Tag: gray whale

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.

Stories from a whale’s life: how whale photo-identification is an essential part of the whale physiology toolkit

By Alejandro Fernandez Ajo, PhD student at the Department of Biology, Northern Arizona University, Visiting scientist in the GEMM Lab working on the gray whale physiology and ecology project  

Two years ago, in August 2018, I came to Newport and visited the Hatfield Marine Science Center for the first time with an NSF/RCN-founded laboratory exchange with the GEMM Lab and met Dr. Leigh Torres. My goals during this exchange where to learn about non-invasive fieldwork techniques for studying free-range whales while interacting, exchanging ideas, and networking with the GEMM Lab members; also, to discuss some projects and thoughts for future collaborations with Dr. Torres. During those two weeks in Newport, I had the opportunity to help with field work on the project “Evaluation of gray whale ecology and physiology in response to variable ambient ocean noise conditions”, which aims to evaluate the hormonal variability and health of the gray whales that forage along the Oregon coast in the context of multiple stressors. I would return during the summers of 2019 and 2020 as a visiting scientist and research assistant to work on this project. This year the experience has been a bit different in terms of interactions with the HMSC community due to COVID-19; however, we were able to successfully start the field season in time and now we are wrapping up our second month of surveys with many new and interesting data gathered, and many new, unforgettable memories to be treasured. Working with these animals is incredibly fascinating because there are so many things we don´t know about them, and the questions can become both overwhelming and exciting.

An essential part of this project, and arguably any research project done with cetaceans, is the identification of individuals. Hence, considerable effort is expended each year attempting to photograph every gray whale possible within our study region and to identify each whale we encounter. The GEMM Lab maintains a catalog of the gray whales that visit the Oregon coast, a sub-population known as the Pacific Coast Feeding Group (PCFG). This catalog currently consists of 173 individuals. which we frequently compare with a larger catalog of gray whales that includes 2060 individuals observed since 1977 (Cascadia Research Collective). These methods allow us to know who is who among the whales we encounter each day at sea.

The different species of cetaceans can be individually identified by markings on their bodies, very much like fingerprints in humans. Some features on these animals are unique and conserved through life. For example, Southern and Northern right whales are identified by the callosity patters in their heads (Picture 1), while humpback whales are mostly identified individually by the shape and the patterns of black and white pigmentation on the underside of their fluke (Picture 2). Gray whales have very mottled skin coloration, so we use a combination of markings and features to identify individuals: pigmentation patterns, scars, shape and pigmentation of their fluke, and sometimes the shape of their knuckles, which are a series of “humps” that gray whales have instead of a dorsal fin on their back. It might sound very difficult to do, and it can be a tedious task, however as you train your eye it becomes easier, and features that at first seemed undistinguishable become recognizable and unique (Picture 3). As a reward, it is such a joy to find a match and recognize old friends when they arrive from their long journeys in the vast ocean each year to the Oregon coast.

The callosity pattern of a southern right whale. Photo by Alejandro Fernández Ajó – Instituto de Conservación de Ballenas.
Three different humpback whales with notably different pigmentation patterns on their flukes. Photographs captured under NOAA/NMFS permit #21678.
The gray whale “Knife” we have observed frequently this summer off the Oregon coast. Can you see how the right pigmentation pattern identified looks like a knife? Photograph captured under NOAA/NMFS permit #21678.

As a result of our photo-identification efforts and the high site-fidelity of the whales we study, the large majority of the gray whales we observe here in Oregon are known individually. For many whales, we also have detailed sightings records that can span years and decades, that document calving history, lactation, appearance of scars indicative of injury or entanglement, minimum age, sex, habitat-use patterns, behaviors, etc. Holding such detailed information of individual whales provides incredible contributions to our understanding of the basic patterns in life history of whales, such as reproduction rates, calving intervals, age of first reproduction, etc. Moreover, when these life history events are linked with physiological sample collection large steps can be made in the development and validation of physiological methods. Many endocrine assays currently in use for whales are based on non-traditional sample types including feces, respiratory vapor, and baleen, which have been validated using the catalogs of well-known individuals to verify that measured hormones reflect patterns expected for various physiological states. For example, we can compare endocrine data of confirmed pregnant females, known mature males, and known-injured whales to learn how whale physiological responses are different during different life history events (e.g., Burgess et al. 2017, 2018, Corkeron et al. 2017, Hunt et al. 2006, 2016, 2018, Lysiak et al. 2018, Rolland et al. 2005).

Here in Oregon we are learning from the lives of the gray whales we study, and here I want to share with you two of their stories, one happy and one not-so-happy.

