Tag: foraging ecology

New publication shows humpback whale distribution in the Northern California Current is related to krill swarm biomass, energetic density, and species composition

By Rachel Kaplan, PhD candidate, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

What does a whale look for at mealtime? Is it a lot of food, its quality, or the type of food? An improved understanding of what makes krill swarms, an important prey item, appetizing for humpback whales can help us anticipate where and when we will see them in our ocean backyard, the Northern California Current (NCC) foraging grounds. In a new paper, we found that humpback whale presence in the NCC is tied to several different metrics of krill swarm quality and quantity, particularly species composition (what types of krill are in the swarm), energetic density (the caloric richness of the average mouthful), and biomass (how much krill is in the swarm). Interestingly, relationships between humpback whales and these krill swarm quality metrics are variable in time and space, dependent on whether the whale is foraging on or off the continental shelf and if it is early or late in the foraging season.

This study required a special, fine-scale dataset of simultaneous observations of krill and whales at sea. While GEMM Lab members conducted marine mammal surveys, we simultaneously observed the prey that whales had access to, using active acoustics (essentially a fancy fish finder) to profile the water column and net tows to collect krill. When we put all these data streams together, we found that increases in biomass, energetic density, and the amount of a particular species, Thysanoessa spinifera, in a krill swarm were positively related to humpback whale presence. These results suggest that humpback whales balance multiple prey quality factors to select feeding areas that offer both plentiful and high-quality krill.

Figure 1. Top photo: Marine mammal observers Clara Bird (left) and Dawn Barlow (right) collect humpback whale distribution data. Bottom photo: At the same time, Talia Davis (left) and Rachel Kaplan (right) collect krill samples.

Species composition

Euphausia pacifica and T. spinifera are the two most common krill species in the NCC region, and other research has shown that many krill foragers, including blue whales, seabirds, and fish, preferentially consume T. spinifera. Although this pickiness is well-warranted – individual T. spinifera tend to be larger than E. pacifica and much higher in calories during the late foraging season – targeting this juicy prey item could place humpback whales in competition with these other species, which may make it harder for them to find a square meal. Nevertheless, we found positive relationships between the proportion of T. spinifera in a krill swarm and humpback whale presence, suggesting humpback whales do in fact preferentially prey upon T. spinifera, particularly during the late foraging season (about July-November).

Energetic density

Humpback whales’ preference for T. spinifera during the late foraging season may be due to its higher caloric content. Although the two krill species offer a similar number of calories early in the foraging season,we found that the energetic density of T. spinifera was elevated during the late foraging season, after productive upwelling conditions have revved up the food web over several months. Krill swarm energetic density had a positive effect on humpback whale occurrence, particularly in the late season when T. spinifera and E. pacifica have significantly different caloric contents. Interestingly, this positive relationship was not present onshore during the early season, when the two krill species have similar caloric contents.

Figure 2. In terms of caloric content, Thysanoessa spinifera krill like this one are the winners in the NCC region! They pack on the milligrams through the productive summer season, making them advantageous prey for hungry whales.

Humpback whales also target forage fish on the continental shelf that have higher energetic densities than krill, indicating that whales may selectively forage on fish – even though it is more energetically expensive to capture them. Variation in seasonal and spatial relationships with krill swarm energetic density may explain why humpback whales prey-switch, selecting prey based on availability and quality. As flexible foragers, humpback whales can consistently target higher-quality swarms that offer more energy per lunge.

Biomass

Biomass, or the total amount of krill in a swarm, was the single best predictor of humpback whale presence that we tested. This result emphasizes the importance of large krill swarms in explaining where humpback whales forage. We found that krill swarm biomass tended to be higher offshore, where swarms were also located deeper in the water column. During the late season offshore, krill quality (elevated due to higher late season caloric contents) together with quantity (higher offshore biomass) may make these offshore swarms the most favorable for foraging whales, despite being deeper.

Figure 3. When humpback whales “fluke,” as seen in this picture, it may indicate the beginning of a foraging dive to capture prey.

Future food webs

Environmental conditions are changing in the NCC, with events like marine heatwaves and strong El Niño events shifting food webs. E. pacifica and T. spinifera may respond to climate change differently based on their life history strategies. Distributional shifts, such as the disappearance of T. spinifera from the NCC during the 2014–2015 “Blob” marine heatwave that transformed the northeast Pacific Ocean, could diminish or entirely remove this key prey item. As a result of such climate and environmental changes, humpback whales may encounter lower quality prey and/or shifts in prey distribution that could make it harder for them to find a meal. In changing oceans, better understanding krill prey quality for humpback whales will shape improved tools for conservation management.

References

Chenoweth, E., Boswell, K., Friedlaender, A., McPhee, M., Burrows, J., Heintz, R., and Straley, J. 2021. Confronting assumptions about prey selection by lunge‐feeding whales using a process‐based model. Funct. Ecol., 35.

Croll, D., Marinovic, B., Benson, S., Chavez, F., Black, N., Ternullo, R., and Tershy, B. 2005. From wind to whales: trophic links in a coastal upwelling system. Mar. Ecol. Prog. Ser., 289: 117–130.

Derville, S., Buell, T. V., Corbett, K. C., Hayslip, C., and Torres, L. G. 2023. Exposure of whales to entanglement risk in Dungeness crab fishing gear in Oregon, USA, reveals distinctive spatio-temporal and climatic patterns. Biol. Conserv., 281: 109989.

Fiedler, P. C., Reilly, S. B., Hewitt, R. P., Demer, D., Philbrick, V. A., Smith, S., Armstrong, W., et al. 1998. Blue whale habitat and prey in the California Channel Islands. Deep Sea Res. Part II, 45: 1781–1801.

Fisher, J. L., Menkel, J., Copeman, L., Shaw, C. T., Feinberg, L. R., and Peterson, W. T. 2020. Comparison of condition metrics and lipid content between Euphausia pacifica and Thysanoessa spinifera in the northern California Current, USA. Prog. Oceanogr., 188.

Murdoch, W. W. 1969. Switching in General Predators: Experiments on Predator Specificity and Stability of Prey Populations. Ecol. Monog., 39: 335–354.

Nickels, C. F., Sala, L. M., and Ohman, M. D. 2018. The morphology of euphausiid mandibles used to assess selective predation by blue whales in the southern sector of the California Current System. J. Crustacean Biol., 38: 563–573.

Price, S. E., Savoca, M. S., Kumar, M., Czapanskiy, M. F., McDermott, D., Litvin, S. Y., Cade, D. E., et al. 2024. Energy densities of key prey species in the California Current Ecosystem. Front. Mar. Sci., 10: 1345525.

Robertson, R. R., and Bjorkstedt, E. P. 2020. Climate-driven variability in Euphausia pacifica size distributions off northern California. Prog. Oceanogr., 188.

Santora, J. A., Mantua, N. J., Schroeder, I. D., Field, J. C., Hazen, E. L., Bograd, S. J., Sydeman, W. J., et al. 2020. Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat Commun, 11: 536.

