Oregon Coast – Page 2 – Geospatial Ecology of Marine Megafauna Laboratory

A MOSAIC of species, datasets, tools, and collaborators

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

Imagine you are 50 nautical miles from shore, perched on the observation platform of a research vessel. The ocean is blue, calm, and seems—for all intents and purposes—empty. No birds fly overhead, nothing disturbs the rolling swells except the occasional whitecap from a light breeze. The view through your binoculars is excellent, and in the distance, you spot a disturbance at the surface of the water. As the ship gets closer, you see splashing, and a flurry of activity emerges as a large group of dolphins leap and dive, likely chasing a school of fish. They swim along with the ship, riding the bow-wave in a brief break from their activity. Birds circle in the air above them and float on the water around them. Together with your team of observers, you rush to record the species, the number of animals, their distance to the ship, and their behavior. The research vessel carries along its pre-determined trackline, and the feeding frenzy of birds and dolphins fades off behind you as quickly as it came. You return to scanning the blue water.

Craig Hayslip and Dawn Barlow scan for marine mammals from the crow’s nest (elevated observation platform) of the *R/V Pacific Storm*.

The marine environment is highly dynamic, and resources in the ocean are notoriously patchy. One of our main objectives in marine ecology is to understand what drives these ephemeral hotspots of species diversity and biological activity. This objective is particularly important now as the oceans warm and shift. In the context of rapid global climate change, there is a push to establish alternatives to fossil fuels that can support society’s energy needs while minimizing the carbon emissions that are a root cause of climate change. One emergent option is offshore wind, which has become a hot topic on the West Coast of the United States in recent years. The technology has the potential to supply a clean energy source, but the infrastructure could have environmental and societal impacts of its own, depending on where it is placed, how it is implemented, and when it is operational.

Northern right whale dolphins leap into the air. Photo by Craig Hayslip.

Any development in the marine environment, including alternative energy such as offshore wind, should be undertaken using the best available scientific knowledge of the ecosystem where it will be implemented. The Marine Mammal Institute’s collaborative project, Marine Offshore Species Assessments to Inform Clean energy (MOSAIC), was designed for just this reason. As the name “MOSAIC” implies, it is all about using different tools to compile different datasets to establish crucial baseline information on where marine mammals and seabirds are distributed in Oregon and Northern California, a region of interest for wind energy development.

A MOSAIC of species

The waters of Oregon and Northern California are rich with life. Numerous cetaceans are found here, from the largest species to ever live, the blue whale, to one of the smallest cetaceans, the harbor porpoise, with many species filling in the size range in between: fin whales, humpback whales, sperm whales, killer whales, Risso’s dolphins, Pacific white-sided dolphins, northern right whale dolphins, and Dall’s porpoises, to name a few. Seabirds likewise rely on these productive waters, from the large, graceful albatrosses that feature in maritime legends, to charismatic tufted puffins, to the little Leach’s storm petrels that could fit in the palm of your hand yet cover vast distances at sea. From our data collection efforts so far, we have already documented 16 cetacean species and 64 seabird species.

A Laysan albatross glides over the water’s surface. Photo by Will Kennerley.

A MOSAIC of data and tools

Schematic of the different components of the MOSAIC project. Graphic created by Solene Derville.

Through the four-year MOSAIC project, we are undertaking two years of visual surveys and passive acoustic monitoring from Cape Mendocino to the mouth of the Columbia River on the border of Oregon and Washington and seaward to the continental slope. Six comprehensive surveys for cetaceans and seabirds are being conducted aboard the R/V Pacific Storm following a carefully chosen trackline to cover a variety of habitats, including areas of interest to wind energy developers.

These dedicated surveys are complemented by additional surveys conducted aboard NOAA research vessels during collaborative expeditions in the Northern California Current, and ongoing aerial surveys in partnership with the United States Coast Guard through the GEMM Lab’s OPAL project. Three bottom-mounted hydrophones were deployed in August 2022, and are recording cetacean vocalizations and the ambient soundscape, and these recordings will be complemented by acoustic data that is being collected continuously by the Oceans Observing Initiative. In addition to these methods to collect broad-scale species distribution information, concurrent efforts are being conducted via small boats to collect individual identification photographs of baleen whales and tissue biopsy samples for genetic analysis. Building on the legacy of satellite tracking here at the Marine Mammal Institute, the MOSAIC project is breathing new life into tag data from large whales to assess movement patterns over many years and determine the amount of time spent within our study area.

A curious fin whale approaches the *R/V Pacific Storm* during one of the visual surveys. Photo by Craig Hayslip.