Let´s start with the not-so-happy story so we can end with some brighter news. On June 24 this year, we encountered a whale near Cape Foulweather, which is a very tricky area to work as there are many rocks and shallow water that make the sea conditions very choppy even with low swell. We started documenting the sighting as usual, taking pictures of the left side, the right side and ideally also the fluke of the whale. As we approached this whale, we started noting that something was wrong with its fluke. With the challenging sea conditions, it was not easy to approach the whale and the whale was not exposing its fluke when diving. When we put our drone up to collect photogrammetry and behavior data we gained a much better perspective. This whale has a bad injury on it fluke (Picture 4.C). On the boat we started making conjectures about the cause of this terrible injury that had basically amputate most of its left fluke lobe. Once back on shore, we sorted out the photos and compared the field images captured during the day with the photo-ID catalog and we made a match. This whale is known in our catalog as “ROLLER SKATE”, is a female, and was first sighted in 2015, so she is at least 5 years old today.

The story unfolded when we reviewed Roller Skate’s sighting history. Interestingly we observed this same whale in the same location last September 2019. Unfortunately, it was a very brief encounter but enough for photo documentation of the whale and an interesting observation. Here I quote the field notes that Dr. Torres wrote from this sighting: “September 6th, 2019. Sighting 9: Scattered whales feeding and/or traveling across area to north of Cape Foulweather. One whale had recently chopped fluke; tried to re-find to get better photos but could not (looking at photos now, this whale is clearly entangled in line!). Ceiling too low for UAS [drone flight].” (Picture 4.B).

Progressive photos of Roller Skate’s fluke. In 2017, her fluke was healthy (A). In 2019, we observed her fluke with a line entanglement and “chewed up” (B). In 2020, we have observed the resulting wound and healing process (C). Photographs captured under NOAA/NMFS permit #21678.

Roller skate’s story is an example of how essential is to keep an ID catalog. After a close-up examination of the 2019 picture, we can clearly see a rope entanglement (Picture 4.B). Photos from previous years show how beautiful and healthy her fluke was before this event (Picture 4.A). This event is heart breaking to witness, but this whale could be considered lucky because she was able to shed the gear and survive this entanglement, at least in the short term. Additionally, we can learn from Roller Skate’s misfortune to help us understand what the consequences of such an injury (stressor) may be on the physiology of a whale. We have been eager to collect a fecal sample from Roller Skate to analyze how her hormone levels compare to non-injured whales. Fortunately, we got lucky a couple weeks ago and collected this sample, so now we need to get in the lab and analyze the samples. But more questions remain: Will this injury impact her ability to reproduce? If so, for how long? And at a larger scale, what are the population consequences of such events? If we can understand the magnitude of lethal and sublethal human caused impacts on individual whales and their populations from events such as entanglements, we can develop better methods to mitigate and limit such hazards for whales in their environments.

As I promised, there is also some good news to share. A very well-known PCFG whale, almost a celebrity I dear to say, is “Scarback”, or as we like to call her “Scarlett”. Scarlett is a female known since 1996, making her at least 24 years old, and she also has a very bad injury of unknown origin. Scarlett has a terrible scar on her back that is theorized to have been caused by an explosive harpoon, or maybe a bad ship-strike (Picture 5), but we really do not know. However, we do know she survived this injury and this year she brought a new calf into the population (Picture 6). This is the second calf we have documented from Scarlett, with her previous calf sighted during the 2016 field season and we call it “Brown”. Scarlett is an example of how resilient these amazing giants can be; however, it is likely that while she was recovering from this injury, she was unable to reproduce. How many calves from Scarlett did the PCFG population “lose” due to such a tragedy? We can´t know, but we are learning, and her story will also help us understand whale physiology as we will analyze her fecal hormones and body condition during pregnancy, lactation, and resting phases.

The scar on Scarlett’s back. This significant wound has an unknown origin. Photograph captured under NOAA/NMFS permit #21678.

Scarlett is a survivor. We need to recognize that we are sharing the ocean with different forms of life. We need to acknowledge their existence and understand how our use of the oceans is affecting them, and, more importantly, work toward improving their conditions. I hope that with our research we highlight and communicate how amazing are these animals, and how important are they for marine ecosystems. And ultimately, I hope our work helps minimize the impacts that affect other forms of ocean life that coexist with us, both above and below the surface.

The well-known gray whale “Scarlett” surfaces under her calf while it checks out the GEMM Lab field team. Photo by Alejandro Fernandez Ajo taken under NOAA/NMFS permit #21678.

References:

Burgess, E., Hunt, K. E., Kraus, S. D. and Rolland, R. M. (2016). Get the most out of blow hormones: validation of sampling materials, field storage and extraction techniques for whale respiratory vapor samples. Conservation Physiology, 4, cow024.

Burgess, E. A., Hunt, K. E., Kraus, S. D. and Rolland, R. M. (2018). Quantifying hormones in exhaled breath for physiological assessment of large whales at sea. Scientific Reports, 8, 10031.

