By Hadley Robinson, undergraduate student, OSU College of Earth, Ocean, and Atmospheric Sciences and School of Language, Culture, and Society
My name is Hadley Robinson and I am a sophomore undergraduate at OSU, double majoring in Environmental Science and Spanish. This summer, I had the privilege of working with Rachel on her PhD research project involving bomb calorimetry, a technique that allows you to quantify the caloric content of organisms like the zooplankton krill.
Hadley preparing the bomb calorimetry machine to run a sample (photo by Rachel Kaplan).
Prior to this internship, I had never worked in a lab before, and as an environmental science major, I had no previous exposure to oceanography. The connection that Rachel made between our labwork and the broader goal of helping decrease whale entanglement events sparked my interest in this project. Our work this summer aimed to process a set of krill samples collected off the coast of Oregon and Washington, so that we could find the number of calories in single krill, and then look at patterns in krill caloric content based on their species, sex, and other characteristics.
We first identified the krill by species and sex (this was my favorite part of the experiment!). I not only loved looking at them under the microscope, but I also loved how it became a collaborative process. We quickly began getting each other’s opinions on whether or not a krill was Euphausia pacifica, Thysanoessa spinifera, male, female, sexless, gravid (carrying eggs), and much more.
Female Thysanoessa spinifera krill (photo by Abby Tomita).
After identification, we weighed and dried the krill, and finally turned them into small pellets that could fit in an instrument called a bomb calorimeter. These pellets were placed individually into in a “bomb cell” that could then be filled with oxygen and receive a shock from a metal wire. When the machine sent an electric pulse through the wire and combusted the krill pellet, the water surrounding the bomb cell warmed very slightly. The instrument measures this minute temperature change and uses it to calculate the amount of energy in the combusted material. With this information, we were able to quantify how many calories each krill sample contained. Eventually, this data could be used to create a seasonal caloric map of the ocean. Assuming that foraging whales seek out regions with calorically dense prey, such a map could play a crucial role in predicting whale distributions.
Working with Rachel taught me how dynamic the world of research really is. There were many variables that we had to control and factor into our process, such as the possibility of high-calorie lipids being lost if the samples became too warm during the identification process, the risk of a dried krill becoming rehumidified if it sat out in the open air, and even the tiny amount of krill powder inevitably lost in the pelletization process. This made me realize that we cannot control everything! Grappling with these realities taught me to think quickly, adapt, and most importantly, realize that it is okay to refine the process of research as it is being conducted.
Intern Abby (left) pressing the krill powder into a pellet and Hadley (right) prepping the bomb (photo by Rachel Kaplan).
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Obtaining enough food is crucial for predators to ensure adequate energy gain for maintenance of vital functions and support for energetically costly life history events (e.g., reproduction). Foraging involves decisions at every step of the process, including prey selection, capture, and consumption, all of which should be as efficient as possible. Making poor foraging decisions can have long-term repercussions on reproductive success and population dynamics (Harris et al. 2007, 2008, Grémillet et al. 2008), and for marine predators that rely on prey that is spatially and temporally dynamic and notoriously patchy (Hyrenbach et al. 2000), these decisions can be especially challenging. Prey abundance and density are frequently used as predictors of marine predator distribution, movement, and foraging effort, with predators often selecting highly abundant or dense prey patches (e.g., Goldbogen et al. 2011, Torres et al. 2020). However, there is increased recognition that prey quality is also an important factor to consider when assessing a predator’s ecology and habitat use (Spitz et al. 2012), and marine predators do show a preference for higher quality prey items (e.g., Haug et al. 2002, Cade et al. 2022). Moreover, negative impacts of low-quality prey on the health and breeding success of some marine mammals (Rosen & Trites 2000, Trites & Donnelly 2003) have been documented. Therefore, examining multiple prey metrics, such as prey quantity and quality, in predator ecology studies is critical.
Figure 1. Site map of the Port Orford TOPAZ/JASPER integrated projects. Blue squares represent the location of the 12 sampling stations within the 2 study sites (site boundaries demarcated with black lines). Brown dot represents the cliff-top observation site where theodolite tracking occurred.
Our integrated TOPAZ/JASPER projects in Port Orford do just this! We collect both prey quantity and quality data from a tandem research kayak, while we track Pacific Coast Feeding Group (PCFG) gray whales from shore. The prey and whale sampling overlap spatially (and often temporally within the same day). This kind of concurrent predator-prey sampling at similar scales is often logistically challenging to achieve, yet because PCFG gray whales have an affinity for nearshore, coastal habitats, it is something we have been able to achieve in Port Orford. Since 2016, a field team comprised of graduate, undergraduate, and high school students has collected data during the month of August to investigate gray whale foraging decisions relative to prey. Every day, a kayak team collects GoPro videos (to assess relative prey abundance; AKA: quantity) and zooplankton samples using a tow net (to assess prey community composition; AKA: quality through caloric content of different species) (Figure 1). At the same time, a cliff team surveys for gray whales from shore and tracks them using a theodolite, which provides us with tracklines of individual whales; We categorize each location of a whale into three broad behavior states (feeding, searching, transiting) based on movement patterns. Over the years, the various students who have participated in the TOPAZ/JASPER projects have written many blog posts, which I encourage you to read here (particularly to get more detailed information about the field methods).
Figure 2. An example daily layer of relative prey abundance (increasing color darkness corresponds with increasing abundance) in one study site with a whale theodolite trackline recorded on the same day overlaid and color-coded by behavioral state.
Several years of data are needed to conduct a robust analysis for our ecological questions about prey choice, but after seven years, we finally had the data and I am excited to share the results, which are due to the many years of hard work from many students! Our recent paper in Marine Ecology Progress Series aimed to determine whether PCFG gray whale foraging decisions are driven by prey quantity (abundance) or quality (caloric content of species) at a scale of 20 m (which is slightly less than 2 adult gray whale body lengths). In this study, we built upon results from my previous Master’s publication, which revealed that there are significant differences in the caloric content between the six common nearshore zooplankton prey species that PCFG gray whales feed on (Hildebrand et al. 2021). Therefore, in this study we addressed the hypothesis that individual whales will select areas where the prey community is dominated by the mysid shrimp Neomysis rayii, since it is significantly higher in caloric content than the other two prey species we identified, Holmesimysis sculpta (a medium quality mysid shrimp species) and Atylus tridens (a low quality amphipod species) (Hildebrand et al. 2021). We used spatial statistics and model to make daily maps of prey abundance and quality that we compared to our whale tracks and behavior from the same day. Please read our paper for the details on our novel methods that produced a dizzying amount of prey layers, which allowed us to tease apart whether gray whales target prey quantity, quality, or a mixture of both when they forage.
Figure 3. Figure shows the probability of gray whale foraging relative to prey abundance (color-coded by prey species). Dark grey vertical line represents the mean threshold for the H. sculpta curves (12.0); light grey vertical lines: minimum (7.2) and maximum (15.3) thresholds for the H. sculpta curves. Inflection points could not be calculated for the N. rayii curves
So, what did we find? The models proved our hypothesis wrong: foraging probability was significantly correlated with the quantity and quality of the mysid H. sculpta, which has significantly lower calories than N. rayii. This result puzzled us, until we started looking at the overall quantity of these two prey types in the study area and realized that the amount of calorically-rich N. rayii never reached a threshold where it was beneficial for gray whales to forage. But, there was a lot of H. sculpta, which likely made for an energetic gain for the whales despite not being as calorically rich as N. rayii. We determined a threshold of H. sculpta relative abundance that is required to initiate the gray whale foraging behavior, and the abundance of N. rayii never came close to this level (Figure 3). Despite not having the highest quality, H. sculpta did have the highest abundance and showed a significant positive relationship with foraging behavior, unlike the other prey items. Interestingly, whales never selected areas dominated by the low-calorie species A. tridens. These results demonstrate trade-off choices by whales for this abundant, medium-quality prey.