Survey tracklines extending between the Columbia River and Cape Mendocino, designed for the MOSAIC visual surveys aboard the *R/V Pacific Storm*.

The resulting species occurrence data from visual surveys and acoustic monitoring will be integrated to develop Species Distribution Models for the many different species in our study region. Identification photographs of individual baleen whales, DNA profiles from whale biopsy samples, and data from satellite-tagged whales will provide detailed insight into whale population structure, behavior, and site fidelity (i.e., how long they typically stay in a given area), which will add important context to the distribution data we collect through the visual surveys and acoustic monitoring. The models will be implemented to produce maps of predicted species occurrence patterns, describing when and where we expect different cetaceans and seabirds to be under different environmental conditions.

With five visual surveys down, the MOSAIC team is gearing up for one final survey this month. The hydrophones will be retrieved this summer. Then, with data in-hand, the team will dive deep into analysis.

A MOSAIC of collaborators

The MOSAIC-4 team waves from the crow’s nest (observation platform) of the *R/V Pacific Storm*. Photo by Craig Hayslip.

The collaborative MOSAIC team brings together a diverse set of tools. The depth of expertise here at the Marine Mammal Institute spans a broad range of disciplines, well-positioned to provide robust scientific knowledge needed to inform alternative energy development in Oregon and Northern California waters.

I have had the pleasure of participating in three of the six surveys aboard the R/V Pacific Storm, including leading one as Chief Scientist, and have collected visual survey data aboard NOAA Ship Bell M. Shimada and from United States Coast Guard helicopters over the years that will be incorporated in the MOSAIC of datasets for the project. This ecosystem is one that I feel deeply connected to from time spent in the field. Now, I am thrilled to dive into the analysis, and will lead the modeling of the visual survey data and the integration of the different components to produce species distribution maps for cetaceans and seabirds our study region.

This project is funded by the United States Department of Energy. The Principal Investigator is the Institute’s Director Dr. Lisa Ballance, and Co-Principal Investigators include Scott Baker, Barbara Lagerquist, Rachael Orben, Daniel Palacios, Kate Stafford, and Leigh Torres of the Marine Mammal Institute; John Calambokidis of the Cascadia Research Collective; and Elizabeth Becker of ManTech International Corp. For more information, please visit the project website, and stay tuned for updates as we enter the analysis phase.

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The Dark Side of Upwelling: It’s getting harder and harder to breathe off the Oregon coast

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

The depths of the productive coastal Oregon ecosystem have long held a mystery – an increasing paucity in the concentration of dissolved oxygen at depth. When dissolved oxygen concentrations dips low enough, the condition “hypoxia” can alter biogeochemical cycling in the ocean environment and threaten marine life. Essentially, organisms can’t get enough oxygen from the water, forcing them to try to escape to more favorable waters, stay and change their behavior, or suffer the consequences and potentially suffocate.

Recent work has illuminated the cause of this mysterious rise in hypoxic waters: an increase in the wind-driven oceanographic process of upwelling (Barth et al., 2024). The seasonal upwelling of cold, nutrient-rich waters underlies the incredible productivity of the Oregon coast, but its dark twin is hypoxia: when organic material in the upper layer of the water column sinks, microbial respiration processes consume dissolved oxygen in the surrounding water. In addition, the deep waters brought to the surface by upwelling are depleted in oxygen compared to the aerated surface waters. These effects combine to form an oxygen-poor water layer over the continental shelf, which typically lasts from May until October in the Northern California Current (NCC) region. The spatial extent of this layer is highly variable – hypoxic bottom waters cover 10% of the shelf in some years and up to 62% in others, presenting challenging conditions for life occupying the Oregon shelf (Peterson et al., 2013).

Figure 1. An article in The Oregonian from 2004 documents research on a hypoxia-driven “dead zone” off the Oregon coast.

While effects of hypoxia on benthic communities and some fish species are well-documented, is unclear how increasing levels of hypoxia off Oregon may impact highly mobile, migratory organisms like whales. A primary pathway is likely through their prey – particularly species that occupy hypoxic regions and depths, like the zooplankton krill. Over the continental shelf and slope, which are important krill habitat, seasonally hypoxic waters tend to extend from about 150 meters depth to the bottom. The vertical center of krill distribution in the NCC region is around 170 meters depth, suggesting that these animals encounter hypoxic conditions regularly.

