How we plan to follow whales

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

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

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

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

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

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

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

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

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

Figure 3. Screenshot of BORIS layout.

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

References

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

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

Torres, Leigh G., Sharon L. Nieukirk, Leila Lemos, and Todd E. Chandler. 2018. “Drone up! Quantifying Whale Behavior from a New Perspective Improves Observational Capacity.” Frontiers in Marine Science 5 (SEP). https://doi.org/10.3389/fmars.2018.00319.

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.

Coding stories, tips, and tricks

Clara Bird1 and Karen Lohman2

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.

Our Stories

Clara

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()

Karen

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.

I 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 day.

In 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.

Advice

Getting Started

  • 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!

Coding Tips

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.

Figure 1. An example of project based workflow directory organization from Nice R Code (https://nicercode.github.io/blog/2013-04-05-projects/ )

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!

Figure 2. Example code with comments explaining the purpose of each line.

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.

7. Functions!

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!

9. Troubleshooting

  • 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….)

  • Learnxinyminutes.com
    • 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.
  • https://swirlstats.com/
    • This is a really good resource for getting an introduction to R

Parting Thoughts

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!

The complex relationship between behavior and body condition

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

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

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

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

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

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

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


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

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

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

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

References

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

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

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

What are the ecological impacts of gray whale benthic feeding?

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

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

How do gray whales feed?

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

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

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

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

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

The impacts of benthic feeding

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

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

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

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

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

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

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

References

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

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

Torres, Leigh G., Sharon L. Nieukirk, Leila Lemos, and Todd E. Chandler. 2018. “Drone up! Quantifying Whale Behavior from a New Perspective Improves Observational Capacity.” Frontiers in Marine Science 5 (SEP). https://doi.org/10.3389/fmars.2018.00319.

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

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

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

GEMM Lab 2019: A Year in the Life

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

Another year has come and gone, and with the final days of 2019 upon us, it is fulfilling to look back and summarize all of the achievements in the GEMM Lab this year. So, snuggle up with your favorite holiday drink and enjoy our recap of 2019!

We wrapped up two intense but rewarding gray whale field seasons this summer. Our project investigating the health of Pacific Coast Feeding Group (PCFG) gray whales through fecal hormone and body condition sampling in the context of ocean noise went into its fourth year, while the Port Orford project where we track whales and prey at a very fine-scale celebrated its wood anniversary (five years!). The dedication and hard work of lots of people to help us collect our data meant that we were able to add a considerable amount of samples to our growing gray whale datasets. Our trusty red RHIB Ruby zipped around the Pacific and enabled us to collect 58 fecal samples, fly the drone 102 times, undertake 105 GoPro drops and record 141 gray whale sightings. Our Newport crew was a mix of full-time GEMMers (Leigh, Todd, Dawn, Leila, Clara, and myself) as well as part-time summer GEMMers (Ale, Sharon, and Cassy). Further south in Port Orford, my team of undergraduate and high school students and I had an interesting field season. We only encountered four different individuals (Buttons, Glacier, Smudge, and Primavera), however saw them repeatedly throughout the month of August, resulting in as many as 15 tracklines for one individual. Furthermore, we collected 249 GoPro drops and 248 zooplankton net samples.  

The GEMM Lab’s fieldwork was not just restricted to gray whales. After last year’s successes aboard the NOAA ship Bell M. Shimada, Alexa and Dawn both boarded the ship again this year as marine mammal observers for the May and September cruises, respectively. They spied humpback, blue, sperm, and fin whales, as well as many dolphins and seabirds, adding to the GEMM Lab’s growing database of megafauna distribution off the Oregon coast. 

After winning the prestigious L’Oréal-UNESCO For Women in Science fellowship and the inaugural Louis Herman Scholarship, GEMM Lab grad Solène Derville lead her first research cruise aboard the French R/V Alis. She and her team conducted line transect surveys and micronekton/oceanographic sampling over several seamounts to try to solve the mystery of why humpbacks hang out there. We are also very excited to announce that Solène will be returning to the GEMM Lab as a post-doc in 2020! She will be creating distribution models of whales off the coast of Oregon with the data collected by Leigh during helicopter flights with the US Coast Guard. The primary aim of this work is to identify potential whale hotspots in an effort to avoid spatial overlap with fisheries gear and reduce entanglement risk.

Switching the focus from marine mammals to seabirds, Rachael has had an extremely busy year of field work all across the globe. She island-hopped from Midway (Hawaiian Northwest island) to the Falkland Islands in the first half of the year, and is currently overwintering on South Georgia, where she will be until end of February. Rachael is tracking albatross at all three locations by tagging individual birds to understand movements relative to fishing vessels and flight energetics. 

