Category: Gray Whale Foraging Ecology and Vessel Disturbance

Florence Sullivan’s masters project.

Hearing Gray: Diving into the Sonic World of the Gray Whale

By Natalie Nickells, visiting PhD Student, British Antarctic Survey

For the last three months, I’ve been lucky enough to be welcomed into the GEMM lab as a visiting PhD student to work on the acoustic data from hydrophones in CATS tags deployed on gray whales. This work has been a huge change for me! I’ve gone from studying Antarctic baleen whale foraging, the topic of my PhD, from a distance at my desk in Cambridge England, to studying PCFG gray whales in Newport- and finally being in the same country, state, and even county to the whales I am studying! Unlike my Antarctic research, where whale blows in the distance become tiny points in a sea of data, listening to the CATS tag data has allowed me to really connect with these animals on an emotional level, as I’ve spent days, weeks and months listening to the world as they hear it.

Humans are fundamentally visual creatures- we take in information through sight first, with hearing probably our second, or for some even third, sense in line. However, for marine mammals, the same cannot be said: their world is auditory first. This fact is an important realisation to get our heads around, highlighted beautifully by the phrase “the ears are the window to the soul of the whale” (Sonic Sea (2017)) or Tim Donaghy’s emotive statement that “a deaf whale is a dead whale”. High levels of ocean noise therefore have a huge impact on baleen whales. Imagine trying to do your groceries or find a friend while blindfolded or in a thick fog– you might struggle to access food or communicate with others, and your stress would certainly be high. To succeed, you would likely need to change your behaviour.

Behavioural changes in response to ocean noise are observed in baleen whales: for example, humpback whales change their foraging behaviour when ship noise increases (Blair et al., 2016), and gray whales have been shown to call more frequently and possibly more loudly in conditions of high ocean noise (Dahlheim & Castellote, 2016). However, even in the absence of notable behaviour change due to ocean noise,  North Atlantic  right whales  may still be experiencing a stress response. When shipping traffic in the Bay of Fundy significantly decreased in the aftermath of 9/11, North Atlantic  right whales in the area had decreased chronic stress levels (Rolland et al., 2012).

Previous work by the GEMM lab observed this stress response to ocean noise in gray whales. They found a correlation between high levels of glucocorticoid (a stress indicator) in male gray whale faeces with high vessel noise and vessel counts in the area. Vessel noise was measured using two static hydrophones off the Oregon coast, and it was assumed all animals in the area experienced the same noise (Lemos et al., 2022; Pirotta et al., 2023). However, a static hydrophone is an imperfect measure of the sound levels a mobile animal experiences, particularly as we might expect animals to change behaviour when disturbed (Sullivan & Torres, 2018).  This previous work became the starting point for the question I have addressed during my time in the GEMM Lab: can we measure and characterise the sound levels  an individual whale was exposed to? Enter CATS tags. These are suction-cup tags fitted with a host of sensors, which have been used by the GEMM lab since 2021 (see Image 1). So far, they have mostly been used for their accelerometry data (Colson et al. (in press), see also Kate’s blog post). However, the GEMM lab had the foresight to put hydrophones on these tags, and as a result I was welcomed into the lab by a bumper-crop of hydrophone data just waiting to be analysed!

Image 1: A gray whale (“Slush”) being tagged with a CATS tag and Natalie (right) with the same tag.

This tag data is particularly valuable, not only for its ability to follow the acoustic world of an individual whale, but also due to the whole suite of data that comes with the acoustics: essentially, the acoustic data comes with behavioural data. Or at least, it comes with data from which we can infer behaviour (Colson et al, in press)! Incorporating behaviour into passive acoustics work hugely strengthens its ecological usefulness (Oestreich et al., 2024). We can hear what an individual whale is hearing, and we can also infer what they were doing before, during, and after they heard or made that sound. Having behavioural data also means that we can ground-truth the sounds we hear. When hearing an interesting sound, I can go back to the video data and accelerometer data to check what the whale sees, what its body-position is doing (e.g., is it headstand foraging?) and the speed and direction of its travel. Context is key!

The importance of context was highlighted in my very first week here in the GEMM lab. I became very interested in a sound I could hear frequently when the whale would surface- a distorted bark-like noise, but the whale was surely too far offshore for any barking dog to be heard? And almost every time the whale surfaced? After a few days pondering, I shared my mystery with Leigh, who laughingly revealed that one of the whale-watching boats in this area has a ‘whale-alerting’ dog on board! Sometimes if it sounds like a dog… it’s a dog! Besides my slightly anticlimactic discovery of dogs barking, committing time to listening to the tags and hearing what the whales hear, has been a magical experience. My favourite hydrophone sound, that still gets me excited when I hear it, is the gray whale ‘bongo call’- or as it’s more formally known in the literature, M1 vocalisation (Guazzo et al., 2019). I’ll let you decide which name is more appropriate! I first heard this call when investigating a time on “Scarlett’s” tag when we knew her 14 year-old daughter “Pacman” had been close: about 15 minutes before “Pacman” appears on the video, Scarlett makes this call (you can play the clip below to listen).  In “Lunita’s” tag, we even hear this call three times in a row!

Image 2: A ‘bongo call’ made by “Scarlett” when her daughter “Pacman” was nearby.

Relatively little research has been done on gray whale calls compared to other more studied species like humpbacks. Most of this research has taken place on gray whale migratory routes (Guazzo et al., 2019, 2017; Burnham et al. 2018)  or in captivity (Fish et. al, 1974 ) so these tag recordings could be a valuable addition to a small sample from the foraging grounds (Clayton et al., 2023; Haver et al., 2023)- as well as being very personally exciting to hear!

We’ve also been able to use the tag hydrophone data to look at close calls with ships. As I was going through the data on “Scarlett’s” tag, I noticed a spike in vessel noise. Looking at the video from the same timestamp, I could see a small vessel passing directly over her as she surfaced. At the time this vessel passed over her, the tag was only 0.8 m under the surface of the water!

Image 3: A close encounter between a small vessel and “Scarlett”, shown both on the video from the CATS tag (top) and the spectrogram (bottom). The close call is outlined in a yellow box, when a greater intensity of noise occurred as illustrated by the brighter colour intensity compared to the white box (quieter vessel noise). Brighter colours denote a louder volume. The red boxes show surfacing noise- this can essentially be ignored when interpreting the echogram for our purposes.

Sometimes vessels may be more distant, but possibly equally harmful: we have seen vessel noise from larger and presumably more distant vessels dominate the soundscape in some of the tag data. Remembering that to a whale, the sonic world is as important as the visual world is to us, this elevated background noise from ships could have major consequences. So, the first step is to try to quantify the gray whales’ exposure to this vessel noise. I’ve been running some systematic sampling on the tag data to try to quantify background noise levels, and how this changes depending on the time of day: do individual whales experience the same daily spikes in ocean noise that were detected on the static hydrophones, at around 6am and noon due to vessel traffic (Haver et al., 2023)? If not, are they taking evasive action to avoid these spikes? These are just some of the questions that these CATS tags can help us answer, although ideally we need longer acoustic data recordings to capture day and night data, as well as potentially improving the hydrophones on the CATS tags themselves to minimise the impacts of tag interference and random noise.

