Clara Bird, PhD Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
In order to understand a species’ distribution, spatial ecologists assess which habitat characteristics are most often associated with a species’ presence. Incorporating behavior data can improve this analysis by revealing the functional use of each habitat type, which can help scientists and managers assign relative value to different habitat types. For example, habitat used for foraging is often more important than habitat that a species just travels through. Further complexity is added when we consider that some species, such as gray whales, employ a variety of foraging tactics on a variety of prey types that are associated with different habitats. If individual foraging tactic specialization is present, different foraging habitats could be valuable to specific subgroups that use each tactic. Consequently, for a population that uses a variety of foraging tactics, it’s important to study the associations between tactics and habitat characteristics.
Lukoschek and McCormick’s (2001) study investigating the spatial distribution of a benthic fish species’ foraging behavior is a great example of combining data on behavior, habitat, and morphology. They collected data on the diet composition of individual fish categorized into different size classes (small, medium, and large) and what foraging tactics were used in which reef zones and habitat types. The foraging tactics ranged from feeding in the water column to digging (at a range of depths) in the benthic substrate. The results showed that an interesting combination of fish behavior and morphology explained the observed diet composition and spatial distribution patterns. Small fish foraged in shallower water, on smaller prey, and primarily employed the water column and shallow digging tactics. In contrast, large fish foraged in deep water, on larger prey, and primarily fed by digging deeper into the seafloor (Figure 1). This pattern is explained by both morphology and behavior. Morphologically, the size of the feeding apparatus (mouth gape size) affects the size of the prey that a fish can feed on. The gape of the small fish is not large enough to eat the larger prey that large fish are able to consume. Behaviorally, predation risk also affects habitat selection and tactic use. Small fish are at higher risk of being predated on, so they remain in shallow areas where they are more protected from predators and they don’t dig as deep to forage because they need to be able to keep an eye out for predators. Interestingly, while they found a relationship between the morphology of the fish and habitat use, they did not find an association between specific feeding tactics and habitat types.
Conversely, Torres and Read (2009) did find associations between theforaging tactics of bottlenose dolphins in Florida Bay, FL and habitat type. Dolphins in this bay employ three foraging tactics: herd and chase, mud ring feeding, and deep diving. Observations of the foraging tactics were linked to habitat characteristics and individual dolphins. The study found that these tactics are spatially structured by depth (Figure 2), with deep diving occurring in deep water whereas mud ring feeding occurrs in shallower water. They also found evidence of individual specialization! Individuals that were observed deep diving were not observed mud ring feeding and vice-versa. Furthermore, they found that individuals were found in the habitat type associated with their preferred tactic regardless of whether they were foraging or not. This result indicates that individual dolphins in this bay have a foraging tactic they prefer and tend to stay in the corresponding habitat type. These findings are really intriguing and raise interesting questions regarding how these tactics and specializations are developed or learned. These are questions that I am also interested in asking as part of my thesis.
Both of these studies are cool examples that, combined, exemplify questions I am interested in examining using our study population of Pacific Coast Feeding Group (PCFG) gray whales. Like both studies, I am interested in assessing how specific foraging tactics are associated with habitat types. Our hypothesis is that different prey types live in different habitat types, so each tactic corresponds to the best way to feed on that prey type in that habitat. While predation risk doesn’t have as much of an effect on foraging gray whales as it does on small benthic fish, I do wonder how disturbance from boats could similarly affect tactic preference and spatial distribution. I am also curious to see if depth has an effect on tactic choice by using the morphology data from our drone-based photogrammetry. Given that these whales forage in water that is sometimes as deep as they are long, it stands to reason that maneuverability would affect tactic use. As described in a previous blog, I’m also looking for evidence of individual specialization. It will be fascinating to see how foraging preference relates to space use, habitat preference, and morphology.
These studies demonstrate the complexity involved in studying a population’s relationship to its habitat. Such research involves considering the morphology and physiology of the animals, their social, individual, foraging, and predator-prey behaviors, and the relationship between their prey and the habitat. It’s a bit daunting but mostly really exciting because better understanding each puzzle piece improves our ability to estimate how these animals will react to changing environmental conditions.
While I don’t have any answers to these questions yet, I will be working with a National Science Foundation Research Experience for Undergraduates intern this summer to develop a habitat map of our study area that will be used in this analysis and potentially answer some preliminary questions about PCFG gray whale habitat use patterns. So, stay tuned to hear more about our work this summer!
Lukoschek, V., & McCormick, M. (2001). Ontogeny of diet changes in a tropical benthic carnivorous fish, Parupeneus barberinus (Mullidae): Relationship between foraging behaviour, habitat use, jaw size, and prey selection. Marine Biology, 138(6), 1099–1113. https://doi.org/10.1007/s002270000530
Torres, L. G., & Read, A. J. (2009). Where to catch a fish? The influence of foraging tactics on the ecology of bottlenose dolphins ( Tursiops truncatus ) in Florida Bay, Florida. Marine Mammal Science, 25(4), 797–815. https://doi.org/10.1111/j.1748-7692.2009.00297.x
What I mean is that the vastness of the ocean is very hard to mentally visualize. When facing a conservation issue such as increased whale entanglement along the US West Coast (see OPAL project ), a tempting solution may be to suggest « let’s go see where the whales are and report their location to the fishermen?! ». But, it only takes a little calculation to realize how impractical this idea is.
Let’s roll out the numbers. The US West Coast exclusive economic zone (EEZ) stretches from the coast out to 200 nautical miles offshore, as prescribed by the 1982 United Nations Convention on the Law of the Sea. It covers an area of 825,549 km² (Figure 1). Now, imagine that you wish to survey this area for marine mammals. Using a vessel such as the R/V Bell M. Shimada that is used for the Northern California Current Ecosystem surveys cruises (NCC cruises, see Dawn and Rachel’s last blog), we may detect whales at a distance of roughly 6 km (based on my preliminary results). This distance of detection depends on the height of the observer, hence the height of the flying bridge where she/he is standing (the observer’s height may also be accounted for, but unless she/he is a professional basket-ball player, I think it can be neglected here). The Shimada is quite a large ship and it’s flying bridge is 13 meters above the water. Two observers may survey the water on each side of the trackline.
Considering that the vessel is moving at 8 knots (~15 km/h), we may expect to be effectively surveying 180 km² per hour (6x2x15). That’s not too bad, right?
Again, perspective is the key. If we divide the West Coast EEZ surface by 180 km² we can estimate that it would take 2,752 hours to survey this entire region. With an average of 12 hours of daylight, this takes us to…
382 DAYS OF SURVEY, searching for marine mammals over the US West Coast. Considering that observations cannot be undertaken on days with bad weather (fog, heavy rain, strong winds…), it might take more than a year and a half to complete the survey! And what would the marine mammals have done in the meantime? Move…
This little math exercise proves that exhaustively searching for the needle in the haystack from a vessel is not the way to go if we are to describe whale distribution and help mitigate the risk of entanglement. And using another platform of observation is not necessarily the solution. The OPAL project has relied on a great collaboration with the United States Coast Guard to survey Oregon waters. The USCG helicopters travel fast compared to a vessel, about 90 knots (167 km/h). As a result, more ground is covered but the speed at which it is traveling prevents the observer from detecting whales that are very far away. Based on the last analysis I ran for the OPAL project, whales are usually detected up to 3 km from the helicopter (only 5 % of sightings exceed that distance). In addition, the helicopter generally only has capacity for one observer at a time.
