric analysis and waits for the system to give you measurements. Simple! Our goal is for these softwares to be a massive time-saver while  still providing vital, accurate body measurements  to the researchers in record time. Furthermore, an advantage of this approach is that researchers can follow the methods in our to-be-soon-published research paper to make  a few adjustments enabling the software to measure other marine species, thus expanding the impact of this work to many other life forms.  

A sneak peek of the results. Each image  illustrates a frame from a drone video that was  identified and extracted through automation, followed by another automation process that identified important points along the whale’s body and curvature.

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References 

  1. Torres LG, Bird CN, Rodríguez-González F, Christiansen F, Bejder L, Lemos L, Urban R J, Swartz S, Willoughby A, Hewitt J, Bierlich K (2022) Range-Wide Comparison of Gray Whale Body Condition Reveals Contrasting Sub-Population Health Characteristics and Vulnerability to Environmental Change. Front Mar Sci 910.3389/fmars.2022.867258 
  1. Bierlich KC, Kane A, Hildebrand L, Bird CN, Fernandez Ajo A, Stewart JD, Hewitt J, Hildebrand I, Sumich J, Torres LG (2023) Downsized: gray whales using an alternative foraging ground have smaller morphology. Biol Letters 19:20230043 doi:10.1098/rsbl.2023.0043 
  1. Torres et al., (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), 1825, https://doi.org/10.21105/joss.01825 
  1. Bird et al., (2020). CollatriX: A GUI to collate MorphoMetriX outputs. Journal of Open Source Software, 5(51), 2328, https://doi.org/10.21105/joss.02328 
  1. Patton, P. T., Cheeseman, T., Abe, K., Yamaguchi, T., Reade, W., Southerland, K., Howard, A., Oleson, E. M., Allen, J. B., Ashe, E., Athayde, A., Baird, R. W., Basran, C., Cabrera, E., Calambokidis, J., Cardoso, J., Carroll, E. L., Cesario, A., Cheney, B. J. … Bejder, L. (2023). A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species. Methods in Ecology and Evolution, 00, 1–15. https://doi.org/10.1111/2041-210X.14167 
  1. Green, K.M., Virdee, M.K., Cubaynes, H.C., Aviles-Rivero, A.I., Fretwell, P.T., Gray, P.C., Johnston, D.W., Schönlieb, C.-B., Torres, L.G. and Jackson, J.A. (2023), Gray whale detection in satellite imagery using deep learning. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.352 
  1. Gray, PC, Bierlich, KC, Mantell, SA, Friedlaender, AS, Goldbogen, JA, Johnston, DW. Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry. Methods Ecol Evol. 2019; 10: 1490–1500. https://doi.org/10.1111/2041-210X.13246 
  1. Fernandez Ajó A, Pirotta E, Bierlich KC, Hildebrand L, Bird CN, Hunt KE, Buck CL, New L, Dillon D, Torres LG (2023) Assessment of a non-invasive approach to pregnancy diagnosis in gray whales through drone-based photogrammetry and faecal hormone analysis. Royal Society Open Science 10:230452 

That’s so Real: Adult Beginners, Serial Podcast(s), and a whole lotta of Baja Gray Whale Video Analysis.

Celest Sorrentino, Research Technician, Geospatial Ecology of Marine Megafauna Lab

Hello again GEMM Lab family. I write to you exactly a year after (okay maybe 361 days after but who’s counting…) from my previous blog post describing my 2022 summer working in the GEMM Lab as an NSF REU intern. Since then, so much has changed, and I can’t wait to fill you in on it.

In June I walked across the commencement stage at UC Santa Barbara, earning my BS in Ecology, Evolution, and Marine Biology and my minor in Italian language. A week later, I packed my bags and headed straight back to the lukewarm beaches of Newport, Oregon as a Research Technician in the GEMM Lab. I am incredibly fortunate to have been invited back to the OSU Marine Mammal Institute to lend a hand analyzing drone footage of gray whales collected back in March 2023 when Leigh and Clara went down to Baja California, as mentioned previously in Clara’s blog. 

