As the largest animals on the planet, blue whales have massive prey requirements to meet energy demands. Despite their enormity, blue whales feed on a tiny but energy-rich prey source: krill. Furthermore, they are air-breathing mammals searching for aggregations of prey in the expansive and deep ocean, and must therefore budget breath-holding and oxygen consumption, the travel time it takes to reach prey patches at depth, the physiological constraints of diving, and the necessary recuperation time at the surface. Additionally, blue whales employ an energetically demanding foraging strategy known as lunge feeding, which is only efficient if they can locate and target dense prey aggregations that compensate for the energetic costs of diving and lunging. In our recent paper, published today in PeerJ, we examine how blue whales in New Zealand optimize their energy use through preferentially feeding on dense krill aggregations near the water’s surface.
To understand how predators such as blue whales optimize foraging strategies, knowledge of predator behavior and prey distribution is needed. In 2017, we surveyed for blue whales in New Zealand’s South Taranaki Bight region (STB, Fig. 2) while simultaneously collecting prey distribution data using an echosounder, which allowed us to identify the location, depth, and density of krill aggregations throughout the region. When blue whales were located, we observed their behavior from the research vessel, recorded their dive times, and used an unmanned aerial system (UAS; “drone”) to assess their body condition and behavior.
Much of what is known about blue whale foraging behavior and energetics comes from extensive studies off the coast of California, USA using accelerometer tags to track fine-scale kinematics (i.e., body movements) of the whales. In the California Current, the krill species targeted by blue whales are denser at depth, and therefore blue whales regularly dive to depths of 300 meters to lunge on the most energy-rich prey aggregations. However, given the reduced energetic costs of feeding closer to the surface, optimal foraging theory predicts that blue whales should only forage at depth when the energetic gain outweighs the cost. In New Zealand, we found that blue whales foraged where krill aggregations were relatively shallow and dense compared to the availability of krill across the whole study area (Fig. 3). Their dive times were quite short (~2.5 minutes, compared to ~10 minutes in California), and became even shorter in locations where foraging behavior and surface lunge feeding were observed.
Describing whale foraging behavior and prey in the surface waters has been difficult due to logistical limitations of conventional data collection methods, such as challenges inferring surface behavior from tag data and quantifying echosounder backscatter data in surface waters. To compliment these existing methods and fill the knowledge gap surrounding surface behavior, we highlight the utility of a different technological tool: UAS. By analyzing video footage of a surface lunge feeding sequence, we obtained estimates of the whale’s speed, acceleration, roll angle, and head inclination, producing a figure comparable to what is typically obtained from accelerometer tag data (Fig. 4, Fig. 5). Furthermore, the aerial perspective provided by the UAS provides an unprecedented look at predator-prey interactions between blue whales and krill. As the whale approaches the krill patch, she first observes the patch with her right eye, then turns and lines up her attack angle to engulf almost the entire prey patch through her lunge. Furthermore, we can pinpoint the moment when the krill recognize the impending danger of the oncoming predator—at a distance of 2 meters, and 0.8 seconds before the whale strikes the patch, the krill show a flee response where they leap away from the whale’s mouth (see video, below).
In this study, we demonstrate that surface waters provide important foraging opportunities and play a key role in the ecology of New Zealand blue whales. The use of UAS technology could be a valuable and complimentary tool to other technological approaches, such as tagging, to gain a comprehensive understanding of foraging behavior in whales.
To see the spectacle of a blue whale surface lunge feeding, we invite you to take a look at the video footage, below:
The publication is led by GEMM Lab Principal Investigator Dr. Leigh Torres. I led the prey data analysis portion of the study, and co-authors include our drone pilot extraordinaire Todd Chandler and UAS analysis guru Dr. Jonathan Burnett. We are grateful to all who assisted with fieldwork and data collection, including Kristin Hodge, Callum Lilley, Mike Ogle, and the crew of the R/V Star Keys (Western Workboats, Ltd.). Funding for this research was provided by The Aotearoa Foundation, The New Zealand Department of Conservation, The Marine Mammal Institute at Oregon State University, Greenpeace New Zealand, OceanCare, Kiwis Against Seabed Mining, The International Fund for Animal Welfare, and The Thorpe Foundation.
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.
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.
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.
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.
By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife,
Geospatial Ecology of Marine Megafauna Lab
We live in an interesting time. Many of us academic
scientists sit in the confines of our homes, reading scientific papers,
analyzing years-worth of data, working through a years-worth of house projects,
or simply watching Netflix. While we are confined to a much smaller area,
wildlife is not.
During this challenging situation we have unique
opportunities to study what happens when people are not outside for recreation.
All of us who feel trapped inside our homes are not only saving human lives, we
are changing ecosystems. Humans are constantly molding our ecosystems on fine
and grand scales, from xeriscaping our lawns with native, drought-resistant
plants to developing large plots of land for new homes. We manipulate nature,
for better or for worse.
