Little whale, big whale, swimming in the water: A quick history on how aerial photogrammetry has revolutionized the ability to obtain non-invasive measurements of whales

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

The morphology and body size of an animal is one of the most fundamental factors for understanding a species ecology. For instance, fish body size and fin shape can influence its habitat use, foraging behavior, prey type, physiological performance, and predator avoidance strategies (Fig 1). Morphology and body size can thus reflect details of an individual’s current health, likelihood of survival, and potential reproductive success, which directly influences a species life history patterns, such as reproductive status, growth rate, and energetic requirements. Collecting accurate morphological measurements of individuals is often essential for monitoring populations, and recent studies have demonstrated how animal morphology has profound implications for conservation and management decisions, especially for populations inhabiting anthropogenically-altered environments (Miles 2020) (Fig. 1). For example, in a study on the critically endangered European eel, De Meyer et al. (2020) found that different skulls sizes were associated with different ecomorphs (a local variety of a species whose appearance is determined by its ecological environment), which predicted different diet types and resulted with some ecomorphs having a greater exposure to pollutants and toxins than others. However, obtaining manual measurements of wild animal populations can be logistically challenging, limited by accessibility, cost, danger, and animal disturbance. These challenges are especially true for large elusive animals, such as whales that are often in remote locations, spend little time at the surface of the water, and their large size can preclude safe capture and live handling.

Figure 1. Top) A pathway framework depicting how the morphology of an animal influences its habitat use, behavior, foraging, physiology, and performance. These traits all affect how successful an animal is in its environment and can reflect an individual’s current health, likelihood of survival, and potential reproductive success. This individual success can then be scaled up to assess overall population health, which in turn can have direct implications for conservation. Bottom) an example of morphological differences in fish body size and fin shape from Walker et al. (2013). Fineness ratio (f) = length of body ­÷ max body width. 

Photogrammetry is a non-invasive method for obtaining accurate morphological measurements of animals from photographs. The two main types of photogrammetry methods used in wildlife biology are 1) single camera photogrammetry, where a known scale factor is applied to a single image to measure 2D distances and angles and 2) stereo-photogrammetry, where two or more images (from a single or multiple cameras) are used to recreate 3D models. These techniques have been used on domestic animals to measure body condition and estimate weight of dairy cows and lactating Mediterranean buffaloes (Negretti et al., 2008; Gaudioso et al., 2014) and on wild animals to measure sexual dimorphism in Western gorillas (Breuer et al., 2007), shoulder heights of elephants (Schrader et al., 2006), nutritional status of Japanese macaques (Kurita et al., 2012), and the body condition of brown bears (Shirane et al., 2020). Over 70 years ago, Leedy (1948) encouraged wildlife biologists to use aerial photogrammetry from aircraft for censusing wild animal populations and their habitats, where photographs can be collected at nadir (straight down) or an oblique angle, and the scale can be calculated by dividing the focal length of the camera by the altitude or by using a ratio of selected points in an image of a known size. Indeed, aerial photogrammetry has been wildly adopted by wildlife biologists and has proven useful in obtaining measurements in large vertebrates, such as elephants and whales.

Whitehead & Payne (1978) first demonstrated the utility of using aerial photogrammetry from airplanes and helicopters as a non-invasive technique for estimating the body length of southern right whales. Prior to this technique, measurements of whales were traditionally limited to assessing carcasses collected from scientific whaling operations, or opportunistically from commercial whaling, subsistence hunting, stranding events, and bycatch. Importantly, aerial photogrammetry provides a method to collect measurements of whales without killing them. This approach has been widely adopted to obtain body length measurements on a variety of whale and dolphin species, including bowhead whales (Cubbage & Calambokidis, 1987), southern right whales (Best & Rüther, 1992), fin whales (Ratnaswamy and Wynn, 1993), common dolphins (Perryman and Lynn, 1993), spinner dolphins (Perryman & Westlake 1998), and killer whales (Fearnbach et al. 2012). Aerial photogrammetry has also been used to measure body widths to estimate nutritive condition related to reproduction in gray whales (Perryman and Lynn, 2002) and northern and southern right whales (Miller et al., 2012). However, these studies collected photographs from airplanes and helicopters, which can be costly, limited by weather and infrastructure to support aircraft research efforts and, importantly, presents a potential risk to wildlife biologists (Sasse 2003). 

The recent advancement and commercialization of unoccupied aircraft systems (UAS, or drones) has revolutionized the ability to obtain morphological measurements from high resolution aerial photogrammetry across a variety of ecosystems (Fig. 2). Drones ultimately bring five transformative qualities to conservation science compared to airplanes and helicopters: affordability, immediacy, quality, efficiency, and safety of data collection. Durban et al. (2015) first demonstrated the utility of using drones for non-invasively obtaining morphological measurements of killer whales in remote environments. Since then, drone-based morphological measurements have been applied to a wide range of studies that have increased our understanding on different whale populations. For example, Leslie et al. (2020) used drone-based measurements of the skull to distinguish a unique sub-species of blue whales off the coast of Chile. Groskreutz et al. (2019) demonstrated how long-term nutritional stress has limited body growth in northern resident killer whales, while Stewart et al. (2021) found a decrease in body length of North Atlantic Right whales since 1981 that was associated with entanglements from fishing gear and may be a contributing factor to the decrease in reproductive success for this endangered population. 

Drone imagery is commonly used to estimate the body condition of baleen whales by measuring the body length and width of individuals. Recently, the GEMM Lab used body length and width measurements to quantify intra- and inter-seasonal changes in body condition across individual gray whales (Lemos et al., 2020). Drones have also been used to measure body condition loss in humpback whales during the breeding season (Christiansen et al., 2016) and to compare the healthy southern right whales to the skinnier, endangered North Atlantic right whales (Christiansen et al., 2020). Drone-based assessments of body condition have even been used to measure how calf growth rate is directly related to maternal loss during suckling (Christiansen et al., 2018), and even estimate body mass (Christiansen et al., 2019). 

Drone-based morphological measurements can also be combined with whale-borne inertial sensing tag data to study the functional morphology across several different baleen whale species. Kahane-Rapport et al. (2020) used drone measurements of tagged whales to analyze the biomechanics of how larger whales require longer times for filtering the water through their baleen when feeding. Gough et al. (2019) used size measurements from drones and swimming speeds from tags to determine that a whale’s “walking speed” is 2 meters per second – whether the largest of the whales, a blue whale, or the smallest of the baleen whales, an Antarctic minke whale. Size measurements and tag data were combined by Segre et al. (2019) to quantify the energetic costs of different sized whales when breaching. 

Taken together, drones have revolutionized our ability to obtain morphological measurements of whales, greatly increasing our capacity to better understand how these animals function and perform in their environments. These advancements in marine science are particularly important as these methods provide greater opportunity to monitor the health of populations, especially as they face increased threats from anthropogenic stressors (such as vessel traffic, ocean noise, pollution, fishing entanglement, etc.) and climate change. 

Drone-based photogrammetry is one of the main focuses of the GEMM Lab’s project on Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE). This summer we have been collecting drone videos to measure the body condition of gray whales feeding off the coast of Newport, Oregon (Fig. 2). As we try to understand the physiological stress response of gray whales to noise and other potential stressors, we have to account for the impacts of overall nutritional state of each individual whale’s physiology, which we infer from these body condition estimates. 

Figure 2. Drones can help collect images of whales to obtain morphological measurements using photogrammetry and help us fill knowledge gaps for how these animals interact in their environment and to assess their current health. Bottom photo is an image collected by the GEMM Lab of a gray whale being measured in MorphoMetriX software to estimate its body condition. 

References

Best, P. B., & Rüther, H. (1992). Aerial photogrammetry of southern right whales, Eubalaena australis. Journal of Zoology228(4), 595-614.

Breuer, T., Robbins, M. M., & Boesch, C. (2007). Using photogrammetry and color scoring to assess sexual dimorphism in wild western gorillas (Gorilla gorilla). American Journal of Physical Anthropology134(3), 369–382. https://doi.org/10.1002/ajpa.20678 

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. Marine Ecology Progress Series592, 267–281. 

Christiansen, F. (2020). A population comparison of right whale body condition reveals poor state of North Atlantic right whale, 1–43. 

Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. Y., & Bejder, L. (2016). Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere7(10), e01468–18. 

Christiansen, F., Sironi, M., Moore, M. J., Di Martino, M., Ricciardi, M., Warick, H. A., … Uhart, M. M. (2019). Estimating body mass of free-living whales using aerial photogrammetry and 3D volumetrics. Methods in Ecology and Evolution10(12), 2034–2044. 

Cubbage, J. C., & Calambokidis, J. (1987). Size-class segregation of bowhead whales discerned through aerial stereo-photogrammetry. Marine Mammal Science3(2), 179–185. 