Fig. 1. View from the top! (of the bridge at Yaquina Bay Bridge in Newport, OR)

During my first meeting with Clara at the beginning of the summer we discussed that a primary goal of my position was to process all the drone footage collected in Baja so that the generated video clips could be later used in other analytical software such as BORIS and SLEAP A.I. Given my previous internships and past summer project, this video processing is familiar to me. My initial thoughts were:

Sweet! Watch drone footage, pop in some podcasts, note down when I see whales, let’s do this!*

Like any overly eager 23-year-old, I might have mentally cracked open a Celsius and kicked my feet up too soon. We added another layer to the goal: develop an ethogram – which requires me to identify and define the behaviors that the gray whales appear to be demonstrating within the videos (more on ethogram development in Clara’s previous blog.) This made me nervous. 

I don’t have any experience with behavior. How do I tell what is a real behavior or if the whale is just existing? What if I’m wrong and ruin the project? What if I totally mess this up?

Naturally, as any sane person, to resolve these thoughts I took to the Reddit search bar: “How to do a job you’ve never done before.” No dice. 

I pushed these thoughts aside and decided to just start the video analysis process. Clara provided me with the ethogram she is developing during her PhD as a point of reference (based on the published gray whale ethogram in Torres et al. 2018), I was surrounded by an insanely supportive lab, and I could Google anything at my fingertips. Fast-forward 6 weeks later: I had analyzed 128 drone videos of adult gray whales as well as mother-calf pairs, and developed an ethogram describing, 26 behaviors**. I named one of my favorite behaviors  a “Twirl” to describe when a gray whale lifts their head out of the water and performs a 360 turn. Reminds me of times when as a kid, sometimes all you really needed is a good spin!

Now I was ready to start a productive, open conversation with Leigh and Clara about this ethogram and my work. However, even walking up to that last meeting, remnants of those daunting, doubtful early summer thoughts persisted. Even after I double checked all the definitions I wrote, rewatched all videos with said behaviors, and had something to show for my work. What gives Brain?

A few days ago, as I sat on my family’s living room couch with my two younger sisters, Baylie and Cassey, Baylie wanted to watch some TikToks with me. One video that came up was of a group of adults taking a beginner dance class, having so much fun and radiating joy. The caption read, Being a beginner as an adult is such a fun and wild thing. Baylie and I watched the video at least 10x, repeating to each other phrases like, “Wow!” and “They’re so cool.” That caption and video has been on my mind since: 

Being a beginner as an adult is such a fun and wild thing.

Being a beginner as an adult is also scary. 

Having just graduated, I can no longer say I am undergraduate student. Now, I am a young adult. This was my first research technician job, as an adult. Don’t adults usually have everything figured out? Can adults be beginners too?

Yes. In fact, we’re beginners more than we realize. 

  • I was a beginner cooking my mother’s turkey recipe 3 years ago for my housemates during the pandemic (Even after having her on Facetime, I still managed to broil it a little too long.) 
  • I was a beginner driver 5 years ago in a rickety Jeep driving myself to school (Now, since I’ve been back home, I’ve been driving my little sisters to school.)
  • I was a beginner NSF REU intern just a year ago. (This summer I was the alumni on the panel for the current NSF REU interns at Hatfield.)
  • I was a beginner science communicator presenting my NSF REU project at Hatfield last summer. (This summer, I presented my research at the Animal Behavior Society Conference.) 
Fig 2A. Group Pic with the LABIRINTO Lab and GEMM Lab at the ABS Portland Conference!
Fig 2B. Clara Bird (left), Dr. Leigh Torres (middle), and I (right) at the ABS Portland Conference. 

I now recognize that during my time identifying and defining behaviors of gray whales in videos made me take on the seat of a “beginner video and behavioral analyst”. I could not rely on the automated computer vision lens I gained from previous internships, which felt familiar and secure. 

