Decisions, decisions: New GEMM Lab publication reveals trade-offs in prey quantity and quality in gray whale foraging

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

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). 


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

Wavelet analysis to describe biological cycles and signals of non-stationarity

By Allison Dawn, GEMM Lab Master’s student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab 

During my second term of graduate school, I have been preparing to write my research proposal. The last two months have been an inspiring process of deep literature dives and brainstorming sessions with my mentors. As I discussed in my last blog, I am interested in questions related to pattern and scale (fine vs. mesoscale) in the context of the Pacific Coast Feeding Group (PCFG) of gray whales, their zooplankton prey, and local environmental variables.

My work currently involves exploring which scales of pattern are important in these trophic relationships and whether the dominant scale of a pattern changes over time or space. I have researched which analysis tools would be most appropriate to analyze ecological time series data, like the impressive long-term dataset the GEMM lab has collected in Port Orford as part of the TOPAZ  project, where we have monitored the abundance of whales and zooplankton, as well as environmental variables since 2016. 

A useful analytical tool that I have come across in my recent coursework and literature review is called wavelet analysis. Importantly, wavelet analysis can handle non-stationarity and edge detection in time series data. Non-stationarity is when a dataset’s mean and/or variance can change over time or space, and edge detection is the identification of the change location (in time or space). For example, it is not just the cycles or “ups and downs” of zooplankton abundance I am interested in, but when in time or where in space these cycles of “ups and downs” might change in relation to what their previous values, or distances between values, were. Simply stated, non-stationarity is when what once was normal is no longer normal. Wavelet analysis has been applied across a broad range of fields, such as environmental engineering (Salas et al. 2020), climate science (Slater et al. 2021), and bio-acoustics (Buchan et al. 2021). It can be applied to any time series dataset that might violate the traditional statistical assumption of stationarity. 

In a recent review of climate science methodology, Slater et al. (2021) outlined the possible behavior of time series data. Using theoretical plots, the authors show that data can a) have the same mean and variance over time, or b) have non-stationarity that can be broken into three major groups – trend, step change, or shifts in variance. Figure 1 further demonstrates the difference between stationary vs. non-stationary data in relation to a given variable of interest over time. 

Figure 1. Plots showing the possible magnitude of a given variable across a time series: a) Stationary behavior, b) Non-stationary trend, step-change, and a shift in variance. [Taken from Slater et. al (2021)].

Traditional correlation statistics assumes stationarity, but it has been shown that ecological time series are often non-stationary at certain scales (Cazelles & Hales, 2006). In fact, ecological data rarely meets the requirements of a controlled experiment that traditional statistics require. This non-stationarity of ecological data means that while widely-used methods like generalized linear models and analyses of variances (ANOVAs) can be helpful to assess correlation, they are not always sufficient on their own to describe the complex natural phenomena ecologists seek to explain. Non-stationarity occurs frequently in ecological time series, so it is appropriate to consider analysis tools that will allow us to detect edges to further investigate the cause.

Wavelet analysis can also be conducted across a time series of multiple response variables to assess if these variables share high common power (correlation). When data is combined in this way it is called a cross-wavelet analysis. An interesting paper used cross-wavelet analysis to assess the seasonal response of zooplankton life history in relation to climate warming (Winder et. al 2009). Results from their cross-wavelet analysis showed that warming temperatures over the past two decades increased the voltinism (number of broods per year) of copepods. The authors show that where once annual recruitment followed a fairly stationary pattern, climate warming has contributed to a much more stochastic pattern of zooplankton abundance. From these results, the authors contribute to the hypothesis that climate change has had a temporal impact on zooplankton population dynamics, and recruitment has increasingly drifted out of phase from the original annual cycles. 

Figure 2. Cross-wavelet spectrum for immature and adult Leptodiaptomus ashlandi for 1965 through either 2000 or 2005. Plots show a) immatures and temperature, b) adults and temperature, c) immatures and phytoplankton, and d) adults and phytoplankton. Arrows indicate phase between combined time series. 0 degrees is in-phase and 180 degrees is anti-phase. Black contour lines show “cone of influence” or the 95% significance level, every value within the cone is considered significant. Left axis shows the temporal period, and the color legend shows wavelet frequency power, with low frequencies in blue and high frequencies in red. Plots show strong covariation of high common power at the 12-month period until the 1980s. This pattern is especially evident in plot c) and d). [Taken from (Winder et. al 2009)].

While wavelet and cross-wavelet analyses should not be the only tool used to explore data, due to its limitations with significance testing, it is still worth implementing to gain a better understanding of how time series variables relate to each other over multiple spatial and/or temporal scales. It is often helpful to combine multiple methods of analysis to get a larger sense of patterns in the data, especially in spatio-temporal research.

When conducting research within the context of climate change, where the concentration of CO2 in ppm in the atmosphere is a non-stationary time series itself (Figure 3), it is important to consider how our datasets might be impacted by climate change and wavelet analysis can help identify the scales of change. 

Figure 3. Plot showing the historic fluctuations of CO^2 and the recent deviation from normal levels. Source:

When considering our ecological time series of data in Port Orford, we want to evaluate how changing ocean conditions may be related to data trends. For example, has the annual mean or variance of zooplankton abundance changed over time, and where has that change occurred in time or space? These changes might have occurred at different scales and might be invisible at other scales. I am eager to see if wavelet analysis can detect these sorts of changes in the abundance of zooplankton across our time series of data, particularly during the seasons of intense heat waves or upwelling. 

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Buchan, S. J., Pérez-Santos, I., Narváez, D., Castro, L., Stafford, K. M., Baumgartner, M. F., … & Neira, S. (2021). Intraseasonal variation in southeast Pacific blue whale acoustic presence, zooplankton backscatter, and oceanographic variables on a feeding ground in Northern Chilean Patagonia. Progress in Oceanography, 199, 102709.

Cazelles, B., & Hales, S. (2006). Infectious diseases, climate influences, and nonstationarity. PLoS Medicine, 3(8), e328.

Salas, J. D., Anderson, M. L., Papalexiou, S. M., & Frances, F. (2020). PMP and climate variability and change: a review. Journal of Hydrologic Engineering, 25(12), 03120002.

Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., … & Wilby, R. L. (2021). Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management. Hydrology and Earth System Sciences, 25(7), 3897-3935.

Winder, M., Schindler, D. E., Essington, T. E., & Litt, A. H. (2009). Disrupted seasonal clockwork in the population dynamics of a freshwater copepod by climate warming. Limnology and Oceanography, 54(6part2), 2493-2505.

The Unpredictable Nature of Field Work & a Mystery Mysid

By Jasen C. White, GEMM Lab summer intern, OSU senior, Department of Fisheries, Wildlife, and Conservation Sciences

Field work is predictably unpredictable. Even with years of experience and exhaustive planning, nature always manages to throw a few curveballs, and this gray whale foraging ecology field season is no exception. We are currently in our sixth week of data collection here in Port Orford, and we have been battling the weather, our equipment, and a notable lack of whales and their zooplankton prey. Throughout all of these setbacks, Team “Heck Yeah” has lived up to its mantra as we have approached each day ready to hit the ground running. When faced with any of our myriad of problems, we have managed to work collaboratively to assess our options and develop solutions to keep the project on track. 