Spitz, J., Trites, A. W., Becquet, V., Brind’Amour, A., Cherel, Y., Galois, R., and Ridoux, V. 2012. Cost of Living Dictates what Whales, Dolphins and Porpoises Eat: The Importance of Prey Quality on Predator Foraging Strategies. PLoS ONE, 7: e50096.

Tanasichuk, R. 1998a. Interannual variations in the population biology and productivity of Thysanoessa spinifera in Barkley Sound, Canada, with special reference to the 1992 and 1993 warm ocean years. Mar. Ecol. Prog. Ser., 173: 181–195.

Videsen, S. K. A., Simon, M., Christiansen, F., Friedlaender, A., Goldbogen, J., Malte, H., Segre, P., et al. 2023. Cheap gulp foraging of a giga-predator enables efficient exploitation of sparse prey. Sci. Adv., 9: eade3889.

Weber, E. D., Auth, T. D., Baumann-Pickering, S., Baumgartner, T. R., Bjorkstedt, E. P., Bograd, S. J., Burke, B. J., et al. 2021. State of the California Current 2019–2020: Back to the Future With Marine Heatwaves? Front. Mar. Sci., 8.

 

Burning Krillories – Determining Krill Caloric Content in New Zealand’s South Taranaki Bight

By Nina Mahalingam, University of California Davis, OSU CEOAS REU program

Hello! I’m Nina Mahalingam, a rising junior at the University of California, Davis studying biochemistry and molecular biology. Growing up in New Hampshire and Massachusetts, the Boston Aquarium was practically in my backyard –  and with just one feel of a touch tank, a lifelong affinity for marine sciences began. CEOAS has provided me with a grand opportunity to pursue this passion, and I can’t wait to dip my toes into the salt water!

Figure 1. Nina posing with a Parr Semimicro Calorimeter.

Here at OSU, I’m researching how our tiny friends, the krill, can provide a krill-uminating perspective on trophic ecology and the vitality of marine ecosystems by investigating the caloric content of an understudied species of krill off the coast of New Zealand. Nyctiphanes australis serves as a key prey species to numerous higher trophic levels. Limited knowledge exists regarding the distribution of N. australis in the South Taranaki Bight (STB), with only a handful of studies focused exclusively on the species. The majority of recent information available on the species in the STB came out of research on blue whales and their foraging behaviors (e.g., Barlow et al., 2020). However, given that the spatial distribution of N. australis directly influences the distribution of predator species that depend on them for sustenance (Barlow et. al. 2020), studying the krill may yield a more comprehensive understanding of blue whale behavior as well as ecosystem resilience.

Figure 2. Nyctiphances australis. Photo by A. Slotwinski, CSIRO.

Seawater temperatures around New Zealand have been increasing since 1981 (Sutton & Bowen, 2019), and there is a growing concern about the implications to marine life. In particular, increasing ocean temperatures have had significant impacts on local aquaculture and fisheries (Sutton et al. 2005; Bowen et al. 2017). Although warming trends along the North Island, north of East Cape, have been more severe (around 0.4℃ increase per decade), warming has also been observed in the central and western areas of the STB, averaging around 0.15-0.20℃ increase per decade (Sutton & Bowen, 2019). During Marine Heat Waves (MHWs) (data collected between 2002 and 2018), warming anomalies were observed to decrease phytoplankton presence (Chiswell & Sutton, 2020). Being krill’s primary food source, this suggests a consequent decrease in krill health and reproduction. A recent study on blue whale reproductive patterns in the STB found that whale feeding activity decreased during MHWs, leading to a decline in their reproductive activity during the following breeding season (Barlow et al., 2020). Concurrently, the study observed that there were less krill aggregations and that they were less dense on average (Barlow et al., 2020). This is presumed to be a result of less upwelling nutrients, and therefore poor conditions for krill feeding and reproduction. These findings indicate that the absence of their primary food source, krill, during MHWs can lead to severely negative consequences for the blue whale populations (Barlow et al., 2023).

Anthropogenic activity in the STB, including high vessel traffic, as well as petroleum and mineral exploration and extraction activities, has also been identified as a threat to the local blue whale population (Torres et. al., 2013). Given the cultural significance of the blue whales in this region, there is an urgent need for improved, dynamic management practices in the STB that can be achieved using predictive models to forecast blue whale spatial distribution. Using environmental factors to inform predictive spatial distribution models (SDMs) of blue whales (Redfern et al. 2006, Elith & Leathwick 2009), Barlow et al. (2021) designed a blue whale forecasting tool for managers and decision-makers in New Zealand.

Given the ecological and cultural significance of blue whales and their krill prey in the STB, a Project SAPPHIRE (Synthesis of Acoustics, Physiology, Prey, and Habitat in a Rapidly changing Environment) was developed to examine the impacts of climate change on the health of these crucial species. The overarching goal of Project SAPPHIRE is to measure prey (krill) and predator (blue whales) response to environmental change off the coast of New Zealand. Despite forecasts of high probability of occurrence of blue whales in the STB during the first field season conducted in January-February 2024, both the blue whales and their krill prey were scarce, and it is currently unclear why. My research will focus on examining the calorie content of N. australis in order to advance understanding of how they fulfill the energetic needs of blue whales. Thus, this data can inform future SDMs to forecast impacts of climate change on New Zealand’s marine ecosystem.

Figure 3. Map of SAPPHIRE’s survey effort for 2024. Gray lines represent visual tracking, dotted lines represent aerial tracking. Red dots represent whale sightings and purple stars indicate where two hydrophones were deployed.

This project has already proven tricky – but I’m ready to embrace the challenge. I would like to thank the CEOAS REU program as well as my mentors Kim Bernard, Rachel Kaplan, and Abby Tomita for their continued support. I can’t wait to see what this summer brings!

References

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. 2023;13:e9770.

Barlow D, Kim S. Bernard, Pablo Escobar-Flores, Daniel M. Palacios, Leigh G. 2020. Torres Links in the trophic chain: modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Marine Ecology Progress Series.

Sutton, P.J.H., & Bowen, M. 2019. Ocean temperature change around New Zealand over the last 36 years. New Zealand Journal of Marine and Freshwater Research, 53(3), 305–326.

Sutton P.J.H., Bowen M, Roemmich D. 2005. Decadal temperature changes in the Tasman Sea. New Zealand Journal of Marine and Freshwater Research. 39:1321–1329.

Bowen M, Markham J, Sutton P, Zhang X, Wu Q, Shears N, Fernandez D. 2017. Interannual variability of sea surface temperatures in the Southwest Pacific and the role of ocean dynamics. Journal of Climate.

Stephen M. Chiswell & Philip J. H. Sutton. 2020. Relationships between long-term ocean warming, marine heat waves and primary production in the New Zealand region. New Zealand Journal of Marine and Freshwater Research.

Reflecting on a solitary journey surrounded by an incredible team

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

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

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

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

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

The GRANITE team during our first in person meeting

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

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

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

The GRANITE team after our third in person meeting.