When explaining to the public what it is to be a PhD student, I often refer to myself as a ‘scientist in training’, or to young children, a ‘baby scientist’. As I look toward my departure from the GEMM lab, I hope to have developed into at least a scientific toddler, having gained the ability to walk through reams of acoustic data with (relative) independence. More than that, I’m excited to take home a refreshed sense of curiosity about what drives marine mammals to behave as they do, an openness to collaboration and new approaches, and a large dose of ‘American emotion’! Let’s hope my British colleagues can handle it!

My heartfelt thanks to all those who welcomed me so warmly at the GEMM lab and Oregon State University, particularly my mentors Leigh Torres and Samara Haver.

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Bibliography

Sonic Sea (2017) Directed by Michelle Dougherty [Film] Distributed by the Natural Resources Defense Council.

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. Biology Letters. 12 (8), 20160005. doi:10.1098/rsbl.2016.0005.

Burnham, R., Duffus, D. & Mouy, X. (2018) Gray Whale (Eschrictius robustus) Call Types Recorded During Migration off the West Coast of Vancouver Island. Frontiers in Marine Science. 5, 329. doi:10.3389/fmars.2018.00329.

Colson, K., E. Pirotta L. New, D Cade, J Calambokidis, K. Bierlich, C Bird, A Fernandez Ajó, L. Hildebrand, A. Trites, L. Torres. (in press). Using accelerometry tags to quantify gray whale foraging behavior. Marine Mammal Science.

Clayton, H., Cade, D.E., Burnham, R., Calambokidis, J. & Goldbogen, J. (2023) Acoustic behavior of gray whales tagged with biologging devices on foraging grounds. Frontiers in Marine Science. 10, 1111666. doi:10.3389/fmars.2023.1111666.

Dahlheim, M. & Castellote, M. (2016) Changes in the acoustic behavior of gray whales Eschrichtius robustus in response to noise. Endangered Species Research. 31, 227–242. doi:10.3354/esr00759.

Fish, J.F., Sumich, J.L. & Lingle, G.L. (n.d.) Sounds Produced by the Gray Whale, Eschrichtius robustus.

Guazzo, R., Schulman-Janiger, A., Smith, M., Barlow, J., D’Spain, G., Rimington, D. & Hildebrand, J. (2019) Gray whale migration patterns through the Southern California Bight from multi-year visual and acoustic monitoring. Marine Ecology Progress Series. 625, 181–203. doi:10.3354/meps12989.

Guazzo, R.A., Helble, T.A., D’Spain, G.L., Weller, D.W., Wiggins, S.M. & Hildebrand, J.A. (2017) Migratory behavior of eastern North Pacific gray whales tracked using a hydrophone array S. Li (ed.). PLOS ONE. 12 (10), e0185585. doi:10.1371/journal.pone.0185585.

Haver, S.M., Haxel, J., Dziak, R.P., Roche, L., Matsumoto, H., Hvidsten, C. & Torres, L.G. (2023) The variable influence of anthropogenic noise on summer season coastal underwater soundscapes near a port and marine reserve. Marine Pollution Bulletin. 194, 115406. doi:10.1016/j.marpolbul.2023.115406.

Lemos, L.S., Haxel, J.H., Olsen, A., Burnett, J.D., Smith, A., Chandler, T.E., Nieukirk, S.L., Larson, S.E., Hunt, K.E. & Torres, L.G. (2022) Effects of vessel traffic and ocean noise on gray whale stress hormones. Scientific Reports. 12 (1), 18580. doi:10.1038/s41598-022-14510-5.

Oestreich, W.K., Oliver, R.Y., Chapman, M.S., Go, M.C. & McKenna, M.F. (2024) Listening to animal behavior to understand changing ecosystems. Trends in Ecology & Evolution. S0169534724001459. doi:10.1016/j.tree.2024.06.007.

Pirotta, E., Fernandez Ajó, A., Bierlich, K.C., Bird, C.N., Buck, C.L., Haver, S.M., Haxel, J.H., Hildebrand, L., Hunt, K.E., Lemos, L.S., New, L. & Torres, L.G. (2023) Assessing variation in faecal glucocorticoid concentrations in gray whales exposed to anthropogenic stressors S. Cooke (ed.). Conservation Physiology. 11 (1), coad082. doi:10.1093/conphys/coad082.

Rolland, R.M., Parks, S.E., Hunt, K.E., Castellote, M., Corkeron, P.J., Nowacek, D.P., Wasser, S.K. & Kraus, S.D. (2012) Evidence that ship noise increases stress in right whales. Proceedings of the Royal Society B: Biological Sciences. 279 (1737), 2363–2368. doi:10.1098/rspb.2011.2429.

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

Are You Seeing Scars Too?: Examining Gray Whale Scars and Skin Conditions

By Serina Lane, GEMM Lab NSF REU Intern, Georgia Gwinnett College

Hello, everyone! My name is Serina and I’m a Research Experience for Undergraduates (REU) Intern at the Hatfield Marine Science Center (HMSC) this summer. I’ve had a love for the ocean for as long as I can remember. Honestly, it started off with just dolphins, but I soon started to realize that the ocean is full of fascinating creatures!

How I ended up here…well, I’ve never been to Oregon, I’m escaping the hot weather of Georgia, but I’m also getting to interact with like-minded marine biologists and experienced individuals at an amazing marine laboratory. At the age of 29, I’m also an older undergraduate student, and I will be graduating soon! I took a very long break from academics and coming back was hard, especially switching from business to biology. I have participated in surveys that asked how I felt about the statement “I am a scientist,” along with the degrees of agree and disagree. For most of my undergraduate career, I picked “slightly disagree”. I was getting great grades, but I did not feel like I was ever going to be able to accomplish the type of work scientific papers are written about. I really felt the need to gain more experience in the career path I intended to follow. All of these are the whirlwind ingredients that went into applying for the HMSC REU Internship at OSU! I’m being mentored by the lovely Natalie Chazal and Leigh Torres, and I am grateful for the opportunity and very excited to experience everything Hatfield has to offer. A little over a week of being here, I already feel my answer sliding from “neutral” to even “slightly agree”. There is still so much to learn!

The project I’m helping with is analyzing the scarring and skin conditions of Eastern North Pacific gray whales alongside the GRANITE team. My job will be analyzing over 100,000 pictures from the past eight years to detect various scars and potential skin conditions (yes, the comma is in the correct spot and no, there are no extra 0’s). Scars can come from a variety of sources such as boat propellers, fishing gear, and killer whales! A study conducted by Corsi et al. consisted of documenting killer whale rake marks (bites, essentially) on different types of whales in the eastern North Pacific. Their results showed that gray whales had the highest percentage of observed rake marks in sighted individuals, and provided insight into why body sections of observed marks are important. Most baleen whales had rake marks predominantly on their flukes, because they are often used for defense and if fleeing, are the closest area to bite. Fascinatingly, Corsi et al. consider that the higher occurrences of gray whale rake marks are due to killer whales adopting species-specific hunting approaches. Gray whales have predictable migratory routes, and we already know how intelligent killer whales can be. If I knew a truck had a specific delivery route and I could wait to intercept a fresh delivery of Krispy Kreme donuts, why wouldn’t I? 