For those of you that are unfamiliar with Port Orford, it is windy here, and when it is not, it can be foggy. Both of these weather patterns have the potential to make unsafe paddling conditions for our kayak sampling team. This summer we have frequently delayed or altered our field work routines to accommodate these weather patterns. Occasionally, we had to call off kayaking altogether as the winds and swell precluded us from maintaining our boat “on station” at the predetermined GPS coordinates during our samples, only for the winds to die down once we had returned to shore and completed the daily gear maintenance. Despite weather challenges, we have made the most of our data collection opportunities over these past six weeks, and we have only been forced to give up four total days of data collection. Flexibility to take advantage of the good weather windows when they arrive is the key!

Equipment issues can be even more unpredictable than the weather. The first major stumbling block for our equipment was a punctured membrane in the dissolved oxygen probe that we lower into the water at each of our twelve sample locations. This puncture was likely the result of a stray urchin’s spine that was in the wrong place at the wrong time. Soon after noticing the problem, we quickly rallied to refurbish the membrane, recalibrate the sensor, and design a protective housing using some plumbing parts from the local hardware store to prevent any future damage to the membrane (Figures 1a-d). Within 6 days, we were back up and running with the dissolved oxygen sensor.

Figure 1. a) Punctured dissolved oxygen sensor membrane; b) plans for constructing a protective housing for the sensor; c) the new protective housing for the dissolved oxygen sensor (yellow) is attached to the sensor array; d) intern Jasen White measuring seawater for the dissolved oxygen sensor calibration after replacing the punctured membrane. Source: A. Dawn

The next major equipment issue involved a GoPro camera whose mounting hardware snapped while being retrieved at a sample site. This event was captured on the camera itself (see below). Fortunately, thanks to our collaborators at the Oregon Institute of Marine Biology, we were soon able to recover the lost GoPro camera, and in the meantime, we relied on our spare to continue sampling. 

Figure 2. The steel cable of the downrigger used to deploy and retrieve our sensor array had worn down until only two strands remained intact. Source: J. White.

The most recent equipment problem was a fraying cable (Figure 2) on our downrigger. We use the downrigger as a winch to lower and raise our sensor array and zooplankton nets into the water to obtain our samples. Fortunately, keen eyes on our team noticed the fray before it fully separated while the sensor array was in the water which could have resulted in losing our gear. We were quickly able to find the necessary repair part locally and get back on the water to finish out our sample regime within an hour of noticing the problem. 

Finally, as Damian mentioned in his post last week, this season seemed to start much slower than the previous field seasons. In the early weeks, many of our zooplankton sampling nets repeatedly came up almost empty. There was often nothing but murky water to see in the GoPro videos that accompany the zooplankton samples. Likely due to the lack of prey, we have only managed to spot a couple of transitory whales that rarely entered our study area. Those few whales that we did observe were difficult to track as the relatively high winds and waves quickly dissipated the tell-tale blows and camouflaged their briefly exposed backs and flukes. 

Our determination and perseverance have recently started to pay off, however, as the prey abundance in at least some of our sample sites has begun to increase. This increase in prey has also corresponded to a slight increase in whale sightings. One whale even spent nearly 30 minutes around the sampling station that consistently yields the most prey, likely indicating foraging behavior. These modest increases in zooplankton prey and whale sightings provide more evidence in support of the hypothesis Damian mentioned last week that reduced whale abundance in the area is likely the result of low prey abundance.

Figure 3. Example of a previously unidentified mysid that dominates several of our zooplankton samples. Due to the unique fat and flat telson (the “tail”) portion, we have been affectionately calling these “beavertail” mysids. Source: J. White.

As the zooplankton abundance finally started to increase, we noticed an interesting shift in the kinds of prey that we are capturing compared to previous seasons. Donovan Burns, an intern from the 2019 field season, noted in his blog post that the two most common types of zooplankton they found in their samples were the mysid species Holmesimysis sculpta and members of the genus Neomysis. While Neomysis mysid shrimp are continuing to make up a large proportion of our prey samples this year, we have noticed that many of our samples are dominated by a different type of mysid shrimp (Figure 3) which, in previous years, was a very rare capture. After searching through several mysid identification guides, this unknown mysid appears to be a member of the genus Lucifer, identified based on the presence of some distinctive characteristics that are unique to this genus (Omori 1992). 

This observation is interesting because historically, Lucifer mysid shrimp are typically found in warmer tropical and subtropical waters and were rarely reported in the eastern North Pacific Ocean before the year 1992 (Omori 1992). Additionally, a key to common coastal mysid shrimp of Oregon, Washington, and British Columbia does not include members of the Lucifer genus, nor does it include any examples of mysids that resemble these new individuals showing up in our zooplankton nets (Daly and Holmquist 1986). If our initial identification of this mysid species is correct, then the sudden rise in the abundance of a typically warm water mysid species in Port Orford may indicate some fascinating shifts in oceanographic conditions that could lend some insight into why our prey and subsequent whale observations are so different this year than in years past.

Figure 4. View from the cliff site where we track gray whales using a theodolite. Source: A. Dawn.

As the 2021 field season draws to a close in Port Orford, I cannot help but reflect on what a wonderful opportunity we have been given through this summer internship program. I have loved the short time that I have spent living in this small but lively community for these past five weeks. Most days we could either be found kayaking around the nearshore to sample for the tiny creatures that our local gray whales call dinner, or we were on a cliff, gazing at the tirelessly beautiful, rugged coastline (Figure 4), hoping to glimpse the blow of a foraging whale so that we could track its course with our theodolite. Though the work can be physically exhausting during long and windy kayaking trips, mentally taxing when processing the data for each of the new samples after a full day of fieldwork, or incredibly frustrating with equipment failures, weather delays and shy whales, it is also tremendously satisfying to know that I contributed in a small but meaningful way to the mission of the GEMM Lab. I cannot imagine a better way to obtain the experience that my fellow interns and I have gained from this work, and I know that it will serve each of us well in our future ambitions.


Daly, K. L., and C. Holmquist. 1986. A key to the Mysidacea of the Pacific Northwest. Canadian Journal of Zoology 64:1201–1210.

Omori, M. 1992. Occurrence of Two Species of Lucifer (Dendrobranchiata: Sergestoidea: Luciferidae) off the Pacific Coast of America. Journal of Crustacean Biology 12:104–110.

Taking a breather

Allison Dawn, new GEMM Lab Master’s student, OSU Department of Fisheries, Wildlife and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

While standing at the Stone Shelter at the Saint Perpetua Overlook in 2016, I took in the beauty of one of the many scenic gems along the Pacific Coast Highway. Despite being an East Coast native, I felt an unmistakable draw to Oregon. Everything I saw during that morning’s hike, from the misty fog that enshrouded evergreens and the ocean with mystery, to the giant banana slugs, felt at once foreign and a place I could call home. Out of all the places I visited along that Pacific Coast road trip, Oregon left the biggest impression on me.

Figure 1. View from the Stone Shelter at the Cape Perpetua Overlook, Yachats, OR. June 2016.

For my undergraduate thesis, which I recently defended in May 2021, I researched blue whale surface interval behavior. Surface interval events for oxygen replenishment and rest are a vital part of baleen whale feeding ecology, as it provides a recovery period before they perform their next foraging dive (Hazen et al., 2015; Roos et al., 2016). Despite spending so much time studying the importance of resting periods for mammals, that 2016 road trip was my last true extended resting period/vacation until, several years later in 2021, I took another road trip. This time it was across the country to move to the place that had enraptured me.

