Gray Whales – Page 5 – Geospatial Ecology of Marine Megafauna Laboratory

Drones with lasers: almost as cool as “sharks with laser beams attached to their heads”

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

The recent advancement in drones (or unoccupied aircraft systems, UAS) has greatly enhanced opportunities for scientists across a broad range of disciplines to collect high-resolution aerial imagery. Wildlife researchers in particular have utilized this technology to study large elusive animals, such as whales, to observe their behavior (see Clara Bird’s blog) and obtain morphological measurements via photogrammetry (see previous blog for a brief history on photogrammetry and drones). However, obtaining useful measurement data is not as easy as flying the drone and pressing record. For this blog, I will provide a brief overview on the basics of using photogrammetry to extract morphological measurements from images collected with drones, as well as the associated uncertainty from using different drone platforms.

During my PhD at Duke University, I co-developed an open-source photogrammetry software called MorphoMetriX to measure whales in images I collected using drones (Fig. 1) (Torres and Bierlich, 2020) (see this blog for some fieldwork memoirs!). The software is designed to be flexible, simple to use, and customizable without knowledge of scripting languages. Using MorphoMetriX, measurements are made in pixels and then multiplied by the ground sampling distance (GSD) to convert to standard units (e.g., meters) (Fig. 2A). GSD represents the distance on the ground each pixel represents (i.e., the linear size of the pixel) and therefore sets the scale of the image (i.e., cm per pixel). Figure 2A describes how GSD is dependent on the camera sensor, focal length lens, and altitude. Thus, drones equipped with different cameras and focal length lenses will have inherent differences in GSD as altitude increases (Fig. 2B). A larger GSD increases the length each pixel represents in a photo and results in a lower resolution image, potentially obscuring important features in the photo and introducing measurement error.

Figure 1. An example of a Pacific Coast Feeding Group gray whale measured in MorphoMetriX (Torres & Bierlich, 2020).

Figure 2: Overview of photogrammetry methods and calculating ground sampling distance (GSD). A) Photogrammetry methods for how each image is scaled to convert measurements in pixels to standard units (e.g., meters). Altitude is the distance between the camera lens and whale (usually at the surface of the water). Figure from Torres and Bierlich (2020). B) The exact (not accounting for distortion or altitude error) ground sampling distance (GSD) for two drone platforms commonly used to obtain morphological measurements of cetaceans. The difference in GSD between the P4Pro and Inspire 1 is due to the difference in sensor width and focal lengths of the cameras used. Figure from Bierlich et al. (2021).

Obtaining accurate altitude information is a key component in obtaining accurate measurements. All drones are equipped with a barometer, which measures altitude from changes in pressure. In general, barometers usually yield low accuracy in the altitude recorded, particularly for low-cost sensors commonly found on small, off-the-shelf drones (Wei et al., 2016). Dawson et al. (2017) added a laser altimeter (i.e., LightWare SF11/C, https://www.mouser.com//datasheet//2//321//28054-SF11-Laser-Altimeter-Manual-Rev8-1371857.pdf) to a drone, which yields higher accuracy in the altitude recorded. Since then, several studies have adopted use of a laser altimeter to study different species of baleen whales (i.e., Gough et al., 2019; Christiansen et al., 2018).

The first chapter of my dissertation, which was published last year in Marine Ecology Progress Series, compared the accuracy of several drones equipped with different camera sensors, focal length lenses, and a barometer vs. laser altimeter (Bierlich et al., 2021). We flew each drone over a known sized object floating at the surface and collected images at various altitudes (between 10 – 120 m). We used the known size of the floating object to determine the percent error of each measurement at each altitude. We found that 1) there is a lot of variation in measurement error across the different drones when using a barometer to measure altitude and 2) using a laser altimeter dramatically reduces measurement error for each drone (Fig. 3).

Figure 3. The % error for measurements from different drones. Black dashed line represents 0% error (true length = 1.48 m). The gray dashed lines represent under- and over-estimation of the true length by 5% (1.41 and 1.55 m, respectively).

These findings are important because if a study is analyzing measurements that are from more than one drone, the uncertainty associated with those measurements must be taken into account to know if measurements are reliable and comparable. For instance, let’s say we are comparing the body length of two different populations and found that population A is significantly longer than population B. From looking at Figure 3, that significant difference in length between population A and B could be unreliable as the difference may be due to the bias introduced by the type of drone, camera sensor, focal length lens, and whether a barometer or laser altimeter was used for recording altitude. In other words, without incorporating uncertainty associated with each measurement, how do you trust your measurement?

Hence, the National Institute of Standards and Technology (NIST) states that a measurement is complete only when accompanied by a quantitative statement of its uncertainty (Taylor & Kuyatt, 1994). In our Bierlich et al. (2021) study, we develop a Bayesian statistical model where we use the measurements of the known-sized object floating at the surface (what was used for Fig. 3) as training data to predict the lengths of unknown-sized whales. This Bayesian approach views data and the underlying parameters that generated the data (such as the mean or standard deviation) as random, and thus can be described by a statistical distribution. Using Bayes’ Theorem, a model of the observed data (called the likelihood function), is combined with prior knowledge pertaining to the underlying parameters (called the prior probability distribution) to form the posterior probability distribution, which serves as updated knowledge about the underlying parameter. For example, if someone told me they saw a 75 ft blue whale, I would not be phased. But if someone told me they saw a 150 ft blue whale, I would be skeptical – I’m using prior knowledge to determine the probability of this statement being true.

The posterior probability distribution produced by this Bayesian approach can also serve as new prior information for subsequent analyses. Following this framework, we used the known-sized objects to first estimate the posterior probability distribution for error for each drone. We then used that posterior probability distribution for error specific to each drone platform as prior information to form a posterior predictive distribution for length of unknown-sized whales. The length of an individual whale can then be described by the mean of this second posterior predictive distribution, and its uncertainty defined as the variance or an interval around the mean (Fig. 4).

Figure 4. An example of a posterior predictive distribution for total length of an individual blue whale. The black bars represent the uncertainty around the mean value (the black dot) – the longer black bars represent the 95% highest posterior density (HPD) interval, and the shorter black bars represent the 65% HPD interval.

For over half a decade, the GEMM Lab has been collecting drone images of Pacific Coast Feeding Group (PCFG) of gray whales off the coast of Oregon to measure their morphology and body condition (see GRANITE Project Blog). We have been using several different types of drones equipped with different cameras, focal length lenses, barometers, and laser altimeters. These measurements from different drones will inherently have different levels of error associated with them, so adapting these methods for incorporating uncertainty will be key to ensure our measurements are comparable and analyses are robust. To do this, we fly over a known-sized board (1 m) at the start of each flight to use as training data to generate a posterior predictive distribution for length of the an unknown-sized PCFG gray whale that we fly over (Fig. 5). Likewise, we are working closely with several other collaborators who are also using different drones. Incorporating measurement uncertainty from drones used across research labs and in different environments will help ensure robust analyses and provide great opportunity for some interesting comparisons – such as differences in gray whale body condition on their feeding grounds in Oregon vs. their breeding grounds in Baja, Mexico, and morphological comparisons with other baleen whale species, such as blue and humpback whales. We are currently wrapping up measurement from thousands of boards (Fig. 5) and whales (Fig. 1) from 2016 – 2021, so stay tuned for the results!

Figure 5. An example of a known-sized object (1 m long board) used as training data to assess measurement uncertainty.

References

Bierlich, K.C., Schick, R.S., Hewitt, J., Dale, J., Goldbogen, J.A., Friedlaender, A.S., Johnston D.J. (2021). A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS. Marine Ecology Progress Series. DOI: https://doi.org/10.3354/meps13814

Christiansen F, Vivier F, Charlton C, Ward R, Amerson A, Burnell S, Bejder L (2018) Maternal body size and condition determine calf growth rates in southern right whales. Mar Ecol Prog Ser 592: 267−281

Dawson SM, Bowman MH, Leunissen E, Sirguey P (2017) Inexpensive aerial photogrammetry for studies of whales and large marine animals. Front Mar Sci 4: 366

Gough, W.T., Segre, P.S., Bierlich, K.C., Cade, D.E., Potvin, J., Fish, F. E., Dale, J., di Clemente, J., Friedlaender, A.S., Johnston, D.W., Kahane-Rapport, S.R., Kennedy, J., Long, J.H., Oudejans, M., Penry, G., Savoca, M.S., Simon, M., Videsen, S.K.A., Visser, F., Wiley, D.N., Goldbogen, J.A. (2019). Scaling of swimming performance in baleen whales. Journal of Experimental Biology, 222(20).https://doi.org/10.1242/jeb.204172

Taylor, B. N., and Kuyatt, C. E. (1994). Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results. Washington, DC: National Institute of Standards and Technology. 1–25.