To our knowledge, individual baleen whale foraging decisions relative to available prey quantity and quality have not been addressed previously at this very fine-scale. Interestingly, this trade-off between prey quantity and quality has also been detected in humpback whales foraging in Antarctica at depths deeper than where the densest krill patches occur; while the whales are exploiting less dense krill patches, these krill composed of larger, gravid, higher-quality krill (Cade et al. 2022). While it is unclear how baleen whales differentiate between prey species or reproductive stages, several mechanisms have been suggested, including visual and auditory identification (Torres 2017). We assume here that gray whales, and other baleen whale species, can differentiate between prey species. Thus, our results showcase the importance of knowing the quality (such as caloric content) of prey items available to predators to understand their foraging ecology (Spitz et al. 2012).
References
Cade DE, Kahane-Rapport SR, Wallis B, Goldbogen JA, Friedlaender AS (2022) Evidence for size-selective pre- dation by Antarctic humpback whales. Front Mar Sci 9:747788
Goldbogen JA, Calambokidis J, Oleson E, Potvin J, Pyenson ND, Schorr G, Shadwick RE (2011) Mechanics, hydrody- namics and energetics of blue whale lunge feeding: effi- ciency dependence on krill density. J Exp Biol 214:131−146
Grémillet D, Pichegru L, Kuntz G, Woakes AG, Wilkinson S, Crawford RJM, Ryan PG (2008) A junk-food hypothesis for gannets feeding on fishery waste. Proc R Soc B 275: 1149−1156
Harris MP, Beare D, Toresen R, Nøttestad L, and others (2007) A major increase in snake pipefish (Entelurus aequoreus) in northern European seas since 2003: poten- tial implications for seabird breeding success. Mar Biol 151:973−983
Harris MP, Newell M, Daunt F, Speakman JR, Wanless S (2008) Snake pipefish Entelurus aequoreus are poor food for seabirds. Ibis 150:413−415
Haug T, Lindstrøm U, Nilssen KT (2002) Variations in minke whale (Balaenoptera acutorostrata) diet and body condi- tion in response to ecosystem changes in the Barents Sea. Sarsia 87:409−422
Hildebrand L, Bernard KS, Torres LG (2021) Do gray whales count calories? Comparing energetic values of gray whale prey across two different feeding grounds in the eastern North Pacific. Front Mar Sci 8:1008
Hyrenbach KD, Forney KA, Dayton PK (2000) Marine pro- tected areas and ocean basin management. Aquat Con- serv 10:437−458
Rosen DAS, Trites AW (2000) Pollock and the decline of Steller sea lions: testing the junk-food hypothesis. Can J Zool 78:1243−1250
Spitz J, Trites AW, Becquet V, Brind’Amour A, Cherel Y, Galois R, Ridoux V (2012) Cost of living dictates what whales, dolphins and porpoises eat: the importance of prey quality on predator foraging strategies. PLOS ONE 7:e50096
Torres LG, Barlow DR, Chandler TE, Burnett JD (2020) Insight into the kinematics of blue whale surface forag- ing through drone observations and prey data. PeerJ 8: e8906
Torres LG (2017) A sense of scale: foraging cetaceans’ use of scale-dependent multimodal sensory systems. Mar Mamm Sci 33:1170−1193
Trites AW, Donnelly CP (2003) The decline of Steller sea lions Eumetopias jubatus in Alaska: a review of the nutri- tional stress hypothesis. Mammal Rev 33:3−28
Marissa Garcia, PhD Student, Cornell University, Department of Natural Resources and the Environment, K. Lisa Yang Center for Conservation Bioacoustics
It was a June morning on the Pacific Ocean, and the R/V Pacific Storm had come to a halt on its journey back to shore. The night before, the Holistic Assessment of Living marine resources off Oregon (HALO) project team had disembarked from Newport and began the long transit to NH 65, a site 65 nautical miles offshore along the Newport Hydrographic line (NH line). Ever since the 1960s, researchers have been conducting oceanographic studies along the NH line; the HALO project seeks to add the biological dimension to these historical data collections.
We were on a mission to recover our first set of Rockhoppers that we had deployed in October 2021, just nine months earlier. The Rockhopper is an underwater passive acoustic recording unit developed by K. Lisa Yang Center for Conservation Bioacoustics at Cornell University. Earlier versions of underwater recorders were optimized to record baleen whales. By contrast, the Rockhopper is designed to record both baleen whales and dolphins on longer and deeper deployments, making it apt for research endeavors such as the HALO project. Three units, deployed at NH 25, 45, and 65, continuously recorded the soundscape of the Oregon waters for six months.In June, we were headed out to sea to recover these three units, collect the acoustic data, and deploy three new units.
Figure 1: The HALO project routinely surveys the trackline spanning between NH 25 and NH 65 on the NH line. Credit: Leigh Torres.
With the ship paused, our first task was to recover the Rockhopper we had deployed at NH 65. This Rockhopper deployment at NH 65 was our deepest successful deployment to date, moored at nearly 3,000 m.
So, how does one recover an underwater recording unit that is nearly 3,000 m below the surface? When the Rockhopper was deployed, it was anchored to the seafloor with a 60 kg cast iron anchor. It seems improbable that an underwater recording unit — anchored by such heavy weights — can eventually rise to the surface, but this capability is made possible through a piece of attached equipment called the acoustic release. By sending a signal of a numbered code from a box on the boat deck through the water column to the Rockhopper, the bottom of the acoustic release will begin to spin and detach from the weights. The weights are then left on the seafloor, as the Rockhopper slowly rises to the surface, now unhindered by the weights. Since these weights are composed of iron, they will naturally erode, without additional pollution contributed to the ecosystem. At NH 65, it took approximately an hour for the Rockhopper to reach the surface.
Figure 2: A diagram of the Rockhopper mooring. Of particular importance to this blog post is the acoustic release (Edgtech PORT MFE release) and the 60 kg anchor (Source: Klinck et al., 2020).
The next challenge is finding the Rockhopper bobbing amongst the waves in the vast ocean — much like searching for a needle in a haystack. The color of the Rockhopper helps aid in this quest. It’s imperative anyone out on the boat deck wears a life jacket; if someone goes overboard while wearing a life-jacket, on-board passengers can more easily spot a bright orange spot in an otherwise blue-green ocean with white caps. The design of the Rockhopper functions similarly; the unit is contained in a bright orange hard hat, helping researchers on-board to more easily spot the device, especially in an ocean often characterized by high sea state.
We also use a Yagi antenna to listen for the VHF (Very High Frequency) signal of the recovery gear, a signal the Rockhopper emits once it’s surfaced above the waterline. Pointing the antenna toward the ocean, we can detect the signal, which will become stronger when we point antenna in the direction of the Rockhopper; once we hear that strong signal, we can recommend to the boat captain to start moving the vessel in that direction.
Figure 3: Derek Jaskula, a member of the field operations team at the K. Lisa Yang Center for Conservation Bioacoustics, points the Yagi antenna to detect the signal from the surfaced Rockhopper. Credit: Marissa Garcia.
At that point, all eyes are on the water, binoculars scanning the horizon for the orange. All ears are eager for the exciting news: “I see the Rockhopper!”
Once that announcement is made, the vessel carefully inches toward the Rockhopper until it is just next to the vessel’s side. Using a hook, the Rockhopper is pulled upward and back onto the deck.
What we weren’t expecting, however, during this recovery was to have our boat surrounded by two dolphin species: Pacific white-sided dolphins (Lagenorhynchus obliquidens) and Northern right whale dolphins (Lissodelphis borealis).
One HALO team member shouted, “I see Northern right whale dolphins!”
Charged with excitement, I quickly climbed up the crow’s nest to get a birds-eye look at the ocean bubbling around us with surfacing dolphins. Surely enough, I spotted the characteristic stripe of the Pacific-white sided dolphins zooming beneath the surface, in streaks of white. But what I was even more eager to see were the Northern right whale dolphins, flipping themselves out of the water, unveiling their bright white undersides. Because they lack dorsal fins, we on-board colloquially refer to Northern right whale dolphins as “sea slugs” to describe their appearance as they surface.