Now that I am settled in Corvallis, I have reflected on my journey to grad school and my recent road trip; both prepared me for a challenging and exciting new chapter as an incoming MSc student within the Marine Mammal Institute (MMI).

Part 1: Journey to Grad School

When I took that photo at the Cape Perpetua Overlook in 2016, I had just finished the first two semesters of my undergraduate degree at UNC Chapel Hill. As a first-generation, non-traditional student those were intense semesters as I made the transition from a working professional to full-time undergrad.

By the end of my freshman year I was debating exactly what to declare as my major, when one of my marine science TA’s, Colleen, (who is now Dr. Bove!), advised that I “collect experiences, not degrees.” I wrote this advice down in my day planner and have never forgotten it. Of course, obtaining a degree is important, but it is the experiences you have that help lead you in the right direction.

That advice was one of the many reasons I decided to participate in the Morehead City Field Site program, where UNC undergraduates spend a semester at the coast, living on the Duke Marine Lab’s campus in Beaufort, NC. During that semester, students take classes to fulfill a marine science minor while participating in hands-on research, including an honors thesis project. The experience of designing, carrying out, and defending my own project affirmed that graduate school in the marine sciences was right for me. As I move into my first graduate TA position this fall, I hope to pay forward that encouragement to other undergraduates who are making decisions about their own future path.

Figure 2. Final slide from my honors thesis defense. UNC undergraduates, and now fellow alumni, who participated in the Morehead City Field Site program in Fall 2018.

Part 2: Taking a Breather

Like the GEMM Lab’s other new master’s student Miranda, my road trip covered approximately 2,900 miles. I was solo for much of the drive, which meant there was no one to argue when I decided to binge listen to podcasts. My new favorite is How To Save A Planet, hosted by marine biologist Dr. Ayana Elizabeth Johnson and Alex Blumberg. At the end of each episode they provide a call to action & resources for listeners – I highly recommend this show to anyone interested in what you can do right now about climate change.

Along my trip I took a stop in Utah to visit my parents. I had never been to a desert basin before and engaged in many desert-related activities: visiting Zion National Park, hiking in 116-degree heat, and facing my fear of heights via cliff jumping.

Figure 3. Sandstone Rocks at Sand Hollow National Park, Hurricane, Utah. June 2021.

 My parents wanted to help me settle into my new home, as parents do, so we drove the rest of the way to Oregon together. As this would be their first visit to the state, we strategically planned a trip to Crater Lake as our final scenic stop before heading into Corvallis.

Figure 4. Wizard Island in Crater Lake National Park, Klamath County, OR. June 2021.

This time off was filled with adventure, yet was restorative, and reminded me the importance of taking a break. I feel ready and refreshed for an intense summer of field work.

Part 3: Rested and Ready

Despite accumulating skills to do research in the field over the years, I have yet to do marine mammal field work (or even see a whale in person for that matter.) My mammal research experience included analyzing drone imagery, behind a computer, that had already been captured. As you can imagine, I am extremely excited to join the Port Orford team as part of the TOPAZ/JASPER projects this summer, collecting ecological data on gray whales and their prey. I will be learning the ropes from Lisa Hildebrand and soaking up as much information as possible as I will be taking over as lead for this project next year.

It will take some time before my master’s thesis is fully developed, but it will likely focus on assessing the environmental factors that influence gray whale zooplankton prey availability, and the subsequent impacts on whale movements and health. For five years, the Port Orford project has conducted GoPro drops at 12 sampling stations to collect data on zooplankton relative abundance.

Figures 5 & 6. GEMM GoPro drop stick assembly and footage demonstrating mysid data collection. July 2021.

Paired with this GoPro is a Time-Depth Recorder (TDR) that provides temperature and depth data. The 2021 addition to this GoPro system is a new dissolved oxygen (DO) sensor the GEMM Lab has just acquired. This new piece of equipment will add to the set of parameters we can analyze to describe what and how oceanographic factors drive prey variability and gray whale presence in our study site.My first task as a GEMM Lab student is to get to know this DO sensor, figure out how it works, set it up, test it, attach it to the GoPro device, and prepare it for data collection during the upcoming Port Orford project starting in 1 week!

Figure 7. The GEMM lab’s new RBR solo3 getting ready for Port Orford. July 2021.

Dissolved oxygen plays a vital role in the ocean; however, climate change and increased nutrient loading has caused the ocean to undergo deoxygenation. According to the IUCN’s 2019 Issues Brief, these factors have resulted in an oxygen decline of 2% since the middle of the 20th century, with most of this loss occurring within the first 1000 meters of the ocean. Two percent may not seem like much, but many species have a narrow oxygen threshold and, like pH changes in coral reef systems, even slight changes in DO can have an impact. Additionally, the first 1000 meters of the ocean contains the greatest amount of species richness and biodiversity.

Previous research done in a variety of systems (i.e., estuarine, marine, and freshwater lakes) shows that dissolved oxygen concentrations can have an impact on predator-prey interactions, where low dissolved oxygen results in decreased predation (Abrahams et al., 2007; Breitburg et al., 1997; Domenici et al., 2007; Kramer et al., 1987); and changes in DO also change prey vertical distributions (Decker et al., 2004). In Port Orford, we are interested in understanding the interplay of factors driving zooplankton community distribution and abundance while investigating the trophic interaction between gray whales and their prey.

I have spent some time with our new DO sensor and am looking forward to its first deployments in Port Orford! Stay tuned for updates from the field!


Abrahams, M. V., Mangel, M., & Hedges, K. (2007). Predator–prey interactions and changing environments: who benefits?. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1487), 2095-2104.

Breitburg, D. L., Loher, T., Pacey, C. A., & Gerstein, A. (1997). Varying effects of low dissolved oxygen on trophic interactions in an estuarine food web. Ecological Monographs, 67(4), 489-507.

​​Decker, M. B., Breitburg, D. L., & Purcell, J. E. (2004). Effects of low dissolved oxygen on zooplankton predation by the ctenophore Mnemiopsis leidyi. Marine Ecology Progress Series, 280, 163-172.

Domenici, P., Claireaux, G., & McKenzie, D. J. (2007). Environmental constraints upon locomotion and predator–prey interactions in aquatic organisms: an introduction.

Hazen, E. L., Friedlaender, A. S., & Goldbogen, J. A. (2015). Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Science Advances, 1(9), e1500469.

Kramer, D. L. (1987). Dissolved oxygen and fish behavior. Environmental biology of fishes, 18(2), 81-92.

Roos, M. M., Wu, G. M., & Miller, P. J. (2016). The significance of respiration timing in the energetics estimates of free-ranging killer whales (Orcinus orca). Journal of Experimental Biology, 219(13), 2066-2077.

Marine mammals of the Northern California Current, 2020 edition

By Dawn Barlow, PhD student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Clara and I have just returned from ten fruitful days at sea aboard NOAA Ship Bell M. Shimada as part of the Northern California Current (NCC) ecosystem survey. We surveyed between Crescent City, California and La Push, Washington, collecting data on oceanography, phytoplankton, zooplankton, and marine mammals (Fig. 1). This year represents the third year I have participated in these NCC cruises, which I have come to cherish. I have become increasingly confident in my marine mammal observation and species identification skills, and I have become more accepting of the things out of my control – the weather, the sea state, the many sightings of “unidentified whale species”. Careful planning and preparation are critical, and yet out at sea we are ultimately at the whim of the powerful Pacific Ocean. Another aspect of the NCC cruises that I treasure is the time spent with members of the science team from other disciplines. The chatter about water column features, musings about plankton species composition, and discussions about what drives marine mammal distribution present lively learning opportunities throughout the cruise. Our concurrent data collection efforts and ongoing conversations allow us to piece together a comprehensive picture of this dynamic NCC ecosystem, and foster a collaborative research environment.  

