Who, where, when: Estimating individual space use patterns of PCFG gray whales

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

Understanding how baleen whales are affected by human activity is a central goal for many research projects in the GEMM Lab. The overarching goal of the GRANITE (Gray whale Response to Ambient Noise Informed by Technology and Ecology) project is to quantify baleen whale physiological response to different stressors (e.g., boat presence and noise) and model the subsequent impacts of these stressors on the population. We will achieve this goal by implementing our long-term, replicate dataset of Pacific Coast Feeding Group (PCFG) gray whales into a framework called population consequences of disturbance (PCoD). I will not go into the details of PCoD in this blog (but I wrote a post a few years ago that you can revisit). Instead, I will explain the approach I am taking to assess where and when individual whales spend time in our study area, which will form an essential component of PCoD and be one of the chapters of my PhD dissertation.

Individuals in a population are unlikely to be exposed to a stressor in a uniform way because they make decisions differently based on intrinsic (e.g., sex, age, reproductive status) and extrinsic (e.g., environment, prey, predators) factors (Erlinge & Sandell 1986). For example, a foraging female gray whale who is still nursing a calf will need to consider factors that are different to ones that an adult single male might need to consider when choosing a location to feed. These differences in decision-making exist across the whole population, which makes it important to understand where individuals are spending time and how they overlap with stressors in space and time before trying to quantify the impacts of stressors on the population as a whole (Pirotta et al. 2018). I am currently working on an analysis that will determine an individual’s exposure to a number of stressors based on their space use patterns. 

We can monitor space use patterns of individuals in a population through time using spatial capture-recapture techniques. As the name implies, a spatial capture-recapture technique involves capturing an individual in a marked location during a sampling period, releasing it back into the population, and then (hopefully) re-capturing it during another sampling period in the future, at either the same or a different location. With enough repeat sampling events, the method should build spatial capture histories of individuals through time to better understand an individual’s space use patterns (Borchers & Efford 2008). While the use of the word capture implies that the animal is being physically caught, this is not necessarily the case. Individuals can be “captured” in a number of non-invasive ways, including by being photographed, which is how we “capture” individual PCFG gray whales. These capture-recapture methods were first pioneered in terrestrial systems, where camera traps (i.e., cameras that take photos or videos when a motion sensor is triggered) are set up in a systematic grid across a study area (Figure 1; Royle et al. 2009, Gray 2018). Placing the cameras in a grid system ensures that there is an equal distribution of cameras throughout the study area, which means that an animal theoretically has a uniform chance of being captured. However, because we know that individuals within a population make space use decisions differently, we assume that individuals will distribute themselves differently across a landscape, which will manifest as individuals having different centers of their spatial activity. The probability of capturing an individual is highest when a camera trap is at that individual’s activity center, and the cameras furthest away from the individual’s activity center will have the lowest probability of capturing that individual (Efford 2004). By using this principle of probability, the data generated from spatial capture-recapture field methods can be modelled to estimate the activity centers and ranges for all individuals in a population. The overlap of an individual’s activity center and range can then be compared to the spatiotemporal distribution of stressors that an individual may be exposed to, allowing us to determine whether and how an individual has been exposed to each stressor. 

Figure 1. Example of camera trap grid in a study area. Figure taken from Gray (2018).

While capture-recapture methods were first developed in terrestrial systems, they have been adapted for application to marine populations, which is what I am doing for our GRANITE dataset of PCFG gray whales. Together with a team of committee members and GRANITE collaborators, I am developing a Bayesian spatial capture-recapture model to estimate individual space use patterns. In order to mimic the camera trap grid system, we have divided our central Oregon coast study area into latitudinal bins that are approximately 1 km long. Unfortunately, we do not have motion sensor activated cameras that automatically take photographs of gray whales in each of these latitudinal bins. Instead, we have eight years of boat-based survey effort with whale encounters where we collect photographs of many individual whales. However, as you now know, being able to calculate the probability of detection is important for estimating an individual’s activity center and range. Therefore, we calculated our spatial survey effort per latitudinal bin in each study year to account for our probability of detecting whales (i.e., the area of ocean in km2 that we surveyed). Next, we tallied up the number of times we observed every individual PCFG whale in each of those latitudinal bins per year, thus creating individual spatial capture histories for the population. Finally, using just those two data sets (the individual whale capture histories and our survey effort), we can build models to test a number of different hypotheses about individual gray whale space use patterns. There are many hypotheses that I want to test (and therefore many models that I need to run), with increasing complexity, but I will explain one here.