Interestingly, the two main krill species off the Oregon coast, Euphausia pacifica and Thysanoessa spinifera, use different strategies to deal with hypoxic conditions. Thysanoessa spinifera krill decrease their oxygen consumption rate to better tolerate ambient hypoxia, a behavioral modification strategy called “oxyconformity”. Euphausia pacifica, on the other hand, use “oxyregulation” to maintain the same, quite high, oxygen utilization rate regardless of ambient levels – which may indicate that this species will be less able to tolerate increasingly hypoxic waters (Tremblay et al., 2020).

Figure 2. This figure from Barth et al. 2024 maps the concentration of dissolved oxygen (uM/kg; cooler colors indicate less dissolved oxygen) to show an increase in hypoxic conditions over the continental shelf and slope (green and blue colors) across seven decades in the NCC region.

Over long time scales, such environmental pressures shape species physiology, life history, and evolution. The krill species Euphausia mucronate is endemic to the Humboldt Current System off the coast of South America, which includes a region of year-round upwelling and a persistent Oxygen Minimum Zone (OMZ). Fascinatingly, Humboldt krill can live in the core of the OMZ, using metabolic adaptations that even let them survive in anoxic conditions (i.e., no oxygen in the water). Humboldt krill abundances actually increase with shallower OMZ depths and lower levels of dissolved oxygen, pointing to the huge success of this species in evolving to thrive in conditions that challenge other local krill species (Díaz-Astudillo et al., 2022).

Back home in the NCC region, will Euphausia pacifica and Thysanoessa spinifera be pressured to adapt to continually increasing levels of hypoxia? If so, will they be able to adapt? One of krill’s many superpowers is an ability to tolerate a wide range of environmental conditions, including the dramatic gradients in temperature, water density, and dissolved oxygen that they encounter during their daily vertical migrations through the water column. Both species have strategies to deal with hypoxic conditions, and this capacity has allowed them to thrive in the active upwelling region that is the NCC. Now, the question is whether increasingly hypoxic waters will eventually force a threshold that compromises the capacity of krill to adapt – and then, what will happen to these species, and the foragers dependent on them?

References

Barth, J. A., Pierce, S. D., Carter, B. R., Chan, F., Erofeev, A. Y., Fisher, J. L., Feely, R. A., Jacobson, K. C., Keller, A. A., Morgan, C. A., Pohl, J. E., Rasmuson, L. K., & Simon, V. (2024). Widespread and increasing near-bottom hypoxia in the coastal ocean off the United States Pacific Northwest. Scientific Reports, 14(1), 3798. https://doi.org/10.1038/s41598-024-54476-0

Díaz-Astudillo, M., Riquelme-Bugueño, R., Bernard, K. S., Saldías, G. S., Rivera, R., & Letelier, J. (2022). Disentangling species-specific krill responses to local oceanography and predator’s biomass: The case of the Humboldt krill and the Peruvian anchovy. Frontiers in Marine Science, 9, 979984. https://doi.org/10.3389/fmars.2022.979984

Peterson, J. O., Morgan, C. A., Peterson, W. T., & Lorenzo, E. D. (2013). Seasonal and interannual variation in the extent of hypoxia in the northern California Current from 1998–2012. Limnology and Oceanography, 58(6), 2279–2292. https://doi.org/10.4319/lo.2013.58.6.2279

Tremblay, N., Hünerlage, K., & Werner, T. (2020). Hypoxia Tolerance of 10 Euphausiid Species in Relation to Vertical Temperature and Oxygen Gradients. Frontiers in Physiology, 11, 248. https://doi.org/10.3389/fphys.2020.00248

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.

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)

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.

When do male whales get randy? Exploring the seasonal testosterone patters in the PCFG gray whale

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

A year in a baleen whale life typically involves migrating between polar or subpolar “feeding grounds” in summer and subtropical “breeding grounds” in winter. Calves are typically born during a specific portion of the winter months (Lockyer and Brown, 1981), suggesting a regular alternation between reproductively active and inactive states (Bronson, 1991). Seasonal reproduction in mammals often includes pronounced annual cycles in reproductive hormones triggered by changes in the photoperiod or other environmental cues, along with endogenous circannual cycles (Hau 2007).

Testosterone (T), a key reproductive hormone, is crucial for male spermatogenesis (development of sperm) and influences behaviors such as courtship, mating, and male to male competition. Seasonally breeding mammals exhibit an annual peak in T. The amplitude of T can be influenced by age, with immature males having low T levels that rise sharply at sexual maturity (Beehner et al. 2009; Chen et al. 2009) and then, in some species, declines in the older males (i.e., reproductive senescence; Hunt et al. 2020; Chen et al. 2009). This variability, combined with social cues and exposure to stressors, contributes to individual differences in hormone patterns.