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

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

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

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Good enough to eat: Dynamics of krill prey quality

By Rachel Kaplan, PhD candidate, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

The Northern California Current region feeds a taxonomically diverse suite of top predators, including numerous species of seabirds, fish, and marine mammals. Baleen whales such as blue, fin, and humpback whales make this productive area a long stop on their seasonal migration, drawn in large part by abundant krill, a shrimp-like zooplankton that serves as an important prey item.

Aspects of both quality and quantity determine whether a prey resource is advantageous for a predator. In the case of whales, sheer biomass is key. It takes a lot of tiny krill to sustain a large whale – literally tons for a blue whale’s daily diet (Goldbogen et al., 2015). Baleen whales are such big eaters that they actually reshape the ocean ecosystem around them (Savoca et al., 2021).

Figure 1. A blue whale lunge feeds on a shallow krill swarm. Read more here.

But the quality of prey, in addition to its quantity, is crucial to ener­getic profitability, and baleen whales must weigh both elements in their foraging decisions. The outcome of those calculations manifest in the diverse feeding strategies that whales employ across ecosystems. In the California Current region, blue whales prefer­entially target the larger, more lipid-rich krill Thysanoessa spinifera (Fiedler et al., 1998). In Antarctica, humpback whales target larger and reproductive krill with higher energetic value, if these extra-juicy varieties are available (Cade et al., 2022). Prey-switching, a strategy in which animals target prey based on relative availability, allows fin whales to  have a more broad diet than blue whales, which are obligate krill predators.

So, what makes krill of high enough quality for a whale to pursue – or low enough quality to ignore? Krill are widely distributed across the NCC region, so why do foraging whales target one krill patch over another?

That whale of a question combines behavior, foraging theory, biochemistry, physics, climate, and more. One key aspect is the composition of a given prey item. Just as for human diet, nutrients, proteins, and calories are where the rubber hits the road in an animal’s energetic budget. The energy density of prey items sets the cost of living for cetaceans, and shapes the foraging strategies they use (Goldbogen et al., 2015; Spitz et al., 2012). In the NCC, T. spinifera krill are more lipid-rich than Euphausia pacifica (Fisher et al., 2020). Pursuing more energy dense prey increases the profitability of a given mouthful and helps a whale offset the energy expended to earn it, including the costly hunt for prey on the foraging grounds (Videsen et al., 2023).

Krill are amazingly dynamic animals in their own right, and they have evolved life history strategies to accommodate a broad range of ocean conditions. They can even exhibit “negative growth,” shrinking their body length in response to challenging conditions or poor food quality. This plasticity in body size can allow krill to survive lean times – but from the perspective of a hungry whale, this strategy also shrinks the available biomass into smaller packages (Robertson & Bjorkstedt, 2020).

One reason why krill are such advantageous prey type for baleen whales is their tendency to aggregate into dense swarms that may contain hundreds of thousands of individuals. The large body size of baleen whales requires them to feed on such profitable patches (Benoit-Bird, 2024). The packing density of krill within aggregations determines how many a whale can capture in one mouthful, and drives patch selection, such as for blue whales in Antarctica (Miller 2019).

Figure 2. The dense swarms formed by krill make them a prime target for many predators, including these juvenile Pacific sardines. (Photo: Richard Herrmann)

However, even the juiciest, densest krill won’t benefit a foraging whale if the energy required to consume it outweighs the gains. The depth of krill in the water column shapes the acrobatic foraging maneuvers blue whales use to feed (Goldbogen et al., 2015), and is a key driver of patch selection (Miller et al., 2019). The horizontal distance between the whale and a new krill patch is important too. Foraging humpback whales adapt their movements to the hierarchical structure of the preyfield, and feeding on neighboring prey schools can reduce the energy and time expended during interpatch travel, increasing net foraging gain (Kirchner et al., 2018).

Prey quality is dynamic, shaped by environmental conditions, extreme events, and climate change processes (Gomes et al., 2024). We can’t yet fully predict how change will alter prey and predator relationships in the NCC region (Muhling et al., 2020), making every step toward understanding prey dynamics relative to environmental variability key to anticipating how whales will fare in an unknown future (Hildebrand et al., 2021). If you are what you eat, then learning more about krill prey quality will give us unique insights into the baleen whales that come from far and wide to the NCC foraging grounds.

References

Benoit-Bird, K. J. (2024). Resource Patchiness as a Resolution to the Food Paradox in the Sea. The American Naturalist, 203(1), 1–13. https://doi.org/10.1086/727473

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

Fiedler, P. C., Reilly, S. B., Hewitt, R. P., Demer, D., Philbrick, V. A., Smith, S., Armstrong, W., Croll, D. A., Tershy, B. R., & Mate, B. R. (1998). Blue whale habitat and prey in the California Channel Islands. Deep Sea Research Part II: Topical Studies in Oceanography, 45(8–9), 1781–1801. https://doi.org/10.1016/S0967-0645(98)80017-9

Fisher, J. L., Menkel, J., Copeman, L., Shaw, C. T., Feinberg, L. R., & Peterson, W. T. (2020). Comparison of condition metrics and lipid content between Euphausia pacifica and Thysanoessa spinifera in the northern California Current, USA. Progress in Oceanography, 188. https://doi.org/10.1016/j.pocean.2020.102417

Goldbogen, J. A., Hazen, E. L., Friedlaender, A. S., Calambokidis, J., DeRuiter, S. L., Stimpert, A. K., & Southall, B. L. (2015). Prey density and distribution drive the three‐dimensional foraging strategies of the largest filter feeder. Functional Ecology, 29(7), 951–961. https://doi.org/10.1111/1365-2435.12395

Gomes, D. G. E., Ruzicka, J. J., Crozier, L. G., Huff, D. D., Brodeur, R. D., & Stewart, J. D. (2024). Marine heatwaves disrupt ecosystem structure and function via altered food webs and energy flux. Nature Communications, 15(1), 1988. https://doi.org/10.1038/s41467-024-46263-2

Hildebrand, L., Bernard, K. S., & Torres, L. G. (2021). Do Gray Whales Count Calories? Comparing Energetic Values of Gray Whale Prey Across Two Different Feeding Grounds in the Eastern North Pacific. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.683634

Kirchner, T., Wiley, D., Hazen, E., Parks, S., Torres, L., & Friedlaender, A. (2018). Hierarchical foraging movement of humpback whales relative to the structure of their prey. Marine Ecology Progress Series, 607, 237–250. https://doi.org/10.3354/meps12789

Miller, E. J., Potts, J. M., Cox, M. J., Miller, B. S., Calderan, S., Leaper, R., Olson, P. A., O’Driscoll, R. L., & Double, M. C. (2019). The characteristics of krill swarms in relation to aggregating Antarctic blue whales. Scientific Reports, 9(1), 16487. https://doi.org/10.1038/s41598-019-52792-4

