Familiar flukes and flanks: The 9th GRANITE field season is underway

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

The winds are consistently (and sometimes aggressively) blowing from the north here on the Oregon coast, which can only mean one thing – summer has arrived! Since mid-May, the GRANITE (Gray whale Response to Ambient Noise Informed by Technology and Ecology) team has been looking for good weather windows to survey for gray whales and we have managed to get five great field work days already. In today’s blog post, I am going to share what (and who) we have seen so far.

On our first day of the field season, PI Leigh Torres, postdoc KC Bierlich and myself, were joined by a special guest: Dr. Andy Read. Andy is the director of the Duke University Marine Lab, where he also runs his own lab, which focuses on conservation biology and ecology of marine vertebrates. Andy was visiting the Hatfield Marine Science Center as part of the Lavern Weber Visiting Scientist program and was hosted here by Leigh. For those of you that do not know, Andy was Leigh’s graduate school advisor at Duke where she completed her Master’s and doctoral degrees. It felt very special to have Andy on board our RHIB Ruby for the day and to introduce him to some friends of ours. The first whale we encountered that day was “Pacman”. While we are always excited to re-sight an individual that we know, this sighting was especially mind-blowing given the fact that Leigh had “just” seen Pacman approximately two months earlier in Guerrero Negro, one of the gray whale breeding lagoons in Mexico (read this blog about Leigh and Clara’s pilot project there). Aside from Pacman, we saw five other individuals, all of which we had seen during last year’s field season. 

The first day of field work for the 2023 GRANITE field season! From left to right: Leigh Torres, Lisa Hildebrand, Andy Read, and KC Bierlich. Source: L. Torres.

Since that first day on the water, we have conducted field work on four additional days and so far, we have only encountered known individuals in our catalog. This fact is exciting because it highlights the strong site fidelity that Pacific Coast Feeding Group (PCFG) gray whales have to areas within their feeding range. In fact, I am examining the residency and space use of each individual whale we have observed in our GRANITE study for one of my PhD chapters to better understand the level of fidelity individuals have to the central Oregon coast. Furthermore, this site fidelity underpins the unique, replicate data set on individual gray whale health and ecology that the GRANITE project has been able to progressively build over the years. So far during this field season in 2023, we have seen 13 unique individuals, flown the drone over 10 of them and collected four fecal samples from two, which represent critical data points from early on in the feeding season.

Our sightings this year have not only highlighted the high site fidelity of whales to our study area but have also demonstrated the potential for internal recruitment of calves born to “PCFG mothers” into the PCFG. Recruitment to a population can occur in two ways: externally (individuals immigrate into a population from another population) or internally (calves born to females that are part of the population return to, or stay, within their mothers’ population). Three of the whales we have seen so far this year are documented calves from females that are known to consistently use the PCFG range, including our central Oregon coast study area. In fact, we documented one of these calves, “Lunita”, just last year with her mother (see Clara’s recap of the 2022 field season blog for more about Lunita). The average calf survival estimate between 1997-2017 for the PCFG was 0.55 (Calambokidis et al. 2019), though it varied annually and widely (range: 0.34-0.94). Considering that there have been years with calf survival estimates as low as ~30%, it is therefore all the more exciting when we re-sight a documented calf, alive and well!

“Lunita”, an example of successful internal recruitment

We have also been collecting data on the habitat and prey in our study system by deploying our paired GoPro/RBR sensor system. We use the GoPro to monitor the benthic substrate type and relative prey densities in areas where whales are feeding. The RBR sensor collects high-frequency, in-situ dissolved oxygen and temperature data, enabling us to relate environmental metrics to relative prey measurements. Furthermore, we also collect zooplankton samples with a net to assess prey community and quality. On our five field work days this year, we have predominantly collected mysid shrimp, including gravid (a.k.a. pregnant) individuals, however we have also caught some Dungeness and porcelain crab larvae. The GEMM Lab is also continuing our collaboration with Dr. Susanne Brander’s lab at OSU and her PhD student Lauren Kashiwabara, who plan on conducting microplastic lab experiments on wild-caught mysid shrimp. Their plan is to investigate the growth rates of mysid shrimp under different temperature, dissolved oxygen, and microplastic load conditions. However, before they can begin their experiments, they need to successfully culture the mysids in the lab, which is why we collect samples for them to use as their ‘starter culture’. Stay tuned to hear more about this project as it develops!

So, all in all, it has been an incredibly successful start to our field season, marked by the return of many familiar flukes and flanks! We are excited to continue collecting rock solid GRANITE data this summer to increase our efforts to understand gray whale ecology and physiology. 

References

Calambokidis, J., Laake, J., and Perez, A. (2019). Updated analyses of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2017. IWC, SC/A17/GW/05 for the Workshop on the Status of North Pacific Gray Whales. La Jolla: IWC.

Title: “Blown away”: measuring the blowholes of whales from drones

By Annie Doron, Undergraduate Intern, Oregon State University, GEMM Laboratory  

Hey up! My name is Annie Doron, and I am an undergraduate Environmental Science student from the University of Sheffield (UK) on my study year abroad. One of my main motivations for undertaking this year abroad was to gain experience working in a marine megafauna lab. Whales in particular have always captivated my interest, and I have been lucky enough to observe  humpback whales in Iceland and The Azores, and even encountered one whilst diving in Australia! For the past 10 months, I have had the unique opportunity to work in the GEMM Lab analyzing Pacific Coast Feeding Group (PCFG) gray whales off the Oregon Coast (Figure 1). I must admit, it has been simply wonderful! 

Figure 1. Aerial image of a PCFG gray whale off the Oregon Coast. 

How did I end up getting involved with the GEMM Lab? I was first accepted into Scarlett Arbuckle’s research-based class in fall term 2022, which is centered around partnering with a mentor for a research project. Having explored the various fields of research at HMSC, I contacted Leigh Torres with interest in getting involved in the GEMM Lab and to establish a research project suitable for a totally inexperienced, international, undergraduate student. Thankfully, Leigh forwarded my email to KC Bierlich who offered to be my mentor for the class, and the rest is history! I first began analyzing drone imagery to measure length and body condition of  PCFG gray whales, which provided an opportunity to get involved with the lab and gain experience using the photogrammetry software MorphoMetriX (Torres & Bierlich, 2020) (see KC’s blog), which is used to make morphometric measurements of whales. Viewing drone imagery of whales sparked my interest in how they use their blowholes (otherwise called ‘nares’) to replenish their oxygen stores; this led to us establishing a research project for the class where we tested if we could use MorphoMetriX to measure blowholes from drone imagery.

