Tag: Gray Whales

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 whales’ 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 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 appears 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 our 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 (a.k.a. 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 was 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!

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly message when we post a new blog. Just add your name and email into the subscribe box below.

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.

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly alert when we make a new post! Just add your name into the subscribe box below!

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

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly alert when we make a new post! Just add your name into the subscribe box below!

References

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

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly message when we post a new blog. Just add your name and email into the subscribe box below.

References

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

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

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

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

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

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

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

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

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

Significant others? Thinking beyond p-values in science

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

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

What is a p-value?

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

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

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

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

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

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

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

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

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

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

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

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

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

The Debate

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

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

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

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

Moving Beyond P-values

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Did you enjoy this blog? Want to learn more about marine life, research, and
conservation? Subscribe to our blog and get a weekly message when we post a new
blog. Just add your name and email into the subscribe box below.

References

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

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

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

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

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

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

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

An ‘X’travaganza! Introducing the Marine Mammal Institute’s Center of Drone Excellence (CODEX)

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

Drones are becoming more and more prevalent in marine mammal research, particularly for non-invasively obtaining morphological measurements of cetaceans via photogrammetry to identify important health metrics (see this and this previous blog). For example, the GEMM Lab uses drones for the GRANITE Project to study Pacific Coast Feeding Group (PCFG) gray whales and we have found that PCFG whales are skinnier and morphologically shorter with smaller skulls and flukes compared to the larger Eastern North Pacific (ENP) population. The GEMM Lab has also used drones to document variation in body condition across years and within a season, to diagnose pregnancy, and even measure blowholes.

While drone-based photogrammetry can provide major insight into cetacean ecology, several drone systems and protocols are used across the scientific community in these efforts, and no consistent method or centralized framework is established for quantifying and incorporating measurement uncertainty associated with these different drones. This lack of standardization restricts comparability across datasets, thus hindering our ability to effectively monitor populations and understand the drivers of variation (e.g., pollution, climate change, injury, noise).

We are excited to announce the Marine Mammal Institute’s (MMI) Center of Drone Excellence (CODEX), which focuses on developing analytical methods for using drones to non-invasively monitor marine mammal populations. CODEX is led by GEMM Lab member’s KC Bierlich, Leigh Torres, and Clara Bird and consists of other team members within and outside OSU. We draw from many years of trials, errors, headaches, and effort working with drones to study cetacean ecology in a variety of habitats and conditions on many different species.

Already CODEX has developed several open-source hardware and software tools. We developed, produced, and published LidarBoX (Bierlich et al., 2023), which is a 3D printed enclosure for a LiDAR altimeter system that can be easily attached and swapped between commercially available drones (i.e., DJI Inspire, DJI Mavic, and DJI Phantom) (Figure 1). Having a LidarBoX installed helps researchers obtain altitude readings with greater accuracy, yielding morphological measurements with less uncertainty. Since we developed LidarBoX, we have received over 35 orders to build this unit for other labs in national and international universities.

Figure 1. A ‘LidarBoX’ attached to a DJI Inspire 2. The LidarBoX is a 3D printed enclosure containing a LiDAR altimeter to help obtain more accurate altitude readings.

Additionally, CODEX recently released MorphoMetriX version 2 (v2), an easy-to-use photogrammetry software that provides users with the flexibility to obtain custom morphological measurements of megafauna in imagery with no knowledge of any scripting language (Torres and Bierlich, 2020). CollatriX is a user-friendly software for collating multiple MorphoMetriX outputs into a single dataframe and linking important metadata to photogrammetric measurements, such as altitude measured with a LidarBoX (Bird and Bierlich, 2020). CollatriX also automatically calculates several body condition metrics based on measurements from MorphoMetriX v2. CollatriX v2 is currently in beta-testing and scheduled to be released late Spring 2024. 

Figure 2. An example of a Pygmy blue whale imported into MorphoMetriX v2, open-source photogrammetry software. 