Figure 4: The Northern right whale dolphin (Lissodelphis borealis) surfaces during a HALO cruise. Source: HALO Project Team Member. Permit: NOAA/NMFS permit #21678.
In my analysis of the HALO project data for my PhD, I am interested in using acoustics to describe how the distribution of dolphins and toothed whales in Oregon waters varies across space and time. One species I am especially fascinated to study in-depth is the Northern right whale dolphin. To my knowledge, only three papers to date have attempted to describe their acoustics — two of which were published in the 1970s, and the most recent of which was published fifteen years ago (Fish & Turl, 1976; Leatherwood & Walker, 1979; Rankin et al., 2007).
Leatherwood & Walker (1979) proposed that Northern right whale dolphins produced two categories of whistles: a high frequency whistle that turned into burst-pulse vocalizations, and low frequency whistles. However, Rankin et al. (2007) proposed that Northern right whale dolphins may not actually produce whistles, based on two lines of evidence. First, Rankin et al. (2007) combined visual and acoustic survey, and all vocalizations recorded were localized via beamforming methods to verify that recorded vocalizations were produced by the visually observed dolphins. The visual surveying component is key to validating the vocalizations of the species, which also hints that the HALO project’s multi-surveying approach (acoustic and visual) could help arrive at similar results. Second, the Rankin et al. (2007) explored the taxonomy of the Northern right whale dolphin to verify which vocalizations the species is likely to produce based on the vocal repertoire of its close relatives. The right whale dolphin is closely related to dolphins in the genus Lagenorhynchus — which includes white-sided dolphins — and Cephalorhynchus — which includes Hector’s dolphin. The vocal repertoire of these relatives don’t produce whistles, and instead predominantly produced pulsed sounds or clicks (Dawson, 1991; Herman & Tavolga, 1980). Northern right whale dolphins primarily produce echolocation clicks trains and burst-pulses. Although Rankin et al. (2007) claims that the Northern right whale dolphin does not produce whistles, stereotyped burst-pulse series may be unique to individuals, just as dolphin species use stereotyped signature whistles, or they may be relationally shared just as discrete calls of killer whales are.
Figure 5: The Northern right whale dolphin (Lissodelphis borealis) produces burst-pulses. There exists variation in series of burst-pulses. The units marked by (a) and (b) ultimately get replaced by the unit marked by (c). (Source: Rankin et al., 2007).
We have just finished processing the first round of acoustic data for the HALO project, and it is ready now for analysis. Already previewing an hour of data on the Rockhopper by NH 25, we identified potential Northern right whale dolphin recordings . So far, we have only visually observed Northern right whale dolphins nearby Rockhopper units placed at sites NH 65 and NH 45, so it was surprising to acoustically detect this species on the most inshore unit at NH 25. I look forward to demystifying the mystery of Northern right whale dolphin vocalizations as our research on the HALO project continues!
Figure6: Potential Northern right whale dolphin vocalizations recorded at the Rockhopper deployed at NH 25.
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Fish, J. F. & Turl, C. W. (1976). Acoustic Source Levels of Four Species of Small Whales.
Herman, L. M., and Tavolga, W. N. (1980). “The communication systems of cetaceans,” in Cetacean behavior: Mechanisms and functions, edited by L. M. Herman (Wiley, New York), 149–209.
Klinck, H., Winiarski, D., Mack, R. C., Tessaglia-Hymes, C. T., Ponirakis, D. W., Dugan, P. J., Jones, C., & Matsumoto, H. (2020). The Rockhopper: a compact and extensible marine autonomous passive acoustic recording system. Global Oceans 2020: Singapore – U.S. Gulf Coast, 1–7. https://doi.org/10.1109/IEEECONF38699.2020.9388970
Leatherwood, S., and Walker, W. A. (1979). “The northern right whale dolphin Lissodelphis borealis Peale in the eastern North Pacific,” in Behavior of marine animals, Vol. 3: Cetaceans, edited by H. E. Winn and B. L. Olla (Plenum, New York), 85–141.
Rankin, S., Oswald, J., Barlow, J., & Lammers, M. (2007). Patterned burst-pulse vocalizations of the northern right whale dolphin, Lissodelphis borealis. The Journal of the Acoustical Society of America, 121(2), 1213–1218. https://doi.org/10.1121/1.2404919
Hello! My name is Kate Colson and I am a master’s student at the University of British Columbia, co-supervised by Dr. Andrew Trites of the Marine Mammal Research Unit and Dr. Leigh Torres of the GEMM Lab. As part of my thesis work, I have had the opportunity to spend the summer field season with Leigh and the GEMM Lab team.
For my master’s I am studying the foraging energetics of Pacific Coast Feeding Group (PCFG) gray whales as part of the much larger Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project. Quantifying the energy expenditure of PCFG gray whales during foraging can help establish a baseline for how disturbance impacts the ability of this unique population to meet their energy needs. Additionally, determining how many calories are burned during different PCFG foraging behaviors might help explain why some gray whales are in better body condition than others.
To understand how much energy different PCFG foraging behaviors cost, I am using data from suction cup tags we have temporarily applied on PCFG gray whales (Figure 1). You can read more about the why the GEMM Lab started using these tags in an earlier blog here. What I want to talk about in this blog is how exactly we can use this tag data to estimate energy expenditure of PCFG gray whales.
Figure 1. The famous “Scarlett” with a suction cup tag just attached using a carbon fiber pole (seen on far right). This minimally invasive tag has many data sensors, all of which sample at high frequencies, that can allow for an estimation of energy expenditure for different gray whale behaviors. Source: GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678
The suction cups tags used in this project have many data sensors that are useful for describing the movement of the tagged whale including accelerometers, magnetometers, gyroscopes, and pressure sensors, and all are sampling at high frequencies. For example, the accelerometer is taking 400 measurements per second! The accelerometer, magnetometer, and gyroscope take measurements in 3 dimensions along the X, Y, and Z-axes. The whale’s movement around the X-axis indicates roll (if the whale is swimming on its side), while movement around the Y-axis indicates pitch (if the whales head is oriented towards the surface or the sea floor). Changes in the whale’s movement around the Z-axis indicates if the whale is changing its swimming direction. Together, all of these sensors can describe the dive profile, body orientation, fluking behavior, and fine-scale body movements of the animal down to the second (Figure 2). This allows for the behavior of the tagged whale to be specifically described for the entirety of the tag deployment.
Figure 2.An example of what the tag sensor data looks like. The top panels show the depth of the animal and can be used to determine the diving behavior of the whale. The middle panels show the body roll of the whale (the X axis) —a roll value close to 0 means the whale is swimming “normally” with no rotation to either side, while a higher roll value means the whale is positioned on its side. The bottom panels show the fluking behavior of the animal: each spike is the whale using its tail to propel itself through the water, with higher spikes indicating a stronger fluke stroke. Source: GEMM Lab, NMFS permit no. 21678
Although these suction cup tags are a great advancement in collecting fine-scale data, they do not have a sensor that actually measures the whale’s metabolism, or rate of calories burned by the whale. Thus, to use this fine-scale tag data as an estimate for energy expenditure, a summary metric must be calculated from the data and used as a proxy. The most common metric found in the literature is Overall Dynamic Body Acceleration (ODBA) and many papers have been published discussing the pros and cons of using ODBA as a proxy for energy expenditure (Brown et al., 2013; Gleiss et al., 2011; Halsey, 2017; Halsey et al., 2011; Wilson et al., 2020). The theory behind ODBA is that because an animal’s metabolic rate is primarily comprised of movement costs, then measuring the acceleration of the body is an effective way of determining energy expenditure. This theory might seem very abstract, but if you have ever worn a Fitbit or similar fitness tracking device to estimate how many calories you’ve burned during a workout, the same principle applies. Those fitness devices use accelerometers and other sensors, to measure the movement of your limbs and produce estimates of energy used.