Torres, W.I., & Bierlich, K.C. (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), 1825. https://doi.org/10.21105/joss.01825

Wei S, Dan G, Chen H (2016) Altitude data fusion utilizing differential measurement and complementary filter. IET Sci Meas Technol (Singap) 10: 874−879

The costs and benefits of automated behavior classification

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

“Why don’t you just automate it?” This is a question I am frequently asked when I tell someone about my work. My thesis involves watching many hours of drone footage of gray whales and meticulously coding behaviors, and there are plenty of days when I have asked myself that very same question. Streamlining my process is certainly appealing and given how wide-spread and effective machine learning methods have become, it is a tempting option to pursue. That said, machine learning is only appropriate for certain research questions and scales, and it’s important to consider these before investing in using a new tool.

The application of machine learning methods to behavioral ecology is called computational ethology (Anderson & Perona, 2014). To identify behaviors from videos, the model tracks individuals across video frames and identifies patterns of movement that form a behavior. This concept is similar to the way we identify a whale as traveling if it’s moving in a straight line and as foraging if it’s swimming in circles within a small area (Mayo & Marx, 1990, check out this blog to learn more). The level of behavioral detail that the model is able to track depends on the chosen method (Figure 1, Pereira et al., 2020). These methods range from tracking each animal as a simple single point (called a centroid) to tracking the animal’s body positioning in 3D (this method is called pose estimation), which range from providing less detailed to more detailed behavior definitions. For example, tracking an individual as a centroid could be used to classify traveling and foraging behaviors, while pose estimation could identify specific foraging tactics.

*Figure 1. Figure from Pereira et al. (2020) illustrating the different methods of animal behavior tracking that are possible using machine learning.*

Pose estimation involves training the machine learning algorithm to track individual anatomical features of an individual (e.g., the head, legs, and tail of a rat), meaning that it can define behaviors in great detail. A behavior state could be defined as a combination of the angle between the tail and the head, and the stride length.

For example, Mearns et al. (2020) used pose estimation to study how zebrafish larvae in a lab captured their prey. They tracked the tail movements of individual larvae when presented with prey and classified these movements into separate behaviors that allowed them to associate specific behaviors with prey capture (Figure 2). The authors found that these behaviors occurred in a specific sequence, that the behaviors kept the prey within the larvae’s line of sight, and that the sequence was triggered by visual cues. In fact, when they removed the visual cue of the prey, the larvae terminated the behavior sequence, meaning that the larvae are continually choosing to do each behavior in the sequence, rather than the sequence being one long behavior event that is triggered only by the initial visual cue. This study is a good example of the applicability of machine learning models for questions aimed at kinematics and fine-scale movements. Pose estimation has also been used to study the role of facial expression and body language in rat social communication (Ebbesen & Froemke, 2021).

Figure 2. Excerpt from figure 1 of Mearns et al. (2020) illustrating (A) the camera set up for their experiment, (B) how the model tracked the eye angles and tail of the larvae fish, (C) the kinematics extracted from the footage. In panel (C) the top plot shows how the eyes converged on the same object (the prey) during prey capture event, the middle plot shows when the tail was curved to the left or the right, and the bottom plot shows the angle of the tail tip relative to the body.

While previous machine learning methods to track animal movements required individuals to be physically marked, the current methods can perform markerless tracking (Pereira et al., 2020). This improvement has broadened the kinds of studies that are possible. For example, Bozek et al., (2021) developed a model that tracked individuals throughout an entire honeybee colony and showed that certain individual behaviors were spatially distributed within the colony (Figure 3). Machine learning enabled the researchers to track over 1000 individual bees over several months, a task that would be infeasible for someone to do by hand.

*Figure 3. Excerpt from figure 1 of Bozek et al., (2021) showing how individual bees and their trajectories were tracked.*

These studies highlight that the potential benefits of using machine learning when studying fine scale behaviors (like kinematics) or when tracking large groups of individuals. Furthermore, once it’s trained, the model can process large quantities of data in a standardized way to free up time for the scientists to focus on other tasks.

While machine learning is an exciting and enticing tool, automating behavior detection via machine learning could be its own PhD dissertation. Like most things in life, there are costs and benefits to using this technique. It is a technically difficult tool, and while applications exist to make it more accessible, knowledge of the computer science behind it is necessary to apply it effectively and correctly. Secondly, it can be tedious and time consuming to create a training dataset for the model to “learn” what each behavior looks like, as this step involves manually labeling examples for the model to use.

As I’ve mentioned in a previous blog, I came quite close to trying to study the kinematics of gray whale foraging behaviors but ultimately decided that counting fluke beats wasn’t necessary to answer my behavioral research questions. It was important to consider the scale of my questions (as described in Allison’s blog) and I think that diving into more fine-scale kinematics questions could be a fascinating follow-up to the questions I’m asking in my PhD.

For instance, it would be interesting to quantify how gray whales use their flukes for different behavior tactics. Do gray whales in better body condition beat their flukes more frequently while headstanding? Does the size of the fluke affect how efficiently they can perform certain tactics? While these analyses would help quantify the energetic costs of different behaviors in better detail, they aren’t necessary for my broad scale questions. Consequently, taking the time to develop and train a pose estimation machine learning model is not the best use of my time.

That being said, I am interested in applying machine learning methods to a specific subset of my dataset. In social behavior, it is not only useful to quantify the behaviors exhibited by each individual but also the distance between them. For example, the distance between a mom and her calf can be indicative of the calves’ dependence on its mom (Nielsen et al., 2019). However, continuously measuring the distance between two individuals throughout a video is tedious and time intensive, so training a machine learning model could be an effective use of time. I plan to work with an intern this summer to develop a machine learning model to track the distance between pairs of gray whales in our drone footage and then relate this distance data with the manually coded behaviors to examine patterns in social behavior (Figure 4). Stay tuned to learn more about our progress!

*Figure 4. A mom and calf pair surfacing together. Image collected under NOAA/NMFS permit #21678*

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References

Anderson, D. J., & Perona, P. (2014). Toward a Science of Computational Ethology. Neuron, 84(1), 18–31. https://doi.org/10.1016/j.neuron.2014.09.005

Bozek, K., Hebert, L., Portugal, Y., Mikheyev, A. S., & Stephens, G. J. (2021). Markerless tracking of an entire honey bee colony. Nature Communications, 12(1), 1733. https://doi.org/10.1038/s41467-021-21769-1

Ebbesen, C. L., & Froemke, R. C. (2021). Body language signals for rodent social communication. Current Opinion in Neurobiology, 68, 91–106. https://doi.org/10.1016/j.conb.2021.01.008

Mayo, C. A., & Marx, M. K. (1990). Surface foraging behaviour of the North Atlantic right whale, Eubalaena glacialis , and associated zooplankton characteristics. Canadian Journal of Zoology, 68(10), 2214–2220. https://doi.org/10.1139/z90-308

Mearns, D. S., Donovan, J. C., Fernandes, A. M., Semmelhack, J. L., & Baier, H. (2020). Deconstructing Hunting Behavior Reveals a Tightly Coupled Stimulus-Response Loop. Current Biology, 30(1), 54-69.e9. https://doi.org/10.1016/j.cub.2019.11.022

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, T. D., Shaevitz, J. W., & Murthy, M. (2020). Quantifying behavior to understand the brain. Nature Neuroscience, 23(12), 1537–1549. https://doi.org/10.1038/s41593-020-00734-z

Of snakes and whales: How food availability and body condition affect reproduction

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

Over six field seasons the GEMM lab team has conducted nearly 500 drone flights over gray whales, equaling over 100 hours of footage. These hours of footage are the central dataset for my PhD dissertation, so it’s up to me to process them all. This process can be challenging, tedious, and daunting, but it is also quite fun and a privilege to be the one person who gets to watch all the footage. It’s fascinating to get to know the whales and their behaviors and pick up on patterns. It motivates me to get through this video processing step and start doing the data analysis. Recently, it’s been especially fun to notice patterns that I’ve seen mentioned in the literature. One example is adult social behavior.

