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).

Feasts of junk food or morsels of fine dining: is prey quality or quantity more important to marine predators?

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

Knowing what and how much prey a predator feeds on are key components to better understanding and conserving that predator. Prey abundance and availability are frequently predictors for marine predator reproductive success and population dynamics. It is the reason why the GEMM Lab makes a concerted effort to not only track our main taxa of interest (marine mammals) but to simultaneously measure their prey. However, over the last decade or two, there has been increased recognition that prey quality is also highly important in understanding a predator’s ecology (Spitz et al. 2012). Optimal foraging theory is a widely accepted framework that posits that predators should attempt to maximize energy gained and minimize energy spent during a foraging event (Charnov 1976, Krebs 1978, Pyke 1984). Thus, knowledge of how valuable a prey item is in terms of its energetic content is an important part of the equation when applying optimal foraging theory to a predator of interest.

Ideally, the prey species with the highest energetic value would also be the easiest, most ubiquitous and least energetically expensive prey item to capture and consume, such that a predator truly could expend very little energy to get very high energetic rewards. However, it rarely is this straightforward. The caloric content of several marine prey species has been shown to increase with increasing size (e.g. Benoit-Bird 2004; Fig. 1), both length and weight. Yet, increasing size often also means increased mobility and, as a result, ability to evade and escape predation. Furthermore, increasing size also inherently means decreasing abundances – there will always be billions more krill in the ocean than whales based solely on cost of reproduction. Therefore, just based on sheer numbers, there are fewer big prey items, which increases the time between, and decreases the likelihood of, a predator encountering big prey items. So, there are clear trade-offs here. It may take longer to locate and capture a high value prey item, which costs more energy to capture, but the payout could potentially be much bigger. However, if a predator gambles too much, then their net energy expenditure to obtain high value prey may be higher than the net energy gained. Instead, it may be worth pursuing smaller prey items with lower energetic values, where discovery and capture success are higher and more frequent. However, in this case, many, many more pursuits are likely needed, thus costing more energy to meet daily energetic demands. 

Figure 1. Increasing caloric content with increasing length (a) and wet weight (b). Figures and caption reproduced from Benoit-Bird 2004.

Is your head spinning as much as mine? Let me try and simplify this complex web of interactions with a tangible example. Bowen et al. (2002) investigated foraging of harbor seals in Nova Scotia to assess prey profitability of different species. By attaching camera systems to the backs of 39 adult male harbor seals, the authors identified sand lance and flounder to be the most targeted prey species. However, there were significant differences in pursuit/handling cost per prey type (kJ/min) with sand lance only requiring 14.8 ± 2.7, whereas flounder required significantly more at 30.3 ± 7.9. Therefore, based solely on energy required to capture prey, the sand lance would seem to be the better option. In fact, to a certain degree, this hypothesis is actually true when we compare the energetic content of the two prey types. Sand lance have a higher energetic value at lengths of 10 and 15 cm (53.6 and 95.8 kJ, respectively) compared to flounder (22.6 and 88.6 kJ, respectively). So, the net gain of a harbor seal foraging on a 15 cm sand lance (assuming that it only takes 1 minute to catch the fish – this is more for explanatory purposes as it likely takes much longer for a harbor seal to capture a fish) would be 81 kJ. This gain is larger than that of a 15 cm flounder (58.3 kJ). However, once we compare these fish at 20 and 25 cm lengths, the flounder actually becomes the more beneficial prey item at 232.6 and 492.3 kJ, respectively, over the sand lance (158.1 and 233.8 kJ). Now, assuming once again that it only takes 1 minute to catch the fish, the harbor seal enjoys a net energetic gain of a whopping 462 kJ when capturing a 25 cm flounder compared to 219 kJ for a sand lance of the same size – that makes the flounder more than twice as profitable!

The Bowen et al. study is an excellent demonstration of the importance of considering the quality of prey items when studying the ecology of marine predators. However, the authors did not assess the relative availability of sand lance and flounder. Ideally, foraging ecology studies aimed at understanding prey choice would try to address both important prey metrics – quality and quantity. This goal is the exact aim of my second Master’s thesis chapter where I am investigating whether prey quality (determined through community composition and caloric content) or prey quantity (measured as relative density) is more important in driving fine-scale gray whale foraging behavior in Port Orford, Oregon (Fig. 2). This question can be simplified by asking does it matter more what prey is in an area, or how much prey there is in an area? Or we can relate it back to the title of this post by asking whether individual gray whales would rather attend a cheap all-you-can-eat buffet or an expensive fine-dining restaurant. I am unfortunately not quite done with my analyses yet (but I’m getting closer!) and therefore am not ready to answer these questions. However, I have done extensive research on this topic and therefore am in a position to briefly mention a few other studies that have investigated these questions for other marine predators. 

Figure 2. A question of what or how much. Left image: example of the screenshots we take to estimate relative prey density in Port Orford. Right images: two examples of the main prey species we find (top: mysid shrimp Neomysis rayii with a full brood pouch; bottom: amphipod Polycheria osborni).