So now that we’ve established that the goal of my research is to essentially use these suction cup tags as Fitbits for PCFG gray whales, let’s look at how accelerometry data has been used to detect foraging behavior in large whales so far. Many accelerometry tagging studies have used rorquals as a focal species (see Shadwick et al. (2019) for a review). Well-known rorqual species include humpback, fin, and blue whales. These species forage by using lunges to bulk feed on dense prey patches in the water column. Foraging lunges are indicated by isolated periods of high acceleration that are easily detectable in the tag data (Figure 3; Cade et al., 2016; Izadi et al., 2022).
Figure 3.Top image: A foraging blue whale performing a surface lunge (Photo credit: GEMM Lab). Note the dense aggregation of krill in the whale’s mouth. Bottom image: The signature acceleration signal for lunge feeding (adapted from Izadi et al., 2022). Each color represents one of the 3D axes of whale movement. The discrete periods of high acceleration represent lunges.
However, gray whales feed very differently from rorquals. Gray whales primarily suction feed on the benthos, using their head to dig into the sediment and filter prey out of the mud using their baleen. Yet, PCFG gray whales often perform many other foraging behaviors such as headstanding and side-swimming (Torres et al., 2018). Additionally, PCFG gray whales tend to feed in water depths that are often shallower than their body length. This shallow depth makes it difficult to isolate signals of foraging in the accelerometry data from random variation in the data and separate the tag data into periods of foraging behaviors (Figure 4).
Figure 4.Top image: A foraging PCFG gray whale rolls on its side to feed on mysid prey. Bottom image: The graph shows the accelerometry data from our suction cup tags that can be used to calculate Overall Dynamic Body Acceleration (ODBA) as a way to estimate energy expenditure. Each color represents a different axis in the 3D motion of the whale. The X-axis is the horizontal axis shows forward and backward movement of the whale, the Y-axis shows the side-to-side movement of the whale, and the Z-axis shows the up-down motion of the whale. Note how there are no clear periods of high acceleration in all 3 axes simultaneously to indicate different foraging behaviors like is apparent during lunges of rorqual whales. However, there is a pattern showing that when acceleration in the Z-axis (blue line) is positive, the X- and Y-axes (red and green lines) are negative. Source: GEMM Lab; NMSF permit no. 21678
But there is still hope! Thanks to the GEMM Lab’s previous work describing the foraging behavior of the PCFG sub-group using drone footage, and the video footage available from the suction cup tags deployed on PCFG gray whales, the body orientation calculated from the tag data can be a useful indication of foraging. Specifically, high body roll is apparent in many foraging behaviors known to be used by the PCFG, and when the tag data indicates that the PCFG gray whale is rolled onto its sides, lots of sediment (and sometimes even swarms of mysid prey) is seen in the tag video footage. Therefore, I am busy isolating these high roll events in the collected tag data to identify specific foraging events.
My next steps after isolating all the roll events will be to use other variables such as duration of the roll event and body pitch (i.e., if the whales head is angled down), to define different foraging behaviors present in the tag data. Then, I will use the accelerometry data to quantify the energetic cost of performing these behaviors, perhaps using ODBA. Hopefully when I visit the GEMM Lab again next summer, I will be ready to share which foraging behavior leads to PCFG gray whales burning the most calories!
References:
Brown, D. D., Kays, R., Wikelski, M., Wilson, R., & Klimley, A. P. (2013). Observing the unwatchable through acceleration logging of animal behavior. Animal Biotelemetry, 1(1), 1–16. https://doi.org/10.1186/2050-3385-1-20
Cade, D. E., Friedlaender, A. S., Calambokidis, J., & Goldbogen, J. A. (2016). Kinematic diversity in rorqual whale feeding mechanisms. Current Biology, 26(19), 2617–2624. https://doi.org/10.1016/j.cub.2016.07.037
Gleiss, A. C., Wilson, R. P., & Shepard, E. L. C. (2011). Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure. Methods in Ecology and Evolution, 2(1), 23–33. https://doi.org/10.1111/j.2041-210X.2010.00057.x
Halsey, L. G. (2017). Relationships grow with time: A note of caution about energy expenditure-proxy correlations, focussing on accelerometry as an example. Functional Ecology, 31(6), 1176–1183. https://doi.org/10.1111/1365-2435.12822
Halsey, L. G., Shepard, E. L. C., & Wilson, R. P. (2011). Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comparative Biochemistry and Physiology – A Molecular and Integrative Physiology, 158(3), 305–314. https://doi.org/10.1016/j.cbpa.2010.09.002
Izadi, S., Aguilar de Soto, N., Constantine, R., & Johnson, M. (2022). Feeding tactics of resident Bryde’s whales in New Zealand. Marine Mammal Science, 1–14. https://doi.org/10.1111/mms.12918
Shadwick, R. E., Potvin, J., & Goldbogen, J. A. (2019). Lunge feeding in rorqual whales. Physiology, 34, 409–418. https://doi.org/10.1152/physiol.00010.2019
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, 1–14. https://doi.org/10.3389/fmars.2018.00319
Wilson, R. P., Börger, L., Holton, M. D., Scantlebury, D. M., Gómez-Laich, A., Quintana, F., Rosell, F., Graf, P. M., Williams, H., Gunner, R., Hopkins, L., Marks, N., Geraldi, N. R., Duarte, C. M., Scott, R., Strano, M. S., Robotka, H., Eizaguirre, C., Fahlman, A., & Shepard, E. L. C. (2020). Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. Journal of Animal Ecology, 89(1), 161–172. https://doi.org/10.1111/1365-2656.13040
Celest Sorrentino, University of California, Santa Barbara, Department of Ecological, Evolution, and Marine Biology, GEMM Lab NSF REU intern
Are you thinking “Did anyone proofread this blog beforehand? Don’t they know how to spell SLEEP?” I completely understand this concern, but not to fear: the spelling of SLEAP is intentional! We’ll address that clickbait in just a moment.
My name is Celest Sorrentino, a first-generation Latina undergrad who leaped at the opportunity to depart from the beaches of Santa Barbara, California to misty Newport, Oregon to learn and grow as a scientist under the influential guidance of Clara Bird, Dr. Leigh Torres and the powerhouse otherwise known as the GEMM lab. As a recent NSF REU (Research Experience for Undergraduates) intern in the GEMM Lab at Oregon State University, I am thrilled to have the chance to finally let you in on the project Clara, Leigh and I have been working on all summer. Ready for this?
Our project uses a deep-learning platform called SLEAP A.I. ( https://sleap.ai/) that can predict and track multiple animals in video to track gray whale mother calf pairs in drone footage. We also took this project a step further and explored how the distance between a gray whale mother and her calf, a proxy for calf independence, varied throughout the season and by different calf characteristics.
In this story, we’ve got a little bit for everyone: the dynamic duo of computer vision and machine learning for my data scientist friends, and ecological inquest for my cetacean researcher friends.
About the Author
Before we begin, I’d like to share that I am not a data scientist. I’ve only ever taken one coding class. I also do not have years of gray whale expertise under my belt (not yet at least!). I’m entering my 5th year at University of California, Santa Barbara as a double major in Ecology and Evolution (BS) as well as Italian Studies (BA). I am sharing this information to convey the feasibility of learning how to use machine-learning as a solution to streamline the laborious task of video analysis, which would permit more time towards answering your own ecological question, as we did here.
Essential Background
Hundreds of Hours of Drone footage
Since 2016, the GEMM Lab has been collecting drone footage of gray whales off the Oregon Coast to observe gray whale behavior in more detail (Torres et al. 2018). Drones have been shown to increase observational time of gray whales by a three-fold (Torres et al. 2018), including the opportunity to revisit the video with fresh eyes at any time one pleases. The GEMM Lab has flow over 500 flights in the past 6 years, including limited footage of gray whale mother-calf pairs. Little is known about gray whale mother-calf dynamics and even less about factors that influence calf development. As we cannot interview hundreds of gray-whale mother-calf pairs to develop a baseline for this information, we explore potential proxies for calf development instead (similar to how developmental benchmarks are used for human growth).