If we replicate the survey time calculation from above for the USCG helicopter, we realize that even with a fast-moving aerial survey platform it would still take 137 days to cover the West Coast EEZ.
First, we can model and extrapolate. This approach is the path we are taking with the OPAL project: we survey Oregon waters in 4 different areas along the coast each month, then model observed whale densities as a function of topographic and oceanographic variables, and then predict whale probability of presence over the entire region. These predictions are based on the assumption that our survey design effectively sampled the variety of environmental conditions experienced by whales over the study region, which it certainly did considering that all sites are surveyed year-round.
An alternative approach that has been recently discussed in the GEMM Llab, is the use of satellite images to detect whales along the coast. A communication entitled « The Potential of Satellite Imagery for Surveying Whales » was published last month in the Sensors Journal (Höschle et al., 2021) and presents the opportunities offered by this relatively new technology. The WorldView-3 satellite, owned by the company Digitalglobe and launched in 2016, has made it possible to commercialize imagery with a resolution never reached before, of the order of 30 cm per pixel. These very high resolution (VHR) satellite images make it possible to identify several species of large whales (Cubaynes et al. al., 2019) and to estimate their density (Bamford et al., 2020). Furthermore, machine learning algorithms, such as Neural Networks, have proved quite efficient at automatically detecting whales in satellite images (Guirado et al., 2019, Figure 2). While several new ultra-high resolution imaging satellites are expected to be launched in 2021 (by Maxar Technologies and Airbus), this “remote” approach looks like a promising avenue to detect whales over vast regions while drinking a cup of coffee at the office.
But like any other data collection method, satellites have their drawbacks. We recently discovered that these VHR satellites are routinely switched off while passing above the ocean. Specific inquiries would need to be made to acquire data over our study areas, which would be at great expense. One of the cheapest provider I found is the Soar platform, that provides images at 50 cm resolution in partnership with the Chinese Aerospace Science and Technology Corporation. They advertise daily images anywhere on earth at $10 USD per km². This might sound cheap at first glance, but circling back to our US West Coast EEZ area calculations, we estimate that surveying this region entirely with satellite imagery would cost more than $8 million USD.
Yet, we have to look forward. The use of satellite imagery is likely to broaden and increase in the coming years, with a possible decrease in cost. Quoting Höschle et al. (2021) ‘To protect our world’s oceans, we need a global effort and we need to create opportunities for that to happen’.
Will satellites soon save whales?
Bamford, C. C. G. et al. A comparison of baleen whale density estimates derived from overlapping satellite imagery and a shipborne survey. Sci. Rep. 10, 1–12 (2020).
Cubaynes, H. C., Fretwell, P. T., Bamford, C., Gerrish, L. & Jackson, J. A. Whales from space: Four mysticete species described using new VHR satellite imagery. Mar. Mammal Sci. 35, 466–491 (2019).
Guirado, E., Tabik, S., Rivas, M. L., Alcaraz-Segura, D. & Herrera, F. Whale counting in satellite and aerial images with deep learning. Sci. Rep. 9, 1–12 (2019).
Höschle, C., Cubaynes, H. C., Clarke, P. J., Humphries, G. & Borowicz, A. The potential of satellite imagery for surveying whales. Sensors 21, 1–6 (2021).
Clara Bird, PhD Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
When I started working on my thesis, I anticipated many challenges related to studying the behavioral ecology of gray whales. From processing five-plus years of drone footage to data analysis, there has been no shortage of anticipated and unexpected issues. I recently hit an unexpected challenge when I started video processing that piqued my interest. As I’ve discussed in a previous blog, ethograms are lists of defined behaviors that help us properly and consistently collect data in a standardized approach. Ethograms form a crucial foundation of any behavior study as the behaviors defined ultimately affect what questions can be asked and what patterns are detected. Since I am working off of the thorough ethogram of Oregon gray whales from Torres et al. (2018), I had not given much thought to the process of adding behaviors to the ethogram. But, while processing the first chunk of drone videos, I noticed some behaviors that were not in the original ethogram and struggled to decide whether or not to add them. I learned that ethogram development can lead down several rabbit holes. The instinct to try and identify every movement is strong but dangerous. Every minute movement does not necessarily need to be included and it’s important to remember the ultimate goal of the analysis to avoid getting bogged down.
Fundamental behavior questions cannot be answered without ethograms. For example, Baker et al. (2017) developed an ethogram for bottlenose dolphins in Ireland in order to conduct an initial quantitative behavior analysis. They did so by reviewing published ethograms for bottlenose dolphins, consulting with multiple experts, and revising the ethogram throughout the study. They then used their data to test inter-observer variability, calculate activity budgets, and analyze how the activity budgets varied across space and time.
Howe et al. (2015) also developed an ethogram in order to conduct quantitative behavior analyses. Their goals were to use the ethogram and subsequent analyses to better understand the behavior of beluga whales in Cook Inlet, AK, USA and to inform conservation. They started by writing down all behaviors they observed in the field, then they consolidated their notes into a formal ethogram that they used and refined during subsequent field seasons. They used their data to analyze how the frequencies of different behaviors varied throughout the study area at different times. This study served as an initial analysis investigating the effect of anthropogenic disturbance and was refined in future studies.
My research is similarly geared towards understanding behavior patterns to ultimately inform conservation. The primary questions of my thesis involve individual specialization, patterns of behavior across space, the relationship between behavior and body condition, and social behavior (check out this blog to learn more). While deciding what behaviors to add to my ethogram I’ve had to remind myself of these main questions and the bigger picture. The drone footage lets us see so much detail that it’s tempting to try to define every movement we can observe. One rabbit hole I’ve had to avoid a few times is locomotion. From the footage, it is possible to document fluke beats and pectoral fin strokes. While it could be interesting to investigate how different whales move in different ways, it could easily become a complicated mess of classifying different movements and take me deep into the world of whale locomotion. Talking through what that work would look like reminded me that we cannot answer every question and trying to assess all exciting side projects can cause us to lose focus on the main questions.
While I avoided going down the locomotion rabbit hole, there were some new behaviors that I did add to my ethogram. I’ll illustrate the process with the examples of two new behaviors I recently added: fluke swish and pass under (Clips 1 and 2). Clip 1 shows a whale rapidly moving its fluke to the side. I chose to add fluke swish because it’s such a distinct movement and I’m curious to see if there’s a pattern across space, time, individual, or nearby human activity that might explain its function. Clip 2 shows a calf passing under its mom. It’s not nursing because the calf doesn’t spend time under its mom, it just crosses underneath her. The calf pass under behavior could be a type of mom-calf tactile interaction. Analyzing how the frequency of this behavior changes over time could show how a calf’s dependency on its mom changes over as it ages.
In defining these behaviors, I had to consider how many different variations of this behavior would be included in the definition. This process involves considering at what point a variation of that behavior could serve a different function, even without knowing the function of the original behavior. For fluke swish this process involved deciding to only count a behavior as a fluke swish if it was a big, fast movement. A small and slow movement of the fluke a little to the side could serve a different function, such as turning, or be a random movement.
The next step involved deciding if the behavior would be a ‘state’ or ‘point’ event. A state event is a behavior with a start and stop moment; a point event is instantaneous and assigned to just a point in time. I would categorize a behavior as a state event if I was interested in questions about its duration. For example, I could ask “what percentage of the total observation time was spent in a certain behavior state?” A point event would be a behavior where duration is not applicable, but I could ask a question like “Did whale 1 perform more point event A than whale 2?”. Both fluke swish and pass under are point events because they only happen for an instant. In a pass under the calf is passing under its mom for just a brief point in time, making it a point event. The final step was to name the behavior. As I discussed in this blog, the name of the behavior does not matter as much as the definition but it is important that the name is clear and descriptive. We chose the name fluke swish because the fluke rapidly moves from side to side and pass under because the calf crosses under its mom.