Corkeron, P. J., Rolland, R. M., Hunt, K. E. and Kraus, S. D. (2017). A right whale PooTree: Fecal hormones and classification trees identify reproductive states in North Atlantic right whales (Eubalaena glacialis). Conservation Physiology, 5, cox006. DOI: 10.1093/conphys/cox006.

Hunt, K., Lysiak, N., Moore, M. and Rolland, R. (2017). Multi-year longitudinal profiles of cortisol and corticosterone recovered from baleen of North Atlantic right whales (Eubalaena glacialis). General and Comparative Endocrinology, 254, 50-59. DOI: 10.1016/j.ygcen.2017.09.009.

Hunt, K., Lysiak, N. S. J., Matthews, C. J. D., et al. (2018). Multi-year patterns in testosterone, cortisol and corticosterone in baleen from adult males of three whale species. Conservation Physiology, 6, coy049. DOI: 10.1093/conphys/coy049.

Hunt, K. E., Rolland, R. M., Kraus, S. D. and Wasser, S. K. (2006). Analysis of fecal glucocorticoids in the North Atlantic Right Whale (Eubalaena glacialis). General and Comparative Endocrinology, 148, 260-272.

Lysiak, N., Trumble, S., Knowlton, A. and Moore, M. (2018). Characterizing the duration and severity of fishing gear entanglement on a North Atlantic right whale (Eubalaena glacialis) using stable isotopes, steroid and thyroid hormones in baleen. Frontiers in Marine Science. DOI: 10.3389/fmars.2018.00168.

Rolland, R. M., Hunt, K. E., Kraus, S. D. and Wasser, S. K. (2005). Assessing reproductive status of right whales (Eubalaena glacialis) using fecal hormone metabolites. General and Comparative Endocrinology, 142, 308-317.

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).

Figure 2. Comparison of body condition between a lactating female gray whale on the feeding grounds in Newport, Oregon, 2020 (GEMM Lab, OSU; NOAA/NMFS permit # 21678) and a pregnant female gray whale on the breeding grounds in San Ignacio Lagoon, 2019 (provided by Laguna San Ignacio Ecosystem Program). Photographer Hunter Warick. Note the very different body shapes: thin lactating female relative to the rotund pregnant female.

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).

How we plan to follow whales

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

The GEMM Lab gray whale team is in the midst of preparing for our fifth field season studying the Pacific Coast Foraging Group (PCFG): whales that forage off the coast of Newport, OR, USA each summer. On any given good weather day from June to October, our team is out on the water in a small zodiac looking for gray whales (Figure 1). When we find a gray whale, we try to collect photo ID data, fecal samples, drone data, and behavioral data. We use the drone data to study both the whale’s body condition and their behavior. In a previous blog, I described ethograms and how I would like to use the behavior data from drone videos to classify behaviors, with the ultimate goal of understanding how gray whale behavior varies across space, time, and by individual. However, this explanation of studying whale behavior is actually a bit incomplete. Before we start fieldwork, we first need to decide how to collect that data.

Figure 1. Image of GEMM lab team collecting gray whale UAS data. Image taken under NOAA/NMFS permit #16111

As observers, we are far from omnipresent and there is no way to know what the animals are doing all of the time. In any environment, scientists have to decide when and where to observe their animals and what behaviors they are interested in recording. In many studies, behavior is recorded live by an observer. In those studies, other limitations need to be taken into account, such as human error and observer fatigue. Collecting behavioral data is particularly challenging in the marine environment. Cetaceans spend most of their lives out of sight from humans, their time at the surface is brief, and when they appear together in large groups it can be very difficult to keep track of who is doing what when. Imagine being in a boat trying to keep track of what three different whales are doing without a pre-determined method – the task could quickly become overwhelming and biased. This is why we need a methodology for collecting and classifying behavior. We cannot study behavior without acknowledging these limitations and the potential biases that come with the methods we choose. Different data collection methods are better suited to address different questions.

The use of drones gives us the ability to record cetacean behavior non-invasively, from a perspective that allows greater observation (Figure 2, Torres et al. 2018), and for later review, which is a significant improvement. However, as we prepare to collect more behavior data, we need to study the methods and understand the benefits and disadvantages of each approach so that we capture the information we need without bias. Altmann (1974) provides a thorough overview of behavioral sampling methods.

Figure 2. Diagram illustrating “whale surface time” relative to “whale visible time” data as collected from an unmanned aerial systems (UAS) aircraft flying over a gray whale as it moves sequentially (from right to left) from “headstand” foraging to surfacing. Figure from Torres et al. (2018).

