Tag: Gray Whales

The Hitchhiker’s Guide to the Gray Whale: Cetacean Cyamid Coverage Explained

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

When most people think about monitoring the health of a 40-ton gray whale, they picture blubber thickness, dive patterns, or perhaps growth rates. But what if some of the most telling signs are found not in the whale’s bulk, but right on the surface–embedded in its skin, and even crawling across it?

As part of the GRANITE project, my research focuses on using a long-term photographic dataset (>347,000 photos from 10 years!) to evaluate epidermal indicators of stress and health in Pacific Coast Feeding Group (PCFG) gray whales (Eschrichtius robustus) foraging off the Oregon coast. My central questions ask:

  • Can we use features visible on the skin like epidermal diseases, lesion severity, scarring from orcas, boats, and fishing gear, and potentially cyamid loads as biomarkers of physiological stress or nutritional status?
  • How do these skin-based indicators correlate with environmental variables, prey availability, fecal hormones, and overall body condition?

By tracking these patterns across individuals and years, my goal is to understand how gray whales are responding to a changing ocean and whether their skin can tell us more about what’s under the surface.

What are cyamids?

Cyamids, more commonly known as “whale lice”, are small crustaceans that live exclusively on marine mammals. Despite their nickname, cyamids are not true lice—they’re actually amphipod ectoparasites, more closely related to beach hoppers than anything you’d find in your hair. For gray whales (Eschrichtius robustus), these tiny passengers are a constant presence throughout their lives.

Figure 1. Gray whale blow hole area covered in barnacles and cyamids. The circle inset shows a zoomed in area where you can see the orange cyamids aggregating near the more yellow barnacles.

Each whale can host thousands of cyamids at a time, with individuals often clustering in specific areas of the body that provide physical refuge from the currents: around the blowhole, in the crevices of flukes, along the rostrum, genital slits, and especially around wounds or skin irregularities (Figure 1). Unlike barnacles, which attach directly to the skin and remain stationary while they feed on nutrients in the passing water, cyamids grasp onto the whale’s body using claw-like appendages, feeding on sloughed skin and bodily fluids. This relationship is generally not thought to be harmful to the whale, but high cyamid loads can be indicative of poor health, injury, or compromised immune function.

There are several species of cyamids, and many are host-specific—meaning they’ve evolved alongside particular whale species. In gray whales, the most common is Cyamus scammoni, which specializes on gray whales and is rarely found elsewhere. Other species found on gray whales include Cyamus kessleri, and the rarer Cyamus ceti. Cyamids are transmitted primarily from mother to calf, which helps explain their host fidelity, but horizontal transmission (between unrelated individuals) may also occur during close contact which can explain some rare occurrences of cyamids that are found outside of their general host species. In fact, Cyamus ceti was only found once on gray whales in 1861 but is generally thought to be specific to bowhead whales, giving us potential insight into interspecies interactions (bowhead and gray whales can spatially overlap on Arctic foraging grounds).

Figure 2. Cyamus scammoni close up photographs of (A) aggregation, (B) juvenile stage, (C) dorsal side of an adult, and (D) ventral side of an adult showcasing the cyamids corkscrew shaped gills (Takeda et al. 2005)

Because cyamids are permanent residents of the whale’s skin, they offer a unique window into both individual whale life histories and broader ecological trends. Their location, abundance, and distribution can potentially inform us about wound healing, residency duration in foraging areas, and even stress or health status—which makes them an unexpectedly valuable focal point in drone and photograph-based monitoring efforts like in the GRANITE project.

Cyamid Life History

Cyamids are obligate ectoparasites, meaning they spend their entire life on a whale and cannot survive independently in the open ocean. Unlike free-swimming crustaceans, cyamids are permanently attached to the skin or embedded within crevices of the whale’s body, often clinging to roughened areas, scars, embedded barnacles, or calloused skin where they can anchor themselves more securely.

They begin life as tiny juveniles, hatching from eggs carried in the brood pouch of a female cyamid. Rather than undergoing a larval phase in the water column like many marine invertebrates, cyamids develop directly into miniature versions of adults and remain on the whale from birth. This direct development is essential because there’s no safe habitat for a larval cyamid in the open ocean: the host whale is both nursery and home.