CODEX also recently developed two automated tools to help speed up the laborious manual processing of drone videos for obtaining morphological measurements (Bierlich & Karki et al., in revision). DeteX is a graphical user interface (GUI) that uses a deep learning model for automated detection of cetaceans in drone-based videos. Researchers can input their drone-based videos and DeteX will output frames containing whales at the surface. Users can then select which frames they want to use for measuring individual whales and then input these selected frames into XtraX, which is a GUI that uses a deep learning model to automatically extract body length and body condition measurements of cetaceans (Figure 4). We found automated measurements from XtraX to be similar (within 5%) of manual measurements. Importantly, using DeteX and XtraX takes about 10% of the time it would take to manually process the same videos, demonstrating how these tools greatly speed up obtaining key morphological data while maintaining accuracy, which is critical for effectively monitoring population health.

Figure 3. An example of an automated body length (top) and body condition (bottom) measurement of a gray whale using XtraX (Bierlich & Karki et al., in revision).

CODEX is also in the process of developing Xcertainty, an R package that uses a Bayesian statistical model to quantify and incorporate uncertainty associated with measurements from different drones (see this blog). Xcertainty is based on the Bayesian statistical model developed by Bierlich et al., (2021b; 2021a), which has been utilized by many studies with several different drones to compare body condition and body morphology across individuals and populations  (Bierlich et al., 2022; Torres et al., 2022; Barlow et al., 2023). Rather than a single point-estimate of a length measurement for an individual, Xcertainty produces a distribution of length measurements for an individual so that the length of a whale can be described by the mean of this distribution, and its uncertainty as the the variance or an interval around the mean (Figure 4). These outputs ensure measurements are robust and comparable across different drones because they provide a measure of the uncertainty around each measurement. For instance, a measurement with more uncertainty will have a wider distribution. The uncertainty associated with each measurement can be incorporated into analyses, which is key when detecting important differences or changes in individuals or populations, such as changes in body condition (blog).

Figure 4. An example of a posterior predictive distribution for total length of an individual blue whale produced by the ‘Xcertainty’ R package. The black bars represent the uncertainty around the mean value (the black dot) – the longer black bars represent the 95% highest posterior density (HPD) interval, and the shorter black bars represent the 65% HPD interval. 

CODEX has integrated all these lessons learned, open-source tools, and analytical approaches into a single framework of suggested best practices to help researchers enhance the quality, speed, and accuracy of obtaining important morphological measurements to manage vulnerable populations. These tools and frameworks are designed to be accommodating and accessible to researchers on various budgets and to facilitate cross-lab collaborations. CODEX plans to host workshops to educate and train researchers using drones on how to apply these tools within this framework within their own research practices. Potential future directions for CODEX include developing a system for using drones to drop suction-cup tags on whales and to collect thermal imagery of whales for health assessments. Stay up to date with all the CODEX ‘X’travaganza here: https://mmi.oregonstate.edu/centers-excellence/codex.  

Huge shout out to Suzie Winquist for designing the artwork for CODEX!

References

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

Bierlich, K., Karki, S., Bird, C.N., Fern, A. and Torres, L.G., n.d. Automated body length and condition measurements of whales from drone videos for rapid assessment of population health. Marine Mammal Science.

Bierlich, K.C., Hewitt, J., Bird, C.N., Schick, R.S., Friedlaender, A., Torres, L.G., Dale, J., Goldbogen, J., Read, A.J., Calambokidis, J. and Johnston, D.W., 2021a. Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.749943.

Bierlich, K.C., Hewitt, J., Schick, R.S., Pallin, L., Dale, J., Friedlaender, A.S., Christiansen, F., Sprogis, K.R., Dawn, A.H., Bird, C.N., Larsen, G.D., Nichols, R., Shero, M.R., Goldbogen, J., Read, A.J. and Johnston, D.W., 2022. Seasonal gain in body condition of foraging humpback whales along the Western Antarctic Peninsula. Frontiers in Marine Science, 9(1036860), pp.1–16. https://doi.org/10.3389/fmars.2022.1036860.