Extending this project into winter and spring terms (via research credits) has enabled me to continue working with Leigh and KC, as well as to collaborate with Clara Bird and Jim Sumich. Thanks to KC, who has patiently guided me through the ins and outs of working on a research project, I now feel more confident handling and manipulating large datasets, analyzing drone footage (i.e., differentiating between behavioral states, recording breathing sequences, detecting when a whale is exhaling vs inhaling, etc.), and speaking in public (although I still get pretty bad stage fright, but I think that is a typical conundrum undergrads face). Whatsmore, applying  R – a programming language used for statistical analysis and data visualization, which I have been trying to wrap my head around for years – to my own dataset has helped me greatly enhance my skills using it. 

So, what exciting things have we been working on this year? Given that we often cannot simply study a whale from inside a laboratory – due to size-related logistical implications – we must use proxies (i.e., a variable that is representative of an immeasurable variable). Since cetaceans must return to the surface to offload carbon dioxide and replenish their oxygen stores, measuring their breath frequency and magnitude is one way to study a whale’s oxygen consumption, in turn offering insight into its energy expenditure (Williams, 1999). Blowholes are one proxy we can use to study breath magnitude. Blowholes can be utilized in this way by measuring inhalation duration (the amount of time a whale is inhaling, which is based on a calculation developed by Jim Sumich) and blowhole area (the total area of a blowhole) to gauge variations in tidal volume (the amount of air flowing in and out of the lungs).

Measuring inhalation duration and blowhole area is important because a larger blowhole area (i.e., one that is more dilated) and a longer inhalation duration is indicative of higher oxygen intake, which can infer stress. For example, in this population, higher stress levels are associated with increased vessel traffic (Lemos et al., 2022), and skinnier whales have higher stress levels compared to chubby, healthy whales (Lemos, Olsen, et al., 2022). Hence, measuring the variation around blowholes could be utilized to predict challenges whales face from climate change and anthropogenic disturbance, including fishing (Scordino et al., 2017) and whale watching industry threats (Sullivan & Torres, 2018) (see Clara’s blog), as well as to inform effective management strategies. Furthermore, measuring the variables inhalation duration and blowhole area could help to identify whether whales are taking larger breaths associated with certain ‘gross behavior states’, otherwise known as ‘primary states’, which include: travel, forage, rest, social (Torres et al., 2018). This could enable us to assess the energetic costs of different foraging tactics (i.e., head standing, side-swimming, and bubble blasting (Torres et al., 2018), as well as consequences of disturbance events, on an individual and population health perspective. 

Inhalation duration has been explored in the past by using captive animals. For example, there have been studies on heart rate and breathing of bottlenose dolphins in human care facilities (Blawas et al., 2021; Fahlman et al., 2015). Recently, Nazario et al. (2022) was able to measure inhalation duration and blowhole area using suction-cup video tags. Her study led us to consider if it was possible to measure the parameters and variation around respiration by measuring blowhole area and inhalation duration of PCFGs from drone imagery. We employed MorphoMetriX to study the length, width, and area of a blowhole (Figure 2). Preliminary analyses verified that the areas of the left and right blowholes are very similar (Figure 3); this finding saved us a lot of time because from thereon we only measured either the left or right side. Interestingly, we see some variation in blowhole area within and across individuals (Figure 4). This variation changes within individuals based on primary state. For example, the whales “Glacier”, “Nimbus”, and “Rat” show very little variation whilst traveling but a large amount whilst foraging. Comparatively, “Dice” shows little variation whilst foraging and large variation whilst traveling. Whilst considering cross-individual comparisons, we can see that “Sole”, “Rat”, “Nimbus”, “Heart”, “Glacier”, “Dice”, and “Coal” each exhibit relatively large amounts of variation, yet “Mahalo”, “Luna”, “Harry”, “Hummingbird” and “Batman” exhibit very little. One potential reason for some individuals displaying higher levels of variation than others could be higher levels of exposure to disturbance events that we were unable to measure or evaluate in this study.

Figure 2. How we measured the length, width, and area of a blowhole using MorphoMetriX.

Figure 3. Data driven evidence that the left and the right blowhole areas are very similar. 

Figure 4. Variation in blowhole area amongst individual PCFG whales. The hollow circles represent the means, and the color represents the primary state the whale is exhibiting, foraging (purple) vs. traveling (blue), which will be further explored in Clara’s PhD.

Now, we are venturing into June and are at a stage where we (KC, Clara, Jim, Leigh, and I) are preparing to publish a manuscript! What a way to finish such a fantastic year! The transition from a 3-month-long pilot study to a much larger data analysis and eventual preparation for a manuscript has been a monumental learning experience. If anybody had told me a year ago that I would be involved in publishing a body of work – especially one that is so meaningful to me – I would simply not have believed them! We hope this established methodology for measuring blowholes will help other researchers carry out blowhole measurements using drone imagery across different populations and species. Further research is required to explore the differences in inhalation duration and blowhole area between different primary states, specifically across different foraging tactics.

It has been a great privilege working with the GEMM Lab these past months, and I was grateful to be included in their monthly lab meetings, during which members gave updates and we discussed recently published papers. Seeing such an enthusiastic, kind, and empathic group of people working together taught me what working in a supportive lab could look and feel like. In spite of relocating from Corvallis to Bend after my first term, I was happy to be able to continue working remotely for the lab for the remainder of my time (even though I was ~200 miles inland). I thoroughly enjoyed living in Corvallis, highlights of which were scuba diving adventures to the Puget Sound and coastal road trips with friends. The appeal to move arose from Bend’s reputation as an adventure hub – with unlimited opportunities for backcountry ski access – as well as its selection of wildlife ecology courses (with a focus on species specific to central Oregon). I moved into ‘Bunk & Brew’ (Bend’s only hostel, which is more like a big house of friends with occasional hostel guests) on January 1st after returning from spending Christmas with friends in my old home in Banff, Canada. I have since been enjoying this wonderful multifaceted lifestyle; working remotely in the GEMM Lab, attending in-person classes, working part-time at the hostel, as well as skiing volcanoes (Mount Hood, Middle and South Sister (Figure 5) or climbing at Smith Rock during my days off. Inevitably, I do miss the beautiful Oregon coast, and I will always be grateful for this ideal opportunity and hope this year marks the start of my marine megafauna career!

Figure 5. What I get up to when I’m not studying blowholes! (This was taken at 5am on the long approach to Middle and North Sister. North Sister is the peak featured in the backdrop).