Most transmission occurs from mother to calf during the close physical contact of early life. Calves born in the warm lagoons of Baja California, Mexico where gray whales calve and nurse during the winter inherit their cyamid colonies during nursing, rubbing, and swimming alongside their mothers. These early colonizers will multiply as the calf grows and can remain with the whale for years, forming the basis of a persistent, host-specific population.

For Cyamus scammoni specifically (our gray whale specific cyamid), adults will breed in the summer just before the southbound migration. Females will have around 1,000 eggs in their brood pouch, although only about a 60% are fertilized (Leung, 1976). These eggs will hatch in the fall while the gray whales take on their southbound migration but they will stay in the safety of the brood pouch for around 2 to 3 months. The juveniles will be released in the winter, when gray whales arrive in the Baja lagoons where they will then find shelter within the crevices of their host gray whale. Juveniles reach maturity during the northbound migration and will be a full-grown brood upon arrival to summer grounds. While the cycle takes about 8 months to complete, there are juveniles found along the gray whales year-round, leading us to believe that there is likely overlap between broods. For our less abundant Cyamus kessleri, the life cycle is very similar, but the juveniles reach maturity before the gray whales northbound migration to summer feeding grounds. Also, there are around 300 eggs in the Cyamus kessleri brood pouches that have a higher rate of fertilization (75-80%) than Cyamus scammoni (60%) (Leung, 1976)

In short, the life of a cyamid is fully bound to the life of a whale. Every migration, dive, foraging event, and scar the whale experiences becomes part of the cyamid’s environment. By studying them, we gain another lens through which to interpret the health, behavior, and ecology of gray whales on the Oregon Coast.

Uses in Cetacean Health Assessments

As we’ve established, cyamids have unique life histories as ectoparasites and may be valuable indicators in cetacean health assessments across multiple whale species. Because they often congregate around wounds, lesions, and areas of poor skin integrity, their presence and distribution can reveal important clues about a whale’s physical condition, injury history, and immune response. However, studies that have made these connections have variable results.

In species like North Atlantic right whales (Pettis et al. 2004, Pirotta et al. 2023), harbor porpoises (Lehnert et al. 2021), and gray whales (Raverty et al. 2024), researchers have used visual surveys and photographic analysis to quantify cyamid loads in living, stranded, and hunted whales. Researchers can score cyamid presence by identifying attachment sites (e.g. blowhole, scar, dorsal ridge) and estimating the relative coverage by using standardized reference images to maintain consistency. In these studies, whales with heavy cyamid coverage, especially in sensitive regions like the blowhole, mouthline, and genital area, often show signs of poor health or stress, such as emaciation, scarring from entanglement, or chronic skin conditions. Cyamid coverage is sometimes used alongside body condition indices and lesion scoring to build a more complete health profile (Pirotta et al. 2023). There are also studies that show no connections, or even positive connections between body condition and cyamid coverage (Von Duyke et al. 2016).

While cyamids are often associated with injured, inflamed, or otherwise damaged skin, there is no evidence that points towards cyamids directly damaging the skin themselves. However, more work needs to be done to assess their role in the healing processes. Additionally, it’s been noted that more work is needed on the role of cyamids and disease spread (Overstreet et al. 2009). For the PCFG, there is an iconic whale we call “Scarlett” (also known as “Scarback”) who has a large scar on the right side of her back that is highly identifiable due to the orange swarm of cyamids that are constantly surrounding the edges of the wound. She has managed to survive and thrive, producing many calves over the years, but questions remain: How are the cyamids affecting the healing process? Are they increasing or decreasing the risk of infection? How does the frequency of large injuries like this on whales contribute to the cyamid population over evolutionary time?

Figure 3. Right side of PCFG icon, “Scarlett” showing her massive scar covered with orange aggregations of cyamids.

Because whales are complex, highly mobile, long-lived creatures with a constant population of cyamid hitchhikers their skin condition is likely representative of specific to life history, phylogeography, and demographic traits of individuals. While we know that cyamids generally eat sloughed or damaged skin on the whale, what this behavior and symbiosis means for each whale’s individual physiology can be highly complex. Through our high-resolution drone and lateral imagery of the same individuals over time paired with other data sources, such as body condition and prey availability, cyamid scores can offer key insights into how environmental stressors and foraging success affect individual and population-level whale health.