Ludynia et al. (2010) explored reasons why African penguin (Spehniscus demersus) numbers have declined in Namibia. They found that after the collapse of pelagic fish stocks in the 1970s (including the principal penguin prey item, sardine), African penguins switched to feeding on bearded goby, which are considered a low-energy prey species. Bearded goby are relatively abundant along Namibia’s southern coast and as such, limited prey availability is not the reason for declining African penguin numbers. Therefore, the authors concluded that the low quality of bearded goby (compared to sardine) appears to be the reason for declining population trends  of the penguins. This study demonstrates that African penguins do better when eating at a fine-dining restaurant, rather than loading up a whole plate of junk food. 

Grémillet et al. (2004) studied the foraging effort and number of successful prey captures per foraging trip (yield) of great cormorants (Phalacrocorax carbo) in Greenland in relation to prey abundance and quality within their foraging areas. The authors radio-tracked 11 great cormorants during a total of 163 foraging trips to estimate foraging effort and yield. The study found that contrary to the authors’ hypothesis, great cormorants foraged in areas of low prey abundance where the average caloric value was also relatively low. Therefore, in this example, it would seem that the predator of interest prioritizes neither high quality nor quantity when foraging.

Haug et al. (2002) investigated the variations in minke whale (Balaenoptera acutorostrata) diet and body condition in response to ecosystem changes in the Barents Sea. The main prey item of minke whales in the Barents Sea is immature herring. However, when recruitment failure and subsequent weak cohorts leads to reduced availability of immature herring, minke whales switched their diet to other prey items such as krill, capelin, and sometimes other gadoid fish species. The authors found a correlation between body condition of minke whales and immature herring abundances, such that minke whales displayed a poor body condition during low immature herring abundances. However, in the years of low immature herring abundance, abundances of krill and capelin were not low. Therefore, similar to the Ludynia et al. (2010) study, it seems that minke whales in the Barents Sea also do better in years when the prey type of highest caloric value is the most abundant. However, decreases in high quality prey has not led to population declines in minke whales in the Barents Sea, indicating that they likely take advantage of high quantities of low quality prey, unlike the African penguins.

Clearly, the answer as to whether marine predators prefer quality over quantity is not simple and constant. Rather, prey preference varies based on predator needs and ecology, falling anywhere on a broad spectrum from low to high prey quality and low to high prey quantity (Fig. 3). To a certain extent, it probably also is not solely predator choice that determines what they eat but many other factors, such as climate, disturbance, and health. As a result, these preferences and choices will likely be fluid, rather than fixed. While I anticipate that individual gray whales will be flexible foragers, I do hypothesize that when there is a prey patch of a higher energetic value in the area, whales will preferentially consume these patches over areas where there is less energetically rich prey, even if it is more abundant. 

Figure 3. A spectrum of prey quantity and quality. Giant cormorants forage on low prey quality & quantity (Grémillet et al. 2004). African penguin populations are declining despite high abundances of low quality prey, suggesting that high prey quality is important for their survival (Ludynia et al. 2010). Body condition of Barents Sea minke whales decreases when high quality prey is less abundant, however their populations have not declined, suggesting they instead exploit high abundances of low quality prey (Haug et al. 2002). What will the gray whales do?

Literature cited

Benoit-Bird, K. J. 2004. Prey caloric value and predator energy needs: foraging predictions for wild spinner dolphins. Marine Biology 145:435-444.

Bowen, W. D., D. Tuley, D. J. Boness, B. M. Bulheier, and G. J. Marshall. 2002. Prey-dependent foraging tactics and prey profitability in a marine mammal. Marine Ecology Progress Series 244:235-245.

Charnov, E. L. 1976. Optimal foraging, the marginal value theorem. Theoretical Population Biology 9(2):129-136.

Grémillet D., G. Kuntz, F. Delbart, M. Mellet, A. Kato, J-P. Robin, P-E. Chaillon, J-P. Gendner, S-H. Lorentsen, and Y. Le Maho. 2004. Linking the foraging performance of a marine predator to local prey abundance. Functional Ecology 18(6):793-801.

Haug, T., U. Lindstrøm, and K. T. Nilssen. 2002. Variations in minke whale (Balaenoptera acutorostrata) diet and body condition in response to ecosystem changes in the Barents Sea. Sarsia 87(6):409-422. 

Krebs, J. R. 1978. Optimal foraging: decision rules for predators. Behvaioral Ecology: An Evolutionary Approach, eds. Krebs, J. R., and N. B. Davies. Oxford: Blackwell. 

Ludynia, J., J-P. Roux, R. Jones, J. Kemper, and L. G. Underhill. 2010. Surviving off junk: low-energy prey dominates  the diet of African penguins Spheniscus demersus at Mercury Island, Namibia, between 1996 and 2009. African Journal of Marine Science 32(3):563-572.

Pyke, G. H. 1984. Optimal foraging theory: a critical review. Annual Reviews of Ecology and Systematics 15:523-575.

Spitz, J., A. W. Trites, V. Becquet, A. Brind’Amour, Y. Cherel, R. Galois, and V. Ridoux. 2012. Cost of living dictates what whales, dolphins and porpoises eat: the importance of prey quality on predator foraging strategies. PLoS ONE 7(11):e50096.