De Meyer, J., Verhelst, P., & Adriaens, D. (2020). Saving the European Eel: How Morphological Research Can Help in Effective Conservation Management. Integrative and Comparative Biology23, 347–349. 

Gaudioso, V., Sanz-Ablanedo, E., Lomillos, J. M., Alonso, M. E., Javares-Morillo, L., & Rodr\’\iguez, P. (2014). “Photozoometer”: A new photogrammetric system for obtaining morphometric measurements of elusive animals, 1–10.

Gough, W. T., Segre, P. S., Bierlich, K. C., Cade, D. E., Potvin, J., Fish, F. E., … Goldbogen, J. A. (2019). Scaling of swimming performance in baleen whales. Journal of Experimental Biology222(20), jeb204172–11. 

Groskreutz, M. J., Durban, J. W., Fearnbach, H., Barrett-Lennard, L. G., Towers, J. R., & Ford, J. K. B. (2019). Decadal changes in adult size of salmon-eating killer whales in the eastern North Pacific. Endangered Species Research40, 1 

Kahane-Rapport, S. R., Savoca, M. S., Cade, D. E., Segre, P. S., Bierlich, K. C., Calambokidis, J., … Goldbogen, J. A. (2020). Lunge filter feeding biomechanics constrain rorqual foraging ecology across scale. Journal of Experimental Biology223(20), jeb224196–8. 

Leedy, D. L. (1948). Aerial Photographs, Their Interpretation and Suggested Uses in Wildlife Management. The Journal of Wildlife Management12(2), 191. 

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

Leslie, M. S., Perkins-Taylor, C. M., Durban, J. W., Moore, M. J., Miller, C. A., Chanarat, P., … Apprill, A. (2020). Body size data collected non-invasively from drone images indicate a morphologically distinct Chilean blue whale (Balaenoptera musculus) taxon. Endangered Species Research43, 291–304. 

Miles, D. B. (2020). Can Morphology Predict the Conservation Status of Iguanian Lizards? Integrative and Comparative Biology

Miller, C. A., Best, P. B., Perryman, W. L., Baumgartner, M. F., & Moore, M. J. (2012). Body shape changes associated with reproductive status, nutritive condition and growth in right whales Eubalaena glacialis and E. australis. Marine Ecology Progress Series459, 135–156. 

Negretti, P., Bianconi, G., Bartocci, S., Terramoccia, S., & Verna, M. (2008). Determination of live weight and body condition score in lactating Mediterranean buffalo by Visual Image Analysis. Livestock Science113(1), 1–7. https://doi.org/10.1016/j.livsci.2007.05.018 

Ratnaswamy, M. J., & Winn, H. E. (1993). Photogrammetric Estimates of Allometry and Calf Production in Fin Whales, \emph{Balaenoptera physalus}. American Society of Mammalogists74, 323–330. 

Perryman, W. L., & Lynn, M. S. (1993). Idendification of geographic forms of common dolphin(\emph{Delphinus Delphis}) from aerial photogrammetry. Marine Mammal Science9(2), 119–137. 

Perryman, W. L., & Lynn, M. S. (2002). Evaluation of nutritive condition and reproductive status of migrating gray whales (\emph{Eschrichtius robustus}) based on analysisof photogrammetric data. Journal Cetacean Research and Management4(2), 155–164. 

Perryman, W. L., & Westlake, R. L. (1998). A new geographic form of the spinner dolphin, stenella longirostris, detected with aerial photogrammetry. Marine Mammal Science14(1), 38–50. 

Sasse, B. (2003). Job-Related Mortality of Wildlife Workers in the United States, 1937- 2000, 1015–1020. 

Segre, P. S., Potvin, J., Cade, D. E., Calambokidis, J., Di Clemente, J., Fish, F. E., … & Goldbogen, J. A. (2020). Energetic and physical limitations on the breaching performance of large whales. Elife9, e51760.

Shirane, Y., Mori, F., Yamanaka, M., Nakanishi, M., Ishinazaka, T., Mano, T., … Shimozuru, M. (2020). Development of a noninvasive photograph-based method for the evaluation of body condition in free-ranging brown bears. PeerJ8, e9982. https://doi.org/10.7717/peerj.9982 

Shrader, A. M., M, F. S., & Van Aarde, R. J. (2006). Digital photogrammetry and laser rangefinder techniques to measure African elephants, 1–7. 

Stewart, J. D., Durban, J. W., Knowlton, A. R., Lynn, M. S., Fearnbach, H., Barbaro, J., … & Moore, M. J. (2021). Decreasing body lengths in North Atlantic right whales. Current Biology.

Walker, J. A., Alfaro, M. E., Noble, M. M., & Fulton, C. J. (2013). Body fineness ratio as a predictor of maximum prolonged-swimming speed in coral reef fishes. PloS one8(10), e75422.

The learning curve never stops as the GRANITE project begins its seventh field season

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

When I thought about what doing fieldwork would be like, before having done it myself, I imagined that it would be a challenging, but rewarding and fun experience (which it is). However, I underestimated both ends of the spectrum. I simultaneously did not expect just how hard it would be and could not imagine the thrill of working so close to whales in a beautiful place. One part that I really did not consider was the pre-season phase. Before we actually get out on the boats, we spend months preparing for the work. This prep work involves buying gear, revising and developing protocols, hiring new people, equipment maintenance and testing, and training new skills. Regardless of how many successful seasons came before a project, there are always new tasks and challenges in the preparation phase.

For example, as the GEMM Lab GRANITE project team geared up for its seventh field season, we had a few new components to prepare for. Just to remind you, the GRANITE (Gray whale Response to Ambient Noise Informed by Technology and Ecology) project’s field season typically takes place from June to mid-October of each year. Throughout this time period the field team goes out on a small RHIB (rigid hull inflatable boat), whenever the weather is good enough, to collect photo-ID data, fecal samples, and drone imagery of the Pacific Coast Feeding Group (PCFG) gray whales foraging near Newport, OR, USA. We use the data to assess the health, ecology and population dynamics of these whales, with our ultimate goal being to understand the effect of ambient noise on the population. As previous blogs have described, a typical field day involves long hours on the water looking for whales and collecting data. This year, one of our exciting new updates is that we are going out on two boats for the first part of the field season and starting our season 10 days early (our first day was May 20th). These updates are happening because a National Science Foundation funded seismic survey is being conducted within our study area starting in June. The aim of this survey is to assess geophysical structures but provides us with an opportunity to assess the effect of seismic noise on our study group by collecting data before, during, and after the survey. So, we started our season early in order to capture the “before seismic survey” data and we are using a two-boat approach to maximize our data collection ability.

While this is a cool opportunistic project, implementing the two-boat approach came with a new set of challenges. We had to find a second boat to use, buy a new set of gear for the second boat, figure out the best way to set up our gear on a boat we had not used before, and update our data processing protocols to include data collected from two boats on the same day. Using two boats also means that everyone on the core field team works every day. This core team includes Leigh (lab director/fearless leader), Todd (research assistant), Lisa (PhD student), Ale (new post-doc), and me (Clara, PhD student). Leigh and Todd are our experts in boat driving and working with whales, Todd is our experienced drone pilot, I am our newly certified drone pilot, and Lisa, Ale, and myself are boat drivers. Something I am particularly excited about this season is that Lisa, Ale, and I all have at least one field season under our belts, which means that we get to become more involved in the process. We are learning how to trailer and drive the boats, fly the drones, and handling more of the post-field work data processing. We are becoming more involved in every step of a field day from start to finish, and while it means taking on more responsibility, it feels really exciting. Throughout most of graduate school, we grow as researchers as we develop our analytical and writing skills. But it’s just as valuable to build our skillset for field work. The ocean conditions were not ideal on the first day of the field season, so we spent our first day practicing our field skills.

For our “dry run” of a field day, we went through the process of a typical day, which mostly involved a lot of learning from Leigh and Todd. Lisa practiced her trailering and launching of the boat (figure 1), Ale and Lisa practiced driving the boat, and I practiced flying the drone (figure 2). Even though we never left the bay or saw any whales, I thoroughly enjoyed our dry run. It was useful to run through our routine, without rushing, to get all the kinks out, and it also felt wonderful to be learning in a supportive environment. Practicing new skills is stressful to say the least, especially when there is expensive equipment involved, and no one wants to mess up when they’re being watched. But our group was full of support and appreciation for the challenges of learning. We cheered for successful boat launchings and dockings, and drone landings. I left that day feeling good about practicing and improving my drone piloting skills, full of gratitude for our team and excited for the season ahead.

Figure 1. Lisa (driving the truck) launching the boat.
Figure 2. Clara (seated, wearing a black jacket) landing the drone in Ale’s hands.