Figure 1. Data collection effort for the NCC September 2020 cruise, between Crescent City, CA, and La Push, WA. Red points represent oceanographic sampling stations, and black lines show the track of the research vessel during marine mammal survey effort.

Every time I head to sea, I am reminded of the patchy distribution of resources in the vast and dynamic marine environment. On this recent cruise we documented a stark contrast between  expansive stretches of warm, blue, stratified, and seemingly empty ocean and areas that were plankton-rich and supported multi-species feeding frenzies that had marine mammal observers like me scrambling to keep track of everything. This year, we were greeted by dozens of blue and humpback whales in the productive waters off Newport, Oregon. Off Crescent City, California, the water was very warm, the plankton community was dominated by gelatinous species like pyrosomes, salps, and other jellies, and the marine mammals were virtually absent except for a few groups of common dolphins. To the north, the plume of water flowing from the Columbia River created a front between water masses, where we found ourselves in the midst of pacific white-sided dolphins, northern right whale dolphins, and humpback whales. These observations highlight the strength of ecosystem-scale and multi-disciplinary data collection efforts such as the NCC surveys. By drawing together information on physical oceanography, primary productivity, zooplankton community composition and abundance, and marine predator distribution, we can gain a nearly comprehensive picture of the dynamics within the NCC over a broad spatial scale.

This year, the marine mammals delivered and kept us observers busy. We lucked out with good survey conditions and observed many different species throughout the NCC (Table 1, Fig. 2).

Table 1. Summary of all marine mammal sightings from the NCC September 2020 cruise.

Figure 2. Maps showing kernel densities of four frequently observed and widely distributed species seen during the cruise. Black lines show the track of the research vessel during marine mammal survey effort, white points represent sighting locations, and colors show kernel density estimates weighted by group size at each sighting.

This year’s NCC cruise was unique. We went to sea as a global pandemic, wildfires, and political tensions continue to strain this country and our communities. This cruise was the first NOAA Fisheries cruise to set sail since the start of the pandemic. Our team of scientists and the ship’s crew went to great lengths to make it possible, including a seven-day shelter-in-place period and COVID-19 tests prior to cruise departure. As a result of these extra challenges and preparations, I think we were all especially grateful to be on the water, collecting data. At-sea fieldwork is always challenging, but morale was up, spirits were high, and laughs were frequent despite smiles being concealed by our masks. I am grateful for the opportunity to participate in this ongoing valuable data collection effort, and to be part of this team. Thanks to all who made it such a memorable cruise.

Figure 3. The NCC September 2020 science team at the end of a successful research cruise! Fieldwork in the time of COVID-19 presents many logistical challenges, but this team rose to the occasion and completed a safe and fruitful survey despite the circumstances.

Do gray whales count calories?

By Lisa Hildebrand, MSc student, OSU Department of Fisheries & Wildlife, Marine Mammal Institute, Geospatial Ecology of Marine Megafauna Lab

When humans count calories it is typically to regulate and limit calorie intake. What I am wondering about is whether gray whales are aware of caloric differences in the prey that is available to them and whether they make foraging decisions based on those differences. In last week’s post, Dawn discussed what makes a good meal for a hungry blue whale. She discussed that total prey biomass of a patch, as well as how densely aggregated that patch is, are the important factors when a blue whale is picking its next meal. If these factors are important for blue whales, is it same for gray whales? Why even consider the caloric value of their prey?

Gray and blue whales are different in many ways; one way is that blue whales are krill specialists whereas gray whales are more flexible foragers. The Pacific Coast Feeding Group (PCFG) of gray whales in particular are known to pursue a more varied menu. Previous studies along the PCFG range have documented gray whales feeding on mysid shrimp (Darling et al. 1998; Newell 2009), amphipods (Oliver et al. 1984Darling et al. 1998), cumacean shrimp (Jenkinson 2001; Moore et al. 2007; Gosho et al. 2011), and porcelain crab larvae (Dunham and Duffus 2002), to name a few. Based on our observations in the field and from our drone footage, we have observed gray whales feeding on reefs (likely on mysid shrimp), benthically (likely on burrowing amphipods), and at the surface on crab larvae (Fig. 1). Therefore, while both blue and PCFG whales must make decisions about prey patch quality based on biomass and density of the prey, gray whales have an extra decision to make based on prey type since their prey menu items occupy different habitats that require different feeding tactics and amount of energy to acquire them. In light of these reasons, I hypothesize that prey caloric value factors into their decision of prey patch selection. 

Figure 1. Gray whales use several feeding tactics to obtain a variety of coastal Oregon zooplankton prey including jaw snapping (0:12 of video), drooling mud (0:21), and head standing (0:32), to name a few.

This prey selection process is crucial since PCFG gray whales only have about 6 months to consume all the food they need to migrate and reproduce (even less for the Eastern North Pacific (ENP) gray whales since their journey to their Arctic feeding grounds is much longer). You may be asking, well if feeding is so important to gray whales, then why not eat everything they come across? Surely, if they ate every prey item they swam by, then they would be fine. The reason it isn’t quite this simple is because there are energetic costs to travel to, search for, and consume food. If an individual whale simply eats what is closest (a small, poor-quality prey patch) and uses up more energy than it gains, it may be missing out on a much more beneficial and rewarding prey patch that is a little further away (that patch may disperse or another whale may eat it by the time this whale gets there). Scientists have pondered this decision-making process in predators for a long time. These ponderances are best summed up by two central theories: the optimal foraging theory (MacArthur & Pianka 1966) and the marginal value theorem (Charnov 1976). If you are a frequent reader of the blog, you have probably heard these terms once or twice before as a lot of the questions we ask in the GEMM Lab can be traced back to these concepts.

Optimal foraging theory (OFT) states that a predator should pick the most beneficial resource for the lowest cost, thereby maximizing the net energy gained. So, a gray whale should pick a prey patch where it knows that it will gain more energy from consuming the prey in the patch than it will lose energy in the process of searching for and feeding on it. Marginal value theorem elaborates on this OFT concept by adding that the predator also needs to consider the cost of giving up a prey patch to search for a new one, which may or may not end up being more profitable or which may take a very long time to find (and therefore cost more energy). 

The second chapter of my thesis will investigate whether individual gray whales have foraging preferences by relating feeding location to prey quality (community composition) and quantity (relative density). However, in order to do that, I first must know about the quality of the individual prey species, which is why my first chapter explores the caloric content of common coastal zooplankton species in Oregon that may serve as gray whale prey. The lab work and analysis for that chapter are completed and I am in the process of writing it up for publication. Preliminary results (Fig. 2) show variation in caloric content between species (represented by different colors) and reproductive stages (represented by different shapes), with a potential increasing trend throughout the summer. These results suggest that some species and reproductive stages may be less profitable than others based solely on caloric content. 

Figure 2. Mean caloric content (J/mg) of coastal Oregon zooplankton (error bars represent standard deviation) from May-October in 2017-2018. Colors represent species and shapes represent reproductive stage.