Ad libitum behavioral sampling has no structure and occurs when we find a group of whales and just write down everything they are doing. This method is a good first step, however it comes with bias.  Without structure, we cannot be sure that there was an equal probability of detecting each kind of behavior; this problem is called detectability bias. This type of bias is an issue if we are trying to answer questions about how often a behavior occurs, or what percent of time is spent in each behavior state. This is a bias to be especially concerned about when it comes to cetaceans because there are many examples of behaviors with different levels of detectability. An extreme example would be the detectability of breaching versus a behavior that takes place under the surface. A breaching whale is easier to spot and more exciting, which could lead to results suggesting that whales breach more often than they do relative to underwater behaviors. While it’s impossible to eliminate detectability bias, other sampling methods employ decision rules to try and reduce its effect. Many decision rules revolve around time, such as setting a minimum or maximum observation time interval. Other time rules involve recording the behavior state at set intervals of time (e.g., every 5 minutes). Setting observation boundaries helps standardize the methods and the data being collected.

In a structured sampling plan, the first big decision that needs to be addressed is the need to know the duration of behaviors. Point events do not include duration data but can be used to study the frequencies of behaviors. For example, if my research question was “Do whales perform “headstands” in a specific habitat type?”, then I would need point events of headstanding behavior. But, if I wanted to ask, “Do whales spend more time spent headstanding in a specific habitat type than in other habitat types?”, I would need headstanding to be a state event. State events are events with associated duration information and can be used for activity budgets. Activity budgets show how much time an animal spends in each behavior state. Some sampling methods focus on collecting only point events. However, to get the most complete understanding of behavior I think it’s important to collect both. Focal animal follows are another method of collecting more detailed data and is commonly used in cetacean studies.

The explanation of a focal follow method is in the name.  We focus on one individual, follow it, and record all of its behaviors. When employing this method, decisions are made about how an individual is chosen and how long it is followed. In some cases, the behavior of this animal is used as a proxy for the behavior of an entire group. I essentially use the focal follow method in my research. While I review drone footage to record behavioral data instead of recording behaviors live in the field, I focus on one individual a time as I go through the videos. To do this I use a software called BORIS (Friard and Gamba 2016) to mark the time of each behavior per individual (Figure 3). If there are three individuals in a video, I’ll review the footage three times to record behaviors once per individual, focusing on each in turn.

Figure 3. Screenshot of BORIS layout.

While the drone footage brings the advantages of time to review and a better view of the whale, we are constrained by the duration of a flight. Focal follows would ideally last longer than the ~15 minutes of battery life per drone flight. Our previously collected footage gives us snapshots of behavior, and this makes it challenging to compare and analyze durations of behaviors. Therefore, I am excited that we are going to try conducting drone focal follows this summer by swapping out drones when power runs low to achieve longer periods of video coverage of whale behavior. I’ll be able to use these data to move from snapshots to analyzing longer clips and better understanding the behavioral ecology of gray whales. As exciting as this opportunity is, it also presents the challenge of method development. So, I now need to develop decision rules and data collection methods to answer the questions that I have been eagerly asking.

References

Altmann, Jeanne. 1974. “Observational Study of Behavior: Sampling Methods.” Behaviour 49 (3–4): 227–66. https://doi.org/10.1163/156853974X00534.

Friard, Olivier, and Marco Gamba. 2016. “BORIS: A Free, Versatile Open-Source Event-Logging Software for Video/Audio Coding and Live Observations.” Methods in Ecology and Evolution 7 (11): 1325–30. https://doi.org/10.1111/2041-210X.12584.

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.

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.

What are the ecological impacts of gray whale benthic feeding?

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

Happy new year from the GEMM lab! Starting graduate school comes with a lot of learning. From skills, to learning about how much there is to learn, to learning about the system I will be studying in depth for the next few years. This last category has been the most exciting to me because digging into the literature on a system or a species always leads to the unearthing of some fascinating and surprising facts. So, for this blog I will write about one of the aspects of gray whale foraging that intrigues me most: benthic feeding and its impacts.

How do gray whales feed?

Gray whales are a unique species. Unlike other baleen whales, such as humpback and blue whales, gray whales regularly feed off the bottom of the ocean (Nerini, 1984). They roll to one side and swim along the bottom, they then suction up (by depressing their tongue) the sediment and prey, then the sediment and water is filtered out of the baleen. In fact, we use sediment streams, shown in Figure 1, as an indicator of benthic feeding behavior when analyzing drone footage (Torres et al. 2018).

Figure 1. Screenshot of drone video showing sediment streaming from mouth of a whale after benthic feeding. Video taken under NOAA/NMFS permit #21678

Locations of benthic feeding can be identified without directly observing a gray whale actively feeding because of the excavated pits that result from benthic feeding (Nerini 1984). These pits can be detected using side-scan sonar that is commonly used to map the seafloor. Oliver and Slattery (1985) found that the pits typically are from 2-20 m2. In some of the imagery, consecutive neighboring pits are visible, likely created by one whale in series during a feeding event. Figure 2 shows different arrangements of pits.