Muhling, B. A., Brodie, S., Smith, J. A., Tommasi, D., Gaitan, C. F., Hazen, E. L., Jacox, M. G., Auth, T. D., & Brodeur, R. D. (2020). Predictability of Species Distributions Deteriorates Under Novel Environmental Conditions in the California Current System. Frontiers in Marine Science, 7. https://doi.org/10.3389/fmars.2020.00589

Robertson, R. R., & Bjorkstedt, E. P. (2020). Climate-driven variability in Euphausia pacifica size distributions off northern California. Progress in Oceanography, 188. https://doi.org/10.1016/j.pocean.2020.102412

Savoca, M. S., Czapanskiy, M. F., Kahane-Rapport, S. R., Gough, W. T., Fahlbusch, J. A., Bierlich, K. C., Segre, P. S., Di Clemente, J., Penry, G. S., Wiley, D. N., Calambokidis, J., Nowacek, D. P., Johnston, D. W., Pyenson, N. D., Friedlaender, A. S., Hazen, E. L., & Goldbogen, J. A. (2021). Baleen whale prey consumption based on high-resolution foraging measurements. Nature, 599(7883), 85–90. https://doi.org/10.1038/s41586-021-03991-5

Spitz, J., Trites, A. W., Becquet, V., Brind’Amour, A., Cherel, Y., Galois, R., & Ridoux, V. (2012). Cost of Living Dictates what Whales, Dolphins and Porpoises Eat: The Importance of Prey Quality on Predator Foraging Strategies. PLoS ONE, 7(11), e50096. https://doi.org/10.1371/journal.pone.0050096

Videsen, S. K. A., Simon, M., Christiansen, F., Friedlaender, A., Goldbogen, J., Malte, H., Segre, P., Wang, T., Johnson, M., & Madsen, P. T. (2023). Cheap gulp foraging of a giga-predator enables efficient exploitation of sparse prey. Science Advances, 9(25), eade3889. https://doi.org/10.1126/sciadv.ade3889

Significant others? Thinking beyond p-values in science

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

Scientific inquiry relies on quantifying how certain we are of the differences we see in observations. This means that we must look at phenomena based on probabilities that we calculate from observed data, or data that we collect from sampling efforts. Historically, p-values have served as a relatively ubiquitous tool for assessing the strength of evidence in support of a hypothesis. However, as our understanding of statistical methods evolves, so does the scrutiny surrounding the appropriateness and interpretation of p-values. In the realm of research, the debate surrounding the use of p-values for determining statistical significance has sparked some controversy and reflection within the academic community. 

What is a p-value?

To understand the debate itself, we need to understand what a p-value is. The p-value represents the probability of obtaining a result as extreme as, or more extreme than, the observed data, under the assumption that there is no true difference or relationship between groups or variables. Traditionally, a p-value below a predetermined threshold (often 0.05) is considered statistically significant, suggesting that the observed data are unlikely (i.e., a 5% probability) to have occurred by chance alone. Many statistical tests provide p-values, which gives us a unified framework for interpretation across a range of analyses.

To illustrate this, imagine a study aimed at investigating the effects of underwater noise pollution on the foraging behavior of gray whales. Researchers collect data on the diving behavior of gray whales in both noisy and quiet regions of the ocean.

Drawings of gray whales with tags (depicted by orange shapes) in quiet areas (left) and noisy areas (right). 

In this example, the researchers hypothesize that gray whales stop foraging and ultimately change their diving behavior in response to increased marine noise pollution. The data collected from this hypothetical scenario could come from tags equipped with sensors that record diving depth, duration, and location, allowing us to calculate the exact length of time spent foraging. Data would be collected from both noisy areas (maybe near shipping lanes or industrial sites) and quiet areas (more remote regions with minimal human activity). 

To assess the significance of the differences between the two noise regimes, researchers may use statistical tests like t-tests to compare two groups. In our example, researchers use a t-test to compare the average foraging time between whales in noisy and quiet regimes. The next step would be to define hypotheses about the differences we expect to see. The null hypothesis (HN) would be that there is no difference in the average foraging time (X) between noisy and quiet areas: 

Scenario where the noisy area does not elicit a behavioral response that can be detected by the data collected by the tags (orange shapes on whales back). The lower graph shows the distribution of the data (foraging time) for the noisy and the quiet areas. The means of this data (X) are not different. 

And the alternative hypothesis (HA) would be that there is a difference between the noisy and quiet areas: 

Scenario where the noisy area elicits a behavioral response (swimming more towards the surface instead of foraging) that can be detected by the data collected by the tags (orange shapes on whales back). The lower graph shows the distribution of the data (foraging time) for the noisy and the quiet areas. The means of this data (X) are different with the noisy mean foraging time (pink) being lower than the quiet mean foraging time (blue).

For now, we will skip over the nitty gritty of a t-test and just say that the researchers get a “t-score” that says whether or not there is a difference in the means (X) of the quiet and noisy areas. A larger t-score means that there is a difference in the means whereas a smaller t-score would indicate that the means are more similar. This t-score comes along with a p-value. Let’s say we get a t-score (green dot) that is associated with a p-value of 0.03 shown as the yellow area under the curve: 

The t-score is a test statistic that tells us how different the means of our observed data groups are from each other (green dot). The area under the t-distribution that is above the t-score is the p-value (yellow shaded area).

A p-value of 0.03 means that there is a 3% probability of obtaining these observed differences in foraging time between noisy and quiet areas purely by chance, which assumes that the null hypothesis is true (that there is no difference). We usually compare this p-value to a threshold value to say whether this finding is significant. We set this threshold before looking at the results of the test. If the threshold is above our value, like 0.05, then we can “reject the null hypothesis” and conclude that there is a significant difference in foraging time between noisy and quiet areas (green check mark scenario). On the flip-side, if the threshold that we set before our results is too low (0.01), then we will “fail to reject the null hypothesis” and conclude that there was no significant difference in foraging time between noisy and quiet areas (red check mark scenario). The reason that we don’t ever “accept the null” is because we are testing an alternative hypothesis with observations and if those observations are consistent with the null rather than the alternative, this is not evidence for the null because it could be consistent with a different alternative hypothesis that we are not yet testing for.

When our pre-set threshold to determine significance is above or greater than our p-value that was calculated we have enough evidence to ‘reject the null hypothesis’ (left figure) whereas if our p-value is lower or smaller than our calculated p-value, then we ‘fail to reject the null hypothesis’ (right figure).

In this example, the use of p-values helps the researchers quantify the strength of evidence for their hypothesis and determine whether the observed differences in gray whale behavior are likely to be meaningful or merely due to chance. 

The Debate

Despite its widespread use, the reliance on p-values has been met with criticism. Firstly, because p-values are so ubiquitous, it can be easy to calculate them with or without enough critical thinking or interpretation. This critical thinking should include an understanding of what is biologically relevant and avoid the trap of using binary language like significant or non-significant results instead of looking directly at the uncertainty of your results. One of the other most common misconceptions about p-values is that they can measure the direct probability of the null hypothesis being true. As amazing as that would be, in reality we can only use p-values to understand the probability of our observed data. Additionally, it’s common to conflate the significance or magnitude of the p-value with effect size (which is the strength of the relationship between the variables). You can have a small p-value for an effect that isn’t very large or meaningful, especially if you have a large sample size. Sample size is an important metric to report. Larger number of samples generally means more precise estimates, higher statistical power, increased generalizability, and higher possibility for replication.