These tiny crustaceans, clinging to the folds and scars of their hosts, might seem like background noise in a study focused on body condition or foraging ecology—but they’re far from incidental. In my research, I’ve come to see cyamids as part of the bigger story: silent indicators of stress, recovery, movement, and resilience. By pairing imagery of PCFG gray whale skin with data on prey availability and environmental conditions, I’m working to understand how foraging success and anthropogenic stressors (such as vessel traffic and entanglements) manifest not just in a whale’s body condition, but in the skin itself. The presence, distribution, and density of cyamids may offer yet another layer of insight into how gray whales are coping with changing ocean conditions. It’s a reminder that even the smallest details, like a patch of whale lice, can help us ask bigger questions about the health, resilience, and future of these cetaceans.

References

Callahan, C.M., n.d. MOLECULAR SYSTEMATICS AND POPULATION GENETICS OF WHALE LICE (AMPHIPODA: CYAMIDAE) LIVING ON GRAY WHALE ISLANDS.

Lehnert, K., IJsseldijk, L.L., Uy, M.L., Boyi, J.O., van Schalkwijk, L., Tollenaar, E.A.P., Gröne, A., Wohlsein, P., Siebert, U., 2021. Whale lice (Isocyamus deltobranchium & Isocyamus delphinii; Cyamidae) prevalence in odontocetes off the German and Dutch coasts – morphological and molecular characterization and health implications. International Journal for Parasitology: Parasites and Wildlife 15, 22–30. https://doi.org/10.1016/j.ijppaw.2021.02.015

Leung, Y., 1976. Life cycle of cyamus scammoni (amphipoda: cyamidae), ectoparasite of gray whale, with a remark on the associated species. Scientific Reports of the Whales Research Institute 28, 153–160.

Overstreet, R.M., Jovonovich, J., Ma, H., 2009. Parasitic crustaceans as vectors of viruses, with an emphasis on three penaeid viruses. Integrative and Comparative Biology 49, 127–141. https://doi.org/10.1093/icb/icp033

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. Can. J. Zool. 82, 8–19. https://doi.org/10.1139/z03-207

Pirotta, E., Schick, R.S., Hamilton, P.K., Harris, C.M., Hewitt, J., Knowlton, A.R., Kraus, S.D., Meyer-Gutbrod, E., Moore, M.J., Pettis, H.M., Photopoulou, T., Rolland, R.M., Tyack, P.L., Thomas, L., 2023. Estimating the effects of stressors on the health, survival and reproduction of a critically endangered, long-lived species. Oikos 2023, e09801. https://doi.org/10.1111/oik.09801

Raverty, S., Duignan, P., Greig, D., Huggins, J.L., Huntington, K.B., Garner, M., Calambokidis, J., Cottrell, P., Danil, K., D’Alessandro, D., Duffield, D., Flannery, M., Gulland, F.M., Halaska, B., Lambourn, D.M., Lehnhart, T., Urbán R., J., Rowles, T., Rice, J., Savage, K., Wilkinson, K., Greenman, J., Viezbicke, J., Cottrell, B., Goley, P.D., Martinez, M., Fauquier, D., 2024. Gray whale (Eschrichtius robustus) post-mortem findings from December 2018 through 2021 during the Unusual Mortality Event in the Eastern North Pacific. PLoS One 19, e0295861. https://doi.org/10.1371/journal.pone.0295861

Stimmelmayr, R., Gulland, F.M.D., 2020. Gray Whale (Eschrichtius robustus) Health and Disease: Review and Future Directions. Front. Mar. Sci. 7. https://doi.org/10.3389/fmars.2020.588820

Takeda, M., Ogino, M., n.d. Record of a Whale Louse, Cyamus scammoni Dall (Crustacea: Amphipoda: Cyamidae), from the Gray Whale Strayed into Tokyo Bay, the Pacific Coast of Japan.

Von Duyke, A.L., Stimmelmayr, R., Sheffield, G., Sformo, T., Suydam, R., Givens, G.H., George, J.C., 2016. Prevalence and Abundance of Cyamid “Whale Lice” (Cyamus ceti) on Subsistence Harvested Bowhead Whales (Balaena mysticetus). Arctic 69, 331–340.

Würsig, B., Thewissen, J.G.M., Kovacs, K.M., 2017. Encyclopedia of Marine Mammals. Elsevier Science & Technology, Chantilly, UNITED STATES.