 Instead, I had to allow myself to be creative. Dig into the unfamiliar in an effort to complete a task or job I had never done before. Allowing myself to be imperfect, make mistakes, meanwhile unconsciously building a new skill. 

This is what makes being a beginner as an adult such a fun thing. 

I don’t think being a beginner is a wild thing, although it can definitely make you feel a wild range of emotions. Being a beginner means you’re allowing yourself to try something new. Being a beginner means you’re allowing yourself the chance to learn.

Whether you’re an adult beginner as you enter your 30s, adult beginner as you enter parenthood, adult beginner grabbing a drink with friends after a long day in lab, adult beginner as a dancer, or like me, a beginner of leaving behind my college student persona and entering a new identity of adulthood, being a beginner as an adult is such a fun and normal thing.

I am not sure what will be next, but I hope to write to you all again from this blog a year from now, as an adult beginner as a grad student in the GEMM Lab. For anyone approaching the question of “What’s next”, I encourage you to read “Never a straight Path” by GEMM Lab MSc alum Florence Sullivan, a blog that has brought me such solace in my new adult journey and advice that never gets old.

Being a beginner—that, is so real. 

Fig 3A. Kayaking as an adult beginner of the Port Orford Field Team!
Fig 3B “See you soon:” Wolftree evenings with the lab.
Fig 3C. GEMM Lab first BeReal!

*I listened to way too many podcasts to list them all, but I will include two that have been a GEMM Lab “gem” —-thanks to Lisa and Clara for looping me in and now, looping you in!)

**(subject to change)

References

Torres LG, Nieukirk SL, Lemos L, Chandler TE (2018) Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Front Mar Sci 510.3389/fmars.2018.00319

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Individual Specialization Part 3: How do individual characteristics relate to individual specialization?

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

While I did not mean to start a mini-blog series on individual specialization, here I am with my third blog about individual specialization in as many years. Looking back, these blogs are actually a lovely documentation of my own journey of learning about individual specialization and I hope you’re enjoying being along for the ride.

So, what have we learned so far? In my first blog I described the concept of individual specialization, why it matters, and presented some case studies. In my second blog I discussed the roles of competition and learning as drivers of individual specialization. Let’s review: Individual specialization is when individuals within a population only use a subset of the resources that the full population uses, but different individuals use different subsets. This is important to quantify for two reasons: (1) it affects how we think about conserving and managing a population (Bolnick et al., 2003), and (2) it can affect the relationships between the population and the other species in its community (Bolnick et al., 2011). Common drivers of specialization are competition and learning. Competition can lead to specialization because it reduces the availability of a resource, driving individuals to switch resource use (Pianka, 1974). Learning can also lead to specialization through ‘one-to-one’ learning, where one individual learns from one demonstrator (Sheppard et al., 2018). This individual tends to then use, and eventually teach, that specialized technique.

While understanding these drivers is important, the question of why specific individuals employ specific specializations remains. If learning is not the driver of specialization, then how do individuals end up using their specific subset. Is it random? Or are there underlying patterns? The common sources of variation are related to sex, age, or size (and often these three can be inter-connected) (Dall et al., 2012). 

Individual differences related to the sex of the individuals are called sexual dimorphisms. Physical and ecological differences between the sexes are common throughout nature (peacocks for example) and these differences can lead to different specializations. Northern elephant seals provide a fascinating example (Kienle et al., 2022). Northern elephant seal males are the distinctly larger sex as they engage in competitionfor females. Because of their larger body size and energy expenditure during competition, they have much higher energetic requirements than females during the breeding season, meaning that they need to consume more prey. This elevated requirement has led to a difference in foraging behaviors. Males forage near the continental shelf where there is more prey while females forage further offshore in the open ocean. However, the tradeoff of feeding on the continental shelf is an increased predation risk due to overlap with predator habitat, and indeed Kienle et al. found that the mortality rate for foraging trips was 5-6 times higher for males than females. So, while males need to take the risk of foraging in an area with higher predator presence to meet their energetic demands, females can forage in a safer habitat with less prey because their energetic requirements are lower. This study presents an excellent example of how sexual dimorphism can cause individual specialization and the subsequent consequences.