Now that we have established that there may be bigger benefits to feeding on some species over others, we have to consider the availability of these zooplankton species to PCFG whales. Availability can be thought of in two ways: 1) is the prey species present and at high enough densities to make searching and foraging profitable, and 2) is the prey species in a habitat or depth that is accessible to the whale at a reasonable energetic cost? Some prey species, such as crab larvae, are not available at all times of the summer. Their reproductive cycles are pulsed (Roegner et al. 2007) and therefore these prey species are less available than species, such as mysid shrimp, that have more continuous reproduction (Mauchline 1980). Mysid shrimp appear to seek refuge on reefs in rock crevices and among kelp, whereas amphipods often burrow in soft sediment. Both of these habitat types present different challenges and energetic costs to a foraging gray whale; it may take more time and energy to dislodge mysids from a reef, but the payout will be bigger in terms of caloric gain than if the whale decides to sift through soft sediment on the seafloor to feed on amphipods. This benthic feeding tactic may potentially be a less costly foraging tactic for PCFG whales, but the reward is a less profitable prey item.  

My first chapter will extend our findings on the caloric content of Oregon coastal zooplankton to facilitate a comparison to the caloric values of the main ampeliscid amphipod prey of ENP gray whales feeding in the Arctic. Through this comparison I hope to assess the trade-offs of being a PCFG whale rather than an ENP whale that completes the full migration cycle to the primary summer feeding grounds in the Arctic. 


Charnov, E. L. 1976. Optimal foraging: the marginal value theorem. Theoretical Population Biology 9:129-136.

Darling, J. D., Keogh, K. E. and T. E. Steeves. 1998. Gray whale (Eschrichtius robustus) habitat utilization and prey species off Vancouver Island, B.C. Marine Mammal Science 14(4):692-720.

Dunham, J. S. and D. A. Duffus. 2002. Diet of gray whales (Eschrichtius robustus) in Clayoquot Sound, British Columbia, Canada. Marine Mammal Science 18(2):419-437.

Gosho, M., Gearin, P. J., Jenkinson, R. S., Laake, J. L., Mazzuca, L., Kubiak, D., Calambokidis, J. C., Megill, W. M., Gisborne, B., Goley, D., Tombach, C., Darling, J. D. and V. Deecke. 2011. SC/M11/AWMP2 submitted to International Whaling Commission Scientific Committee.

Jenkinson, R. S. 2001. Gray whale (Eschrichtius robustus) prey availability and feeding ecology in Northern California, 1999-2000. Master’s thesis, Humboldt State University.

MacArthur, R. H., and E. R. Pianka. 1966. On optimal use of a patchy environment. American Naturalist 100:603-609.

Mauchline, J. 1980. The larvae and reproduction in Blaxter, J. H. S., Russell, F. S., and M. Yonge, eds. Advances in Marine Biology vol. 18. Academic Press, London.

Moore, S. E., Wynne, K. M., Kinney, J. C., and C. M. Grebmeier. 2007. Gray whale occurrence and forage southeast of Kodiak Island, Alaska. Marine Mammal Science 23(2)419-428.

Newell, C. L. 2009. Ecological interrelationships between summer resident gray whales (Eschrichtius robustus) and their prey, mysid shrimp (Holmesimysis sculpta and Neomysis rayii) along the central Oregon coast. Master’s thesis, Oregon State University.

Oliver, J. S., Slattery, P. N., Silberstein, M. A., and E. F. O’Connor. 1984. Gray whale feeding on dense ampeliscid amphipod communities near Bamfield, British Columbia. Canadian Journal of Zoology 62:41-49.

Roegner, G. C., Armstrong, D. A., and A. L. Shanks. 2007. Wind and tidal influences on larval crab recruitment to an Oregon estuary. Marine Ecology Progress Series 351:177-188.

What makes a good meal for a hungry whale?

By Dawn Barlow, PhD student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

In the vast and dynamic marine environment, food is notoriously patchy and ephemeral [1]. Predators such as marine mammals and seabirds must make a living in this dynamic environment by locating and capturing those prey patches. Baleen whales such as blue and humpback whales have a feeding strategy called “lunge feeding”, whereby they accelerate forward and open their massive jaws, engulf prey-laden water in their buccal pouch that expands like an accordion, and filter the water out through baleen plates so that they are left with a mouthful of food (Fig. 1) [2]. This approach is only efficient if whales can locate and target dense prey patches that compensate for the energetic costs of diving and lunging [3]. Therefore, not only do these large predators need to locate enough food to survive in the expansive and ever-changing ocean, they need to locate food that is dense enough to feed on, otherwise they actually lose more energy by lunging than they gain from the prey they engulf.

Figure 1. Schematic of a humpback whale lunge feeding on a school of fish. Illustration by Alex Boersma.

Why do baleen whales rely on such a costly feeding approach? Interestingly, this tactic emerged after the evolution of schooling behavior of prey such as zooplankton and forage fish (e.g., herring, anchovy, sand lance) [4]. Only because the prey aggregate in dense patches can these large predators take advantage of them by lunge feeding, and by engulfing a whole large patch they efficiently exploit these prey patches. Off the coast of California, where krill aggregations are denser in deeper water, blue whales regularly dive to depths of 100-300 m in order to access the densest krill patches and get the most bang for their buck with every lunge [5]. In New Zealand, we have found that blue whales exploit the dense krill patches near the surface to maximize their energetic gain [6], and have documented a blue whale bypassing smaller krill patches that presumably were not worth the effort to feed on.

By now hopefully I have convinced you of the importance of dense prey patches to large whales looking for a meal. It is not necessarily only a matter of total prey biomass in an area that is important to a whale, it is whether that prey biomass is densely aggregated. What makes for a dense prey patch? Recent work has shown that forage species, namely krill and anchovies, swarm in response to coastal upwelling [7]. While upwelling events do not necessarily change the total biomass of prey available to a whale over a spatial area, they may aggregate prey to a critical density to where feeding by predators becomes worthwhile. Forage species like zooplankton and small fish may school because of enhanced food resources, for predator avoidance, or reproductive grouping. While the exact behavioral reason for the aggregation of prey may still only be partially understood, the existence of these dense patches allows the largest animals on the planet to survive.

Another big question is, how do whales actually find their food? In the vast, seemingly featureless, and ever-changing ocean environment, how does a whale know where to find a meal, and how do they know it will be worthwhile before they take a lunge? In a review paper written by GEMM Lab PI Dr. Leigh Torres, she suggests it is all a matter of scale [8]. On a very large scale, baleen whales likely rely on oceanographic stimuli to home in on areas where prey are more likely to be found. Additionally, recent work has demonstrated that migrating blue whales return to areas where foraging conditions were best in previous years, indicating a reliance on memory [9,10]. On a very fine scale, visual cues may inform how a blue whale chooses to lunge [6,8,11].

What does it matter what a blue whale’s favorite type of meal is? Besides my interest in foundational research in ecology such as predator-prey dynamics, these questions are fundamental to developing effective management approaches for reducing impacts of human activities on whales. In the first chapter of my PhD, I examined how oceanographic features of the water column structure krill aggregations, and how blue whale distribution is influenced by oceanography and krill availability [12]. Currently, I am deep into my second chapter, analyzing the pathway from wind to upwelling to krill to blue whales in order to better understand the links and time lags between each step. Understanding the time lags will allow us to make more informed models to forecast blue whale distribution in my third chapter. Environmental managers in New Zealand plan to establish a protected area to conserve the population of blue whales that I study [13] on their foraging grounds. Understanding where blue whales will be distributed, and consequently how their distribution patterns might shift with environmental conditions or overlap with human activities, comes down the fundamental question I started this blog post with: What makes a good meal for a hungry whale?