Furthermore, in studies that require multiple comparisons (i.e. multiple statistical analyses are done in a single study), there is an increased likelihood of observing false positives because each test introduces a chance of obtaining a significant result by random variability alone. In p-value language, a “false positive” is when you say something is significant (below your p-value threshold) when it actually is not, and a “false negative” is when you say something is not significant (above the p-value threshold) when it actually is. So, in terms of multiple comparisons, if there are no adjustments made for the increased risk of false positives, this can potentially lead to inaccurate conclusions of significance.

In our example using foraging time in gray whales, we didn’t consider the context of our findings. To make this a more reliable study, we have to consider factors like the number of whales tagged (sample size!), the magnitude of noise near the tagged whales, other variables in the environment (e.g. prey availability) that could affect our results, and the ecological significance in the difference in foraging time that was found. To make robust conclusions, we need to carefully build hypotheses and study designs that will answer the questions we seek. We must then carefully choose the statistical tests that we use and explore how our data align with the assumptions that these tests make. It’s essential to contextualize our results within the bounds of our study design and broader ecological system. Finally, performing sensitivity analyses (e.g. running the same tests multiple times on slightly different datasets) ensures that our results are stable over a variety of different model parameters and assumptions. 

In the real world, there have been many studies done on the effects of noise pollution on baleen whale behavior that incorporate multiple sources of variance and bias to get robust results that show behavioral responses and physiological consequences to anthropogenic sound stressors (Melcón et al. 2012, Blair et al. 2016, Gailey et al. 2022, Lemos et al. 2022).

Moving Beyond P-values

There has been growing interest in reassessing the role of p-values in scientific inference and publishing. Scientists appreciate p-values because they provide one clear numeric threshold to determine significance of their results. However, the reality is more complicated than this binary approach. We have to explore the uncertainty around these estimates and test statistics (e.g. t-score) and what they represent ecologically. One avenue to explore might be focusing more on effect sizes and confidence intervals as more informative measures of the magnitude and precision of observed effects. There has also been a shift towards using Bayesian methods, which allow for the incorporation of prior knowledge and a more nuanced quantification of uncertainty.

Bayesian methods in particular are a leading alternative to p-values because instead of looking at how likely our observations are given a null hypothesis, we get a direct probability of the hypothesis given our data. For example, we can use Bayes factor for our noisy vs quiet gray whale behavioral t-test (Johnson et al. 2023). Bayes factor measures the likelihood of the data being observed for each hypothesis separately (instead of assuming the null hypothesis is true) so if we calculate a Bayes factor of 3 for the alternative hypothesis (HA), we could directly say that it is 3 times more likely for there to be decreased foraging time in a noisy area than for there to be no difference in the noisy vs quiet group. But that is just one example of Bayesian methods at work. The GEMM lab uses Bayesian methods in many projects from Lisa’s spatial capture-recapture models (link to blog) and Dawn’s blue whale abundance estimates (Barlow et al. 2018) to quantifying uncertainty associated with drone photogrammetry data collection methods in KC’s body size models (link to blog). 

Ultimately, the debate surrounding p-values highlights the necessity of nuanced and transparent approaches to statistical inference in scientific research. Rather than relying solely on arbitrary thresholds, researchers can consider the context, relevance, and robustness of their findings. From justifying our significance thresholds to directly describing parameters based on probability, we have increasingly powerful tools to improve the methodological rigor of our studies. 

References

Agathokleous, E., 2022. Environmental pollution impacts: Are p values over-valued? Science of The Total Environment 850, 157807. https://doi.org/10.1016/j.scitotenv.2022.157807

Barlow, D.R., Torres, L.G., Hodge, K.B., Steel, D., Baker, C.S., Chandler, T.E., Bott, N., Constantine, R., Double, M.C., Gill, P., Glasgow, D., Hamner, R.M., Lilley, C., Ogle, M., Olson, P.A., Peters, C., Stockin, K.A., Tessaglia-Hymes, C.T., Klinck, H., 2018. Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endangered Species Research 36, 27–40. https://doi.org/10.3354/esr00891

Blair, H.B., Merchant, N.D., Friedlaender, A.S., Wiley, D.N., Parks, S.E., 2016. Evidence for ship noise impacts on humpback whale foraging behaviour. Biol Lett 12, 20160005. https://doi.org/10.1098/rsbl.2016.0005

Brophy, C., 2015. Should ecologists be banned from using p-values? Journal of Ecology Blog. URL https://jecologyblog.com/2015/03/06/should-ecologists-be-banned-from-using-p-values/ (accessed 4.19.24).

Castilho, L.B., Prado, P.I., 2021. Towards a pragmatic use of statistics in ecology. PeerJ 9, e12090. https://doi.org/10.7717/peerj.12090

Gailey, G., Sychenko, O., Zykov, M., Rutenko, A., Blanchard, A., Melton, R.H., 2022. Western gray whale behavioral response to seismic surveys during their foraging season. Environ Monit Assess 194, 740. https://doi.org/10.1007/s10661-022-10023-w

Halsey, L.G., 2019. The reign of the p-value is over: what alternative analyses could we employ to fill the power vacuum? Biology Letters 15, 20190174. https://doi.org/10.1098/rsbl.2019.0174

Johnson, V.E., Pramanik, S., Shudde, R., 2023. Bayes factor functions for reporting outcomes of hypothesis tests. Proceedings of the National Academy of Sciences 120, e2217331120. https://doi.org/10.1073/pnas.2217331120

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. Sci Rep 12, 18580. https://doi.org/10.1038/s41598-022-14510-5

LU, Y., BELITSKAYA-LEVY, I., 2015. The debate about p-values. Shanghai Arch Psychiatry 27, 381–385. https://doi.org/10.11919/j.issn.1002-0829.216027

Melcón, M.L., Cummins, A.J., Kerosky, S.M., Roche, L.K., Wiggins, S.M., Hildebrand, J.A., 2012. Blue Whales Respond to Anthropogenic Noise. PLOS ONE 7, e32681. https://doi.org/10.1371/journal.pone.0032681

Murtaugh, P.A., 2014. In defense of P values. Ecology 95, 611–617. https://doi.org/10.1890/13-0590.1

Vidgen, B., Yasseri, T., 2016. P-Values: Misunderstood and Misused. Front. Phys. 4. https://doi.org/10.3389/fphy.2016.00006

Baleen analyses reveals patterns in foraging ecology and stress physiology in gray whales prior to death.

