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

It Takes a Village to Raise a PhD Student

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

This year in late February is the 2022 Ocean Sciences Meeting, an interdisciplinary bonanza of ocean scientists from all over the world. The conference will be held online this year as a precaution against Covid-19, and a week of virtual talks and poster sessions will cover new research in diverse topics from microbial ecology to ocean technology to whale vocalizations.

The meeting will also include my first poster presentation at a major conference, and so I have the typical grad student jitters that accompany each new thing I do (read more about the common experience of “imposter syndrome” here). This poster is the first time since starting graduate school and joining Project OPAL that I’m trying to craft a full science story that connects whales, their prey, and oceanographic conditions.

Learning how to do the analyses to assess and quantify these connections has involved plenty of head-scratching and periodic frustration on my part, but it has also offered a surprisingly joyful and even moving experience. In my efforts to troubleshoot a problem with my prey analysis, I’ve reached out to nearly everyone who works with krill acoustic data on the West Coast. Every single person has been incredibly welcoming and ready to help me, and excited to learn about my work in return. This experience has made me realize how many people I have on my team, and that even strangers are willing to support me on the whacky journey that is a PhD.

Through these collaborations, I am learning to analyze the acoustic signal of krill, small animals that are important food for whales foraging off the coast of Oregon and beyond. As part of Project OPAL, we plan to compare krill swarms with whale survey data to learn about the types of aggregations that whales are drawn to. From the perspective of a hungry whale, not all krill are created equal.

Analysis of a layer of krill in the upper ocean. The blue color in the top panel indicates scattering of acoustic signal by the krill, and the outline in the bottom panel shows the results of an algorithm programmed to detect krill aggregations.

In addition to developing great remote relationships through this work, the ability to meet in person as we continue adapting to life during the pandemic has absolutely not lost its thrill. After over a year of meetings and collaborating on Zoom, I was delighted to meet GEMM Lab postdoc Solène Derville this January, after she journeyed from her home in New Caledonia to Oregon. It was so exciting to see her in real life (we’re more similar in height than I knew!) and a few minutes into our first lunch together she was already helping me refine my analysis plans and think of new approaches.

Our interaction also made me think about how impressive the GEMM Lab is. The first two people Solène saw upon her arrival in Oregon were me and fellow GEMM Lab student Allison Dawn, two newer members who joined the lab after her last trip to Oregon. Without a moment of hesitation, Allison stepped up to give Solène a ride to Newport from Corvallis to finish her long journey. The connection our lab has developed and maintained during a pandemic, across borders and time zones, is special.

Hiking on gorgeous days is just one of the many benefits of being in the same place! This adventure included spotting a whale blow off the coast and a lot of GEMM excitement.

As I look out at the next few weeks until the Ocean Sciences meeting, and out towards the rest of my PhD, I inevitably feel worried about all I need to accomplish. But, I know that the dynamics in our lab and the other collaborative relationships I’m forming are what will carry me through. Every meeting and new connection reminds me that I’m not doing this alone. I’m grateful that there’s a team of people who are ready and willing to help me muddle my way through my first Principal Components Analysis, puzzle over algorithm errors, and celebrate with me as we make progress.

The benefits of play: A review of cetacean behavior.

Imogen Lucciano, Graduate Student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab.

Beluga whale. Photo credit: https://www.salon.com/2020/07/13/beluga-whales-are-the-oceans-extroverts-research-finds/

Since coming back from winter holiday, things have picked back up to my normal pace of GO! and I’ve taken little to no “down time” in my awaken hours. As a grad student who is also a mother to an active 11-year-old daughter and two dogs, my days are packed. Although I do enjoy a life of steady movement and accomplishment, I also need to do “nothing” sometimes, like a recluse who needs to see the sun on occasion. So, this evening I decided that I would have a night of fun and I took my daughter to see a movie. We haven’t been to the movies much since the pandemic started, but it is one of our most beloved things to do. I heard the theatres were like ghost towns since the recent omicron surge anyway, so we showed up and were one of two families there. We picked a comedy and ordered a bucket of popcorn, nachos (no jalapeños, just the cheese), slurpies, soft pretzels, and sour patch kids (I told the cashier to have two wheelchairs ready to haul us out of there post feast). We laughed and sang and by the near end of the movie, I had a moment of self-realization: I felt really relaxed. This epiphany was synaptically followed by thinking about how cetaceans engage in play.

Humans often recognize play through sports or games, and mostly through smiling and the vocalization of laughter. If we’re laughing it usually means that we are not aggressing. From what we currently understand, play in cetaceans has evolved as an ontogenetic behavior in many species for the purposes of developing survival skills (Paulos et al., 2010). This “purpose of play” makes a lot of sense, and I see it in my dogs when they are growling, snapping, tugging rope, and chasing each other in the yard. They are having the time of their lives and certainly not really fighting one another, yet they are also clearly practicing important skills if they were to come across predators or prey in the wild.

Two dolphins play-fighting. https://www.youtube.com/watch?v=StuGe3dkCU4.

Most cetaceans vocalize often, whether in the form of pulsed calls, whistles, screams, songs, clicks or combination calls. The element of play associated with a utilized sound or other behavior opens the door for cetaceans to develop important social relationships among conspecifics, as well as developing crucial survival skills (Paulos et al., 2010). To quantify the vocal signals produced by cetacean species, researchers examine their complex repertoires to understand more about the function of certain sounds made specifically during play (Boisseau, 2004). Bottlenose dolphins provide each other with a distinct signal, pulse whistles that start around 13 kHz and end at around 10 kHz (Fig 1), to tell one another that the behavior they are exhibiting is play rather than aggression (Blomqvist et al., 2005).

Figure 1. Spectrogram of bottlenose dolphin pulse whistles during play. Blomqvist et al., 2005.

Cetacean play is defined as behavior that is spontaneous, intentional, pleasurable, and rewarding (Hill et al., 2017). Although cetacean play is conducted in a relaxed setting when there is no immediate need for survival, it has a role in growth and sociability (Hill et al., 2017). For example, cetaceans participate in interspecies play, where they actively engage with one another for no apparent ecological benefit (excluding periods of symbiotic behavior, such as working together to herd prey). Yet, these periods of interspecies play may suggest that these animals are comfortable practicing for real world situations with one another. Large baleen whales have few predators and thus have opportunities to engage in play with pods of dolphins. In some cases, large baleen whales such as humpback and gray whales will lift smaller mammals out of the water, possibly to practice for maternal care (Hill et al., 2017).