So, what happens when we change our behavior? Rather than
driving, we’re gardening, instead of playing at parks, we’re playing board
games at our kitchen tables; we as a society are completely changing our
habitat-use patterns. When any top predator changes its habitat-use, switches
niches, or drastically changes its behaviors, there are top-down ecosystem
effects. When one species changes its behavior, there are major downstream
impacts on predation, foraging, diet, and habitat use. For example, when
bluegill sunfish underwent large shifts in both diet and habitat, major
predator-mediated habitat use changes in other species occurred (Mittelbach
1986). There are multiple studies describing the impacts of human-mediated
drivers on ecosystems worldwide. In coastal environments, anthropogenic
activities, specifically shipping, industry, and urban development, dramatically
change both the coastal and marine ecosystems (Mead et al. 2013).
By far the most pronounced example of how an international halt on travel can alter ecosystems comes from the tragic terrorist attacks on September 11, 2001. Prior to this current, viral pandemic, the events following 9/11 were the first time that nearly all major transit stopped in the USA—including airplanes and major shipping traffic. This halt created a unique opportunity to study some of the secondary impacts, such as a reduction in shipping traffic noise, on cetaceans. Following 9/11, there was a six decibel decrease in underwater noise that co-occurred with a decrease in stress hormones of endangered North Atlantic right whales (Rolland et al. 2012). When I first read about this study, my first thought was “leave it to scientists to make the best out of a terrible situation.” Truly, learning from nature, even in the darkest of days, is an incredible skillset. Research like this inspires me to ask questions about what changes are happening in ecosystems now because of recent events. For example, the entire port of San Diego, its beaches and bays, are closed for all recreational activity and I wonder how this reduction in traffic is similar to the post-9/11 study but on bottlenose dolphins, gray whales, and pinnipeds that are coast-associated. Are urban and suburban neighborhoods slowly becoming more rural and making space for wildlife again?
Mead, A., Griffiths, C.L., Branch, G.M.,
McQuaid, C.D., Blamey, L.K., Bolton, J.J., Anderson, R.J., Dufois, F., Rouault,
M., Froneman, P.W. and Whitfield, A.K., 2013. Human-mediated drivers of
change—impacts on coastal ecosystems and marine biota of South Africa. African
Journal of Marine Science, 35(3), pp.403-425.
Mittelbach, Gary. 1986. Predator-mediated
habitat use: some consequences for species interactions. Environ Biol
Fish16, 159–169. https://doi.org/10.1007/BF00005168
Rolland, R.M., Parks, S.E., Hunt, K.E.,
Castellote, M., Corkeron, P.J., Nowacek, D.P., Wasser, S.K. and Kraus, S.D.,
2012. Evidence that ship noise increases stress in right whales. Proceedings
of the Royal Society B: Biological Sciences, 279(1737),
I want to start my post this week with a disclaimer – I am not a virologist or an epidemiologist. My knowledge and understanding on what a virus is, how it changes and spreads, and predicting its trajectory, is very limited (though it has definitely improved in recent weeks). Nevertheless, I did not want that to stop me from shifting my focus and time currently spent reading about a certain virus in humans, to thinking about viruses in marine mammals. So, after several hours of reading papers and reports, I believe I have a good enough grasp on viruses in marine mammals to write a blog post on this topic.
To answer the question in my title – yes, marine mammals can get coronavirus! Coronaviruses have been detected in several marine mammals – mostly in captive ones (harbor seal, beluga whale, Indo-Pacific bottlenose dolphin), but it was also detected in a wild harbor seal1. It is at this point where I am going to step back from marine mammals for a moment and give a very short ‘lesson’ on viruses.
Viruses are microscopic infectious agents that replicate inside living cells of organisms. They have the ability to infect all forms of life – anything from bacteria to plants to animals to humans. Nothing is excluded. Viruses are classified similarly to how living organisms are classified. Try to think back to middle school science when your teacher used mnemonic devices like, “Kids prefer candy over fancy green salad” or “Kings play chess on fine glass surfaces”, to get you to remember the Kingdom-Phylum-Class-Order-Family-Genus-Species classification. Well, viruses have almost the same classification tree. The only difference is that instead of Kingdom at the top, viruses have a Realm. As of 2019, the International Committee on Taxonomy of Viruses (ICTV) has defined 5,560 species of viruses in over 1,000 genera and 150 families. Different species of virus are classified based on their genomic material and key elements of structure and replication. That is as far as I am going to go with virus background – back to marine mammals!
So, yes, coronaviruses have been detected in marine mammals before. But, no, they were not the same species of coronavirus that is currently spreading across the globe in humans. Coronavirus, or Coronaviridae, is a family of viruses that contains around 40 species. However, coronavirus is not the family that has plagued marine mammals the most since research on marine mammal diseases began. The infectious disease that I have found to be the most common and recurring in marine mammals is morbillivirus and I will therefore focus on that virus for the rest of this post.