Over eight years of field work for the GRANITE project, consisting of over 40,000 km2 of ocean surveyed with 2,169 sightings of gray whales, our observations lead us to hypothesize that there are two broad space use strategies that whales use to optimize how they find enough prey to meet their energetic needs. For the moment, we are calling these strategies ‘home-body’ and ‘roamer’. As the name implies, a home-body is an individual that stays in a relatively small area and searches for food in this area consistently through time. A roamer, on the other hand, is an individual that travels and searches over a greater spatial area to find good pockets of food and does not generally tend to stay in just one place. In other words, we except a home-body to have a consistent activity center through time and a small activity range, while a roamer will have a much larger activity range and its activity center may vary more throughout the years (Figure 2). 

Figure 2. Schematic representing one of the hypotheses we will be testing with our Bayesian spatial capture-recapture models. The schematic shows the activity centers (the circles) and activity ranges (vertical lines attached to the circles) of two individuals (green and orange) across three years in our central Oregon study area. The green individual represents our hypothesized idea of a home-body, whereas the orange individuals represents our idea of a roamer.

While this hypothesis sounds straightforward, there are a lot of decisions that I need to make in the Bayesian modeling process that can ultimately impact the results. For example, do all home-bodies in a population have the same size activity range or can the size vary between different home-bodies? If it can vary, by how much can it vary? These same questions apply for the roamers too. I have a long list of questions just like these, which means a lot of decision-making on my part, and that long list of hypotheses I previously mentioned. Luckily, I have a fantastic team made up of Leigh, committee members, and GRANITE collaborators that are guiding me through this process. In just a few more months, I hope to reveal how PCFG individuals distribute themselves in space and time throughout our central Oregon study area, and hence describe their exposure to different stressors. Stay tuned! 

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References

Borchers DL, Efford MG (2008) Spatially explicit maximum likelihood methods for capture-recapture studies. Biometrics 64:377-385.

Efford M (2004) Density estimation in live-trapping studies. Oikos 106:598-610.

Erlinge S, Sandell M (1986) Seasonal changes in the social organization of male stoats, Mustela erminea: An effect of shifts between two decisive resources. Oikos 47:57-62.

Gray TNE (2018) Monitoring tropical forest ungulates using camera-trap data. Journal of Zoology 305:173-179.

Pirotta E, Booth CG, Costa DP, Fleishman E, Kraus SD, and others (2018) Understanding the population consequences of disturbance. Ecology and Evolution 8(19):9934–9946.Royle J, Nichols J, Karanth KU, Gopalaswamy AM (2009) A hierarchical model for estimating density in camera-trap studies. Journal of Applied Ecology 46:118-127.

Learning from the unexpected: the first field season of the SAPPHIRE project

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

The SAPPHIRE project’s inaugural 2024 field season has officially wrapped up, and the team is back on shore after an unexpected but ultimately fruitful research cruise. The project aims to understand the impacts of climate change on blue whales and krill, by investigating their health under variable environmental conditions. In order to assess their health, however, a crucial first step is required: finding krill, and finding whales. The South Taranaki Bight (STB) is a known foraging ground where blue whales typically feed on krill found in the cool and productive upwelled waters. This year, however, both krill and blue whales were notoriously absent from the STB, leaving us puzzled as we compulsively searched the region in between periods of unworkable weather (including an aerial survey one afternoon).