Gray whales swim/interact with white-sided dolphins, playing with one another. Image credit: https://www.youtube.com/watch?v=kZBnZ6iAdRI.

Cetaceans engage in play not only with one another, but as solitary individuals as well. This play (which can occur parallel to conspecifics simultaneously) includes surfing, aerial breaches and leaps, slapping the surface of the water with a fin or tail fluke, and erratic swimming (Paulos et al., 2010). Some cetaceans play with objects they find in the wild. One example being bowhead whales, which are known to balance, sink, and lift logs (Paulos et al., 2010).

Another interesting cetacean play behavior is bubble blowing. Though humpback whales blow bubbles as a means of trapping prey while foraging (Moreno & Macgregor, 2019), beluga whales, particularly females, blow mouth ring bubbles and perform blowhole bursts when engaging in solitary play (Hill et al., 2011). Just for the fun of it. It appears that cetaceans also need to be actively involved in “nothing” sometimes, as there is some good use for it. For me, engaging in play is a way to reset and relax, which is necessary even for those us who gain a lot of pleasure from our accomplishments. As I sit in the desolate theatre connecting with my daughter and nurturing my own needs, I feel completely justified in my relaxing night off. Pass the nachos, please.

Beluga mouth ring bubble. Photo credit: https://todropscience.tumblr.com/post/135072665727/volk-morya-new-study-reveals-belugas-blow.

Literature Cited

Blomqvist, C., Mello, I., Amundin, M. 2005. An acoustic play-fight signal in bottlenose dolphins (Tursiops truncatus) in human care. Aquatic Mammals, 31 (2), 187-194. 

Boisseau, O. 2004. Quantifying the acoustic repertoire of a population: The vocalizations of free-ranging bottlenose dolphins in Fiordland, New Zealand. The Journal of the Acoustical Society of America, 117, 2318-2329. https://doi.org/10.1121/1.1861692.

Hill, H., Dietrich, S., Cappiello, B. 2017. Learning to play: A review and theoretical investigation of the development mechanisms and functions of cetacean play. Learning & Behavior, 45, 335-354. https://link.springer.com/content/pdf/10.3758/s13420-017-0291-0.pdf.

Hill, H., Kahn, M., Brilliott, L., Roberts, B., Gutierrez, C. 2011. Beluga (Delphinaptera leucas) bubble bursts: surprise, protection, or play? International Journal of Comparative Psychology, 24, 235-243.

Moreno, K. & Macgregor, R. 2019. Bubble trails, bursts, rings, and more: A review of multiple bubble types produced by cetaceans. Animal Behavior and Cognition, 6 (2), 105-126. https://www.animalbehaviorandcognition.org/uploads/journals/23/AB_C_Vol6(2)_Moreno_Macgregor.pdf.

Paulos, R., Trone, M., Kuczaj II, S. 2010. Play in wild and captive cetaceans. International Journal of Comparative Psychology, 23, 701-722.

Provine, R. 2016. Laughter as an approach to vocal evolution: The bipedal theory. Psychonomic Bulletin & Review, 24, ­238-244. https://link.springer.com/content/pdf/10.3758/s13423-016-1089-3.pdf.

New year’s hindsight: will it ever be the same?

By Solène Derville, Postdoc, OSU Department of Fisheries, Wildlife, and Conservation Science, Geospatial Ecology of Marine Megafauna Lab

As I sit down at my desk during the first week of 2022 to write the first blog of this new year, more than ever before I feel like I am at a pivotal time. Standing in front of an invisible frontier, contemplating the past, and anxiously looking ahead.

Globally, 2021 was yet another challenging year. The COVID pandemic is persisting in endless waves of contamination and new variants. Climate change is all the more on our minds as the COP26 failed to live up to the expectations of many.

For me personally, 2021 was a very strange year too. I recovered from an accident I had in November 2020 that shook me to the bones and pushed me into living life to its fullest. On the other hand, the pandemic prevented me from moving to Oregon and I have been remotely working on the OPAL project for a year. I feel very lucky to participate in this work and I have enjoyed every bit of time I have spent on my computer processing data and teasing out the ecological drivers of whale distribution in Oregon. Yet, despite the numerous zoom meeting and email exchanges, I have been frustrated by the long-distance relationship I had with my dear GEMM lab colleagues and friends. Like so many others, I have felt the tow of the virtual life the pandemic has imposed on us.

As I reflect on the mixed feelings I am experiencing in this first week of 2022, I realize that the global context we live in and my individual questionings are intertwined. The pandemic and environmental issues triggered the same ethical and philosophical questions about individual responsibility, freedom, and equity. For instance, why should I make sacrifices that will cost me a lot personally but only have a very minor effect on the broader scale? The year 2021 has confronted us with a harsh reality: however strongly you believe your answer to the above question is the right one, other people might think otherwise.

The term eco-anxiety has emerged in recent years to describe people suffering from ‘persistent worries about the future of Earth and the life it shelters’. These symptoms of chronic fear are rising worldwide, which sadly but frankly, is only normal given that the degradation of our climate and biosphere deserves our full attention. More disturbingly, I found out that eco-anxiety is mostly affecting children and young people around the globe. Despite acting for the environment on an everyday basis and working as a conservation biologist, I can relate to this feeling of overwhelming helplessness.

In the first week of this new year, I would like to turn this distress into motivation to act and do better. To that extent, ‘adaptation’ is the word that keeps coming up to my mind. In biology, adaptation is the process of change by which an organism or species becomes better suited to its environment. Contrary to ‘acclimation’ that refers to a temporary change occurring on the short term, adaptation is a more profound evolution occurring at the scale of multiple generations. Somewhat, we need to combine the best of both worlds, adapt profoundly but adapt fast.

As I stayed at my family house in Toulouse (France) during the last couple weeks, I went through my old stuff in the room I occupied as a teenager and found a note book written by a 13 year-old Solène. I smiled at my words “One day, I will become a Biologist so that maybe I can save our beautiful planet, […] it’s the only thing that matters”. I was both impressed by the strength of the conviction I was holding to back then and stunned that I have now reached a place, as an independent adult and early career marine ecologist, where I could actually put these words in action.

So here is my 2022 New Year’s resolution: despite the waves of anxiety that sometimes hit us, let’s keep fighting our battles and trust that we can make this world a better place!