There are two categories of social behavior that I’m interested in studying: maternal behavior, defined as interactions between a mom and its calf, and general social behaviors, defined as social interactions between non-mom/calf pairs. In this blog I’ll focus on general social behaviors, but if you’re interested in maternal behavior check out this blog. General social behavior, which I’ll refer to as social behavior moving forward, includes tactile interactions and promiscuous behaviors (Torres et al. 2018; Clip 1). While gray whales in the PCFG range are primarily foraging, researchers have observed increases in social behavior towards the end of the foraging season (Stelle et al., 2008; Torres et al., 2018). We think that this indicates that the whales are starting to focus less on feeding and more on breeding. This tradeoff of foraging vs. socializing time is interesting because it comes at an energetic cost.

Clip 1. Example of social interaction between a male and female gray whale off the coast of Oregon, USA. Collected under NOAA/NMFS permit #21678

Broadly, animals need to balance the energetic demands of survival with those of reproduction. They need to reproduce to pass on their genes, but reproduction is energetically demanding, and animals also need to survive and grow to be able to reproduce. The decision to reproduce is costly because reproduction requires energetic investment and time investment since animals do not forage (gaining energy) when they are socializing. Consequently, only animals with sufficient energy reserves (i.e., body condition) to invest in reproduction actually engage in reproduction. Given these costs associated with reproduction, we expect to see a relationship between social behavior and body condition (Green, 2001) with mainly animals in good body condition engaging in social behavior because these animals have sufficient reserves to sustain the cost. Furthermore, since body condition is an indicator of foraging success and prey availability, environmental conditions can also affect social behavior and reproduction through this pathway.

Rahman et al. (2014) used a lab experiment to study the relationship between nutritional stress and male guppy courtship behavior (Figure 1). In their experiment they tested for the effects of both decreased diet quantity and quality on the frequency of male courtship behaviors. Rahman et al (2014) found that individuals in the low-quantity group were significantly smaller than those in the high-quality group and that diet quantity had a significant effect on the frequency of courtship behaviors. Males fed a low-quantity diet performed fewer courtship behaviors. Interestingly, there was no significant effect of diet quality on courtships behavior, although there was some evidence of an interaction effect, which suggests that within the low-quantity group, males fed with high-quality food performed more courtship behaviors that those fed with low-quality food. This study is interesting because it shows how foraging success (diet quantity and quality) can affect courting behavior.

However, guppies are not the ideal species for comparison to gray whales because gray whales and guppies have quite different life history traits. A more fitting comparison would be with an example species with more in common with gray whales, such as viviparous capital breeders. Viviparous animals develop the embryo inside the body and give live birth. Capital breeders forage to build energy reserves and then rely on those energy reserves during reproduction. Surprisingly, I found asp vipers to be a good example species for comparison to gray whales.

Asp vipers (Figure 2) are viviparous snakes who are considered capital breeders because they forage prior to hibernation, and then begin reproduction immediately following hibernation without additional foraging. Naulleau & Bonnet (1996) conducted a field study on female asp vipers to determine if there was a difference in body condition at the start of the breeding season between females who reproduced or not during that season. To do this they marked individuals and measured their body condition at the start of the breeding season and then recaptured those individuals at the end of the breeding season and recorded whether the individual had reproduced. Interestingly, they found that there was a strongly significant difference in body condition between females that did and did not reproduce. In fact, they discovered that no female below a certain body condition value reproduced, meaning that they found a body condition threshold for reproduction.

Additionally, a study on water pythons found that their body condition threshold for reproduction shifted over time in response to prey availability (Madsen & Shine, 1999). These authors found that females lowered their threshold after several consecutive years of poor prey availability. These studies are really exciting to me because they address questions that the GRANITE project team is interested in tackling.

Understanding the relationship between body condition and reproduction in gray whales is an important puzzle piece for our work. The aim of the GRANITE project is to understand how the effects of stressors on individual whales scales up to population level impacts (read Lisa’s blog to learn more). Reproduction rates play a big role in population dynamics, so it is important to understand what factors affect reproduction. Since we’re studying these whales on their foraging grounds, assessing body condition provides an important link between foraging behavior and reproduction.

For example, if an individual’s response to a stressor is to forage less, that may lead to poorer body condition, meaning that they may be less likely to reproduce. While reduced reproduction in one individual may not have a big effect on the population, the same response from multiple individuals could impact the population’s dynamics (i.e., increasing or decreasing abundance). Understanding these different relationships between behavior, body condition, and reproduction rates is a big undertaking, but it’s exciting to be a member of the GRANITE team as this strong group of scientists works to bring together different data streams to work on this big picture question. We’re all deep into data processing right now so stay tuned over the next few years to learn more about gray whale social behavior and to find out if fat whales are more social than skinny whales.

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References

Green, A. J. (2001). Mass/Length Residuals: Measures of Body Condition or Generators of Spurious Results? Ecology, 82(5), 1473–1483. https://doi.org/10.1890/0012-9658(2001)082[1473:MLRMOB]2.0.CO;2

Madsen, T., & Shine, R. (1999). The adjustment of reproductive threshold to prey abundance in a capital breeder. Journal of Animal Ecology, 68(3), 571–580. https://doi.org/10.1046/j.1365-2656.1999.00306.x

Naulleau, G., & Bonnet, X. (1996). Body Condition Threshold for Breeding in a Viviparous Snake. Oecologia, 107(3), 301–306.

Rahman, M. M., Kelley, J. L., & Evans, J. P. (2013). Condition-dependent expression of pre- and postcopulatory sexual traits in guppies. Ecology and Evolution, 3(7), 2197–2213. https://doi.org/10.1002/ece3.632

Rahman, M. M., Turchini, G. M., Gasparini, C., Norambuena, F., & Evans, J. P. (2014). The Expression of Pre- and Postcopulatory Sexually Selected Traits Reflects Levels of Dietary Stress in Guppies. PLOS ONE, 9(8), e105856. https://doi.org/10.1371/journal.pone.0105856

Stelle, L. L., Megill, W. M., & Kinzel, M. R. (2008). Activity budget and diving behavior of gray whales (Eschrichtius robustus) in feeding grounds off coastal British Columbia. Marine Mammal Science, 24(3), 462–478. https://doi.org/10.1111/j.1748-7692.2008.00205.x

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science, 5(SEP). https://doi.org/10.3389/fmars.2018.00319

Memoirs from above: drone observations of blue, humpback, Antarctic minke, and gray whales

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

With the GRANITE field season officially over, we are now processing all of the data we collected this summer. For me, I am starting to go through all the drone videos to take snapshots of each whale to measure their body condition. As I go through these videos, I am reflecting on the different experiences I am fortunate enough to have with flying different drones, in different environments, over different species of baleen whales: blue, humpback, Antarctic minke, and now gray whales. Each of these species have a different morphological design and body shape (Woodward et al., 2006), which leads to different behaviors that are noticeable from the drone. Drones create immense opportunity to learn how whales thrive in their natural environments [see previous blog for a quick history], and below are some of my memories from above.

I first learned how drones could be used to study the morphology and behavior of large marine mammals during my master’s degree at Duke University, and was inspired by the early works of John Durban (Durban et al., 2015, 2016) Fredrick Christiansen (Christiansen et al., 2016) and Leigh Torres (Torres et al., 2018). I immediately recognized the value and utility of this technology as a new tool to better monitor the health of marine mammals. This revelation led me to pursue a PhD with the Duke University Marine Robotics and Remote Sensing (MaRRS) Lab led by Dr. Dave Johnston where I helped further develop tools and methods for collecting drone-based imagery on a range of species in different habitats.

When flying drones over whales, there are a lot of moving parts; you’re on a boat that is moving, flying something that is moving, following something that is moving. These moving elements are a lot to think about, so I trained hard, so I did not have to think about each step and flying felt intuitive and natural. I did not grow up playing video games, so reaching this level of comfort with the controls took a lot of practice. I practiced for hours over the course of months before my first field excursion and received some excellent mentorship and training from Julian Dale, the lead engineer in the MaRRS Lab. Working with Julian and the many hours of training helped me establish a solid foundation in my piloting skills and feel confident working in various environments on different species.

Blue whales offshore of Monterey, California.

In 2017 and 2018 I was involved in collaborative project with the MaRRS Lab and Goldbogen Lab at Stanford University, where we tagged and flew drones over blue whales offshore of Monterey, California. We traveled about an hour offshore and reliably found groups of blue whales actively feeding. Working offshore typically brought a large swell, which can often make landing the drone back into your field partner’s hands tricky as everything is bobbing up and down with the oscillations of the swell. Fortunately, we worked from a larger research vessel (~56 ft) and quickly learned that landing the drone in the stern helped dampen the effects of bobbing up and down. The blue whales we encountered often dove to a depth of around 200 m for about 20-minute intervals, then come to the surface for only a few minutes. This short surface period provided only a brief window to locate the whale once it surfaced and quickly fly over it to collect the imagery needed before it repeated its dive cycle. We learned to be patient and get a sense of the animal’s dive cycle before launch in order to time our flights so the drone would be in the air a couple of minutes before the whale surfaced.