Frankly, in the beginning, I was a bit overwhelmed by the realization that the content of my ethogram would ultimately control the questions I could answer. I could not help but worry that after processing all the videos, I would end up regretting not defining more behaviors. However, after reading some of the literature, chatting with Leigh, and reviewing the initial chunk of videos several times, I am more confidence in my judgment and my ethogram. I have accepted the fact that I can’t anticipate everything, and I am confident that the behaviors I need to answer my research questions are included. The process of reviewing and updating my ethogram has been a rewarding challenge that resulted in a valuable lesson that I will take with me for the rest of my career.
Baker, I., O’Brien, J., McHugh, K., & Berrow, S. (2017). An ethogram for bottlenose dolphins (Tursiops truncatus) in the Shannon Estuary, Ireland. Aquatic Mammals, 43(6), 594–613. https://doi.org/10.1578/AM.43.6.2017.594
Howe, M., Castellote, M., Garner, C., McKee, P., Small, R. J., & Hobbs, R. (2015). Beluga, Delphinapterus leucas, ethogram: A tool for cook inlet beluga conservation? Marine Fisheries Review, 77(1), 32–40. https://doi.org/10.7755/MFR.77.1.3
Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science, 5(SEP). https://doi.org/10.3389/fmars.2018.00319
“Hurry up and wait.” A familiar phrase to anyone who has conducted field research. A flurry of preparations, followed by a waiting game—waiting for the weather, waiting for the right conditions, waiting for unforeseen hiccups to be resolved. We do our best to minimize unknowns and unexpected challenges, but there is always uncertainty associated with any endeavor to collect data at sea. We cannot control the whims of the ocean; only respond as best we can.
On 15 February 2021, we were scheduled to board the NOAA Ship Bell M. Shimada as marine mammal observers for the Northern California Current (NCC) ecosystem survey, a recurring research cruise that takes place several times each year. The GEMM Lab has participated in this multidisciplinary data collection effort since 2018, and we are amassing a rich dataset of marine mammal distribution in the region that is incorporated into the OPAL project. February is the middle of wintertime in the North Pacific, making survey conditions challenging. For an illustration of this, look no further than at the distribution of sightings made during the February 2018 cruise (Fig. 1), when rough sea conditions meant only a few whales were spotted.
Now, this is February 2021and the world is still in the midst of navigating the global coronavirus pandemic that has affected every aspect of our lives. The September 2020 NCC cruise was the first NOAA fisheries cruise to set sail since the pandemic began, and all scientists and crew followed a strict shelter-in-place protocol among other COVID risk mitigation measures. Similarly, we sheltered in place in preparation for the February 2021 cruise. But here’s where the weather comes in yet again. Not only did we have to worry about winter weather at sea, but the inclement conditions across the country meant our COVID tests were delayed in transit—and we could not board the ship until everyone tested negative. By the time our results were in, the marine forecast was foreboding, and the Captain determined that the weather window for our planned return to port had closed.
So, we are still on shore. The ship never left the dock, and NCC February 2021 will go on the record as “NAs” rather than sightings of marine mammal presence or absence. So it goes. We can dedicate all our energy to studying the ocean and these spectacularly dynamic systems, but we cannot control them. It is an important and humbling reminder. But as we have continued to learn over the past year, there are always silver linings to be found.
Even though we never made it to the ship, it turns out there’s a lot you can get done onshore. Dawn has sailed on several NCC cruises before, and one of the goals this time was to train Rachel for her first stint at marine mammal survey work. This began at Dawn’s house in Newport, where we sheltered in place together for the week prior to our departure date.
We walked through the iPad program we use to enter data, looked through field guides, and talked over how to respond in different scenarios we might encounter while surveying for marine mammals at sea. We also joined Solene, a postdoc working on the OPAL project, for a Zoom meeting to edit the distance sampling protocol document. It was great training to discuss the finer points of data collection together, with respect to how that data will ultimately be worked into our species distribution models.
The February NCC cruise is famously rough, and a tough time to sight whales (Fig. 1). This low sighting rate arises from a combination of factors: baleen whales typically spend the winter months on their breeding grounds in lower latitudes so their density in Oregon waters is lower, and the notorious winter sea state makes sighting conditions difficult. Solene signed off our Zoom call with, “Go collect that high-quality absence data, girls!” It was a good reminder that not seeing whales is just as important scientifically as seeing them—though sometimes, of course, it’s not possible to even get out where you can’t see them. Furthermore, all absence data is not created equal. The quality of the absence data we can collect deteriorates along with the weather conditions. When we ultimately use these survey data to fuel species distribution models, it’s important to account for our confidence in the periods with no whale sightings.
In addition to the training we were able to conduct on land, the biggest silver lining came just from sheltering in place together. We had only met over Zoom previously, and spending this time together gave us the opportunity to get to know each other in real life and become friends. The week involved a lot of fabulous cooking, rainy walks, and an ungodly number of peanut butter cups. Even though the cruise couldn’t happen, it was such a rich week. The NCC cruises take place several times each year, and the next one is scheduled for May 2021. We’ll keep our fingers crossed for fair winds and negative COVID tests in May!
As a GEMM lab post-doc working on the OPAL project, my main goal for 2021 will be to produce accurate predictive models of baleen whale distribution off the Oregon coast to reduce entanglement risk. For the past months, I have been compiling, cleaning, and processing about two years of data collected by Leigh Torres and Craig Hayslip during monthly repeat surveys conducted onboard United States Coast Guard (USCG) helicopters. These standardized surveys record where and when whales are observed off the Oregon coast. These presence and absence data may now be modeled in relation to habitat, while accounting for effort and detection (as several parameters, such as weather and sea state, can affect the capacity of observers to detect whales at the surface). Considering that several baleen whale species (namely, humpback, fin, blue and gray whales) are known to feed in the area, prey availability is expected to be a major driver of their distribution.
As prey distribution data are frequently the lacking component in the habitat model equation, whale ecologists often resort to using environmental proxies. Variables such as topography (e.g., the depth or slope of the seafloor), water physical and chemical characteristics (e.g., temperature, salinity, oxygen concentration) or ocean circulation (e.g., currents, turbulence) have proved to be good predictors for fish or krill distribution, and in turn potential predictors for whale suitable habitats. In my search for such environmental variables to be tested in our future OPAL models, I have been focusing my research on a fascinating ocean feature: sea height.
Sea height varies both temporally and spatially under the influence of multiple factors, from internal mass of the solid Earth to the orbital revolution of the moon. After reading this blog you will realize that the flatness of the horizon at sea is a deceiving perspective (Figure 1) …
Gravity and the geoid
We all know of Newton’s s discovery of gravity: the attraction force exerted by any object with a given mass on its surroundings. Yet, it is puzzling to think that the rate of acceleration of the apple falling on Newton’s head would have been different if Newton had been anywhere else on Earth.
Why is that and what does it have to do with sea height? On Earth, the standard gravity g is set at 9.80665 m/s2. This constant is called a “standard” because in fact, gravity varies at the surface of our planet, even if estimated at a fixed altitude. Indeed, as gravity is caused by mass, any change in relief or rock composition results in a change in gravity. For instance, magmatic activity in the upper mantle of the Earth and the crust causes a change in rock density and results in a change in gravity measured at the surface.