“Sometimes you have the feeling that nothing makes sense anymore, and sometimes it just feels right.”
A picture of myself taken during a research cruise in New Caledonia this summer. We were searching for humpback whales in the Chesterfield archipelago (South Pacific), one of the most remote and pristine reef in the world (Photo credit: Marine Reveilhac, mission MARACAS/IRD/Opération Cétacés/WWF/GouvNC/Parc naturel de la mer de Corail).

GEMM Lab 2021: A Year in the Life

Another year has come and gone, and the GEMM Lab has expanded in many facets! Every year it gets just a little bit harder to succinctly summarize all of the research, outreach, and successes that the GEMMs accomplish but it is an absolute honor and thrill to be a member of this lab. So, please enjoy the 6th edition of a GEMM Lab Year in the Life!

Our lab has almost doubled in size since I wrote the 2020 edition of this blog! This year we welcomed the arrival of two postdocs (Drs. Alejandro [Ale] Fernández Ajó and KC Bierlich) and two Master’s students (Allison Dawn and Miranda Mayhall). Ale and KC joined us as freshly minted Drs., as Ale defended his doctoral thesis from Northern Arizona University in April, while KC graduated in May from Duke University. Both of them immediately jumped into GRANITE fieldwork, scooping gray whale poop and flying drones (more below). Allison also dove headfirst into gray whale fieldwork as she co-led the TOPAZ and JASPER projects with me (Lisa) after defending her undergraduate thesis and graduating from the University of North Carolina with highest honors in the spring. Miranda, a U.S. Army Intelligence veteran, also joins us from the East Coast as she moved from Virginia to Oregon with her 10-year-old daughter, Mia, and two dogs, Angus and Mr. Gibbs. Unlike our other new arrivals, Miranda’s research does not relate to gray whales as she is part of the GEMM Lab’s newest research project…

There are exciting developments in the research project realm of the GEMM Lab every year. This year’s new project, HALO (Holistic Assessment of Living marine resources off Oregon), is particularly exciting as it is a joint project with the Cornell Lab, with GEMM Lab PI Leigh collaborating with Dr. Holger Klinck to better understand cetacean distributions off Oregon. HALO will involve monthly survey cruises aboard MMI’s R/V Pacific Storm along the Newport Hydrographic line (65 nm to 5 nm off Newport), where three Rockhopper hydrophones have been deployed and are passively monitoring cetacean acoustics. The HALO team, which includes GEMM students Miranda and PhD candidates Dawn Barlow and Rachel Kaplan, has already had two successful cruises this year! Check out the HALO website to stay tuned for updates throughout 2022. In addition to starting new research projects in our Oregon backyard, the GEMM Lab has also ventured further north, to the more frigid waters of Kodiak, Alaska. Postdocs KC and Ale went on a scouting mission to Kodiak Island to see whether the multidisciplinary methods we use in the GRANITE project to study PCFG gray whales in Oregon, can also be applied to other gray whales in other study areas. The reconnaissance trip was a huge success with KC and Ale making vital connections with potential collaborators and managing to collect some pilot data (drone flights, prey samples, and one fecal sample!). Both of these new ventures are funded by sales and renewals of the special Oregon gray whale license plate, which benefits MMI. We gratefully thank all the gray whale license plate holders, who made this research possible, and encourage any Oregonians that don’t have a whale on their tale yet, to do so in 2022!

These new research ventures certainly do not mean that we neglected our already established field research projects – in fact, most of them have flourished and thrived this year! Rachel and Dawn returned as marine mammal observers to the R/V Shimada for the May stint of the Northern California Current research cruise. They observed Dall’s porpoise, Northern right whale & Pacific white-sided dolphins, as well as killer, humpback, & fin whales. These sightings will add to the growing OPAL (Overlap Predictions About Large whales) dataset that both Rachel and postdoc Solène Derville are analyzing to better understand whale distribution patterns in Oregon waters. Speaking of OPAL, MMI Faculty Research Assistant Craig Hayslip and Leigh continued to take to the skies in U.S. Coast Guard helicopters to obtain monthly cetacean distribution data, which is also being used in the OPAL project to identify the co-occurrence between whales and fishing effort in Oregon to reduce entanglement risk. Both of our gray whale projects, GRANITE (Gray whale Response to Ambient Noise Informed by Technology & Ecology) and TOPAZ (Theodolite Overlooking Predators & Zooplankton)/JASPER (Journey for Aspiring Scientists Pursuing Ecological Research) had another year of successful field seasons. The GRANITE team, which includes Leigh, Todd Chandler, Ale, KC, PhD student Clara Bird, and myself, headed out in search of gray whales earlier than usual this year to document the potential effects of a National Science Foundation (NSF) funded seismic survey, which was conducted off the Oregon coast, on gray whales in the area. By the end of October, we had conducted 80 drone flights, collected 48 and 66 fecal and prey samples (respectively), and seen 36 individual whales during 201 sightings. Down south in Port Orford, the TOPAZ/JASPER project experienced a passing of the torch as I stepped down from the team lead position (which I held since 2018) and handed the project reins over to Allison. We co-led another fantastic field season this year. While whale sightings were much lower than in previous years (read some musings here), the project continued to be successful at making real impacts on young people’s lives as we once again engaged a local Pacific High School student (Damian Amerman-Smith) and two OSU undergraduates (Nadia Leal & Jasen White) in the field work. While our annual reach may be small in terms of numbers, the impact we have is huge, with many of the high school interns (including this year’s) deciding to go to college and/or to study biology directly as a result of our project.

TOPAZ/JASPER certainly is not the only project in our lab that engages students in ecological research. This year, we collectively oversaw and mentored 13 students. The OBSIDIAN (Observing Blue whale Spatial ecology to Investigate Distribution In Aotearoa New Zealand) project was assisted by three interns (Grace Hancock, Mateo Estrada Jorge, and Mattea Holt Colberg) overseen by Dawn and Leigh. Grace worked on maintaining the New Zealand blue whale photo-ID catalogue and won best student poster at our department’s annual student conference (RAFWE) for this work. Mattea, a 2020 TOPAZ/JASPER team member, switched study species and assisted Dawn in validating blue whale calls and songs. Mateo was a NSF Research Experience Undergraduate (REU) who conducted an analysis on blue whales and earthquakes. Clara also supervised a REU student with Leigh: Marc Donnelly, who created a habitat map for the GRANITE project. Rachel mentored Amanda Kent, an Undergraduate Research, Scholarship, & the Arts (URSA) Engage student, who helped her conduct a literature review about two Oregon krill species that are primary prey of whales. Over the summer, we had two student workers (Noah Goodwin-Rice & Julia Parker) join us in our efforts to better understand gray whale prey. Noah assisted us by sorting and identifying gray whale prey samples collected this summer and Julia wrapped up the microplastics analysis of gray whale prey and fecal samples. In the fall, both Clara and Allison supervised students (Kathryne Macallan & Jasen White, respectively) taking the Coastal & Estuarine Research Management class in our department who produced independent research projects during the term. Kathryne investigated the relationship between body length and blow intervals of gray whales during different behavioral states, while Jasen dove into the relationship between zooplankton abundance and environmental covariates. 