Distance and Development
During our own life journey, each of us became less and less dependent on our parents to survive on our own. Formulating our first words so that we can talk for ourselves, cracking an egg for our parents so that we can one day cook for ourselves, or even letting go of their hand when crossing the street. For humans, we spend many years with our kin preparing for these moments, but gray whale mother-calf pairs only have a few months after birth until they separate. Gray whale calves are born on their wintering grounds in Baja California, Mexico (around February), migrate north with their mothers to the foraging grounds, and are then weaned during the foraging season (we think around August). This short time with their mother means that they have to become independent pretty quickly (about 6 months!).
Distance between mother and calf can be considered a measure of independence because we would expect increased distance between the pair as calf independence increases. In a study by Nielson et al (2019), distance between Southern Right Whale mother-calf pairs was found to increase as the calf grew, indicating that it can serve as a good proxy for independence. The moment a mother-calf pair separates has not been documented, but the GEMM lab has footage of calves during the foraging season pre-weaning that can be used to investigate this process. However, video analysis is no easy feat: video analysis can range from post-processing, diligent evaluation, and video documentation (Torres et al. 2018). Although the use of UAS has become a popular method for many researchers, the extensive time required for video analysis is a limitation. As mentioned in Clara’s blog, the choice to pursue different avenues to streamline this process, such as automation through machine learning, is highly dependent on the purpose and the kind of questions a project intends to answer.
SLEAP A.I.
In a world where modern technology is constantly evolving to cater towards making everyday tasks easier, machine learning leads the charge with its ability for a machine to perform human tasks. Deep learning is a subset of machine learning in which the model learns and adapts the ability to perform a task given a dataset. SLEAP (Social LEAP Estimation of Animal Poses) A.I. is an open-source deep-learning framework created to be able to track multiple subjects, specifically animals, throughout a variety of environmental conditions and social dynamics. In previous cases, SLEAP has tracked animals with distinct morphologies and conditions such as mice interactions, fruit flies engaging in courtship, and bee behavior in a petri dish (Pereira 2020). While these studies show that SLEAP could help make video analysis more efficient, these experiments were all conducted on small animals and in controlled environments. However, large megafauna, such as gray whales, cannot be cultivated and observed in a controlled Petri dish. Could SLEAP learn and adapt to predict and track gray whales in an uncontrolled environment, where conditions are never the same (ocean visibility, sunlight, obstructions)?
Methods
In order to establish a model within SLEAP, we split our mother-calf drone video dataset into training (n=9) and unseen/testing (n=3) videos. Training involves teaching the model to recognize gray whales, and necessitated me to label every four frames using the following labels (anatomical features): rostrum, blowhole, dorsal, dorsal-knuckle, and tail (Fig. 1). Once SLEAP was trained and able to successfully detect gray whales, we ran the model on unseen video. The purpose of using unseen video was to evaluate whether the model could adapt and perform on video it had never seen before, eliminating the need for a labeler to retrain it.
We then extracted the pixel coordinates for the mom and calf, calculated the distance between their respective dorsal knuckles, and converted the distance to meters using photogrammetry (see KC’s blog for a great explanation of these methods). The distance between each pair was then summarized on a daily scale as the average distance and the standard deviation. Standard deviation was explored to understand how variable the distance between mother-calf pair was throughout the day. We then looked at how distance and the standard deviation of distance varied by day of year, calf Total Length (TL), and calf Body Area Index (BAI; a measure of body condition). We hypothesized that these three metrics may be drivers of calf independence (i.e., as the calf gets longer or fatter it becomes more independent from its mother).
Fig 1. Example of a labelled frame from SLEAP, highlighting labels: rostrum, blowhole, dorsal, dorsal-knuckle, and tail.
Results
SLEAP A.I. was able to successfully detect and track gray whale mother-calf pairs across all videos (that’s a total of 1318 frames!). When evaluating how the average distance changed across Day of Year, calf Total length, and calf BAI, the plots did not demonstrate the positive relationship we anticipated (Fig 2A). However, when evaluating the standard deviation of distance across Day of Year, calf Total Length, and calf BAI, we did notice that there does appear to be an increase in variability of distance with an increase in Day of Year and calf Total length (Fig 2B)
Fig 2A: Relationship between average distance(m) between each mother and calf (colors represent different named calves) relative to Day of Year, Total length of calf, and BAI of calf.
Fig 2B: Relationship between standard deviation of distance(m) between each mother and calf (colors represent different named calves) relative to Day of Year, Total length of calf, and BAI of calf.
Concluding thoughts
These results are monumental! We demonstrated the feasibility to use AI to create a model that can track gray whale pairs in drone footage, which is a fantastic tool that can be applied to updated datasets in the future. As more footage of gray whale mother-calf pairs are collected, this video can be quickly uploaded to SLEAP for model evaluation, predictions can be exported, and results subsequently included in the distance analysis to update our plots and increase our understanding. Our data currently provide a preliminary understanding of how the distance between mother-calf pairs changes with Day of Year, Total length, and BAI, but we are now able to continue updating our dataset as we collect more drone footage.
I suppose you can say I did mislead you a bit with my title, as I have lost some SLEEP recently. But, not over video analysis per say but rather in the form of inspiration. Inspiration toward expanding my understanding of machine learning so that it can be applied toward answering pressing ecological questions. This project has only propelled me to dig my heels in and investigate further the potential of machine learning to analyze dense datasets for huge knowledge gains.
Fig 3A: Snapshot of Celest working in SLEAP GUI.
Acknowledgements
This project was made possible in partnership by the continuous support by Clara Bird, Dr. Leigh Torres, KC Bierlich, and the entire GEMM Lab!
References
Nielsen, M., Sprogis, K., Bejder, L., Madsen, P., & Christiansen, F. (2019). Behavioural development in southern right whale calves. Marine Ecology Progress Series, 629, 219–234. https://doi.org/10.3354/meps13125
Pereira, Talmo D., Nathaniel Tabris, Junyu Li, Shruthi Ravindranath, Eleni S. Papadoyannis, Z. Yan Wang, David M. Turner, et al. “SLEAP: Multi-Animal Pose Tracking.” Preprint. Animal Behavior and Cognition, September 2, 2020. https://doi.org/10.1101/2020.08.31.276246.
Torres, 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. https://doi.org/10.3389/fmars.2018.00319
Allison Dawn, GEMM Lab Master’s student, OSU Department of Fisheries, Wildlife and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab
The 8th year of Port Orford Gray Whale Foraging Ecology Project (TOPAZ/JASPER) has come to an end and it feels truly bittersweet. Last Friday, the team hosted our annual community presentation to close out the project and I was filled with pride to see them confidently convey all they learned over this summer to an audience of family, friends, and community members.
Figure 1: Team B.W.E poses for the annual team photo after the community presentation alongside Tom Calvanese (field station manager) and Lisa Hildebrand (previous project lead).
I am amazed by all that you can accomplish in one summer, especially with an enthusiastic and adaptable team. I’ve compiled a “by the numbers” table (Fig. 2) that summarizes our hard work this season.
Figure 2: Port Orford Gray Whale Forage Ecology (GWFE) field season 2022 by the numbers.
Every Spring, the GEMM lab works diligently to hire a solid team of students for this project, which just finished its 8th consecutive year. These students are initially total strangers who come together to live and work at the Port Orford field station on a project that is as physically and mentally tasking as it is rewarding. Although attention to all the daily details is critical, without a genuine desire to form strong connections and learn from each other – the real “glue” for teamwork – this project would not be as successful as it has been. Like the teams before them, team Big Whale Energy (B.W.E.) started off with little to no gray whale knowledge, sea kayaking experience, zooplankton ID, theodolite operation, or other skills that this project demands. The learning curve required of these students in such a short time is steep, but each year these bright, young scientists prove that with patience, determination, and a positive mindset you can gain not only valuable skills but lifelong connections.