1.        Hyrenbach KD, Forney KA, Dayton PK. 2000 Marine protected areas and ocean basin management. Aquat. Conserv. Mar. Freshw. Ecosyst. 10, 437–458. (doi:10.1002/1099-0755(200011/12)10:6<437::AID-AQC425>3.0.CO;2-Q)

2.        Goldbogen JA, Cade DE, Calambokidis J, Friedlaender AS, Potvin J, Segre PS, Werth AJ. 2017 How Baleen Whales Feed: The Biomechanics of Engulfment and Filtration. Ann. Rev. Mar. Sci. 9, 367–386. (doi:10.1146/annurev-marine-122414-033905)

3.        Goldbogen JA, Calambokidis J, Oleson E, Potvin J, Pyenson ND, Schorr G, Shadwick RE. 2011 Mechanics, hydrodynamics and energetics of blue whale lunge feeding: efficiency dependence on krill density. J. Exp. Biol. 214, 131–146. (doi:10.1242/jeb.048157)

4.        Cade DE, Carey N, Domenici P, Potvin J, Goldbogen JA. 2020 Predator-informed looming stimulus experiments reveal how large filter feeding whales capture highly maneuverable forage fish. Proc. Natl. Acad. Sci. U. S. A. (doi:10.1073/pnas.1911099116)

5.        Hazen EL, Friedlaender AS, Goldbogen JA. 2015 Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Sci. Adv. 1, e1500469–e1500469. (doi:10.1126/sciadv.1500469)

6.        Torres LG, Barlow DR, Chandler TE, Burnett JD. 2020 Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ (doi:10.7717/peerj.8906)

7.        Benoit-Bird KJ, Waluk CM, Ryan JP. 2019 Forage Species Swarm in Response to Coastal Upwelling. Geophys. Res. Lett. 46, 1537–1546. (doi:10.1029/2018GL081603)

8.        Torres LG. 2017 A sense of scale: Foraging cetaceans’ use of scale-dependent multimodal sensory systems. Mar. Mammal Sci. 33, 1170–1193. (doi:10.1111/mms.12426)

9.        Abrahms B et al. 2019 Memory and resource tracking drive blue whale migrations. Proc. Natl. Acad. Sci. U. S. A. (doi:10.1073/pnas.1819031116)

10.      Szesciorka AR, Ballance LT, Širovi A, Rice A, Ohman MD, Hildebrand JA, Franks PJS. 2020 Timing is everything: Drivers of interannual variability in blue whale migration. Sci. Rep. 10, 1–9. (doi:10.1038/s41598-020-64855-y)

11.      Friedlaender AS, Herbert-Read JE, Hazen EL, Cade DE, Calambokidis J, Southall BL, Stimpert AK, Goldbogen JA. 2017 Context-dependent lateralized feeding strategies in blue whales. Curr. Biol. (doi:10.1016/j.cub.2017.10.023)

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

13.      Barlow DR et al. 2018 Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res. 36, 27–40. (doi:

Introducing the Theyodelers – the Port Orford Gray Whale Foraging Ecology Team of 2020

By Lisa Hildebrand, MSc student, OSU Department of Fisheries & Wildlife, Marine Mammal Institute, Geospatial Ecology of Marine Megafauna Lab

Yodel-Ay-Ee-Ooooo! Hello from the Theyodelers, this year’s Port Orford gray whale foraging ecology field team. In case you were wondering, no, we aren’t hobby yodelers and we don’t plan on becoming them. The team name this year actually has to be attributed to a parent of one of my interns. Shout out to Scott Holt who during the first week of the field season asked his daughter Mattea (our OSU undergraduate intern) whether using a theodolite (the instrument we use to track gray whales from our cliff site) is anything like yodeling. The name was an immediate hit with the team and so the team name discussion was closed fairly early on in the season. Now that I have explained our slightly unconventional team name, let me tell you a little about this year’s team and what has been going on down here on the Oregon south coast so far.

As you can tell from the byline, I (Lisa) am back as the project’s team lead in this, the 6th year of the Port Orford gray whale research and internship project. Going into this year’s field season with two years of experience under my belt has made me feel more confident and comfortable with diving straight back into our fine-scale research with a new team of interns. Yet, I am beginning to realize that no matter how much experience I have, there will always be unforeseeable curve balls thrown at me that I can’t anticipate no matter how prepared or experienced I am. However, my knowledge and experience now certainly inform how I tackle these curve balls and hopefully allow my problem-solving to be better and quicker. I am so thrilled that Leigh and I were able to get the field season approved here in Port Orford despite the ongoing pandemic. There were many steps we had to take and protocols to write and get approved, but it was worth the work. It certainly is strange living in a place that is meant to be your home for six weeks but having to wear a face covering everywhere except your own bedroom. However, mask wearing, frequent hand washing, and disinfecting is a very small price to pay to avoid having a lapse in our gray whale data collected here in Port Orford (and minimize transmission). Doing field research amidst COVID has certainly been a big curve ball this year but, so far, I have been able to handle these added challenges pretty well, especially with a lot of help from my team. Speaking of which, time to introduce the other Theyodelers…

Figure 1. Noah watching and waiting for whales on the cliff. When we are outside in the wind and are able to maintain a minimum 6-ft distance, we are able to remove our face coverings. Source: T. McCambridge.

First up, we have Noah Dolinajec. Noah is a fellow graduate student who is currently doing a Master’s in Marine & Lacustrine Science and Management at the Vrije Universiteit Brussel in Brussels, Belgium. While he is attending graduate school in Belgium, Noah is not actually from this European country. In fact, he is a Portlandian! As an Oregonian with a passion for the marine environment, Noah is no stranger to the Oregon coast and has spent quite some time exploring it in the past. Some other things about Noah: before going to college he played semi-professional ice hockey, he is a bit of a birder, and he likes to cook (he and I have been tag-teaming the team cooking this year). 

Figure 2. Mattea outside the field station holding local fisher-pup Jim. Source: L. Hildebrand.

Next, we have Mattea Holt Colberg. As I mentioned before, Mattea is the team’s OSU undergraduate intern this year. By participating in a running-start program at her high school where she took two years of college classes, Mattea entered OSU as a junior at just 18 years old! However, she has decided to somewhat extend her undergraduate career at OSU by completing a dual major in Biology and Music. She plays the piano and the violin (which she brought to Port Orford, but we have yet to be serenaded by her). Mattea has previously conducted field research on killer whales in the Salish Sea and I can tell that she is hoping for killer whales to show up in Port Orford (while not entirely ludicrous, the chance of this happening is probably very, very slim). 

Figure 3. Liz in the bow of the kayak in Tichenor Cove. Source: L. Hildebrand.

Last but certainly not least, is Liz Kelly, our Pacific High School intern from Port Orford. Liz has lived in several different states across the country (I’m talking Kentucky to Florida) and so I am really excited that she currently lives here in Oregon because she has been an absolute joy to have on the team so far. Liz brings a lot of energy and humor to the team, which we have certainly needed whenever those curve balls come flying. Besides her positivity, Liz brings a lot of determination and perseverance and seeing her work through tough situations here already has made me very proud. I really hope this internship provides Liz with the life, STEM, and communication skills she needs to help her succeed in pursuing her goals of doing wildlife research after college. As you may have read in my last blog, our previous high school interns have had successes in being admitted to various colleges to follow their goals, and I feel confident that Liz will be no different. When she is not here at the field station, she can probably be found taking care of and riding one of her four horses (Millie, Maricja, Miera, and Jeanie). 