The whale’s scale: Emphasizing the scale of process, not the scale of observation

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

Baleen whales must navigate a seemingly featureless world to locate the resources they need to survive. The task of finding prey to feed on in the vast seascapes relies on the use of several sensory modalities that operate at different scales (Torres 2017; Figure 1). For example, baleen whale vision is believed to be rather limited, with the ability to see objects about 10-100 meters away. Yet, baleen whale somatosensory perception of oceanographic stimuli is thought to be on the order of 100-1000s kilometers. This diversity in sensory ability has led scientists to believe that whales, in fact all animals, perceive cues and make decisions at several scales. As ecologists, we endeavor to understand why and when animals are found (or not found) in certain locations as this knowledge allows us to better manage and conserve animal populations. With this information we can aim to minimize potential anthropogenic disturbance and protect important resource areas, such as foraging or nursing grounds. In order to accomplish this goal, we ourselves must conduct studies and test hypotheses at several scales (Levin 1992; Hobbs 2003). As someone who tackles spatial foraging ecology questions, I am particularly interested in understanding whale behavior and movement in the context of feeding. Since accurately measuring predator and prey distribution at the same scales can be challenging, we often resort to environmental variables to serve as proxies for prey, whereby we look for correlations between environmental variables and whales to understand and predict the distribution of our population. 

Figure 1. Schematic of hypothetical interchange of sensory modalities used by baleen whales to locate prey at variable scales. X-axis represents log distance to prey from micro (left) to macro (right). Y-axis represents the relative use of each sensory modality between 0 (no contribution) to 10 (highest contribution). Each line and color represent a different sensory modality. Figure taken and caption adapted from Torres 2017.

What do I mean when I use the word ‘scale’? The term scale is typically explained by two components: grain and extent (Wiens 1989). The grain is the finest resolution measured; in other words, how detailed we are measuring. The extent is the overall coverage of what we are measuring. These components can be applied to both spatial scale and temporal scale. For example, spatially, if we were using a 1×1 meter sampling quadrat to count the number of crabs on a rocky shore, then our grain would be the 1 m2 quadrat and the extent would be the entire exposed rocky intertidal area that we are surveying (Figure 2). Temporally, if we placed a temperature logger at the mouth of Yaquina Bay that took a temperature recording every minute for two years, then our grain would be one minute and the extent would be two years. So, when designing a study, it is imperative for us to decide on the spatiotemporal scales of the ecological questions we are asking and the hypotheses we are testing, as it will inform what data we need to collect. When making this decision, it is important to think about the scale at which the ecological process happens, as opposed to the scale at which we can observe the process (Levin 1992). In other words, we need to think from the perspective of our study species, as opposed to from our own human perspective. Making informed and ecologically reasonable decisions regarding the choice of scale relies on having prior knowledge of an animal’s biology, such as knowing that baleen whales might see a prey patch that is 50 meters away, but it may also somatosensorily perceive an oceanic front where zooplankton prey aggregate from 500 kilometers away.

Figure 2. Schematic of spatial scale where the extent (depicted by dashed orange box) is the entire exposed rocky intertidal area being surveyed and the grain (solid yellow box) is the 1×1 m quadrat being used to count crabs.

There is a wealth of studies that have explored space use patterns of wildlife relative to environmental variables to better understand foraging behavior. I want to share a couple from the marine mammal realm with you that I find particularly fascinating. In their 2018 study, González García and colleagues used opportunistic sightings of blue whales around the Azorean islands of Portugal and modeled their distribution patterns relative to physiographic and oceanographic variables summarized at different spatial (fine [1-10 km] and meso [10-100 km]) and temporal (daily, weekly, monthly) scales. The two variables that were most correlated with blue whale occurrence was distance from the coast and eddy kinetic energy (a measure of mesoscale variability of ocean dynamics). Both of these variables were interestingly found to be scale invariant, meaning that no matter which spatial and temporal scale was investigated, the relationship between blue whales and these two variables stayed the same; blue whale occurrence increased with increasing distance from the coast and was maximal at an eddy kinetic energy value of 0.007 cm2/s2 (Figure 3).