Seasonal testosterone patterns are well-documented in many vertebrate males, including terrestrial mammals, pinnipeds, and odontocetes (Wells, 1984; Kellar et al., 2009; Funasaka et al., 2011, 2018; O’Brien et al., 2016; Richard et al., 2017). However, our understanding of seasonal patterns of testosterone in large whales, especially baleen whales, remains incomplete due to their cryptic nature. Improved understanding of cyclic changes in male reproductive hormones could enhance population management and conservation of whale species. For instance, a clear comprehension of male testosterone cycling in a species can potentially improve the accuracy of sex identification for unknown individuals through hormone ratios. It can also aid in better discriminating sexually active adults from juveniles, understanding the age of sexual maturity (often challenging to determine in males), the potential occurrence of reproductive senescence in older males, and determining the month and location of the conceptive season—which, in turn, may inform estimates of gestation length in females. Insight into these aspects of baleen whale reproductive biology would enhance our ability to understand variation in population abundance and vital rates.

Recent advancements in hormone extraction from non-plasma (blood) samples, such as blow, fecal, blubber, earplugs, and baleen, offer new avenues for studying baleen whale physiology (Hunt et al., 2013). However, obtaining repeated samples from an individual, and over an extended period, from whales to assess hormone patterns is challenging. In this context, earplug endocrine analyses, focusing on cerumen layers (ear wax), have provided insights into sexual maturity in male blue whales (Trumble et al., 2013). However, the temporal resolution (e.g., years) in this sample type limits the detection of seasonal patterns. On the other hand, baleen data provides longitudinal information with sufficient resolution for understanding male reproductive biology and it has been successfully applied to the study of whale species with longer baleen plates (over a decade of an individual’s life), such as the bowhead whale, North Atlantic right whale, and a blue whale (Hunt et al., 2018; Hunt et al., 2020). Additionally, seasonal trends in testosterone have been documented in male humpback whales through blubber biopsy analyses (Cates et al. 2019).

Photos: This is Orange Knuckles (AKA OK). He is one of the males that regularly visit the Oregon coast. He was first observed in 2005, which means he is an adult male and is at least 19 years old (as of 2024). Do you want to learn more about him and other PCFG whales that frequent the Oregon coast? Visit IndividuWhale. Credit: GEMM Lab.

With the GEMM Lab’s GRANITE project, we are delving into an eight-year dataset of individual gray whale morphometrics and fecal hormone data to investigate important aspects of male reproduction in detail. Our non-invasive data collection methods (fecal samples and drone overflights) allow important repeated measurements of the same individual throughout and between foraging seasons. Preliminary results from our analysis reveal a significant association of the day of the year with elevation in T, suggesting that in the late summer the Oregon Coast could be an important area for gray whale social behavior in preparation for reproduction. Furthermore, we are uncovering an association between age and T levels, highlighting the potential for us to identify the age for onset of sexual maturity in males. Additionally, we are exploring the relationship between T levels, exposure to stressors, body condition, and other factors that might influence male reproductive attempts. These data will provide valuable information for conservation and management efforts, aiding in critical habitat identification and reproductive timing for gray whales. Stay tuned for the new results to come!