Gravity therefore is the first reason why the ocean surface is not flat. Gravity shapes an irregular surface called the “geoid”. This hypothetical ocean surface has equal gravitational potential anywhere on Earth and differs from the ellipsoid of reference by as much as 100 m! So to the question whether Earth is round or flat, I would say it is potato shaped (Figure 2)!
The geoid is an essential reference for understanding ocean currents and monitoring changes in sea-level. Hypothetically, if ocean water had equal density everywhere and at any depth, the sea surface should match with the geoid… but that’s not the case. Let’s see why.
Ocean dynamic topography
Not unlike the hills and valleys covering landscapes, the ocean surface also has its highs and lows. Except that in the ocean, the surface topography is ever changing. Sea surface height (SSH) measures the average height difference between the observed sea level and the ellipsoid of reference (Figure 3). SSH is mostly affected by ocean circulation and may vary by as much as ±1 m. Indeed, just like the rocks inside the Earth, the water in the ocean varies in density. The vertical and horizontal physical structuring of the ocean was extensively discussed by Dawn last November while she was preparing for her PhD Qualifying Exams. Temperature clearly is at the core of the processes. As thermal expansion increases the space between warming water particles, the volume of a given amount of liquid water increases with increasing temperature. Warmer waters therefore take up “more space” than cooler waters, resulting in an elevated SSH.
SSH may therefore be used as an indicator of oceanographic phenomena such as upwellings, where warm surface waters are replaced by deep, cooler, and nutrient-rich waters moving upwards. The California Current that moves southwards along the North American coast is known as one of the world’s major currents affiliated with strong upwelling zones, which often triggers increased biological productivity. Several studies conducted in the California Current system have found a link between the variations in SSH and whale abundance or foraging activity (Abrahms et al. 2019; Pardo et al. 2015; Becker et al. 2016; Hazen et al. 2016).
SSH is measured by altimeter satellites and is made freely available by the European Space Agency and the US National Aeronautics and Space Administration. Lucky me! Numerous variables are derived from SSH, as shown in Figure 3. Among other things, I was able to download the daily maps of Sea Surface Height Anomaly (SSHa, also referred to as Sea Level Anomaly: SLA) over the Oregon coast from February 2019 to December 2020. SSHa is the difference between observed SSH at a specific time and place from the mean SSH field of reference calculated over a long period of time. Negative values of SSHa potentially suggest upwellings of cooler waters that could be associated with higher prey availability. Figure 4 shows an example of environmental data mining as I try to match SSHa with whale observations made during OPAL surveys. Figure 4B suggests increased whale occurrence where/when SSHa is lower.
Although encouraging, these preliminary insights are just the tip of the modeling iceberg. Many more testing and modeling steps will be required to determine confounding factors and relevant spatio-temporal scales at which these oceanographic variables may be influencing whale distribution off the Oregon coast. I am only at the start of a long road…
Abrahms, Briana, Heather Welch, Stephanie Brodie, Michael G. Jacox, Elizabeth A. Becker, Steven J. Bograd, Ladd M. Irvine, Daniel M. Palacios, Bruce R. Mate, and Elliott L. Hazen. 2019. “Dynamic Ensemble Models to Predict Distributions and Anthropogenic Risk Exposure for Highly Mobile Species.” Diversity and Distributions, no. December 2018: 1–12. https://doi.org/10.1111/ddi.12940.
Becker, Elizabeth, Karin Forney, Paul Fiedler, Jay Barlow, Susan Chivers, Christopher Edwards, Andrew Moore, and Jessica Redfern. 2016. “Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?” Remote Sensing 8 (2): 149. https://doi.org/10.3390/rs8020149.
Hazen, Elliott L, Daniel M Palacios, Karin A Forney, Evan A Howell, Elizabeth Becker, Aimee L Hoover, Ladd Irvine, et al. 2016. “WhaleWatch : A Dynamic Management Tool for Predicting Blue Whale Density in the California Current.” Journal of Applied Ecology 54 (5): 1415–28. https://doi.org/10.1111/1365-2664.12820.
Pardo, Mario A., Tim Gerrodette, Emilio Beier, Diane Gendron, Karin A. Forney, Susan J. Chivers, Jay Barlow, and Daniel M. Palacios. 2015. “Inferring Cetacean Population Densities from the Absolute Dynamic Topography of the Ocean in a Hierarchical Bayesian Framework.” PLOS One 10 (3): 1–23. https://doi.org/10.1371/journal.pone.0120727.
Clara Bird, PhD Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
As anyone who has ever been, or raised, a picky eater knows, humans have a wide range of food preferences. The diversity of available cuisines is a testament to the fact that we have individual food preferences. While taste is certainly a primary influence, nutritional benefits and accessibility are other major factors that affect our eating choices. But we are not the only species to have food preferences. In cetacean research, it is common to study the prey types consumed by a population as a whole. Narrowing these prey preferences down to the individual level is rare. While the individual component is challenging to study and to incorporate into population models, it is important to consider what the effects of individual foraging specialization might be.
To understand the role and drivers of individual specialization in population ecology, it is important to first understand the concepts of niche variation and partitioning. An animal’s ecological niche describes its role in the ecosystem it inhabits (Hutchinson, 1957). A niche is multidimensional, with dimensions for different environmental conditions and resources that a species requires. One focus of my research pertains to the dimensions of the niche related to foraging. As discussed in a previous blog, niche partitioning occurs when ecological space is shared between competitors through access to resources varies across different dimensions such as prey type, foraging location, and time of day when foraging takes place. Niche partitioning is usually discussed on the scale of different species coexisting in an ecosystem. Pianka’s theory stating that niche partitioning will increase as prey availability decreases uses competing lizard species as the example (Pianka, 1974). Typically, niche partitioning theory considers inter-specific competition (competition between species), but niche partitioning can take place within a species in response to intra-specific competition (competition between individuals of the same species) through individual niche variation.
A species that consumes a multitude of prey types is considered a generalist while one with a specific prey type is considered a specialist. Gray whales are considered generalists (Nerini, 1984). However, we do not know if each individual gray whale is a generalist or if the generalist population is actually composed of individual specialists with different preferences. One way to test for the presence of individual specialization is to compare the niche width of the population to the niche width of each individual (Figure 1, Bolnick et al., 2003). For example, if a population eats five different types of prey and each individual consumed those prey types, those individuals would be generalists. However, if each individual only consumed one of the prey types, then those individuals would be specialists within a generalist population.
If individual specialization is present in a population the natural follow-up question is why? To answer this, we look for common characteristics between the individuals that are similarly specialized. What do all the individuals that feed on the same prey type have in common? Common characterizations that may be found include age, sex, or distinct morphology (such as different beak or body shapes) (Bolnick et al., 2003).
Woo et al. (2008) studied individual specialization in Brünnich’s guillemot, a generalist sea bird species, using diet and tagging data. They found individual specialization in both diet (prey type) and behavior (dive depth, shape, and flight time). Specialization occurred across multiple timescales but was higher over short-time scales. The authors found that it was more common for an individual to specialize in a prey-type/foraging tactic for a few days than for that specialization to continue across years, although a few individuals were specialists for the full 15-year period of the study. Based on reproductive success of the studies birds, the authors concluded that the generalist and specialist strategies were largely equivalent in terms of fitness and survival. The authors searched for common characteristics in the individuals with similar specialization and they found that the differences between sexes or age classes were so small that neither grouping explained the observed individual specialization. This is an interesting result because it suggests that there is some missing attribute, that of the authors did not examine, that might explain why individual specialists were present in the population.