Once over the whales, the streamlined body of the blue whales was noticeable, with their small, high aspect ratio flippers and fluke that make them so well adapted for fast swimming in the open ocean (Fig. 1) (Woodward et al., 2006). I also noticed that because these whales are so large (often 21 – 24 m), I often flew at higher altitudes to be able fit them within the field of view of the camera. It was also always shocking to see how small the tagging boat (~8 m) looked when next to Earth’s largest creatures.

Figure 1. Two blue whales surface after a deep dive offshore of Monterey, Ca. *(Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03)*

Antarctic minke whales and humpback whales along the Western Antarctic PeninsulaA lot of the data included in my dissertation came from work along the Western Antarctic Peninsula (WAP), which had a huge range of weather conditions, from warm and sunny days to cold and snowy/foggy/rainy/windy/icy days. A big focus was often trying to keep my hands warm, as it was often easier to fly without gloves in order to better feel the controls. One of the coldest days I remember was late in the season in mid-June (almost winter!) in Wilhemina Bay where ice completely covered the bay in just a couple hours, pushing the whales out into the Gerlache Strait; I suspect this was the last ice-free day of the season. Surprisingly though, the WAP also brought some of the best conditions I have ever flown in. Humpback and Antarctic minke whales are often found deep within the bays along the peninsula, which provided protection from the wind. So, there were times where it would be blowing 40 mph in the Gerlache Strait, but calm and still in the bays, such as Andvord Bay, which allowed for some incredible conditions for flying. Working from small zodiacs (~7 m) allowed us more maneuverability for navigating around or through the ice deep in the bays (Fig. 2)

Figure 2. Navigating through ice-flows along the Western Antarctic Peninsula. *(Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)*

Flying over Antarctic minke whale was always rewarding, as they are very sneaky and can quickly disappear under ice flows or in the deep, dark water. Flying over them often felt like a high-speed chase, as their small streamlined bodies makes them incredibly quick and maneuverable, doing barrel rolls, quick banked turns, and swimming under and around ice flows (Fig. 3). There would often be a group between 3-7 individuals and it felt like they were playing tag with each other – or perhaps with me!

Figure 3. Two Antarctic minke whales swimming together along the Western Antarctic Peninsula. *(Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)*

Humpbacks displayed a wide range of behaviors along the WAP. Early in the season they continuously fed throughout the entire day, often bubble net feeding in groups typically of 2-5 animals (Fig. 4). For as large as they are, it was truly amazing to see how they use their pectoral fins to perform quick accelerations and high-speed maneuvering for tight synchronized turns to form bubble nets, which corral and trap their krill, their main food source (Fig. 4) (Woodward et al., 2006). Later in the season, humpbacks switched to more resting behavior in the day and mostly fed at night, taking advantage of the diel vertical migration of krill. This behavior meant we often found humpbacks snoozing at the surface after a short dive, as if they were in a food coma. They also seemed to be more curious and playful with each other and with us later in the season (Fig. 5).

We also encountered a lot of mom and calf pairs along the WAP. Moms were noticeably skinny compared to their plump calf in the beginning of the season due to the high energetic cost of lactation (Fig. 6). It is important for moms to regain this lost energy throughout the feeding season and begin to wean their calves. I often saw moms refusing to give milk to their nudging calf and instead led teaching lessons for feeding on their own.

Figure 4. Two humpback whales bubble-net feeding early in the feeding season (December) along the Western Antarctic Peninsula. *(Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)*

Figure 5. A curious humpback whale dives behind our Zodiac along the Western Antarctic Peninsula. (Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)

Figure 6. A mom and her calf rest at the surface along the Western Antarctic Peninsula. Note how the mom looks skinnier compared to her plump calf, as lactation is the most energetically costly phase of the reproductive cycle. *(Image credit: Duke University Marine Robotics and Remote Sensing under NOAA permit 14809-03 and ACA permits 2015-011 and 2020-016.)*

Gray whales off Newport, Oregon

All of these past experiences helped me quickly get up to speed and jump into action with the GRANITE field team when I officially joined the GEMM Lab this year in June. I had never flown a DJI Inspire quadcopter before (the drone used by the GEMM Lab), but with my foundation piloting different drones, some excellent guidance from Todd and Clara, and several hours of practice to get comfortable with the new setup, I was flying over my first gray whale by day three of the job.

The Oregon coast brings all sorts of weather, and some days I strangely found myself wearing a similar number of layers as I did in Antarctica. Fog, wind, and swell could all change within the hour, so I learned to make the most of weather breaks when they came. I was most surprised by how noticeably different gray whales behave compared to the blue, Antarctic minke, and humpback whales I had grown familiar with watching from above. For one, it is absolutely incredible to see how these huge whales use their low-aspect ratio flippers and flukes (Woodward et al., 2006) to perform low-speed, highly dynamic maneuvers to swim in very shallow water (5-10 m) so close to shore (<1m sometimes!) and through kelp forest or surf zones close to the beach. They have amazing proprioception, or the body’s ability to sense its movement, action, and position, as gray whales often use their pectoral fins and fluke to stay in a head standing position (see Clara Bird’s blog) to feed in the bottom sediment layer, all while staying in the same position and resisting the surge of waves that could smash them against the rocks (Video 1) . It is also remarkable how the GEMM Lab knows each individual whale based on natural skin marks, and I started to get a better sense of each whale’s behavior, including where certain individuals typically like to feed, or what their dive cycle might be depending on their feeding behavior.

Video 1. Two Pacific Coast Feeding Group (PCFG) gray whales “head-standing” in shallow waters off the coast of Newport, Oregon. NOAA/NMFS permit #21678

I feel very fortunate to be a part of the GRANITE field team and to contribute to data collection efforts. I look forward to the data analysis phase to see what we learn about how the morphology and behavior of these gray whales to help them thrive in their environment.

References:

Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. Y., and Bejder, L. (2016).Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7, e01468–18.

Durban, J. W., Fearnbach, H., Barrett-Lennard, L. G., Perryman, W. L., & Leroi, D. J. (2015). Photogrammetry of killer whales using a small hexacopter launched at sea. Journal of Unmanned Vehicle Systems, 3(3), 131-135.

Durban, J. W., Moore, M. J., Chiang, G., Hickmott, L. S., Bocconcelli, A., Howes, G., et al.(2016). Photogrammetry of blue whales with an unmanned hexacopter. Mar. Mammal Sci. 32, 1510–1515.

Woodward, B. L., Winn, J. P., and Fish, F. E. (2006). Morphological specializations of baleen whales associated with hydrodynamic performance and ecological niche. J. Morphol. 267, 1284–1294.

How much energy does that mouthful cost?

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

Tagging a whale is no easy feat, nor is it without some impact to the whale – no matter how minimized through the use of non-penetrating suction cup tags. Yet, in August 2021 the GEMM Lab initiated a new phase in our research on gray whales, aimed at obtaining a better understanding of the underwater lives and energetics of a gray whale (Figure 1, top image). We captured some amazing data through these specialized, non-invasive tags that provide a brief window into their world and physiology. The video recordings from the tags showed us whales digging their heads into the benthos generating billowing clouds of sediment, likely exploiting desirable prey patches (Figure 1, middle images). We also saw foraging whales undertake dizzying spins and headstands for hours, demonstrating the fascinating maneuverability and flexibility of gray whales (Figure 1, bottom image). But what is motivating us to capture this information?

**Figure 1.** **Top image:** *One of the suction cup tags attached to the right side of the gray whale “RAT”*. **Middle images:** still images from the video recorded on the tag attached to gray whale “DICE”, where left image depicts the whale “seeing” our research vessel RUBY as “DICE” comes to the surface to breathe, and the right image shows a large sediment cloud generated by “DICE” as it digs its head into the benthos feeding on benthic prey. **Bottom image:** a visualization of a trackline (grey, blue, & yellow line) of a foraging gray whale (indicated by the blue arrow). The yellow segments depict times during which the whale was on its side or headstanding (suggesting feeding behavior), while the blue segments represent when the whale was traveling or surfacing. You can see how often in the track the whale is presumably feeding based on the amount of yellow segments in the track. Also, note the amount of circular loops the whale performs, displaying the maneuverability of foraging gray whales. Source: GEMM Lab.