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

The Eastern North Pacific (ENP) gray whale population has experienced at least two recorded Unusual Mortality Events (UMEs), from 1999–2000 and from 2019 to 2024, during which many gray whales stranded along the Pacific coast from northern Mexico to the Alaskan Arctic, USA (Martínez-Aguilar et al., 2019; Urbán, 2020). Several factors have been considered as possible causes for the high number of gray whale’s strandings, including variation in Arctic prey availability and the duration of their feeding season caused by the timing of sea ice formation and breakup (Stewart et al., 2023), starvation, anthropogenically derived toxicants, biotoxins, infectious diseases, parasites, fisheries interactions, and ship strikes (F. Gulland et al., 2005). In the most recent UME, many of the stranded whales showed signs of emaciation, indicating malnutrition as a causal factor of death (Christiansen et al., 2021; Torres et al., 2022). While the poor condition of many of the stranded whales supports the idea of starvation as a cause for these mortalities, the underlying causes of malnutrition are unknown, and it is also unclear whether the whales’ decline in body condition was rapid or gradual.

Figure 1. Gray whale with baleen exposed. Photo: GEMM Lab  NOAA/NMFS permit #16111.

Large whales face a multitude of stressors in their environment, ranging from ocean noise to contaminants, climate change, and prey shifts. Understanding how individual whales respond to these disturbances is crucial for assessing potential impacts on the population as a whole. However, monitoring the health parameters and vital rates of whales presents significant challenges due to their large size, mobility, and the vast ranges of their marine habitat. Studying stranded whales can provide valuable insights into health risks, disease susceptibility, and the impacts of pollutants and other stressors on whale populations, thus informing conservation strategies (see post). Nonetheless, the quality of information obtained from necropsies heavily relies on the timeliness of stranding reports, as decomposition begins immediately after death, limiting detailed investigations into the cause of death. Therefore, establishing a robust network capable of promptly reporting and addressing stranding events is essential (Gulland & Stockin, 2020). An effective network involves having well-trained staff, proper infrastructure, sufficient funding, and the expertise and tools necessary to gather and analyze data and samples to infer their health and causes of mortality.

During my doctoral dissertation, I worked to develop and ground truth the endocrine analyses of whale baleen as a novel sample type that can be used for retrospective assessments of the whale’s physiology (see my previous post & post). Baleen, the filter-feeding apparatus of mysticetes whales (Figure 1), consists of long fringed plates of keratinized tissue that grow continuously and slowly downward from the whale’s upper jaw. These plates are routinely collected at necropsies; and unlike other tissue types, they are durable and have minimum storage requirements; they can be preserved dry at room temperature, allowing for the analysis of both historical and current whale populations. Moreover, while most sample types used for studying whale health and physiology provide a single time-point measure of current circulating hormone levels (e.g., skin or respiratory vapor) or hold integrated information from the previous few hours or days (e.g., urine and feces), baleen tissue provides a unique opportunity for retrospective and longitudinal analyses of multiple biological parameters of the individual during the time that the tissue was grown, i.e., months to years prior to death, helping to describe the whale’s physiology, migration patterns, and exposure to pollutants (see my previous post).

In our recent study, “A longitudinal study of endocrinology and foraging ecology of subadult gray whales prior to death based on baleen analysis”, published in the journal General and Comparative Endocrinology, we examine isotope and hormone levels in the baleen of five young gray whales stranded in central Oregon during the most recent UME. Our primary objectives were to retrospectively examine the hormone and isotopic profiles of the individual whales prior to mortality, assess potential factors contributing to death, and identify the timing for the onset of chronic illness leading to mortality. Our analysis included tracing longitudinal changes in (1) stable isotope values in baleen (δ13C and δ15N), which allowed us to infer the baleen growth rate and assess the seasonal changes in diet and foraging location in large whales (Figure 2), along with the quantification of (2) two adrenal glucocorticoid steroids that are biomarkers for the whale’s stress response, (3) one thyroid hormone (triiodothyronine, T3) as an indicator of nutritional state, and (4) two sex hormones, progesterone and testosterone, to infer about reproductive status and sexual maturity. By integrating isotopic and hormonal methodologies, our study demonstrates how baleen analysis offers a comprehensive narrative of the endocrine and trophic ecology of individual whales over time.

Figure 2. Gray whales, like other large marine mammals that rely on built-up energy reserves, exhibit distinct seasonal shifts in their feeding habits. During summer, these whales feed at the ocean’s bottom, consuming organisms lower in the food chain, which is reflected in lower nitrogen values in their baleen (summer foraging). In winter, however, they must rely on their own fat reserves, causing an increase in nitrogen values (wintering). In this plot we can observe the oscillations in δ15N over time; this information allows us to estimate the baleen growth rate. Our results suggest that gray whale baleen holds a record of around 1.3 years of stable isotopes and hormone data prior to the time of death (Fernandez Ajo et al. 2024). The red cross in the X-axis, indicate the time of death. Gray whale illustration https://www.fisheries.noaa.gov/species/gray-whale

Our endocrine assessments revealed detailed profiles of stress-related hormones (glucocorticoids, cortisol) and thyroid hormones along the lengths of the baleen. We found increased levels of cortisol in whales that died from unknown causes, starting about eight months prior to their deaths. This suggests these whales were under prolonged stress before dying. In contrast, in the case of a whale killed acutely by a killer whale, cortisol levels were low and constant prior to death, indicating this individual was likely in good health prior to the sudden attack. In terms of thyroid activity, indicated by T3 hormone levels, we found a gradual increase over several months in the whales that died of unknown causes. This pattern is not typically expected, as stress usually suppresses thyroid function. This anomaly could suggest an adaptive response to maintain body temperature and metabolism in potentially malnourished whales. Regarding the sex hormones, as expected for this age class, we found no significant fluctuations or spikes that would indicate sexual maturity in these young whales (Figure 3).

Figure 3. Longitudinal hormone profiles in an individual gray whale that died due to unknown causes (left) and one that died acutely due to orca predation (right). Note the pronounced elevations in cortisol levels (indicative of stress) and T3 prior to death in the case of unknown cause of death, while hormone levels remained low and constant prior to death in the whale acutely killed. Sex hormones do not present any clear oscillations, indicating that these whales were likely sexually immature. The red cross in the X-axis, indicate the time of death. Killer whale (Orcinus orca) illustration https://www.fisheries.noaa.gov/species/ killer-whale

Although commercial whaling is currently banned and several whale populations show evidence of recovery, today’s whales are exposed to a variety of other human stressors that cause significant lethal and non-lethal impacts (e.g., entanglement in fishing gear, vessel strikes, shipping noise, climate change, etc.; reviewed in Thomas et al., 2016). The recovery and conservation of large whale populations is particularly important to the oceanic environment due to their key ecological role and unique biological traits (See my previous post). Our research demonstrates the strengths of using baleen as a tool for the retrospective assessments of whale endocrinology and trophic ecology. As the Eastern North Pacific gray whale population faces recurring challenges, indicated by fluctuating numbers and unusual mortality events, innovative techniques like the baleen analysis presented here, are essential to investigate the causes of mortality and inform management, helping us understand not only the immediate causes of death but also broader environmental and ecological changes affecting their survival. Broadly implementing this approach with a greater sample size of baleen collected across a larger spatial and temporal range could significantly improve our strategies for conservation and management of baleen whales.