All the diligent prep work paid off on Saturday with a great first day (figure 3). We conducted five GoPro drops (figure 4), collected seven fecal samples from four different whales (figure 5), and flew four drone flights over three individuals including our star from last season, Sole. Combined, we collected two trifectas (photo-ID images, fecal samples, and drone footage)! Our goal is to get as many trifectas as possible because we use them to study the relationship between the drone data (body condition and behavior) and the fecal sample data (hormones). We were all exhausted after 10 hours on the water, but we were all very excited to kick-start our field season with a great day.

Figure 3. Lisa on the bow pulpit during our first sighting of the day.
Figure 4. Lisa doing a GoPro drop, she’s lowering the GoPro into the water using the line in her hands.
Figure 5. Clara and Ale collecting a fecal sample.

On Sunday, just one boat went out to collect more data from Sole after a rainy morning and I successfully flew over her from launching to landing! We have a long season ahead, but I am excited to learn and see what data we collect. Stay tuned for more updates from team GRANITE as our season progresses!

Defining Behaviors

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

When I started working on my thesis, I anticipated many challenges related to studying the behavioral ecology of gray whales. From processing five-plus years of drone footage to data analysis, there has been no shortage of anticipated and unexpected issues. I recently hit an unexpected challenge when I started video processing that piqued my interest. As I’ve discussed in a previous blog, ethograms are lists of defined behaviors that help us properly and consistently collect data in a standardized approach. Ethograms form a crucial foundation of any behavior study as the behaviors defined ultimately affect what questions can be asked and what patterns are detected. Since I am working off of the thorough ethogram of Oregon gray whales from Torres et al. (2018), I had not given much thought to the process of adding behaviors to the ethogram. But, while processing the first chunk of drone videos, I noticed some behaviors that were not in the original ethogram and struggled to decide whether or not to add them. I learned that ethogram development can lead down several rabbit holes. The instinct to try and identify every movement is strong but dangerous. Every minute movement does not necessarily need to be included and it’s important to remember the ultimate goal of the analysis to avoid getting bogged down.

Fundamental behavior questions cannot be answered without ethograms. For example, Baker et al. (2017) developed an ethogram for bottlenose dolphins in Ireland in order to conduct an initial quantitative behavior analysis. They did so by reviewing published ethograms for bottlenose dolphins, consulting with multiple experts, and revising the ethogram throughout the study. They then used their data to test inter-observer variability, calculate activity budgets, and analyze how the activity budgets varied across space and time.

Howe et al. (2015) also developed an ethogram in order to conduct quantitative behavior analyses. Their goals were to use the ethogram and subsequent analyses to better understand the behavior of beluga whales in Cook Inlet, AK, USA and to inform conservation. They started by writing down all behaviors they observed in the field, then they consolidated their notes into a formal ethogram that they used and refined during subsequent field seasons. They used their data to analyze how the frequencies of different behaviors varied throughout the study area at different times. This study served as an initial analysis investigating the effect of anthropogenic disturbance and was refined in future studies.

My research is similarly geared towards understanding behavior patterns to ultimately inform conservation. The primary questions of my thesis involve individual specialization, patterns of behavior across space, the relationship between behavior and body condition, and social behavior (check out this blog to learn more). While deciding what behaviors to add to my ethogram I’ve had to remind myself of these main questions and the bigger picture. The drone footage lets us see so much detail that it’s tempting to try to define every movement we can observe. One rabbit hole I’ve had to avoid a few times is locomotion. From the footage, it is possible to document fluke beats and pectoral fin strokes. While it could be interesting to investigate how different whales move in different ways, it could easily become a complicated mess of classifying different movements and take me deep into the world of whale locomotion. Talking through what that work would look like reminded me that we cannot answer every question and trying to assess all exciting side projects can cause us to lose focus on the main questions.

While I avoided going down the locomotion rabbit hole, there were some new behaviors that I did add to my ethogram. I’ll illustrate the process with the examples of two new behaviors I recently added: fluke swish and pass under (Clips 1 and 2). Clip 1 shows a whale rapidly moving its fluke to the side. I chose to add fluke swish because it’s such a distinct movement and I’m curious to see if there’s a pattern across space, time, individual, or nearby human activity that might explain its function. Clip 2 shows a calf passing under its mom.  It’s not nursing because the calf doesn’t spend time under its mom, it just crosses underneath her. The calf pass under behavior could be a type of mom-calf tactile interaction. Analyzing how the frequency of this behavior changes over time could show how a calf’s dependency on its mom changes over as it ages.

In defining these behaviors, I had to consider how many different variations of this behavior would be included in the definition. This process involves considering at what point a variation of that behavior could serve a different function, even without knowing the function of the original behavior. For fluke swish this process involved deciding to only count a behavior as a fluke swish if it was a big, fast movement. A small and slow movement of the fluke a little to the side could serve a different function, such as turning, or be a random movement.

Clip 1: Fluke swish behavior (Video filmed under NOAA/NMFS research permit #16111​​ by certified drone pilot Todd Chandler).
Clip 2: Pass under behavior (Video filmed under NOAA/NMFS research permit #16111​​ by certified drone pilot Todd Chandler).

The next step involved deciding if the behavior would be a ‘state’ or ‘point’ event. A state event is a behavior with a start and stop moment; a point event is instantaneous and assigned to just a point in time. I would categorize a behavior as a state event if I was interested in questions about its duration. For example, I could ask “what percentage of the total observation time was spent in a certain behavior state?” A point event would be a behavior where duration is not applicable, but I could ask a question like “Did whale 1 perform more point event A than whale 2?”. Both fluke swish and pass under are point events because they only happen for an instant. In a pass under the calf is passing under its mom for just a brief point in time, making it a point event. The final step was to name the behavior. As I discussed in this blog, the name of the behavior does not matter as much as the definition but it is important that the name is clear and descriptive. We chose the name fluke swish because the fluke rapidly moves from side to side and pass under because the calf crosses under its mom.

Frankly, in the beginning, I was a bit overwhelmed by the realization that the content of my ethogram would ultimately control the questions I could answer. I could not help but worry that after processing all the videos, I would end up regretting not defining more behaviors. However, after reading some of the literature, chatting with Leigh, and reviewing the initial chunk of videos several times, I am more confidence in my judgment and my ethogram. I have accepted the fact that I can’t anticipate everything, and I am confident that the behaviors I need to answer my research questions are included. The process of reviewing and updating my ethogram has been a rewarding challenge that resulted in a valuable lesson that I will take with me for the rest of my career.

References

Baker, I., O’Brien, J., McHugh, K., & Berrow, S. (2017). An ethogram for bottlenose dolphins (Tursiops truncatus) in the Shannon Estuary, Ireland. Aquatic Mammals, 43(6), 594–613. https://doi.org/10.1578/AM.43.6.2017.594

Howe, M., Castellote, M., Garner, C., McKee, P., Small, R. J., & Hobbs, R. (2015). Beluga, Delphinapterus leucas, ethogram: A tool for cook inlet beluga conservation? Marine Fisheries Review, 77(1), 32–40. https://doi.org/10.7755/MFR.77.1.3

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

Connecting Research Questions

Clara Bird, Masters Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

The field season can be quite a hectic time of year. Between long days out on the water, trouble-shooting technology issues, organizing/processing the data as it comes in, and keeping up with our other projects/responsibilities, it can be quite overwhelming and exhausting.

But despite all of that, it’s an incredible and exciting time of year. Outside of the field season, we spend most of our time staring at our computers analyzing the data that we spend a relatively short amount of time collecting. When going through that process it can be easy to lose sight of why we do what we do, and to feel disconnected from the species we are studying. Oftentimes the analysis problems we encounter involve more hours of digging through coding discussion boards than learning about the animals themselves. So, as busy as it is, I find that the field season can be pretty inspiring. I have recently been looking through our most recent drone footage of gray whales and feeling renewed excitement for my thesis.

At the moment, my thesis has four central questions: (1) Are there associations between habitat type and gray whale foraging tactic? (2) Is there evidence of individualization? (3) What is the relationship between behavior and body condition? (4) Do we see evidence of learning in the behavior of mom and calf pairs? As I’ve been organizing my thoughts, what’s become quite clear is how interconnected these questions are. So, I thought I’d take this blog to describe the potential relationships.

Let’s start with the first question: are there associations between habitat types and gray whale foraging tactics? This question is central because it relates foraging behavior to habitat, which is ultimately associated with prey. This relationship is the foundation of all other questions involving foraging tactics because food is necessary for the whales to have the energy and nutrients they need to survive. It’s reasonable to think that the whales are flexible and use different foraging tactics to eat different prey that live in different habitats. But, if different prey types have different nutritional value (this is something that Lisa is studying right now; check out the COZI project to learn more), then not all whales may be getting the same nutrients.