The GEMM Lab has researched the ecology and physiology of Pacific Coast Feeding Group (PCFG) gray whales since 2015. Our efforts have filled crucial knowledge gaps to better understand this sub-group of the Eastern North Pacific (ENP) gray whale population. We now know that gray whale body condition increases throughout a foraging season and can fluctuate considerably between years (Soledade Lemos et al. 2020). Additionally, body condition varies significantly by reproductive state, with calves and pregnant females displaying higher body conditions (Soledade Lemos et al. 2020). We have also validated and quantified fecal steroid and thyroid hormone metabolite concentrations, providing us with thresholds to identify a stressed vs. a not stressed whale based on its hormone levels (Lemos et al. 2020). These validations have allowed us to make correlations between poor body condition and the steroid hormone cortisol which confirm that slim whales are stressed, while chubby whales are relaxed (Lemos et al. 2021). These physiological results are particularly salient in the light of our recent findings that PCFG gray whales select prey quality over prey quantity when foraging (Hildebrand et al. in review) and that the caloric content of available prey species in the PCFG range vary significantly (Hildebrand et al. 2021).

While we have addressed several fundamental questions about the PCFG in the last 7 years, answering one question has led to asking 10 more questions – a common pattern in science. Given that we know (1) PCFG whales improve their body condition over the course of the foraging season (Soledade Lemos et al. 2020), (2) PCFG females are able to successfully give birth to and wean calves (Calambokidis & Perez 2017), and (3) certain prey in the PCFG region are of higher caloric value than prey in the ENP Arctic foraging grounds (Hildebrand et al. 2021), a big question that we continue to scratch our heads about is why does the PCFG sub-group have such a small abundance (~250 individuals; Calambokidis et al. 2017) in comparison to the much larger ENP population (~21,000 individuals; Stewart & Weller 2021). Several hypotheses have been suggested including that the energetic costs of feeding may differ between ENP and PCFG whales, with the latter having to expend more energy to obtain prey due to the different foraging behaviors employed (Torres et al. 2018) to obtain diverse prey types, thus justifying the larger abundance of the ENP (Hildebrand et al. 2021).

Quantifying the energetic cost of baleen whale behaviors is not simple. However, the development of animal-borne tags has allowed scientists to make big strides regarding behavioral cost quantification. The majority of this work has focused on rorqual whales (i.e., blue, humpback, fin whales; e.g., Goldbogen et al. 2013; Cade et al. 2016) as their characteristic lunge-feeding strategy produces a distinct signal in the accelerometer sensors integrated within the tags, making feeding events easier to identify. Gray whales, unlike rorquals, do not lunge-feed. ENP gray whales predominantly feed benthically; diving down to the benthos where they turn onto their side and suction mouthfuls of soft sediment (mud) that contains amphipods that they filter out of the mud (Nerini & Oliver 1983). PCFG whales feed benthically as well, but they also use a number of other feeding behaviors to obtain a variety of prey in a variety of benthic habitats, including headstands, bubble blasts, and sharking (Torres et al. 2018). The above-mentioned gray whale feeding behaviors involve much subtler movements than the powerful, distinctive lunges displayed by rorquals, yet they undoubtedly still incur some energetic cost to the whales. However, exactly how energetically costly the various gray whale feeding behaviors are remains unknown.

One of the three suction cup tags we deployed on gray whales. Dr. Cade printed special “kelp shields” (blue part of the tag) to prevent kelp from potentially getting caught underneath the tag since PCFG whales often forage on reefs with a lot of kelp. This tag includes a video camera (the lens can be seen in the center of the tag) to record video of the whale’s underwater behavior. Source: L. Torres.

This knowledge gap is one of the reasons why the GEMM Lab initiated a new project in close collaboration with Dr. Dave Cade from Stanford University and John Calambokidis from Cascadia Research Collective to quantify and understand the energetics and underwater behavior of gray whales using suction-cup tags. The project was kick-started with a very successful pilot effort the week of August 16^th this year. Tags were placed on the backs of three different PCFG gray whales with a long carbon fiber pole and attached to the whales with four suction cups. The tags recorded video, position, accelerometry, and magnetometry data, which we will use to recreate the animal’s movements (pitch, roll), heading, trackline, and environment. Although the weather forecast did not look promising for most of the week, we lucked out with perfect conditions for one day during which we managed to deploy three tags on three different gray whales that are well-known, long-term study animals of the GEMM Lab. The tags stayed on the whales for 1-6 hours and were all recovered (including an adventurous trip up the Alsea River which involved a kayak deployment!).

Left: Postdoc Alejandro Ajo Fernandez uses a Yagi antenna to try and locate a tagged whale since the tags have a transmitter that sends a signal when the whale is at the surface to breathe. Right: Dr. Dave Cade proudly showing off the tag that floated up the Alsea River and required a kayak to recover. Source: L. Torres.

Dr. Cade spent the rest of the week teaching GEMM Lab PI Leigh Torres, University of British Columbia Master’s student Kate Colson (who is co-advised by Leigh and Dr. Andrew Trites), and myself the intricacies of data download, processing, and preliminary analysis of the tag data. For her Master’s research, Kate will develop a bioenergetics model for the PCFG sub-group that includes data on foraging energetics (estimated from the tag data) and prey availability in the PCFG foraging range. I plan on using the tag data to assess behavior patterns of PCFG whales relative to habitat as part of my PhD research. All together analysis of the data from these short-term tag deployments will help us get closer to understanding the behavioral choices, habitat needs, and energetic trade-offs of whales living in a rapidly changing ocean. With the success of this pilot effort, we plan to conduct another suction-cup tagging effort next summer to hopefully capture and explore more mysterious underwater behaviors of the PCFG.

*An ecstatic team at the end of a very long yet successful day of suction cup tagging. Bottom (from left): Leigh Torres, Lisa Hildebrand, Clara* *Bird, Dave Cade, KC Bierlich. Top: John Calambokidis.*

This project was funded by sales and renewals of the special Oregon whale license plate, which benefits MMI. We gratefully thank all the gray whale license plate holders, who made this research effort possible.

Literature cited

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. doi:10.1016/j.cub.2016.07.037.

Calambokidis, J., & Perez, A. 2017. Sightings and follow-up of mothers and calves in the PCFG and implications for internal recruitment. IWC Report SC/A17/GW/04 for the Workshop on the Status of North Pacific Gray Whales (La Jolla: IWC).

Calambokidis, J., Laake, J., & Perez, A. 2017. Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2015. IWC Report SC/A17/GW/05 for the Workshop on the Status of North Pacific Gray Whales (La Jolla: IWC).

Goldbogen, J. A., Friedlaender, A. S., Calambokidis, J., McKenna, M. F., Simon, M., & Nowacek, D. P. 2013. Integrative approaches to the study of baleen whale diving behavior, feeding performance, and foraging ecology. BioScience 63(2):90-100. doi:10.1525/bio.2013.63.2.5.

Hildebrand, L., Bernard, K. S., & Torres, L. G. 2021. Do gray whales count calories? Comparing energetic values of gray whale prey across two different feeding grounds in the eastern North Pacific. Frontiers in Marine Science 1008. doi:10.3389/fmars.2021.683634.

Lemos, L. S., Olsen, A., Smith, A., Burnett, J. D., Chandler, T. E., Larson, S., Hunt, K. E., & Torres, L. G. 2021. Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Marine Mammal Science. doi:10.111/mms.12877.

Lemos, L.S., Olsen, A., Smith, A., Chandler, T.E., Larson, S., Hunt, K., and L.G. Torres. 2020. Assessment of fecal steroid and thyroid hormone metabolites in eastern North Pacific gray whales. Conservation Physiology 8:coaa110.

Nerini, M. K., & Oliver, J. S. 1983. Gray whales and the structure of the Bering Sea benthos. Oecologia 59:224-225. doi:10.1007/bf00378840.

Soledade Lemos, L., Burnett, J. D., Chandler, T. E., Sumich, J. L., & Torres, L. G. 2020. Intra- and inter-annual variation in gray whale body condition on a foraging ground. Ecosphere 11(4):e03094.

Stewart, J. D., & Weller, D. W. 2021. Abundance of eastern North Pacific gray whales 2019/2020. Department of Commerce, NOAA Technical Memorandum NMFS-SWFSC-639. United States: NOAA. doi:10.25923/bmam-pe91.

Torres, L.G., Nieukirk, S.L., Lemos, L., and T.E. Chandler. 2018. Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Frontiers in Marine Science: https://doi.org/10.3389/fmars.2018.00319.