References

Blawas, A. M., Nowacek, D. P., Allen, A. S., Rocho-Levine, J., & Fahlman, A. (2021). Respiratory sinus arrhythmia and submersion bradycardia in bottlenose dolphins (Tursiops truncatus). Journal of Experimental Biology, 224(1), jeb234096. https://doi.org/10.1242/jeb.234096

Fahlman, A., Loring, S. H., Levine, G., Rocho-Levine, J., Austin, T., & Brodsky, M. (2015). Lung mechanics and pulmonary function testing in cetaceans. Journal of Experimental Biology, 218(13), 2030–2038. https://doi.org/10.1242/jeb.119149

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), 18580. https://doi.org/10.1038/s41598-022-14510-5

Lemos, L. S., Olsen, A., Smith, A., Burnett, J. D., Chandler, T. E., Larson, S., Hunt, K. E., & Torres, L. G. (2022). Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Marine Mammal Science, 38(2), 801–811. https://doi.org/10.1111/mms.12877

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

Scordino, J., Carretta, J., Cottrell, P., Greenman, J., Savage, K., & Scordino, J. (2017). Ship Strikes and Entanglements of Gray Whales in the North Pacific Ocean. Cambridge: International Whaling Commission, 1924–2015.

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

Sumich, J. L. (1994). Oxygen extraction in free-swimming gray whale caves. Marine Mammal Science, 10(2), 226–230. https://doi.org/10.1111/j.1748-7692.1994.tb00266.x

Torres, W., & Bierlich, K. (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

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, 319. https://doi.org/10.3389/fmars.2018.00319
Williams, T. M. (1999). The evolution of cost efficient swimming in marine mammals: Limits to energetic optimization. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 354(1380), 193–201. https://doi.org/10.1098/rstb.1999.0371

“May the Force be with you” – or how to be a good mentor?

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

This year, for the 15th consecutive summer, the Hatfield Marine Science Center (HMSC) will be hosting undergraduate interns as part of the NSF-funded Research Experiences for Undergraduate (REU) program. The GEMM Lab will provide research experiences to no less than three REU interns for a 10-week period starting mid-June. Along with Leigh, Dawn, and Allison, I will be a daily “mentor” to one of these students; a role that the HMSC REU program takes very seriously.

I used quotation marks here because although I have been supervising/helping/engaging/leading teams and students for many years, I only really learned this new word, “mentoring”, after moving to the US. In my native language, French, the word “mentor” exists and has the same meaning as in English (i.e. a person who gives a younger or less experienced person help and advice over a period of time, especially at work or school; Cambridge Dictionary). However, the verb derived from this noun – “mentoring” – does not exist in French and the word we use instead sounds more like “supervising”. Actually, an advisor to a trainee or intern is often called “maître de stage”, literally meaning “internship master”, which conveys a pretty different message from the title mentor…

Why does that semantic nuance matter? Well, I believe that the words we use are an extension of the way we see the world. Fact is, although I have been an advisor to several students before, I had never received any formal mentoring training up to today and I never put that much thought into what mentoring meant to me. Well, I certainly did not see myself being anyone’s Master! So, what is the difference between mentoring and supervising?

A quick google search gave me a hint… supervision is very much task-oriented, it’s about overseeing a person’s activities and providing instructions and recommendations to ensure that the task is successfully completed. That’s all good and well, but mentoring adds an additional layer of care for the person’s long-term development, in an approach that strives to be more holistic. That approach may seem obvious to many academics today, but unfortunately things don’t always happen that way. Many of us could cite several cases where we have observed students being used as additional work force without much attention given to their wellbeing, learning, and personal development. Who has never seen a real-life professor like that of Phd Comics (Illustration below)? On top of that, mentoring styles, and the academic system as a whole, have long shaped the new generations of scientists to resemble their senior mentors, hence perpetuating inequity in education and a lack of diversity in research carriers.

PhD Comics is probably one of the most hilarious yet highly accurate depictions of the flaws of academic mentoring. Comic reproduced from “Piled Higher and Deeper” by Jorge Cham, www.phdcomics.com.

I discovered that there are a lot of great resources out there to help early-career scientists navigate the waters of mentorship (e.g., Center for Improved Mentored Experiences in Research, OSU guidance for DEI learning). I also really appreciated the fact that the HMSC REU program director, Itchung Cheung, would take the time to meet all future mentors ahead of time, and make sure that they had the tools and resources to be good mentors. He made it clear that a student has many mentoring needs (e.g., role models, emotional support, access to opportunities, professional development…) and that it is not possible for one person to fill all these shoes. As PhD student Rachel Kaplan pointed out “It takes a village to raise a PhD student”! That being said, there are a couple simple rules that everyone should agree on before taking on interns or new students. I will not list all these best practices here but some of main take-away messages for me were the importance of planning, having clear expectations while staying flexible, encouraging interns to take an active role in setting goals and providing critical feedback, and fostering a welcoming environment in which the student can feel a sense of belonging.

Along those lines, I would like to end on a more personal note. Although I never received formal mentorship training, I do believe that I learned some of these skills in the most traditional way;  that is by learning by example. And (hopefully!) this process did not turn too badly because I was lucky to have great mentors to look up to. Among other qualities, my mentors always made me feel like I belonged, like what I had to say mattered. Reflecting upon my years as a graduate student, I now realize that this feeling is one of the things that allowed me to love research, with all its setbacks and challenges. My mentors always made me feel like I was among their priorities, whether it be by returning manuscript edits in time or listening to me present all of my latest analysis outputs and coding tribulations. Holistic mentoring is a bit of a jargony word, and although I am still learning the theories underlying that approach, I know that if that’s what I experienced as a mentee, then that’s what I will try to do as a mentor!

To learn more about research experiences for undergraduate at Oregon State University, check out this link.

As waters warm, what are “anomalous conditions” in the face of climate change?

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

Recently, I had the opportunity to attend the Effects of Climate Change on the World’s Ocean (ECCWO) conference. This meeting brought together experts from around the world for one week in Bergen, Norway, to gather and share the latest information on how oceans are changing, what is at risk, responses that are underway, and strategies for increasing climate resilience, mitigation, and adaptation. I presented our recent findings from the EMERALD project, which examines gray whale and harbor porpoise distribution in the Northern California Current over the past three decades. Beyond sharing my postdoctoral research widely for the first time and receiving valuable feedback, the ECCWO conference was an incredibly fruitful learning experience. Marine mammals can be notoriously difficult to study, and often the latest methodological approaches or conceptual frameworks take some time to make their way into the marine mammal field. At ECCWO, I was part of discussions at the ground floor of how the scientific community can characterize the impacts of climate change on the ecosystems, species, and communities we study.