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References

Christiansen, F., Rodríguez-González, F., Martínez-Aguilar, S., Urbán, J., Swartz, S., Warick, H., Vivier, F., & Bejder, L. (2021). Poor body condition associated with an unusual mortality event in gray whales. Marine Ecology Progress Series, 658, 237–252. https://doi.org/10.3354/meps13585

Gulland, F. M. D., & Stockin, K. A. (2020). Harmonizing global strandings response. European Cetacean Society Special Publication Series.

Gulland, F., Pérez-Cortés, H., Urbán, J. R., Rojas-Bracho, L., Ylitalo, G., Weir, J., Norman, S., Muto, M., Rugh, D., Kreuder, C., & Rowles, T. (2005). Eastern North Pacific gray whale (Eschrichtius robustus) unusual mortality event, 1999-2000. U.S. Department of Commerce. NOAA Technical Memorandum. NMFS-AFSC-150., March, 33 pp. http://www.afsc.noaa.gov/publications/AFSC-TM/NOAA-TM-AFSC-150.pdf

Martínez-Aguilar, S., Mariano-Meléndez, E., López-Paz, N., Castillo-Romero, F., Zaragoza-aguilar, G. A., Rivera-Rodriguez, J., Zaragoza-Aguilar, A., Swartz, S., Viloria-Gómora, L., & Urbán, J. R. (2019). Gray whale (Eschrichtius robustus) stranding records in Mexico during the winter breeding season in 2019. Report of the International Whaling Commission. Document SC/68A/CMP/14, May.

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

Torres, L. G., Bird, C. N., Rodríguez-González, F., Christiansen, F., Bejder, L., Lemos, L., Urban R, J., Swartz, S., Willoughby, A., Hewitt, J., & Bierlich, KC. (2022). Range-Wide Comparison of Gray Whale Body Condition Reveals Contrasting Sub-Population Health Characteristics and Vulnerability to Environmental Change. Frontiers in Marine Science, 9. https://doi.org/10.3389/fmars.2022.867258

Urbán, R. (2020). Gray whale stranding records in Mexico during the 2020 winter breeding season. Unpublished Paper SC/68B/CMP/13 Presented to the IWC Scientific Committee, Cambridge.

How big, how blue, how beautiful! Studying the impacts of climate change on big, (and beautiful) blue whales

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

The SAPPHIRE Project is in full swing, as we spend our days aboard the R/V Star Keys searching for krill and blue whales (Figure 1) in the South Taranaki Bight (STB) region of Aotearoa New Zealand. We are investigating how changing ocean conditions impact krill availability and quality, and how this in turn impacts blue whale behavior, health, and reproduction. Understanding the link between changing environmental conditions on prey species and predators is key to understanding the larger implications of climate change on ocean food webs and each populations’ resiliency. 

Figure 1. The SAPPHIRE team searching for blue whales. Top left) KC Bierlich, top right) Dawn Barlow, bottom left) Dawn Barlow, Kim Bernard (left to right), bottom right) KC Bierlich, Dawn Barlow, Leigh Torres, Mike Ogle (left to right).  

One of the many components of the SAPPHIRE Project is to understand how foraging success of blue whales is influenced by environmental variation (see this recent blog written by Dr. Dawn Barlow introducing each component of the project). When you cannot go to a grocery store or restaurant any time you are hungry, you must rely on stored energy from previous feeds to fuel energy needs. Body condition reflects an individual’s stored energy in the body as a result of feeding and thus represents the foraging success of an individual, which can then affect its potential for reproductive output and the individual’s overall health (see this previous blog). As discussed in a previous blog, drones serve as a valuable tool for obtaining morphological measurements of whales to estimate their body condition. We are using drones to collect aerial imagery of pygmy blue whales to obtain body condition measurements late in the foraging season between years 2024 and 2026 of the SAPPHIRE Project (Figure 2). We are quantifying body condition as Body Area Index (BAI), which is a relative measure standardized by the total length of the whale and well suited for comparing individuals and populations (Figure 3). 

The GEMM Lab recently published an article led by Dr. Dawn Barlow where we investigated the differences in BAI between three blue whale populations: Eastern North Pacific blue whales feeding in Monterey Bay, California; Chilean blue whales feeding in the Corcovado Gulf; and New Zealand Pygmy blue whales feeding in the STB (Barlow et al., 2023). These three populations are interesting to compare since blue whales that feed in Monterey Bay and Corcovado Gulf migrate to and from these seasonally productive feeding grounds, while the Pygmy blue whales stay in Aotearoa New Zealand year-round. Interestingly, the Pygmy blue whales had higher BAI (were fatter) compared to the other two regions despite relatively lower productivity in their foraging grounds. This difference in body condition may be due to different life history strategies where the non-migratory Pygmy blue whales may be able to feed as opportunities arrive, while the migratory strategies of the Eastern North Pacific and Chilean blue whales require good timing to access high abundant prey. Another interesting and unexpected result from our blue whale comparison was that Pygmy blue whales are not so “pygmy”; they are actually the same size as Eastern North Pacific and Chilean blue whales, with an average size around 22 m. Our findings from this blue whale comparison leads us to more questions about how environmental conditions that vary from year to year influence body condition and reproduction of these “not so pygmy” blue whales. 

Figure 2. An aerial image of a Pygmy blue whale in the South Taranaki Bight region of Aotearoa New Zealand collected during the SAPPHIRE 2024 field season using a DJI Inspire 2 drone. 
Figure 3. A drone image of a Pygmy blue whale and the length and body width measurements used to estimate Body Area Index (BAI), represented by the shaded blue region. Width measurements will also be used to help identify pregnant individuals.

The GEMM Lab has been studying this population of Pygmy blue whales in the STB since 2013 and found that years designated as a marine heatwave resulted with a reduction in blue whale feeding activity. Interestingly, breeding activity is also reduced during marine heatwaves in the following season when compared to the breeding season following a more productive, typical foraging season. These findings indicate that fluctuations in the environment, such as marine heatwaves, may affect not only foraging success, but also reproduction in Pygmy blue whales. 

To help us better understand reproductive patterns across years, we will use body width measurements from drone images paired with hormone concentrations collected from fecal and biopsy samples to identify pregnant individuals. Progesterone is a hormone secreted in the ovaries of mammals during the estrous cycle and gestation, making it the predominant hormone responsible for sustaining pregnancy. Recently, the GEMM Lab’s Dr. Alejandro Fernandez-Ajo wrote a blog discussing his publication identifying pregnant individual gray whales using drone-based body width measurements and progesterone concentrations from fecal samples (Fernandez et al., 2023). While individuals that were pregnant had higher levels of progesterone compared to when they were not pregnant, the body width at 50% of the body length served as a more reliable method for detecting pregnancy in gray whales. We will use similar methods to help identify pregnancy in Pygmy blue whales for the SAPPHIRE Project where will we examine body width measurement paired with progesterone concentrations collected from fecal and biopsy samples to identify pregnant individuals. We hope our work will help to better understand how climate change will influence Pygmy blue whale body condition and reproduction, and thus the overall health and resiliency of the population. Stay tuned! 