Figure 2. Different arrangements of pits created by feeding whales (Nerini 1984).

Aside from how fascinating the behavior is, benthic feeding is also interesting because it has a large impact on the environment. Coming from a background of studying baleen whales that primarily feed on krill, I had not really considered the potential impacts of whale foraging other than removing prey from the environment. However, when gray whales feed, they excavate large areas of the benthic substrate that disturb and impact the habitat.

The impacts of benthic feeding

Weitkamp et al. (1992) conducted a study on gray whale benthic foraging on ghost shrimp in Puget Sound, WA, USA. This study, conducted over two years, focused on measuring the impact of benthic foraging by its effect on prey abundance. They found that the standing stock of ghost shrimp within a recently excavated pit was two to five times less than that outside the pit, and that 3100 to 5700 grams of shrimp can be removed per pit. From aerial surveys they estimated that within one season feeding gray whales created between 2700 and 3200 pits. Using these values, they calculated that 55 to 79% of the standing stock of ghost shrimp was removed each season by foraging gray whales. Interestingly, they found that the shrimp biomass within an excavated pit recovered within about two months.

Oliver and Slattery (1985) also found a recovery period of about 2 months per pit in their study on the effect of gray whale benthic feeding on the prey community in the Bering Sea. They sampled prey within and outside feeding excavations, both actual whale pits and man-made, to test the response of the benthic community to the disturbance of a feeding event. They found that after the initial feeding disturbance, the excavated area was rapidly colonized by scavenging lysianassid amphipods, which are small (10 mm) crustaceans that typically eat dead organic material. These amphipods rushed in and attacked the organisms that were injured or dislodged by the whale feeding event, typically small crustaceans and polychaete worms. Within hours of the whale feeding event, these amphipods had dispersed and a different genre of scavenging lysianassid amphipods slowly invaded the excavated pit further and stayed much longer. After a few days or weeks these pits collected and trapped organic debris that attracted more colonists. Indeed, they found that the number of colonists remained elevated within the excavated areas for over two months.

Notably, these results on how the disturbance of gray whale benthic feeding changes sediment composition support the idea that this foraging behavior maintains the sand substrate and therefore helps to maintain balanced levels of benthic dwelling amphipods, their primary source of prey in this study area (Johnson and Nelson, 1984). Gray whales scour the sea floor when they feed and this process leads to the resuspension of lots of sediments and nutrients that would otherwise remain on the seafloor. Therefore, while this feeding may seem like a violent disturbance, it may in fact play a large role in benthic productivity (Johnson and Nelson, 1984; Oliver and Slattery, 1985).

These ecosystem impacts of gray whale benthic feeding I have described above demonstrate the various stages of invaders after a feeding disturbance, and the process of succession. Succession is the ecological process of how a community structure builds and grows. Primary succession is when the structure grows from truly nothing and secondary succession occurs after a disturbance, such as a fire. In secondary succession, there are typically pioneer species that first appear and then give way to other species and a more complex community eventually emerges. Succession is well documented in many terrestrial studies after disturbance events, and the processes of secondary succession is very important to community ecology and resilience.

Since gray whale benthic foraging does not impact an entire habitat all at once, the process is not perfectly comparable to secondary succession in terrestrial systems. Yet, when thinking about the smaller scale, another example of succession in the marine environment takes place at a whale fall. When a whale dies and sinks to the ocean floor, a small ecosystem emerges. Different organisms arrive at different stages to scavenge different parts of the carcass and a food web is created around it.

To me the impacts of gray whale benthic feeding are akin to both terrestrial disturbance events and whale falls. The excavation serves as a disturbance, and through secondary succession the habitat is refreshed via stages of different species colonization until the system eventually returns to the pre-disturbance levels. However, like a whale fall the feeding event leaves behind injured or displaced organisms that scavengers consume; in fact seabirds are known to take advantage of benthic invertebrates that are brought to the surface by a gray whale feeding event (Harrison, 1979). 

So much of our research is focused on questions about how the changing environment impacts our study species and not the other way around. This venture into the literature has provided me with an important reminder to think about flipping the question. I have enjoyed starting 2020 with a reminder of how cool gray whales are, and that while a disturbance can initially be thought of as negative, it may actually bring about important, and positive, change.

References

Nerini, Mary. 1984. “A Review of Gray Whale Feeding Ecology.” In The Gray Whale: Eschrichtius Robustus, 423–50. Elsevier Inc. https://doi.org/10.1016/B978-0-08-092372-7.50024-8.

Oliver, J. S., and P. N. Slattery. 1985. “Destruction and Opportunity on the Sea Floor: Effects of Gray Whale Feeding.” Ecology 66 (6): 1965–75. https://doi.org/10.2307/2937392.