One particular theme became increasingly apparent to me throughout the conference: as the oceans warm, what are “anomalous conditions”? There was an interesting dichotomy between presentations focusing on “extreme events,” “no-analog conditions,” or “non-stationary responses,” compared with discussions about the overall trend of increasing temperatures due to climate change. Essentially, the question that kept arising was, what is our frame of reference? When measuring change, how do we define the baseline?

Marine heatwaves have emerged as an increasingly prevalent phenomenon in recent years (see previous GEMM Lab blogs about marine heatwaves here and here). The currently accepted and typically applied definition of a marine heatwave is when water temperatures exceed a seasonal threshold (greater than the 90th percentile) for a given length of time (five consecutive days or longer) (Hobday et al. 2016). These marine heatwaves can have substantial ecosystem-wide impacts including changes in water column structure, primary production, species composition, distribution, and health, and fisheries management such as closures and quota changes (Cavole et al. 2016, Oliver et al. 2018). Through some of our own previous research, we documented that blue whales in Aotearoa New Zealand shifted their distribution (Barlow et al. 2020) and reduced their reproductive effort (Barlow et al. 2023) in response to marine heatwaves. Concerningly, recent projections anticipate an increase in the frequency, intensity, and duration of marine heatwaves under global climate change (Frölicher et al. 2018, Oliver et al. 2018).

However, as the oceans continue to warm, what baseline do we use to define anomalous events like marine heatwaves? Members of the US National Oceanic and Atmospheric Administration (NOAA) Marine Ecosystem Task Force recently put forward a comment article in Nature, proposing revised definitions for marine heatwaves under climate change, so that coastal communities have the clear information they need to adapt (Amaya et al. 2023). The authors posit that while a “fixed baseline” approach, which compares current conditions to an established period in the past and has been commonly used to-date (Hobday et al. 2016), may be useful in scenarios where a species’ physiological limit is concerned (e.g., coral bleaching), this definition does not incorporate the combined effect of overall warming due to climate change. A “shifting baseline” approach to defining marine heatwaves, in contrast, uses a moving window definition for what is considered “normal” conditions. Therefore, this shifting baseline approach would account for long-term warming, while also calculating anomalous conditions relative to the current state of the system.

An overview of two different definitions for marine heatwaves, relative to either fixed or shifting baselines. Reproduced from Amaya et al. 2023.

Why bother with these seemingly nuanced definitions and differences in terminology, such as fixed versus shifting baselines for defining marine heatwave events? The impacts of these events can be extreme, and potentially bear substantial consequences to ecosystems, species, and coastal communities that rely on marine resources. With the fixed baseline definition, we may be headed toward perpetual heatwave conditions (i.e., it’s almost always hotter than it used to be), at which point disentangling the overall warming trends from these short-term extremes becomes nearly impossible. What the shifting baseline definition means in practice, however, is that in the future temperatures would need to be substantially higher than the historical average in order to qualify as a marine heatwave, which could obscure public perception from the concerning reality of warming oceans. Yet, the authors of the Nature comment article claim, “If everything is extremely warm all of the time, then the term ‘extreme’ loses its meaning. The public might become desensitized to the real threat of marine heatwaves, potentially leading to inaction or a lack of preparedness.” Therefore, clear messaging surrounding both long-term warming and short-term anomalous conditions are critically important for adaptation and resource allocation in the face of rapid environmental change.

While the findings presented and discussed at an international climate change conference could be considered quite disheartening, I left the ECCWO conference feeling re-invigorated with hope. Crown Prince Haakon of Norway gave the opening plenary and articulated that “We need wise and concerned scientists in our search for truth”. Later in the week, I was a co-convenor of a session that gathered early-career ocean professionals, where we discussed themes such as how we deal with uncertainty in our own climate change-related ocean research, and importantly, how do we communicate our findings effectively. Throughout the meeting, I had formal and informal discussions about methods and analytical techniques, and also about what connects each of us to the work that we do. Interacting with driven and dedicated researchers across a broad range of disciplines and career stages gave me some renewed hope for a future of ocean science and marine conservation that is constructive, collaborative, and impactful.

Enjoying the ~anomalously~ sunny April weather in Bergen, Norway, during the ECCWO conference.

Now, as I am diving back in to understanding the impacts of environmental conditions on harbor porpoise and gray whale habitat use patterns through the EMERALD project, I am keeping these themes and takeaways from the ECCWO conference in mind. The EMERALD project draws on a dataset that is about as old as I am, which gives me some tangible perspective on how things have things changed in the Northern California Current during my lifetime. We are grappling with what “anomalous” conditions are in this dynamic upwelling system on our doorstep, whether these anomalies are even always bad, and how conditions continue to change in terms of cyclical oscillations, long-term trends, and short-term events. Stay tuned for what we’ll find, as we continue to disentangle these intertwined patterns of change.

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

Amaya DJ, Jacox MG, Fewings MR, Saba VS, Stuecker MF, Rykaczewski RR, Ross AC, Stock CA, Capotondi A, Petrik CM, Bograd SJ, Alexander MA, Cheng W, Hermann AJ, Kearney KA, Powell BS (2023) Marine heatwaves need clear definitions so coastal communities can adapt. Nature 616:29–32.

Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG (2020) Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar Ecol Prog Ser 642:207–225.

Barlow DR, Klinck H, Ponirakis D, Branch TA, Torres LG (2023) Environmental conditions and marine heatwaves influence blue whale foraging and reproductive effort. Ecol Evol 13:e9770.

Cavole LM, Demko AM, Diner RE, Giddings A, Koester I, Pagniello CMLS, Paulsen ML, Ramirez-Valdez A, Schwenck SM, Yen NK, Zill ME, Franks PJS (2016) Biological impacts of the 2013–2015 warm-water anomaly in the northeast Pacific: Winners, losers, and the future. Oceanography 29:273–285.

Frölicher TL, Fischer EM, Gruber N (2018) Marine heatwaves under global warming. Nature 560.

Hobday AJ, Alexander L V., Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Sen Gupta A, Wernberg T (2016) A hierarchical approach to defining marine heatwaves. Prog Oceanogr.