An illustration depicting a male (background) and female (foreground) northern elephant seal. source: https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/northern-elephant-seal. Illustration by Pieter Folken.

Individual specializations attributed to differences between distinct morphs are called resource polymorphisms. A morph is the physical appearance of an individual; distinct morphs are when there are clearly different kinds of morphs within a population. Morphs can range from color polymorphism (ex. lizards of different colors within the same species) to differences in skull shape and size. A study on the European eel found that differences in skull morphology were related to different foraging strategies (Cucherousset et al., 2011).  Eels with larger head widths consumed larger prey types. Interestingly, they found that eels on either end of the head width spectrum (i.e., very narrow or very wide) were more successful (i.e., in better body condition) than eels with intermediate head widths. They suggest that this difference in nutrition success is because the intermediate head width eels were less efficient foragers than eels at the extremes. In this example we see that morphology is related to the ability to feed on a prey type and has consequences for individual health.

Figure 2. from Cucherousset et al., 2011. Small-bodied eels with narrower (a) and broader (b) heads and large-bodied eels with narrower (c) and broader (d) heads. TL (Total Length) and HW (Head Width):TL are shown for each individual

Individual differences related to changes in size, shape, and behavior that occur as an individual grows are called ontogenetic shifts. As you have experienced yourself, there are many changes that occur as an individual grows, and these changes can mean that different age classes have different specializations. Gustafsson (1988) found an ontogenetic shift in where coal tits (a species of bird) fed within a pine tree. Younger birds were more generalists but tended to feed on the outer sections of the tree, while adults foraged on the more profitable, central portion of the tree. He attributes this difference to dominance of adults over juveniles. Gustafsson also found that the larger individuals within each age class also tended to feed closer to the center of the tree. This within age class difference is attributed to a larger body size being better suited for feeding closer to the center of the tree, while smaller body sizes are better suited for hovering and foraging on the outside. Interestingly, this study occurred over multiple years, and Gustafsson documented several juvenile individuals that shifted foraging behavior when they became adults. 

Photo of a coal tit. Source: https://ebird.org/species/coatit2

These sources of behavioral variation are important to account for because ultimately phenotypic variation can affect not only a population’s niche, but its population size, distribution, evolutionary potential, and vulnerability to environmental change (Wennersten & Forsman, 2012). And it’s important to determine which source(s) of variation are at play to inform best population management practices. Different behaviors between sexes versus age classes have different implications for the population, making it necessary to not only assess if there are differences but also to try and understand their drivers.

This behavioral variability relative to morphs is something that is of particular interest to me, and it is the focus of my first chapter. We’ve documented that the PCFG gray whales in our study region employ a variety of foraging tactics, and I want to know if there is specialization in tactic use and if we can find an underlying source of the variation. I can’t wait to share results with you in the next installment of this individual specialization journey. Stay tuned!

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly alert when we make a new post! Just add your name into the subscribe box below!

References

Bolnick, D. I., Amarasekare, P., Araújo, M. S., Bürger, R., Levine, J. M., Novak, M., Rudolf, V. H. W., Schreiber, S. J., Urban, M. C., & Vasseur, D. A. (2011). Why intraspecific trait variation matters in community ecology. Trends in Ecology & Evolution, 26(4), 183–192. https://doi.org/10.1016/j.tree.2011.01.009

Bolnick, D. I., Svanbäck, R., Fordyce, J. A., Yang, L. H., Davis, J. M., Hulsey, C. D., & Forister, M. L. (2003). The ecology of individuals: Incidence and implications of individual specialization. American Naturalist, 161(1), 1–28. https://doi.org/10.1086/343878

Cucherousset, J., Acou, A., Blanchet, S., Britton, J. R., Beaumont, W. R. C., & Gozlan, R. E. (2011). Fitness consequences of individual specialisation in resource use and trophic morphology in European eels. Oecologia, 167(1), 75–84. https://doi.org/10.1007/s00442-011-1974-4