Figure 3. Functional response curves between presence of Azorean blue whales and distance to the coast (panel 1 on left) and eddy kinetic energy (panel 2 on right). The top row of each panel represents the low spatial scale and the bottom row represents the high spatial scale. Each column represents a different temporal scale (from left to right: daily, weekly, monthly). Note that the general shape of the relationship remains similar across all spatiotemporal scales and that the peak of the curves tend to occur at the same values for distance to coast and eddy kinetic energy across all scales. Figures taken from González García et al. 2018.

However, not all studies find scale invariant relationships. For example, Cotté and co-authors (2009) found that habitat use of Mediterranean fin whales was very much scale dependent. At a large scale (700-1,000 km and annual), fin whales were more densely aggregated during the summer in the Western Mediterranean where there was consistently colder water than in the winter. However, at a meso scale (20-100 km and weekly-monthly), fin whale densities were highest in areas where there were steep changes in temperature, as opposed to consistently cold temperatures. The authors explain that these differences in fin whale density and temperature at different scales are likely due to whale movement being driven by annually persistent prey abundance at the large scale, but at the meso scale, where prey aggregations are less predictable, the fin whales’ distribution becomes more driven by areas of physical ocean mixing.

As I investigate the environmental drivers of individual gray whale space use using our 8-year GRANITE (Gray whale Response to Ambient Noise Informed by Technology and Ecology) dataset, these studies (and many more) are at the top of my mind to interpret the patterns we are detecting. Our goal is to quantify and describe what environmental conditions (1) lead to a higher probability of a gray whale being seen in our central Oregon coast study area (~70 km) at a daily scale, and (2) influence space use patterns (activity range, residency, activity center) of different individual whales at annual scales. Our results show both consistency and variation in the environmental drivers of gray whales across these scales, leading me to deeply consider how gray whales make decisions at different points in their lives, based on information gained through various senses, to maximize their chances of capturing food. Previous work from the GEMM Lab on the relationships between gray whales and prey, at both fine (read more here) and large (read more here) scales have guided my work by providing specific hypotheses regarding environmental variables and lag times for me to test. Investigating the environmental drivers of animal space use and behavior is exciting work as it reveals that no single environmental variable determines animal distribution, but rather that multiple processes are happening concomitantly that animals respond to at different scales continually. It is only by studying animal space use patterns across spatiotemporal scales that we can begin to understand their complex decision-making patterns.

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References

Cotté, C., Guinet, C., Taupier-Letage, I., Mate, B., & Petiau, E. (2009). Scale-dependent habitat use by a large free-ranging predator, the Mediterranean fin whale. Deep Sea Research Part I: Oceanographic Research Papers, 56(5), 801-811.

González García, L., Pierce, G. J., Autret, E., & Torres-Palenzuela, J. M. (2018). Multi-scale habitat preference analyses for Azorean blue whales. PLoS One, 13(9), e0201786.

Hobbs, N. T. (2003). Challenges and opportunities in integrating ecological knowledge across scales. Forest Ecology and Management, 181(1-2), 223-238.

Levin, S. A. (1992). The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology, 73(6), 1943-1967.

Torres, L. G. (2017). A sense of scale: Foraging cetaceans’ use of scale‐dependent multimodal sensory systems. Marine Mammal Science, 33(4), 1170-1193.

Wiens, J. A. (1989). Spatial scaling in ecology. Functional ecology, 3(4), 385-397.

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

Reflecting on a solitary journey surrounded by an incredible team

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

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

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

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

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

The GRANITE team during our first in person meeting

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

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

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

The GRANITE team after our third in person meeting.

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

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

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

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New publication reveals gray whale habitat use patterns over three decades in the Northern California Current

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

The EMERALD project (Examining Marine mammal Ecology through Regional Assessment of Long-term Data) has reached a milestone with a recent publication detailing our findings on long-term gray whale distribution, abundance, and habitat use patterns (Barlow et al. 2024). The study is made possible by an incredible dataset. Every May-July since 1992, a team of observers surveyed the coastline between the Columbia River at the border between Oregon and Washington and San Francisco Bay, California for marbled murrelets, a seabird species of conservation concern. They drive a small vessel along pre-determined tracklines, and record observations of seabirds and marine mammals—not just marbled murrelets—and fortunately for us, that means there is a record of annual gray whale distribution and abundance patterns that spans over three decades.

The Crescent Coastal Research team collecting survey data. We are incredibly grateful to Craig Strong and the many folks who collected these valuable observations over the years!