Donuts aside, I’ll also be categorizing where the scars/skin conditions are located – for example, certain regions on the tail (like above) or on their left or right back (often due to boat collisions). Then I’ll define what I believe to be the source of scarring and rate my confidence in that decision based on the photo. Now, not all of the photos are clear enough for me to make informed decisions, so realistically I could end up with only a few hundred usable photos. At the end of the summer, we’ll gather the results and compare the different rates of scarring sources and the body parts where they occurred, and analyze any patterns in skin conditions, such as whether a skin condition has worsened or improved on an individual we have sighted multiple times over the years.

 Figure 1. A little look into a table I made to give examples of what scarring from different sources look like.

Surprisingly, cetaceans can heal deep wounds on their own without medical intervention. Scientists have discovered that compounds in their blubber layer, such as organohalogens and isovaleric acid, may naturally fight off infections and help wounds heal faster. Unlike humans and other terrestrial animals that form scabs when injured, cetaceans develop a different protective layer over their wounds. This layer consists of degenerative cells mixed with tiny bubbles and covers the injured area. This unique adaptation might help protect the wound from seawater and other environmental factors. While there have been studies on how surface wounds heal in captive dolphins and whales, there’s still much to learn about how these animals heal large, deep wounds. Understanding how wounds heal can help us to more accurately assess the frequency at which whales are wounded, whether it be from fishing gear or boats, to cookie cutter sharks or killer whales.

It seems like a lot, and it is, but our ultimate goal is to assess the effects that scarring and skin conditions can have in the ecology of marine megafauna. Assessing the individual gray whales in the photos can provide a bigger picture of the health of a whole population. We can also look for any patterns of skin conditions between mother and calf, individuals that are around each other often, adults and juveniles, or males and females. Scars may also play a role in a population’s health. If a gray whale had an open wound previously, did it develop into a skin condition? Did a skin condition worsen? Did it leave them more vulnerable to predators? These are the questions we would like to elaborate on with this research. A great read on this topic was conducted by Dawn R. Barlow, Acacia L. Pepper and Leigh G. Torres, which will be in the references below (Barlow et al., 2019). A better understanding of potential patterns is a better assessment of our current marine management practices. Is it enough, or do we need to change and do more?

Okay, lastly, let’s talk about artificial intelligence (AI). Would using AI methods for this project make our lives easier? Yes. If we could train AI to accurately identify specific scars and skin conditions, our 100,000 photos could be done within minutes. For my job security, woo no AI! But on a serious note, this approach could free up time that could be spent on other efforts, or speed up the process of assessing marine management. However, we gain so much by reviewing the photos ourselves which is still important to do when training AI on what specifics to search for. Over the summer, I’m going to get to know different whales and see how they may change over 8 years, just by their pictures. My excitement grew as soon as I looked at my first 3 gray whales and learned their names. It’s forever important to remember that we can always learn from sharing connections with the organisms we study and interact with. We share the same planet and we have to work together to preserve it. I thank you all for taking a trip through our summer research with me and I hope to meet some of you around Hatfield!

References

Barlow, D. R., Pepper, A. L., & Torres, L. G. (2019a). Skin deep: An assessment of New Zealand blue whale skin condition. Frontiers in Marine Science, 6. https://doi.org/10.3389/fmars.2019.00757 

Bradford, A. L., Weller, D. W., Ivashchenko, Y. V., Burdin, A. M., & Brownell, Jr, R. L. (2009). Anthropogenic scarring of Western Gray Whales (Eschrichtius robustus). Marine Mammal Science, 25(1), 161–175. https://doi.org/10.1111/j.1748-7692.2008.00253.x 

Corsi, E., Calambokidis, J., Flynn, K. R., & Steiger, G. H. (2021). Killer whale predatory scarring on Mysticetes: A comparison of rake marks among blue, humpback, and gray whales in the eastern North Pacific. Marine Mammal Science, 38(1), 223–234. https://doi.org/10.1111/mms.12863 

NOAA. (2020, April 4). Fisheries of the United States. https://www.fisheries.noaa.gov/national/sustainable-fisheries/fisheries-united-states

Hamilton, P. K., & Marx, M. K. (2005). Skin lesions on North Atlantic right whales: Categories, prevalence and change in occurrence in the 1990s. Diseases of Aquatic Organisms, 68, 71–82. https://doi.org/10.3354/dao068071 

Pettis, H. M., Rolland, R. M., Hamilton, P. K., Brault, S., Knowlton, A. R., & Kraus, S. D. (2004). Visual health assessment of north atlantic right whales (Eubalaena glacialis) using photographs. Canadian Journal of Zoology, 82(1), 8–19. https://doi.org/10.1139/z03-207 

Silber, G. K., Weller, D. W., Reeves, R. R., Adams, J. D., & Moore, T. J. (2021). Co-occurrence of gray whales and vessel traffic in the North Pacific Ocean. Endangered Species Research, 44, 177–201. https://doi.org/10.3354/esr01093 Sun, L., Engle, C., Kumar, G., & van Senten, J. (2022). Retail market trends for Seafood in the United States. Journal of the World Aquaculture Society, 54(3), 603–624. https://doi.org/10.1111/jwas.12919

Expand your rolodex and meet some more IndividuWhales!

In case you aren’t already aware, I want to remind you of a website called IndividuWhale we created about Pacific Coast Feeding Group (PCFG) gray whales we study as part of our GRANITE project. IndividuWhale features stories of some of the Oregon coast’s most iconic gray whales, as well as information about how we study them, stressors they experience in our waters, and even a game to test your gray whale identification skills. We also provide details about where to best spot gray whales along our coast and the different behaviors you might see gray whales displaying at different times of the year. Since launching the website in late 2021, we have made small tweaks and updates along the way, but now, after about 2.5 years, the time has come for a major content update as we are introducing you to three new individuals and their stories! Head over to IndividuWhale.com to check out the updates or continue reading for a preview of the content…

Lunita

Even though “Lunita” is only two years old (as of 2024), they (sex currently unknown!) have quickly become a star of our dataset and hearts. We documented Lunita as a calf with their mother “Luna” (hence the name Lunita, which means little Luna/moon) in 2022. We observed the mom-calf pair in our study area for almost two weeks during which it seemed like Lunita was a very attentive calf, always staying close to Luna and appearing to benthic feed alongside their mom. As is often the case when we document mom–calf pairs, we wonder whether we will see the calf again and how it will fair in an environment increasingly impacted by human activities. Much to our delight, we were reunited with Lunita later in the same summer when we saw them feeding independently, indicating that they had successfully weaned. We were even more delighted when we were reunited with Lunita again many times during the summer of 2023 as Lunita spent almost the entire feeding season along the central Oregon coast. This is yet another example, much like “Cheetah” and “Pacman,” of successful internal recruitment of calves born to PCFG females into the PCFG sub-population.