I also experienced a learning curve as this was my first year leading the project solo. While Leigh and Lisa trained me well last year, and were always a phone call away, there are certain skills that can only truly be honed with experience, many of which must be learned through the inevitable curve balls each new field season brings. During the six week project, Team B.W.E. grew as individuals and as a team as we encountered every challenge with a positive mindset and creative adaptation – from learning new knots to secure our downrigger line, to creating new songs while patiently watching for whales. I know I speak for all of us when I say we are so grateful for everything this 2022 field season experience has taught us about both the process of scientific research and ourselves.
During our community presentation, Leigh wonderfully conveyed how informative and exciting long term data sets can be, especially because 8 years is long enough for us to begin to observe cycles. We have been able to observe cycles in both the ecological changes in Port Orford and in the succession of students who have taken part in the project. Last year, the ecological habitat suitability seemed to have reached a new low, while this year we have seen more kelp and an uptick of whale activity as compared to 2021. We are hopeful this change is indicative of an ecosystem recovery. The cycle of returning project leads and previous interns (both virtual and in person) allows for a meaningful interchange of wisdom, memories, and excitement for the future of this project.
Figure 3: Mosaic of memories for Team B.W.E.
Thank you Team B.W.E. for helping me grow as a leader, contributing to the GEMM lab legacy, and making the 8th year of this project a great success.
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By Charlie Ells, incoming freshman, Environmental Science major, College of Arts & Sciences at University of Oregon, GEMM Lab intern
Hi! My name is Charlie Ells and I’m an intern at the Port Orford field station. I’m part of the 8th Gray Whale Foraging Ecology Research Team, named this year Team B.W.E (Big Whale Energy!)
The inspiration for our team name originated when the cliff team first spotted a whale named Buttons. Luke, another intern, saw Buttons through the Theodolite and said that he had “Big Whale Energy.” Luke was correct. Pictured below is Zoe, a fellow intern whose blog you might have read a couple weeks ago, and Buttons, an adult gray whale who surprised us both when he appeared out of nowhere behind us while in the kayak. The image doesn’t do him justice, but Buttons is absolutely awesome (and I mean that in the literal definition of the word). Buttons is huge; when he surfaces, it is almost like he is showing off. Buttons pulls a lot more of his body out of the water than seems necessary. His blows are deafening, sounding like an 18-wheeler’s brakes applied with full force. He often exhibits a behavior called ‘sharking’, which is when a whale turns on its side on the surface, bringing a part of their fluke out of the water (see GEMM lab video of sharking behavior). The behavior helps gray whales feed in shallow areas, and was named so because someone thought the whale’s fluke looked like a shark’s fin.
Figure 2: Kayak team gets a surprise visit by Buttons. No craft, whether it has a motor or not, should get this close to a whale. See this GEMM lab website with vessel guidelines and more information. In this case, we had seen Buttons at a safe distance (>100 yards) moments before, and moved in the opposite direction we had seen him going to avoid disturbing him. But Buttons had other plans.
Not only does B.W.E apply to the large whale that Buttons is, but it also encapsulates how much more whale activity we’ve seen this year compared to last year. So far, we have over 17 hours of whale observation time this season, which is 15 hours more than the team had in total last year. We’ve ID’d three unique whales using our study area, learned about some of them on the IndividuWhale website, and collected some great behavior data. Meet Rugged, the first whale I ever photographed. She’s young, and a bit smaller than the other adults, but she’s full of personality (to the extent that we can observe a whale’s personality, anyway).
Figure 3: Rugged. Photo taken from the beach.
Figure 4: Rugged shows us her fluke as she dives behind the jetty.
Rugged likes to feed for a relatively long time; while some whales have searched and left quickly, she often hangs around the foraging grounds for hours. When Rugged travels, she tends to fluke, meaning she brings the end of her tail out of the water (Figure 4), pretty often. She sometimes blows three times in a row, and spends more time at the surface than others typically do. Look closely at Figure 3 and you can see a propeller scar, which is sadly new this year but at least these identification marks help us spot her more easily. So far, Rugged has been a regular customer at this season’s Mill Rocks buffet, where she feasts on a variety of zooplankton. We’ve seen her the most frequently of any whale this season, and when she shows up, she can be counted on to stick around and offer us the opportunity to collect a lot of nice whale behavior data.
My favorite part of the TOPAZ project data collection efforts are the photographs of whales I’ve captured. The camera is my favorite piece of our gear, and since using it so much this summer I’ve been seriously considering investing in one for myself. For any photography nerds, the camera is a Canon EOS 90D with a 400mm telephoto lens and auto-stabilization. Using this camera on challenging subjects, like a whale that can travel over a kilometer in a couple minutes, has taught me a lot about photography. I’ve learned a lot of situation-specific tricks as well as some general knowledge I’d like to share. I found that using such a long lens can introduce enough camera shake to ruin a shot. To prevent this, simply cranking the shutter speed up does wonders. In the main menu, I change the shutter speed to something like 1/1000, which means the shutter is open for 1/1000th of a second, minimizing the effect of the shake. I’ve also discovered that with a subject that is only in frame for a second (such as a whale), there just isn’t enough time to manually focus the camera before it’s gone. There are two solutions here: rely on auto-focus, which is fine with this camera, but might not be sufficient on others, or use manual focus before your subject is in frame. This second trick has helped me get much better whale pictures than when I first started this internship, and I use it all the time now.
Capturing these pictures of the whales is a thrilling process. First, the wait. Second, the moment of panicked excitement when someone spots a blow. Third, the breathless callouts of where the whale is and the direction it’s heading. Fourth, the mad scramble to get the whale in frame, in focus, and open the shutter in the few seconds before it returns to the depths. This last step is tough — I end up with more photos of empty water, rocks I mistake for the whale, and blurry nothingness than usable ID photos. But when I do end up with a good picture, it’s a great feeling.
Figure 5: My best picture yet. This is Rugged, showing off what my teammates have dubbed “RainBlow.”
Figure 6: Dotty, the third whale we ID’d this season. I hustled to the Battle Rock shoreline to get a better angle of this whale, as the sun was causing too much glare from the Cliff site to obtain a good ID photo.
This internship has affirmed my favorite part of conservation, which is the blending of science and art to inform and inspire. One of the things that first got me into science, besides my excellent science teachers, was watching YouTube videos. People like Mark Rober, Steve Mould, Veritasium, and Physics Girl take the scientific process and turn it into creative, accessible, and understandable videos. These artists and scientists have gifted me so much inspiration, which I personally think is one of the most valuable things you can be given. Inspiration can propel you forward, motivate you, and help you take those first steps towards your goal. This internship has propelled my first steps (via kayak strokes) toward my career goals. I’m looking forward to taking these lessons with me as I go off to U of O to study Environmental science. I created the video below in an attempt to capture our work, show off some highlights, and give people the same inspiration that I was given. I hope you enjoy it. This is Team Big Whale Energy, signing off!
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By Luke Donaldson, incoming OSU freshman, Department of Forestry, GEMM Lab intern
When I was a toddler, my grandma took me to the Face Rock viewpoint in Bandon, Oregon during summer to look for migrating whales. Even though we never spotted a blow or fluke, it was a great memory, one that helped spark my ever-growing interest in biology and the environment.
As soon as I was old enough, I volunteered to help scientists at the South Slough National Estuarine Research Reserve (SSNERR) work on a variety of research projects, including European green crab (Carcinus maenas) removal in the Coos Estuary. The removal process of the invasive European green crabs from the Coos estuary is similar to current culling efforts of purple sea urchins (Strongylocentrotus purpuratus) by the Oregon Kelp Alliance (ORKA) of here in Port Orford. Both efforts hope to reduce the negative ecological impacts caused by a lack of natural predators on the Oregon coast. Without natural predators, green crabs and sea urchins dominate food sources and reproduce exponentially in their respective ecosystems. In Port Orford, the decline in population of several species of sea stars since 2013 has led to an abundance of sea urchins, an estimated 350 million alone at Orford Reef (Sommer & Kastelnik, 2021). Read Lisa Hildebrand’s blog for more information about how the cycles of potential phase shifts between sea urchins and kelp impact both the ecology and economics along the Oregon coast. In addition to collecting long term data on gray whale activity and zooplankton abundance, the TOPAZ/JASPER projects have accumulated a yearly inventory of bull kelp canopies in order to record biogeographic changes and monitor areas of concern related to urchin abundance.