The sharing of our research and expertise was not limited to mentoring students. Despite most conferences and seminars still occurring virtually this year due to the pandemic, the GEMM Lab presented numerous talks including at the State of the Coast (Rachel, Dawn, Leigh, & myself), International Biologging Symposium (Solène), HMSC Research Seminar (Ale & Solène, KC), and the Northwest Student Society of Marine Mammalogy chapter conference (Clara, Dawn, & myself), to name a few. Furthermore, Clara and I were guest lecturers once again for Dr. Renee Albertson’s marine mammal classes in our department, and Solène gained her first teaching experience by creating and leading a data visualization workshop (called “Pimp my figure!”) for RAFWE in May, which she reiterated at the University of New Caledonia in October.

Another huge accomplishment comes from the southern hemisphere as the hard work and time that Leigh and Dawn dedicated to OBSIDIAN and the results generated contributed to the denial of a seabed mining permit to extract iron sands in the South Taranaki Bight. This milestone has been years in the making, starting in 2013 when Leigh published her hypothesis that an unrecognized blue whale foraging ground existed in New Zealand. Since then, Leigh and Dawn have been building a tower of knowledge about these resident New Zealand blue whales block-by-block. They first confirmed Leigh’s hypothesis by presenting a bounty of evidence in support of this resident population, then assessed the skin condition of these whales, modeled the functional relationships between oceanography, krill and the distribution of blue whales, discovered temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence, and most recently, developed dynamic models to forecast blue whale distribution three weeks into the future. We are extremely proud of the direct applications that the OBSIDIAN research outputs have had on the management and conservation of these New Zealand blue whales – hurrah to Leigh & Dawn!!

Other hurrahs this year include that Rachel passed her College of Earth, Ocean, & Atmospheric Sciences qualifying exam, now making her a PhD Candidate. Clara also reached a graduate milestone this year as she not only formed her PhD committee but also successfully defended her research review in the spring. Additionally, Clara became a certified drone pilot right before the start of the GRANITE field season and joined Todd and KC as pilots this summer. The lab and its members also received numerous grants and awards. There are too many to name for this blog, but we are very grateful for all of them! I do want to highlight two here: Dawn was awarded the Bob Moch memorial endowment award that recognizes service to the Hatfield Marine Science Center (HMSC) and broader Oregon coast community. I cannot think of anyone more deserving of this award than Dawn who truly does so much to serve and better the HMSC and Oregon coast communities! Clara was awarded a prestigious ARCS (Achievement Rewards for College Scientists) scholarship which provides awards to academically outstanding students to further their scientific knowledge. 

We have once again been prolific writers, contributing 24 total peer-reviewed publications to 17 different scientific journals. If you are in the mood for some holiday reading, you will find the full list of publications at the end of this post.

And YOU, our awesome, supportive readers, have once again been supportive viewers, with a whopping 27,135 views of our blog this year!!! Thank you for joining us on our 2021 journey! We hope you have enjoyed the tales that we have told and the knowledge we have (hopefully) conveyed. We wish you all restful, happy, and most importantly, healthy holidays and hope you will join us again in 2022!

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Publications

Andréfouët, S., Derville, S., Buttin, J., Dirberg, G., Wabnitz, C.C.C., Garrigue, C., & Payri, C. E. 2021. Nation-wide hierarchical and spatially-explicit framework to characterize seagrass meadows in New Caledonia, and its potential application to the Indo-Pacific. Marine Pollution Bulletin 173:113036. https://doi.org/10.1016/j.marpolbul.2021.113036. 

Barlow, D.R., & Torres, L.G. 2021. Planning ahead: Dynamic models forecast blue whale distribution with applications for spatial management. Journal of Applied Ecology. (Link)

Barlow, D.R., Klinck, H., Ponirakis, D., Garvey, C., & Torres, L.G. (2021). Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Scientific Reports 11(1):1-10. (Link)

Beal, M., … Torres, L.G., et al. 2021. Global political responsibility for the conservation of albatrosses and large petrels. Science Advances 7(10):eabd7225.

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

Bierlich, K.C., Hewitt, J., Bird, C.N., Schick R.S., Friedlaender, A.S., Torres, L.G., Dale, J., Goldbogen, J.A., Read, A., Calambokidis J., & Johnston, D.W. 2021. Comparing uncertainty associated with 1-, 2-, and 3D aerial photogrammetry-based body condition measurements of baleen whales. Frontiers in Marine Science 8:749943. doi: 10.3389/fmars.2021.749943  

Bonneville, C.D., Derville, S., Luksenburg, J.A., Oremus, M., Garrigue, C. 2021. Social structure, habitat use and injuries of Indo-Pacific Bottlenose Dolphins (Tursiops aduncus) reveal isolated, coastal, and threatened communities in the South Pacific. Frontiers in Marine Science 8:1–14. https://doi.org/10.3389/fmars.2021.606975.

Clatterbuck, C.A., Lewison, R.L., Orben, R.A., Ackerman, J.T., Torres, L.G., Suryan, R.M., Warzybok, P., Jahncke, J., & Shaffer, S.A. 2021. Foraging in marine habitats increases mercury concentrations in a generalist seabird. Chemosphere 279:130470.

D’Agostino, V.C., Fernandez, A.A.A., Degrati M., Krock, B., Hunt, K.E., Uhart, M.M., & Buck, C.L. 2021. Potential endocrine correlation with exposure to domoic acid in Southern Right Whale (Eubalaena australis) at the Península Valdés breeding ground. Oecologia 1-14.

Dillon, D., Fernandez, A.A.A., Hunt, K.E., & Buck, C.L. 2021. Investigation of keratinase digestion to improve steroid hormone extraction from diverse keratinous tissues. General and Comparative Endocrinology 309:113795.