Hoelzel et al. (1989) studied minke whale foraging specialization by observing the foraging behaviors of 23 minke whales over five years from a small boat. They identified two foraging tactics: lunge feeding and bird-associated feeding. Lunge feeding involved lunging up through the water with an open mouth to engulf a group of fish, while bird-associated feeding took advantage of a group of fish being preyed on by sea birds to attack the fish from below while they were already being attacked from above. They found that nine individuals used lunge feeding, and of those nine, six whales used this tactic exclusively. Five of those six whales were observed in at least two years. Seventeen whales were observed using bird-associated feeding, 14 exclusively. Of those 14, eight were observed in at least two years. Interestingly, like Woo et al. (2008), this study did not find any associations between foraging tactic use and sex, age, or size of whale. Through a comparison of dive durations and feeding rates, they hypothesized that lunge feeding was more energetically costly but resulted in more food, while bird-associated feeding was energetically cheaper but had a lower capture rate. This result means that these two strategies might have the similar energetic payoffs.
Both of these studies are examples of questions that I am excited to ask using our data on the PCFG gray whales feeding off the Oregon coast (especially after doing the research for this blog). We have excellent individual-specific data to address questions of specialization because the field teams for this project always carefully link observed behaviors with individual whale ID. Using these data, I am curious to find out if the whales in our study group are individual specialists or generalists (or some combination of the two). I am also interested in relating specific tactics to their energetic costs and benefits in order to assess the payoffs of each foraging tactic. I then hope to combine the results of both analyses to assess the payoffs of each individual whale’s strategy.
Figure 2. Example images of two foraging tactics, side swimming (left) and headstanding (right).Images captured under NOAA/NMFS permit #21678.
Studying individual specialization is important for conservation. Consider the earlier example of a generalist population that consumes five prey items but is composed of individual specialists. If the presence of individual specialization is not accounted for in management plans, then regulations may protect certain prey types or foraging tactics/areas of the whales and not others. Such a management plan could be a dangerous outcome for the whale population because only parts of the population would be protected, while other specialists are at risk, thus potentially losing genetic diversity, cultural behaviors, and ecological resilience in the population as a whole. A plan designed to maximize protection for all the specialists would be better for the population because populations with increased ecological resilience are more likely to persist through periods of rapid environmental change. Furthermore, understanding individual specialization could help us better predict how a population might be affected by environmental change. Environmental change does not affect all prey species in the same way. An individual specialization study could help identify which whales might be most affected by predicted environmental changes. Therefore, in addition to being a fascinating and exciting research question, it is important to test for individual specialization in order to improve management and our overall understanding of the PCFG gray whale population.
Bolnick, D. I., Svanbäck, R., Fordyce, J. A., Yang, L. H., Davis, J. M., Hulsey, C. D., & Forister, M. L. (2003). The ecology of individuals: Incidence and implications of individual specialization. American Naturalist, 161(1), 1–28. https://doi.org/10.1086/343878
Hoelzel, A. R., Dorsey, E. M., & Stern, S. J. (1989). The foraging specializations of individual minke whales. Animal Behaviour, 38(5), 786–794. https://doi.org/10.1016/S0003-3472(89)80111-3
Hutchinson, G. E. (1957). Concluding Remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22(0), 415–427. https://doi.org/10.1101/sqb.1957.022.01.039
Nerini, M. (1984). A Review of Gray Whale Feeding Ecology. In The Gray Whale: Eschrichtius Robustus (pp. 423–450). Elsevier Inc. https://doi.org/10.1016/B978-0-08-092372-7.50024-8
Pianka, E. R. (1974). Niche Overlap and Diffuse Competition. 71(5), 2141–2145.
Woo, K. J., Elliott, K. H., Davidson, M., Gaston, A. J., & Davoren, G. K. (2008). Individual specialization in diet by a generalist marine predator reflects specialization in foraging behaviour. Journal of Animal Ecology, 77(6), 1082–1091. https://doi.org/10.1111/j.1365-2656.2008.01429.x
At this year’s virtual State of the Coast conference, I enjoyed tuning into a range of great talks, including one by Zach Penney from the Columbia River Inter-Tribal Fish Commission. In his presentation, “More Than a Tradition: Treaty rights and the Columbia River Inter-Tribal Fish Commission,” Penney described a tribal “covenant with resources,” and noted the success of this approach — “You don’t live in a place for 15,000 years by messing it up.”
Indigenous management of resources in the Pacific Northwest dates back thousands of years. From oak savannahs to fisheries to fires, local tribes managed diverse natural systems long before colonial settlement of the area that is now Oregon. We know comparatively little, however, about how Indigenous groups in Oregon interacted with whale populations before the changes brought by colonialism and commercial whaling.
I’m curious about how this missing knowledge could inform our understanding of the coastal Oregon ecosystems in which many GEMM Lab projects take place. My graduate research will be part of the effort to identify co-occurrence between whales and fishing in Oregon, with the goal of helping to reduce whale entanglement risk. Penney’s talk, ongoing conversations about decolonizing science, and my own concerns about becoming the scientist that I want to be, have all led me to ask a new set of questions: What did humans know in the past about whale distributions along the Oregon coast? What lost knowledge can be reclaimed from history?
As I started reading about historical Indigenous whale use in Oregon, I was struck by how little we know today, and how this learning process became a multidisciplinary treasure hunt. Clues as to how Indigenous groups interacted with whales along the Oregon coast lie in oral histories, myths, journals, and archaeological artifacts.
Much of what I read hinged on the question: did Indigenous tribes in Oregon historically hunt whales? Many signs point to yes, but it’s a surprisingly tricky question to answer conclusively. Marine systems and animals, including seals and whales, remain an important part of cultures in the Pacific Northwest today – but historically, documentation of hunting whales in Oregon has been limited. Whale bones have been found in coastal middens, and written accounts describe opportunistic harvests of beached whales. However, people have long believed that only a few North American tribes outside of the Arctic regularly hunted whales.
But in 2007, archaeologists Robert Losey and Dongya Yang found an artifact that started to shift this narrative. While studying a collection of tools housed at the Smithsonian Institution, they discovered the tip of a harpoon lodged in a whale flipper bone. This artifact came from the Partee site, which was inhabited around AD 300-1150 and is located near present-day Seaside, Oregon.
Through DNA testing, Losey and Yang determined that the harpoon was made of elk bone, and that the elk was not only harvested locally, but also used locally. This new piece of evidence suggested that whaling did in fact take place at the Partee site, likely by the Tillamook or Clatsop tribes that utilized this area.
Several years later, this discovery inspired Smithsonian Museum of Natural History archaeologist Torben Rick and University of Oregon PhD student Hannah Wellman to comb through the rest of the animal remains in the Smithsonian’s collection from northwest Oregon. Rick and Wellman scrutinized 187 whale bones for signs of hunting or processing, and found that about a quarter of the marks they inspected could have come from either hunting or the opportunistic harvest of stranded whales. They examined tools from the midden as well, and found that they were more suited to hunting animals, like seals and sea lions, or fishing.
However, Wellman and Rick also used DNA testing to identify which whale species were represented in the midden – and the DNA analyses suggested a different story. Genetic results revealed that the majority of whale bones in the midden came from gray whales, a third from humpback whales, and a few from orca and minke. Modern gray whale stranding events are not uncommon, and so it follows logically that these bones could have simply come from people harvesting beached whales. However, humpback strandings are rare – suggesting that such a large proportion of humpback bones in the midden is likely evidence of people actively hunting humpback whales.