Lunita’s high site fidelity to our study area in 2023 meant that she was an excellent candidate for the suction-cup tagging we have been conducting in the last few years. During suction-cup tagging, we attach a device (or tag) via suction cups to a whale’s back. The tag contains a number of different sensors, including an accelerometer (to measure speed), a gyroscope (to measure direction), and a magnetometer (to measure magnetic field), as well as a high-definition video camera and hydrophone (or underwater microphone). These tags typically stay on for a maximum of 24 hours before they pop off the whale leaving no harm to the whale. Upon retrieval, we can recreate the whale’s dive path and see the environment and conditions that the whale experienced over several hours. We sometimes refer to tagging as giving the gray whales some temporary jewelry because the tags are a very flashy, bright orange color. From the video from Lunita’s tag shows how they soared through kelp forests feeding on mysids for many, many hours. Check out their profile here: https://www.individuwhale.com/whales/lunita/

Burned

There are many ways to assess the health of a whale. In our lab, we calculate body condition from drone images to determine how fat or skinny a whale is, examine different hormones from their poop, and assess growth rates via length measurements from drone images. Another health assessment metric that we explore in the lab is the skin and scarring on the individuals that we see in our central Oregon study area. By conducting a skin and scarring analysis, we can identify scarring patterns and lesions that may indicate interactions with human activities and track the progression of skin diseases that will help us understand the prevalence and impacts of pathogens on whales. One skin condition that we are particularly interested in tracking appears as a thick white or gray layer that can mask a gray whale’s natural pigmentation. An example of a whale that has experienced this skin condition is “Burned.”

Burned is a female who is at least 9 years old (as of 2024), as she was first documented in the PCFG range in 2015. We saw Burned for the first time in 2016. At the time, we noticed small, isolated, gray patches of the skin condition on both sides of Burned’s body. Throughout the years as we have continued to resight Burned, we noticed the skin condition spreading progressively across her body. We saw the skin condition at its maximum extent in 2022 when, at first glance, Burned was hardly recognizable. Luckily, we can identify gray whales using more than just their pigmentation patterns (learn more on our whale identification page). Interestingly, when we saw Burned in June 2024, it appeared that the skin condition completely disappeared! Burned is just one example of whales with this skin condition, leaving us with many questions about its origin and impact on the whales: What causes the skin condition (viral, fungal, bacterial?); How it is transmitted (via air or contact?); Is it harmful to the whale (weakened immune system?). Our research is aimed at addressing these questions to make this skin condition a little less mysterious. Check out her profile here: https://www.individuwhale.com/whales/burned/

Heart

“Heart,” who is also known as “Ginger,” is a very well known and popular whale in the Depoe Bay region. Heart is a female who is particularly famous for being a “tall fluker,” meaning that when she dives, she arches her tail fluke high in the air before it glides elegantly into the water. Heart was first documented as a calf in 2010, which means that she is 14 years old (as of 2024). At 14 years of age, we would expect for Heart to have had at least one, if not more, calves by now, as it is believed that gray whales reach sexual maturity at age 8 or 9. However, Heart has never been documented with a calf. Why?

While we cannot know for sure, we have a theory that it might be linked to her body length. Recent work in our lab has explored how growth of PCFG whales has changed over time. Using measurements of whales from our drone data, we  investigated how the asymptotic length (i.e. the final length reached once an individual stops growing) for the PCFG whales has changed since the 1980s. Shockingly, we found that starting in the year 2000 the asymptotic length of PCFG whales has declined at an average rate of 0.05–0.12 meters per year. Over time, this means that a whale born in 2020 is expected to reach an adult body length that is 13% shorter than a gray whale born prior to 2000. In Heart’s case specifically, when we last measured her length at 13 years old, she was 10.65 meters long. If she had been born prior to 2000, then she would be 12.04 meters long by now at the age of 13. That’s a whole 1.5 meters (or almost 5 feet) shorter!

You might be wondering how Heart’s length links back to her ability to have a calf. It takes a lot of energy to be pregnant and support the fetus, so by being smaller, Heart may not be able to store and allocate enough energy towards reproduction. Many of the whales we commonly see are shorter than expected based on their age (including “Zorro”), so we are monitoring the number and frequency of calves in the PCFG to see how this decline in length may impact the population. Check our her profile here: https://www.individuwhale.com/whales/heart/

Be sure to head over to IndividuWhale.com to explore all of the whale profiles and lots of other information that we have provided there about PCFG gray whales and how we study them here in Oregon waters!

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Blubber and Barnacles: An Introduction to Cetacean Skin Disease

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

Ever noticed how our skin gets pruny and overly soft after just ten minutes in the water? That’s because human skin is adapted for life on dry land, where retaining moisture is a primary concern. In contrast, cetaceans have evolved remarkable adaptations to thrive in the cold, salty ocean water for their entire lives. Understanding cetacean skin is crucial for conservation efforts, as it allows us to monitor and assess the overall health of these marine populations. By analyzing skin conditions, we can identify scarring patterns and lesions that may indicate interactions with human activities, such as entanglements or boat strikes, which can inform more effective risk assessment and mitigation strategies. Additionally, tracking the progression of skin diseases provides vital information on the prevalence and impact of pathogens, in order to guide more targeted management strategies to improve whale health and population resilience in their changing environments. To fully appreciate why monitoring skin diseases in cetaceans matters, let’s first explore the anatomy and physiology of cetacean skin and understand how scarring and diseases occur.

Whale skin has similar layers to our own, but modified over millions of years of evolution. Thicker than terrestrial mammals, the epidermis (the outermost layer) in marine mammals is designed to help maintain hydration in a hyperosmotic (very salty) environment where water is trying to flow into the cells of the whale. This top layer sloughs off at the surface as new cells are continuously renewed. The hypodermis, or blubber layer, is composed of primarily vascularized fat cells which insulate, store energy, and regulate buoyancy (Figure 1). 

Figure 1. Major layers of whale skin with the pop up showing a detailed figure of the epidermal/hypodermal junction (Mouton et al. 2011). 

Some other interesting skin adaptations that allow whales to maximize their efficiency underwater include near hairlessness, no sweat glands, and high levels of melanin. First, cetacean hairlessness helps them reduce drag in the water, but they don’t quite lack all hair. Most species of whales have hair around their mouths when they’re developing in the womb and then lose their hair either before birth or shortly after. Some species, like the humpback, have tubercles that are modified hair follicles to help them sense their surroundings, similar to whiskers on a dog. Second, because sweating is not effective for thermoregulation in the aquatic environment, whales have lost the sweat gland structure in their skin, making it slightly less permeable than terrestrial mammals. Their lack of glands also means that whales don’t secrete their own oils to maintain the moisture of the skin. So, if they’re exposed to dry air, their skin will dry out faster than the skin of terrestrial mammals. Lastly, melanin pigments vary from species to species. You can easily see this when we compare lateral surface photos of different species (Figure 2).