The next question relates to the first question but is not necessarily dependent on it. It’s the question of individualization, a topic Lisa also explored in a past blog. Within our Oregon field sites we have documented a variety of gray whale foraging tactics (Torres et al. 2018; Video 1) but we do not know if all gray whales use all the tactics or if different individuals only use certain tactics. While I think it’s unlikely that one whale only uses one tactic all the time, I think we could see an individual use one tactic more often than the others. I reason that there could be two reasons for this pattern. First, it could be a response to resource availability; certain tactics are more efficient than others, this could be because the tactic involves capturing the more nutritious prey or because the behavior is less energetically demanding. Second, foraging tactics are socially learned as calves from their mothers, and hence individuals use those learned tactics more frequently. This pattern of maternally inherited foraging tactics has been documented in other marine mammals (Mann and Sargeant 2009; Estes et al. 2003). These questions between foraging tactic, habitat and individualization also tie into the remaining two questions.

My third question is about the relationship between behavior and body condition. As I’ve discussed in a previous blog, I am interested in assessing the relative energetic costs and benefits of the different foraging tactics. Is one foraging tactic more cost-effective than another (less energy out per energy in)? Ever since our lab’s cetacean behavioral ecology class, I’ve been thinking about how my work relates to niche partitioning theory (Pianka 1974).This theory states that when there is low prey availability, niche partitioning will increase. Niche partitioning can occur across several different dimensions: for instance, prey type, foraging location, and time of day when active. If gray whales partition across the prey type dimension, then different whales would feed on different kinds of prey. If whales partition resources across the foraging location dimension, individuals would feed in different areas. Lastly, if whales partition resources across the time axis, individuals would feed at different times of day. Using different foraging tactics to feed on different prey would be an example of partitioning across the prey type dimension. If there is a more preferable prey type, then maybe in years of high prey availability, we would see most of the gray whales using the same tactics to feed on the same prey type. However, in years of low prey availability we might expect to see a greater variety of foraging tactics being used. The question then becomes, does any whale end up using the less beneficial foraging tactic? If so, which whales use the less beneficial tactic? Do the same individuals always switch to the less beneficial tactic? Is there a common characteristic among the individuals that switched, like sex, age, size, or reproductive status? Lemos et al. (2020) hypothesized that the decline in body condition observed from 2016 to 2017 might be a carryover effect from low prey availability in 2016. Could it be that the whales that use the less beneficial tactic exhibit poor body condition the following year?

My fourth, and final, question asks if foraging tactics are passed down from moms to their calves. We have some footage of a mom foraging with her calf nearby, and occasionally it looks like the calf could be copying its mother. Reviewing this footage spiked my interest in seeing if there are similarities between the behavior tactics used by moms and those used by their calves after they have been weaned. While this question clearly relates to the question of individualization, it is also related to body condition: what if the foraging tactics used by the mom is influenced by her body condition at the time?

I hope to answer some of these fascinating questions using the data we have collected during our long field days over the past 6 years. In all likelihood, the story that comes together during my thesis research will be different from what I envision now and will likely lead to more questions. That being said, I’m excited to see how the story unfolds and I look forward to sharing the evolving ideas and plot lines with all of you.

References

Estes, J A, M L Riedman, M M Staedler, M T Tinker, and B E Lyon. 2003. “Individual Variation in Prey Selection by Sea Otters: Patterns, Causes and Implications.” Source: Journal of Animal Ecology. Vol. 72.

Mann, Janet, and Brooke Sargeant. 2009. “ Like Mother, like Calf: The Ontogeny of Foraging Traditions in Wild Indian Ocean Bottlenose Dolphins ( Tursiops Sp.) .” In The Biology of Traditions, 236–66. Cambridge University Press. https://doi.org/10.1017/cbo9780511584022.010.

Pianka, Eric R. 1974. “Niche Overlap and Diffuse Competition” 71 (5): 2141–45.

Soledade Lemos, Leila, Jonathan D Burnett, Todd E Chandler, James L Sumich, and Leigh G. Torres. 2020. “Intra‐ and Inter‐annual Variation in Gray Whale Body Condition on a Foraging Ground.” Ecosphere 11 (4). https://doi.org/10.1002/ecs2.3094.

Torres, Leigh G., Sharon L. Nieukirk, Leila Lemos, and Todd E. Chandler. 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.

Dual cameras provide bigger picture

By Hunter Warick, Research Technician, Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute

When monitoring the health of a capital breeding species, such as whales that store energy to support reproduction costs, it is important to understand what processes and factors drive the status of their body condition. Information gained will allow for better insight into their cost of reproduction and overall life history strategies.

For the past four years the GEMM Lab has utilized the perspective that Unoccupied Aerial Systems (UAS; or ‘drones’) provide for observations of marine mammals. This aerial perspective has documented gray whale behavior such as jaw snapping, drooling mud, and headstands, all of which shows or suggest foraging (Torres et al. 2018). However, UAS is limited to a bird’s eye view, allowing us to see WHAT whales are doing, but limited information about the reasons WHY. To overcome this hurdle, Leigh Torres and team have equipped their marine mammal research utility belts with the use of GoPro cameras. They developed a technique known as the “GoPro drop” where a GoPro camera mounted to a weighted pole is lowered off the side of the research vessel in waters < 20 m deep via a line to record video data. This technique allows the team to obtain fine-scale habitat and prey variation information, like what the whale experiences. Along with the context provided by the UAS, this dual camera perspective allows for deeper insight into gray whale foraging strategies and efficiency. Torres’s GoPro data analysis protocol examines kelp density, kelp health, benthic substrate, rock fish density, and mysid density. These characteristics are graded along a scale (Figure 1), allowing for relative comparisons of habitat and prey availability between where whales spend time and forage. These GoPro drops will also help create a fine-scale benthic habitat map of the Newport field area. So, why are these data on gray whale habitat and prey important to understand?

Figure 1. The top row shows varying degrees of mysid density (low to high, left to right). Middle row illustrates different types of substrate you might encounter (reef, sandy, boulders; left to right). Bottom row shows the different levels of kelp health (poor, medium, good).

The foraging grounds are the first step in the life history domino chain reaction for many rorqual whales; if this step doesn’t go off cleanly then everything else fails to fall into place. Gray whales partake on a 15,000-20,000 km (round trip) migration, which is the longest of any known mammal (Swartz 1986). During this migration, whales spend around three months fasting in their breeding grounds (Highsmith & Coyle 1992), living only off the energy stores that they accumulated in their feeding grounds (Næss et al. 1998). These extreme conditions of existence for gray whales drive the need to be a successful forager and is why it is so crucial for them to forage in high prey density areas (Newell, C. 2009).

Mysids are a critical part of the gray whale diet in Oregon waters (Newell, C. 2009; Sullivan, F. 2017) and mysids have strong predator-prey relationships with both top-down and bottom-up control (Dunham & Duffus 2001; Newell & Cowles 2006). This unique tie illustrates the great dependency that gray whales have on mysids, further showing the benefit to looking at the density of mysids where gray whales are seen foraging. The quality of mysids may also be as important as quantity; with higher water temperatures resulting in lower lipid content in mysids (Mauchline 1980), suggesting density might not be the only factor for determining efficient whale foraging. The overall goal of gray whales on their foraging grounds is to get as fat as possible in order to reproduce as often as possible. But, this isn’t always as easy as it sounds. Gray whales typically have a two-year breeding interval but can be anywhere from 1-4 years (Blokhin 1984). The longer time it takes to build up adequate energy stores to support reproduction costs, the longer it will take to breed successfully. Building back up these energy stores can prove to be difficult, especially for lactating females (Figure 2).

Being able to track the health and behavior of gray whales on an individual level, including comparisons between variation in body condition, foraging behavior, and fine scale information on benthic communities gained through the use of GoPros, can provide a better understanding of the driving factors and impacts on their health and population trends (Figure 3).


Figure 3. A compilation of video clips captured by the GEMM Lab during their research on gray whale ecology and physiology off Newport, Oregon using Unoccupied Aerial Systems (UAS, or “drones”) and GoPro cameras. UAS are used to observe gray whale behavior and conduct photogrammetry assessment of body condition. GoPro camera drops assess the benthic habitat and prey density across the study region, with a couple chance encounters of whales. Research is conducted under NOAA/NMFS permit # 21678.

Milling around in definitions

Clara Bird, Masters Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

A big part of graduate school involves extensive reading to learn about the previous research conducted in the field you are joining and the embedded foundational theories. A firm understanding of this background literature is needed in order to establish where your research fits. Science is a constructive process; to advance our disciplines we must recognize and build upon previous work. Hence, I’ve been reading up on the central topic of my thesis: behavioral ecology. It is equally important to study the methods used in these studies as to understand the findings. As discussed in a previous blog, ethograms are a central component of the methodology for studying behavior. Ethograms are lists of defined behaviors that help us properly and consistently collect data in a standardized approach. It is especially important in a project that spans years to know that the data collected at the beginning was collected in the same way as the data collected at the end of the project.

While ethograms and standardized methods are commonly used within a study, I’ve noticed from reading through studies on cetaceans, a lack of standardization across studies. Not all behaviors that are named the same way have matching definitions, and not all behaviors with similar definitions have matching names. Of all the behaviors, “milling” may be the least standardized.