A map of our survey effort during the 2024 field season. Gray lines represent our visual survey tracklines, with the aerial survey shown in the dashed line. Red points show blue whale sighting locations. Purple stars are the deployment locations of two hydrophones, which will record over the next year.

The tables felt like they were turning when we finally found a blue whale off the west coast of the South Island, and were able to successfully fly the drone to collect body condition information, and collect a fecal sample for genetic and hormone analysis. Then, we returned to the same pattern. Days of waiting for a weather window in between fierce winds, alternating with days of searching and searching, with no blue whales or krill to be found. Photogrammetry measurements of our drone data over the one blue whale we found determined it to be quite small (only ~17 m) and in poor body condition. The only krill we were able to find and collect were small and sparsely mixed in to a massive gelatinous swarm of salps. Where were the whales? Where was their prey?

Above: KC Bierlich and Dawn Barlow search for blue whales. Below: salps swarm beneath the surface.

Then, a turn of events. A news story with the headline “Acres of krill washing up on the coastline” made its way to our inboxes and news feeds. The location? Kaikoura. On the other side of the Cook Strait, along the east coast of the South Island. With good survey coverage in the STB resulting in essentially no appearances of our study species, this report of krill presence along with a workable weather forecast in the Kaikoura area had our attention. In a flurry of quick decision-making (Leigh to Captain: “Can we physically get there?” Captain to Leigh: “Yes, we can.” Leigh to Captain: “Let’s go.”), we turned the vessel around and surfed the swells to the southeast at high speed.

The team in action aboard the R/V Star Keys, our home for the duration of the three-week survey.

Twelve hours later we arrived at dusk and anchored off the small town of Kaikoura, with plans to conduct a net tow for krill before dawn the next morning. But the krill came to us! In the wee hours of the morning, the research vessel was surrounded by swarming krill. The dense aggregation made the water appear soup-like, and attracted a school of hungry barracuda. These abundant krill were just what was needed to run respiration experiments on the deck, and to collect samples to analyze their calories, proteins, and lipids back in the lab.

Left: An illuminated swarm of krill just below the surface. Right: A blue whale comes up for air with an extended buccal pouch, indicating a recent mouthful of krill. Drone piloted by KC Bierlich.

With krill in the area, we were anxious to find their blue whale predators, too. Once we began our visual survey effort, we were alerted by local whale watchers of a blue whale sighting. We headed straight to this location and got to work. The day that followed featured another round of krill experiments, and a few more blue whale sightings. Predator and prey were both present, a stark contrast to our experience in the previous weeks within the STB and along the west coast of the South Island. The science team and crew of the R/V Star Keys fell right into gear, carefully maneuvering around these ocean giants to collect identification photos, drone flights, and fecal samples, finding our rhythm in what we came here to do. We are deeply grateful to the regional managers, local Iwi representatives, researchers, and tourism operators that supported making our time in Kaikoura so fruitful, on just a moment’s notice.

The SAPPHIRE 2024 field team on a day of successful blue whale sightings. Clockwise, starting top left: Dawn Barlow and Leigh Torres following a sunset blue whale sighting, Mike Ogle in position for biopsy sample collection, Kim Bernard collecting blue whale dive times, KC Bierlich collecting identification photos.

What does it all mean? It’s hard to say right now, but time and data analysis will hopefully tell. While this field season was certainly unexpected, it was valuable in many ways. Our experiences this year emphasize the pay-off of being adaptable in the field to maximize time, money, and data collection efforts (during our three-week cruise we slept in 10 different ports or anchorages, did an aerial survey, and rapidly changed our planned study area). Oftentimes, the cases that initially “don’t make sense” are the ones that end up providing key insights into larger patterns. No doubt this was a challenging and at times frustrating field season, but it could also be the year that provides the greatest insights. After two more years of data collection, it will be fascinating to compare this year’s blue whale and krill data in the greater context of environmental variability.