These results shed new light on whale harvesting practices at the Partee Site, and, like so much research, they suggest a new set of questions. What does the fact that there were orca, minke, gray, and humpback whales off the Oregon coast 900 years ago tell us about the history of this ecosystem? Could artifacts that have not yet been found provide more conclusive evidence of hunting? What would it mean if these artifacts are found one day, or if they are never found?
What we learn about the past can also change how we understand this ecosystem today, and provide new context as we try to understand the impacts of climate change on whale populations in Oregon. I’m interested in how learning more about historical Indigenous whaling practices could provide more information about whale population baselines, ideas for management strategies, and a new lens on the importance of whales in the Pacific Northwest. Even if we can’t fully reclaim lost knowledge from history, maybe we can still read enough clues to help us see both the past and present more fully.
Braun, Ashley. “New Research Offers a Wider View on Indigenous North American Whaling.” Hakai Magazine, November 2016, www.hakaimagazine.com/news/new-research-offers-wider-view-indigenous-north-american-whaling/.
Eligon, John. “A Native Tribe Wants to Resume Whaling. Whale Defenders Are Divided.” New York Times, November 2019.
Hannah P. Wellman, Torben C. Rick, Antonia T. Rodrigues & Dongya Y. Yang (2017) Evaluating Ancient Whale Exploitation on the Northern Oregon Coast Through Ancient DNA and Zooarchaeological Analysis, The Journal of Island and Coastal Archaeology, 12:2, 255-275, DOI: 10.1080/15564894.2016.1172382
Losey, R., & Yang, D. (2007). Opportunistic Whale Hunting on the Southern Northwest Coast: Ancient DNA, Artifact, and Ethnographic Evidence. American Antiquity, 72(4), 657-676. doi:10.2307/25470439
Sanchez, Gabriel (2014). Conference paper: Cetacean Hunting at the Par-Tee site (35CLT20)?: Ethnographic, Artifact and Blood Residue Analysis Investigation.
Clara Bird, PhD Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
Based on my undergrad experience I assumed that most teaching in grad school would be as a teaching assistant, and this would consist of teaching labs, grading, leading office hours, etc. However, now that I’m in graduate school, I realize that there are many different forms of teaching as a graduate student. This summer I worked as an instructor for an e-campus course, which mainly involved grading and mentoring students as they developed their own projects. Yet, this past week I was a guest teacher for Physiology and Behavior of Marine Megafauna, which was a bit more involved.
I taught a whale photogrammetry lab that I originally developed as a workshop with a friend and former lab mate, KC Bierlich, at the Duke University Marine Robotics and Remote Sensing (MaRRS) lab when I worked there. Similar to Leila’s work, we were using photogrammetry to measure whales and assess their body condition. Measuring a whale is a deceivingly simple task that gets complicated when taking into account all the sources of error that might affect measurement accuracy. It is important to understand the different sources of error so that we are sure that our results are due to actual differences between whales instead of differences in errors.
Error can come from distortion due to the camera lens, inaccurate altitude measurements from the altimeter, the whale being arched, or from the measurement process. When we draw a line on the image to make a measurement (Image 1), measurement process errors come from the line being drawn incorrectly. This potential human error can effect results, especially if the measurer is inexperienced or rushing. The quality of the image also has an effect here. If there is glare, wake, blow or refraction covering or distorting the measurer’s view of the full body of the whale then the measurer has to estimate where to begin and end the line. This estimation is subjective and, therefore, a source of error. We used the workshop as an opportunity to study these measurement process errors because we could provide a dataset including images of varying qualities and collect data from different measurers.
This workshop started as a one-day lecture and lab that we designed for the summer drone course at the Duke Marine Lab. The idea was to simultaneously teach the students about photogrammetry and the methods we use, while also using all the students’ measurements to study the effect of human error and image quality on measurement accuracy. Given this one-day format, we ambitiously decided to teach and measure in the morning, compile and analyze the students’ measurements over lunch, and then present the results of our error analysis in the afternoon. To accomplish this, we prepared as much as we could and set up all the code for the analysis ahead of time. This preparation meant several days of non-stop working, discussing, and testing, all to anticipate any issues that might come up on the day of the class. We used the measuring software MorphoMetriX (Torres & Bierlich, 2020) that was developed by KC and a fellow Duke Marine Lab grad student Walter Torres. MorphoMetriX was brand new at the time, and this newness of the software meant that we didn’t yet know all the issues that might come up and we did not have time to troubleshoot. We knew this meant that helping the students install the software might be a bit tricky and sure enough, all I remember from the beginning of that first lab is running around the room helping multiple people troubleshoot at the same time, using use all the programming knowledge I had to discover new solutions on the fly.
While troubleshooting on the fly can be stressful and overwhelming, I’ve come to appreciate it as good practice. Not only did we learn how to develop and teach a workshop, we also used what we had learned from all the troubleshooting to improve the software. I also used the code we developed for the analysis as the starting blocks for a software package I then wrote, CollatriX (Bird & Bierlich, 2020), as a follow up software to MorphoMetriX. Aside from the initial troubleshooting stress, the workshop was a success, and we were excited to have a dataset to study measurement process errors. Given that we already had all the materials for the workshop prepared, we decided to run a few more workshops to collect more data.
That brings me to my time at here at OSU. I left the Duke MaRRS lab to start graduate school shortly after we taught the workshop. Interested in running the workshop here, I reached out to a few different people. I first ran the workshop here as an event organized by the undergraduate club Ocean11 (Image 2). It was fun running the workshop a second time, as I used what I learned from the first round; I felt more confident, and I knew what the common issues would likely be and how to solve them. Sure enough, while there were still some troubleshooting issues, the process was smoother and I enjoyed teaching, getting to know OSU undergraduate students, and collecting more data for the project.
The next opportunity to run the lab came through Renee Albertson’s physiology and behavior of marine megafauna class, but during the COVID era this class had other challenges. While it’s easier to teach in person, this workshop was well suited to be converted to a remote activity because it only requires a computer, the data can be easily sent to the students, and screen sharing is an effective way to demonstrate how to measure. So, this photogrammetry module was a good fit for the marine megafauna class this term that has been fully remote due to COVID-19. My first challenge was converting the workshop into a lab assignment with learning outcomes and analysis questions. The process also involved writing R code for the students to use and writing step-by-step instructions in a way that was clear and easy to understand. While stressful, I appreciated the process of developing the lab and these accompanying materials because, as you’ve probably heard from a teacher, a good test of your understanding of a concept is being able to teach it. I was also challenged to think of the best way to communicate and explain these concepts. I tried to think of a few different explanations, so that if a student did not understand it one way, I could offer an alternative that might work better. Similar to the preparation for the first workshop, I also prepared for troubleshooting the students’ issues with the software. However, unlike my previous experiences, this time I had to troubleshoot remotely.
After teaching this photogrammetry lab last week my respect for teachers who are teaching remotely has only increased. Helping students without being able to sit next to them and walk them through things on their computer is not easy. Not only that, in addition to the few virtual office hours I hosted, I was primarily troubleshooting over email, using screen shots from the students to try and figure out what was going on. It felt like the ultimate test of my programming knowledge and experience, having to draw from memories of past errors and solutions, and thinking of alternative solutions if the first one didn’t work. It was also an exercise in communication because programming can be daunting to many students; so, I worked to be encouraging and clearly communicate the instructions. All in all, I ended this week feeling exhausted but accomplished, proud of the students, and grateful for the reminder of how much you learn when you teach.