While milling is not in our ethogram (Leigh believes this term is a “cheat” for when behavior is actually “unknown”), we occasionally use “milling” in the field to describe when the gray whales are swimming around in an area, not foraging, but not in any other primary behavior state (travel, social, or rest). Sometimes we use when we think the whale may be searching, but we aren’t 100% sure yet. A recent conversation during a lab meeting on the confusing nature of the term “milling” inspired me to dig into the literature for this blog. I searched through the papers I’ve saved for my literature review and found 18 papers that used the term milling. It was fascinating to read how variably the term has been defined and used.

When milling was defined in these papers, it was most commonly described as numerous directional changes in movement within a restricted area 1–8. Milling often co-occurred with other behavior states. Five of these eight studies described milling as co-occurring with foraging behavior 3–6,8. In one case, milling was associated with foraging and slow movement 8. While another study described milling as passive, slow, nondirectional movement 9.

Eight studies used the term milling without defining the behavior 10–17. Of these, five described milling as being associated with other behavior states. Three studies described milling as co-occurring with foraging 10,14,16, one said that it co-occurred with social behavior 13, and another described milling as being associated with resting/slow movement 12.

In addition to this variety of definitions and behavior associations, there were also inconsistencies with the placement of “milling” within ethograms. In nine studies, milling was listed as a primary state 1,2,4,7–9,15,17,18. But, in two studies that mentioned milling and used an ethogram, milling was not included in the ethogram 6,14.

Diving into the associations between milling and foraging reveal how varied the use of milling has been within the cetacean literature. For example, two studies simply described milling as occurring near foraging in time 10,16. While another two studies explained that milling was applied in situations where there was evidence of feeding without feeding being directly observed 8,14. Bobkov et al. (2019) described milling as occurring between feeding cycles along with breathing. Lastly, two studies describe milling as a behavior within the foraging primary state 3,5, while another study described feeding as a behavior within milling 4.

It’s all rather confusing, huh? Across these studies, milling has been defined, mentioned without being defined, included in ethograms as a primary state, included in ethograms as a sub-behavior, and excluded from ethograms. Milling has also been associated with multiple primary behavior states (foraging, resting, and socializing). It has been described as both passive 9 and slow 12, and strong 16 and active 5.

It appears that milling is often used to describe behaviors that the observer cannot distinctly classify or describe its function. I have also struggled to define these times when a whale is in between behavior states; I often end up calling it “just being a whale”, which includes time spent breathing at the surface, or just swimming around.

As I’ve said above, Leigh thinks that this term is a “cheat” for when a behavior is actually “unknown”. I think we have trouble equating “milling” with “unknown” because it seems like “unknown” should refer to a behavior where we can’t quite tell what the whale is doing. However, during milling, we can see that the whale is swimming at the surface. But here’s the thing, while we can see what the whale is doing, the function of the behavior is still unknown. Instead of using an indistinct term, we should use a term that better describes the behavior.  If it’s swimming at the surface, name the behavior “swimming at the surface”. If we can’t tell what the whale is doing because we can’t quite see what it’s doing, then name the behavior “unknown-partially visible”. Instead of using vague terminology, we should use clear names for behaviors and embrace using the term “unknown”.

I am most certainly not criticizing these studies as they all provided valuable contributions and interesting results. The studies that asked questions about behavioral ecology defined milling. The term was mentioned without being defined in studies focused on other topics. So, defining behaviors mentioned was less important.

With this exploration into the use of “milling” in studies, I am not implying that all behavioral ecologists need to agree on the use of the same behavior terms. However, I have learned clear definitions are critical. This lesson is also important outside of behavioral ecology. Different labs, and different people, use different terms for the same things. As I dig into my thesis, I am keeping a list of terminology I use and how I define those terms, because as I learn more, my terminology evolves and changes. For example, at the beginning of my thesis I used “sub-behavior” to refer to behaviors within the primary state categories. But, now after chatting with Leigh and learning more, I’ve decided to use the term “tactic” instead as these are often processes or events that contribute to the broader behavior state. My running list of terminology helps me remember what I meant when I used a certain word, so that when I read my notes from three months ago, I can know what I meant.  Digging into the literature for this blog reminded me of the importance of clearly defining all terminology and never assuming that everyone uses the same term in the same way.

Check out these videos to see some of the behaviors we observe:

References

1.        Mallonee, J. S. Behaviour of gray whales (Eschrichtius robustus) summering off the northern California coast, from Patrick’s Point to Crescent City. Can. J. Zool. 69, 681–690 (1991).

2.        Clarke, J. T., Moore, S. E. & Ljungblad, D. K. Observations on gray whale (Eschrichtius robustus) utilization patterns in the northeastern Chukchi Sea. Can. J. Zool 67, (1988).

3.        Ingram, S. N., Walshe, L., Johnston, D. & Rogan, E. Habitat partitioning and the influence of benthic topography and oceanography on the distribution of fin and minke whales in the Bay of Fundy, Canada. J. Mar. Biol. Assoc. United Kingdom 87, 149–156 (2007).

4.        Lomac-MacNair, K. & Smultea, M. A. Blue Whale (Balaenoptera musculus) Behavior and Group Dynamics as Observed from an Aircraft off Southern California. Anim. Behav. Cogn. 3, 1–21 (2016).

5.        Lusseau, D., Bain, D. E., Williams, R. & Smith, J. C. Vessel traffic disrupts the foraging behavior of southern resident killer whales Orcinus orca. Endanger. Species Res. 6, 211–221 (2009).

6.        Bobkov, A. V., Vladimirov, V. A. & Vertyankin, V. V. Some features of the bottom activity of gray whales (Eschrichtius robustus) off the northeastern coast of Sakhalin Island. 1, 46–58 (2019).

7.        Howe, M. et al. Beluga, Delphinapterus leucas, ethogram: A tool for cook inlet beluga conservation? Mar. Fish. Rev. 77, 32–40 (2015).

8.        Clarke, J. T., Christman, C. L., Brower, A. A. & Ferguson, M. C. Distribution and Relative Abundance of Marine Mammals in the northeastern Chukchi and western Beaufort Seas, 2012. Annu. Report, OCS Study BOEM 117, 96349–98115 (2013).

9.        Barendse, J. & Best, P. B. Shore-based observations of seasonality, movements, and group behavior of southern right whales in a nonnursery area on the South African west coast. Mar. Mammal Sci. 30, 1358–1382 (2014).

10.      Le Boeuf, B. J., M., H. P.-C., R., J. U. & U., B. R. M. and F. O. High gray whale mortality and low recruitment in 1999: Potential causes and implications. (Eschrichtius robustus). J. Cetacean Res. Manag. 2, 85–99 (2000).

11.      Calambokidis, J. et al. Abundance, range and movements of a feeding aggregation of gray whales (Eschrictius robustus) from California to southeastern Alaska in 1998. J. Cetacean Res. Manag. 4, 267–276 (2002).

12.      Harvey, J. T. & Mate, B. R. Dive Characteristics and Movements of Radio-Tagged Gray Whales in San Ignacio Lagoon, Baja California Sur, Mexico. in The Gray Whale: Eschrichtius Robustus (eds. Jones, M. Lou, Folkens, P. A., Leatherwood, S. & Swartz, S. L.) 561–575 (Academic Press, 1984).

13.      Lagerquist, B. A. et al. Feeding home ranges of pacific coast feeding group gray whales. J. Wildl. Manage. 83, 925–937 (2019).

14.      Barrett-Lennard, L. G., Matkin, C. O., Durban, J. W., Saulitis, E. L. & Ellifrit, D. Predation on gray whales and prolonged feeding on submerged carcasses by transient killer whales at Unimak Island, Alaska. Mar. Ecol. Prog. Ser. 421, 229–241 (2011).

15.      Luksenburg, J. A. Prevalence of External Injuries in Small Cetaceans in Aruban Waters, Southern Caribbean. PLoS One 9, e88988 (2014).

16.      Findlay, K. P. et al. Humpback whale “super-groups” – A novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS One 12, e0172002 (2017).

17.      Villegas-Amtmann, S., Schwarz, L. K., Gailey, G., Sychenko, O. & Costa, D. P. East or west: The energetic cost of being a gray whale and the consequence of losing energy to disturbance. Endanger. Species Res. 34, 167–183 (2017).

18.      Brower, A. A., Ferguson, M. C., Schonberg, S. V., Jewett, S. C. & Clarke, J. T. Gray whale distribution relative to benthic invertebrate biomass and abundance: Northeastern Chukchi Sea 2009–2012. Deep. Res. Part II Top. Stud. Oceanogr. 144, 156–174 (2017).