We analyzed these valuable data using density surface modeling to better understand what drives gray whale distribution and abundance, what their habitat preferences are, and whether and how these occurrence patterns have changed over time. I am excited to share a few of our findings here!

Long-term, stable hotspots

The survey data revealed three main areas with consistently high gray whale density: the central Oregon Coast off Newport, Cape Blanco off Oregon’s south coast, and the mouth of the Klamath River in northern California. Despite fluctuations in how many whales were observed over the years, these areas have remained predicable hotspots for gray whales during their summer feeding season.

(A) Mean gray whale encounter rate (whales/kilometers surveyed) summarized by year, across all latitudes. (B) Mean gray whale encounter rate summarized by 1° latitude bin, across all years. White indicates times and locations with no survey effort. (C) Mean gray whale encounter rate summarized by year and 1° latitude bin. (D) Map of the study area, with region boundaries shown by the dashed lines, and major placenames denoted. Figure and caption reproduced from Barlow et al. 2024.

Key regional differences

Major features like prominent capes divide the California Current into different regions with distinct oceanographic characteristics. We found that gray whales showed different habitat preferences in the different regions. In the northern part of our study area between the Columbia River and Cape Blanco, we found that rocky bottom substrate was strongly related to areas of higher gray whale abundance, despite being far less available than soft, sandy bottom habitat. In the region between Cape Blanco and Cape Mendocino, gray whales were more abundant in areas south of prominent capes and in closer proximity to river estuaries.

Coastal upwelling and relaxation are key

Coastal upwelling—the process by which winds in the spring and summer push surface water offshore that is then replaced by cold, nutrient-rich water that is brought into the sunlight and drives an abundance of marine life—is a critically important influence in the oceanography, ecology, and biodiversity of our study region. But relaxation of those upwelling winds is also important for coastal species, as relaxation events allow the upwelled nutrients to be retained in the nearshore waters and enhance and aggregate local productivity and prey. We found that gray whale abundance was highest when there was a combination of both upwelling and relaxation events—a critical balance of “enough but not too much”—that seems to be optimal for gray whale feeding opportunities in nearshore waters.

You are what, where, and how you eat

Gray whales are incredibly flexible predators and have a wide range of prey items they are known to feed on. We found that throughout our study range, gray whales have different habitat preferences. As they spend their summers here to feed, these habitat preferences are linked to their foraging preferences. Off the central Oregon Coast, gray whales are known to feed on zooplankton that aggregate around rocky reefs and kelp forests (Hildebrand et al. 2022, 2024).

A gray whale surfaces in a patch of kelp, foraging around a rocky reef. UAS image credit: GEMM Lab.

Further south, in the region between Cape Blanco and Cape Mendocino that encompassed the long-term hotspot of gray whale sightings off the Klamath River, our models revealed different habitat preferences. In the soft-bottom habitat off the Klamath River, gray whales are known to do more benthic feeding, whereby they scoop up the seafloor and filter out the invertebrates in the sediment such as amphipods and cumaceans (Mallonée 1991, Jenkinson 2001).

A gray whale surfaces with a mouth full of muddy sediment, filtering out the invertebrate prey. UAS image credit: GEMM Lab.

These differences in regional habitat preferences and preferred prey likely relate to larger-scale phenomena as well. Indeed, when we looked at how gray whale abundance in different regions related to widespread warm or cool phases in the North Pacific Ocean, the responses differed by region. This aspect of the study indicates that what gray whales eat and where they forage influences how they respond to shifting environmental conditions and prey availability.

Conservation of an iconic nearshore predator

The unique mosaic of habitat characteristics throughout the Northern California Current summer feeding range of gray whales provides them the opportunity to gain the energetic stores they need to survive, reproduce, and migrate. Thus, the reliability of these resources has led them to return to these stable foraging hotspots year after year. Under climate change, one potential impact on upwelling systems is shifts in the intensity and location of upwelling (Bograd et al. 2023); in the Northern California Current, this could mean reduced relaxation events that we found are crucial for gray whales feeding in this habitat. Furthermore, these whales overlap with human activities such as vessel disturbance, entanglement and vessel strike risk, and ocean noise throughout the foraging season, and have to bear the consequences of these anthropogenic stressors (Sullivan & Torres 2018, Lemos et al. 2022, Pirotta et al. 2023) as they also navigate changing environmental conditions. Our study highlights the value of long-term monitoring to better understand present ecological patterns in the context of the past, which can be used to inform conservation management decisions for the future.