Dall, S. R. X., Bell, A. M., Bolnick, D. I., & Ratnieks, F. L. W. (2012). An evolutionary ecology of individual differences. Ecology Letters, 15(10), 1189–1198. https://doi.org/10.1111/j.1461-0248.2012.01846.x

De Meyer, J., Belpaire, C., Boeckx, P., Bervoets, L., Covaci, A., Malarvannan, G., De Kegel, B., & Adriaens, D. (2018). Head shape disparity impacts pollutant accumulation in European eel. Environmental Pollution, 240, 378–386. https://doi.org/10.1016/j.envpol.2018.04.128

Gustafsson, L. (1988). Foraging behaviour of individual coal tits, Parus ater, in relation to their age, sex and morphology. Animal Behaviour, 36(3), 696–704. https://doi.org/10.1016/S0003-3472(88)80152-0

Kienle, S. S., Friedlaender, A. S., Crocker, D. E., Mehta, R. S., & Costa, D. P. (2022). Trade-offs between foraging reward and mortality risk drive sex-specific foraging strategies in sexually dimorphic northern elephant seals. Royal Society Open Science, 9(1), 210522. https://doi.org/10.1098/rsos.210522

Pianka, E. R. (1974). Niche Overlap and Diffuse Competition. 71(5), 2141–2145.

Sheppard, C. E., Inger, R., McDonald, R. A., Barker, S., Jackson, A. L., Thompson, F. J., Vitikainen, E. I. K., Cant, M. A., & Marshall, H. H. (2018). Intragroup competition predicts individual foraging specialisation in a group-living mammal. Ecology Letters, 21(5), 665–673. https://doi.org/10.1111/ele.12933

Wennersten, L., & Forsman, A. (2012). Population-level consequences of polymorphism, plasticity and randomized phenotype switching: A review of predictions. Biological Reviews, 87(3), 756–767. https://doi.org/10.1111/j.1469-185X.2012.00231.x

A Gut Feeling: DNA Metabarcoding Gray Whale Diets

By Charles Nye, graduate student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Cetacean Conservation and Genomics Laboratory

Figure 1: An illustration (by me) of a feeding gray whale whose caudal end transitions into a DNA double helix.

Let’s consider how much stuff organisms shed daily. If you walk down a hallway, you’ll leave a microscopic trail of skin cells, evaporated sweat, and even more material if you so happen to sneeze or cough (as we’ve all learned). The residency of these bits and pieces in a given environment is on the order of days, give or take (Collins et al. 2018). These days, we can extract, amplify, and sequence DNA from leftover organismal material in environments (environmental DNA; eDNA), stomach contents (dietary DNA, dDNA), and other sources (Sousa et al. 2019; Chavez et al. 2021).

You might be familiar with genetic barcoding, where scientists are able to use documented and annotated pieces of a genome to identify a piece of DNA down to a species. Think of these as genetic fingerprints from a crime scene where all (described) species on Earth are prime suspects. With advancements in computing technology, we can barcode many species at the same time—a process known as metabarcoding. In short, you can now do an ecosystem-wide biodiversity survey without even needing to see your species of interest (Ficetola et al. 2008; Chavez et al. 2021).

(Before you ask: yes, people have tried sampling Loch Ness and came up with not a single strand of plesiosaur DNA (University of Otago, 2019).)

I received my crash course on metabarcoding when I was employed at the Monterey Bay Aquarium Research Institute (MBARI), right before grad school. There, I was employed to help refine eDNA survey field and laboratory methods (in addition to some cool robot stuff). Here at OSU, I use metabarcoding to research whale ecology, detection, and even a little bit of forensics  work. Cetacean species (or evidence thereof) I’ve worked on include North Atlantic right whales (Eubalaena glacialis), killer whales (Orcinus spp.), and gray whales (Eschrichtius robustus).