Figure 2. Comparison of surfacing photos between blue whales (upper left), Cuvier’s beaked whale (upper right), gray whale (lower left), and beluga whale (lower right) coloration. Blue and gray whale photos from GEMM Lab, beaked whale photo from Cascadia Research Collective (https://cascadiaresearch.org/files/Discriminating-between-Cuviers-and-Blainvilles-beaked-whales.pdf), and beluga whale photo from NOAA (https://www.fisheries.noaa.gov/event/2022-belugas-count)

This difference in coloration can be used by animals for camouflage either to avoid predators or to help ambush prey, and helps us to identify the species while they are at the surface. Coloration can also change as an animal ages and can help signal to us or other conspecifics the age or reproductive status of the individual (Caro et al. 2011). The melanin that creates these different colorations can protect whales against the harmful effects of UV radiation by absorbing and dissipating UV radiation, which decreases how far it penetrates into the skin, reducing cell damage (Morales-Guerrero et al. 2017). 

Thus, whale skin is very well adapted to the aquatic environment, from thick blubber layers to no sweat glands. However, despite these adaptations, cetaceans remain vulnerable to a range of pathogens. The major skin diseases documented in whales can fall into 4 categories: viral, bacterial, fungal, and parasitic. Viral infections in cetaceans involve the invasion of host cells, where viruses replicate and cause cell death or dysfunction, leading directly to skin lesions or nodules. Viruses can also manipulate the host immune response, suppressing immunity and exacerbating inflammation, which further contributes to skin damage. In contrast, fungal infections typically involve fungal growth and colonization on the skin surface or within tissues, with some fungi producing toxins that directly damage cells or provoke inflammatory responses (Espregueira et al. 2023). Bacterial infections in cetaceans often result from bacterial invasion and multiplication within skin tissues, accompanied by toxin production that damages cells and triggers a robust inflammatory response (Bressem et al. 2009). Parasitic infections, such as barnacle and whale lice infestations, can cause irritation, abrasions, and compromise the skin’s protective function, leading to localized inflammation and potential secondary infections. 

Understanding the specific causes of skin conditions in cetaceans is crucial because different pathogens spread through populations in distinct ways, impacting both individuals and population level health. Viral infections, for instance, can spread rapidly within populations through direct contact or respiratory droplets, potentially leading to widespread outbreaks and systemic effects. Fungal infections may persist in environmental reservoirs (spores of fungus can exist in seawater, sediment, organic marine debris, and the air) and can affect multiple individuals over time, particularly in conditions favoring fungal growth. Bacterial infections often spread through direct contact or contaminated environments, posing risks of localized outbreaks and secondary complications. Parasitic infestations, such as barnacles and whale lice, can transmit between individuals through close contact or shared habitat spaces (Romero et al 2012). By accurately identifying the causative agents of skin diseases, we can assess their transmission dynamics, anticipate population-level impacts, and implement targeted management strategies to mitigate disease spread and preserve whale health.

There are complex factors that contribute to skin disease prevalence in cetaceans. Environmental degradation, chemical pollution, climate change, and other anthropogenic stressors are known to lower immune systems, and degrade prey quality and quantity (Bressem et al. 2009). To understand the interactions between disease and the environment, we have to begin by establishing baseline health metrics. This summer, we will characterize an emerging skin disease in gray whales (see Zorro’s progression in Figure 3) using the photographs taken from the last 9 years of GRANITE fieldwork. Gray whales are particularly vulnerable to environmental threats because of their reliance on nearshore habitats. Unlike some other cetacean species that venture into deeper waters, gray whales are primarily coastal dwellers, feeding on benthic and epi-benthic organisms found in shallow, nutrient-rich waters. This dependence on nearshore environments exposes them to numerous risks. Pollution from runoff, oil spills, and plastic debris accumulates in these coastal waters, disrupting their immune systems leaving them more susceptible to disease. Climate change can induce shifts in the environment that alter the availability and quality of these habitats, potentially forcing them into proximity of other animals or places that harbor more disease. Habitat degradation due to coastal development and human activities like overfishing and increased vessel traffic further restricts their access to critical feeding areas (Bressem et al. 2009).

Figure 3. Comparisons of Zorro (a PCFG gray whale) between a year with no skin condition, 2020 (left panels) and this year where he came back covered in an unknown skin condition, 2024 (right panels). The upper panels capture his left side and the lower panels capture his right side.

These cumulative impacts increase the susceptibility of gray whales to diseases and stressors, highlighting the urgent need for baseline health assessments and identifying early signs of environmental stress (Stimmelmayr 2020). By documenting and analyzing skin conditions of gray whales through photographs, we can track changes over time and correlate them with environmental factors like pollution levels or habitat alterations. This non-invasive approach not only provides valuable insights into the prevalence and severity of skin diseases but also helps to understand broader ecological health trends in gray whale populations. 

P.S. Check out IndividuWhale to explore some great examples of how the skin condition of some of the local Oregon PCFG gray whales compare to each other and how we use their specific markings to help identify them in the field. 

References

Barlow, D.R., Pepper, A.L., Torres, L.G., 2019. Skin Deep: An Assessment of New Zealand Blue Whale Skin Condition. Frontiers in Marine Science 6.

Bressem, M.-F.V., Raga, J.A., Guardo, G.D., Jepson, P.D., Duignan, P.J., Siebert, U., Barrett, T., Santos, M.C. de O., Moreno, I.B., Siciliano, S., Aguilar, A., Waerebeek, K.V., 2009. Emerging infectious diseases in cetaceans worldwide and the possible role of environmental stressors. Diseases of Aquatic Organisms 86, 143–157. https://doi.org/10.3354/dao02101

Callewaert, C., Ravard Helffer, K., Lebaron, P., 2020. Skin Microbiome and its Interplay with the Environment. Am J Clin Dermatol 21, 4–11. https://doi.org/10.1007/s40257-020-00551-x

Caro, T., Beeman, K., Stankowich, T., Whitehead, H., 2011. The functional significance of colouration in cetaceans. Evol Ecol 25, 1231–1245. https://doi.org/10.1007/s10682-011-9479-5

Espregueira Themudo, G., Alves, L.Q., Machado, A.M., Lopes-Marques, M., da Fonseca, R.R., Fonseca, M., Ruivo, R., Castro, L.F.C., 2020. Losing Genes: The Evolutionary Remodeling of Cetacea Skin. Front. Mar. Sci. 7. https://doi.org/10.3389/fmars.2020.592375

Menon, G.K., Elias, P.M., Wakefield, J.S., Crumrine, D., 2022. CETACEAN EPIDERMAL SPECIALIZATION: A REVIEW. Anat Histol Embryol 51, 563–575. https://doi.org/10.1111/ahe.12829

Morales-Guerrero, B., Barragán-Vargas, C., Silva-Rosales, G.R., Ortega-Ortiz, C.D., Gendron, D., Martinez-Levasseur, L.M., Acevedo-Whitehouse, K., 2017. Melanin granules melanophages and a fully-melanized epidermis are common traits of odontocete and mysticete cetaceans. Veterinary Dermatology 28, 213-e50. https://doi.org/10.1111/vde.12392

Mouton, M., Botha, A., Mouton, M., Botha, A., 2012. Cutaneous Lesions in Cetaceans: An Indicator of Ecosystem Status?, in: New Approaches to the Study of Marine Mammals. IntechOpen. https://doi.org/10.5772/54432

Pitman, R.L., Durban, J.W., Joyce, T., Fearnbach, H., Panigada, S., Lauriano, G., 2020. Skin in the game: Epidermal molt as a driver of long-distance migration in whales. Marine Mammal Science 36, 565–594. https://doi.org/10.1111/mms.12661

Romero, A., Keith, E.O., 2012. New Approaches to the Study of Marine Mammals. BoD – Books on Demand.