Fernandez, A.A.A., Hunt, K.H., Sironi, M., Uhart, M., Rowntree, V., Giese, A.C., Maron, C.F., DiMartino, M., Dillon, D., & Buck, C.L. 2021. Retrospective analysis of the lifetime endocrine response of southern right whales calves to gull wounding and harassment: a baleen hormone approach. Integrative and Comparative Biology 61.

Fernandez, A.A.A., Hunt, K.E., Dillon, D., Uhart, M., Sironi, M., Rowntree, V., & Buck, C.L. 2021. Optimizing hormone extraction protocols for whale baleen: tackling questions of solvent: sample ratio and variation. General and Comparative Endocrinology 113828.

Garrigue, C., & Derville, S. 2021. Behavioral responses of humpback whales to biopsy sampling on a breeding ground : the influence of age-class , reproductive status , social context , and repeated sampling. Marine Mammal Science 1–16. https://doi.org/10.1111/mms.12848. 

Gough, W.T., Smith, H.J., Savoca, M.S., Czapanskiy M.F., Fish, F.E., Potvin, J., Bierlich, K.C., Cade, D.E., Di Clemente, J., Kennedy, J., Segre, P., Stanworth, A., Weir, C., & Goldbogen, J.A. 2021. Scaling of oscillatory kinematics and Froude efficiency in baleen whales. Journal of Experimental Biology224(13):jeb237586. DOI: https://doi.org/10.1242/jeb.237586

Hildebrand, L., Bernard, K.S., & Torres, L.G. 2021. Go 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, https://doi.org/10.3389/fmars.2021.683634

Jones, D.C., Ceia, F.R., Murphy, E., Delord, K., Furness, R.W., Verdy, A., Mazloff, M., Phillips, R.A., Sagar, P.M., Sallée, J-B., Schreiber, B., Thompson, D.R., Torres, L.G., Underwood, P.J., Weimerskirch, H., & Xavier J.C. 2021. Untangling local and remote influences in two major petrel habitats in the oligotrophic Southern Ocean. Global Change Biology 27(22):5773-5785.

Kone, D.V., Tinker, M.T., & Torres, L.G. 2021. Informing sea otter reintroduction through habitat and human interaction assessment. Endangered Species Research 55:159-176. 

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.

Lemos, L.S., Haxel, J.H., Olsen, A., Burnett, J.D., Smith, A., Chandler, T.E., Nieukirk, S.L., Larson, S.E., Hunt, K.E., & Torres, L.G. 2021. Sounds of stress: assessment of relationships between ambient noise, vessel traffic, and gray whale stress hormone. Scientific Reports. DOI:10.21203/rs.3.rs-923450/v1

Maron, C.F., Lábaque, M.C., Beltramino L., DiMartino, M., Alzugaray, L., Ricciardi, M., Fernandez, A.A.A., Adler, F.R., Seger, J., Sironi, M., Rowntree, V.J., & Uhart, M.M. 2021. Patterns of blubber fat deposition and evaluation of body condition in growing southern right whale calves (Eubalaena australis). Marine Mammal Science. DOI: 10/1111/mms.12818.

Orben, R.A., Adams, J., Hester, M., Shaffer, S.A., Suryan, R.M., Deguchi, T., Ozaki, K., Sato, F., Young, L.C., Clatterbuck, C., Conners, M.G., Kroodsma, D.A., & Torres, L.G. 2021. Across borders: External factors and prior behavior influence North Pacific albatross associations with vessel traffic. Journal of Applied Ecology.

Savoca, M.S. Czapanskiy, M.F., Kahane-Rapport, S.R., Gough, W.T., Falhbusch, J.A., Bierlich, K.C., Segre, P.S., Di Clemente, J., Penry G.S., Wiley, D.N., Calambokids, J., 
Nowacek, D.P., Johnston, D.W., Pyenson, N.D., Friedlaender, A.S., Hazen, E.L., & Goldbogen, J.A. 2021. Baleen whale prey consumption based on high-resolution foraging measurements. Nature 599:85–90. https://doi.org/10.1038/s41586-021-03991-5

Stephenson, F., Hewitt, J.E., Torres, L.G., Mouton, T.L., Brough, T., Goetz, K.T., Lundquist, C.J., MacDiarmid, A.B., Ellis, J., & Constantine, R. 2021. Cetacean conservation planning in a global diversity hotspot: dealing with uncertainty and data deficiencies. Ecosphere 12(7):e03633.

Thompson, D.R., Goetz, K.T., Sagar, P.M., Torres, L.G., Kroeger, C.E., Sztukowski, LA., Orben, R.A., Hoskins, A.J., & Phillips, R.A. 2021. The year-round distribution and habitat preferences of Campbell albatross (Thalassarche impavida). Aquatic Conservation: Marine and Freshwater Ecosystems 31(10):2967-2978.

Looking for micro in the macro: microplastics in cetaceans

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

Since we find ourselves well into the cozy winter season, I thought it was an appropriate time to update you all on our project COZI (Coastal Oregon Zooplankton Investigation). COZI is a cross-college collaborative effort, led by GEMM PI Leigh Torres, that aims to better understand the quality of Oregon coast zooplankton prey and its impacts on gray whale foraging ecology and health. Leigh is joined by three other early-career female scientists, Dr. Sarah Henkel, Dr. Kim Bernard, and Dr. Susanne Brander, that each contribute a different area of expertise to the project. The quartet recently graced the cover of the Oregon Stater in an article all about COZI written by Nancy Steinberg (which I highly recommend reading!). To date, the COZI team (which includes myself as well as many other students) has found that the caloric content of the six predominant zooplankton species in Oregon coastal waters differs significantly, with Dungeness crab megalopae coming out on top as a caloric goldmine (Hildebrand et al. 2021). We found that these Oregon prey are calorically competitive with the predominant benthic amphipod that gray whales feed on in the Arctic, which has interesting implications for foraging ground selection and use of gray whales in the eastern North Pacific (read about it in detail in my blog about the publication). Now that we know that Oregon zooplankton quality differs in terms of calories, we are curious to determine whether these species are impacted by microplastics in the environment, to what extent, and how gray whales may be affected.

What is in those zooplankton? A microscopic view of several mysid shrimp collected in Oregon coastal waters. Source: L. Hildebrand.