References

Barlow, D. R., Bierlich, K. C., Oestreich, W. K., Chiang, G., Durban, J. W., Goldbogen, J. A., Johnston, D. W., Leslie, M. S., Moore, M. J., Ryan, J. P., & Torres, L. G. (2023). Shaped by Their Environment: Variation in Blue Whale Morphology across Three Productive Coastal Ecosystems. Integrative Organismal Biology, 5(1). https://doi.org/10.1093/iob/obad039

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

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

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

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

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

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

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

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

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

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

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

Video 1: NBC6 news report on the sighting

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

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

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References

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

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

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

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

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

El Niño de Navidad: What is atmospheric Santa Claus bringing to Oregon krill and whales?

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

Early June marked the onset of El Niño conditions in the Pacific Ocean , which have been strengthening through the fall and winter. For Oregonians, this climate event means unseasonably warm December days, less snow and overall precipitation (it’s sunny as I write this!), and the potential for increased wildfires and marine heatwaves next summer.

This phenomenon occurs about every two to seven years as part of the El Niño Southern Oscillation (ENSO), a cyclical rotation of atmospheric and oceanic conditions in the Pacific Ocean that is initiated by departures from and returns to “normal conditions” at the equator. Typically, the trade winds blow warm water west along the equator, and El Niño occurs when these winds weaken or reverse. As a result, the upwelling of cold water at the equator ceases, and warm water flows towards the west coast of the Americas, rather than its typical pathway towards Asia. When the trade winds resume their normal direction, usually after months or a year, the system returns to “normal” conditions – or, it can enter the cool La Niña part of the cycle, in which the trade winds are stronger than normal. “El Niño de Navidad” was named by South American fisherman in the 1600s because this event tends to peak in December – and El Niño is clearly going to be a guest for Christmas this year.

Figure 1. Maps of sea surface temperature anomalies show Pacific Ocean conditions during a strong La Niña (top) and El Niño (bottom). Source: NOAA climate.gov

These events at the equator trigger changes in global atmospheric circulation patterns, and they can shape weather around the world. Teleconnection, the coherence between meteorological and environmental phenomena occurring far apart, is to me one of the most incredible things about the natural world.  This coherence means that the biological community off the Oregon coast is strongly impacted by events initiated at the equator, with consequences that we don’t yet fully understand.

The effects of El Niño are diverse – floods in some places, droughts in others – and their onset can mean wildly different things for Oregon, Peru, Alaska, and beyond. As we tap our fingers waiting to be able to ski and snowboard in Oregon, what does our current El Niño event mean for the life in the waters off our coast?

Figure 2. Anomalous conditions at the equator qualified as an El Niño event in June 2023.

ENSO plays a big role in the variability in our local Northern California Current (NCC) system, and the outcomes of these events can differ based on the strength and how the signal propagates through the ocean and atmosphere (Checkley & Barth, 2009). Large-scale “coastal-trapped” waves flowing alongshore can bring the warm water signal of an El Niño to our ocean backyard in a matter of weeks. One of the first impacts is a deepening of the thermocline, the upper ocean’s steep gradient in temperature, which changes the cycling of important nutrients in the surface ocean. This can result in a decrease in upwelling and primary productivity that sends ramifications through the food web, including consequences for grazers and predators like zooplankton, marine mammals, and seabirds (Checkley & Barth, 2009).

In addition to these ecosystem effects that result from local changes, the ocean community can also receive new visitors from afar, and see others flee . For krill, the shrimp-like whale prey that I spend a lot of my time thinking about, community composition can change as subtropical species typically found off southern and Baja California are displaced by horizontal ocean flow, or as resident species head north (Lilly & Ohman, 2021).

Figure 3. This Euphausia gibboides krill is typically found in offshore subtropical habitats but moves north and inshore during El Niño events, and tends to persist awhile in these new environments, impacting the local zooplankton community. Source: Solvn Zankl

The two main krill species that occur in the NCC, Euphausia pacifica and Thysanoessa spinifera, favor the cool, coastal waters typical off the coast of Oregon. During El Niño events, E. pacifica tends to contract its distribution inshore in order to continue occupying these conditions, increasing its spatial overlap with T. spinifera (Lilly & Ohman, 2021). In addition, both tend to shift their populations north, toward cooler, upwelling waters (Lilly & Ohman, 2021).

These krill species are a favored prey of rorqual whales, and the coast of Oregon is an important foraging ground for humpback, blue, and fin whales. Predators tend to follow their prey, and shifting distributions of these krill species may cause whales to move, too. During the 2014-2015 “Blob” event in the Pacific Ocean, a marine heatwave was exacerbated by El Niño conditions. Humpback whales in central California shifted their distributions inshore in response to sparse offshore krill, increasing their overlap with fishing gear and leading to an increase in entanglement events (Santora et al., 2020). Further north, these conditions even led humpback whales to forage in the Columbia River!

Figure 4. In September 2015, El Niño conditions led humpback whales to follow their prey and forage in the Columbia River.

As El Niño events compound with the impacts of global climate change, we can expect these distributional shifts – and perhaps surprises – to continue. By the year 2100, the west coast habitat of both T. spinifera and E. pacifica will likely be constrained due to ocean warming – and when El Niños occur, this habitat will decrease even further (Lilly & Ohman, 2021). As a result, the abundances of both species are expected to decrease during El Niño events, beyond what is seen today (Lilly & Ohman, 2021). This decline in prey availability will likely present a problem for future foraging whales, which may already be facing increased environmental challenges.

Understanding connections is inherent to the field of ecology, and although these environmental dependencies are part of what makes life so vulnerable, they can also be a source of resilience. Although humans have known about ENSO for over 400 years, the complex interplay between nature, anthropogenic systems, and climate change means that we are still learning the full implications of these events. Just as waiting for Santa Claus always keeps kids guessing, the dynamic ocean keeps surprising us, too.

References

Checkley, D. M., & Barth, J. A. (2009). Patterns and processes in the California Current System. Progress in Oceanography, 83(1–4), 49–64. https://doi.org/10.1016/j.pocean.2009.07.028

Lilly, L. E., & Ohman, M. D. (2021). Euphausiid spatial displacements and habitat shifts in the southern California Current System in response to El Niño variability. Progress in Oceanography, 193, 102544. https://doi.org/10.1016/j.pocean.2021.102544

Santora, J. A., Mantua, N. J., Schroeder, I. D., Field, J. C., Hazen, E. L., Bograd, S. J., Sydeman, W. J., Wells, B. K., Calambokidis, J., Saez, L., Lawson, D., & Forney, K. A. (2020). Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat Commun, 11(1), 536. https://doi.org/10.1038/s41467-019-14215-w

Migrating back east

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

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

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

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

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

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

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

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

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

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

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

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References

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