Long-time readers of the GEMM Lab Blog are probably quite knowledgeable about the summertime grays—the Pacific Coast Feeding Group (PCFG). All of us here at OSU’s Marine Mammal Institute (MMI) are keenly interested in understanding why these whales hang out in the Pacific Northwest during the summer months and what sets them apart from the rest of the Eastern North Pacific gray whale population. What interests me? Well, I want to double-check what they’re eating—genetically.

“What does my study species eat?” is a straightforward but underappreciated question. It’s also deceptively difficult to address. What if your species live somewhere remote or relatively inaccessible? You can imagine this is a common logistical issue for most research in marine sciences. How many observations do you need to make to account for seasonal or annual changes in prey availability? Do all individuals in your study population eat the same thing? I certainly like to mix and match my diet.

Gray whale foraging ecology has been studied comprehensively over the last several decades, including an in-depth stomach content evaluation by Mary Nerini in 1984 and GEMMer Lisa Hildebrand’s MSc research. PCFG whales seem to prefer shrimpy little creatures called mysids, along with Dungeness crab (Cancer magister) larvae, during their stay in the Pacific Northwest (PNW), most notably the mysid Neomysis rayii (Guerrero 1989; Hildebrand et al. 2021). Indeed, the average energetic values of common suspected prey species in PNW waters rival the caloric richness of Arctic amphipods (Hildebrand et al. 2021). However, despite our wealth of visual foraging observations, metabarcoding may add an additional layer of resolution. For example, the ocean sunfish (Mola mola) was believed to exclusively forage on gelatinous zooplankton, but a metabarcoding approach revealed a much higher diversity of prey items, including other bony fishes and arthropods (Sousa et al. 2016).

Given all this exposition, you may be wondering: “Charles—how do you intend on getting dDNA from gray whales? Are you going to cut them open?”

Figure 2: The battle station, a vacuum pump that I use to filter out all of the particulate matter from a gray whale dDNA sample. The filter is made of polycarbonate track etch material, which melts away in the DNA extraction process—quite handy, indeed!

No. I’m going to extract DNA from their poop.

Well, actually, I’ve been doing that for the last two years. My lab (Cetacean Conservation and Genomics Laboratory, CCGL) and GEMM Lab have been collaborating to make lemonade out of, er…whale poop. An archive of gray whale fecal samples (with ongoing collections every field season) originally collected for hormone analyses presented itself with new life—the genomics kind. In addition to community-level data, we are also able to recover informative DNA from the gray whales, including sex ID from “depositing” individuals, though the recovery rate isn’t perfect.

Because the GEMM Lab/MMI can non-invasively collect multiple samples from the same individuals over time, dDNA metabarcoding is a great way to repeatedly evaluate the diets of the PCFG, just shy of being at the right place at the right time with a GoPro or drone to witness a feeding event.  While we can get stomach contents and even usable dDNA from a naturally deceased whale, those data may not be ideal. How representative a stranded whale is of the population is dependent on the cause of death; an emaciated or critically injured individual, for example, is a strong outlier.

Figure 3: Presence/absence of the top 10 most-common taxonomic Families observed in the PCFG gray whale dDNA dataset (n = 20, randomly selected). Filled-in dots indicate at least one genetic read associated with that Family, and empty dots indicate none. Note the prey taxa: mysids (Mysidae), krill (Euphausiidae), and olive snails (Olividae).

Here’s a snapshot of progress to date for this dDNA metabarcoding project. I pulled out twenty random samples from my much larger working dataset (n = 82) for illustrative purposes (and legibility). After some bioinformatic wizardry, we can use a presence/absence approach to get an empirical glimpse at what passes through a PCFG gray whale. While I am able to recover species-level information, using higher-level taxonomic rankings summarizes the dataset in a cleaner fashion (and also, not every identifiable sequence resolves to species).

The title of most commonly observed prey taxa belongs to our friends, the mysids (Mysidae). Surprisingly, crabs and amphipods are not as common in this dataset, instead losing to krill (Euphausiidae) and olive snails (Olividae). The latter has been found in association with gray whale foraging grounds but not documented in a prey study (Jenkinson 2001). We also get an appreciable amount of interference from non-prey taxa, most notably barnacles (Balanidae), with an honorable mention to hydrozoans (Clytiidae, Corynidae). While easy to dismiss as background environmental DNA, as gray whales do forage at the benthos, these taxa were physically present and identifiable in Nerini’s (1984) gray whale stomach content evaluation.