Oliver ECJ, Donat MG, Burrows MT, Moore PJ, Smale DA, Alexander L V., Benthuysen JA, Feng M, Sen Gupta A, Hobday AJ, Holbrook NJ, Perkins-Kirkpatrick SE, Scannell HA, Straub SC, Wernberg T (2018) Longer and more frequent marine heatwaves over the past century. Nat Commun 9:1–12.

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!

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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]

Navigating the Research Rollercoaster

By Amanda Rose Kent, College of Earth Ocean and Atmospheric Sciences, OSU, GEMM Lab/Krill Seeker undergraduate intern

If you asked me five years ago where I’d thought I’d be today, the answer I would give would not reflect where I am now. Back then, I was a customer service representative for a hazardous waste company, and I believed that going to university and participating in research was a straightforward experience. I learned soon after I left that career and began my journey at OSU in 2020 that I wasn’t even remotely aware of the process. I knew that as part of my oceanography degree I would need to become involved in some form of research, but I had no idea where to start.

I started looking through the Oregon State website and I eventually found an outdated flier from 2018 that advertised a lab that studied plankton in Antarctica, and that was when I first reached out to Dr. Kim Bernard. My journey took off from there. As an undergraduate researcher in the URSA Engage program working with Kim and one of her graduate students, Rachel, I conducted a literature review on the ecosystem services provided by two species of krill off the coast of Oregon, including their value to baleen whales. After learning all I could from the literature about krill and how important they were to the ocean, I knew that there was so much more to learn and that this was the topic I wanted to continue to pursue. After I completed the URSA program, I remained a member of Kim’s zooplankton ecology lab.

While continuing to work with Rachel, I was given the opportunity to join the GEMM Lab’s Project HALO for a daylong cruise conducting a whale survey along the Newport Hydrographic Line. I was initially brought on to learn how to use the echosounder to collect krill data but unfortunately, the device had technical difficulties and Rachel and I were no longer needed. We decided to go on the cruise anyway, and I was able to instead learn how to survey for marine mammals (it’s not as easy as it may seem, but still very fun!).

Figure 1. Enjoying the point of view from the crow’s nest on the R/V Pacific Storm, but also very cold.

Soon, another opportunity arose to apply for a brand-new program called ARC-Learn. This two-year research program focuses on studying the Arctic using publicly available data, and with the support of my mentors, I applied and was accepted. Initially I found that there were no mentors within the program that studied krill, so I found myself becoming immersed in a new topic: harmful algal blooms (HABs). Determined to incorporate krill into this research, I started looking through the literature trying to develop my hypothesis that HABs affected zooplankton in some way. There was evidence to potentially support my hypothesis, but I ended up encountering numerous data gaps in the region I was studying. After months of roadblocks, I eventually started feeling defeated and regretted applying for the program. Rachel was quick to remind me that all experiences are valuable experiences, and that I was still gaining new skills I could use in graduate school or my career.

As my undergraduate degree progressed, I continued supporting Rachel in her graduate research, spending some time during the summer processing krill samples by sorting, sexing, and drying them to crush them into pellets. Our goal was to process them in an instrument called a bomb calorimeter, which is used to quantify the caloric content of prey species and help us better understand the energy flux required for animals higher up the food chain (like whales) and the amount they need to eat. I was only able to do this for a few weeks before heading out on the experience of a lifetime, spending three weeks on a ship traveling around the Bering, Chukchi, and Beaufort Seas with one of my ARC-Learn mentors. It was a great opportunity for me to see the toxic phytoplankton (which can form HABs) I had been studying and learn about methods of sample collection and processing. If I could go back and do it again, I’d go in a heartbeat.

Figure 2. Pulling out all of the animal biomass out of the Arctic sediment.

At the beginning of my bachelor’s degree, I had expected to just work with Kim and conduct research within her lab. Instead, I have had opportunities I would never have expected five years ago. I have learned a vast amount from my graduate mentor, Rachel, which has helped influence my trajectory in my degree. I have had the privilege to not only meet giants in the field I’m interested in, but also work with them and learn from them, and to spend three weeks in the Arctic Ocean.  The experiences I have had throughout this roller-coaster helped me develop a project idea with new mentors that I eventually hope to pursue in my master’s degree. I wasn’t prepared for the number of adjustments I would make to find new experiences and start new projects, but all the experiences I had were necessary to learn about what I was interested in and what I wanted to pursue. Looking back on it all today, I have zero regrets.

Figure 3. A picture of the Norseman II, the ship I was on in the Arctic, taken by the Japanese ship JAMSTEC on a short rendezvous between the Chukchi and Beaufort Seas.

Migrating south to another foraging ground

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 

Krill, a shrimplike crustacean found across our oceans, embodies the term “small but mighty”. Though individuals tend to be small, sometimes weighing in at less than a gram, the numerous species of krill have a global distribution and are estimated to collectively outweigh the entire human population. Much of my graduate research focuses on relationships between foraging whales and krill (Euphausia pacifica and Thysanoessa spinifera) in the Northern California Current (NCC) region. This work hinges on themes that are universal across environments: just as krill are ubiquitous across the global ocean, questions of prey quality, distribution, and ecological relationships with predators are universal.

Next week, I’m headed south to consider these questions in a very different foraging environment: the Western Antarctic Peninsula (WAP). One benefit of being a co-advised student is the incredible opportunity to be exposed to diverse projects and types of research. My graduate co-advisor, Kim Bernard, has studied krill in the WAP region for over a decade, and she is currently leading research into the implications of the shifting polar food web for Antarctic krill (Euphasia superba). Through a series of laboratory experiments and fieldwork, the project, titled “The Omnivore’s Dilemma: The effect of autumn diet on winter physiology and condition of juvenile Antarctic krill”,  investigates the impact of climate-driven changes in diet on the health of juvenile krill in autumn and winter, a key time for their survival and recruitment. Winter is a poorly studied season in Antarctica, and this project has already shed light on the physiology, respiration, and growth potential of juvenile krill (Bernard et al., 2022).

 Figure 1: Antarctic krill are much bigger than those found in the NCC region – they can be as long as your thumb! (Source: Australian Antarctic Program)

Just as in the NCC region, krill are an essential link in Southern Ocean food webs, where they transfer energy from their microscopic prey to the higher trophic levels that eat them, including several species of fish, seals, penguins, and whales (Bernard & Steinberg, 2013; Cavan et al., 2019; Ducklow et al., 2013). These predators depend upon this high-quality prey to fuel their seasonal migrations and to build the energy reserves they need to survive the frigid Antarctic winter (Cade et al., 2022; Schaafsma et al., 2018). But, the quality of krill depends upon the food that it can consume itself, and climate change may alter their diet.