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.

Weitkamp, Laurie A, Robert C Wissmar, Charles A Simenstad, Kurt L Fresh, and Jay G Odell. 1992. “Gray Whale Foraging on Ghost Shrimp (Callianassa Californiensis) in Littoral Sand Flats of Puget Sound, USA.” Canadian Journal of Zoology 70 (11): 2275–80. https://doi.org/10.1139/z92-304.

Johnson, Kirk R., and C. Hans Nelson. 1984. “Side-Scan Sonar Assessment of Gray Whale Feeding in the Bering Sea.” Science 225 (4667): 1150–52.

Harrison, Craig S. 1979. “The Association of Marine Birds and Feeding Gray Whales.” The Condor 81 (1): 93. https://doi.org/10.2307/1367866.

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

Current gray whale die-off: a concern or simply the circle of life?

By Leila Lemos, PhD Candidate in Wildlife Sciences, Fisheries and Wildlife Department / OSU

Examination of a dead gray whale found in Pacifica, California, in May 2019.
Source: CNN 2019.

 

The avalanche of news on gray whale deaths this year is everywhere. And because my PhD thesis focuses on gray whale health, I’ve been asked multiple times now why this is happening. So, I thought it was a current and important theme to explore in our blog. The first question that comes to (my) mind is: is this a sad and unusual event for the gray whales that raises concern, or is this die-off event expected and simply part of the circle of life?

At least 64 gray whales have washed-up on the West Coast of the US this year, including the states of California, Oregon and Washington. According to John Calambokidis, biologist and founder of the Cascadia Research Collective, the washed-up whales had one thing in common: all were in poor body condition, potentially due to starvation (Calambokidis in: Paris 2019). Other than looking skinny, some of the whale carcasses also presented injuries, apparently caused by ship strikes (CNN 2019).

Cascadia Research Collective examining a dead gray whale in 9 May 2019, washed up in Washington state. Cause of death was not immediately apparent but appeared consistent with nutritional stress.
Source: Cascadia Research Collective 2019.

To give some context, gray whales migrate long distances while they fast for long periods. They are known for performing the longest migration ever seen for a mammal, as they travel up to 20,000 km roundtrip every year from their breeding grounds in Baja California, Mexico, to their feeding grounds in the Bering and Chukchi seas (Calambokidis et al. 2002, Jones and Swartz 2002, Sumich 2014). Thus, a successful feeding season is critical for energy replenishment to recover from the previous migration and fasting periods, and for energy storage to support their metabolic needsduring the migration and fasting periods that follow. An unsuccessful feeding season could likely result in poor body condition, affecting individual performance in the following seasons, a phenomenon known as the carry-over effect(Harrison et al., 2011).

In addition, environmental change, such as climate variations, might impact shifts in prey availability and thus intensify energetic demands on the whales as they need to search harder and longer for food. These whales already fast for months and spend large energy reserves supporting their migrations. When they arrive at their feeding grounds, they need to start feeding. If they don’t have access to predictable food sources, their fitness is affected and they become more vulnerable to anthropogenic threats, including ship strikes, entanglement in fishery gear, and contamination.

For the past three years, I have been using drone-based photogrammetry to assess gray whale body condition along the Oregon coast, as part of my PhD project. Coincident to this current die-off event, I have observed that these whales presented good body condition in 2016, but in the past two years their condition has worsened. But these Oregon whales are feeding on different prey in different areas than the rest of the ENP that heads up to the Bering Sea to feed. So, are all gray whales suffering from the same broad scale environmental impacts? I am currently looking into environmental remote sensing data such as sea surface temperature, chlorophyll-a and upwelling index to explore associations between body condition and environmental anomalies that could be associated.

Trying to answer the question I previously mentioned “is this event worrisome or natural?”, I would estimate that this die-off is mostly due to natural patterns, mainly as a consequence of ecological patterns. This Eastern North Pacific (ENP) gray whale population is now estimated at 27,000 individuals (Calambokidis in: Paris 2019) and it has been suggested that this population is currently at its carrying capacity(K), which is estimated to be between 19,830 and 28,470 individuals (Wade and Perryman, 2002). Prey availability on their primary foraging grounds in the Bering Sea may simply not be enough to sustain this whole population.

The plot below illustrates a population in exponential growth over the years. The population reaches a point (K) that the system can no longer support. Therefore, the population declines and then fluctuates around this K point. This pattern and cycle can result in die-off events like the one we are currently witnessing with the ENP gray whale population.

Population at a carrying capacity (K)
Source: Conservation of change 2019.

 

According to the American biologist Paul Ehrlich: “the idea that we can just keep growing forever on a finite planet is totally imbecilic”. Resources are finite, and so are populations. We should expect die-off events like this.