So—can we conclude that barnacles and hydrozoans are an important part of a gray whale’s diet, as much as mysids? From decades of previous observations, we might say…probably not. Gray whales are actively targeting patches of crabby, shrimpy zooplankton things, and even employ novel foraging strategies to do so (Newell & Cowles 2006; Torres et al. 2018). However, the sheer diversity of consumed species does present additional dimensionality to our understanding of gray whale ecology.

The whales are eating these ancillary organisms, whether they intend to or not, and this probably does influence population dynamics, recruitment, and succession in these nearshore benthic habitats. After all, the shallow pits that gray whales leave behind post-feeding provide a commensal trophic link with other predatory taxa, including seabirds and groundfish (Oliver & Slattery 1985). Perhaps the consumption of these collateral species affects gray whale energetics and reflects on their “performance”?

I hope to address all of this and more in some capacity with my published work and graduate chapters. I’m confident to declare that we can document diet composition of PCFG whales using dDNA metabarcoding, but what comes next is where one can get lost in the sea(weeds). How does the diet of individuals compare to one another? What about at differing time points? Age groups? How many calories are in a barnacle? No need to fret—this is where the fun begins!

References

Chavez F, Min M, Pitz K, Truelove N, Baker J, LaScala-Grunewald D, Blum M, Walz K,

Nye C, Djurhuus A, et al. 2021. Observing Life in the Sea Using Environmental

DNA Oceanog. 34(2):102–119. doi:10.5670/oceanog.2021.218.

Collins R, Wangensteen OS, O’Gorman EJ, Mariani S, Sims DW, Genner M. 2018. Persistence

of environmental DNA in marine systems. Comm Biol. 1(185).

https://doi.org/10.1038/s42003-018-0192-6

Ficetola GF, Miaud C, Pompanon F, Taberlet P. 2008. Species detection using

environmental DNA from water samples. Biol Lett. 4(4):423–425.

doi:10.1098/rsbl.2008.0118.

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. 8:683634.

doi:10.3389/fmars.2021.683634.

Jenkinson R. 2001. Gray whale (Eschrichtius robustus) prey availability and feeding ecology in

northern California, 1999-2000 [thesis]. California State Polytechnic University,

Humboldt. 81 p.

Newell CL, Cowles TJ. 2006. Unusual gray whale Eschrichtius robustus feeding in the summer

of 2005 off the central Oregon Coast. Geophys Res Lett. 33(22):L22S11.

doi:10.1029/2006GL027189.

Oliver JS, Slattery PN. 1985. Destruction and Opportunity on the Sea Floor: Effects of

Gray Whale Feeding. Ecology. 66(6):1965–1975. doi:10.2307/2937392.

Sousa LL, Silva SM, Xavier R. 2019. DNA metabarcoding in diet studies: Unveiling

ecological aspects in aquatic and terrestrial ecosystems. Environmental DNA.

1(3):199–214. doi:10.1002/edn3.27.

Sousa LL, Xavier R, Costa V, Humphries NE, Trueman C, Rosa R, Sims DW, Queiroz N.

2016. DNA barcoding identifies a cosmopolitan diet in the ocean sunfish. Sci

Rep. 6(1):28762. doi:10.1038/srep28762.

Torres LG, Nieukirk SL, Lemos L, Chandler TE. 2018. Drone Up! Quantifying Whale Behavior

From a New Perspective Improves Observational Capacity. Front Mar Sci. 5:319.

doi:10.3389/fmars.2018.00319.

University of Otago. 2019. First eDNA study of Loch Ness points to something fishy.

https://www.otago.ac.nz/news/news/otago717609.html. [accessed 2023 Apr 25]

How do we study the impact of whale watching?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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References

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

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

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

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

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

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