After multiple opportunities to hone my skills on the theodolite during our two training weeks, I spent several hours at our cliff observation site helping map kelp beds (read more about the theodolite and its purposes in Nichola’s recent blog). Not only does operating the theodolite require practice and careful precision, but weather also poses a challenge to mapping the surface expression of kelp effectively. Sunlight itself strains the eye and causes a glare in the theodolite objective lens. Wind gusts, tidal changes and swell can all distort kelp patches, so consistent timing is essential. Some areas of Tichenor Cove and Mill Rocks are obstructed by sea stacks, vegetation, and man-made structures, so for these areas we use a Garmin GPS to mark waypoints via kayak to create the perimeter of each kelp patch. With over 1,500 fixes and 120 kelp patches mapped, it was our first formal assessment of kelp this year within our two study areas, Tichenor Cove and Mill Rocks (Figure 1). While kelp cover in Tichenor appears to have increased a little since 2021, the kelp in Mill Rocks shows a great recovery.
Figure 1. Study site map with kelp cover from 2021 and 2022 shown in green. The gray areas represent land and each kayak sampling station is denoted within a bounding box. Map by A. Dawn
Not only is the kelp different between study years and areas, but our zooplankton catches are also showing signs of recovery. The large kelp beds of Mill Rocks support a sustained population of zooplankton, unlike in 2021 or in Tichenor Cove. Last year’s GEMM lab intern Damian Amerman-Smith noted the decline of kelp also appeared to correlate with decreased zooplankton abundance and gray whale foraging activity in Port Orford. However, not only does Mill Rocks yield higher amounts of zooplankton this year, but their average size, especially the mysid Holmesmysis sculpta, appears larger this year than in 2021.
Consequently, this increase in food availability may be the cause of our higher frequency of gray whale observations in Mill Rocks this year. Despite the continued abundance of sea urchins in our study areas, I am optimistic that the current amount of kelp compared to past year’s data might be indicating a recovery of the ecosystem (Figure 2).
Figure 2. A comparison between Mill Rocks Station 17 in 2021 (left) and 2022 (right). Observe the difference in kelp and mysid shrimp abundance.
The first gray whale that we observed this year was consistently foraging within the kelp beds of Mill Rocks, which was very encouraging for our team. Through this internship I have learned many interesting things about kelp, including how kelp supplies more than just primary productivity, but also a wide range of services directly and indirectly to gray whales. In addition to being a foundation species of Oregon’s coastal ecosystems, bull kelp specifically provides zooplankton with nutrient-rich detritus, protection from predators, and a buffer from strong ocean currents (Schaffer & Feehan, 2020). Kelp provides gray whales not only with habitat for their prey, but keeps them hygienic as well. Gray whales have been observed “kelping”, where they brush against kelp with their skin like a loofah (Morris, 2016). Although kelping is relatively under-investigated, there are claims that this behavior can double as another foraging method (Busch, 1998). When swimming through kelp, gray whales may scrape off tiny crustaceans clinging to the kelp fronds. It has also been noted that gray whale mothers will hide their calves in kelp to conceal them from predators (Busch, 1998).
Ask anyone who has been to Port Orford and they will attest to the abundance and diversity of marine fauna that thrive in the nutrient-rich coastal waters. I hope this will continue, and that we will see a stable bull kelp canopy kelp ecosystem return here in Port Orford. Stay tuned for more results when the team maps kelp canopies again at the end of August!
Figure 3. Kayak sampling at a large patch of kelp in Mill Rocks. Photo credit: Nichola Gregory
This Gray whale foraging ecology (GWFE) internship has prepared me for college in many ways. Being able to study this dynamic ecosystem is any marine science intern’s dream; and, my decision to pursue Natural Resources as my major has been affirmed through this summer’s field and lab experience. It inspires me to focus on ecology and possibly attend graduate school in the future. The college-like environment of living at the field station has conditioned me for dorm life in the fall; and, the opportunity to meet leading experts in a variety of marine science fields has expanded my knowledge of possible career pathways. With the inspiration and guidance of Dr. Leigh Torres, field station manager Tom Calvanese, team leader Allison Dawn, and the rest of the whale team, I am excited to begin my journey as a natural resource student and future scientist.
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References
Busch, R. (1998). Gray Whales: Wandering Giants. Orca Book Publishers.
Feehan, C. J., Grauman-Boss, B. C., Strathmann, R. R., Dethier, M. N., & Duggins, D. O. (2017, October 25). Kelp detritus provides high-quality food for sea urchin larvae. Association for the Sciences of Limnology and Oceanography. Retrieved August 13, 2022, from https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lno.10740
Kastelnik, T. (2021, August 18). Kelp. Oregon Kelp Alliance. Retrieved August 10, 2022, from https://www.oregonkelp.com/
By Nichola Gregory, B.S. Earth Science, College of Earth, Ocean, & Atmospheric Sciences, GEMM Lab Port Orford Intern
As a recent OSU graduate from the College of Earth, Ocean, and Atmospheric Sciences (CEOAS), I gained both knowledge regarding oceanographic and biological concepts through my coursework, and also a passion to be involved in projects that work towards bettering the natural world. Currently, I am pursuing a GIS (Geographic Information System) certificate from Portland Community College. The choice to continue my education with this certification was driven by its applicability as well as my desire to equip myself with skill sets that are applicable in addressing questions in marine science. This desire leads to the primary reason I was drawn to the TOPAZ/ JASPER projects that I am fortunate to be a part of this summer. These projects located in Port Orford have allowed me to become more familiar with various softwares and instruments used within marine sciences, and the instrument that I have been most excited to learn more about this summer is the theodolite.
My first introduction to the theodolite was during my biology of marine mammals course in Newport where PhD student Lisa Hildebrand (then Master’s student and graduate student leader of the Port Orford project since 2018) visited us in Depoe Bay with the instrument. That day, I was intimidated yet intrigued by how theodolites work and learned from Lisa that it can be used to create ‘tracklines’ of gray whale movements.
Now that the 2022 field season is underway, I’ve spent the last couple weeks at the Port Orford Field Station under the guidance of Master’s student Allison Dawn where I have gained familiarity with operating the theodolite (or as we affectionately call it, the Theo). I have also learned how vital of a tool it can be in helping us understand the habits and ecology of PCFG gray whales that visit the Oregon coast.
Figure 1: Four out of five members of the 2022 team pictured during cliff training. From left to right: Charlie watches whales with binoculars, Zoe learns how to use Pythagoras software for trackline creation, and Allison instructs me on how to use the theodolite. Photo credit: Luke Donaldson
Figure 2: A basic diagram of a digital theodolite. Top “Theo” pictured is facing out toward the object while the bottom “Theo” shows the user side. Diagram credit: Johnson Level & Tool Mfg. Co
Theodolites became popular in the early 1800’s and have been used for land surveying since. They combine optical plummets, a bubble level, and graduated circles to find vertical and horizontal angles while surveying. For a more visual introduction to theodolite and some of its uses, check out this link to a youtube video.
When the cliff team begins the day, their primary objective is to set up the theodolite and be prepared to track the locations and movements of gray whales. First, the surveying point (which is used to ensure repeatability of station location) is placed on the ground to position the tripod and theodolite. Then, once the tripod is set up and theodolite attached, leveling the instrument takes place. The 3 screws on the base plate of the Theo allow for leveling, which is of utmost importance so that the instrument is perfectly level with the horizon. The Theo has two bubble levelers to promote accuracy while moving the tripod legs as well as the leveling screws. Once the instrument is level, we complete the “start fix”, which is our first data point for each day and used as our reference point. The telescope includes an eyepiece for the user and an objective lens with internal mirrors to magnify the object(s) being viewed. Now we are ready to start fixing whale locations! And while the set up involved with “Theo” can be difficult to remember and tedious (leveling specifically) it has become somewhat automatic after a few weeks of practice.
