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 experiencedpersonhelp 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.
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.
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.
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.
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.
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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.
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.
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.
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.
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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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
By Charles Nye, graduate student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Cetacean Conservation and Genomics Laboratory
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?”
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.
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!
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.
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!).
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.
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.