To answer these questions, we are analyzing both zooplankton and gray whale fecal samples for microplastics to see what kind, and how many, microplastics we find, and whether microplastics biomagnify up the food chain. The lab analysis has just been completed and we are working on interpreting the results. We can’t let the cat out of the bag yet, but a little sneak-peek of what we have found is that there are different levels of microplastic loads by zooplankton species, which also end up in the whale poop. So, until we finalize those results for sharing, I am going to review the field of microplastics research, with a particular focus on cetaceans. Avid readers of our blog may recall that I wrote a blog about marine plastics at the start of 2019. In that blog, I mentioned that a GoogleScholar search of “microplastics marine” generated 7,650 results. To get an idea of how microplastics research in the marine environment has progressed since I wrote my 2019 blog, I conducted the same GoogleScholar search for this blog but I limited the results to studies published between 2019-2021. GoogleScholar presented me with a whopping 18,000+ results, which shows the rapidity at which the field of marine microplastics research has grown in the last couple of years. The studies span all kinds of topics from distribution & occurrence, to chemical behaviors & interactions with other toxins, to sources & sinks (to name a few!). The results encompass both laboratory and field studies investigating samples from all five oceans of the world. Unfortunately, the title of my blog from two years ago still rings very true: plastics truly are ubiquitous in the marine environment. 

In my last blog, I listed three cetacean species that had been found to contain microplastics: a True’s beaked whale (Lusher et al. 2015), a humpback whale (Besseling et al.2015) and an Indo-Pacific humpback dolphin (Zhu et al.2018). Reflective of the marine microplastics field in general, this list has also grown considerably in the last two years. Since 2019, microplastics have been detected in harbor porpoises (Philipp et al. 2021), common dolphins (Nelms et al. 2019), striped dolphins (Novillo et al. 2020), bottlenose dolphins (Battaglia et al. 2020), Atlantic white-sided dolphins (Nelms et al. 2019), beluga whales (Moore et al. 2020), and Bryde’s & sei whales (Zantis et al. 2021). At this point, I would posit that the main reason this list is not longer is due to the time it takes to collect and analyze samples for microplastics, rather than microplastics being absent in other cetacean species. During my research for this blog, I noticed that the studies on microplastics in cetaceans are starting to shift from focusing on simply determining microplastic occurrence to attempting to estimate levels of exposure and/or ingestion, determine the main source (from water vs. from prey), and long-term consequences. 

Graphical abstract taken from Zantis et al. (2021) representing the pathway of microplastics exposure of large marine filter-feeders. Source: Zantis et al. (2021).

A study published this year examined fecal samples of Bryde’s and sei whales in coastal waters in New Zealand and detected 32 ± 24 microplastics per 6 g of feces (Zantis et al. 2021). By extrapolating these values to the proportions of prey species in the whales’ diet, the authors estimate that these whales consume over 24,000 pieces of microplastics per mouthful of prey, or more than 3 million microplastics per day. Another study (Shetty 2021) in the same geographic region investigated the levels of microplastics in coastal surface waters, which allowed the authors to estimate whether the source of the microplastics that the Bryde’s and sei whales ingest come from the water or the prey. They found that the estimated level of microplastics that the whales consume daily from their prey is four orders of magnitude higher than the microplastic levels in the coastal waters. This finding strongly suggests that the predominant mode of exposure of large filter feeders, such as baleen whales, for microplastic pollution comes from their prey through biomagnification (not just from the ambient sea water).

The GEMM Lab collecting a gray whale fecal sample along the Oregon coast captured from a drone. Source: GEMM Lab.

COZI aims to conduct similar analyses as these studies described above to understand the exposure of coastal Oregon zooplankton to microplastics and how this may be affecting gray whales. Stay tuned for those results!

I am aware that I have painted a very bleak (but true) picture of microplastic pollution in our oceans in this blog but there are things you can do to help reduce microdebris in the environment!

  1. A major source of pollution in the ocean comes from microfibers through our laundry. You can help stop this pathway by simply using a Cora Ball or installing a filter (such as this one) in your washing machine that captures microfleece & polyester fibers.
  2. Minimize your use of single-use plastics. There are so many ways to do so including reuseable water bottles, travel mugs for coffee or tea, fabric totes as shopping bags, carry a set of utensils for takeout food, beeswax wraps instead of plastic wrap or sandwich bags.
  3. Use public transport when possible as another huge source of microplastics comes from tire treads! This solution also helps reduce your carbon footprint.

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References

Battaglia, F.M., Beckingham, B.A., & McFee, W.E. 2020. First report from North America of microplastics in the gastrointestinal tract of stranded bottlenose dolphins (Tursiops truncatus). Marine Pollution Bulletin 160:111677.

Besseling, E., et al. 2015. Microplastic in a macro filter feeder: humpback whale Megaptera novaeangliae. Marine Pollution Bulletin 95: 248-252.

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. https://doi.org/10.3389/fmars.2021.683634

Lusher, A.L., et al. 2015. Microplastic and macroplastic ingestion by a deep diving, oceanic cetacean: the True’s beaked whales Mesoplodon mirus. Environmental Pollution 199: 185-191.

Moore, R.C., et al. 2020. Microplastics in beluga whales (Delphinapterus leucas) from the eastern Beaufort Sea. Marine Pollution Bulletin 150:110723.

Nelms, S.E., et al. 2019. Microplastics in marine mammals stranded around the British coast: ubiquitous bus transitory? Scientific Reports 9:1075.

Novillo, O., Raga, J. A., & Tomás, J. 2020. Evaluating the presence of microplastics in striped dolphins (Stenella coeruleoalba) stranded in the western Mediterranean Sea. Marine Pollution Bulletin 160:111557.

Philipp, C., et al. 2021. First evidence of retrospective findings of microplastics in harbor porpoises (Phocoena phocoena) from German waters. Frontiers in Marine Science. https://doi.org/10.3389/fmars.2021.682532

Shetty, D. 2021. Incidence of microplastics in coastal inshore fish species and surface waters in the Hauraki Gulf, New Zealand. Master’s thesis, University of Auckland, New Zealand.

Zantis, L.J., et al. 2021. Assessing microplastic exposure of large marine filter-feeders. Science of The Total Environment 151815.

Zhu, J., et al. 2018. Cetaceans and microplastics: First report of microplastic ingestion by a coastal delphinid, Sousa chinensis. Science of the Total Environment 659: 649-654.

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. 