Morbillivirus is a genus of viruses in the family Paramyxoviridae and hosts of this genus include humans, dogs, cats, cattle, seals, and cetaceans. There are seven described species of morbillivirus, three of which have been detected in marine mammals, namely canine distemper virus (CDV), cetacean morbillivirus (CeMV), and phocine distemper virus (PDV). The earliest, traceable case of morbillivirus in a marine mammal occurred in 1982 in bottlenose dolphins in the Indian and Banana Rivers in Florida2. This case was followed by hundreds of others in subsequent years all along the Atlantic U.S. coast and resulted in the first Unusual Mortality Event (UME; 1987-1988) that was concluded to have been caused by morbillivirus (Table 1).
Table 1. Unusual Mortality Events (UMEs) of marine mammals in the U.S. where the cause was determined to be or is suspected to be morbillivirus. Data obtained from NOAA Fisheries.
Interestingly, at the same time as this 1980s morbillivirus in the US, the first documented marine mammal morbillivirus epidemic occurred in Europe in the North Sea. This outbreak led to the death of more than 23,000 harbor seals, which accounted for roughly 60% of all North Sea harbor seals at the time3. The virus that was isolated from the stranded seals in the North Sea was similar to CDV but not exactly the same. Resultantly, it was described as a new species of morbillivirus and it was therefore the first outbreak of PDV. Another interesting thing about this case in Europe is that while the infection originated at the Danish island of Anholt, new centers of infection appeared quite far from this first epicenter within a relatively short amount of time (~3-4 weeks) from the initial outbreak, some as far as the Irish Sea (~2,000 km away; Figure 1). Harbor seals typically have a limited home range and do not travel such distances, leading scientists to speculate that grey seals may have been a carrier of the virus and transported it from Anholt to haul-out sites in the Irish Sea. Mixed species haul-out sites of harbor and grey seals are very common across the North Sea and is the most logical explanation for the rapid spread of the virus across such distances.
Harbor seals seem to be the most susceptible to PDV based on all documented cases of PDV outbreaks, however the reason for this pattern remains unknown1. While CDV has only been detected in Baikal and Caspian seals, CeMV has occurred in a larger number of cetaceans including harbor porpoises, striped, bottlenose, Guiana and Fraser’s dolphins, pilot whales, and a minke whale. This list is not extensive as morbillivirus has been found in 23 of the 90 cetacean species. In fact, it has been suggested that CeMV should be divided into more than one species as the morbilliviruses detected in the Northern Hemisphere show significant divergence from those found in the Southern Hemisphere.
Transmission is believed to mostly occur horizontally, meaning that the morbillivirus is passed from one individual to another. This transfer happens when one individual inhales the aerosolized virus breathed out by an infected individual. This is likely the reason why odontocete and pinniped groups are most affected due to their social group behavior and/or high density of individuals within groups4. However, vertical transmission has also been suggested as a possible transmission route as morbillivirus antigens have been detected in the mammary glands of bottlenose dolphins along the U.S. Atlantic Coast5 and striped dolphins in the Mediterranean Sea affected by CeMV6. Thus, it has been postulated that CeMV infected females could transmit the infection to their fetuses and neonates in utero, as well as to their calves during lactation.
Morbilliviruses mostly affect the respiratory and neurologic systems in marine mammals, wherein affected individuals may display ocular and naval discharge, erratic swimming, respiratory distress, raised body temperature, and/or cachexia (weakness and wasting away of the body due to severe illness). However, most diagnoses occur post-mortem. Some individuals may survive the initial acute infection of morbillivirus, yet the general weakening of the immune system will make individuals more susceptible to other infections, diseases, and disturbance events7.
It is impossible to know whether marine mammals take precautions when a virus has taken grip of a group or population, or if marine mammals even have an awareness of such things occurring. There obviously is no such thing as an emergency room or a doctor in the lives of marine mammals, but do individuals perhaps demonstrate social distancing by increasing the space between each other when traveling in groups? Do groups decrease their traveling distances or foraging ranges to isolate themselves in a smaller area? Are sick individuals ‘quarantined’ by being forced out of a group? These are just some of the questions I have been asking myself while working from home (day 16 for me now). I hope you are all staying safe and healthy and have enjoyed distracting yourselves from thinking about one virus to learn about another in a different kind of mammal.
1 Bossart, G. D., and P. J. Duignan. 2018. Emerging viruses in marine mammals. CAB Reviews 13(52): doi:10.1079/PAVSNNR201913052.
2 Duignan, P. J., C. House, D. K. Odell, R. S. Wells, L. J. Hansen, M. T. Walsh, D. J. St. Aubin, B. K. Rima, and J. R. Geraci. 1996. Morbillivirus infection in bottlenose dolphins: evidence for recurrent epizootics in the western Atlantic and Gulf of Mexico. Marine Mammal Science 12(4):499-515.
3 Härkönen, T., R. Dietz, P. Reijnders, J. Teilmann, K. Harding, A. Hall, S. Brasseur, U. Siebert, S. J. Goodman, P. D. Jepson, T. D. Rasmussen, and P. Thompson. 2006. A review of the 1988 and 2002 phocine distemper virus epidemics in European harbor seals. Diseases of Aquatic Organisms 68:115-130.