Now that I have introduced the 2020 field team, here is a short play-by-play of what we have been seeing, or perhaps more aptly, not seeing. Our whale sighting numbers have been pretty low so far and when we do see them, they seem to be foraging a little further away from our study site than I am used to seeing in past years. However, this shift in behavior is not entirely surprising to me since our zooplankton net has been coming up pretty empty at our sampling stations. While there are mysids and amphipods scattered here and there, their numbers are in the low 10s when we do our zooplankton ID lab work in the afternoons. These low counts are also reflected by the low densities I am anecdotally seeing on our GoPro drops (Fig 4).

While I am not entirely certain why we are seeing this low prey abundance, I do have some hypotheses. The most likely reason is that this year we experienced some delayed upwelling on our coast. Dawn wrote a great blog about upwelling and wind a few weeks ago and I suggest checking it out to better understand what upwelling is and how it can affect whales (and the whole ecosystem). Typically, we see our peak upwelling occur here in Oregon in May-June. However, if you look at Figure 5 you will see that both the indices remained low at that time this year, whereas in previous years, they were already increasing by May/June.

Figure 5. 10 year time series of the Coastal Upwelling Transport Index (CUTI; top plot) and Biologically Effective Upwelling Transport Index (BEUTI; bottom plot) at 44ºN. CUTI represents the amount of upwelling (positive numbers) or downwelling (negative numbers) while BEUTI estimates the amount of nitrate (i.e. nutrients) upwelled (positive numbers) and downwelled (negative numbers). The light-colored lines represent the CUTI and BEUTI at that point in time while the dark, bold lines represent the long-term average.

A delayed upwelling means that there was likely less nutrients in the water to support little critters like zooplankton to start reproducing and increasing their abundances. Simply put, it means our coastal waters appear to be less productive than they usually are at this time of the year. If there is not much prey around (as we have been finding in our two study sites – Mill Rocks and Tichenor Cove), then it makes sense to me why gray whales are not hanging around since there is not much to feed on. Fortunately, the tail of the trend line in Figure 5 is angling upward, which means that the upwelling finally started in June so hopefully the nutrients, zooplankton and whales will follow soon too. In fact, since I wrote the draft of this blog at the end of last week, we have actually seen an increase in the numbers of mysids in our zooplankton net and on our GoPro videos.

We are almost halfway done with the field season already and I cannot believe how quickly it goes by! During the first two weeks we were busy getting familiar with all of our gear and completing First Aid/CPR and kayak paddle & rescue courses. This week the team started the real data collection. We have had some hiccups (we lost our GoPro stick and our backup GoPro stick, but thankfully have already recovered one of them) but overall, we are off to a pretty good start. Now we just need the upwelling to really kick in, for there to be thick layers of mysids, and for the whales to come in close. Over the next three weeks, you will be hearing from Noah, Mattea and Liz as they share their experiences and viewpoints with all of you!

You can’t build a pyramid without the base: diving into the foundations of behavioral ecology to understand cetacean foraging

By Lisa Hildebrand, MSc student, OSU Department of Fisheries & Wildlife, Marine Mammal Institute, Geospatial Ecology of Marine Megafauna Lab

The last two months have been challenging for everyone across the world. While I have also experienced lows and disappointments during this time, I always try to see the positives and to appreciate the good things every day, even if they are small. One thing that I have been extremely grateful and excited about every week is when the clock strikes 9:58 am every Thursday. At that time, I click a Zoom link and after a few seconds of waiting, I am greeted by the smiling faces of the GEMM Lab. This spring term, our Principal Investigator Dr. Leigh Torres is teaching a reading and conference class entitled ‘Cetacean Behavioral Ecology’. Every week there are 2-3 readings (a mix of book chapters and scientific papers) focused on a particular aspect of behavioral ecology in cetaceans. During the first week we took a deep dive into the foundations of behavioral ecology (much of which is terrestrial-based) and we have now transitioned into applying the theories to more cetacean-centric literature, with a different branch of behavior and ecology addressed each week.

Leigh dedicated four weeks of the class to discussing foraging behavior, which is particularly relevant (and exciting) to me since my Master’s thesis focuses on the fine-scale foraging ecology of gray whales. Trying to understand the foraging behavior of cetaceans is not an easy feat since there are so many variables that influence the decisions made by an individual on where and when to forage, and what to forage on. While we can attempt to measure these variables (e.g., prey, environment, disturbance, competition, an individual’s health), it is almost impossible to quantify all of them at the same time while also tracking the behavior of the individual of interest. Time, money, and unworkable weather conditions are the typical culprits of making such work difficult. However, on top of these barriers is the added complication of scale. We still know so little about the scales at which cetaceans operate on, or, more importantly, the scales at which the aforementioned variables have an effect on and drive the behavior of cetaceans. For instance, does it matter if a predator is 10 km away, or just when it is 1 km away? Is a whale able to sense a patch of prey 100 m away, or just 10 m away? The same questions can be asked in terms of temporal scale too.

What is that gray whale doing in the kelp? Source: F. Sullivan.

As such, cetacean field work will always involve some compromise in data collection between these factors. A project might address cetacean movements across large swaths of the ocean (e.g., the entire U.S. west coast) to locate foraging hotspots, but it would be logistically complicated to simultaneously collect data on prey distribution and abundance, disturbance and competitors across this same scale at the same time. Alternatively, a project could focus on a small, fixed area, making simultaneous measurements of multiple variables more feasible, but this means that only individuals using the study area are studied. My field work in Port Orford falls into the latter category. The project is unique in that we have high-resolution data on prey (zooplankton) and predators (gray whales), and that these datasets have high spatial and temporal overlap (collected at nearly the same time and place). However, once a whale leaves the study area, I do not know where it goes and what it does once it leaves. As I said, it is a game of compromises and trade-offs.

Ironically, the species and systems that we study also live a life of compromises and trade-offs. In one of this week’s readings, Mridula Srinivasan very eloquently starts her chapter entitled ‘Predator/Prey Decisions and the Ecology of Fear’ in Bernd Würsig’s ‘Ethology and Behavioral Ecology of Odontocetes’ with the following two sentences: “Animal behaviors are governed by the intrinsic need to survive and reproduce. Even when sophisticated predators and prey are involved, these tenets of behavioral ecology hold.”. Every day, animals must walk the tightrope of finding and consuming enough food to survive and ensure a level of fitness required to reproduce, while concurrently making sure that they do not fall prey to a predator themselves. Krebs & Davies (2012) very ingeniously use the idea of economic analysis of costs and benefits to understand foraging behavior (but also behavior in general). While foraging, individuals not only have to assess potential risk (Fig. 1) but also decide whether a certain prey patch or item is profitable enough to invest energy into obtaining it (Fig. 2).