References

Beehner JC, Gesquiere L, Seyfarth RM, Cheney DL, Alberts SC, Altmann J. 2009. Testosterone related to age and life-history stages in male baboons and geladas. Horm Behav 56:472-80.
Bronson FH (1991) Mammalian Reproductive Biology. University of Chicago Press, Chicago, IL.
Buck CL, Barnes BM. 2003. Androgen in free-living arctic ground squirrels: seasonal changes and influence of staged male-male aggressive encounters. Horm Behav 43:318-26.
Cates KA, Atkinson S, Gabriele CM, Pack AA, Straley JM, Yin S. 2019. Testosterone trends within and across seasons in male humpback whales (Megaptera novaeangliae) from Hawaii and Alaska. Gen Comp Endocrinol 279:164-73.
Chen H, Ge R-S, Zirkin BR. 2009. Leydig cells: from stem cells to aging. Mol Cell Endocrinol 306:9-16.
Funasaka N, Yoshioka M, Suzuki M, Ueda K, Miyahara H, Uchida S (2011) Seasonal difference of diurnal variations in serum melatonin, cortisol, testosterone, and rectal temperature in Indo-Pacific bottlenose dolphins (Tursiops aduncus). Aquatic Mamm 37: 433–443.
Hau M. 2007. Regulation of male traits by testosterone: implications for the evolution of vertebrate life histories. BioEssays 29:133-44.
Hunt KE, Moore MJ, Rolland RM, Kellar NM, Hall AJ, Kershaw J, Raverty SA, Davis CE, Yeates LC, Fauquier DA. 2013. Overcoming the challenges of studying conservation physiology in large whales: a review of available methods. Cons Physiol 1:cot006.
Hunt KE, Buck CL, Ferguson S, Fernández Ajo A., Heide-Jørgensen MP, Matthews CJD, Male Bowhead Whale Reproductive Histories Inferred from Baleen Testosterone and Stable Isotopes, Integrative Organismal Biology, Volume 4, Issue 1, 2022, obac014 https://doi.org/10.1093/iob/obac014
Kellar N, Trego M, Marks C, Chivers S, Danil K (2009) Blubber testosterone: a potential marker of male reproductive status in shortbeaked common dolphins. Mar Mamm Sci 25: 507–522
Lockyer C, Brown S (1981) The migration of whales. In Aldley D, ed. Animal Migration Society for Experimental Biology Seminar Series, Book 13. Cambridge University Press, Cambridge, England.
O’Brien JK, Steinman KJ, Fetter GA, Robeck TR (2016) Androgen and glucocorticoid production in the male killer whale (Orcinus orca): influence of age, maturity, and environmental factors. Andrology 5: 180–190.
Richard JT, Robeck TR, Osborn SD, Naples L, McDermott A, LaForge R, Romano TA, Sartini BL (2017) Testosterone and progesterone concentrations in blow samples are biologically relevant in belugas (Delphinapterus leucas). Gen Comp Endocrinol 246: 183–193.
Trumble S, Robinson E, Berman-Kowalewski M, Potter C, Usenko S (2013) Blue whale earplug reveals lifetime contaminant exposure and hormone profiles. Proc Nat Acad Sci 110: 16922–16926.
Wells RS (1984) Reproductive behavior and hormonal correlates in Hawaiian spinner dolphins (Stenella longirostris). In Perrin WR, Brownell RL Jr, DeMaster DP, eds. Reproduction in Whales, Dolphins, and Porpoises. Cambridge: Reports of the International Whaling Commission, pp 465–472.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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
SeaBeam Instruments. (2000) Multibeam Sonar Theory of Operation. L-3 Communications, East Walpole MA. https://www3.mbari.org/data/mbsystem/sonarfunction/SeaBeamMultibeamTheoryOperation.pdf
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
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
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
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
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
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 Canada, 19(5), 815–838. https://doi.org/10.1139/f62-051

A non-invasive approach to pregnancy diagnosis in Gray whales is possible!

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

In a previous post (link to blog), I discussed the crucial importance of acquiring knowledge on the reproductive parameters of individual animals in wild populations for designing effective strategies in conservation biology. Specifically, the ability to quantify the number of pregnancies within a population offers valuable insights into the health of individual females and the population as a whole [1,2]. This knowledge provides tools to describe important life-history parameters, including the age of sexual maturity, frequency of pregnancy, duration of gestation, timing of reproduction, and population fecundity; all of which are essential components for monitoring trends in reproduction and the overall health of a species [3]. Additionally, I explained some of the challenges inherent in obtaining such information when working with massive wild animals that spend most of their time underwater in vast expanses of the oceans. Yes, I am talking about whales.

As a result of the logistical and methodological challenges that involve the study of large whales, detailed knowledge of the life-history and general reproductive biology of whales is sparse for most species and populations. In fact, much of the available information is derived from whaling records [4], which may be outdated for application in population models [5].

If you are an avid reader of the GEMM Lab blog posts, you might be familiar with the gray whale (Eschrichtius robustus), and with the distinct subgroup of gray whales, known as the Pacific Coast Feeding Group (PCFG). PCFG gray whales are characterized by their shorter migration to spend their feeding season in the coastal waters of Northern California, Oregon, and southeastern Alaska [6], relative to the larger Eastern North Pacific gray whale that forage in the Arctic region.

The GEMM Lab has monitored individual gray whales within the PCFG off the Oregon coast since 2016 (check the GRANITE project). Each individual whale presents a unique pigmentation pattern, or unique marks that we can use to identify who is who among the whales who visit the Oregon coast. In this way, we keep a detailed record of re-sightings of known individuals (visit IndividuWhale to learn more), and we have high individual re-sighting rates, resulting in a long-term data series for individual whales which enables us to monitor their health, body condition, and thus further develop and advance our non-invasive study methods.