A blue whale comes up for air. Photo by Dawn Barlow.

One thing is clear, the oceans are without question already experiencing the impacts of global climate change. This year solidified the importance of our research, emphasizing the need to understand how krill—a crucial marine prey item—and their predators are being affected by warming and shifting oceans.  

A blue whale at sunset, off Kaikoura. Photo by Leigh Torres.

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

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

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

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

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

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

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

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

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

References

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

Fernandez Ajó, A., Pirotta, E., Bierlich, K. C., Hildebrand, L., Bird, C. N., Hunt, K. E., Buck, C. L., New, L., Dillon, D., & Torres, L. G. (2023). Assessment of a non-invasive approach to pregnancy diagnosis in gray whales through drone-based photogrammetry and faecal hormone analysis. Royal Society Open Science10(7), 230452. https://doi.org/10.1098/rsos.230452

Phases and Feelings of the Scientific Journey

Leigh Torres, Associate Professor, PI of the GEMM Lab

There are many phases of a scientific journey, which generally follows a linear path (although I recognize that the process is certainly iterative at times to improve and refine). The scientific journey typically starts with an idea or question, bred from curiosity and passion. The journey hopefully ends with new knowledge, a useful application (e.g., tool or management outcome), and more questions in need of answers, providing a sense of success and pride. But along this path, there are many more phases, with many more emotions. As we begin the four-year SAPPHIRE project, I have already experienced a range of emotions, and I am certain more will come my way as I again wander through the many phases and feeling of science:

PHASEFEELINGS
Generation of idea or questionCuriosity, passion, wonder
Build the team and develop the funding proposalDrive, dreaming big, team management, belief in the importance of your proposed work
Notice of funding proposal successDisbelief, excitement, and pride, followed quickly by feeling daunted, and self-doubt about the ability to pull off what you said you would do.
*Prep for fieldwork/experiment/data collectionFrantic and overwhelmed by the need to remember all the details that make or break the research; lists, lists, lists; pressure to get organized and stay within your budget. Anticipation, exhaustion.
*Outreach/Engagement/CommunicationEagerness to share and connect; Pressure to build relationships and trust; make sure the research is meaningful and accessible to local communities
*Fieldwork/experiment/data collection/data analysisSigh of relief to be underway, accompanied by big pressure to achieve: gotta do what you said you would do.
Preparation of scientific publications and reportsExcitement for data synthesis: What will the results say? What are the answers to your burning questions? Were your hypotheses correct? With a good dose of apprehension of peer feedback and critical reviews.
Publications and reportsSatisfaction to see outputs and results from hard work being broadly disseminated.
Project end with final reportFeeling of great accomplishment, but now need to develop the next project and get the funding… the cycle continues.

*After months of intense preparation for our field research component of the SAPPHIRE project in Aotearoa New Zealand (permits, equipment purchasing, community engagement, gathering supplies, learning how to use new equipment, vessel contracting, overseas shipping, travel arrangements, vessel mobilization, oh the list goes on!), we have just stepped off the vessel after 3 full days collecting data. I have cycled through all these emotions many times, and now I feel both exhausted and elated. We are implementing our plan, and we now have data in-hand. Worry creeps in all the time: we need to do more, do better. But I also know that our team is excellent and with patience, blessings from the weather gods, and our continued hard work, we will succeed, learn, and share. As SAPPHIRE chargers ahead to understand the impacts of climate change on marine prey (krill) and predators (blue whales), I am ready for the continued mix of emotions that comes with science.

Photo montage of our awesome SAPPHIRE team in prep mode and during data collection in the South Taranaki Bight within Aotearoa New Zealand.