Bird, C. N., & Bierlich, K. (2020). CollatriX: A GUI to collate MorphoMetriX outputs. Journal of Open Source Software, 5(51), 2328. https://doi.org/10.21105/joss.02328
Torres, W., & Bierlich, K. (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), 1825. https://doi.org/10.21105/joss.01825
As I solidified my grad school plans last spring, one of the things that made me most excited to join the GEMM Lab was the direct applicability of its research to management and conservation practices. Seeing research directly plugged into current problems facing society is always inspirational to me. My graduate research will be part of the GEMM Lab’s project to identify co-occurrence between whales and fishing effort in Oregon, with the goal of helping to reduce whale entanglement risk. Recently, watching the Oregon Department of Fish and Wildlife (ODFW) Commission in action gave me a fascinating, direct look at how the management sausage gets made.
At the September Commission meeting, ODFW Marine Resources Program Manager Caren Braby presented proposed rule changes in the management of the Oregon dungeness crab fleet. As part of a coordinated effort with Washington and California, the main goal of these changes is to reduce the risk of whale entanglements, which have increased sharply in US West Coast waters since 2014.
With the aim of maximizing the benefit to whales while minimizing change to the fishery, Braby and her staff developed a recommendation for a shift in summer fishing effort, when whales are most abundant in Oregon waters. Based on diverse considerations — including the distributions of humpback whales off Oregon and season fishery economics — she outlined options along what she termed a “spectrum of reduced risk,” which included possible shifts in the fishing season, spatial extent, and number of pots deployed.
Although the GEMM Lab project to provide a robust understanding of whale distribution in Oregon waters is not yet complete, the data collected to-date has already significantly refined knowledge of whale distributions off the coast — and it directly informed the proposed monthly depth limitations for fishing effort. It is never possible to have perfect knowledge of an ecosystem, and resource managers must navigate this inherent complexity as they make decisions. As the GEMM Lab collects and analyzes more data on the distribution of whales and their prey, our ability to inform management decisions can become even more precise and effective.
Braby proposed that the fleet reduce the number of crab pots deployed by 20% and prohibit fishing at depths greater than 30 fathoms, starting May 1, for the next three seasons. The goal of this recommendation is to effectively separate the bulk of fishing effort from the deep waters where humpback whales forage, when they visit their feeding grounds off the coast of Oregon during the summer.
Following Braby’s presentation, a public comment period allowed stakeholders to offer their own opinions and requests for the Commission to consider. Fisherman, lawyers, and members of conservation nonprofits each provided succinct three-minute statements, offering a wide range of opinions and amendments to the proposed rule changes.
This comment period highlighted how truly multifaceted this decision-making process is, as well as the huge number of livelihoods, economic impacts, and types of data that must be considered. It also raised essential questions — how do you make regulations that protect whales without favoring one group of stakeholders over another? How can you balance multiple levels of law with the needs of local communities?
Even during heated moments of this meeting, the tone of the dialog impressed me. This topic is inevitably a contentious and emotional issue. Yet even people with opposing viewpoints maintained focus on their common goals and common ground, and frequently reiterated their desire to work together.
After more than six hours of presentations, comments, and deliberation, the Commission voted on the proposed rule changes. They decided to adopt somewhat more liberal rule changes than Braby had proposed — a 20% reduction in crab pots and a prohibition on fishing at depths greater than 40 fathoms, starting May 1. After three years, the Commission will evaluate the efficacy of these new policies, and plan to refine or change the rules based on the best available data.
Witnessing this decision-making process gave me a new perspective on the questions and context my research will fit into, and this understanding will help me become a better collaborator. Watching the Commission in action also underscored the difficult position managers are often put in. They must make decisions based on incomplete knowledge that will inevitably impact people’s lives — but they also need to protect the species and biodiversity, that also have an innate right to exist in natural ecosystems. Seeing the intricacies of this balancing act made me glad that I get to be part of research that can inform important management decisions in Oregon.
Fall has arrived in the Pacific Northwest. For humans, it means packing away the shorts and sandals, and getting the boots, raincoats and firewood ready. For gray whales, it means gulping down the last meal of zooplankton they will eat for several months and commencing the journey to warmer waters and sunnier skies in Mexico where they will spend the winter fasting, calving, and nursing. While the GEMM Lab may still squeeze in a day or two of field work this week, we are slowly wrapping up the 2020 field season as conditions get rougher and our beloved gray whales gradually depart our waters. This year marked the 6th year of data collection for both of our gray whale projects: the Newport project that investigates the impacts of multiple stressors on gray whale ecology and health, and the Port Orford project that explores fine-scale foraging ecology of gray whales and their zooplankton prey. Since it will be several months before the GEMM Lab heads back out onto the water again, I thought I would summarize our two field seasons, share some highlights, and muse about the drivers of our observations this summer.
Our RHIB Ruby zipped around the central and southern Oregon coast on 33 different days. The summer started slow, with several days of field work where we encountered no whales despite surveying our entire study region. Our encounters picked up towards the end of June and by the end of the summer we totaled 107 sightings, encountering 46 unique individuals, 36 of which were resightings of known individuals we have identified in previous years. Our Newport star of the summer was Solé, a female gray whale we have seen every year since 2015, and we also saw many of our other regulars including Casper, Rafael, Spray, Bit, and Heart. None of these whales shone as bright as Solé though. We flew the drone over her 8 times and collected 7 fecal samples (one of which was the biggest whale fecal sample I have ever seen!). In total, we collected 30 fecal samples and flew the drone 88 times. These data will allow us to continue measuring body condition and hormone levels of Pacific Coast Feeding Group (PCFG) gray whales that use the Oregon coast.
Our tandem research kayak Robustus may not be as zippy as Ruby (it is powered by human muscle rather than a powerful outboard engine after all), but it certainly continues to be a trusty vessel for the Port Orford team. The Port Orford research team, named the Theyodelers this year, collected 181 zooplankton samples and conducted 180 GoPro drops during the month of August from Robustus. Despite the many samples collected, the size of our prey samples remained relatively small throughout the whole season compared to previous years. The cliff team surveyed for a total of 117 hours, of which 15 were spent tracking whales with the theodolite and resulted in 40 different tracklines of whale movements. The whale situation in Port Orford was similar to the pattern of whale sightings in Newport, with low whale sightings at the start of the field season. Luckily, by the start of August (which marked the start of data collection for the Theyodelers), the number of whales using the Port Orford area, especially the two study sites, Mill Rocks & Tichenor Cove, had increased. Of the whales that came close enough to shore for us to identify using photo-id, we tracked 5 unique individuals, 3 of which we also saw in Newport this year. The Port Orford star of the summer was Smudge, with his tracklines making up a quarter of all of our tracklines collected. Smudge is also the whale we sighted most often last year in Port Orford.