There’s a lot to love about krill, but my fascination with them is directly tied to their value as a food source for predators. I want to know how the caloric content of individuals and the aggregations they form changes spatially along the WAP, and how this might shift under climate-forced food web changes. This work will clarify the climate-driven variability in the quality of krill as prey, and the implications this might have for top predators in the region.

Figure 2: The upcoming field season will involve sampling krill along a latitudinal gradient in the WAP region, spanning approximately from the Gerlache Strait in the north to Marguerite Bay in the south (Bernard et al., 2022).

In order to investigate these questions, I’ll be spending the next six months based out of Palmer Station, the smallest of the United States’ research bases in Antarctica, along with Kim and our undergraduate intern Abby. During this upcoming field season, we’ll spend about a month at sea collecting krill samples and active acoustic data using an echosounder, and the rest of the time conducting experiments and sampling in the nearshore. Over the last year, Abby has worked with me to quantify krill caloric content in the NCC, as well as processing samples collected in Antarctica last year. I’m so impressed by everything she’s accomplished, and excited to see her take in this environment, learn a fresh set of experimental and field sampling approaches, and be inspired to ask new questions.

Figure 3: Abby preparing NCC krill samples for caloric analysis (Kim Kenny/OSU CEOAS).

For me, heading south will be a bit like coming home. After graduating from college, I spent about nine months living at Palmer Station and working on the microbial ecology component of the long-term ecological research station there. The experience of being immersed in the WAP environment was foundational to my curiosity about ocean ecology and the impacts of climate change. It is also where I met Kim! All in all, this environment fueled my desire to study krill with Kim and spatial ecology with Leigh, and set me on the course I’m on today.

It also feels meaningful to return here again at this point in my educational journey. With new knowledge and questions I have formed while working in the NCC, I am now excited to apply this knowledge and consider similar questions in the WAP. Abby and I will write blogs through the season and post them here, so stay tuned for news from down south!

Figure 4: Kim and I (the two farthest right in the front row) prepare for a group costumed polar plunge in 2015. Will we do it again? We’ll keep you posted!

References

Bernard, K. S., & Steinberg, D. K. (2013). Krill biomass and aggregation structure in relation to tidal cycle in a penguin foraging region off the Western Antarctic Peninsula. ICES Journal of Marine Science, 70(4), 834–849. https://doi.org/10.1093/icesjms/fst088

Bernard, K. S., Steinke, K. B., & Fontana, J. M. (2022). Winter condition, physiology, and growth potential of juvenile Antarctic krill. Frontiers in Marine Science, 9, 990853. https://doi.org/10.3389/fmars.2022.990853

Cade, D. E., Kahane-Rapport, S. R., Wallis, B., Goldbogen, J. A., & Friedlaender, A. S. (2022). Evidence for Size-Selective Predation by Antarctic Humpback Whales. Frontiers in Marine Science, 9, 747788. https://doi.org/10.3389/fmars.2022.747788

Cavan, E. L., Belcher, A., Atkinson, A., Hill, S. L., Kawaguchi, S., McCormack, S., Meyer, B., Nicol, S., Ratnarajah, L., Schmidt, K., Steinberg, D. K., Tarling, G. A., & Boyd, P. W. (2019). The importance of Antarctic krill in biogeochemical cycles. Nat Commun, 10(1), 4742. https://doi.org/10.1038/s41467-019-12668-7

Ducklow, H., Fraser, W., Meredith, M., Stammerjohn, S., Doney, S., Martinson, D., Sailley, S., Schofield, O., Steinberg, D., Venables, H., & Amsler, C. (2013). West Antarctic Peninsula: An Ice-Dependent Coastal Marine Ecosystem in Transition. Oceanography, 26(3), 190–203. https://doi.org/10.5670/oceanog.2013.62

Schaafsma, F. L., Cherel, Y., Flores, H., van Franeker, J. A., Lea, M.-A., Raymond, B., & van de Putte, A. P. (2018). Review: The energetic value of zooplankton and nekton species of the Southern Ocean. Marine Biology, 165(8), 129. https://doi.org/10.1007/s00227-018-3386-z

SST, EKE, SSH: Wading Through the Alphabet Soup of Oceanographic Parameters related to Deep-Dwelling Odontocetes

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

Predator-Prey Inference: A Tale as Old as Time

It’s a tale as old as time: where there’s prey, there’ll be predators.

As apex predators, cetaceans act as top-down regulators of ecosystem function. While baleen whales act as “ecosystem engineers,” facilitating nutrient cycling in the ocean (Roman et al., 2014), toothed whales, or “odontocetes,” can impart keystone-level effects — that is, they disproportionately control the marine community’s food-web structure (Valls, Coll, & Christensen, 2015). The menus of prey vary widely by species — ranging from mircronekton to fish to squid – and by extension, vary widely across trophic levels.

So, it naturally follows the old adage: where there’s an abundance of prey, there’ll be an abundance of cetaceans. Yet, creating models that accurately depict this predator-prey relationship is, perhaps unsurprisingly, not as straightforward.

Detecting the ‘Predator’ Half of the Equation

Scientists have successfully documented cetacean presence drawing upon a myriad of methods, each bearing its unique advantages and limitations.

Visual surveys — spanning viewpoints from land, boats, and air — can attain precise spatial data and species ID. However, this data can be constrained by “availability bias” — that is, scientists can only observe cetaceans visible at the surface, not those obscured by the ocean’s depths. Species that spend less time near the surface are more likely to elude the observer’s line of sight, thereby being missed in the data. Consequently, visual surveys have historically undersampled deep-diving species. For instance, since its discovery by western science in 1945, the Hubb’s beaked whale (Mesoplodon carlshubbi) has only been observed alive twice by OSU MMI’s very own Bob Pitman, once in 1994 and another time in 2021.