Right now, we are early on the 2019 feeding season for these giant migrators. Mortality numbers are likely to increase and might even exceed previous die-off events. The last ENP gray whale die-off event occurred in the 1999-2000 season, when a total of 283 stranded whales in 1999 and 368 in 2000 were found displaying emaciated conditions (Gulland et al. 2005). This last die-off event occurred 20 years ago, and thus in my opinion, it is too soon to raise concerns about the long-term impacts on the ENP gray whale population, unless this event continues over multiple years.

 

References

Calambokidis, J. et al. 2002. Abundance, range and movements of a feeding aggregation of gray whales (Eschrichtius robustus) from California to southeastern Alaska in 1998. Journal of Cetacean research and Management. 4, 267-276.

Cascadia Research Collective (2019, May 10). Cascadia and other Washington stranding network organizations continue to respond to growing number of dead gray whales along our coast and inside waters. Retrieved from http://www.cascadiaresearch.org/washington-state-stranding-response/cascadia-and-other-washington-stranding-networkorganizations?fbclid=Iw AR1g7zc4EOMWr_wp_x39ertvzpjOnc1zZl7DoMbBcjI1Ic_EbUx2bX8_TBw

Conservation of change (2019, May 31). Limits to Growth: the first law of sustainability. Retrieved from http://www.conservationofchange.org/limits

CNN (2019, May 15). Dead gray whales keep washing ashore in the San Francisco Bay area.Retrieved from https://www.cnn.com/2019/05/15/us/gray-whale-deaths-trnd-sci/index.html

Gulland, F. M. D., H. Pérez-Cortés M., J. Urbán R., L. Rojas-Bracho, G. Ylitalo, J. Weir, S. A. Norman, M. M. Muto, D. J. Rugh, C. Kreuder, and T. Rowles. 2005. Eastern North Pacific gray whale (Eschrichtius robustus) unusual mortality event, 1999-2000. U. S. Dep. Commer., NOAA Tech. Memo. NMFS-AFSC-150, 33 p.

Harrison, X. A., et al., 2011. Carry-over effects as drivers of fitness differences in animals. Journal of Animal Ecology. 80, 4-18.

Jones, M. L., Swartz, S. L., Gray Whale, Eschrichtius robustus. Encyclopedia of Marine Mammals. Academic Press, San Diego, 2002, pp. 524-536.

Paris (2019, May 27). Gray Whales Wash Up On West Coast At Near-Record Levels.Retrieved from https://www.wbur.org/hereandnow/2019/05/27/gray-whales-wash-up-record-levels

Sumich, J. L., 2014. E. robustus: The biology and human history of gray whales. Whale Cove Marine Education.

Wade, P. R., Perryman, W., An assessment of the eastern gray whale population in 2002. IWC, Vol. SC/54/BRG7 Shimonoseki, Japan, 2002, pp. 16.

 

The Beauty of Scientific Conferences

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

Science is truly meaningful because it is shared amongst colleagues and propagated to the wider public. There are many mediums through which information dissemination can occur. A common and most rigorous form is the peer-review scientific publication of papers. The paper approval process is vigorous, can last a long time – sometimes on the scale of several years – and is therefore an excellent way of vetting science that is occurring all over the world in many different disciplines. New studies build upon the results and downfalls of others, and therefore the process of research and communication of knowledge is continuous.

However, scientific journals and the publications within them can be quite exclusive; they are often only accessible to certain members of the scientific community or of an educational institution. For a budding scientist who is not affiliated with an institution, it can be very hard to get your hands on current research. Having said that, this issue is slowly becoming inconsequential since open access and free journals, such as PeerJ, are becoming more prevalent.

How some students feel after reading scientific publications. Source: Know Your Meme.

Something that is perhaps more restrictive is the amount of topic-specific jargon used in publications. While a certain degree of jargon is to be expected, it can sometimes overwhelm a reader to the point where the main findings of the research become lost. This typically tends to be the case for those just at the beginning of their scientific journeys, however I have also known professors to comment on confusing sections of publications due to the heavy use of specific jargon.

Conferences on the other hand offer an opportunity to disseminate meaningful science in a more open and (sometimes) more laid-back setting (this may not always be true depending on the field of science and the calibre of the conference). Researchers of a particular field congregate for a few days to learn about current research efforts, ponder potential collaborations, peruse posters of new studies, and argue over which soccer team is going to win the next World Cup. That is the beauty of conferences – it is very possible to get to know each other on a personal level. These face-to-face opportunities are especially beneficial to students as this relaxed atmosphere lends itself to asking questions and engaging with scientists that are leaders in their fields.

Logo for the Marine Technology Summit. Source: MTS.