Young, J. K., B. A. Black, J. T. Clarke, S. V. Schonberg, and K. H. Dunton. 2017. Abundance, biomass and caloric content of Chukchi Sea bivalves and association with Pacific walrus (Odobenus rosmarus divergens) relative density and distribution in the northeastern Chukchi Sea. Deep-Sea Research Part II 144:125-141.

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

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

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

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

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

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

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

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

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

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

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

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

Literature cited

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

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

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

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

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.

Makah Gray Whale Hunt Waiver – a long-time coming, but still premature?

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

Archaeological site of Ozette Village. Source: Makah Museum.

The Makah, an indigenous people of the Pacific Northwest Coast living in Washington State, have a long history with whaling. Deposits from a mudslide in the village of Ozette suggest that whaling may date back 2,000 years as archaeologists uncovered humpback and gray whale bones and barbs from harpoons (Kirk 1986). However, the history of Makah whaling is also quite recent. On January 29 of this year, the National Marine Fisheries Service (NMFS; informally known as NOAA Fisheries) announced a 45-day public comment period regarding a NMFS proposed waiver on the Marine Mammal Protection Act’s (MMPA) moratorium on the take of marine mammals to allow the Makah to take a limited number of eastern North Pacific gray whales (ENP). To understand how the process reached this point, we first must go back to 1855.

1855 marks the year in which the U.S. government and the Makah entered into the Treaty of Neah Bay (in Washington state). The Makah ceded thousands of acres of land to the U.S. government, and in return reserved their right to whale. Following the treaty, the Makah hunt of gray whales continued until the 1920s. At this point, commercial hunting had greatly reduced the ENP population, so much so that the Makah voluntarily ceased their whaling. The next seven decades brought about the formation of the International Whaling Commission (IWC), the enactment of the Whaling Convention Act, the listing of gray whales as endangered under the U.S. Endangered Species Act, and the enactment of the MMPA. For gray whales, these national and international measures were hugely successful, leading to the removal of the ENP from the Federal List of Endangered Wildlife in 1994 when it was determined that the population had recovered to near its estimated original population size.

One year later on May 5, 1995 (just one month after I was born!), the Makah asked the U.S. Department of Commerce to represent its interest to obtain a quota for gray whales from the IWC in order to resume their treaty right for ceremonial and subsistence harvest of the ENP. The U.S. government pursued this request at the next IWC meeting, and subsequently NMFS issued a final Environmental Assessment that found no significant impact to the ENP population if the hunt recommenced. The IWC set a catch limit and NMFS granted the Makah a quota in 1998. In 1999 the Makah hunted, struck and landed an ENP gray whale.

“Makahs cutting up whale, Neah Bay, ca. 1930. Photo by Asahel Curtis, Courtesy UW Special Collections (CUR767)”. Source and caption: History Link.

I will not go into detail about what happened between 1999 and now because frankly, a lot happened, particularly a lot of legal events including summary judgements, appeals, and a lot of other legal jargon that I do not quite understand. If you want to know the specifics of what happened in those two decades, I suggest you look at NMFS’ chronology of the Makah Tribal Whale Hunt. In short, cases brought against NMFS argued that they did not take a “hard [enough] look” at the National Environmental Policy Act when deciding that the Makah could resume the hunt. Consequently, the hunt was put on hold. Yet, in 2005 NMFS received a waiver request from the Makah on the MMPA’s take moratorium and NMFS published a notice of intent to review this request. A lot more happened between that event and now, including on January 29 of this year when NMFS announced the availability of transcripts from the Administrative Law Judge’s (ALJ) hearing (which happened from November 14-21, 2019) on the proposed regulations and waiver to allow the Makah to resume hunting the ENP. We are currently in the middle of the aforementioned 45-day public comment period on the formal rulemaking record. 

It has been 15 years since the Makah requested the waiver and while the decision has not yet been reached, we are likely nearing the end of this long process. This blog has turned into somewhat of a history lesson (not really my intention) but I feel it is important to understand the lengthy and complex history associated with the decision that is probably going to happen sometime this year. My actual intent for this blog is to ruminate on a few questions, some of which remain unanswered in my opinion, that are large and broad, and important to consider. Some of these questions point out gaps in our ecological knowledge regarding gray whales that I believe should be addressed for a truly informed decision to be made on NMFS’ proposed waiver now or anytime in the near future. 

1. Should the Pacific Coast Feeding Group (PCFG) of gray whales be recognized as its own stock?

Currently, the PCFG are considered a part of the ENP stock. This decision was published following a workshop held by a NMFS task force (Weller et al. 2013). The report concluded that based on photo-identification, genetics, tagging, and other data, there was a substantial level of uncertainty in the strength of the evidence to support the independence of the PCFG from the ENP. Nevertheless, mitochondrial genetic data have indicated a differentiation between the PCFG and the ENP, and the exchange rate between the two groups may be small enough for the two to be considered demographically independent (Frasier et al. 2011). Based on all currently available data, it seems that matrilineal fidelity plays a role in creating population structure within and between the PCFG and the ENP, however there has not been any evidence to suggest that whales from one feeding area (i.e. the PCFG range) are reproductively isolated from whales that utilize other feeding areas (i.e. the Arctic ENP feeding grounds) (Lang et al. 2011). Several PCFG researchers do argue that there needs to be recognition of the PCFG as an independent stock. It is clear that more research, especially efforts to link genetic and photo-identification data within and between groups, is required.