Figure 1. A guppy (Rahman et al., 2013)

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. 

Figure 2. An asp viper

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

Harmful algal blooms expose southern right whales to domoic acid and can potentially cause endocrine alterations

Dr. Alejandro Fernández Ajó, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Rises in ocean temperatures can lead to multiple alterations in marine ecosystems, including the increase and the frequency of Harmful Algal Blooms (HABs). HABs are characterized by the rapid growth of toxin-producing species of algae that can be harmful to people, animals, and the local ecology, even causing death in severe cases. Species of marine diatom within the genus Pseudo-nitzschia and Nitzschia can form HABs when they produce domoic acid (DA), a potent neurotoxin responsible for amnesic shellfish poisoning (D’Agostino et al., 2018, 2017).

Figure 1. Southern right whale (E. australis) mother and calf swimming at the gulfs of Peninsula Valdes, Argentina, during a phytoplankton bloom. Photo: Mariano Sironi / Instituto de Conservacion de Ballenas de Argentina.

During HABs, DA is transferred to higher organisms through the pelagic food web and is accumulated by intermediate vectors, such as copepods, euphausiids (i.e., krill), shellfish, and fish. As this neurotoxin affects top predators, DA poisoning poses a risk to the safety and health of humans and wildlife. This neurotoxin has caused mortality in many marine mammal species, including both pinnipeds and cetaceans (Gulland 1999; Lefebvre et al. 1999; Fire et al. 2010, 2021; Broadwater et al. 2018). In addition, the exposure to DA constitutes a stressor that may affect glucocorticoids (hormones involved in the stress response) concentrations.

The glucocorticoids (GCs; cortisol and corticosterone) are adrenal steroid hormones that maintain the essential functions of metabolism and energy balance in mammals. GCs can increase sharply in response to environmental stressors to elicit physiological and behavioral adaptations by individuals to support survival (Sapolsky et al. 2000; Bornier et al. 2009). However, with the chronic exposure to a stressor, this relationship can reverse, with GCs sometimes declining below its baseline levels (Dickens and Romero, 2013; Fernández Ajó et al., 2018). Moreover, DA can interfere with the stress response in mammals, and cause alterations in their physiological response. DA is an excitatory amino acid analog of glutamate (Pulido 2008), a well-known brain neurotransmitter that play an important role in the activation of the adrenal axis (which in turn regulate the production and secretion of the GCs) and regulate many of the pituitary hormones involved in the stress response (Brann and Mahesh 1994; Johnson et al. 2001). Hence, monitoring GC levels in marine mammals can be a potential useful metric for assessing the physiological impacts of exposure to DA.

Glucocorticoids are traditionally measured in plasma, but given that plasma sampling from free-ranging large whales is currently impossible, alternative sample types such as fecal samples, among others, can be utilized to quantify GCs in large whales (Ajó et al., 2021; Burgess et al., 2018, 2016; Fernández Ajó et al., 2020, 2018; Hunt et al., 2019, 2014, 2006; Rolland et al., 2017, 2005)(Figure 2). The analyses of fecal glucocorticoid metabolites (fGCm) is particularly useful for endocrine assessments of free-swimming whales, with several studies showing that fGCm correlate in meaningful ways with presumed stressors. For example, high levels of fGCm in North Atlantic right whales (NARW, Eubalaena glacialis) and in gray whales (Eschrichtius robustus) correlate with poor body condition (Hunt et al., 2006; Lemos et al., 2021), and fGCm increases were associated with whale entanglements and ship strikes (i.e., Lemos et al., 2020; Rolland et al., 2017).

Figure 2. Alternative samples types can be used to study hormones in large whales. 1-2-3 are sample types that can be obtained from free-living whales and provide a more instantaneous and acute measurement of the whales´ physiology. 4-5 can be obtained at necropsy when the whale is found dead at the beach and provide an integrated measure of the whale physiology that can expand through years or even the lifespan of an individual.

In Península Valdés, Argentina, southern right whales (SRW, E. australis) gather in large numbers to mate and nurse their calves during the austral winter months (Bastida and Rodríguez, 2009). SRWs are capital breeders, largely fasting during the breeding season and instead relying on stored blubber fuel reserves. However, they can occasionally feed on calanoid copepods (D’Agostino et al., 2018, 2016), particularly during the phytoplankton blooms that are dominated by diatoms of the genus Pseudo-nitzschia (Sastre et al. 2007; D’Agostino et al. 2015, 2018). Therefore, feeding SRWs in Península Valdés temporally overlap with these Pseudo-nitzschia blooms (D’Agostino et al. 2018, 2015) and represents a test case for assessing the relationship of DA exposure with GC levels (Figure 3).

Figure 3. Southern right whale (E. australis) skim feeding at the Peninsula Valdes breeding ground. Photo: Lucas Beltranino.

In our recent scientific publication (D’Agostino et al. 2021), we investigate SRW exposure to DA at their breeding ground in Peninsula Valdes and assessed its effects on fecal glucocorticoid concentrations. Although the sample size of this study is unavoidably small due to the difficulties of obtaining fecal samples from whales at their calving grounds where defecation is infrequent, we observed significantly lower fGCm in samples from whales exposed to DA (Figure 4). Our results agree with findings from a previous study in California sea lions (Zalophus californianus) exposed to DA, where these authors found a significant association of DA exposure with reduced serum cortisol (Gulland et al., 2009), which can be tentatively attributed to abnormal function of the adrenal axis due to the exposure.

Figure 4. Fecal glucocorticoid metabolite levels in southern right whales exposed (YES, solid triangles) and not-exposed (NO, open circles) to DA. Left panel: immunoreactive fecal corticosterone metabolites. Right panel: immunoreactive fecal cortisol metabolites. Hormone concentrations are expressed in ng of immunoreactive hormone per gram of dry fecal sample. Significant differences between groups are denoted with an asterisk (P<0.05). The black solid line indicates the mean for each group, and in parenthesis is the sample size for each group. Adapted from D’Agostino et al. 2021.

If ingestion of toxins produced by phytoplankton can result in long-term suppression of baseline GCs, whales and marine mammals in general, could suffer reduced ability to cope with additional stressors. The adrenal function is essential to maintain circulating blood glucose and other aspects of metabolism within normal bounds. Additionally, the ability to elevate GCs facilitates energy mobilization to physiologically cope with a stressful event and to initiate appropriate behavioral responses (i.e., flee from predators, heal wounds). Various toxicants have been shown to reduce adrenal function across taxa (Romero and Wingfield, 2016) and could have negative consequences on the ability of cetaceans to respond and adapt to ongoing environmental and anthropogenic changes. Compounding this problem, whales are exposed to an increasing number of stressors from multiple sources and with cumulative effects and they need to be able to physiologically respond to continue to reproduce and survive.