Drone-based image of a Gray whale defecating. Source: GEMM Lab, NOAA/NSF permit #16111

In our recently manuscript published in the Royal Society Open Science journal, armed with our robust dataset comprising fecal hormone metabolites, drone-based photogrammetry, and individual sightings, we delved into the strengths and weaknesses of various diagnostic tools for non-invasive pregnancy diagnosis. Ultimately, we propose a methodological approach that can help with the challenging and important task of identifying pregnancies in gray whales. In particular, we explored the variability in fecal progesterone metabolites and body morphology relative to observed reproductive status and estimated the pregnancy probability for mature females using statistical models.

In mammals, the progesterone hormone is secreted in the ovaries during the estrous cycle and gestation, making it the predominant hormone responsible for sustaining pregnancy [7]. As the hormones are cleared from the blood into the gut, they are metabolized and eventually excreted in feces; fecal samples represent a cumulative and integrated concentration of hormone metabolites [8;9], which are useful indicators for endocrine assessments of free-swimming whales. Additionally, our previous studies in this population [10] detected differences in body condition (see KC blog for more details about how we measure whales) that suggest that changes in the whale’s body widths could be useful in detecting pregnancies.

Our exploratory analyses show that in individual whales, the levels of fecal progesterone were elevated when pregnant as compared to when the same whale was not pregnant. But when looking at progesterone levels at the population level, these differences were masked with the intrinsic variability of this measurement. In turn, the body morphometrics, in particular the body width at the 50% of the total body length, helped discriminate pregnancies better, and the statistical models that included this width variable, effectively classified pregnant from non-pregnant females with a commendable accuracy. Thus, our morphometric approach showcased its potential as a reliable alternative for pregnancy diagnosis.

Below, a comparison of body widths at 5% increments along total body length (from 20 % to 70 %) in female gray whales of known reproductive status from UAS-based photogrammetry (example photograph shown at top). Pregnant females (PF; in blue), presumed nonpregnant juvenile females (JF; yellow), and lactating females (LF; orange). Fernandez Ajó et al. 2023.

Notably, when we ran the pregnancy prediction models on data from our 2022 season and compared results with observations of whales in 2023, we identified a known whale from our study area “Clouds” accompanied by a calf, indicating that she was pregnant in 2022. Our model predicted Clouds to be pregnant with a 70% probability. This validation lends strong confidence to our approach to diagnosing pregnancy. Conversely, some whales predicted to be pregnant in 2022 were not observed with a calf during the 2023 season. However, the absence of calves accompanying these females is likely due to the relatively high mortality of newborn calves in gray whales due to predation or other causes [11].

Overall, our findings underscore some limitations of fecal progesterone metabolite in accurately identifying pregnant PCFG gray whales. However, while acknowledging the challenges associated with fecal sample collection and hormone analysis, we advocate for ongoing exploration of alternative hormone quantification methods and antibodies. Our study highlights the importance of continued research in refining these techniques. The unique attributes of our study system, including high individual re-sighting rates and non-invasive fecal hormone analysis, position it as a cornerstone for future advancements in understanding gray whale reproductive health. By improving our ability to monitor reproductive metrics in baleen whale populations, we pave the way for more effective conservation strategies, ensuring the resilience of these magnificent creatures in the face of a changing marine ecosystems.

References

[1] Burgess EA, Lanyon JM, Brown JL, Blyde D, Keeley T. 2012 Diagnosing pregnancy in free-ranging dugongs using fecal progesterone metabolite concentrations and body morphometrics: A population application. Gen Comp Endocrinol 177, 82–92. (doi:10.1016/J.YGCEN.2012.02.008)

[2] Slade NA, Tuljapurkar S, Caswell H. 1998 Structured-Population Models in Marine, Terrestrial, and Freshwater Systems. J Wildl Manage 62. (doi:10.2307/3802363)

[3] Madliger CL, Love OP, Hultine KR, Cooke SJ. 2018 The conservation physiology toolbox: status and opportunities. Conserv Physiol 6, 1–16. (doi:10.1093/conphys/coy029)

[4] Rice DW, Wolman AA. 1971 Life history and ecology of the gray whale (Eschrichtius robustus). Stillwater, Oklahoma: American Society of Mammalogists.

[5] Melicai V, Atkinson S, Calambokidis J, Lang A, Scordino J, Mueter F. 2021 Application of endocrine biomarkers to update information on reproductive physiology in gray whale (Eschrichtius robustus). PLoS One 16. (doi:10.1371/journal.pone.0255368)

[6] Calambokidis J, Darling JD, Deecke V, Gearin P, Gosho M, Megill W, et al. Abundance, range and movements of a feeding aggregation of gray whales (Eschrichtius robustus) from California to south-eastern Alaska in 1998. J Cetacean Res Manag 2002;4:267–76.