Stimmelmayr, R., Gulland, F.M.D., 2020. Gray Whale (Eschrichtius robustus) Health and Disease: Review and Future Directions. Frontiers in Marine Science 7.

Su, C.-Y., Hughes, M.W., Liu, T.-Y., Chuong, C.-M., Wang, H.-V., Yang, W.-C., 2022. Defining Wound Healing Progression in Cetacean Skin: Characteristics of Full-Thickness Wound Healing in Fraser’s Dolphins (Lagenodelphis hosei). Animals (Basel) 12, 537. https://doi.org/10.3390/ani12050537

Van Bressem, M.-F., Van Waerebeek, K., Duignan, P.J., 2022. Tattoo Skin Disease in Cetacea: A Review, with New Cases for the Northeast Pacific. Animals 12, 3581. https://doi.org/10.3390/ani12243581

Kelp to whales: New GEMM Lab publication explores indirect effects of a classic trophic cascade on gray whales

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

As many of our avid readers already know, the Pacific Coast Feeding Group (PCFG) of gray whales employs a wide range of foraging tactics to feed on a number of different prey items in various benthic substrate types (Torres et al. 2018). One example foraging tactic is when PCFG whales, particularly when they are in the Oregon portion of their feeding range, forage on mysid shrimp in and near kelp beds on rocky reefs. We have countless drone video clips of whales weaving their large bodies through kelp and many photographs of whales coming to the surface to breath completely covered in kelp, looking more like a sea monster than a whale (Figure 1). So, when former intern Dylan Gregory made an astute observation during the 2018 TOPAZ/JASPER field season in Port Orford about a GoPro video the field team collected that showed many urchins voraciously feeding on an unhealthy-looking kelp stalk (Figure 2a), it made us wonder if and how changes to kelp forests may impact gray whales. 

Fig 1. Gray whale surfacing in a large kelp patch. Photograph captured under NOAA/NMFS research permit #16111. Source: GEMM Lab.

Kelp forests are widely used as a marine example of trophic cascades. Trophic cascades are trigged by the addition/removal of a top predator to/from a system, which causes changes further down the trophic chain. Sea urchins are common inhabitants of kelp forests and in a balanced, healthy system, urchin populations are regulated by predators as they behave cryptically by hiding in crevices in the reef and individual urchins feed passively on drift kelp that breaks off from larger plants. When we think about who controls urchins in kelp forests, we probably think of sea otters first. However, sea otters have been absent from Oregon waters for over a century (Kone et al. 2021), so who controls urchins here? The answer is the sunflower sea star (Figure 2b). Sunflower sea stars are large predators with a maximum arm span of up to 1 m! Unfortunately, a disease epidemic that started in 2013 known as sea star wasting disease caused 80-100% population decline of sunflower sea stars along the coastline between Mexico and Alaska (Harvell et al. 2019). Shortly thereafter, a record-breaking marine heatwave caused warm, nutrient-poor water conditions to persist in the northeast Pacific Ocean from 2014 to 2016 (Jacox et al. 2018). These co-occurring stressors caused unprecedented and long-lasting decline of a previously robust kelp forest in northern California (Rogers-Bennett & Catton 2019), where sea otters are also absent. Given the biogeographical similarity between southern Oregon and northern California and the observation made by Dylan in 2018, we decided to undertake an analysis of the eight years of data collected during the TOPAZ/JASPER project in Port Orford starting in 2016, to investigate the trends of four trophic levels (purple sea urchins, bull kelp, zooplankton, and gray whales) across space and time. The results of our study were published last week in Scientific Reports and I am excited to be able to share them with you today.

Fig 2a. Sea urchins actively feeding on kelp stalks. Source: GEMM Lab.
Fig 2b. Diver holding a sunflower sea star near Port Orford, OR. Source: Scott Groth.

Every day during the TOPAZ/JASPER field season, two teams head out to collect data. One team is responsible for tracking gray whales from shore using a theodolite, while the other team heads out to sea on a tandem research kayak to collect prey data (Figure 3). The kayak team samples prey in multiple ways, including dropping a GoPro camera at each sampling station. When the project was first developed, the original goal of these GoPro videos was to measure the relative abundance of prey. Since the sampling stations occur on or near reefs that are shallow with dense surface kelp, traditional methods to assess prey density, such as using a boat with an echosounder, are not suitable options. Instead, GEMM Lab PI Leigh Torres, together with the first Master’s student on this project Florence Sullivan, developed a method to score still images extracted from the GoPro videos to estimate relative zooplankton abundance. However, after we saw those images of urchins feeding on kelp in 2018, we decided to develop another protocol that allowed us to use these GoPro videos to also characterize sea urchin coverage and kelp condition. Once we had occurrence values for all four species, we were able to dig into the spatiotemporal trends.

Figure 3. Map of Port Orford, USA study area showing the 10 kayak sampling stations (white circles) within the two study sites (Tichenor Cove and Mill Rocks). The white triangle represents the cliff top location where theodolite tracking of whales was conducted. Figure and caption taken from Hildebrand et al. 2024.

When we examined the trends for each of the four study species across years, we found that purple sea urchin coverage in both of our study sites within Port Orford increased dramatically within our study period (Figure 4). In 2016, the majority of our sampled stations contained no visible urchins. However, by 2020, we detected urchins at every sampling station. For kelp, we saw the reverse trend; in 2016 all sampling stations contained kelp that was healthy or mostly healthy. But by 2019, there were many stations that contained kelp in poor health or where kelp was absent entirely. Zooplankton and gray whales experienced similar temporal trends as the kelp, with their occurrence metrics (abundance and foraging time, respectively) having higher values at the start of our study period and declining steadily during the eight years. While the rise in urchin coverage across our study area occurred concurrently with the decrease in kelp condition, zooplankton abundance, and gray whale foraging, we wanted to explicitly test how these species are related to one another based on prior ecological knowledge.

Figure 4. Temporal trends of purple sea urchin coverage, bull kelp condition, relative zooplankton abundance, and gray whale foraging time by year across the eight-year study period (2016–2023), from the generalized additive models. The colored ribbons represent approximate 95% confidence intervals. Line types represent the two study sites, Mill Rocks (MR; solid) and Tichenor Cove (TC; dashed). All curves are statistically significant (P < 0.05). Figure and caption taken from Hildebrand et al. 2024.

