Questions that drive my research curiosity

By Mattea Holt Colberg, GEMM Lab summer intern, OSU junior

Science is about asking new questions in order to make new discoveries. Starting every investigation with a question, sparked by an observation, is enshrined in the scientific method and pursued by researchers everywhere. Asking questions goes beyond scientific research though; it is the best way to learn new things in any setting.

When I first arrived in Port Orford, I did not know much about gray whales. The extent of my knowledge was that they are large baleen whales that migrate every year and feed on plankton. I did, however, know quite a bit about killer whales. I have been interested in killer whales since I was 5 years old, so I have spent years reading about, watching, and listening to them (my current favorite book about them is Of Orcas and Men, by David Neiwert and I highly recommend it!). I have also had opportunities to research them in the Salish Sea, both on a sailing trip and through the dual-enrollment program Ocean Research College Academy, where I explored how killer whales respond to ambient underwater noise for a small independent project. Knowing more about killer whales than other species has caused killer whales to be the lens through which I approach learning and asking questions about other whales. 

At first, I was not sure how to apply what I know about killer whales specifically to research on gray whales, since killer whales are toothed whales, while gray whales are baleen whales. There are several differences between toothed whales and baleen whales; toothed whales tend to be more social, occurring in pods or groups, eat larger prey like fish, squid, and seals, and they echolocate. In comparison, baleen whales are less social, eat mostly tiny zooplankton prey, and do not echolocate. Because of these differences, I wanted to learn more about gray whales, so I started asking Lisa questions. Killer whales only sleep with half of their brain at a time, so I asked if gray whales do the same. They do. Killer whales typically travel in stable, long-term matriarchal groups, and I recently learned that gray whales frequently travel alone (though not exclusively). This new knowledge to me led me to ask if gray whales vocalize while traveling. They typically do not. Through asking these questions, and others, I have begun to learn more about gray whales. 

Figure 2. Mattea on the tandem research kayak taking a break in between prey sampling. Source: L. Hildebrand.

I am still learning about marine mammal research, and from what I have experienced so far, marine mammal acoustics intrigues me the most. As a child, I developed a general interest in whale vocalizations after hearing recordings of them in museums and aquariums. Then, two years ago, I heard orcas vocalizing in the wild, and I decided I wanted to learn more about their vocalizations as a long-term career goal. 

To pursue a career studying marine mammal acoustics, I will need scientific and communication skills that this internship is helping me develop. Sitting on the cliff for hours at a time, sometimes with gray whales swimming in our view-scape and sometimes without, is teaching me the patience and attention needed to review hours of sound recordings with or without vocalizations. Identifying and counting zooplankton most days is teaching me the importance of processing data regularly, so it does not build up or get too confusing, as well as attention to detail and keeping focused. Collecting data from a kayak is teaching me how to assess ocean conditions, keep track of gear, and stay calm when things go wrong. I am also practicing the skill of taking and identifying whale photos, which can be applied to many whale research topics I hope to pursue. Through writing this blog post and discussing the project with Lisa and my fellow interns, I am improving my science communication skills. 

Figure 3. Mattea manning the theodolite watching and waiting for a gray whale to show up in our study area. Source: L. Hildebrand.

As an undergraduate student, it can sometimes be difficult to find opportunities to research marine mammals, so I am very grateful for and excited about this internship, both because of the skills it is helping me build and the field work experiences that I enjoy participating in. Another aspect of research this internship is helping me learn about is to ask engaging questions. As I mentioned at the beginning of this post, asking questions is a key element of conducting research. By asking questions about gray whales based on both prior knowledge and new observations, I am practicing this skill, as well as thinking of topics I am curious about and might want to explore in the future. While watching for whales, I have thought of questions such as: How is whale behavior affected by surface conditions? Do gray whales prefer feeding at certain times of the day? Questions like these help me learn about whales, and they keep me excited about research. Thanks to this internship, I can continue working towards my dreams of pursuing similar questions about whales as a career.