Whale blow: good for more than spotting whales

Clara Bird, Masters Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Whale blow, the puff of air mixed with moisture that a whale releases when it comes to the surface, is a famously thrilling indicator of the presence of a whale. From shore, spotting whale blow brings the excitement of knowing that there are whales nearby. During boat-based field work, seeing or hearing blow brings the rush of adrenaline meaning that it’s game time. Whale blow can also be used to identify different species of whales, for example gray whale blow is heart shaped (Figure 1). However, whale blow can be used for more than just spotting and identifying whales. We can use the time between blows to study energetics.

Figure 1. Gray whale blow is often heart shaped (when there is very little wind). Source: https://www.lajollalight.com/sdljl-natural-la-jolla-winter-wildlife-2015jan08-story.html

A blow interval is the time between consecutive blows when a whale is at the surface (Stelle, Megill, and Kinzel 2008). These are also known as short breath holds, whereas long breath holds are times between surfacings (Sumich 1983).  Sumich (1983) hypothesized that short breath holds lead to efficient rates of oxygen use. The body uses oxygen to create energy, so “efficient rate of oxygen use” means that longer breath holds do not use much more oxygen and subsequently do not produce more energy.  Surfacings, during which short blow intervals occur, are often thought of as recovery periods for whales. Think of it this way, when you sprint, immediately afterwards you typically need to take a break to just breathe and recover.

We hypothesize that we can use blow intervals as a measure of how strenuous an activity is; shorter blow intervals may indicate that an activity is more energetically demanding (Wursig, Wells, and Croll 1986). Let’s go back to the sprinting analogy and compare the energetic demands of walking and running. Imagine I asked you to walk for five minutes, stop and measure the time between each breath, and then run for five minutes and do the same; after running, you would likely breathe more heavily and take more breaths with less time between them. This result indicates that running is more demanding, which we already know because we can do other experiments with humans to study metabolic rate and related metrics. In the case of gray whales, we cannot do experiments in the same way, but we can use the same analogy. Several studies have examined how blow intervals differ between travelling and foraging.

Wursig, Wells, and Croll (1986) measured blow interval, surfacing time, and estimated dive depth and duration of gray whales in Alaska from a boat during the foraging season. They found that blow intervals were shorter during feeding. They also found that the number of blows per surfacing increased with increasing depth. Overall these findings suggest that during the foraging season, feeding is more strenuous than other behaviors and that deeper dives may be more physiologically stressful.

Stelle, Megill, and Kinzel (2008) studied gray whales foraging off of British Columbia, Canada. They found shorter blow intervals during foraging, intermediate blow intervals during searching, and longer blow intervals during travelling. Interestingly, within feeding behaviors, they found a difference between whales feeding on mysids (krill-like animals that swim in the water column) and whales feeding benthically on amphipods. They found that whales feeding on mysids made more frequent but shorter dives with short blow intervals at surface, while whales feeding benthically had longer dives with longer blow intervals. They hypothesized that this difference in surfacing pattern is because mysids might scatter when disturbed, so gray whales surface more often to allow the mysids swarm to reform. These studies inspired me to start investigating these same questions with my drone video data.

As I review the drone footage and code the behaviors I also mark the time of each blow. I’ve done some initial video coding and using this data I have started to look into differences in blow intervals. As it turns out, we see a similar difference in blow interval relative to behavior state in our data: whales that are foraging have shorter blow intervals than when traveling (Figure 2). It is encouraging to see that our data shows similar patterns.

Figure 2. Boxplot of mean blow interval per sighting of foraging whales and travelling whales.

Next, I would like to examine how blow intervals differ between foraging tactics. A significant part of my thesis is dedicated to studying specific foraging tactics. The perspective from the drone allows us to identify behaviors in greater detail than studies from shore or boat (Torres et al. 2018), allowing us to dig into the differences between the different foraging behaviors. The purpose of foraging is to gain energy. However, this gain is a net gain. To understand the different energetic “values” of each tactic we need to understand the cost of each behavior, i.e. how much energy is required to perform the behavior. Given previous studies, maybe blow intervals could help us measure this cost or at least compare the energetic demands of the behaviors relative to each other. Furthermore, because different behaviors are likely associated with different prey types (Dunham and Duffus 2001), we also need to understand the different energetic gains of each prey type (this is something that Lisa is studying right now, check out the COZI project to learn more). By understanding both of these components – the gains and costs – we can understand the energetic tradeoffs of the different foraging tactics.

Another interesting component to this energetic balance is a whale’s health and body condition. If a whale is in poor health, can it afford the energetic costs of certain behaviors? If whales in poor body condition engage in different behavior patterns than whales in good body condition, are these patterns explained by the energetic costs of the different foraging behaviors? All together this line of investigation is leading to an understanding of why a whale may choose to use different foraging behaviors in different situations. We may never get the full picture; however, I find it really exciting that something as simple and non-invasive as measuring the time between breaths can contribute such a valuable data stream to this project.

References

Dunham, Jason S., and David A. Duffus. 2001. “Foraging Patterns of Gray Whales in Central Clayoquot Sound, British Columbia, Canada.” Marine Ecology Progress Series 223 (November): 299–310. https://doi.org/10.3354/meps223299.

Stelle, Lei Lani, William M. Megill, and Michelle R. Kinzel. 2008. “Activity Budget and Diving Behavior of Gray Whales (Eschrichtius Robustus) in Feeding Grounds off Coastal British Columbia.” Marine Mammal Science 24 (3): 462–78. https://doi.org/10.1111/j.1748-7692.2008.00205.x.

Sumich, James L. 1983. “Swimming Velocities, Breathing Patterns, and Estimated Costs of Locomotion in Migrating Gray Whales, Eschrichtius Robustus.” Canadian Journal of Zoology 61 (3): 647–52. https://doi.org/10.1139/z83-086.

Torres, Leigh G., Sharon L. Nieukirk, Leila Lemos, and Todd E. Chandler. 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.

Wursig, B., R. S. Wells, and D. A. Croll. 1986. “Behavior of Gray Whales Summering near St. Lawrence Island, Bering Sea.” Canadian Journal of Zoology 64 (3): 611–21. https://doi.org/10.1139/z86-091.

The complex relationship between behavior and body condition

Clara Bird, Masters Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Imagine that you are a wild foraging animal: In order to forage enough food to survive and be healthy you need to be healthy enough to move around to find and eat your food. Do you see the paradox? You need to be in good condition to forage, and you need to forage to be in good condition. This complex relationship between body condition and behavior is a central aspect of my thesis.

One of the great benefits of having drone data is that we can simultaneously collect data on the body condition of the whale and on its behavior. The GEMM lab has been measuring and monitoring the body condition of gray whales for several years (check out Leila’s blog on photogrammetry for a refresher on her research). However, there is not much research linking the body condition of whales to their behavior. Hence, I have expanded my background research beyond the marine world to looked for papers that tried to understand this connection between the two factors in non-cetaceans. The literature shows that there are examples of both, so let’s go through some case studies.

Ransom et al. (2010) studied the effect of a specific type of contraception on the behavior of a population of feral horses using a mixed model. Aside from looking at the effect of the treatment (a type of contraception), they also considered the effect of body condition. There was no difference in body condition between the treatment and control groups, however, they found that body condition was a strong predictor of feeding, resting, maintenance, and social behaviors. Females with better body condition spent less time foraging than females with poorer body condition. While it was not the main question of the study, these results provide a great example of taking into account the relationship between body condition and behavior when researching any disturbance effect.

While Ransom et al. (2010) did not find that body condition affected response to treatment, Beale and Monaghan (2004) found that body condition affected the response of seabirds to human disturbance. They altered the body condition of birds at different sites by providing extra food for several days leading up to a standardized disturbance. Then the authors recorded a set of response variables to a disturbance event, such as flush distance (the distance from the disturbance when the birds leave their location). Interestingly, they found that birds with better body condition responded earlier to the disturbance (i.e., when the disturbance was farther away) than birds with poorer body condition (Figure 1). The authors suggest that this was because individuals with better body condition could afford to respond sooner to a disturbance, while individuals with poorer body condition could not afford to stop foraging and move away, and therefore did not show a behavioral response. I emphasize behavioral response because it would have been interesting to monitor the vital rates of the birds during the experiment; maybe the birds’ heart rates increased even though they did not move away. This finding is important when evaluating disturbance effects and management approaches because it demonstrates the importance of considering body condition when evaluating impacts: animals that are in the worst condition, and therefore the individuals that are most vulnerable, may appear to be undisturbed when in reality they tolerate the disturbance because they cannot afford the energy or time to move away.

Figure 1.  Figure showing flush distance of birds that were fed (good body condition) and unfed (poor body condition).

These two studies are examples of body condition affecting behavior. However, a study on the effect of habitat deterioration on lizards showed that behavior can also affect body condition. To study this effect, Amo et al. (2007) compared the behavior and body condition of lizards in ski slopes to those in natural areas. They found that habitat deterioration led to an increased perceived risk of predation, which led to an increase in movement speed when crossing these deteriorated, “risky”, areas. In turn, this elevated movement cost led to a decrease in body condition (Figure 2). Hence, the lizard’s behavior affected their body condition.