To test whether urchin coverage had an effect on kelp condition, we hypothesized that increased urchin coverage would be correlated with reduced kelp condition based on the decades of research that has established a negative relationship between the two when a trophic cascade occurs in kelp forest systems. Next, we wanted to test whether kelp condition had an effect on zooplankton abundance and hypothesized that increased kelp condition would be correlated with increased zooplankton abundance. We based this hypothesis on several pieces of prior knowledge, particularly as they pertain to mysid shrimp: (1) high productivity within kelp beds provides food for mysids, including kelp zoospores (VanMeter & Edwards 2013), (2) current velocities are one third slower inside kelp beds compared to outside (Jackson & Winant 1983), which might support the retention of mysids within kelp beds since they are not strong swimmers, and (3) the kelp canopy may serve as potential protection for mysids from predators (Coyer 1984). Finally, we wanted to test whether both kelp condition and zooplankton abundance have an effect on gray whales and we hypothesized that increased values for both would be correlated with increased gray whale foraging time. While the reasoning behind our hypothesized correlation between zooplankton prey and gray whales is obvious (whales eat zooplankton), the reasoning behind the kelp-whale connection may not be. We speculated that since kelp habitat may aggregate or retain zooplankton prey, gray whales may use kelp as an environmental cue to find prey patches. 

When we tested our hypotheses through generalized additive models, we found that increased urchin coverage was significantly correlated with decreased kelp condition in both study sites, providing evidence that a shift from a kelp forest to an urchin barren may have occurred in the Port Orford area. Additionally, increased kelp condition was correlated with increased zooplankton abundance, supporting our hypothesis that kelp forests are an important habitat and resource for nearshore zooplankton prey. Interestingly, this relationship was bell-shaped in one of our two study sites, suggesting that there are other factors besides healthy bull kelp that influence zooplankton abundance, which likely include upwelling dynamics, habitat structure, and local oceanographic characteristics. For the whale model, we found that increased kelp condition was significantly correlated with increased gray whale foraging time, which may corroborate our hypothesis that gray whales use kelp as an environmental cue to locate prey. Zooplankton abundance was significantly correlated with gray whale foraging time in one of our two sites. Once again, this relationship was bell-shaped, which suggests other factors influence gray whale foraging time, including prey quality (Hildebrand et al. 2022) and density.

Figure 5. Effects derived from trophic path generalized additive models of purple sea urchin coverage on kelp condition (A), kelp condition on relative zooplankton abundance (B), and kelp condition and relative zooplankton abundance on gray whale foraging time (C). The colored ribbons represent approximate 95% confidence intervals. Line types represent the two study sites, Mill Rocks (MR; solid) and Tichenor Cove (TC; dashed). Curves with asterisks indicate statistically significant (P < 0.05) relationships. Figure and caption taken from Hildebrand et al. 2024.

Our results highlight the potential larger impacts of reduced gray whale foraging time as a result of these trophic dynamics may cause at the individual and population level. If an area that was once a reliable source of food (like Port Orford) is no longer favorable, then whales likely search for other areas in which to feed. However, if the areas affected by these dynamics are widespread, then individuals may spend more time searching for, and less time consuming, prey, which could have energetic consequences. While our study took place in a relatively small spatial area, the trophic dynamics we documented in our system may be representative of patterns across the PCFG range, given ecological and topographic similarities in habitat use patterns. In fact, in the years with the lowest kelp, zooplankton, and whale occurrence (2020 and 2021) in Port Orford, the GRANITE field team also noted low whale numbers and minimal surface kelp extent in the central Oregon field site off of Newport. However, ecosystems are resilient. We are hopeful that the dynamics we documented in Port Orford are just short-term changes and that the system will return to its former balanced state with less urchins, more healthy bull kelp, zooplankton, and lots of feeding gray whales.

If you are interested in getting a more detailed picture of our methods and analysis, you can read our open access paper here: https://www.nature.com/articles/s41598-024-59964-x

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References

Coyer, J. A. (1984). The invertebrate assemblage associated with the giant kelp, Macrocystis pyrifera, at Santa Catalina Island, California: a general description with emphasis on amphipods, copepods, mysids, and shrimps. Fishery Bulletin, 82(1), 55-66.

Harvell, C. D., Montecino-Latorre, D., Caldwell, J. M., Burt, J. M., Bosley, K., Keller, A., … & Gaydos, J. K. (2019). Disease epidemic and a marine heat wave are associated with the continental-scale collapse of a pivotal predator (Pycnopodia helianthoides). Science advances, 5(1), eaau7042.

Hildebrand, L., Sullivan, F. A., Orben, R. A., Derville, S., & Torres, L. G. (2022). Trade-offs in prey quantity and quality in gray whale foraging. Marine Ecology Progress Series, 695, 189-201.

Jackson, G. A., & Winant, C. D. (1983). Effect of a kelp forest on coastal currents. Continental Shelf Research, 2(1), 75-80.

Jacox, M. G., Alexander, M. A., Mantua, N. J., Scott, J. D., Hervieux, G., Webb, R. S., & Werner, F. E. (2018). Forcing of multi-year extreme ocean temperatures that impacted California Current living marine resources in 2016. Bull. Amer. Meteor. Soc, 99(1).

Kone, D. V., Tinker, M. T., & Torres, L. G. (2021). Informing sea otter reintroduction through habitat and human interaction assessment. Endangered Species Research, 44, 159-176.

Rogers-Bennett, L., & Catton, C. A. (2019). Marine heat wave and multiple stressors tip bull kelp forest to sea urchin barrens. Scientific reports, 9(1), 15050.

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science, 5, 319.

VanMeter, K., & Edwards, M. S. (2013). The effects of mysid grazing on kelp zoospore survival and settlement. Journal of Phycology, 49(5), 896-901.

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.

Sonar savvy: using echo sounders to characterize zooplankton swarms

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

I’m Natalie Chazal, the GEMM Lab’s newest PhD student! This past spring I received my MS in Biological and Agricultural Engineering with Dr. Natalie Nelson’s Biosystems Analytics Lab at North Carolina State University. My thesis focused on using shellfish sanitation datasets to look at water quality trends in North Carolina and to forecast water quality for shellfish farmers in Florida. Now, I’m excited to be studying gray whales in the GEMM Lab!

Since the beginning of the Fall term, I’ve jumped into a project that will use our past 8 years of sonar data collected using a Garmin echo sounder during the GRANITE project work with gray whales off the Newport, OR coast. Echo sounder data is commonly used recreationally to detect bottom depth and for finding fish and my goal is to use these data to assess relative prey abundance at gray whale sightings over time and space. 

There are also scientific grade echo sounders that are built to be incredibly precise and very exact in the projection and reception of the sonar pulses. Both types of echosounders can be used to determine the depth of the ocean floor, structures within the water column, and organisms that are swimming within the sonar’s “cone” of acoustic sensing. The precision and stability of the scientific grade equipment allows us to answer questions related to the specific species of organisms, the substrate type at the sea floor, and even animal behavior. However, scientific grade echo sounders can be expensive, overly large for our small research vessel, and require expertise to operate. When it comes to generalists, like gray whales, we can answer questions about relative prey abundances without the use of such exact equipment (Benoit-Bird 2016; Brough 2019). 