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Oceanographic Alchemy: How Winds Become Whale Food in Oregon

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

Here in the GEMM lab, we love the Oregon coast for its amazing animals – the whales we all study, the seabirds we can sometimes spot from the lab, and the critters that come up in net tows when we’re out on the water. Oregonians owe the amazing biological productivity of the Oregon coast to the underlying atmospheric and oceanographic processes, which make our local Northern California Current (NCC) ecosystem one of the most productive places on earth.

While the topographical bumps of the Oregon coastline and vagaries of coastal weather do have a big impact on the physical and biological processes off the coast, the dominant forces shaping the NCC are large-scale, atmospheric heavy hitters. As the northeasterly trade winds blow across the globe, they set up the clockwise-rotating North Pacific Subtropical Gyre, a major feature covering about 20 million square kilometers of the Pacific Ocean. The equatorward-flowing part of the gyre is the California Current. It comprises an Eastern Boundary Upwelling Ecosystem, one of four such global systems that, while occupying only 1% of the global ocean, are responsible for a whopping 11% of its total primary productivity, and 17% of global fish catch.

Figure 1. Important features of the California Current System (Checkley and Barth, 2009).

At its core, this incredible ocean productivity is due to atmospheric pressure gradients. Every spring, an atmospheric system called the North Pacific High strengthens, loosening the hold of the stormy Aleutian Low. As a result, the winds begin to blow from the north, pushing the surface water in the NCC with them towards the equator.

This water is subject to the Coriolis effect – an inertial force that acts upon objects moving across a rotating frame of reference, and the same force that airplane pilots must account for in their flight trajectories. As friction transmits the stress of wind acting upon the ocean’s surface downward through the water column, the Coriolis effect deflects deeper layers of water successively further to the right, before the original wind stress finally peters out due to frictional losses.

This process creates an oceanographic feature called an Ekman spiral, and its net effect in the NCC is the offshore transport of surface water. Deep water flows up to replace it, bringing along nutrients that feed the photosynthesizers at the base of the food web. Upwelling ecosystems like the NCC tend to be dominated by food webs full of large organisms, in which energy flows from single-celled phytoplankton like diatoms, to grazers like copepods and krill, to predators like fish, seabirds, and our favorite, whales. These bountiful food webs keep us busy: GEMM Lab research has explored how upwelling dynamics impact gray whale prey off the Oregon coast, as well as parallel questions far from home about blue whale prey in New Zealand.

Figure 2. The Coriolis effect creates an oceanographic feature called an Ekman Spiral, resulting in water transport perpendicular to the wind direction (Source: NOAA).

Although the process of upwelling lies at the heart of the productive NCC ecosystem, it isn’t enough for it to simply happen – timing matters, too. The seasonality of ecological events, or phenology, can have dramatic consequences for the food web, and individual populations in it. When upwelling is initiated as normal by the “spring transition”, the delivery of freshly upwelled nutrients activates the food web, with reverberations all the way from phytoplankton to predators. When the spring transition is late, however, the surface ocean is warm, nutrients are depleted, primary productivity is low, and the life cycles and abundances of some species can change dramatically. In 2005, for example, the spring transition was delayed by a month, resulting in declines and spatial redistributions of the taxa typically found in the NCC, including hake, rockfish, albacore tuna, and squid. The Cassin’s auklet, which feeds on plankton, suffered its worst year on record, including reproductive failure that may have resulted from a lack of food.

Upwelling is alchemical in its power to transform, modulating physical and atmospheric processes and turning them into ecosystem gold – or trouble. As oceanographers and Oregonians alike wonder how climate change may reshape our coast, changes to upwelling will likely play a big role in determining the outcome. Some expect that upwelling-favorable winds will become more prevalent, potentially increasing primary productivity. Others suspect that the timing of upwelling will shift, and ecological mismatches like those that occurred in 2005 will be increasingly detrimental to the NCC ecosystem. Whatever the outcome, upwelling is inherent to the character of the Oregon coast, and will help shape its future.