Figure 2. Figure showing the difference in body condition of lizards in natural and deteriorated habitats.

Together, these case studies provide an interesting overview of the potential answers to the question: does body condition affect behavior or does behavior affect body condition? The answer is that the relationship can go both ways. Ransom et al. (2004) showed that regardless of the treatment, behavior of female horses differed between body conditions, indicating that regardless of a disturbance, body condition affects behavior. Beale and Monaghan (2004) demonstrated that seabird reactions to disturbance differed between body conditions, indicating that disturbance studies should take body condition into account. And, Amo et al. (2007) showed that disturbance affects behavior, which consequently affects body condition.

Looking at the results from these three studies, I can envision finding similar results in my gray whale research. I hypothesize that gray whale behavior varies by body condition in everyday circumstances and when the whale is disturbed. Yet, I also hypothesize that being disturbed will affect gray whale behavior and subsequently their body condition. Therefore, what I anticipate based on these studies is a circular relationship between behavior and body condition of gray whales: if an increase in perceived risk affects behavior and then body condition, maybe those affected individuals with poor body condition will respond differently to the disturbance. It is yet to be determined if a sequence like this could ever be detected, but I think that it is important to investigate.

Reading through these studies, I am ready and eager to start digging into these hypotheses with our data. I am especially excited that I will be able to perform this investigation on an individual level because we have identified the whales in each drone video. I am confident that this work will lead to some interesting and important results connecting behavior and health, thus opening avenues for further investigations to improve conservation studies.

References

Beale, Colin M, and Pat Monaghan. 2004. “Behavioural Responses to Human Disturbance: A Matter of Choice?” Animal Behaviour 68 (5): 1065–69. https://doi.org/10.1016/j.anbehav.2004.07.002.

Ransom, Jason I, Brian S Cade, and N. Thompson Hobbs. 2010. “Influences of Immunocontraception on Time Budgets, Social Behavior, and Body Condition in Feral Horses.” Applied Animal Behaviour Science 124 (1–2): 51–60. https://doi.org/10.1016/j.applanim.2010.01.015.

Amo, Luisa, Pilar López, and José Martín. 2007. “Habitat Deterioration Affects Body Condition of Lizards: A Behavioral Approach with Iberolacerta Cyreni Lizards Inhabiting Ski Resorts.” Biological Conservation 135 (1): 77–85. https://doi.org/10.1016/j.biocon.2006.09.020.

Classifying cetacean behavior

Clara Bird, Masters Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

The GEMM lab recently completed its fourth field season studying gray whales along the Oregon coast. The 2019 field season was an especially exciting one, we collected rare footage of several interesting gray whale behaviors including GoPro footage of a gray whale feeding on the seafloor, drone footage of a gray whale breaching, and drone footage of surface feeding (check out our recently released highlight video here). For my master’s thesis, I’ll use the drone footage to analyze gray whale behavior and how it varies across space, time, and individual. But before I ask how behavior is related to other variables, I need to understand how to best classify the behaviors.

How do we collect data on behavior?

One of the most important tools in behavioral ecology is an ‘ethogram’. An ethogram is a list of defined behaviors that the researcher expects to see based on prior knowledge. It is important because it provides a standardized list of behaviors so the data can be properly analyzed. For example, without an ethogram, someone observing human behavior could say that their subject was walking on one occasion, but then say strolling on a different occasion when they actually meant walking. It is important to pre-determine how behaviors will be recorded so that data classification is consistent throughout the study. Table 1 provides a sample from the ethogram I use to analyze gray whale behavior. The specificity of the behaviors depends on how the data is collected.

Table 1. Sample from gray whale ethogram. Based on ethogram from Torres et al. (2018).

In marine mammal ecology, it is challenging to define specific behaviors because from the traditional viewpoint of a boat, we can only see what the individuals are doing at the surface. The most common method of collecting behavioral data is called a ‘focal follow’. In focal follows an individual, or group, is followed for a set period of time and its behavioral state is recorded at set intervals.  For example, a researcher might decide to follow an animal for an hour and record its behavioral state at each minute (Mann 1999). In some studies, they also recorded the location of the whale at each time point. When we use drones our methods are a little different; we collect behavioral data in the form of continuous 15-minute videos of the whale. While we collect data for a shorter amount of time than a typical focal follow, we can analyze the whole video and record what the whale was doing at each second with the added benefit of being able to review the video to ensure accuracy. Additionally, from the drone’s perspective, we can see what the whales are doing below the surface, which can dramatically improve our ability to identify and describe behaviors (Torres et al. 2018).

Categorizing Behaviors

In our ethogram, the behaviors are already categorized into primary states. Primary states are the broadest behavioral states, and in my study, they are foraging, traveling, socializing, and resting. We categorize the specific behaviors we observe in the drone videos into these categories because they are associated with the function of a behavior. While our categorization is based on prior knowledge and critical evaluation, this process can still be somewhat subjective.  Quantitative methods provide an objective interpretation of the behaviors that can confirm our broad categorization and provide insight into relationships between categories.  These methods include path characterization, cluster analysis, and sequence analysis.

Path characterization classifies behaviors using characteristics of their track line, this method is similar to the RST method that fellow GEMM lab graduate student Lisa Hildebrand described in a recent blog. Mayo and Marx (1990) analyzed the paths of surface foraging North Atlantic Right Whales and were able to classify the paths into primary states; they found that the path of a traveling whale was more linear and then paths of foraging or socializing whales that were more convoluted (Fig 1). I plan to analyze the drone GPS track line as a proxy for the whale’s track line to help distinguish between traveling and foraging in the cases where the 15-minute snapshot does not provide enough context.

Figure 1. Figure from Mayo and Marx (1990) showing different track lines symbolized by behavior category.

Cluster analysis looks for natural groupings in behavior. For example, Hastie et al. (2004) used cluster analysis to find that there were four natural groupings of bottlenose dolphin surface behaviors (Fig. 2). I am considering using this method to see if there are natural groupings of behaviors within the foraging primary state that might relate to different prey types or habitat. This process is analogous to breaking human foraging down into sub-categories like fishing or farming by looking for different foraging behaviors that typically occur together.

Figure 2. Figure from Hastie et al. (2004) showing the results of a hierarchical cluster analysis.

Lastly, sequence analysis also looks for groupings of behaviors but, unlike cluster analysis, it also uses the order in which behaviors occur. Slooten (1994) used this method to classify Hector’s dolphin surface behaviors and found that there were five classes of behaviors and certain behaviors connected the different categories (Fig. 3). This method is interesting because if there are certain behaviors that are consistently in the same order then that indicates that the order of events is important. What function does a specific sequence of behaviors provide that the behaviors out of that order do not?

Figure 3. Figure from Slooten (1994) showing the results of sequence analysis.

Think about harvesting fruits and vegetables from a garden: the order of how things are done matters and you might use different methods to harvest different kinds of produce. Without knowing what food was being harvested, these methods could detect that there were different harvesting methods for different fruits or veggies. By then studying when and where the different methods were used and by whom, we could gain insight into the different functions and patterns associated with the different behaviors. We might be able to detect that some methods were always used in certain habitat types or that different methods were consistently used at different times of the year.

Behavior classification methods such as these described provide a more refined and detailed analysis of categories that can then be used to identify patterns of gray whale behaviors. While our ultimate goal is to understand how gray whales will be affected by a changing environment, a comprehensive understanding of their current behavior serves as a baseline for that future study.

References

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2019). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science, 35(1), 108–139. https://doi.org/10.1111/mms.12527

Darling, J. D., Keogh, K. E., & Steeves, T. E. (1998). Gray whale (Eschrichtius robustus) habitat utilization and prey species off Vancouver Island, B.C. Marine Mammal Science, 14(4), 692–720. https://doi.org/10.1111/j.1748-7692.1998.tb00757.x

Hastie, G. D., Wilson, B., Wilson, L. J., Parsons, K. M., & Thompson, P. M. (2004). Functional mechanisms underlying cetacean distribution patterns: Hotspots for bottlenose dolphins are linked to foraging. Marine Biology, 144(2), 397–403. https://doi.org/10.1007/s00227-003-1195-4

Mann, J. (1999). Behavioral sampling methods for cetaceans: A review and critique. Marine Mammal Science, 15(1), 102–122. https://doi.org/10.1111/j.1748-7692.1999.tb00784.x

Slooten, E. (1994). Behavior of Hector’s Dolphin: Classifying Behavior by Sequence Analysis. Journal of Mammalogy, 75(4), 956–964. https://doi.org/10.2307/1382477

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

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

Remote Sensing Applications

By Leila Lemos, PhD candidate

Fisheries and Wildlife Department, OSU

 

I am finally starting my 3rd and last year of my PhD. Just a year left and yet so many things to do. As per department requirements, I still need to take some class credits, but what classes could I take? In this short amount of time it is important to focus on my research project and on what could help me better understand the many branches of the project and what could improve my analyses. Thinking of that, both my advisor (Dr. Leigh G. Torres) and I agreed that it would be useful for me to take a class on remote sensing. So, I could learn more about this field, as well as try to include some remote sensing analyses in my project, such as sea surface temperature (SST) and chlorophyll (i.e., as a productivity indicator) conditions over the years we have collected data on gray whales off the Oregon coast.