ENP gray whales foraging off the coast of Alaska on their main foraging grounds in the Bering Sea. Photo taken by ASAMM/AFSC. Funded by BOEM IAA No. M11PG00033. Source: NMFS.

2. Is emigration/immigration driving PCFG population growth, or is it births/deaths?

It is unclear whether the current PCFG population growth is a consequence of births and deaths that occur within the group (internal dynamics) or whether it is due to immigration and emigration (external dynamics). Likely, it is a combination of the two, however which of the two has more of an effect or is more prevalent? This question is important to answer because if population growth is driven more by external dynamics, then potential losses to the PCFG population due to the Makah hunt may not be as detrimental to the group as a whole. However, if internal dynamics play a bigger role, then the loss of just a few females could have long-term ramifications for the PCFG (Schubert 2019). NMFS has taken precautions to try and avoid such effects. In their proposed waiver, of the cumulative limit of 16 strikes of PCFG whales over the 10-year waiver period, no more than 8 of the strikes may be PCFG females (Yates 2019a). While a great step, it still begs the question how the loss of 8 females, admittedly over a rather long period of time, may affect population dynamics since we do not know what ultimately drives recruitment. Especially when taken together with potential non-lethal effects on whales (further discussed in question 5 below).

“Scarlet” is a PCFG female who has had multiple calves in the decades that researchers have seen her in the PCFG range. Image captured under NOAA/NMFS permit #21678. Source: L Hildebrand.

3. How important are individual patterns within the PCFG, and how might the loss of these individuals affect the population? 

The hunt will be restricted to the Makah Usual & Accustomed fishing area (U&A), which is off the Washington coast. It has been shown that site fidelity among PCFG individuals is strong. In fact, based on the 143 PCFG gray whales observed in nine or more years from 1996 to 2015, 94.4% were seen in at least one of nine different PCFG regions during six or more of the years they were seen (Calambokidis et al. 2017). While high site-fidelity seems to be common for some PCFG individuals in certain regions, interestingly, an analysis of sighting histories of all individuals that utilized the Makah U&A from 1985-2011 revealed that most PCFG whales do not have strong site fidelity to the Makah U&A (Scordino et al. 2017). Only about 20% of the whales were observed in six or more years of the total 26 years of data analyzed. Since high individual site fidelity does not appear to be strong in this area, perhaps a loss of genetic diversity, cultural knowledge, and behavioral individualism is not of great concern.

“Buttons” seems to have a preference for the southern Oregon coast as in the last 5 years the GEMM Lab has conducted research, he has only been sighted in 1 year in Newport but in all 5 years in Port Orford. However, perhaps such preferences are not common among all PCFG whales. Source: F. Sullivan.

4. How has the current UME affected the situation?

The ENP has experienced two Unusual Mortality Events (UMEs) in the past 20 years; one from 1999-2000 and the second began in May 2019. Many questions arise when thinking about the Makah hunt in light of the UME. 

  • What impacts will the current UME have on ENP and PCFG birth rates in subsequent years? 
  • Could the UME lead to shifts in feeding behavior of ENP whales and result in greater use of PCFG range by more individuals?
  • What caused the UME? Shifting prey availability and a changing climate? Or has the ENP reached carrying capacity? 
  • Will UMEs become more frequent in the future with continued warming of the Arctic? 
  • What is the added impact of such periodic UMEs on population trends?
“A gray whale found dead off Point Reyes National Seashore in northern California [during the 2019 UME]. Photo by M. Flannery, California Academy of Sciences.” Source and caption: NMFS.

A key assumption of the model developed by NMFS (Moore 2019) to forecast PCFG population size for the period 2016-2028, is that the population processes underlying the data from 2002-2015 (population size estimates developed by Calambokidis et al. 2017) will be the same during the forecasted period. In other words, it is assuming that PCFG gray whales will experience similar environmental conditions (with similar variation) during the next decade as the previous one, and that there will be no catastrophic events that could drastically affect population dynamics. The UME that is still ongoing could arguably affect population dynamics enough such that they are drastically different to effects on the population dynamics during the previous decade. The cause of  the 1999/2000 UME remains undetermined and the results of the investigation of the current UME will possibly not be available for several years (Yates 2019b). Even though the ENP did rebound following the 1999/2000 UME and the abundance of the PCFG increased during and subsequent to that UME, much has changed in the 20 years since then. Increased noise due to increased vessel traffic and other anthropogenic activities (seismic surveys, pile driving, construction to name a few) as well as increased coastal recreational and commercial fishing, have all contributed to a very different oceanscape than the ENP and PCFG encountered 20 years ago. Furthermore, the climate has changed considerably since then too, which likely has caused changes in the spatial distribution of habitat and quantity, quality, and predictability of prey. All of these factors make it difficult to predict what impact the UME will have now. If such events were to become more frequent in the future or the impacts of such events are greater than anticipated, then the PCFG population forecasts will not have accounted for this change. 