Following Tracks: A Summer of Research in Quantitative Ecology

**GUEST POST** written by Irina Tolkova from the University of Washington.

R, a programming language and software for statistical analysis, gives me an error message.

I mull it over. Revise my code. Run it again.

Hey, look! Two error messages.

I’m Irina, and I’m working on summer research in quantitative ecology with Dr. Leigh Torres in the GEMM Lab. Ironically, as much as I’m interested in the environment and the life inhabiting it, my background is actually in applied math, and a bit in computer science.

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(Also, my background is the sand dunes of Florence, OR, which are downright amazing.)

When I mention this in the context of marine research, I usually get a surprised look. But from firsthand experience, the mindsets and skills developed in those areas can actually be very useful for ecology. This is partly because both math and computer science develop a problem-solving approach that can apply to many interdisciplinary contexts, and partly because ecology itself is becoming increasingly influenced by technology.

Personally, I’m fascinated by the advancement in environmentally-oriented sensors and trackers, and admire the inventors’ cleverness in the way they extract useful information. I’ve heard about projects with unmanned ocean gliders that fly through the water, taking conductivity, temperature, depth measurements (Seaglider project by APL at the University of Washington), which can be used for oceanographic mapping. Arrays of hydrophones along the coast detect and recognize marine mammals through bioacoustics (OSU Animal Bioacoustics Lab), allowing for analysis of their population distributions and potentially movement. In the GEMM lab, I learned about light and small GPS loggers, which can be put on wildlife to learn about their movement, and even smaller lighter ones that determine the animal’s general position using the time of sunset and sunrise. Finally, scientists even made artificial nest mounds which hid a scale for recording the weight of breeding birds — looking at the data, I could see a distinctive sawtooth pattern, since the birds lost weight as they incubated the egg, and gained weight after coming home from a foraging trip…

On the whole, I’m really hopeful for the ecological opportunities opened up by technology. But the information coming in from sensors can be both a blessing and a curse, because — unlike manually collected data — the sample sizes tend to be massive. For statistical analysis, this is great! For actually working with the data… more difficult. For my project, this trade-off shows as R and Excel crash over the hundreds of thousands of points in my dataset… what dataset, you might ask? Albatross GPS tracking data.

In 2011, 2012, and 2013, a group of scientists (including Dr. Leigh!) tagged grey-headed albatrosses at Campbell Island, New Zealand, with small GPS loggers. This was done in the summer months, when the birds were breeding, so the GPS tracks represent the birds’ flights as they incubated and raised their chicks. A cool fact about albatrosses: they only raise one chick at a time! As a result, the survival of the population is very dependent on chick survival, which means that the health of the albatrosses during the breeding season, and in part their ability to find food, is critical for the population’s sustainability. So, my research question is: what environmental variables determine where these albatrosses choose to forage?

The project naturally breaks up into two main parts.

  • How can we quantify this “foraging effort” over a trajectory?
  • What is the statistical relationship between this “foraging effort metric” and environmental variables?

Luckily, R is pretty good for both data manipulation and statistical analysis, and that’s what I’m working on now. I’ve just about finished part (1), and will be moving on to part (2) in the coming week. For a start, here are some color-coded plots showing two different ways of measuring the “foraging value” over one GPS track:

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Most of my time goes into writing code, and, of course, debugging. This might sound a bit dull, but the anticipation of new results, graphs, and questions is definitely worth it. Occasionally, that anticipation is met with a result or plot that I wasn’t quite expecting. For example, I was recently attempting to draw the predicted spatial distribution of an albatross population. I fixed some bugs. The code ran. A plot window opened up. And showed this:

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I stared at my laptop for a moment, closed it, and got some hot tea from the lab’s electronic kettle, all the while wondering how R came up with this abstract art.

All in all, while I spend most of my time programming, my motivation comes from the wildlife I hope to work for. And as any other ecologist, I love being out there on the Oregon coast, with the sun, the rain, sand, waves, valleys and mountains, cliff swallows and grey whales, and the rest of our fantastic wild outdoors.

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