Figure 3. The GEMM Lab is grateful that the biological productivity generated by upwelling draws humpback whales like this one to the Oregon coast! (photo: Dawn Barlow)
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References

Chavez, Francisco & Messié, Monique. (2009). A comparison of Eastern Boundary Upwelling Ecosystems. Progress In Oceanography. 83. 80-96. 10.1016/j.pocean.2009.07.032.

Chavez, F P., and J R Toggweiler, 1995: Physical estimates of global new production: The upwelling contribution. In Dahlem Workshop on Upwelling in the Ocean: Modern Processes and Ancient Records, Chichester, UK, John Wiley & Sons, 313-320.

Checkley, David & Barth, John. (2009). Patterns and processes in the California Current System. Progress In Oceanography. 83. 49-64. 10.1016/j.pocean.2009.07.028.

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

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

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

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

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

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

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

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

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

Video 1: NBC6 news report on the sighting

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

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

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References

Alter, S. E., Meyer, M., Post, K., Czechowski, P., Gravlund, P., Gaines, C., Rosenbaum, H. C., Kaschner, K., Turvey, S. T., van der Plicht, J., Shapiro, B., & Hofreiter, M. (2015). Climate impacts on transocean dispersal and habitat in gray whales from the Pleistocene to 2100. Molecular Ecology24(7), 1510–1522. https://doi.org/10.1111/mec.13121

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

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

Scheinin, A. P., Kerem, D., MacLeod, C. D., Gazo, M., Chicote, C. A., & Castellote, M. (2011). Gray whale ( Eschrichtius robustus) in the Mediterranean Sea: Anomalous event or early sign of climate-driven distribution change? Marine Biodiversity Records4, e28. https://doi.org/10.1017/S1755267211000042

Stewart, J. D., Joyce, T. W., Durban, J. W., Calambokidis, J., Fauquier, D., Fearnbach, H., Grebmeier, J. M., Lynn, M., Manizza, M., Perryman, W. L., Tinker, M. T., & Weller, D. W. (2023). Boom-bust cycles in gray whales associated with dynamic and changing Arctic conditions. Science382(6667), 207–211. https://doi.org/10.1126/science.adi1847

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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References

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

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

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


Sonar savvy: using echo sounders to characterize zooplankton swarms

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

Migrating back east

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

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

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

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

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

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

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

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

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

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

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

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References

Pike, G. C. (1962). Migration and feeding of the gray whale (Eschrichtius gibbosus). Journal of the Fisheries Research Board of Canada19(5), 815–838. https://doi.org/10.1139/f62-051

Blue whales, krill, and climate change: introducing the SAPPHIRE project

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

The world is warming. Ocean ecosystems are experiencing significant and rapid impacts of climate change. However, the cascading effects on marine life are largely unknown. Thus, it is critical to understand how – not just if – environmental change impacts the availability and quality of key prey species in ocean food webs, and how these changes will impact marine predator health and population resilience. With these pressing knowledge gaps in mind, we are thrilled to launch a new project “Marine predator and prey response to climate change: Synthesis of Acoustics, Physiology, Prey, and Habitat in a Rapidly changing Environment (SAPPHIRE).”  We will examine how changing ocean conditions affect the availability and quality of krill, and thus impact blue whale behavior, health, and reproduction. This large-scale research effort is made possible with funding from the National Science Foundation.

The SAPPHIRE project takes place in the South Taranaki Bight (STB) region of Aotearoa New Zealand, and before diving into our new research plans, let’s reflect briefly on what we know so far about this study system based on our previous research. Our collaborative research team has studied blue whales in the STB since 2013 to document the population, understand their ecology and habitat use, and inform conservation management. We conducted boat-based surveys and used hydrophones to record the underwater soundscape, and found the following:

  • Blue whales in Aotearoa New Zealand are a unique population, genetically distinct from all other known populations in the Southern Hemisphere, with an estimated population size of 718 (95% CI = 279 – 1926).1
  • Blue whales reside in the STB region year-round, with feeding and breeding vocalizations detected nearly every day of the year.2,3
  • Wind-driven upwelling over Kahurangi shoals moves a plume of cold, nutrient-rich waters into the STB, supporting aggregations of krill, and thereby critical feeding opportunities for blue whales in spring and summer.4–6
  • We developed predictive models to forecast blue whale distribution up to three weeks in advance, providing managers with a real-time tool in the form of a desktop application to produce daily forecast maps for dynamic management.7
  • During marine heatwaves, blue whale feeding activity was substantially reduced in the STB. Interestingly, their breeding activity was also reduced in the following season when compared to the breeding season following a more productive, typical foraging season. This finding indicates that shifting environmental conditions, such as marine heatwaves and climate change, may have consequences to not just foraging success, but the population’s reproductive patterns.3
A blue whale comes up for air in the South Taranaki Bight. Photo by Leigh Torres.

Project goals

Building on this existing knowledge, we aim to gain understanding of the health impacts of environmental change on krill and blue whales, which can in turn inform management decisions. Over the next three years (2024-2026) we will use multidisciplinary methods to collect data in the field that will enable us to tackle these important but challenging goals. Our broad objectives are to:

  1. Assess variation in krill quality and availability relative to rising temperatures and different ocean conditions,
  2. Document how blue whale body condition and hormone profiles change relative to variable environmental and prey conditions,
  3. Understand how environmental conditions impact blue whale foraging and reproductive behavior, and
  4. Integrate these components to develop novel Species Health Models to predict predator and prey whale population response to rapid environmental change.

Kicking off fieldwork

This coming January, we will set sail aboard the R/V Star Keys and head out in search of blue whales and krill in the STB! Five of our team members will spend three weeks at sea, during which time we will conduct surveys for blue whale occurrence paired with active acoustic assessment of krill availability, fly Unoccupied Aircraft Systems (UAS; “drones”) over whales to determine body condition and potential pregnancy, collect tissue biopsy samples to quantify stress and reproductive hormone levels, deploy hydrophones to record rates of foraging and reproductive calls by blue whales, and conduct on-board controlled experiments on krill to assess their response to elevated temperature.

The team in action aboard the R/V Star Keys in February 2017. Photo by L. Torres.

The moving pieces are many as we work to obtain research permits, engage in important consultation with iwi (indigenous Māori groups), procure specialized scientific equipment, and make travel and shipping arrangements. The to-do lists seem to grow just as fast as we can check items off; such is the nature of coordinating an international, multidisciplinary field effort. But it will pay off when we are underway, and I can barely contain my excitement to back on the water with this research team.

Our team has not collected data in the STB since 2017. We know so much more now than we did when studies of this blue whale population were just beginning. For example, we are eager to put our blue whale forecast tool to use, which will hopefully enable us to direct survey effort toward areas of higher blue whale density to maximize data collection. We are keen to see what new insights we gain, and what new questions and challenges arise.

Research team

The SAPPHIRE project will only be possible with the expertise and coordination of the many members of our collaborative group. We are all thrilled to begin this research journey together, and eager to share what we learn.

Principal Investigators:

Research partners and key collaborators:

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References:

1.          Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-Hymes CT, Klinck H. Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res. 2018;36:27–40.

2.          Barlow DR, Klinck H, Ponirakis D, Holt Colberg M, Torres LG. Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring. J Mammal. 2022;

3.          Barlow DR, Klinck H, Ponirakis D, Branch TA, Torres LG. Environmental conditions and marine heatwaves influence blue whale foraging and reproductive effort. Ecol Evol. 2023;13:e9770.

4.          Barlow DR, Klinck H, Ponirakis D, Garvey C, Torres LG. Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep. 2021;11(6915):1–10.

5.          Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG. Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar Ecol Prog Ser. 2020;642:207–25.

6.          Torres LG, Barlow DR, Chandler TE, Burnett JD. Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ. 2020;8:e8906.

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