For more details, we invite you to read the full, open access publication here: https://www.nature.com/articles/s41598-024-59552-z

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References

Barlow DR, Strong CS, Torres LG (2024) Three decades of nearshore surveys reveal long-term patterns in gray whale habitat use, distribution, and abundance in the Northern California Current. Sci Rep 14:9352.

Bograd SJ, Jacox MG, Hazen EL, Lovecchio E, Montes I, Pozo Buil M, Shannon LJ, Sydeman WJ, Rykaczewski RR (2023) Climate Change Impacts on Eastern Boundary Upwelling Systems. Ann Rev Mar Sci 15:1–26.

Hildebrand L, Derville S, Hildebrand I, Torres LG (2024) Exploring indirect effects of a classic trophic cascade between urchins and kelp on zooplankton and whales. Sci Rep 14.

Hildebrand L, Sullivan FA, Orben RA, Derville S, Torres LG (2022) Trade-offs in prey quantity and quality in gray whale foraging. Mar Ecol Prog Ser 695:189–201.

Jenkinson RS (2001) Gray whale (Eschrichtius robustus) prey availability and feeding ecology in Northern California, 1999-2000. Humboldt State University

Lemos L, Haxel J, Olsen A, Burnett JD, Smith A, Chandler TE, Nieukirk SL, Larson SE, Hunt KE, Torres LG (2022) Effects of vessel traffic and ocean noise on gray whale stress hormones. Sci Rep 12:1–13.

Mallonée JS (1991) Behaviour of gray whales (Eschrichtius robustus) summering off the northern California coast, from Patrick’s Point to Crescent City. Can J Zool 69:681–690.

Pirotta E, Fernandez Ajó A, Bierlich KC, Bird CN, Buck CL, Haver SM, Haxel JH, Hildebrand L, Hunt KE, Lemos LS, New L, Torres LG (2023) Assessing variation in faecal glucocorticoid concentrations in gray whales exposed to anthropogenic stressors. Conserv Physiol 11:coad082.

Sullivan FA, Torres LG (2018) Assessment of vessel disturbance to gray whales to inform sustainable ecotourism. J Wildl Manage 82:896–905.

Measure faster! New tools for automatically obtaining body length and body condition of whales from drone videos

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

Monitoring the body length and body condition of animals can help provide important information on the health status of individuals and their populations, and can even serve as early warning signs if a population is adapting to habitat changes or is at risk of collapse (Cerini et al., 2023). As discussed in previous blogs, drone-based photogrammetry provides a method for non-invasively collecting important size measurements of whales, such as for detecting differences in body condition and length between populations, and even diagnosing pregnancy. Thus, using drones to collect measurement data on the growth, body condition, and pregnancy rates of whales can help expedite population health assessments to elicit conservation and management actions.

However, it takes a long time to manually measure whales filmed in drone imagery. For every video collected, an analyst must carefully watch each video and manually select frames with whales in good positions for measuring (flat and straight at the surface). Once frames are selected, each image must then be ranked and filtered for quality before finally measuring using a photogrammetry software, such as MorphoMetriX. This entire manual processing pipeline ultimately delays results, which hinders the ability to rapidly assess population health. If only there was a way to automate this process of obtaining measurements…

Well now there is! Recently, a collaboration between researchers from the GEMM Lab, CODEX, and OSU’s Department of Engineering and Computer Science published a manuscript introducing automated methods for obtaining body length and body condition measurements (Bierlich et al., 2024). The manuscript describes two user-friendly models: 1) “DeteX”, which automatically detects whales in drone videos to output frames for measuring and 2) “XtraX”, which automatically extracts body length and body condition measurements from input frames (Figure 1). We found that using DeteX and XtraX produces measurements just as good as manual measurement (Coefficient of Variation < 5%), while substantially reducing the processing time by almost 90%. This increased efficiency not only saves hours (weeks!) of manual processing time, but enables more rapid assessments of populations’ health.