 

Our photogrammetry data indicates that whales gradually increased their body condition over the feeding seasons of 2016 and 2018, while 2017 is different. Whales were still looking skinny in the middle of the season, and we were not collecting many fecal samples up to that point (indicating not much feeding). These findings made us wonder if this was related to delayed seasonal upwelling events and consequently low prey availability. These questions are what motivated me the most to join this class so that we might be able to link environmental correlates with our observations of gray whale body condition.

Figure 01: Skinny body condition state of the gray whale “Pancake” in August 2017.
Source: Leila S. Lemos

 

If we stop to think about what remote sensing is, we have already been implementing this method in our project since the beginning, as my favorite definition for remote sensing is “the art of collecting information of objects or phenomenon without touching it”. So, yes, the drone is a type of sensor that remotely collects information of objects (in this case, whales).

Figure 02: Drone remotely collecting information of a whale in September 2018. Drone in detail. Collected under NOAA/NMFS permit #16111.
Source: Leila Lemos

 

However, satellites, all the way up in the space, are also remotely sensing the Earth and its objects and phenomena. Even from thousands of km above Earth, these sensors are capable of generating a great amount of detailed data that is easily and freely accessible (i.e., NASA, NOAA), and can be used for multiple applications in different fields of study. Satellites are also able to collect data from remote areas like the Antarctica and the Arctic, as well as other areas that are not easily reached by humans. One important application of the use of satellite imagery is wildlife monitoring.

For example, satellite data was used to detect variation in the abundance of Weddell seals (Leptonychotes weddellii) in Erebus Bay, Antarctica (LaRue et al., 2011). Because this is a well-studied seal population, the object of this study was to test if satellite imagery could produce reliable abundance estimates. The authors used high-resolution (0.6 m) satellite imagery (from satellites Quick-Bird-2 and WorldView-1) to compare counts from the ground with counts from satellite images in the same locations at the same time. This study demonstrated a reliable methodology for further studies to replicate.

Figure 03: WorldView-1 image (0.6 m resolution) of Weddell seals hauled out east of Inaccessible Island, Erebus Bay, Antarctica.
Source: LaRue et al. (2011).

 

Satellite imagery was also applied to estimate colony sizes of Adélie penguins in Antarctica (LaRue et al., 2014). High-resolution (0.6 m) satellite imagery combined with spectral analysiswas used to estimate the sizes of the penguin breeding colonies. Ground counts were also used in order to check the reliability of the applied method. The authors then created a model to predict the abundance of breeding pairs as a function of the habitat, which was identified terrain slope as an important component of nesting density.

The identification of whales using satellite imagery is also possible. Fretwell et al. (2014)pioneered this method by successfully identifing Southern Right Whales (Eubalaena australis) in the Golfo Nuevo, Península Valdés, in Argentina in satellite images. By using very high-resolution satellite imagery (50 cm resolution) and a water penetrating coastal band that was able to see deeper into the water column, the researchers were able to successfully identify and count the whales (Fig. 04). The importance of this study was very significant, since this species was extensively hunted from the 17ththrough to the 20thcentury. Since then, the species has shown a strong recovery, but population estimates are still at <15% of historical estimates. Thus, being able to use new tools to identify, count and monitor individuals in this recovering population is a great development, especially in remote and hard to reach areas.

Figure 04: Identification of Southern Right Whales by using imagery from the WorldView2 satellite in the Golfo Nuevo Bay, Península Valdés, Argentina.
Source: Fretwell et al. (2014).

 

Polar bears (Ursus maritimus) have also been studied in the Foxe Basin, in Nunavut and Quebec, Canada (LaRue et al., 2015). Researchers used high-resolution satellite imagery in an attempt to identify and count the bears, but spectral signature differences between bears and other objects were insufficient to yield useful results. Therefore, researchers developed an automated image differencing, also known as change detection, that identifies differences between remotely sensed images collected at different times and “subtract of one image from another”. This method correctly identified nearly 90% of the bears. The technique also generated false positives, but this problem can be corrected by a manual review.

Figure 05 shows the difference in resolution of two types of satellite imagery, the panchromatic (0.6 m resolution) and the multispectral (2.4 m resolution). LaRue et al. (2015)decided not to use the multispectral imagery due to resolution constraints.

Figure 05: Polar Bears on panchromatic (0.6 m resolution) and multispectral (2.4 m resolution) imagery.
Source: LaRue et al. (2015).

 

A more recent study is being conducted by my fellow OSU Fisheries and Wildlife graduate student, Jane Dolliveron breeding colonies of three species of North Pacific albatrosses (Phoebastria immutabilis, Phoebastria nigripes, and Phoebastria albatrus)(Dolliver et al., 2017). Jane is using high-resolution multispectral satellite imagery (DigitalGlobe WorldView-2 and -3) and image processing techniques to enumerate the albatrosses. They are also using albatross species at multiple reference colonies in Hawaii and Japan (Fig. 06) to determine species identification accuracy and required correction factor(s). This will allow scientists to accurately count unknown populations on the Senkakus, which are uninhabited islands controlled by Japan in the East China Sea.

Figure 06: Satellite image of a colony of short-tailed albatrosses (Phoebastria albatrus) in Torishima, Japan, 2016.
Source: Satellite image provided by Jane Dolliver.

 

Using satellite imagery to count seals, penguins, whales, bears and albatrosses is just the start of this rapidly advancing technology. Techniques and resolutions are continuously improving. Methods can also be applied to many other endangered species, especially in remote areas, providing data on presence, abundance, annual productivity, population estimates and trends, changes in distribution, and breeding ground usage.

Other than directly monitoring wildlife, satellite images can also provide information on the environmental variables that can be related to wildlife presence, abundance, productivity and distribution.

Gentemann et al. (2017), for example, used satellite data from NASA to analyze SST variations along the west coast of the United States from 2002 to 2016. The NASA Jet Propulsion Laboratory produces global, daily, 1 km, multiscale ultra-high resolution, motion-compensated analysis of SST, and incorporates SSTs from eight different satellites. Researchers were able to identify warmer than usual SSTs (also called anomalies) along the Washington, Oregon, and California coasts from January 2014 to August 2016 (Fig.07) relative to previous years. This marine heat wave started in the Gulf of Alaska and ended in Southern California, where SST reached a maximum temperature anomaly of 6.2°C, causing major disturbances and substantial economic impacts.

Figure 07: Monthly SST anomalies in the West Coast of United States, from January 2014 to August 2016.
Source: Gentemann et al. (2017).

 

Changes in SST and winds may alter events such as the coastal upwelling that supplies nutrients to sustain a whole food chain. A marine heat-wave event as described by Gentemann et al. (2017)could have significant impacts on the health of the marine ecosystem in the subsequent season (Gentemann et al., 2017).

These findings may even relate to our questions regarding the poor gray whale body condition we noticed in 2017: this marine heat wave that lasted until August 2016 along the US west coast could have impacted the ecosystem in the subsequent season. However, I must conduct a more detailed study to determine if this heat wave was related or if another oceanographic process was involved.

So, whether remotely sensed data is generated by satellites, drones, thermal imagery, robots (as I previously wrote about), or another type of technology, it can have important  and informative applications to monitor wildlife or environmental variables associated with their ecology and biology. We can take advantage of remotely sensed technology to aid wildlife conservation efforts.

 

References

Dolliver, J., et al., Multispectral processing of high resolution satellite imagery to determine the abundance of nesting albatross. Ecological Society of America, Portland, OR, United States., 2017.

Fretwell, P. T., et al., 2014. Whales from Space: Counting Southern Right Whales by Satellite. Plos One. 9,e88655.

Gentemann, C. L., et al., 2017. Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophysical Research Letters. 44,312-319.

LaRue, M. A., et al., 2014. A method for estimating colony sizes of Adélie penguins using remote sensing imagery. Polar Biology. 37,507-517.

LaRue, M. A., et al., 2011. Satellite imagery can be used to detect variation in abundance of Weddell seals (Leptonychotes weddellii) in Erebus Bay, Antarctica. Polar Biology. 34,1727–1737.

LaRue, M. A., et al., 2015. Testing Methods for Using High-Resolution Satellite Imagery to Monitor Polar Bear Abundance and Distribution. Wildlife Society Bulletin. 39,772-779.