4 Van Bressem, M-F., P. J. Duignan, A. Banyard, M. Barbieri, K. M. Colegrove, S. De Guise, G. Di Guardo, A. Dobson, M. Domingo, D. Fauquier, A. Fernandez, T. Goldstein, B. Grenfell, K. R. Groch, F. Gulland, B. A. Jensen, P. D. Jepson, A. Hall, T. Kuiken, S. Mazzariol, S. E. Morris, O. Nielsen, J. A. Raga, T. K. Rowles, J. Saliki, E. Sierra, N. Stephens, B. Stone, I. Tomo, J. Wang, T. Waltzek, and J. F. X. Wellehan. 2014. Cetacean morbillivirus: current knowledge and future directions. Viruses 6(12):5145-5181.
5 Schulman, F. Y., T. P. Lipscomb, D. Moffett, A. E. Krafft, J. H. Lichy, M. M. Tsai, J. K. Taubenberger, and S. Kennedy. 1997. Histologic, immunohistochemical, and polymerase chain reaction studies of bottlenose dolphins from the 1987-1988 United States Atlantic coast epizootic. Veterinary Pathology 34(4):288-295.
6 Domingo, M., J. Visa, M. Pumarola, A. J. Marco, L. Ferrer, R. Rabanal, and S. Kennedy. 1992. Pathologic and immunocytochemical studies of morbillivirus infection in striped dolphins (Stenella coeruleoalba). Veterinary Pathology 29(1):1-10.
7 Wellehan, J., and G. Cortes-Hinojosa. 2019. Marine Mammal Viruses. Fowler’s Zoo and Wild Animal Medicine Current Therapy 9:597-602.
By Leila Lemos, PhD (no more PhD candidate!), OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
Did you read the byline above? Yes! I finally became a PhD last week and I will not be signing as a PhD candidate anymore. The past few months have been really challenging as I wrapped up my PhD, sent my written dissertation to my committee and synthesized all of the results of my four different chapters into a single presentation. On top of that I had family members visiting me for my defense in the middle of this whole coronavirus chaos.
For my PhD defense, everybody was encouraged to watch it online to help contain the virus spread. There were around 10 people in the room seated with at least two empty chairs between each other. I usually get a bit nervous with full rooms and public speaking, so that was a plus for me. However, I was delighted to hear that there were 61 people watching my defense online (Fig. 01), and I was thrilled to share the results of almost five years of research on this amazing project about gray whale body condition, hormones, and associations with ambient noise.
One of the questions I got from one of my committee members, Dr. Kathleen Hunt, in the closed-door session of my defense that actually motivated me to write this blog was: “what do I expect would happen to the whales during this coronavirus situation”. That made me think of the Rolland et al. (2012) article immediately, which looked into North Atlantic Right Whale (NARWs) cortisol responses to decreased ship traffic and ambient noise after the 9/11 event. Those authors found that NARWs decreased their overall cortisol (i.e., stress-related hormone) concentrations, supporting the theory that noise caused by ship traffic affects the physiology of these animals. Thus, I would expect the same to occur with gray whales in the Pacific northwest. If vessel activities in general are reduced, we can expect a quieter and cleaner environment, which would allow the animals and overall nature to “breath”.
In fact, multiple news stories over the last days have pointed out declines in air pollution (Fig. 02) and cleaner waters with no boat traffic (Fig. 03), which demonstrate how poorly we treat the environment during “normal” times.
It is impressive to see how fast nature can take back what we, humans, have been taking from it. In addition, there were lots of photos that went viral on Twitter of animals returning to urban areas, including fish swarms, swans, dolphins, and wild boars. Even though there are reports saying that the apparition of some of these animals is fake (Daly 2020), it definitely can make us all reflect on how dense tourism, boat traffic, and overall anthropogenic activities impacts and changes the environment. Perhaps once this coronavirus scare is over people may act in ways that better balance these activities with also allowing our planet to keep breathing.
Here you can see some of these tweets:
Here's an unexpected side effect of the pandemic – the water's flowing through the canals of Venice is clear for the first time in forever. The fish are visible, the swans returned. pic.twitter.com/2egMGhJs7f
The Guardian also added a video showing some of these cases:
Source: Guardian News (2020).
In a near future, it will also be a great moment for researchers to evaluate potential shifts in ecosystem pollution, flora, and evaluate physiological responses in bioindicator species to inform management and conservation efforts, setting up potential thresholds for these activities. As I mentioned before, I worked with gray whale body condition, hormone quantification, and associations with ambient noise in my PhD project. I explored an association between cortisol levels and ambient noise, and now, with a reduction in overall vessel traffic, would be an ideal moment to see if cortisol levels would decrease in this population. The problem is that we are not able to leave our houses for now to do research. But maybe other variables can be evaluated once this chaos passes. Maybe it will be reflected in individual body condition and reproductive rates, maybe we will see fewer signs of fisheries interactions, or maybe we just need to be creative and think of other possible ways.