5. What impacts will the hunt and associated training exercises have on energy and stress levels of whales?

The proposed waiver would allow hunts to occur in the following manner: in even-years, the hunting period is from December 1 of an odd-numbered year through May 31 of the following even-numbered year. While in odd-years, the hunt is limited from July to October.

In the even-years, the hunt coincides with the northbound migration toward the foraging grounds for ENP whales and with the arrival of PCFG whales to their foraging grounds near the Makah U&A. During the northbound migration, gray whales are at their most nutritionally stressed state as they have been fasting for several months. They are therefore most vulnerable to energy losses due to disturbance at this point (Villegas-Amtmann 2019). Attempted strikes and training exercises would certainly cause some level of disturbance and stress to the whales. Furthermore, the timing of even-year hunts, means that hunters would likely encounter pregnant females, as they are the first to arrive at foraging grounds. A loss of just ~4% of a pregnant female’s energy budget could cause them to abort the fetus or not produce a calf that year (Villegas-Amtmann 2019).

In odd-years, the Makah hunt will most certainly target PCFG whales as the Makah U&A forms one of the nine PCFG regions where PCFG individuals will be feeding during those months. However, NMFS’ waiver limits the number of strikes during odd-years to 2 (Yates 2019a), which certainly protects the PCFG population.

Stress is a difficult response to quantify in baleen whales and research on stress through hormone analysis is still relatively novel. It is unlikely that a single boat training approach of a gray whale will have an adverse effect on the individual. However, a whale is never just experiencing one disturbance at a time. There are typically many confounding factors that influence a whale’s state. In an ideal world, we would know what all of these factors are and how to recognize these effects. Yet, this is virtually impossible. Therefore, while precautions will be taken to try to minimize harm and stress to the gray whales, there may very well still be unanticipated impacts that we cannot anticipate. 

Gray whale fluke. Image captured under NOAA/NMFS permit #21678. Photo: L Hildebrand.

Final thoughts

Many unknowns still remain about the ENP and PCFG gray whale populations. During the ALJ hearing, both sides tried to deal with these unknowns. After reading testimony from both sides, it is clear to me that some of the unknowns still have not been reconciled. Ultimately, a lot of the questions circle back to the first one I posed above: Are the PCFG an independent stock? If there is independent population structure, then the proposed waiver put forth by NMFS would likely change. While NMFS has certainly taken the PCFG into account during the declarations of several experts at the ALJ hearing and has aired on the side of caution, the fact that the PCFG is considered part of the ENP might underestimate the impact that a resumption of the Makah hunt may have on the PCFG. As you can see, there are still many questions that should be addressed to make fully informed decisions on such an important ruling. While this research may take several years to obtain results, the data are within reach through synthesis and collaboration that will fill these critical knowledge gaps. 

Literature cited

Calambokidis, J. C., J. Laake, and A. Pérez. 2017. Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2015. International Whaling Commission SC/A17/GW/05.

Frasier, T. R., S. M. Koroscil, B. N. White, and J. D. Darling. 2011. Assessment of population substructure in relation to summer feeding ground use in eastern North Pacific gray whale. Endangered Species Research 14:39-48.

Kirk, Ruth. 1986. Tradition and change on the Northwest Coast: the Makah, Nuu-chah-nulth, southern Kwakiutl and Nuxalk. University of Washington Press, Seattle.

Lang, A. R., D. W. Weller, R. LeDuc, A. M. Burdin, V. L. Pease, D. Litovka, V. Burkanov, and R. L. Brownell, Jr. 2011. Genetic analysis of stock structure and movements of gray whales in the eastern and western North Pacific. SC/63/BRG10.

Moore, J. E. 2019. Declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

Schubert, D. J. 2019. Rebuttal testimony in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

Scordino, J. J., M. Gosho, P. J. Gearin, A. Akmajian, J. Calambokidis, and N. Wright. 2017. Individual gray whale use of coastal waters off northwest Washington during the feeding season 1984-2011: Implications for management. Journal of Cetacean Research and Management 16:57-69.

Villegas-Amtmann, S. 2019. Declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001.

Weller, D. W., S. Bettridge, R. L. Brownell, Jr., J. L. Laake, J. E. Moore, P. E. Rosel, B. L. Taylor, and P. R. Wade. 2013. Report of the National Marine Fisheries Service Gray Whale Stock Identification Workshop. NOAA-TM-NMFS-SWFSC-507. 

Yates, C. 2019a. Declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

Yates, C. 2019b. Fifth declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

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 is that whale doing? Only residence in space and time will tell…

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

For my research in Port Orford, my field team and I track individual gray whales continuously from a shore-based location: once we spot a whale we will track it for the entire time that it remains in our study site. The time spent tracking a whale can vary widely. In the 2018 field season, our shortest trackline was three minutes, and our longest track was over three hours in duration.