Learning the right stuff – examining social transmission in humans, monkeys, and cetaceans

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

The start of a new school year is always an exciting time. Like high school, it means seeing friends again and the anticipation of preparing to learn something new. Even now, as a grad student less focused on coursework, the start of the academic year involves setting project timelines and goals, most of which include learning. As I’ve been reflecting on these goals, one of my dad’s favorite sayings has been at the forefront of my mind. As an overachieving and perfectionist kid, I often got caught up in the pursuit of perfect grades, so the phrase “just learn the stuff” was my dad’s reminder to focus on what matters. Getting good grades didn’t matter if I wasn’t learning. While my younger self found the phrase rather frustrating, I have come to appreciate and find comfort in it.

Given that my research is focused on behavioral ecology, I’ve also spent a lot of time thinking about how gray whales learn. Learning is important, but also costly. It involves an investment of energy (a physiological cost, Christie & Schrater, 2015; Jaumann et al., 2013), and an investment of time (an opportunity cost). Understanding the costs and benefits of learning can help inform conservation efforts because how an individual learns today affects the knowledge and tactics that the individual will use in the future.

Like humans, individual animals can learn a variety of tactics in a variety of ways. In behavioral ecology we classify the different types of learning based on the teacher’s role (even though they may not be consciously teaching). For example, vertical transmission is a calf learning from its mom, and horizontal transmission is an individual learning from other conspecifics (individuals of the same species) (Sargeant & Mann, 2009). An individual must be careful when choosing who to learn from because not all strategies will be equally efficient. So, it stands to reason than an individual should choose to learn from a successful individual. Signals of success can include factors such as size and age. An individual’s parent is an example of success because they were able to reproduce (Barrett et al., 2017). Learning in a population can be studied by assessing which individuals are learning, who they are learning from, and which learned behaviors become the most common.

An example of such a study is Barrett et al. (2017) where researchers conducted an experiment on capuchin monkeys in Costa Rica. This study centered around the Panama ́fruit, which is extremely difficult to open and there are several documented capuchin foraging tactics for processing and consuming the fruit (Figure 1). For this study, the researchers worked with a group of monkeys who lived in a habitat where the fruit was not found, but the group included several older members who had learned Panamá fruit foraging tactics prior to joining this group. During a 75-day experiment, the researchers placed fruits near the group (while they weren’t looking) and then recorded the tactics used to process the fruit and who used each tactic. Their results showed that the most efficient tactic became the most common tactic over time, and that age-bias was a contributing factor, meaning that individuals were more like to copy older members of the group.

*Figure 1. Figure from Barrett et al. (2017) showing a capuchin monkey eating a Panamá fruit using the canine seam technique.*

Social learning has also been documented in dolphin societies. A long-term study on wild bottlenose dolphins in Shark Bay, Australia assessed how habitat characteristics and the foraging behaviors used by moms and other conspecifics affected the foraging tactics used by calves (Sargeant & Mann, 2009). Interestingly, although various factors predicted what foraging tactic was used, the dominant factor was vertical transmission where the calf used the tactic learned from its mom (Figure 2). Overall, this study highlights the importance of considering a variety of factors because behavioral diversity and learning are context dependent.

*Figure 2. Figure from Sargeant & Mann (2009) showing that the probability of a calf using a tactic was higher if the mother used that tactic.*

Social learning is something that I am extremely interested in studying in our study population of gray whales in Oregon. While studies on social learning for such long-lived animals require a longer study period than of the span of our current dataset, I still find it important to consider the role learning may play. One day I would love to delve into the different factors of learning by these gray whales and answer questions such as those addressed in the studies I described above. Which foraging tactics are learned? How much of a factor is vertical transmission? Considering that gray whale calves spend the first few months of the foraging season with their mothers I would expect that there is at least some degree of vertical transmission present. Furthermore, how do environmental conditions affect learning? What tactics are learned in good vs. poor years of prey availability? Does it matter which tactic is learned first? While the chances that I’ll get to address these questions in the next few years are low, I do think that investigating how tactic diversity changes across age groups could be a good place to start. As I’ve discussed in a previous blog, my first dissertation chapter will focus on quantifying the degree of individual specialization present in my study group. After reading about age-biased learning, I am curious to see if older whales, as a group, use fewer tactics and if those tactics are the most energetically efficient.

The importance of understanding learning is related to that of studying individual specialization, which can allows us to estimate how behavioral tactics might change in popularity over time and space. We could then combine this with knowledge of how tactics are related to morphology and habitat and the associated energetic costs of each tactic. This knowledge would allow us to estimate the impacts of environmental change on individuals and the population. While my dissertation research only aims to provide a few puzzle pieces in this very large and complicated gray whale ecology puzzle, I am excited to see what I find. Writing this blog has both inspired new questions and served as a good reminder to be more patient with myself because I am still, “just learning the stuff”.

A little help from my friends to study gray whales in Port Orford

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

The 2021 TOPAZ (Theodolite Overlooking Predators And Zooplankton) field season in Port Orford has come to a close. Its close also signals the end of my tenure as field project lead, after I took over from my predecessor Florence Sullivan (OSU/GEMM Lab MSc grad) in the summer of 2018. Allison Dawn, incoming GEMM Lab Master’s student, is my successor and I am excited to pass the torch to her and see what new directions she will take the project. In today’s post, I will not recap the field season as I often do at the end of August. However, I strongly encourage you to read the blog posts written by the JASPER (Journey for Aspiring Scientists Pursuing Ecological Research) interns that made up Team “Heck Yeah”, Nadia Leal, Damian Amerman-Smith, and Jasen White, as they did an excellent job summarizing what we saw and experienced over the last six weeks. Instead, I want to take this opportunity to highlight a few people in Port Orford (and their most memorable gray whale encounters) who created a home away from home for me in Port Orford and played a large part in creating rich and meaningful experiences during my time as field project lead.

Up first is Tom Calvanese, the OSU Port Orford Field Station manager. The field station can be an extremely busy place, especially during the summer when ideal weather conditions allow many marine scientists to conduct their research. There can be a lot of comings and goings at the field station, with swift turnarounds between groups and individuals from different departments and projects; some staying just one night, while others (such as the TOPAZ field teams) stay for several weeks. Leigh and I like to call Tom “the man behind the machine” because he manages to keep this busy field station running smoothly. From the get go, Tom has been a solid rock for me in Port Orford and he has never hesitated to give me the time and attention I needed, be it because I was seeking him out for advice about how to handle a personnel issue, a lesson in how to tie strong knots, or just a friendly conversation at the end of a long field day. I know that I have found a life-long friend and colleague in Tom through this project and for this I am very grateful.

One of Tom’s most iconic gray whale encounters happened when he was kayak fishing with a few friends in Tichenor Cove (coincidentally one of the two TOPAZ study sites). The individual kayakers were scattered throughout the cove, all in search of a good spot to hook some rockfish or lingcod. The group had not been out on the water for very long, which likely plays a large part in the shock and surprise that comes next, when Tom suddenly heard the blow of whale. He looked up from his fishing in the direction of the blow, only to see that a gray whale was surfacing right underneath one of his kayak fishing friends. Said friend could do nothing as he sat paralyzed in his kayak which slowly slid off the back of the gray whale as it dove once again. Neither whale nor human was harmed in this encounter, as the whale went back to foraging in the area, and the human (after several minutes of incredulity) went back to fishing. Every year, Tom has warned me of this location where this interaction happened (an uncharacteristically deep spot in Tichenor Cove compared to the rest of the area), though his warning is always accompanied with a twinkle in his eye.

*An image captured by 2018’s Team “Whale Storm” aboard the kayak while sampling in Tichenor Cove, Port Orford. Source: GEMM Lab.*

Dave Lacey owns South Coast Tours (SCT), a tour operating business that offers boat, kayak, and snorkeling tours, as well as surf lessons. Dave has been one of the most generous individuals to the TOPAZ/JASPER projects, never hesitating to loan us wetsuits and/or kayaks and allowing us to use his office and storage areas every day. He has also delivered excellent kayak paddle & safety instruction to the field teams over the last two years. Dave has truly become a vital partner during the Port Orford field seasons. It has been such a pleasure to be able to learn from and work with him, as well as see his business grow each year. Even though I will not be leading the project in Port Orford anymore, I am excited to continue my working relationship with Dave through obtaining important photo identification and sighting data of gray whales in the area when the GEMM Lab team is not there.