Future steps for DeteX and XtraX are to adapt the models so that measurements can be extracted from multiple whales in a single frame, which could be particularly useful for analyzing images containing mothers with their calf. We also look forward to adapting DeteX and XtraX to accommodate more species. While DeteX and XtraX was trained using only gray whale imagery, we were pleased to see that these models performed well when trialing on imagery of a blue whale (Figure 2). These results are encouraging because it shows that the models can be adapted to accommodate other species with different body shapes, such as belugas or beaked whales, with the inclusion of more training data.

We are excited to share these methods with the drone community and the rest of this blog walks through the features and steps for running DeteX and XtraX to make them even easier to use.

Figure 1. Overview of DeteX and XtraX for automatically obtaining body length and body condition measurements from drone-based videos.

Figure 2. Example comparing manual (MorphoMetriX) vs. automated (XtraX) measurements of a blue whale.

DeteX and XtraX walkthrough

Both DeteX and XtraX are web-based applications designed to be intuitive and user-friendly. Instructions to install and run DeteX and XtraX are available on the CODEX website. Once DeteX is launched, the default web-browser automatically opens the application where the user is asked to select 1) the folder containing the drone-based videos to analyze and 2) the folder to save output frames (Figure 3). Then, the user can select ‘start’ to begin. The default for DeteX is set to analyze the entire video from start to finish at one frame per second; if recording a video at 30 frames per second, the last (or 30th) frame is processed for each second in the video. There is also a “finetune” version of DeteX that offers users much more control, where they can change these default settings (Figure 4). For example, users can change the defaults to increase the number of frames processed per second (i.e., 10 instead of 1), to target a specific region in the video rather than the entire video, and adjust the “detection model threshold” to change the threshold of confidence the model has for detecting a whale. These specific features for enhanced control may be particularly helpful when there is a specific surfacing sequence that a user wants to have more flexibility in selecting specific frames for measuring.

Figure 3. A screenshot of the DeteX web-based application interface.

Figure 4. The DeteX “finetune” version provides more control for users to change the default settings to target a specific region in the video (here between 3 min 00 sec and 3 min 05 sec), change the number of frames per second to process (now 10 per second), and the detection threshold, or level of confidence for identifying a whale in the video (now a higher threshold at 0.9 instead of the default at 0.8).

Once output frames are generated by DeteX, the user can select which frames to input into XtraX to measure. Once XtraX is launched, the default web-browser automatically opens the application where the user is asked to select 1) the folder containing the frames to measure and 2) the folder to save the output measurements. If the input frames were generated using DeteX, the barometric altitude is automatically extracted from the file name (note, that altitudes collected from a LiDAR altimeter can be joined in the XtraX output .csv file to then calculate measurements using this altitude). The image width (pixels) is automatically extracted from the input frame metadata. Users can then input specific camera parameters, such as sensor width (mm) and the focal length of the camera (mm), the launch height of the drone (i.e., if launching from hand when on a boat), and the region along the body to measure body condition (Figure 5). This region along the body is called the Head-Tail range and is identified as the area where most lipid storage takes place to estimate body condition. To run, the user selects “start”. XtraX then will output a .png file of each frame showing the keypoints (used for the body length measurement) and the shaded region (used for the body condition estimate) along the body to help visual results so users can filter for quality (Figure 6). XtraX also outputs a single .csv containing all the measurements (in meters and pixels) with their associated metadata.

Figure 5. User interface for XtraX. The user specifies a folder containing the images to measure and a folder to save the outputs measurements, and then can enter in camera specifications, the launch height of the drone (to be added to the barometer altitude) and the range of body widths to include in the body condition measurement (in the case, 0.2 and 0.7 correspond to body region between widths 20% and 70% of the total length, respectively).

Figure 6. Example output from XtraX showing (red) keypoints along the body to measure body length and the (green) shaded region used for body condition.

We hope this walkthrough is helpful for researchers interested in using and adapting these tools for their projects. There is also a video tutorial available online. Happy (faster) measuring!

References

Bierlich, K. C., Karki, S., Bird, C. N., Fern, A., & Torres, L. G. (2024). Automated body length and body condition measurements of whales from drone videos for rapid assessment of population health. Marine Mammal Science, e13137. https://doi.org/10.1111/mms.13137

Cerini, F., Childs, D. Z., & Clements, C. F. (2023). A predictive timeline of wildlife population collapse. Nature Ecology & Evolution, 7(3), 320–331. https://doi.org/10.1038/s41559-023-01985-2

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

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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

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