Scientists have also been increasingly conducting acoustic surveys to document cetacean presence. Acoustic recorders can “hear” each cetacean species at different ranges. Baleen whales, which bellow low-frequency calls, can be heard as far as across ocean basins (Munk et al., 1994). Toothed whales whistle, echolocate, and buzz at frequencies so high they’re considered ultrasonic. But it comes at a trade-off: high-frequency sounds have shorter wavelengths, meaning they are heard across smaller ranges. This high variability, which scientists refer to as “detection range,” translates to not always knowing where the vocalizing cetacean that was recorded is: as such, acoustic data can lack the high-resolution spatial precision often achieved by visual surveys. Nevertheless, acoustic data triumphs in temporal extent, sometimes managing to record continuously at six months at a time. Additionally, animals can elude visual detection in poor weather conditions or if they have a cryptic surface expression, but detected in acoustic surveys (e.g., North Atlantic right whales (Eubalaena glacialis) (Ganley, Brault, & Mayo, 2019; Clark et. al, 2010). Thus, acoustic surveys may be especially optimal for recording elusive deep-dwellers that occupy the often rough Oregon waters, such as beaked whales, the focus of my research in collaboration with the GEMM Lab.

Figure 1: HALO Project researchers Marissa Garcia (left; Yang Center via Cornell) and Imogen Lucciano (right; OSU MMI) among three Rockhopper acoustic recording units, ahead of deployment off the Oregon coast. Credit: Marissa Garcia.

Detecting the ‘Prey’ Half of the Equation

Prey can be measured by numerous methods. Most directly, prey can be measured “in-situ” — that is, prey is collected directly from the site where the cetaceans are detected or observed. A 2020 study combined fish trawls with a towed hydrophone array to identify which fish species odontocetes along the continental shelf of West Ireland (e.g., pilot whales, sperm whales, and Sowerby’s beaked whales) were feasting; the results found that odontocetes primarily fed upon mesopelagic fish and cephalopods (Breen et al., 2020). While trawls can glean species ID of prey, associating this prey data with depth and biomass can prove challenging.

Alternatively, prey can be detected via active acoustics. Echosounders release an acoustic signal that descends through the water column and then echoes back once it hits a sound-scattering organism. Beaked whales forage within deep scattering layers typically composed of myctophid fish and squid, both of which can echo back echosounder pings (Hazen et al., 2011). Thus, echosounder data can map prey density through the water column. When mapping prey density of beaked whales, Hazen et al. 2011 found a strong positive correlation among prey density, ocean vertical structure, and clicks primarily produced while foraging – suggesting beaked whales forage at depth when encountering large, multi-species aggregations of prey.

Figure 2: An example of prey mapping via a Simrad EK60 120 kHz split-beam echosounder. Credit: Rachel Kaplan (OSU MMI) via the HALO Project.

Most relevant to the HALO Project, prey is measured using proximate indices, which are more easily quantifiable metrics of ocean conditions, such as collected from ships via CTD casts or via satellite imagery, that are indirectly related to prey abundance. CTD data can provide information related to the water column structure, including depth and strength of the thermocline, depth of the mixed layer, depth of the euphotic zone, and total chlorophyll concentration in the euphotic zone (Redfern et al. 2006). Satellite imagery can characterize the dynamic patterns of the surface later, including sea surface temperature (SST), salinity, surface chlorophyll a, sea surface height (SSH), and sea surface currents (Virgili et al., 2022; Redfern et al., 2006). Ocean model data products can, such as the Regional Ocean Modeling System (ROMS) which models how an oceanic region of interest responds to physical processes, can provide water column variables related to eddy kinetic energy (EKE) and average temperature gradients (Virgili et al., 2022). In the case of my research with the HALO Project, we will be using oceanographic data collected through the Ocean Observatories Initiative to inform odontocete species distribution models.

Connecting the Dots: Linking Deep-Dwelling Top Predators and Prey

While scientists have made significant advances with collecting both cetacean and prey data, connecting the dots between the ecology of deep-dwelling odontocetes and the oceanographic parameters indicative of their prey still remains a challenge.

In the absence of in situ sampling, species distribution models of marine top predators often derive proxies for “prey data” from static bathymetric and dynamic surface water variables (Virgili et al., 2022). However, surface variables may be irrelevant to toothed whale prey inhabiting great depths (Virgili et al., 2022). Within the HALO Project, the deepest Rockhopper acoustic recording unit is recording odontocetes at nearly 3,000 m below the surface, putting into question the relevance of oceanographic parameters collected at the surface.

Figure 3: Schematic depicting the variation among different zones in the water column. Conditions at the surface may not represent conditions at depth. Credit: Barbara Ambrose, NOAA via NOAA Ocean Explorer.

In my research, I am setting out to estimate which oceanographic variables are optimal for explaining deep-dwelling odontocete presence. A 2022 study using visual survey data found that surface, subsurface, and static variables best explained beaked whale presence, whereas only surface and deep-water variables – not static – best explained sperm whale presence (Virgili et al., 2022). These results are associated with each species’ distinct foraging ecologies; beaked whales may truly only rely on organisms that live near the seabed, whereas sperm whales also feast upon meso-to-bathypelagic organisms, so they may be more sensitive to changes in water column conditions (Virgili et al., 2022). This study expanded the narrative: deep-water variables can also be key to predicting deep-dwelling odontocete presence. The oceanographic variables must be tailored to the ecology of each species of interest.

In the months ahead, I seek to build on this study by investigating which parameters best predict odontocete presence using an acoustic approach instead — I am looking forward to the results to come!

References

Breen, P., Pirotta, E., Allcock, L., Bennison, A., Boisseau, O., Bouch, P., Hearty, A., Jessopp, M., Kavanagh, A., Taite, M., & Rogan, E. (2020). Insights into the habitat of deep diving odontocetes around a canyon system in the northeast Atlantic ocean from a short multidisciplinary survey. Deep-Sea Research. Part I, Oceanographic Research Papers, 159, 103236. https://doi.org/10.1016/j.dsr.2020.103236

Clark, C.W., Brown, M.W., & Corkeron, P. (2010). Visual and acoustic surveys

for North Atlantic right whales, Eubalaena glacialis, in Cape Cod Bay, Massachusetts, 2001–2005: Management implications. Marine Mammal Science, 26(4), 837-854.

Ganley, L.C., Brault, S., & Mayo, C.A. (2019). What we see is not what there is: Estimating North Atlantic right whale Eubalaena glacialis local abundance. Endangered Species Research, 38, 101-113.

Hazen, E. L., Nowacek, D. P., St Laurent, L., Halpin, P. N., & Moretti, D. J. (2011). The relationship among oceanography, prey fields, and beaked whale foraging habitat in the Tongue of the Ocean. PloS One, 6(4), e19269–e19269.