Many of you may be familiar with the whale Scarlett (formally known as Scarback). Scarlett is a female, at least 24 years old (she was first documented in the PCFG range in 1996), who is well-known (and easily identified) by the large concave injury on her back that is covered in whale lice, or cyamids. No one knows for certain how Scarlett sustained this injury (though there are stories), however what we do know is that it has not prevented this female from reproducing and successfully raising several calves over her lifetime. The GEMM Lab last saw Scarlett with a calf (which we named Brown) in 2016. Since Scarlett is such a famous whale with a unique history, it shouldn’t be a surprise that one of our highlights this summer is the fact that Scarlett showed up with a new calf! In keeping with a “shades of red” theme, Leigh came up with the name Rose for the new calf. In July, the mom-calf pair put on quite a cute performance, with Rose rising up on Scarlett’s back, giving the team a glimpse of its face. The Scarlett-Rose highlight doesn’t end there though. Just last week, we had a very brief encounter in choppy, swelly waters with a small whale. The whale surfaced just twice allowing us to capture photo-id images, and as we were looking around to see where it would come up a third time, it suddenly breached approximately 20 m from the boat. Lo-and-behold, after comparing our photos of the whale to our catalogue, we realized that this elusive, breaching whale was Rose! I am excited to see whether Rose will return to the Oregon coast next summer and become a PCFG regular just like her mom.
The highlight of the field season in Port Orford is the trial, failures and small successes of a new element to the project. There is still a lot that we do not know and understand about PCFG gray whales. One such thing is the way in which gray whales maneuver their large bodies in shallow rocky habitats, often riddled with kelp, and how exactly they capture their zooplankton prey in these environments. Using drones has certainly helped bring some light into this darkness and has led to the documentation of many novel foraging behaviors (Torres et al. 2018). However, the view from above is unable to provide the fine-scale interactions between whales, kelp, reefs, and zooplankton. Instead, we must somehow find a way to watch the whales underwater. Enter CamDo. CamDo is a technology company that designs specialty products to allow for GoPro cameras to be used for time-lapsed recordings over long periods of time in harsh environmental conditions. One of their products is a housing specifically designed for long-term filming underwater – exactly what we need! The journey was not as easy as simply purchasing the housing. We also needed to build a lander for the housing to sit on (thankfully our very own Todd Chandler designed and built something for us), and coordinate with divers and a vessel to deploy and retrieve the set-up, as well as undertake weekly battery and SD cards swaps (thankfully Dave Lacey of South Coast Tours and a very generous group of divers* donated their time and resources to make this happen). We unfortunately had some technological difficulties and bad visibility for the first 4 weeks (precisely why this CamDo effort was a pilot season this year), however we had some small success in the last 2 weeks of deployment that give us hope for the future. The camera recorded a lot of things: thick layers of mysids, countless rockfish and lingcod, several swimming and foraging murres, a handful of harbor seals, and two encounters of the species we were hoping to film – gray whales! While the footage is not the ‘money shot’ we are hoping to film (aka, a headstanding gray whale eating zooplankton right in front of the camera), the fact that we captured gray whales in the first place has showed us that this set-up is a promising investment of time, money and effort that will hopefully deliver next year.
You may have picked up on the fact that we had slow starts to our field seasons in both Newport and Port Orford. Furthermore, while the number of whale sightings did increase in both locations throughout the field seasons, the number of sightings and whales per day were lower than they have been in previous years. For example, in 2018, we identified 15 different individuals in the month of August in Port Orford (compared to just 5 this year). In 2019, 63 unique whales were seen in Newport (compared to 46 this year). Interestingly, we had a greater diversity of encountered individuals at the start and end of the season in Newport, with a relatively small number of different individuals in July and August. While I cannot provide a definitive reason (or reasons) as to why patterns were observed (we will need to analyze several years of our data to try and understand why), I have some hypotheses I wish to share with you.
As I mentioned in a previous blog, this summer the coastal upwelling along the Oregon coast was delayed (Figure 1). Typically, peak upwelling occurs during the month of June or shortly thereafter, bringing nutrient-rich, deep waters to the surface and, when mixed with sunlight, a lot of productivity. This productivity sets off a chain of reactions — the input of nutrients leads to increased phytoplankton production, which in turn leads to increased zooplankton production, resulting in growth and development of larger organisms that consume zooplankton, such as rockfish and gray whales. If the timing of upwelling is delayed, then so too is this chain of reactions. As you can see from Figure 1, the red lines show that the peak upwelling this year occurred far later in the summer than any year in the last 10 years, with the exception of 2012. Gray whales may have cued into this delay and therefore also delayed their arrival to the PCFG feeding grounds, hence causing us to have low sighting rates at the start of our season. However, this is mostly speculative as we still do not understand the functional mechanisms by which cetaceans, such as gray whales, detect prey across different scales, and to what extent oceanographic conditions like upwelling may play a role in prey availability (Torres 2017).
Furthermore, the green line in Figure 1shows that even after peak upwelling was reached this year, upwelling conditions were lower than all the other peaks in the previous 10 years. We know that weak upwelling is correlated to poor body condition of PCFG gray whales in subsequent years (Soledade Lemos et al. 2020). Upon arriving to the Oregon coast feeding grounds, gray whales may have noticed that it was shaping up to be a poor prey year (we certainly noticed it in Port Orford in the emptiness of our zooplankton net). Faced with this low resource availability, individuals had to make important decisions – risk staying in a currently prey-poor environment or continue the journey onward, searching for better prey conditions elsewhere. This conundrum is known as the marginal value theorem, whereby an individual must decide whether it should abandon the patch it is currently foraging on and move on to search for a new patch without knowing how far away the next patch may be or its value relative to the current patch (Charnov 1976). If we think of the Oregon coast as the ‘current patch’, then we can see how the marginal value theorem translates to the situation gray whales may have found themselves in at the start of the summer.
Yet, an individual gray whale does not make these decisions in a vacuum. Instead, all gray whales in the same area are faced with the same conundrum. Seminal work by Pianka (1974) showed that when resources, such as food, are abundant, then competition between predators is low because there is enough food to go around. However, when resources dwindle, competition increases and the niches of predators begin to overlap more and more. With Charnov and Pianka’s theories in mind, we can see two groups of gray whales emerge from our 2020 field work observations: those that stayed in the ‘current patch’ (Oregon) and those that decided to seek out a new patch in hopes that it would be a better one. Solé certainly belongs in the first group. We saw her consistently throughout the whole summer. In fact, she was oftentimes so predictable that we would find her foraging on the same reef complex every time we went out to survey. Smudge may also belong in this group, however it is hard to say definitively since we only survey in Port Orford in late July and August. In contrast, I would place whales such as Spray and Heart in the second group since we saw them early in the summer and then not again until mid-to-late September. Where did they go in the interim? Did they go somewhere else in the PCFG range? Or did they venture all the way up to Alaska to the primary Eastern North Pacific (ENP) gray whale feeding grounds? Did their choice to search for food elsewhere pay off?
As I said earlier, these are all just musings for now, but the GEMM Lab is already hard at work trying to answer these questions. Stay tuned to see what we find!
* Thanks to all the divers who assisted with the pilot CamDo season: Aaron Galloway, Ross Whippo, Svetlana Maslakova, Taylor Eaton, Cori Kane, Austin Williams, Justin Smith
Charnov, E.L. 1976. Optimal Foraging, the Marginal Value Theorem. Theoretical Population Biology 9(2):129-136.
Pianka, E.R. 1974. Niche Overlap and Diffuse Competition. PNAS 71(5):2141-2145.
Soledade Lemos, L., Burnett, J.D., Chandler, T.E., Sumich, J.L., and L.G. Torres. 2020. Intra- and inter-annual variation in gray whale body condition on a foraging ground. Ecosphere 11(4):e03094.
Torres, L.G. 2017. A sense of scale: Foraging cetaceans’ use of scale-dependent multimodal sensory systems. Marine Mammal Science 33(4):1170-1193.
Torres, L.G., Nieukirk, S.L., Lemos, L., and T.E. Chandler. 2018. Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Frontiers in Marine Science: https://doi.org/10.3389/fmars.2018.00319.