This variability in foraging time is partly what sparked my curiosity to investigate potential foraging differences between individuals of the Pacific Coast Feeding Group (PCFG) gray whales. I want to know why some individuals, like “Humpy” who was our longest tracked individual in 2018, stayed in an area for so long, while others, like “Smokey”, only stayed for three minutes (Figure 1). It is hard to pinpoint just one variable that drives these decisions (e.g., prey, habitat) made by individuals about where they forage and how long because the marine environment is so dynamic. Foraging decisions are likely dictated by several factors acting in concert with one another. As a result, I have many research questions, including (but certainly not limited to):

  1. Does prey density drive length of individual foraging bouts?
  2. Do individual whales have preferences for a particular prey species?
  3. Are prey patches containing gravid zooplankton targeted more by whales?
  4. Do whales prefer to feed closer to kelp patches?
  5. How does water depth factor into all of the above decisions and/or preferences? 

I hope to get to the bottom of these questions through the data analyses I will be undertaking for my second chapter of my Master’s thesis. However, before I can answer those questions, I need to do a little bit of tidying up of my whale tracklines. Now that the 2019 field season is over and I have all of the years of data that I will be analyzing for my thesis (2015-2019), I have spent the past 1-2 weeks diving into the trackline clean-up and analysis preparation.

The first step in this process is to run a speed filter over each trackline. The aim of the speed filter is to remove any erroneous points or outliers that must be wrong based on the known travel speeds of gray whales. Barb Lagerquist, a Marine Mammal Institute (MMI) colleague who has tracked gray whales for several field seasons, found that the fastest individual she ever encountered traveled at a speed of 17.3 km/h (personal communication). Therefore, based on this information,  my tracklines are run through a speed filter set to remove any points that suggest that the whale traveled at 17.3 km/h or faster (Figure 2). 

Fig 3. Trackline of “Humpy” after interpolation. The red points are interpolated.

Next, the speed-filtered tracklines are interpolated (Figure 3). Interpolation fills spatial and/or temporal gaps in a data set by evenly spacing points (by distance or time interval) between adjacent points. These gaps sometimes occur in my tracklines when the tracking teams misses one or several surfacings of a whale or because the whale is obscured by a large rock. 

After speed filtration and interpolation has occurred, the tracklines are ready to be analyzed using Residence in Space and Time (RST; Torres et al. 2017) to assign behavior state to each location. The questions I am hoping to answer for my thesis are based upon knowing the behavioral state of a whale at a given location and time. In order for me to draw conclusions over whether or not a whale prefers to forage by a reef with kelp rather than a reef without kelp, or whether it prefers Holmesimysis sculpta over Neomysis rayii, I need to know when a whale is actually foraging and when it is not. When we track whales from our cliff site, we assign a behavior to each marked location of an individual. It may sound simple to pick the behavior a whale is currently exhibiting, however it is much harder than it seems. Sometimes the behavioral state of a whale only becomes apparent after tracking it for several minutes. Yet, it’s difficult to change behaviors retroactively while tracking a whale and the qualitative assignment of behavior states is not an objective method. Here is where RST comes in.

Those of you who have been following the blog for a few years may recall a post written in early 2017 by Rachael Orben, a former post-doc in the GEMM Lab who currently leads the Seabird Oceanography Lab. The post discussed the paper “Classification of Animal Movement Behavior through Residence in Space Time” written by Leigh and Rachael with two other collaborators, which had just been published a few days prior. If you want to know the nitty gritty of what RST is and how it works, I suggest reading Rachael’s blog, the GEMM lab’s brief description of the project and/or the actual paper since it is an open-access publication. However, in a nut shell, RST allows a user to identify three primary behavioral states in a tracking dataset based on the time and distance the individual spent within a given radius. The three behavioral categories are as follows:

Fig 4. Visualization of the three RST behavioral categories. Taken from Torres et al. (2017).
  • Transit – characterized by short time and distance spent within an area (radius of given size), meaning the individual is traveling.
  • Time-intensive – characterized by a long time spent within an area, meaning the individual is spending relatively more time but not moving much distance (such as resting in one spot). 
  • Time & distance-intensive – characterized by relatively high time and distances spent within an area, meaning the individual is staying within and moving around a lot in an area, such as searching or foraging. 

What behavior these three categories represent depends on the resolution of the data analyzed. Is one point every day for two years? Then the data are unlikely to represent resting. Or is the data 1 point every second for 1 hour? In which case travel segments may cover short distances. On average, my gray whale tracklines are composed of a point every 4-5 minutes for 1-2 hours.  Bases on this scale of tracking data, I will interpret the categories as follows: Transit is still travel, time & distance-intensive points represent locations where the whale was searching because it was moving around one area for a while, and time-intensive points represent foraging behavior because the whale has ‘found what it is looking for’ and is spending lots of time there but not moving around much anymore. The great thing about RST is that it removes the bias that is introduced by my field team when assigning behavioral states to individual whales (Figure 5). RST looks at the tracklines in a very objective way and determines the behavioral categories quantitatively, which helps to remove the human subjectivity.

While it took quite a bit of troubleshooting in R and overcoming error messages to make the codes run on my data, I am proud to have results that are interesting and meaningful with which I can now start to answer some of my many research questions. My next steps are to create interpolated prey density and distance to kelp layers in ArcGIS. I will then be able to overlay my cleaned up tracklines to start teasing out potential patterns and relationships between individual whale foraging movements and their environment. 