Efforts to identify these potential changes and setting up thresholds for these activities may aid in building a planet that will be in equilibrium, and maybe declines in air pollution, and clearer waters will be more common and the apparition of species in urban areas will not be fake news.
Newburger, E. 2020. Air pollution falls as coronavirus slows travel, but scientists warn of longer-term threat to climate change progress. CNBC. Accessed on 03/23/2020 at https://www.cnbc. com/2020/03/21/air-pollution-falls-as-coronavirus-slows-travel-but-it-forms-a-new-threat.html
Rolland, R. M., S. E. Parks, K. E. Hunt, M. Castellote, P. J. Corkeron, D. P. Nowacek, S. K. Wasser, and S. D. Kraus. 2012. Evidence that ship noise increases stress in right whales Proceedings of the Royal Society B 279:2363–2368.
1Masters Student in Wildlife Science, Geospatial
Ecology of Marine Megafauna Lab
2Masters Student in Wildlife Science, Cetacean
Conservation and Genomics Laboratory
In a departure from my typical science-focused blog, this week I thought I would share more about myself. This week I was inspired by International’s Woman’s Day and, with some reflection on the last eight months as a graduate student, I decided to look back on the role that coding has played in my life. We hear about how much coding can be empowering but I thought it might be cool to talk about my personal experience of feeling empowered by coding. I’ve also invited a fellow grad student in the Marine Mammal Institute, Karen Lohman, to co-author this post. We’re going to briefly talk about our experience with coding and then finish with advice for getting started with coding and coding for data analysis.
I’ve only been coding for a little over two and a half years. In summer 2017 I did an NSF REU (Research Experience for Undergraduates) at Bigelow Laboratory for Ocean Sciences and for my project I taught myself python (with the support of a post-doc) for a data analysis project. During those 10 weeks, I coded all day, every workday. From that experience, I not only acquired the hard skill of programming, but I gained a good amount of confidence in myself, and here’s why: For the first three years of my undergraduate career coding was a daunting skill that I knew I would eventually need but did not know where to start. So, I essentially ended up learning by jumping off the deep end. I found the immersion experience to be the most effective learning method for me. With coding, you find out if you got something right (or wrong) almost instantaneously. I’ve found that this is a double-edged sword. It means that you can easily have days where everything goes wrong. But, the feeling when it finally works is what I think of when I hear the term empowerment. I’m not quite sure how to put it into words, but it’s a combination of independence, confidence, and success.
Aside from learning the fundamentals, I finished that summer with confidence in my ability to teach myself not just new coding skills, but other skills as well. I think that feeling confident in my ability to learn something new has been the most helpful aspect to allow me to hit the ground running in grad school and also keeping the ‘imposter syndrome’ at bay (most of the time).
Clara’s Favorite Command: pd.groupby (python) – Say you have a column of measurements and a second column with the field site of each location. If you wanted the mean of the measurement per each location, you could use groupby to get this. It would look like this: dataframe.groupby(‘Location’)[‘Measurement’].mean().reset_index()
I’m quite new to coding, but once I
started learning I was completely enchanted! I was first introduced to coding
while working as a field assistant for a PhD student (a true R wizard who has
since developed deep learning computer vision packages for automated camera
trap image analysis) in the cloud forest of the Ecuadorian Andes. This remote
jungle was where I first saw how useful coding can be for data management and
analysis. It was a strange juxtaposition between being fully immersed in nature
for remote field work and learning to think along the lines of coding syntax.
It wasn’t the typical introduction to R most people have, but it was an
effective hook. We were able to produce preliminary figures and analysis as we
collected data, which made a tough field season more rewarding. Coding gave us
instant results and motivation.
committed to fully learning how to code during my first year of graduate
school. I first learned linux/command line and python, and then I started
working in R that following summer. My graduate research uses population
genetics/genomics to better understand the migratory connections of humpback
whales. This research means I spend a great deal of time working to develop
bioinformatics and big data skills, an essential skill for this area of
research and a goal for my career. For me, coding is a skill that only returns
what you put in; you can learn to code quite quickly, if you devote the time.
After a year of intense learning and struggle, I am writing better code every
grad school research progress can be nebulous, but for me coding has become a
concrete way to measure success. If my code ran, I have a win for the week. If
not, then I have a clear place to start working the next day. These “tiny wins”
are adding up, and coding has become a huge confidence boost.
Karen’s Favorite Command: grep (linux) – Searches for a string pattern and prints all lines containing a match to the screen. Grep has a variety of flags making this a versatile command I use every time I’m working in linux.
Be kind to yourself, think of it as a foreign language. It takes a long time and a lot of practice.
Once you know the fundamental concepts in any language, learning another will be easier (we promise!).
Ask for help! The chances that you have run into a unique error are quite small, someone out there has already solved your problem, whether it’s a lab mate or another researcher you find on Google!
1. Set yourself up
for success by formatting your datasheets properly
Instead of making your spreadsheet easy to read, try and think about how you want to use the data in the analysis.
Avoid formatting (merged cells, wrap text) and spaces in headers
Try to think ahead when formatting your spreadsheet
Maybe chat with someone who has experience and get their advice!