Just over a week ago, the GEMM Lab had the opportunity to do all of the above-mentioned things. PI Dr Leigh Torres and I participated in the Marine Technology Summit (MTS) in Newport, OR, a “mini-conference” at which shiny, new technologies for use in marine applications were introduced by leading, and many local, tech companies. While Leigh and I are not technologists, we are ecologists that have greatly benefitted from recent, rapid advances in technology. Both of our gray whale (Eschrichtius robustus) research projects use different technologies to unveil hitherto unknown ecological aspects of these marine mammals.

Leigh presented her research that involves flying drones over gray whales that grace the Oregon coastal waters in the spring and summer. Through these flights, many previously undocumented gray whale behaviours have been captured and quantified1, such as headstands, nursing and jaw snapping (check out the video below). Furthermore, still images from the videos have been used to perform photogrammetry to assess health and body condition of the whales2. These drone flights have added a wealth of valuable data to the life histories of individual whales that previously were assessed mainly through photo-identification and genetics. This still fairly new approach to assess health by using drones can be relatively cost-effective, which has always been one of Leigh’s key aims throughout her research so that methods are accessible to many scientists. These productive drones used by the GEMM Lab are commercially available (yup, just like the ones you see on the shelves at your local Best Buy!).

The use of cost-effective technologies is a common theme in the GEMM Lab and is also central to my research. The estimation of zooplankton density is vital to my project to determine whether gray whales in Port Orford select areas of high prey density over areas with less dense prey. However, the traditional technology used to quantify prey densities in the water column are often bulky or expensive. Instead, we developed a relatively cheap method of measuring relative zooplankton density using a GoPro camera that we reel down through the water column from a downrigger attached to our research kayak. While we are unable to exactly quantify the mass of zooplankton in the water column, we have been successful in assessing changes in relative prey density by scoring screenshots of the footage.

Screenshot of a GoPro video from this summer’s field season in Port Orford, OR revealing a thick layer of zooplankton. Source: GEMM Lab.

While our drones and GoPro technology is not without error, technology rarely is. In truth, we lost our GoPro for several days after it became stuck in a rock crevice and Leigh’s team regrettably lost a drone to the depths of the ocean this summer. This technology reality was part of the reason I presented at the MTS as I wanted to involve technologists to find solutions to some of the problems I have experienced. Needless to say, I got a lot of excellent input from many different people, for which I am very grateful. In addition to developing new opportunities to collaborate, I was very content to sit in the audience and hear about the ground-breaking new marine technologies that are in development. Below are short descriptions of two new technologies I learned about that are revolutionising the marine world.

ASV Unmanned Marine Systems develop autonomous surface vehicles that are powered by renewable energies (solar panels and wind turbines). These vessels are particularly useful for oceanographic monitoring as they are more capable than weather buoys and much more cost effective than manned weather ships or research vessels. Additionally, they can be used for a lot of different marine science applications including active acoustic fisheries monitoring, water quality monitoring, and cetacean tracking. Some models even have integrated drones that are launched and retrieved autonomously.

The Ocean Cleanup is a company that develops technologies to clean garbage out of our oceans. There is presently a large mission underway by The Ocean Cleanup to combat the Great Pacific Garbage Patch (GPGP). The GPGP is essentially a large island in the middle of the North Pacific Ocean comprised of diverse plastic particles – wrappers, polystyrene, fishing line, plastic bags, the list is endless3. A recent study estimates the amount of plastic in the GPGP to be at least 79 thousand tonnes of ocean plastic4. Unfortunately, the GPGP is not the only one of its kind. The Ocean Cleanup hopes to reduce this massive plastic accumulation with the development of a system made up of a 600-m long floater that sits on the ocean’s surface with a 3-m deep skirt attached below it. The skirt will collect debris while the float will prevent plastic from flowing over it, as well as keep the whole system afloat. The system arrived at the GPGP last Wednesday and the team of over 80 engineers, researchers, scientists and computational modellers have successfully installed the system. The team posts frequent updates on their Twitter and I would highly recommend you follow this possibly revolutionary technology.

While attending the MTS, it felt like there are no bounds for the types of marine technology that will be developed in the future. I am excited to see what ecologists working with technicians can develop to keep applying technology to address challenging questions and conservation issues.

 

References

  1. Torres, L., et al., Drone up! Quantifying whale behaviour from a new perspective improves observational capacity.Frontiers in Marine Science, 2018. 5, DOI:10.3389/fmars.2018.00319.
  2. Burnett, J.D., et al., Estimating morphometric attributes on baleen whales using small UAS photogrammetry: A case study with blue and gray whales, 2018.Marine Mammal Science. DOI:10.1111/mms.12527.
  3. Kaiser, J., The dirt on the ocean garbage patches. Science, 2018. 328(5985): p. 1506.
  4. Lebreton, L., et al., Evidence that the Great Pacific Garbage Patch is rapidly accumulating plastic. Scientific Reports, 2018. 8(4666).