Literature cited

Torres, L. G., R. A. Orben, I. Tolkova, and D. R. Thompson. 2017. Classification of animal movement behavior through residence in space and time. PLoS ONE: doi. org/10.1371/journal.pone.0168513.

A Series of Short Stories from A Field Season in Port Orford

By Mia Arvizu, Marine Studies Initiative (MSI) & GEMM Lab summer intern, OSU junior

Part 1: The Green Life Jacket

The swells are churning and for once my stomach is calm. I take advantage of it while I can, and head out on the kayak. Another beautiful day, another good data set. After about three hours in the kayak and a long paddle fighting winds and swells, we arrive at TC1. That’s short for Tichenor Cove Station 1. I’m fairly tired by now but my teammate and I are determined to finish all stations today. GPS says we arrived, and I paddle against any slight movement to keep us on station. It’s getting more difficult though, so I check in with Anthony, one of the high school interns this summer. “Anthony, have you sent the GoPro camera down yet?”  I take a quick look back peering over my green life jacket. Red flash, and I know it’s on. Anthony sends it down, and I watch as it plunges into depths I couldn’t see on my own. I’m confident it’s doing its job. 

Part 2: The GoPro Dive

The green life jacket is familiar, but there’s a different soul, a different face every year. It’s the same month though. August – the month of whales. 

Red flash, I’m on,  and it’s my time to shine. The scientists debrief me on my latest mission, and I’m alive. “Secchi depth .75 meters.” Hmm, low visibility. This may be a tough one. “Station TC1” One of my favorites but challenging no doubt. “Time is 10:36. 5, 6, 7, 8…” I’m ready. A flush of swirling water surrounds me as I plunge into the depths of a different realm. I’m cocooned in the beauty of an ocean so blue, so majestic, so entrancing. Oh, the mission! Right, I need to stay focused. They lurk all around but with sand clouding the water, I can barely see. I just need one good visual of the purple spikes and the swaying green leaves, and the mission will be complete. I glance just to the left and oh my!

Sea urchins actively foraging on kelp at station TC1 in Tichenor Cove. Source: GEMM Lab.

A giant purple spike comes too close. I barely caught a glimpse of it. I need a better shot, but I only have so much control especially with these undercurrents. I’m ready now though. I peer through the sediment and nothing, but one quick swivel to the right shows me what I feared and what the green life jackets predicted: The purple spikes have grown too many and reduced the swaying greens down to half chewed, severed, scared dead masses. I thought their hypothesis was right, but I didn’t expect this degree of damage. It’s so frightening I almost look away.

But I don’t. I have a mission. So, I look straight ahead documenting the scene. I haven’t seen it this bad in the past years. I wonder what the green life jackets will do about this. I feel a tug, and I’m reeled in. I guess I’ll find out.

GoPro video taken from tandem research kayak during 2019 gray whale field season in Tichenor Cove, Port Orford. Source: GEMM Lab.

Part 3: The Science, how I see it

After collecting data in the kayak, I go back to the field station ready to do data processing. I grab the GoPro and take a look at the video from TC1. I’m both amazed and terrified for the surrounding habitat from what I see. Sea urchins seem to have been actively foraging on kelp stalks. 

Last summer, around this time, a previous intern pointed out that he was witnessing damaged kelp and a notable number of urchins in the GoPro videos. Thus, the GEMM Lab is looking into the relationship between kelp health and sea urchin abundance in Port Orford, which can have significant trophic cascades for the rest of the ecosystem, including whales and their zooplankton prey. The hypothesis is that if sea urchin populations increase in number they may actively forage on kelp, reducing the health of that habitat. Many creatures depend on this habitat including zooplankton which whales feed on. I have looked at videos from past years and the temporal difference in the abundance of urchins is stark. A detailed methodology for the project and our pending results will be featured in a later post, but for now this story is unfolding before our eyes and the GoPro’s lens as well. 

Part 4: The Transformation from STEM to STEAM

I hope you enjoyed these short stories. As the writer, it was nice to express the ecological phenomena I’ve learned about in the last few weeks between sea urchins and kelp in this creative and artistic outlet. Especially since I feel science can be rigid at times. It can be easy to lose myself in numbers and large datasets. However, by tying together the arts and STEM (Science, Technology, Engineering, Mathematics), there is more space for well-rounded inquiry and expressive results. STEAM, which is STEM with the Arts included, is not a new movement. Examples of STEAM are preserved in the past and is ongoing in present examples. A great example of how the sciences and arts are merged together is in the songs of Aboriginal Australians. These songs can take hours to recite fully and are full of environmental knowledge such as species types, behavior of animals, and edible plants. The combination of art and STEM is also displayed in the modern age and is shown in Leah Heiss’s work to create jewelry that helps measure cardiac data and also helps diabetics administer their insulin.  

This is one of Leah’s feature blends of biotechnology and jewelry. It measures cardiac data and is primarily beneficial for patients at risk of heart attacks. Source: Leah Heiss.

There are many ways in which the two subjects can merge together, making each other stronger and better. As a well-rounded student pursuing Environmental Science and interested dance and writing, I am comforted to know that STEAM can allow me to blend my interests. 

Intricacies of Zooplankton Species Identification

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

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

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

Caprellidae. Source: R. Norman.