[7] Bronson, F. H. (1989). Mammalian reproductive biology. University of Chicago Press.

[8] Wasser SK, Hunt KE, Brown JL, Cooper K, Crockett CM, Bechert U, Millspaugh JJ, Larson S, Monfort SL (2000) A generalized fecal glucocorticoid assay for use in a diverse array of nondomestic mammalian and avian species. Gen Comp Endocrinol120:260–275.

[9] Hunt, K.E., Rolland, R.M., Kraus, S.D., Wasser, S.K., 2006. Analysis of fecal glucocorticoids in the North Atlantic right whale (Eubalaena glacialis). Gen. Comp. Endocrinol. 148, 260–272. https://doi.org/10.1016/j.ygcen.2006.03.01215.

[10] Soledade Lemos L, Burnett JD, Chandler TE, Sumich JL, Torres LG. 2020 Intra‐ and inter‐annual variation in gray whale body condition on a foraging ground. Ecosphere 11. (doi:10.1002/ecs2.3094)

[11] James L. Sumich, James T. Harvey, Juvenile Mortality in Gray Whales (Eschrichtius robustus), Journal of Mammalogy, Volume 67, Issue 1, 25 February 1986, Pages 179–182, https://doi.org/10.2307/1381019

A smaller sized gray whale: recent publication finds PCFG whales are smaller than ENP whales

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

A recent blog post by GEMM Lab’s PhD Candidate Clara Bird gave a recap of our 8^th consecutive GRANITEfield season this year. In her blog, Clara highlighted that we saw 71 individual gray whales this season, 61 of which we have seen in previous years and identified as belonging to the Pacific Coast Feeding Group (PCFG). With an estimated population size of around 212 individuals, this means that we saw almost 1/3 of the PCFG population this season alone. Since the GEMM Lab first started collecting data on PCFG gray whales in 2016, we have collected drone imagery on over 120 individuals, which is over half the PCFG population. This dataset provides incredible opportunity to get to know these individuals and observe them from year to year as they grow and mature through different life history stages, such as producing a calf. A question our research team has been interested in is what makes a PCFG whale different from an Eastern North Pacific (ENP) gray whale, which has a population size around 16,000 individuals and feed predominantly in the Arctic during the summer months? For this blog, I will highlight findings from our recent publication in Biology Letters (Bierlich et al., 2023) comparing the morphology (body length, skull, and fluke size) between PCFG and ENP populations.

Body size and shape reflect how an animal functions in their environment and can provide details on an individual’s current health, reproductive status, and energetic requirements. Understanding how animals grow is a key component for monitoring the health of populations and their vulnerability to climate change and other stressors in their environment. As such, collecting accurate morphological measurements of individuals is essential to model growth and infer their health. Collecting such morphological measurements of whales is challenging, as you cannot ask a whale to hold still while you prepare the tape measure, but as discussed in a previous blog, drones provide a non-invasive method to collect body size measurements of whales. Photogrammetry is a non-invasive technique used to obtain morphological measurements of animals from photographs. The GEMM Lab uses drone-based photogrammetry to obtain morphological measurements of PCFG gray whales, such as their body length, skull length (as snout-to-blowhole), and fluke span (see Figure 1).

Figure 1. Morphological measurements obtained via photogrammetry of a Pacific Coast Feeding Group (PCFG) gray whale. These measurements were used to compare to individuals from the Eastern North Pacific (ENP) population.

As mentioned in this previous blog, we use photo-identification to identify unique individual gray whales based on markings on their body. This method is helpful for linking all the data we are collecting (morphology, hormones, behavior, new scarring and skin conditions, etc.) to each individual whale. An individual’s sightings history can also be used to estimate their age, either as a ‘minimum age’ based on the date of first sighting or a ‘known age’ if the individual was seen as a calf. By combining the length measurements from drone-based photogrammetry and age estimates from photo-identification history, we can construct length-at-age growth models to examine how PCFG gray whales grow. While no study has previously examined length-at-age growth models specifically for PCFG gray whales, another study constructed growth curves for ENP gray whales using body length and age estimates obtained from whaling, strandings, and aerial photogrammetry (Agbayani et al., 2020). For our study, we utilized these datasets and compared length-at-age growth, snout-to-blowhole length, and fluke span between PCFG and ENP whales. We used Bayesian statistics to account and incorporate the various levels of uncertainty associated with data collected (i.e., measurements from whaling vs. drone, ‘minimum age’ vs. ‘known age’).