Although SCT is not even 10 years old (though it will be next year in 2022!), Dave has had so many gray whale encounters that he said it was really hard for him to pick just one. However, he ultimately picked the first time that he smelled a gray whale’s breath. It happened during a kayak tour when the group rounded the corner from Tichenor to Nellie’s Cove and a whale suddenly surfaced right in front of everyone, hitting them with the misty cloud of its blow. Up until this moment, Dave had both seen and heard hundreds of whale blows, but had never smelled one. He says, “to hear and see [the blow] is pretty normal but to get the third sense [of smell] is really phenomenal.”. Upon asking what he thinks of the smell, Dave replied that he does not think it is as gross as some people may think and during tours on his boat, the Black Pearl, he now actually tries to (safely) maneuver the boat downwind of the blow so that his clients can get a whiff as well.

*The misty cloud emitted by whales when they come to the surface to breathe is referred to as the “blow”. Source: GEMM Lab.*

Mike Baran is a co-owner of Port Orford Sustainable Seafood (POSS) and he also occasionally guides kayak and snorkel tours for SCT. POSS is a community supported fishery that delivers wild, line-caught seafood direct from Port Orford to communities throughout western Oregon. I developed a great friendship with Mike through seeing him on the water a lot as a kayak guide for SCT in my first summer leading the TOPAZ/JASPER projects (2018), as well as seeing him at the field station on most days since POSS’ office and fish-processing facility are located there as well. If you are a keen follower of the GEMM Lab blog, you will know by now that the field season in Port Orford is short, yet very intense and taxing. Therefore, uplifting and sometimes goofy interactions with someone can really turn an upsetting day (potentially due to kayak gear loss or simply exhaustion) into a better one. Mike provided me with a lot of uplifting and goofy interactions and always helped put a smile on my face.

As a SCT kayak guide, Mike has also had many gray whale encounters, however none are as memorable as the one he had on August 2^nd, 2019. Mike describes it as a typical Port Orford day: “windy with lots of whale activity all morning”, though all of the activity had been at a distance (the whale blows were far away). Yet, on the paddle back through Tichenor Cove along the backside of the port jetty, Mike and his tour glimpsed a whale that was headstanding along the jetty rocks. The paddlers slowed down and kept their distance, watching as the gray whale foraged, diving down for 3-4 minutes at a time before resurfacing in almost the same location as it had surfaced in before. Suddenly, the whale surfaced right in the middle of the kayak group, with Mike to its left, a mere meter or so away, and the rest of the group to its right. Despite the fact that the sudden appearance of the whale scared the living daylights out of Mike, he was able to take a picture of the surfacing, which features one of the tour clients in the background with her hands lifted up to her face in total shock. So, thankfully for us the moment is not just eternalized in Mike’s memory but also in photographic form.

*The photo of the gray whale that surfaced right next to Mike’s kayak, which also captured the shock & surprise of one of the tour clients in the background. Source: South Coast Tours.*

Last but certainly not least is Tara Ramsey, the coordinator of the Redfish Rocks Community Team since the summer of 2020. Despite arriving to Port Orford and her job in the middle of a pandemic, Tara has developed a lot of exciting new outreach and education material for the Redfish Rocks Marine Reserve, including an excellent walking tour of Port Orford (if you are ever there, I cannot recommend it highly enough – it starts at the Visitor Center!). While I have not known Tara as long as the other individuals featured in this blog, she has become a really great friend of mine, teaching me a lot about the reserve and Port Orford in general, including the best spot on Battle Rock beach for a small nighttime bonfire.

Tara’s most memorable encounter with a gray whale is in fact her only encounter with a gray whale to date, and it happened just a few weeks ago when she was doing an Instagram livestream of the Redfish Rocks Marine Reserve aboard SCT’s Black Pearl. The purpose of the livestream was to bring the public into the reserve without having to leave the comfort and current safety of their homes. Tara describes the conditions in the reserve as “quite eerie” that day as there was a combination of smoke, fog, and no wind in the air. These conditions resulted in some pretty poor visibility, but gave the reserve an almost mystical appearance. Tara was actually mid-sentence on the livestream, talking about how special this moment was for her because it was her first time being in the reserve, when a whale surfaced a few meters from the boat. While the encounter was brief (the whale only surfaced 3 or 4 times before disappearing into the fog), Tara says the vision will be etched in her memory forever as Redfish Rocks is “a circle of islands, kind of like an amphitheater and it was amazing to see the whale just in the middle of it all.”

*An aerial view of Redfish Rocks Marine Reserve. Source: FishTracker.*

I will miss being the field project lead of the TOPAZ and JASPER projects. I will miss kayaking every other day and spying on gray whales from the cliff site. I will miss having the opportunity to work closely with and train a new crop of aspiring marine scientists. I will miss my daily interactions with Tom, Dave, Mike, Tara, and many more individuals, when I do not go to Port Orford for six weeks next summer. I will cherish all the memories I have amassed over my last four summers in Port Orford for a very long time. Most of all, I will always be grateful to the gray whales that brought me back every summer and who (in a way) made all those memories happen.

PI Leigh Torres and Lisa at the end of the 2021 TOPAZ field season in Port Orford after the annual community presentation with Battle Rock Beach, Humbug Mountain, and Redfish Rocks Marine Reserve in the background. Source: L Torres.

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.

References

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.

Where are all the whales: Thoughts from the first half of the Port Orford project 2021

By Damian Amerman-Smith, Pacific High School senior, GEMM Lab summer intern

*Left to right: Damian, Nadia, Jasen. The group scans the ocean looking for whales, while Damian puts on sunscreen. Source: A. Dawn.*

Growing up in Port Orford, a short ten-minute walk from the Pacific Ocean, has certainly shaped my life a lot. It has given me a great regard for the ocean, the diversity of life within it, and how life seems to bypass human derived borders in order to go wherever it can. I often marvel at all the beautiful, intricate ecosystems that are able to exist inside of our planet’s vast oceanic expanses. Along with my love of the ocean has come a great regard for marine mammals and the novelties of these animals that allow them to live entirely in the ocean despite not having gills. Every new discovery of these beautiful ocean creatures brings me such simple and pure joy, such as my very recent discovery that baleen whales have two blow holes. These blow holes look so peculiar on the top of their bodies, like a short upside-down nose.

*Photo of a gray whale’s blow hole.* *Source: NOAA*.

My interest in the ocean and its inhabitants was a large part of what made me so enthused to take a part in the gray whale foraging ecology (GWFE) project in Port Orford this summer. When Elizabeth Kelly, my friend and a previous intern for the GWFE project mentioned her experiences from the previous summer, I was very happy when she put me in contact with Lisa Hildebrand and Leigh Torres so that I could apply to be an intern. Since then, I have been very ecstatically awaiting the beginning of the project and could hardly believe it when it finally began, and I was able to meet my fellow team members: Lisa Hildebrand, the PhD student who has been leading the GWFE project for the last four years; Allison Dawn, a Master’s student who is going to take over the project in Lisa’s stead; Nadia Leal, an OSU undergrad hoping to further pursue the field of marine biology; and Jasen White, an OSU undergrad whose time in the Navy has made him a very steeling presence while out on the water.

The three weeks that we have spent together learning the procedures that make up the project have been well spent, teaching all of us a lot of new things, such as what a theodolite is, how to operate a dissolved oxygen sensor, and (for me) how to use Excel. The first two weeks were largely spent just learning about how we collect data and improving our field skills, but as we have become more comfortable with our skills, we have also begun looking beyond the procedures, towards the data itself and what it can mean. Primarily, we started to notice the distinct lack of gray whales and almost complete lack of zooplankton prey for any gray whales in the area to eat.

*A calm & beautiful, yet whale-less, view from the cliff site. Source: L. Hildebrand.*

As we pass the halfway point in the project, we have only witnessed two whales inside our study area. While in the beginning it was not surprising that there were no whales, it has started to become concerning to me. We have a strong working hypothesis about why there have not been many whale sightings in our monitored sites of Mill Rocks and Tichenor’s Cove: there is not nearly enough zooplankton prey to attract them. Monday, August 9th is a good example to support this hypothesis. On that day, when we pulled up our sample net at Tichenor Cove station #1, we collected fifty-three individual Neomysis mysid shrimp, which are a tasty treat for gray whales. However, all the other prey samples from the remaining eleven kayak sampling stations had perhaps a maximum of five assorted zooplankton each, which is certainly not enough to attract the attention of such a large predator as Eschrichtius robustus (a gray whale). Unfortunately, we have yet to see much change in zooplankton prey availability in our sampling nets over the season so far, but we are hopeful that swarms of zooplankton in the area will resurge and the gray whales will begin using the area around the port as their August feeding grounds.