After a productive day with many whale fixes, a small map (Figure 3) is made on the associated computer program “Pythagoras”. This map shows the station (“Theo”), the reference point, and the relative location and coordinates of each fix made. The tracklines are then analyzed to learn more about movement and behavior of specific whale individuals (read Lisa’s blog here for more information!). We also carefully outline kelp patches with many “fixes” so we can create maps of kelp cover in our study areas. This year we are seeing more bull kelp compared to 2021, but stay tuned for more details about these changes from intern Luke Donaldson’s upcoming blog!
Figure 3: An example of a trackline map made in Pythagoras after gray whale fixes are made. This specific trackline shows a whale coming into Mill Rocks to forage, moving past the cliff station toward Tichenor Cove, and then making its way back to Mill Rocks.
Due to this amazing instrument, the GEMM lab has non-invasively tracked many whales over the many previous field seasons. Two whales that this year’s team has grown particularly fond of are named “Buttons” and “Rugged”. Both have visited Port Orford numerous times over the past couple weeks, giving us the chance to get practice with creating tracklines while also capturing up-to-date ID photos. Buttons is regularly documented along the Oregon coast and is such a local favorite that there is an honorary Port Orford Public Library Card in his name! Rugged also showed up two weeks ago with a brand new marking that is likely a propeller scar. In addition to seeing a greater number of kelp patches, we have already obtained more whale trackline data than the entirety of last year’s season. I hope this means we are observing a recovering ecosystem, and a positive future for Port Orford, through the lens of the Theodolite.
Figure 4: A photo captured of Rugged, our first whale sighting of the 2022 season. Photo credit: Allison Dawn
After being in Port Orford for a couple weeks now, with the first few days of proper sampling behind me, I can tell my time here will be time well spent. Not only have I become familiar with a new instrument, I have learned a great deal in how science in the field is conducted and how broad a project can become. Specifically, I am impressed by the volume of data that is collected at the 12 unique kayak sampling stations on any given field day –secchi depth, water depth & chemistry, underwater footage, and zooplankton. These data complement the data cliff team provides, which, in addition to whale movement data, includes Beaufort Sea State, tidal height, and weather. I now appreciate how important it is to gather as much information as possible in order to find connections between the environment, gray whales, and their prey, even if those connections are not obvious to us today.
Another lesson I’ve found invaluable during this experience is my growing belief in myself and abilities. Prior to this summer, I had minimal experience on the water, mostly limited to rivers and lakes. But after being in Port Orford for a few weeks, I have learned that something that once seemed daunting can become enjoyable. I think almost every young person in science finds themselves in a state of “imposter syndrome” at some point, where despite great education and experiences, they fall short in self confidence. Time spent on the cliff, kayak and lab has helped affirm that marine science is where I belong. Perhaps even more impactful are the experiences I have had while navigating the learning curve of these skills. I hope to keep this growth-mindset and push through future experiences that feel awkward or scary in order to reach my goals and find my place in marine sciences.
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By Zoe Sax, Drake University senior, Department of Environmental Science & Sustainability, GEMM Lab NSF REU intern
My name is Zoe and I am from land-locked Minnesota… so how did I end up on the west coast this summer? Well, I am a rising senior at Drake University studying environmental science on the biological conservation track with a zoo and conservation science concentration and a math minor. Despite the wordy title, there is one thing missing from my education — the ocean. This summer, I am dipping my toes into the field of marine biology as a National Science Foundation (NSF) Research Experience for Undergraduates (REU) student — and I am loving it. As an REU student in the GEMM lab, I am doing both lab and field work surrounding the TOPAZ/JASPER project. In June, I arrived at the Hatfield Marine Science Center (HMSC) in Newport to outline my project with master’s student Allison Dawn, and start data analysis before the busy field season began.
Since 2016, the Port Orford project has collected Secchi disk measurements and GoPro video footage at each kayak sampling station. A Secchi disk is a simple tool with black and white quadrants that we lower into the water until it cannot be seen anymore. As we raise the disk out of the water, we count the marks on the line to calculate a measurement of water clarity (Figure 1). This long time-series of Secchi measurements is an excellent dataset, but what do these Secchi measurements actually reflect? Productivity in the water column, increased turbidity from river runoff, changes in zooplankton abundance? Additionally, what, if anything, does GoPro video color represent? My REU project aims to address these specific questions that the GEMM Lab needs answered. I will compare the Secchi disk measurements to the water color in GoPro video footage, collected at the same time and place, and satellite chlorophyll-a concentrations from MODIS. The goal is to understand if there is a relationship between video color and visibility (Secchi disk data), or a relationship between video color and chlorophyll-a concentrations.
Figure 1. Secchi disk deployment (top) Secchi disk (bottom).
I am using a programming language called Python to take screenshots of the GoPro footage at certain depths and extract color information. Originally, I extracted RGB values from each pixel and converted them to hex color codes. RGB stands for “red, green, blue” and represents the amount of each color present to achieve the color seen. Hex codes are unique codes for every color and contain six letters or numbers; the first two represent red, the second two represent green, and the final two represent green (Figure 2). However, to relate color to numeric data, I need to quantify the color values into a scale. Hex color codes do not have an obvious scale because they are so distinct and use both letters and numbers. On the other hand, RGB values have a numeric scale from 0–255 for each of the three colors, so we ultimately decided to only use these.
Figure 2. Screenshot from MR17 GoPro video footage on August 23rd, 2021 and the hex color code extraction. The donut plot (left) shows the frequency of each hex code in the center GoPro image, and the table (right) lists the hex codes.
Figure 3. Screenshot from TC6 GoPro video footage on August 12th, 2021 (a) and its RGB color extraction histogram (b).
Every image has millions of pixels, and each pixel has an RGB value. My code separates the red, green, and blue values of each pixel and plots a histogram with the RGB color value on the x-axis and the number of pixels where that value is present on the y-axis (Figure 3). I am currently in the process of determining the best mode of summarizing the color values, whether that be the mean, maximum, or range of values. Once determined, the summarized values will be compared to Secchi disk values and satellite chlorophyll-a concentrations. I still have to iron out the code, but I am proud of what I have done so far and cannot wait for it to all come together!
Along with learning new methods of analysis, I am being challenged to learn new field techniques, such as self-rescue in a tandem kayak (Figure 4). I also have enjoyed performing the data collection that, until now, I have only been watching on my laptop. As this year’s team collects data and reviews GoPro footage, which seems to be showing higher zooplankton abundance than in previous years, I get excited at the prospect of analyzing the data after the field season is complete.
Figure 4. Kayak safety training with the whale team and Marcus from South Coast Tours.
At the beginning of the summer, I felt overwhelmed. Yet, I have come to realize that it is okay to not understand something as long as I put in the effort to learn and am not afraid to ask for repeated explanations. I have also learned what it is like to be part of a lab and that lab mates can be a great source of support and knowledge. The GEMM lab is collaborative and members enjoy helping each other brainstorm. I am very thankful that Clara Bird, a GEMM lab PhD candidate, provided base code and additional guidance throughout my analysis. Additionally, I attended a lab meeting where many others provided helpful comments and suggestions that were crucial for my project.
My experience as a GEMM lab intern has allowed me to see my REU project through many phases. I have gained confidence in my R and Python programming skills, and confidence in my capabilities overall. Living and working at both the HMSC and Port Orford field stations has exposed me to a multitude of areas in marine science, from GEMM lab research on foraging behavior or acoustics, to other REU students’ and mentors’ research on seabird behavior or plankton ecology. Although there is still a month left of my internship, I have already affirmed my interest in marine biology, and hands-on exploration, and have a greater sense of what I may want to do in graduate school.
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