Besides several field efforts, the GEMM Lab was also busy disseminating our research and findings to various audiences. Our conferences kicked off in late February when Leigh and Rachael both flew to Kauai to present at the Pacific Seabird Group’s 46th Annual Meeting. In the spring, Leila, Dawn, Alexa, Dom, and myself drove to Seattle where the University of Washington hosted the Northwest Student Society of Marine Mammalogy chapter meeting and we all gave talks. Additionally, the Fisheries & Wildlife grad students in the lab also presented at the department’s annual Research Advances in Fisheries, Wildlife, and Ecology conference. Later in the year, Dom and I attended the State of the Coast conference where Dom was invited to participate in a panel about the holistic approaches to management in the nearshore while I presented a poster on preliminary findings of my Master’s thesis. Most recently, the entire GEMM Lab (bar Rachael) flew to Barcelona to present at the World Marine Mammal Conference (WMMC). 

Our science communication and outreach efforts were not just restricted to conferences though. Over the course of this year, the GEMM Lab supervised a total of 10 undergraduate and high school interns that assisted in a variety of ways (field and/or lab work, data analyses, independent projects) on a number of projects going on in the lab. Leigh and Dawn boarded the R/V Oceanus in the fall to co-lead a STEM research cruise aimed at providing high school students and teachers hands-on marine research. Dawn and I were guests on Inspiration Dissemination, a live radio show run by graduate students about graduate research going on at OSU. Our weekly blog, now in its fifth year, reached its highest viewership with a total of 14,814 views this year!

The GEMMers were once again prolific writers too! The 13 new publications in 10 scientific journals include contributions from Leigh (7), Rachael (6), Solène (2), Dawn (2), and Leila (1). Scroll down to the end of the post to see the list.

Academic milestones were also reached by several of us. Most notably and recently, Dom successfully defended his Master’s thesis this past week – congratulations Dom!! Unsurprisingly, he already has a job lined up starting in January as a Science Officer with the California Ocean Science Trust. Dom is the 6th GEMM Lab graduate, which after just five years of the GEMM Lab existing is a huge testament to Leigh as an advisor. Leila, who is in the 4th year of her PhD, anticipates finishing this coming March. We also had three successful research reviews – I met with my committee in late March to discuss my Master’s proposal, while Alexa and Dawn met with their committees in the summer to review their PhD proposals. All three reviews were fruitful and successful. And we want to highlight the success of a GEMM Lab grad, Florence Sullivan, who started a job in Maui with the Pacific Whale Foundation in September as a Research Analyst.

Leigh was recognized for her expertise in gray whale ecology and was appointed to the IUCN Western Gray Whale Advisory Panel (WGWAP). The western gray whales are a critically endangered population. At one point in the 1960s, the population was so scarce that they were believed to have been extinct. While this concern did not prove to be the case, the population still is not doing well, which is why the IUCN formed WGWAP to provide advice on the conservation of the western gray whales. Leigh was appointed to the panel this year and traveled to Switzerland and Russia for meetings. 

Clara aboard Ruby on her first day of gray whale field work in Oregon. Photo: Leigh Torres

We are excited about a new addition to the lab. Clara Bird started her MS in Wildlife Science in the Department of Fisheries & Wildlife this fall. She jumped straight into field work when she came in early September and got a taste of the Pacific. Clara joins us from the Duke University where she did her undergraduate degree and worked for the past year in their Marine Robotics and Remote Sensing Lab. Clara is digging into the gray whale drone footage collected over the last four field seasons and scrutinize them from a behavioral point of view. 

If you are reading this post, we would like to say that we really appreciate your support and interest in our work! We hope you will continue to join us on our journeys in 2020. Until then, happy holidays from the GEMM Lab!  

GEMM Lab at the beginning of June with some permanents GEMMs and some temporary summer GEMM helpers.

Barlow, D. R., M. Fournet, and F. Sharpe. 2019. Incorporating tides into the acoustic ecology of humpback whales. Marine Mammal Science 35:234-251.

Barlow, D. R., A. L. Pepper, and L. G. Torres. 2019. Skin deep: an assessment of New Zealand blue whale skin condition. Frontiers in Marine Science doi.org/10.3389/fmars.2019.00757.

Baylis, A. M. M., R. A. Orben, A. A. Arkhipkin, J. Barton, R. L. Brownell Jr., I. J. Staniland, and P. Brickle. 2019. Re-evaluating the population size of South American fur seals and conservation implications. Aquatic Conservation: Marine and Freshwater Ecosystems 29(11):1988-1995.