Our hopes aside, it is intriguing to think about why there has been so few zooplankton at our sampling sites. A main factor is likely the decrease of Port Orford’s kelp forests over the past few years. Kelp is very important to zooplankton, particularly mysids, as it allows them to seek shelter from predators. Declines in kelp forests have been documented all along the southern Oregon coast, and are believed to be fueled by many factors (ORKA, 2021). A combination of warming waters with decreasing amount of nutrients available to the kelp (Richardson 2008), and the increasing abundances of purple sea urchins that eat the kelp has vastly impacted the amount of kelp in the area. The decline in local kelp forests may be the reason that we are seeing fewer mysid swarms than in previous years. This reduced kelp and mysid availability could, in turn, be making Port Orford waters an unappetizing area for hungry whales to visit this year. While this trophic cascade is still just an educated hypothesis, it is important for us and others to keep watch on the situation, and to see how it changes. There are organizations such as the Oregon Kelp Alliance (ORKA) that are working hard to study why the kelp populations are hurting and how we can help. We will power through the season with the hopes that the gray whales will come. It is still very possible that the zooplankton will resurge and the whales will return with plenty to feed on.

References

Richardson, Anthony J. 2008. In hot water: zooplankton and climate change, ICES Journal of Marine Science, Volume 65, Issue 3, Pages 279–295, https://doi.org/10.1093/icesjms/fsn028

ORKA, 2021. “Kelp.” Oregon Kelp Alliance, www.oregonkelp.com/.

Food for thought: conscious reasoning among foraging gray whales

By Nadia Leal, GEMM Lab summer intern, OSU senior, Department of Fisheries, Wildlife, and Conservation Sciences

The OSU GEMM Lab gray whale foraging ecology project in Port Orford is in its seventh year of research. I have the honor to serve as a field assistant for the project as part of Team “Heck Yeah” for the summer 2021 field season. In doing so, I have been presented with the opportunity to take part in its enduring legacy. It is a legacy characterized by novel discovery, distinguished leadership, and endless adventure. These particular aspects motivated me to pursue this internship. Further, the desire to seek out gray whales (Eschrichtius robustus) — a species epitomizing the ability to exhibit resilience in the face of adversity after having experienced two unusual mortality events (UME) in the past two decades and having recovered from historically low population abundances due to whaling — sparked immeasurable excitement.

*Figure 1. Nadia operating the theodolite to calculate the location of a gray whale. Source: A. Dawn.*

The skills we are acquiring during this field season are essential to master so that I can pursue my aspirations of becoming a marine conservation biologist. For example, we have learned how to operate a theodolite, which is a surveying tool used regularly in marine mammal research to accurately calculate the location of cetaceans and track their movements (Figure 1). We are also learning how to operate a number of other research equipment, to navigate a tandem kayak using a GPS, to process various forms of data, and to identify gray whales! I have especially enjoyed collecting prey samples and navigating our tandem kayak, as kayaking is a summer tradition for my family and the opportunity to kayak in this context is certainly the high point of this internship. The kayak is named “Robustus” after the scientific name of the gray whale: Eschrichtius robustus! (Figure 2).

*Figure 2. Nadia navigating Robustus, the research kayak.*

The Port Orford project aims to determine how gray whale foraging is affected by prey quantity and quality. In fact, gray whales exhibit specificity in their selection of prey on the basis of caloric content (Hildebrand 2020). I am particularly interested in the underlying implications these findings imply: the notion of conscious reasoning and decision-making by individual whales as they seek the most suitable prey for its dietary needs among other options to maximize its survivability. Are gray whales in possession of an awareness that allows them to exhibit intentional preference? Can the behavior be attributed to instinct and/or learned behavior, or to cognition comparable to human preference? These and similar questions are my motivation for studying the realm of marine mammal biology. These questions concern intelligence and evolution, which can be effectively investigated through an analysis of cetacean brain structure, as it likely has compelling relationships to their extensive behavioral abilities (Hof and Van Der Gucht 2007).

For instance, the brain of the gray whale has expanded and developed extensively over evolutionary time in response to distinct selection pressures. Evidence affirms that the behavioral challenges associated with foraging exert strong selection pressures on the evolution of their brain size and structure (Muller and Montgomery 2019)! Selection pressures associated with social cognition are also believed to have contributed to such growth (Connor et al. 1998; Marino 2002; Shultz and Dunbar 2010 ). Further, their neural organization has increased in complexity, leading to greater function and usage of the cortical portion of the brain, which is the portion responsible for higher level activity (Oelschläger and Oelschläger 2002).

*Figure 3. Structure of humpback whale brain representative of baleen species used to infer about gray whales (Hof and Van Der Gucht 2007).*

Though research about baleen whale brain morphology is not as pervasive as that of toothed whales (due to increased susceptibility of toothed whales to captivity given the feasibility of their capture and subsequent analysis in lab/controlled setting), studies have indicated that the brain of baleen whales share similarities to those of humans (Wade et. al 2012). In particular, similarities exist in the frontal lobe of the brain, which is responsible for the complex activities of self-awareness, reasoning, and behavior, as well as for problem-solving and motivation (Hof and Van Der Gucht 2007) (Figure 3). These findings indicate that baleen whales, including the gray whale, have the capability to exhibit intentional preference and take part in conscious decision-making in the recognition of different prey species. The mechanisms responsible for how gray whales may discern prey likely involve a number of the sensory systems, differing in respect to spatial scale (Torres 2017). Thus, gray whales likely rely on various sensory methods, such as vision, sound perception/reception, chemoreception, or an oceanographic stimulus, at differing scales to locate and discern prey. The sensory method employed is dependent on their distance from prey.

Though we cannot yet confirm whether and/or how gray whales are capable of distinguishing between prey species, what is certain, is that the gray whale is intelligent and quite similar to us. Moreover, they are representative of strength and endurance, providing lessons we can learn from and qualities we can embody. Despite the threats to the species from fishing gear entanglement, ship collisions, climate change, oil industry developments, and being historically hunted, they have remarkably persisted. Thus, we must ensure the existence of the gray whale so they too may thrive for the rest of time, with healthy lives and habitat that is rightfully theirs.

P.S. I would like to thank the GEMM Lab, Oregon State University, Shalynn Pack, Port Orford Sustainable Seafood, Port Orford Co-op, South Coast Tours, Nicki’s Knick Knacks, Leigh Torres, Lisa Hildebrand, Allison Dawn, Clara Bird, Tom Calvanese, Maddie English, Jasen White, and Damian Amerman-Smith for making the internship as special and memorable as it is/was.

References

Connor, R. C., Mann, J., Tyack, P. L., and Whitehead, H. (1998). Social evolution in toothed whales. Trends in Ecology and Evolution, 13(6): 228– 232. doi: https://doi.org/10.1016/S0169‐5347(98)01326‐3

Hildebrand, L. (2020). Tonight’s specials include mysids, amphipods, and more: an examination of the zooplankton prey of Oregon gray whales and its impact on foraging choices and prey selection. Master’s thesis, Oregon State University.

Hof, P.R., and Van Der Gucht, E. (2007). Structure of the cerebral cortex of the humpback whale, Megaptera novaengliae(Cetacea, Mysticeti, Balaenopteridae). The Anatomical Record 290:1-31 doi: 10.1002/ar.a.20407

Marino, L. (2002). Convergence of complex cognitive abilities in cetaceans and primates. Brain, Behavior, and Evolution59: 21–32. doi: https://doi. org/10.1159/000063731

Oelschläger, H.A., and Oelschläger, J.S. (2002). Brains. In: Perrin WF, Wu¨ rsig B, Thewissen JGM, editors. Encyclopedia of marine mammals. San Diego, CA: Academic Press. p 133–158.

Shultz, S., & Dunbar, R. (2010). Encephalization is not a universal macroevolutionary phenomenon in mammals but is associated with sociality. Proceedings of the National Academy of Sciences of the United States of America 107(50): 21582–21586. doi: https://doi.org/10.1073/ pnas.1005246107

Torres, L.G. (2017). A sense of scale: foraging cetaceans’ use of scale-dependent multimodal sensory systems. Marine Mammal Science 33: 1170-1193. doi: 10.1111/mms.12426

Wade, P.R., Reeves, R.R., and Mesnick, S.L. (2012). Social and behavioral factors in cetacean responses to overexploitation: are odontocetes less “resilient” than mysticetes?. The Journal of Marine Biology 2012: 1-15. doi:10.1155/2012/567276

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