We found that while both populations grow at similar rates, PCFG gray whales reach smaller adult lengths than ENP. This difference was more extreme for females, where PCFG females were ~1 m (~3 ft) shorter than ENP females and PCFG males were ~0.5 m (1.5 ft) shorter than ENP males (Figure 2, Figure 3). We also found that ENP males and females have slightly larger skulls and flukes than PCFG male and females, respectively. Our results suggest PCFG whales are shaped differently than ENP whales (Figure 3)! These results are also interesting in light of our previous published study that found PCFG whales are skinnier than ENP whales (see this previous blog post).

Figure 2. Growth curves (von Bertalanffy–Putter) for length-at-age comparing male and female ENP and PCFG gray whales (shading represents 95% highest posterior density intervals). Points represent mean length and median age. Vertical bars represent photogrammetric uncertainty. Dashed horizontal lines represent uncertainty in age estimates.

Figure 3. Schematic highlighting the differences in body size between Pacific Coast Feeding Group (PCFG) and Eastern North Pacific (ENP) gray whales.

Our results raise some interesting questions regarding why PCFG are smaller: Is this difference in size and shape normal for this population and are they healthy? Or is this difference a sign that they are stressed, unhealthy and/or not getting enough to eat? Larger individuals are typically found at higher latitudes (this pattern is called Bergmann’s Rule), which could explain why ENP whales are larger since they feed in the Arctic. Yet many species, including fish, birds, reptiles, and mammals, have experienced reductions in body size due to changes in habitat and anthropogenic stressors (Gardner et al., 2011). The PCFG range is within closer proximity to major population centers compared to the ENP foraging grounds in the Arctic, which could plausibly cause increased stress levels, leading to decreased growth.

The smaller morphology of PCFG may also be related to the different foraging tactics they employ on different prey and habitat types than ENP whales. Animal morphology is linked to behavior and habitat (see this blogpost). ENP whales feeding in the Arctic generally forage on benthic amphipods, while PCFG whales switch between benthic, epibenthic and planktonic prey, but mostly target epibenthic mysids. Within the PCFG range, gray whales often forage in rocky kelp beds close to shore in shallow water depths (approx. 10 m) that are on average four times shallower than whales feeding in the Arctic. The prey in the PCFG range is also found to be of equal or higher caloric value than prey in the Arctic range (see this blog), which is interesting since PCFG were found to be skinnier.

It is also unclear when the PCFG formed? ENP and PCFG whales are genetically similar, but photo-identification history reveals that calves born into the PCFG usually return to forage in this PCFG range, suggesting matrilineal site fidelity that contributes to the population structure. PCFG whales were first documented off our Oregon Coast in the 1970s (Figure 4). Though, from examining old whaling records, there may have been PCFG gray whales foraging off the coasts of Northern California to British Columbia since the 1920s.

Figure 4. First reports of summer-resident gray whales along the Oregon coast, likely part of the Pacific Coast Feeding Group. Capital Journal, August 9, 1976, pg. 2.

Altogether, our finding led us to two hypotheses: 1) the PCFG range provides an ecological opportunity for smaller whales to feed on a different prey type in a shallow environment, or 2) the PCFG range is an ecological trap, where individuals gain less energy due to energetically costly feeding behaviors in complex habitat while potentially targeting lower density prey, causing them to be skinnier and have decreased growth. Key questions remain for our research team regarding potential consequences of the smaller sized PCFG whales, such as does the smaller body size equate to reduced resilience to environmental and anthropogenic stressors? Does smaller size effect fecundity and population fitness? Stay tuned as we learn more about this unique and fascinating smaller sized gray whale.

References

Agbayani, S., Fortune, S. M. E., & Trites, A. W. (2020). Growth and development of North Pacific gray whales (Eschrichtius robustus). Journal of Mammalogy, 101(3), 742–754. https://doi.org/10.1093/jmammal/gyaa028

Bierlich, K. C., Kane, A., Hildebrand, L., Bird, C. N., Fernandez Ajo, A., Stewart, J. D., Hewitt, J., Hildebrand, I., Sumich, J., & Torres, L. G. (2023). Downsized: gray whales using an alternative foraging ground have smaller morphology. Biology Letters, 19(8). https://doi.org/10.1098/rsbl.2023.0043

Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L., & Heinsohn, R. (2011). Declining body size: A third universal response to warming? Trends in Ecology and Evolution, 26(6), 285–291. https://doi.org/10.1016/j.tree.2011.03.005

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