Bierlich, K.C., Schick, R.S., Hewitt, J., Dale, J., Goldbogen, J.A., Friedlaender, A.S. and Johnston, D.W., 2021b. Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones. Marine Ecology Progress Series, 673, pp.193–210. https://doi.org/10.3354/meps13814.

Bird, C. and Bierlich, K.C., 2020. CollatriX: A GUI to collate MorphoMetriX outputs. Journal of Open Source Software, 5(51), pp.2323–2328. https://doi.org/10.21105/joss.02328.

Torres, L.G., Bird, C.N., Rodríguez-González, F., Christiansen, F., Bejder, L., Lemos, L., Urban R, J., Swartz, S., Willoughby, A., Hewitt, J. and Bierlich, K.C., 2022. Range-Wide Comparison of Gray Whale Body Condition Reveals Contrasting Sub-Population Health Characteristics and Vulnerability to Environmental Change. Frontiers in Marine Science, 9(April), pp.1–13. https://doi.org/10.3389/fmars.2022.867258.

Torres, W. and Bierlich, K.C., 2020. MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), pp.1825–1826. https://doi.org/10.21105/joss.01825.

Every breath [a whale] takes: How and why we study cetacean respiration

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

We need energy to function and survive. For animals in the wild who may have limited food availability, knowing how they spend their energy is a critical question for many scientists because it fundamentally informs how we understand their decisions about where they go and what they do. The entire field of foraging theory is founded on the concept that animals optimize their ratio of energy in and energy out so that they have enough energy to survive, reproduce (pass on their genes), watch out for threats, if need be, and rest. And, if we understand an animal’s ‘typical’ energy budget, we can then try to predict how disturbance or environmental change will affect their actual energy budgets as a consequence of that change. But how do we measure energy expenditure?

The most commonly measured energy currency is oxygen. Since our cells use oxygen to produce energy (this is why we need oxygen to live), we can measure oxygen consumption as a metric of energy expenditure. The more oxygen we consume, the more energy we’re expending. In ideal lab settings, oxygen consumption can be accurately measured by placing the subject in a chamber where the oxygen flow can be controlled (Speakman, 1999). However, you can probably see how that approach is problematic for measuring oxygen consumption in most large free-living animals, especially cetaceans. It isn’t exactly feasible to put a whale in a box.

Image 1. A great tit in a metabolic chamber. Figure 1 from Broggi et al., 2009

Fortunately, a tool called a spirometer was developed to measure oxygen consumption in restrained cetaceans. A spirometer is a device that can be placed over the blowhole(s) of an individual to accurately measure the amount of air that is exhaled and inhaled (Figure 1).  For trained cetaceans in captivity (e.g., dolphins), spirometers can be used to quantify how respiration changes after the animal performs certain behaviors (Fahlman et al., 2019). The breathing patterns of diving mammals are particularly interesting because they cannot breathe during most of their exercise (energy expenditure) as they are underwater. So, their breathing patterns after a dive tell us a lot about how much energy they spent during that dive. For example, Fahlman et al. (2019) used spirometer data from dolphins in captivity to study how their breathing patterns changed while recovering from dives of different durations. Interestingly, they found that after longer dives, dolphins took larger breaths (i.e., inhaled more air) while recovering but did not change the number of breaths. This finding is particularly relevant to the work we are conducting in the GEMM lab, where we utilize breathing patterns to quantify the energy expenditure of cetaceans in the wild, where spirometers cannot be used.

Figure 1. Figure 1 from Sumich et al. (2023). Left: a spirometer being held over the blow holes of JJ, a gray whale calf at sea world in 1997; one of the rare times that a large baleen whale was in captivity and available for these measurements. Right: example of a plot created using the data from a spirometer over JJ’s blow holes. The duration of a “blow” (exhale followed by immediate inhale) is on the x-axis, the flow rate (in liters per second) is on the y-axis. The positive curve during the exhale shows that the whale strongly exhales a lot of air very quickly, then the negative curve shows the whale inhaling a lot of air very quickly.