2. Start with a plan, start on paper
This low-tech solution saves countless hours of code confusion. It can be especially helpful when manipulating large data frames or in multistep analysis. Drawing out the structure of your data and checking it frequently in your code (with ‘head’ in R/linux) after manipulation can keep you on track. It is easy to code yourself into circles when you don’t have a clear understanding of what you’re trying to do in each step. Or worse, you could end up with code that runs, but doesn’t conduct the analysis you intended, or needed to do.
3. Good organization and habits will get you far
There is an excellent blog by Nice R Code on project organization and file structure. I highly recommend reading and implementing their self-contained scripting suggestions. The further you get into your data analysis the more object, directory, and function names you have to remember. Develop a naming scheme that makes sense for your project (i.e. flexible, number based, etc.) and stick with it. Temporary object names in functions or code blocks can be a good way to clarify what is the code-in-progress or the code result.
4. Annotate. Then annotate some more.
Make comments in your code so you can remember what each section or line is for. This makes debugging much easier! Annotation is also a good way to stay on track as you code, because you’ll be describing the goal of every line (remember tip 1?). If you’re following a tutorial (or STACKoverflow answer), copy the web address into your annotation so you can find it later. At the end of a coding session, make a quick note of your thought process so it’s easier to pick up when you come back. It’s also a good habit to add some ‘metadata’ details to the top of your script describing what the script is intended for, what the input files are, the expected outputs, and any other pertinent details for that script. Your future self will thank you!
5. Get with git/github already
Github is a great way to manage version control. Remember how life-changing the advent of dropbox was? This is like that, but for code! It’s also become a great open-source repository for newly developed code and packages. In addition to backing up and storing your code, GitHub has become a ‘coding CV’ that other researchers look to when hiring.
Wondering how to get started with GitHub? Check out this guide: https://guides.github.com/activities/hello-world/
Looking for a good text/code editor? Check out atom (https://atom.io/), you can push your edits straight to git from here.
6. You don’t have to learn everything, but you should probably learn the R Tidyverse ASAP
Tidyverse is a collection of data manipulation packages that make data wrangling a breeze. It also includes ggplot, an incredibly versatile data visualization package. For python users hesitant to start working in R, Tidyverse is a great place to start. The syntax will feel more familiar to python, and it has wonderful documentation online. It’s also similar to the awk/sed tools from linux, as dplyr removes any need to write loops. Loops in any language are awful, learn how to do them, and then how to avoid them.
Break your code out into blocks that can be run as functions! This allows easier repetition of data analysis, in a more readable format. If you need to call your functions across multiple scripts, put them all into one ‘function.R’ script and source them in your working scripts. This approach ensures that all the scripts can access the same function, without copy and pasting it into multiple scripts. Then if you edit the function, it is changed in one place and passed to all dependent scripts.
8. Don’t take error messages personally
Repeat after me: Everyone googles for every other line of code, everyone forgets the command some (….er every) time.
Debugging is a lifestyle, not a task item.
One way to make it less painful is to keep a list of fixes that you find yourself needing multiple times. And ask for help when you’re stuck!
Know that you’re supposed to google but not sure what?
start by copying and pasting the error message
When I started it was hard to know how to phrase what I wanted, these might be some common terms
A dataframe is the coding equivalent of a spreadsheet/table
Do you want to combine two dataframes side by side? That’s a merge
Do you want to stack one dataframe on top of another? That’s concatenating
Do you want to get the average (or some other statistic) of values in a column that are all from one group or category? Check out group by or aggregate
A loop is when you loop through every value in a column or list and do something with it (use it in an equation, use it in an if/else statement, etc).
Favorite Coding Resource (other than github….)
This is great ‘one stop googling’ for coding in almost any language! I frequently switch between coding languages, and as a result almost always have this open to check syntax.
This is a really good resource for getting an introduction to R
We hope that our stories and advice have been helpful! Like many skills, you tend to only see people once they have made it over the learning curve. But as you’ve read Karen and I both started recently and felt intimidated at the beginning. So, be patient, be kind to yourself, believe in yourself, and good luck!
In recent years, anomalously warm ocean temperatures known as “marine heatwaves” have sparked considerable attention and concern around the world. Marine heatwaves (MHW) occur when seawater temperatures rise above a seasonal threshold (greater than the 90th percentile) for five consecutive days or longer (Hobday et al. 2016; Fig. 1). With global ocean temperatures continuing to rise, we are likely to see more frequent and more intense MHW conditions in the future. Indeed, the global prevalence of MHWs is increasing, with a 34% rise in frequency, a 17% increase in duration, and a 54% increase in annual MHW days globally since 1925 (Oliver et al. 2018). With sustained anomalously warm water temperatures come a range of ecological, sociological, and economic consequences. These impacts include changes in water column structure, primary production, species composition, marine life distribution and health, and fisheries management including closures and quota changes (Oliver et al. 2018).