Baylis, A. M. M., M. Tierney, R. A. Orben, et al. 2019. Important at-sea areas of colonial breeding marine predators on the southern Patagonian Shelf. Scientific Reports 9:8517. 

Cockerham, S., B. Lee, R. A. Orben, R. M. Suryan, L. G. Torres, P. Warzybok, R. Bradley, J. Jahncke, H. S. Young, C. Ouverney, and S. A. Shaffer. 2019. Microbial biology of the western gull (Larus occidentalis). Microbial Ecology 78:665-676.

Derville, S., L. G. Torres, R. Albertson, O. Andrews, C. S. Baker, P. Carzon, R. Constantine, M. Donoghue, C. Dutheil, A. Gannier, M. Oremus, M. M. Poole, J. Robbins, and C. Garrigue. 2019. Whales in warming water: assessing breeding habitat diversity and adaptability in Oceania’s changing climate. Global Change Biology 25(4):1466-1481.

Derville, S., L. G. Torres, R. Dodémont, V. Perard, and C. Garrigue. 2019. From land and sea, long-term data reveal persistent humpback whale (Megaptera novaeangliae) breeding habitat in New Caledonia. Aquatic Conservation: Marine and Freshwater Ecosystems 29(10):1697-1711.

Fleischman, A. B., R. A. Orben, N. Kokubun, A. Will, R. Paredes, J. T. Ackerman, A. Takahashi, A. S. Kitaysky, and S. A. Shaffer. 2019. Wintering in the western Subantarctic Pacific increases mercury contamination of red-legged kittiwakes. Environmental Science & Technology 53(22):13398-13407.

Holdman, A. K., J. H. Haxel, H. Klinck, and L. G. Torres. 2019. Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A. Marine Mammal Science 35:164-186.

Kroeger, C., D. E. Crocker, D. R. Thompson, L. G. Torres, P. Sagar, and S. A. Shaffer. 2019. Variation in corticosterone levels in two species of breeding albatrosses with divergent life histories: responses to body condition and drivers of foraging behavior. Physiological and Biochemical Zoology 92(2):223:238.

Loredo, S. A., R. A. Orben, R. M. Suryan, D. E. Lyons, J. Adams, and S. W. Stephensen. 2019. Spatial and temporal diving behavior of non-breeding common murres during two summers of contrasting ocean conditions. Journal of Experimental Biology and Ecology 517:13-24.

Monteiro, F., L. S. Lemos, J. Fulgêncio de Moura, R. C. C. Rocha, I. Moreira, A. P. Di Beneditto, H. A. Kehrig, I. C. A. C. Bordon, S. Siciliano, T. D. Saint’Pierre, and R. A. Hauser-Davis. 2019. Subcellular metal distributions and metallothionein associations in rough-toothed dolphins (Steno bredanensis) from southeastern Brazil. Marine Pollution Bulletin 146:263-273.

Orben, R. A., A. B. Fleischman, A. L. Borker, W. Bridgeland, A. J. Gladics, J. Porquez, P. Sanzenbacher, S. W. Stephensen, R. Swift, M. W. McKown, and R. M. Suryan. 2019. Comparing imaging, acoustics, and radar to monitor Leach’s storm-petrel colonies. PeerJ 7:e6721.

Yates, K. L., …, L. G. Torres, et al. 2019. Outstanding challenges in the transferability of ecological models. Trends in Ecology & Evolution 33(10):790-802.

Barcelona-bound! The GEMM Lab heads to the World Marine Mammal Conference

By Dawn Barlow, PhD student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Every two years, an international community of scientists, managers, policy-makers, educators, and students gather to share the most current research and most pressing conservation issues facing marine mammals. This year, the World Marine Mammal Conference will take place in Barcelona, Spain from December 7-12, and the whole GEMM Lab will make their way across the Atlantic to present their latest work. The meeting is an international gathering of scientists ranging from longtime researchers who have shaped the field throughout the course of their careers to students who are just beginning to carve out a niche of their own. This year’s conference has 2,500 registered attendees from 95 different countries, 1,960 abstract submissions, and 700 accepted oral and speed talks and 1,200 posters. Needless to say, it is an incredible platform for learning, networking, and putting our work in the context of research taking place around the globe.

This will be my third time at this conference. I attended in San Francisco in 2015 as a wide-eyed undergraduate and met with Leigh, who I hoped would soon become my graduate advisor. I also presented my Masters research at the conference in Halifax in 2017. This time around, I will be presenting findings from the first two chapters of my PhD. Looking ahead to the Barcelona 2019 meeting and having some sense of what to expect, I feel butterflies rising in my stomach—a perfect mixture of the nerves that come with putting your hard work out in the world, eagerness to learn and absorb new information, and excitement to reconnect with friends and colleagues from around the world. In short, I can’t wait!