Munk, W. H., Spindel, R. C., Baggeroer, A., & Birdsall, T. G. (1994). The Heard Island Feasibility Test. The Journal of the Acoustical Society of America, 96(4), 2330–2342. https://doi.org/10.1121/1.410105

Redfern, J. V., Ferguson, M. C., Becker, E. A., Hyrenbach, K. D., Good, C., Barlow, J., Kaschner, K., Baumgartner, M. F., Forney, K. A., Ballance, L. T., Fauchald, P., Halpin, P., Hamazaki, T., Pershing, A. J., Qian, S. S., Read, A., Reilly, S. B., Torres, L., & Werner, F. (2006). Techniques for cetacean–habitat modeling. Marine Ecology. Progress Series (Halstenbek), 310, 271–295.

Roman, J., Estes, J. A., Morissette, L., Smith, C., Costa, D., McCarthy, J., Nation, J., Nicol, S., Pershing, A., & Smetacek, V. (2014). Whales as marine ecosystem engineers. Frontiers in Ecology and the Environment, 12(7), 377–385.

Valls, A., Coll, M., & Christensen, V. (2015). Keystone species: toward an operational concept for marine biodiversity conservation. Ecological Monographs, 85(1), 29–47.

Virgili, A., Teillard, V., Dorémus, G., Dunn, T. E., Laran, S., Lewis, M., Louzao, M., Martínez-Cedeira, J., Pettex, E., Ruiz, L., Saavedra, C., Santos, M. B., Van Canneyt, O., Vázquez Bonales, J. A., & Ridoux, V. (2022). Deep ocean drivers better explain habitat preferences of sperm whales Physeter macrocephalus than beaked whales in the Bay of Biscay. Scientific Reports, 12(1), 9620–9620.

Dealing with uncertainty in ecology and conservation biology

By Dr. Alejandro A. Fernández Ajó, Postdoctoral Scholar, Marine Mammal Institute – OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab. 

Ecological research focuses on understanding how species and ecosystems interact and function, as well as understanding what drives changes in these interactions and functions over time. Thus, ecology is a critical component of conservation biology. Although uncertainty is present in any research, it is a pervasive characteristic of ecology and conservation biology, often due to our inability to control the complexity of natural systems. Uncertainty poses challenges to decision-making, policy development, and effective conservation strategies, and therefore needs to be understood and addressed when conducting ecological studies and conservation efforts.

There are several sources of uncertainty in ecological research and conservation biology. One of the primary sources arises from incomplete or limited data (epistemic uncertainty). Ecological systems are complex, and obtaining comprehensive data on all relevant variables and scales is often challenging or impossible. Data may be lacking or unavailable for certain species, habitats, or regions, which can hinder the ability to fully understand ecological dynamics and make accurate predictions. Additionally, ecological data may be uncertain or variable due to measurement errors (see blog post), sampling biases, or changes in data collection methods over time (Regan et al. 2002). Furthermore, another source of uncertainty arises from language (linguistic uncertainty). Linguistic uncertainty can result from lack of agreement in the terms and definitions used in the scientific vocabulary (see blog post), which can often result in ambiguous, vague, or context dependent interpretations (Regan et al 2002). These two source-types of uncertainty can create a complex set of challenges.

Uncertainty in ecological research and conservation biology has important implications for decision-making and policy development. When faced with uncertain information, decision-makers may adopt a cautious approach, leading to delayed or ineffective conservation actions. Alternatively, they may make decisions based on incomplete or biased data, which can lead to unintended consequences or wasted resources. Uncertainty can also affect the public’s perception of ecological issues, leading to skepticism, misinformation, or lack of support for conservation initiatives. In addition, uncertainty can also pose challenges in setting conservation priorities. With limited resources, conservation organizations and policymakers must prioritize efforts to protect species or habitats that are at the greatest risk. However, uncertainties in data or predictions can affect the accuracy of risk assessments, leading to potential misallocation of resources. Finally, uncertainty may also arise when assessing the success of conservation interventions, making it difficult to determine the effectiveness of the conservation actions.

Despite the challenges posed by uncertainty, there are ways to address and mitigate its impacts in ecological research and conservation biology. Here are some strategies that the GEMM Lab implements to navigate these nuances in ecological research:

Improving data quality and quantity: Robust data can provide a more accurate understanding of ecological dynamics and facilitate evidence-based decision-making. In this direction, the GEMM Lab develops comprehensive data collection and monitoring efforts that can help reduce uncertainty. The TOPAZ and GRANITE projects, which study gray whale ecology off the Oregon coast, are good examples in this direction due to continuous research efforts since 2015. With these projects we have developed and standardized data collection and analytical methods, improved data accuracy and precision, and are filling knowledge gaps through targeted research.

Emphasizing adaptive management: Adaptive management is an approach that involves learning from ongoing conservation actions and adjusting strategies based on new information (Allen et al. 2015). This approach recognizes that uncertainties are inherent in ecological systems and promotes flexibility in conservation planning. Monitoring and evaluating conservation interventions, and adjusting management strategies, accordingly, can help mitigate the impacts of uncertainty. With OBSIDIAN, OPAL, and HALO projects the GEMM Lab works towards a better understanding of cetaceans’ distribution and its interactions with the oceanographic conditions (e.g., ocean temperature). These research projects can help to forecast the occurrence of whale aggregations and inform management to reduce conflicts when overlapping with human activities. For instance, results from the OPAL project have been incorporated into Dungeness Crab fishing regulations to reduce entanglement risk to whales, and the GEMM Lab is now investigating the effectiveness of these regulations in the SLATE project.

With these projects, along with the many other research efforts conducted by the GEMM lab and the MMI, we are advancing research in marine ecology, through the development and application the best possible science to generate the needed ecological data for effective conservation and management of the marine environment.

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Sources:

Regan, H. M., Colyvan, M., & Burgman, M. A. (2002). A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecological applications, 12(2), 618-628.

Allen, C. R., & Garmestani, A. S. (2015). Adaptive management (pp. 1-10). Springer Netherlands.

https://mmi.oregonstate.edu/gemm-lab/research-projects

https://mmi.oregonstate.edu/gemm-lab/halo-holistic-assessment-living-marine-resources-oregon

https://mmi.oregonstate.edu/gemm-lab/obsidian-observing-blue-whale-spatial-ecology-investigate-distribution-aotearoa-new-zealand

https://mmi.oregonstate.edu/gemm-lab/opal-overlap-predictions-about-large-whales-identifying-co-occurrence-between-whales

https://mmi.oregonstate.edu/gemm-lab/granite-gray-whale-response-ambient-noise-informed-technology-ecology

https://mmi.oregonstate.edu/gemm-lab/topaz-theodolite-overlooking-predators-zooplankton-gray-whale-foraging-ecology