The notorious “warm blob” was an MHW event that plagued the northeast Pacific Ocean from 2014-2016. Some of the most notable consequences of this MHW were extremely high levels of domoic acid, extreme changes in the biodiversity of pelagic species, and an unprecedented delay in the opening of the Dungeness crab fishery, which is an important and lucrative fishery for the West Coast of the United States (Santora et al. 2020). The “warm blob” directly impacted the California Current ecosystem, which is typically a highly productive coastal area driven by seasonal upwelling. Yet, as a consequence of the 2014-2016 MHW, upwelling habitat was compressed and constricted to the coastal boundary, resulting in a contraction in available habitat for humpback whales and a shift in their prey (Santora et al. 2020; Fig. 2).
Shifting to an example from another part of the world, the austral summer of 2015-2016 coincided with a strong regional MHW in the Tasman Sea between Australia and New Zealand, which lasted for 251 days and had a maximum intensity of 2.9°C above the climatological average (Oliver et al. 2017). Subsequently, the conditions were linked to a significant shift in zooplankton species composition and abundance in Australia (Evans et al. 2020). Ocean warming, including MHWs, also appears to decrease primary production in the Tasman Sea and large portions of New Zealand’s marine ecosystem (Chiswell & Sutton 2020). In New Zealand’s South Taranaki Bight region, where we study the ecology of blue whales, we observed a shift in blue whale distribution in the MWH conditions of February 2016 relative to more typical ocean conditions in 2014 and 2017 (Fig. 3). The first chapter of my dissertation includes a detailed analysis of the impacts of the 2016 MHW on New Zealand oceanography, krill, and blue whales, documenting how the warm, stratified water column of 2016 led to consequences across multiple trophic levels, from phytoplankton, to zooplankton, to whales.
The response of marine mammals is tightly linked to shifts in their environment and prey (Silber et al. 2017). With MHWs and changing ocean conditions, there will likely be “winners” and “losers” among marine predators including large whales. Blue whales are highly selective krill specialists (Nickels et al. 2019), whereas other species of whales, such as humpback whales, have evolved flexible feeding tactics that allow them to switch target prey species when needed (Cade et al. 2020). In California, humpback whales have been shown to switch their primary prey from krill to fish during warm years (Fossette et al. 2017, Santora et al. 2020). By contrast, blue whales shift their distribution in response to changing krill availability during warm years (Fossette et al. 2017), however this strategy comes with increased risk and energetic cost associated with searching for prey in new areas. Furthermore, in instances when a prey resource such as krill becomes increasingly scarce for a multi-year period (Santora et al. 2020), krill specialist predators such as blue whales are at a considerable disadvantage. It is also important to acknowledge that although the humpbacks in California may at first seem to have a winning strategy for adaptation by switching their food source, this tactic may come with unforeseen consequences. Their distribution overlapped substantially with Dungeness crab fishing gear during MHW conditions in the warm blob years, resulting in record numbers of entanglements that may have population-level repercussions (Santora et al. 2020).
While this is certainly not the most light-hearted blog
topic, I believe it is an important one. As warming ocean temperatures
contribute to the increase in frequency, intensity, and duration of extreme
conditions such as MHW events, it is paramount that we understand their impacts
and take informed management actions to mitigate consequences, such as lethal
entanglements as a result of compressed whale habitat. But perhaps more
importantly, even as we do our best to manage consequences, it is critical that
we as individuals realize the role we have to play in reducing the root cause
of warming oceans, by being conscious consumers and being mindful of the impact
our actions have on the climate.
Cade DE, Carey N, Domenici P, Potvin J, Goldbogen JA (2020) Predator-informed looming stimulus experiments reveal how large filter feeding whales capture highly maneuverable forage fish. Proc Natl Acad Sci USA.
Chiswell SM, Sutton PJH
(2020) Relationships between long-term ocean warming, marine heat waves and
primary production in the New Zealand region. New Zeal J Mar Freshw Res.
Evans R, Lea MA, Hindell
MA, Swadling KM (2020) Significant shifts in coastal zooplankton populations
through the 2015/16 Tasman Sea marine heatwave. Estuar Coast Shelf Sci.
Fossette S, Abrahms B,
Hazen EL, Bograd SJ, Zilliacus KM, Calambokidis J, Burrows JA, Goldbogen JA,
Harvey JT, Marinovic B, Tershy B, Croll DA (2017) Resource partitioning
facilitates coexistence in sympatric cetaceans in the California Current. Ecol
Hobday AJ, Alexander L
V., Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT,
Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Sen Gupta A, Wernberg T
(2016) A hierarchical approach to defining marine heatwaves. Prog Oceanogr.
Nickels CF, Sala LM,
Ohman MD (2019) The euphausiid prey field for blue whales around a steep
bathymetric feature in the southern California current system. Limnol Oceanogr.
Oliver ECJ, Benthuysen
JA, Bindoff NL, Hobday AJ, Holbrook NJ, Mundy CN, Perkins-Kirkpatrick SE (2017)
The unprecedented 2015/16 Tasman Sea marine heatwave. Nat Commun.