While there are many variations of echo sounders that are specific to their purpose, commercially available, single beam echo sounders generally function in the same way (Fig. 1). First, a “ping” or short burst of sound at a specific frequency is produced from a transducer. The ping then travels downward and once it hits an object, some of the sound energy bounces off of the object and some moves into the object. The sound that bounces off of the object is either reflected or scattered. Sound energy that is either reflected or scattered back in the direction of the source is then received by the transducer. We can figure out the depth of the signal using the amount of travel time the ping took (SeaBeam Instruments 2000).

Figure 1. Diagram of how sound is scattered, reflected, and transmitted in marine environments (SeaBeam Instruments, 2000).

The data produced by this process is then displayed in real-time, on the screen on board the boat. Figure 2 is an example of the display that we see while on board RUBY (the GEMM Lab’s rigid-hull inflatable research boat): 

Figure 2. Photo of the echo sounder display on board RUBY. On the left is a map that is used for navigation. On the right is the real time feed where we can see the ocean bottom shown as the bright yellow area with the distinct boundary towards the lower portion of the screen. The more orange layer above that, with the  more “cloudy” structure  is a mysid swarm.

Once off the boat, we can download this echo sounder data and process it in the lab to recreate echograms similar to those seen on the boat. The echograms are shown with the time on the x-axis, depth on the y-axis, and are colored by the intensity of sound that was returned (Fig. 3). Echograms give us a sort of picture of what we see in the water column. When we look at these images as humans, we can infer what these objects are, given that we know what habitat we were in. Below (Fig. 3) are some example classifications of different fish and zooplankton swarms and what they look like in an echogram (Kaltenberg 2010).

Figure 3. Panel of echogram examples, from Kaltenberg 2010, for different fish and zooplankton aggregations that have been classified both visually (like we do in real time on the boat) as well as statistically (which we hope to do with the mysid aggregations). 

For our specific application, we are going to focus on characterizing mysid swarms, which are considered to be the main prey target of PCFG whales in our study area. With the echograms generated by the GRANITE fieldwork, we can gather relative mysid swarm densities, giving us an idea of how much prey is available to foraging gray whales. Because we have 8 years of GRANITE echosounder data, with 2,662 km of tracklines at gray whale sightings, we are going to need an automated process. This demand is where image segmentation can come in! If we treat our echograms like photographs, we can train models to identify mysid swarms within echograms, reducing our echogram processing load. Automating and standardizing the process can also help to reduce error. 

We are planning to utilize U-Nets, which are a method of image segmentation where the image goes through a series of compressions (encoders) and expansions (decoders), which is common when using convolutional neural nets (CNNs) for image segmentation. The encoder is generally a pre-trained classification network (CNNs work very well for this) that is used to classify pixels into a lower resolution category. The decoder then takes the low resolution categorized pixels and reprojects them back into an image to get a segmented mask. What makes U-Nets unique is that they re-introduce the higher resolution encoder information back into the decoder process through skip connections. This process allows for generalizations to be made for the image segmentation without sacrificing fine-scale details (Brautaset 2020; Ordoñez 2022; Slonimer 2023; Vohra 2023).

Figure 4. Diagram of the encoder, decoder architecture for U-Nets used in biomedical image segmentation. Note the skip connections illustrated by the gray lines connecting the higher resolution image information on the left, with the decoder process on the right (Ronneberger 2015)

What we hope to get from this analysis is an output image that provides us only the parts of the echogram that contain mysid swarms. Once the mysid swarms are found within the echograms, we can use both the intensity and the size of the swarm in the echogram as a proxy for the relative abundance of gray whale prey. We plan to quantify these estimates across multiple spatial and temporal scales, to link prey availability to changing environmental conditions and gray whale health and distribution metrics. This application is what will make our study particularly unique! By leveraging the GRANITE project’s extensive datasets, this study will be one of the first studies that quantifies prey variability in the Oregon coastal system and uses those results to directly assess prey availability on the body condition of gray whales. 

However, I have a little while to go before the data will be ready for any analysis. So far, I’ve been reading as much as I can about how sonar works in the marine environment, how sonar data structures work, and how others are using recreational sonar for robust analyses. There have been a few bumps in the road while starting this project (especially with disentangling the data structures produced from our particular GARMIN echosounder), but my new teammates in the GEMM Lab have been incredibly generous with their time and knowledge to help me set up a strong foundation for this project, and beyond. 

References

  1. Kaltenberg A. (2010) Bio-physical interactions of small pelagic fish schools and zooplankton prey in the California Current System over multiple scales. Oregon State University, Dissertation. https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/z890rz74t
  2. SeaBeam Instruments. (2000) Multibeam Sonar Theory of Operation. L-3 Communications, East Walpole MA. https://www3.mbari.org/data/mbsystem/sonarfunction/SeaBeamMultibeamTheoryOperation.pdf
  3. Benoit-Bird K., Lawson G. (2016) Ecological insights from pelagic habitats acquired using active acoustic techniques. Annual Review of Marine Science. https://doi.org/10.1146/annurev-marine-122414-034001
  4. Brough T., Rayment W., Dawson S. (2019) Using a recreational grade echosounder to quantify the potential prey field of coastal predators. PLoS One. https://doi.org/10.1371/journal.pone.0217013
  5. Brautaset O., Waldeland A., Johnsen E., Malde K., Eikvil L., Salberg A, Handegard N. (2020) Acoustic classification in multifrequency echosounder data using deep convolutional neural networks. ICES Journal of Marine Science 77, 1391–1400. https://doi.org/10.1093/icesjms/fsz235
  6. Ordoñez A., Utseth I., Brautaset O., Korneliussen R., Handegard N. (2022) Evaluation of echosounder data preparation strategies for modern machine learning models. Fisheries Research 254, 106411. https://doi.org/10.1016/j.fishres.2022.106411
  7. Slonimer A., Dosso S., Albu A., Cote M., Marques T., Rezvanifar A., Ersahin K., Mudge T., Gauthier S., (2023) Classification of Herring, Salmon, and Bubbles in Multifrequency Echograms Using U-Net Neural Networks. IEEE Journal of Oceanic Engineering 48, 1236–1254. https://doi.org/10.1109/JOE.2023.3272393
  8. Ronneberger O., Fischer P., Brox T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. https://doi.org/10.48550/arXiv.1505.04597

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

Lemos, L. S., Haxel, J. H., Olsen, A., Burnett, J. D., Smith, A., Chandler, T. E., Nieukirk, S. L., Larson, S. E., Hunt, K. E., & Torres, L. G. (2022). Effects of vessel traffic and ocean noise on gray whale stress hormones. Scientific Reports, 12(1), 18580. https://doi.org/10.1038/s41598-022-14510-5

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

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A Gut Feeling: DNA Metabarcoding Gray Whale Diets

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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