In a previous blog, I described how inter-breath intervals (the time between consecutive blows) are useful for estimating energy expenditure in free-living cetaceans. Essentially, a shorter interval indicates that the whale was just engaged in an energetically demanding activity. When you’re recovering from a sprint, you breathe faster (i.e., with shorter inter-breath intervals), than when you’re recovering from a walk. However, a big assumption in using inter-breath intervals as a proxy for energy expenditure is that every breath is equal. But as Fahlman et al. emphasize in their 2016 paper, every blow is not equal (Fahlman et al., 2016). In addition to varying the time between breaths, an animal can vary the intensity of each breath (e.g., Fahlman et al., 2019), the duration of each breath (Sumich et al., 2023), the number of breaths, and even the expansion of their nostrils (Nazario et al., 2022; check out this blog for more).

Image 2. Gray whale blow. Source: https://www.lajollalight.com/sdljl-natural-la-jolla-winter-wildlife-2015jan08-story.html

Altogether, this means that it’s important to measure every breath and that no one metric tells the complete story. This also means my research question focused on comparing the energetic costs of different tactics is more complicated than I originally thought. If we go back to the first blog I wrote on this topic, I was planning ons only using inter-breath intervals to estimate energy expenditure. Fast forward four years, with all my new knowledge gained on respiration variability, I’ve modified my plan and now I’m working to first understand how all these different metrics of breathing relate to each other. Then, I’ll compare how breathing varies between different foraging tactics, which is an important follow up to my questions around individual specialization of foraging tactics. If different whales are using different foraging behaviors, does that mean they’re spending different amounts of energy? If so, are certain behaviors more advantageous than others? Of course, these answers are incomplete without understanding the prey the whales are eating, but that’s something that PhD student Nat Chazal is working to understand (check out her recent blog here).  For now, I’m working on bringing integrating all the measures of breathing, then we will start putting the story together and finding some answers to our pressing questions. 

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly alert when we make a new post! Just add your name into the subscribe box below!

References

Broggi, J., Hohtola, E., Koivula, K., Orell, M., & Nilsson, J. (2009). Long‐term repeatability of winter basal metabolic rate and mass in a wild passerine. Functional Ecology, 23(4), 768–773. https://doi.org/10.1111/j.1365-2435.2009.01561.x

Fahlman, A., Brodsky, M., Miedler, S., Dennison, S., Ivančić, M., Levine, G., Rocho-Levine, J., Manley, M., Rocabert, J., & Borque-Espinosa, A. (2019). Ventilation and gas exchange before and after voluntary static surface breath-holds in clinically healthy bottlenose dolphins, Tursiops truncatus. Journal of Experimental Biology, 222(5), jeb192211. https://doi.org/10.1242/jeb.192211

Fahlman, A., van der Hoop, J., Moore, M. J., Levine, G., Rocho-Levine, J., & Brodsky, M. (2016). Estimating energetics in cetaceans from respiratory frequency: Why we need to understand physiology. Biology Open,5(4), 436–442. https://doi.org/10.1242/bio.017251

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

Speakman, J. R. (1999). The Cost of Living: Field Metabolic Rates of Small Mammals. In A. H. Fitter & D. G. Raffaelli (Eds.), Advances in Ecological Research (Vol. 30, pp. 177–297). Academic Press. https://doi.org/10.1016/S0065-2504(08)60019-7

Sumich, J. L., Albertson, R., Torres, L. G., Bird, C. N., Bierlich, K. C., & Harris, C. (2023). Using audio and UAS-based video for estimating tidal lung volumes of resting and active adult gray whales (Eschrichtius robustus). Marine Mammal Science, 1(8). https://doi.org/10.1111/mms.13081