Oliver ECJ, Donat MG,
Burrows MT, Moore PJ, Smale DA, Alexander L V., Benthuysen JA, Feng M, Sen
Gupta A, Hobday AJ, Holbrook NJ, Perkins-Kirkpatrick SE, Scannell HA, Straub
SC, Wernberg T (2018) Longer and more frequent marine heatwaves over the past
century. Nat Commun.
Santora JA, Mantua NJ,
Schroeder ID, Field JC, Hazen EL, Bograd SJ, Sydeman WJ, Wells BK, Calambokidis
J, Saez L, Lawson D, Forney KA (2020) Habitat compression and ecosystem shifts
as potential links between marine heatwave and record whale entanglements. Nat
Silber GK, Lettrich MD,
Thomas PO, Baker JD, Baumgartner M, Becker EA, Boveng P, Dick DM, Fiechter J,
Forcada J, Forney KA, Griffis RB, Hare JA, Hobday AJ, Howell D, Laidre KL,
Mantua N, Quakenbush L, Santora JA, Stafford KM, Spencer P, Stock C, Sydeman W,
Van Houtan K, Waples RS (2017) Projecting marine mammal distribution in a
changing climate. Front Mar Sci.
By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
Human-wildlife interactions have occurred since people first inhabited the Earth. However, today, when describing human-wildlife interactions specifically in relation to dolphins, frequently we hear about ‘conflicts’. Interactions between fisheries and dolphins that lead to bycatch or depredation (stealing bait/catching from gear) are particularly common. But, symbiotic relationships with dolphin species and certain human groups can also be mutualistic, with both groups benefitting. These symbiotic relationships have been around for hundreds, if not thousands of years.
In eastern Australia, cooperative fishing interactions occur
between Aboriginal Australians and dolphins—both bottlenose dolphins and orcas.
In Burleigh Heads National Park, Queensland, AUS, the dolphins are thought to
help the local indigenous Kombemerri (saltwater) people hunt for fish. Indigenous
stories recall men wading into the water with their spears and nets. Then, many
of the men would hit the surface waters to make noises with the splashes.
Underwater, this sound was amplified and then the dolphins would begin chasing
the fish toward the men and their nets (Neil 2002). Aboriginal Australians,
especially those in eastern Australia have an emotional and spiritual
connection to both dolphins and orcas. There are widespread accounts of cooperation
between indigenous people and small cetaceans on the eastern Australian
coastline, which create both context and precedent for the economic and
emotional objectives to contemporary human-dolphin interactions such as dolphin
provisioning (Neil 2002).
In the coasts off of Laguna, Brazil, bottlenose dolphins and local fishermen cooperatively fish while tourists gather to watch. Previously, PhD candidate Leila Lemos wrote about these interactions in a blog post. Like many groups of socializing dolphins, these dolphins have a unique whistle to recognize each other. The waters surrounding Laguna, Brazil are murky, turbid and dark green to the point where the fisherman cannot see any of the fish in the water. As the fishermen wade into the murky waters, bottlenose dolphins chase shoals of mullet toward the shore. Then the dolphins tail slap or abruptly dive, “signaling” the fishermen to cast their nets. Research has shown that when the fishermen “work with” the dolphins, both the dolphins and the people catch more, larger fish (Roman 2013). One fisherman claims it is not worth fishing unless the dolphins are around (Roman 2013). Here, the fishermen know the dolphins based on their markings. They know which dolphins participate in the different parts of hunting as well—which dolphin initiates the tail slap, which dolphin usually circles the fish, and which drive the fish towards the coastline. After the dolphins round up and chase the fish for the fishermen and themselves, there is no “reward” from the fishermen for the dolphins—no fish tossed their way. Scientists also found there is a difference in whistle structure between cooperative and non-cooperative dolphin groups (Preston 2017).
Along most coastlines worldwide, humans and dolphins are
competitors. Dolphins are seen as thieves who steal fish out of nets, or get
caught in their gear and ruin fishing opportunities. Thus, dolphins are often unwelcome
near fishing communities. Such negative interactions sometimes lead to
human-caused fatalities of dolphin from gunshots or stabbings, thought to be
from angry fishermen. Yet, in this same
world, fishermen thank the dolphins for bringing their catch to them. Clearly,
both humans and dolphins share high intelligence levels and skills in fishing.
If it is a matter of two minds are better than one, then I think indigenous
communities figured this equation out first: working with the dolphins, and not
against, can better feed their people.
(2002). Cooperative fishing interactions between Aboriginal Australians and
dolphins in eastern Australia. Anthrozoos: A Multidisciplinary Journal of The
Interactions of People & Animals. 15. 10.2752/089279302786992694.
“Dolphins That Work with Humans to Catch Fish Have Unique Accent.” New
Scientist, 2 Oct. 2017,
Roman, Joe. “Fishing with
Dolphins: An astonishing cooperative venture in which every species wins but
the fish.” Slate Magazine, 31 Jan. 2013,
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.
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.
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).
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.
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 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.
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.
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.
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.
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.
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
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
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.
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):
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.