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

Copepod. Source: L. Hildebrand.

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

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

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

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

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

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

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

Fieldwork experience as a GEMM Lab intern

By Anthony Howe, Astoria High School graduate 2019, GEMM Lab summer intern

Murphy’s Law says that “things will go wrong in any given situation if you give them a chance”. This statement certainly applies to research where you never really know what is going to happen when performing fieldwork. You can only try to be prepared for all of the situations. When I arrived at the Oregon State University (OSU) Field Station in Port Orford, I had no idea that it would harbor some of the best educational experiences I have ever had. I had no idea what a theodolite was, nor did I know how to kayak in the ocean, but I learned fast. When we first started being trained on using a theodolite and the program that processes the data, Pythagoras, we had some problems. The theodolite would not stay level, but just as we were learning how to work the theodolite, we also learned how to work as a team. When we finally managed to level the theodolite, which did take a few days, I began to realize the hard work of doing fieldwork. You can be prepared but there will always be something that goes wrong, and that’s okay. I have learned that mistakes happen and cannot be dwelled on. Only learned from. No one is perfect.

Fig 1. Me holding two zooplankton samples after collecting them on the kayak. Source: L. Hildebrand.

Just two days ago I was on our tandem research kayak with Mia Arvizu, the OSU Marine Studies Initiative (MSI) undergraduate intern. When we go out on the kayak, we paddle around our study area and go to GPS-marked “stations” to collect prey samples of zooplankton, test for water visibility using a Secchi disk, and send a GoPro underwater to have a better understanding of what is going on under the surface. While sampling at Station 15 in Mill Rocks I lowered the GoPro into the water using a downrigger. When the GoPro reached the bottom, I began to pull it up, only to realize it had gotten snagged in a crevice. I gave the line to which the GoPro is attached some slack and began to give Mia instructions to move to different spots to try and retrieve the GoPro out of this tight crevice. Unfortunately, I did not realize all of the lines had wrapped themselves underneath the downrigger and as soon as a swell came up, the line broke. My eyes widened as I realized what had just happened. Thankfully, I managed to grasp the last of the remaining line left connected to the GoPro and pulled it back into the kayak using my hand wrapped in a towel since the line is thin and can cut into your hands easily. Only then did I realize that neither Mia nor I had packed a knife in the event we needed to cut a line. We sat and pondered ideas of how to cut the last of the line so that I could reattach the GoPro to the downrigger. Mia came up with the idea to use a barnacle or a mussel, and it worked perfectly. We were proud of ourselves for being resourceful and using nature to our advantage. But as soon as I finished using the mussel to cut the line, Lisa’s voice came over the VHF radio that we always carry with us in the kayak that there were scissors in the First Aid Kit that is stowed in the dry hatch of the kayak. Mia and I looked at each other and could only laugh. The kayak team can be rough at times but it’s made up by the fact that we get beautiful prey samples and stunning GoPro videos of what is below the water.

Fig 2. Mia and myself paddling the kayak across “The Passage”, the approximately 1 km stretch between Mill Rocks and Tichenor Cove, our two study sites. Red Fish Rocks, which is Oregon’s first Marine Reserve, can be seen in the background. Source: L. Hildebrand.

After all of the kayak sampling is done we organize and store our gear, and then go to the lab. In the lab, one person will clean all tools and devices touched by saltwater while the other sieves all of our zooplankton samples. Each sample is individually sieved and then placed in a sample jar with its station name on it and placed into the freezer. We put them in the freezer to increase the longevity of the samples, as well as euthanizing all zooplankton so that they are easier to identify under a dissection scope. After all of that is done we take a 45-minute break before taking over the cliff team job so they can have a lunch break, as well as a rest from staring at the glare of the water all day searching for whales. 

The cliff team generally consists of two people. One person will be on the theodolite, and the other will be on the laptop. The idea is that the theodolite uses the Pythagorean Theorem to get the exact coordinates of the whale we are spotting. This allows us to track exactly where the whales are going, what they are doing, how they’re doing it, and the fashion in which they’re doing it. The fixed points will fall on a plotted map on the laptop. The other job of the person on the laptop is to take pictures when possible so we can identify the whales. For instance, there is a whale named Buttons that has been recorded during past summers in Port Orford. By using the photos we take of a whale, combined with previous data about the whale named Buttons, we can cross-reference the body color and patterns of the whale to be able to re-identify Buttons. We now know that we have seen Buttons for 4 consecutive days feeding in our study area. The camera also acts as a tool to take pictures of whales not just for identity but for rare activity. Today while on the cliff Mia and I spotted a whale in Tichenor Cove (one of our sampling sites) that breached four times! These experiences are rare and beautiful. You never think about how big a whale truly is until you see it almost completely leap out of the water – it is beautiful. 

Fig 3. The post-breach splash created by Buttons. Unfortunately we weren’t able to get a good photo from the cliff because we were too stunned by the fact that we were seeing this rare behavior. Source: GEMM Lab.

I’m sure more mistakes will be made but that’s okay. I have many more experiences to witness, and many more memories to make from this internship, as well as challenges. I couldn’t be more than happy with the team I have to share all of these learning experiences and hardships with.