For those of you reading this blog that are unable to attend, I’d like to share an overview of what the GEMM Lab will be presenting at the conference. If you will be in Barcelona, we warmly invite you to the following posters, speed talks, and oral presentations! In order of appearance:

Lisa Hildebrand, MS Student

What do Oregon gray whales like to eat? Do individual whales have individual foraging habits? To learn more visit Lisa Hildebrand’s poster “Investigating potential gray whale individual foraging specializations within the Pacific Coast Feeding Group”. (Poster presentation, Session: Foraging Ecology – Group A, Time: Monday, 1:30-3:00pm)

Todd Chandler, Faculty Research Assistant

Did you know it is possible to measure the mechanics of how a blue whale feeds using a drone? The GEMM Lab’s all-star drone pilot Todd Chandler will present a poster titled “More than snacks: An analysis of drone observed blue whale surface lunge feeding linked with prey data”. (Poster presentation, Session: Foraging Ecology – Group A, Time: Monday, 1:30-3:00pm)

Clara Bird, MS Student

The GEMM Lab’s newest student Clara Bird will present a poster on work she conducted with the Marine Robotics and Remote Sensing lab at Duke University using new technologies and approaches to investigate scarring patterns on humpbacks. Her poster is titled “A comparison of percent dorsal scar cover between populations of humpback whales (Megaptera novaeangliae) off California and the Western Antarctic Peninsula”. (Poster presentation, Session: New Technology  – Group B, Time: Tuesday, 8:30-9:45am)

Dr. Leigh Torres, Principal Investigator

GEMM Lab PI Leigh Torres will synthesize some exciting new analyses from the GEMM Lab’s gray whale physiology and ecology research off the Oregon Coast. Is it stressful to feed in a noisy coastal environment? Leigh will discuss the latest findings in her talk, “Sounds of stress: Evaluating the relationships between variable soundscapes and gray whale stress hormones”. (Oral presentation, Session: Physiology, Time: Tuesday, 11:30-11:45am)

Leila Lemos, PhD Student

Carrying on with exciting new findings about Oregon gray whales, Leila Lemos will present a speed talk titled “Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability”, in which she will summarize three years of analysis of how gray whale health can be quantified, and how physiology is influenced by ocean conditions. (Speed talk, Session: Physiology, Time: Tuesday, 11:55am-12:m)

Dawn Barlow, PhD Student

Can we predict where blue whales will be using our understanding of their environment and prey? Can this knowledge be used for effective conservation? I (Dawn Barlow) will give a presentation titled “Cloudy with a chance of whales: Forecasting blue whale occurrence based on tiered, bottom-up models to mitigate industrial impacts”, which will share our latest findings on how functional ecological relationships can be modeled in changing ocean conditions. (Oral presentation, Session: Habitat and Distribution I, Time: Wednesday, 10:15-10:30am)

Dr. Solene Derville, Post-Doctoral Scholar

The GEMM Lab’s most recent graduate Solene Derville will present work she has conducted in New Caledonia regarding humpback whale diving and movement patterns around breeding grounds. Her speed talk is titled “Whales of the deep: Horizontal and vertical movements shed light on humpback whale use of critical pelagic habitats in the western South Pacific” (Speed talk, Session: Behavioral Ecology II, Time: Wednesday, 11:35-11:40am)

Dominique Kone, MS Student

Can sea otters make a comeback in Oregon after a long absence? Dom Kone takes a comprehensive look at how Oregon coast habitat could support a reintroduced sea otter population in his speed talk, “An evaluation of the ecological needs and effects of a potential sea otter reintroduction to Oregon, USA”. (Speed talk, Session: Conservation II, Time: Wednesday, 2:45-2:50pm)

Alexa Kownacki, PhD Student

Alexa Kownacki will share her latest findings on dolphin distribution relative to static and dynamic oceanographic variables in her speed talk titled “The biogeography of common bottlenose dolphins (T. truncatus) of the southwestern USA and Mexico”. (Speed talk, Session: Habitat and Distribution II, Time: Wednesday, 3:35-3:40pm)

Other members of the Marine Mammal Mnstitute who will present their work include: Scott Baker, Debbie Steel, Angie Sremba, Karen Lohman, Daniel Palacios, Bruce Mate, Ladd Irvine, and Robert Pitman. For anyone planning to attend, we look forward to seeing you there! For those who wish to stay tuned from home, keep your eye on the GEMM Lab twitter page for our updates during the conference and follow the conference hashtag #WMMC19, and look forward to future blog posts recapping the experience.