To our knowledge, this study provides the first quantification of fGCm levels in whales exposed to DA; and we hope this effort starts a growing dataset to which other researchers can add. Sampling and analysis of non-traditional matrices, such as feces, blubber, baleen and others, would likely increase sample sizes and thus our understanding of the interrelationships among DA exposure and age, sex, and reproductive status of cetaceans. Given that chronic exposure to DA could alter the capacity of animals to respond to stress, and indications that HABs are becoming more frequent and intense world-wide (Van Dolah 2000; Masó et al. 2006; Erdner et al. 2008), we believe that research evaluating the health status of marine mammal populations should include the assessment of stress physiology relative to natural and anthropogenic stressors including exposure to toxicants.

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References

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D’Agostino, V.C., Hoffmeyer, M.S., Degrati, M., 2016. Faecal analysis of southern right whales (Eubalaena australis) in Península Valdés calving ground, Argentina: Calanus australis, a key prey species. J. Mar. Biol. Assoc. United Kingdom 96, 859–868. https://doi.org/10.1017/S0025315415001897

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Weighing-in on scale

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

As the first term of my master’s program comes to an end and we head toward winter break, I am excited by the course material that has already helped direct my research and development as a scientist. There have been new, challenging topics to tackle, and each assignment has fostered deeper thinking into the formation of my thesis. While I learned new methods and analysis approaches this term, a single phrase pervades throughout my studies of ecology – “it depends!”. Ecologists work to uncover patterns driven by natural processes, and this single phrase seems to answer many questions about whether the pattern always exists. A reasonable follow up to that frequently used phrase is, “depends on what?” or “when or where would this pattern change?” In the context of foraging ecology, predator-prey patterns are frequently driven by environmental processes that depend on the scale you choose for your study. 

What do we mean by scale? Simply stated, scale is a graduation from one level of measurement to another. You can imagine a ruler, for example. You can measure how tall you are in inches with a ruler or in yards with a yard stick. When we think about scale in ecology, the “ruler” can have traditional units of space (meters, kilometers, etc.), units of time (minutes, days, hours, months, years, etc.), or sometimes both!  

The ocean is dynamic and heterogeneous, which simply means there is a lot going on at once. Oceanographic processes influence predator-prey interactions but due to the inherent variability in the system, it is important to explore which factors drive processes that influence patterns at different spatial and temporal scales.  

In marine ecology, the “explanatory power” of a factors’ influence on a given process depends on which scale you choose to build your research upon. Ocean ecosystems are hierarchical, with patterns happening at many temporal and spatial scales all at once. So, we could choose to study the same predator-prey interactions at the scale of meters and minutes or 100s of km and months, and we would likely find very different drivers of patterns. The topic of scale is particularly relevant in regard to whale foraging, as marine mammals employ different sensory methods to locate prey at different spatial scales (Torres 2017). 

Among the first papers to conduct multi-scale research on whale foraging was Jaquet and Whitehead, 1996. Here, they studied sperm whale distribution in relation to various physical and environmental variables. Analysis showed that the main drivers of sperm whale distribution were secondary productivity (e.g., bacteria and zooplankton), underwater topography, and the gradient between deep water and surface water productivity. However, these drivers had a different impact depending on the spatial scale. There was no correlation between the drivers and sperm whale distribution at small scales < 320 nautical miles. However, at large scales >= 320 nautical miles, female sperm whale distribution was correlated with high secondary productivity and steep underwater topography. These important findings demonstrate that small scale distribution of prey alone does not drive the distribution of sperm whale predators in this study region, while other factors contribute to predator movement.  

Figure 1. Figure reproduced from Jaquet & Whitehead, 1996. Plots show how the Spearman correlation results between sperm whale density and environmental variables change across multiple spatial scales. (A) Prey distribution, (B) distance to shore and bathymetric contour, and (C) the three main environmental drivers (secondary productivity, topography, and the deep water productivity gradient). 

Ten years later, a study on Mediterranean fin whales tackled a similar question of how interactions between prey and predator change at multiple scales. However, their work investigated responses to both spatial and temporal scale changes. Through spatial modeling relative to oceanographic factors, Cotté et al. 2009 found that at a large-scale (year and ocean basin-wide), fin whales demonstrated two distinct distribution patterns: in the summer they were aggregated, and in the winter they were more dispersed. However, at the meso-scale (weeks -months, and 20-100 km) fin whale fidelity switched to colder, saltier waters with steeper topography and temperature gradients. Based on these results, the authors concluded that at the large scale, whale movement was driven by annually persistent prey abundance. At smaller scales, prey aggregations are less predictable, thus the authors suggest that whale movement at the meso-scale is driven by physical processes, such as frontal zones and strong currents.  

Figure 2. Figure reproduced from Cotté et. al 2009. Map shows Mediterranean fin whale distribution against oceanographic conditions. Color gradient indicates sea surface temperature (SST), fin whale observations shown in white and red circles, black arrows show current direction, with inset temperature/salinity diagram for September 28-30th 2006. 

A key takeaway from these papers is that it is important to investigate how processes and responses can vary at different scales, because results can sometimes depend on the time and space measurement applied in the analysis. For my thesis, I will explore which drivers take a front seat role in gray whale foraging at both fine and meso-scales. I am interested to compare my results on the relationships between PCFG gray whales and their zooplankton prey to the results from the above described studies. Stay tuned for more updates! 

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References: 

Cotté, C., Guinet, C., Taupier-Letage, I., Mate, B., & Petiau, E. (2009). Scale-dependent habitat use by a large free-ranging predator, the Mediterranean fin whale. Deep Sea Research Part I: Oceanographic Research Papers, 56(5), 801-811. 

Jaquet, N., & Whitehead, H. (1996). Scale-dependent correlation of sperm whale distribution with environmental features and productivity in the South Pacific. Marine ecology progress series, 135, 1-9. 

​​Torres, L. G. (2017). A sense of scale: Foraging cetaceans’ use of scale‐dependent multimodal sensory systems. Marine Mammal Science, 33(4), 1170-1193.