Leigh’s class has been great, not only to learn about foundational theories but to then also apply them to each of our study species and systems. It has been exciting to construct hypotheses based on the readings and then dissect them as a group. As an example, Sih’s 1984 paper on the behavioral response race of predators and prey prompted a discussion on responses of predators and prey to one another and how this affects their spatial distributions. Sih posits that since predators target areas with high prey densities, and prey will therefore avoid areas that predators frequent, their responses are in conflict with one another. Resultantly, there will be different outcomes depending on whichever response dominates. If the predator’s response dominates (i.e. predators are able to seek out areas of high prey density before prey can respond), then predators and prey will have positively correlated spatial distributions. However, if the prey responses dominate, then the spatial distributions of the two should be negatively correlated, as predators will essentially always be ‘one step behind’ the prey. Movement is most often the determinant factor to describe the strength of these relationships.

Video 1. Zooplankton closest to the camera will jump or dart away from it. Source: GEMM Lab.

So, let us think about this for gray whales and their zooplankton prey. The latter are relatively immobile. Even though they dart around in the water column (I have seen them ‘jump’ away from the GoPro when we lower it from the kayak on several occasions; Video 1), they do not have the ability to maneuver away fast or far enough to evade a gray whale predator moving much faster. As such, the predator response will most likely always be the strongest since gray whales operate at a scale that is several orders of magnitude greater than the zooplankton. However, the zooplankton may not be as helpless as I have made them seem. Based on our field observations, it seems that zooplankton often aggregate beneath or around kelp. This behavior could potentially be an attempt to evade predators as the kelp and reef crevices may serve as a refuge. So, in areas with a lot of refuges, the prey response may in fact dominate the relationship between gray whales and zooplankton. This example demonstrates the importance of habitat in shaping predator-prey interactions and behavior. However, we have often observed gray whales perform “bubble blasts” in or near kelp (Video 2). We hypothesize that this behavior could be a foraging tactic to tip the see-saw of predator-prey response strength back into their favor. If this is the case, then I would imagine that gray whales must decide whether the energetic benefit of eating zooplankton hidden in kelp refuges outweighs the energy required to pursue them (Fig. 2). On top of all these choices, are the potential risks and threats of boat traffic, fishing gear, noise, and potential killer whale predation (Fig. 1). Bringing us back to the analogy of economic analysis of costs and benefits to predator-prey relationships. I never realized it so clearly before, but gray whales sure do have a lot of decisions to make in a day!

Video 2. Drone footage of a gray whale foraging in kelp and performing a “bubble blast” at 00:40. Footage captured under NMFS permit #21678. Source: GEMM Lab.

Trying to tease apart these nuanced dynamics is not easy when I am unable to simply ask my study subjects (gray whales) why they decided to abandon a patch of zooplankton (Were the zooplankton too hard to obtain because they sought refuge in kelp, or was the patch unprofitable because there were too few or the wrong kind of zooplankton?). Or, why do gray whales in Oregon risk foraging in such nearshore coastal reefs where there is high boat traffic (Does their need for food near the reefs outweigh this risk, or do they not perceive the boats as a risk?). So, instead, we must set up specific hypotheses and use these to construct a thought-out and informed study design to best answer our questions (Mann 2000). For the past few weeks, I have spent a lot of time familiarizing myself with spatial packages and functions in R to start investigating the relationships between zooplankton and kelp hidden in the data we have collected over 4 years, to ultimately relate these patterns to gray whale foraging. I still have a long and steep journey before I reach the peak but once I do, I hope to have answers to some of the questions that the Cetacean Behavioral Ecology class has inspired.

Literature cited

Krebs, J. R., and N. B. Davies. 2012. Economic decisions and the individual in Davies, N. B. et al., eds. An introduction to behavioral ecology. John Wiley & Sons, Oxford.

Mann, J. 2000. Unraveling the dynamics of social life: long-term studies and observational methods in Mann, J., ed. Cetacean societies: field studies of dolphins and whales. University of Chicago Press, Chicago.

Sih, A. 1984. The behavioral response race between predator and prey. The American Naturalist 123:143-150.

Srinivasan, M. 2019. Predator/prey decisions and the ecology of fear in Würsig, B., ed. Ethology and ecology of odontocetes. Springer Nature, Switzerland. 

Intricacies of Zooplankton Species Identification

By Donovan Burns, Astoria High School Junior, GEMM Lab summer intern

The term zooplankton is used to describe a large number of creatures; the exact definition is any animal that cannot move against a sustained current in the marine environment. There are two main types of plankton: holoplankton and meroplankton. Meroplankton are organisms that are plankton for only part of their life cycle. So this makes most sea creatures plankton, for instance, salmon, sunfish, tuna, and most other fish are meroplankton because they start out their lives as plankton. Holoplankton are plankton that remain plankton for their whole lives, these include mysid shrimp, most marine worms, and most jellyfish.

I have spent a good deal of time this summer looking through a microscope at the zooplankton we have captured during sampling from our research kayak, trying to distinguish and identify different species. Telsons, the tail of the tail, are what we use to identify different types of mysid shrimp, which are a primary gray whale prey item along the Oregon coast and the most predominant type of zooplankton we capture in our sampling. For instance Neomysis is a genus of mysid shrimp and is one of the two most abundant zooplankton species we get. Their telsons end with two spikes that are somewhat longer than the spikes on the side of the telson.  This look is distinct from Holmesimysis sculpta, the other of the two most abundant zooplankton species we get, which have four-pronged telsons with varying sizes of spikes along the sides of the telson. Alienacanthomysis macropsis is identified by both their long eye stalks and their rather bland rounded telson.

Caprellidae. Source: R. Norman.

However, creatures that are not mysid shrimp cannot be identified this way.  Like gammarids, they look like fleas.  We have only found one kind of gammarid here in Port Orford this year, Atylus tridens. There are other types but that is the only type we have found this year. After that, we have Caprellidae, also known as skeleton shrimp. They are long and stalky, and have claws in every spot where they could have claws.

Copepod. Source: L. Hildebrand.

Then there are copepods. Copepods are tiny and have long antennae that string down to the sides of their bodies. We also have been seeing lots of crab larvae. I have also seen a couple of polychaete worms, which are marine worms with many legs and segments. The only reason I was able to identify them as polychaetes is due to my marine biology class at Astoria High School where we identified these worms using microscopes before.

We also have had some trouble identifying somethings. For instance, we have found a few individuals of a type of mysid shrimp with a rake-like tail that we are still trying to identify.  Also, we have captured some jellyfish that we are not trying to identify. When the kayak team gets back in from gathering samples, we freeze the samples to kill and preserve the critters in them. This process turns the jellyfish to mush, so they are hard to identify.

To identify these zooplankton and other critters, we put them into a Petri dish and under a dissection scope, at which point we use forceps to move and pivot creatures.  If a jellyfish had just eaten another plankton, we have to cut it open to get the plankton out so we can identify it.  

Sometimes we have large samples of thousands of the same creature, in this case, we would normally sub-sample it. Sub-sampling is when we take a portion of a sample and identify and count individual zooplankton in that sub-sample. Then we multiply those counts by the portion of the whole sample to get the approximate total number that are in that sample.  For instance, say we had a rather large sample, we would take a tenth of that sample and count what is in it. Say we count 500 individuals in that tenth. We would then multiply 500 by ten to get the total number in that whole sample.

Then there are some plankton that we do not catch, like large jellyfish.  The kayak team has gotten photos of a giant jellyfish that was nearly a meter long.

Jellyfish seen by the kayak team. Source: L. Hildebrand.

All in all, Port Orford has an amazing and diverse population of marine life. From gray whales to thresher sharks to mysid shrimp to copepods to jellyfish, this little ecosystem has pretty much some of everything.