From coast to coast: assessing impacts of human threats and climate change from dolphins to blue whales

By Nicole Principe, first-year PhD student, OSU Dept of Fisheries, Wildlife and Conservation Sciences, GEMM Lab

Humans rely on oceans and coastal ecosystems for a variety of resources, such as tourism and recreation, fishing and aquaculture, transport of goods, and resource extraction. However, each use is contributing to new and cumulative stressors that are impacting marine mammals.  The health of marine mammal populations can often serve as indicators of overall environmental health. Therefore, studying the stressors they face can help provide insights into the broader impacts on marine ecosystems and determine if conservation or management measures are necessary. As a master’s student at the College of Charleston in South Carolina and subsequently the stranding and research technician with the Lowcountry Marine Mammal Network (LMMN), I saw first-hand how some of these stressors affect local marine mammal populations.

In my role as the stranding and research technician with LMMN, I led the response and recovery of all deceased marine mammals, mainly bottlenose dolphins (Tursiops erebennus), in South Carolina to determine cause of death and identify main sources of mortality. Threats to these cetaceans can be environmental or anthropogenic in origin. Carefully examining and sampling every individual during a necropsy was critical to determine the presence of infectious disease, the contaminant and microplastic load, and any sign human interaction. While deaths from environmental causes can be more challenging for humans to mitigate, direct threats from human activity can be lessened with conservation actions and increased education to the public. LMMN responds to several strandings of dolphins each year that are the result of entanglement or boat strike. South Carolina has one of the highest rates of crab pot entanglements. In some cases, the call came quick enough that a disentanglement was possible, but in others, we found the animal already deceased with rope and gear still attached. Hundreds, if not thousands, of commercial and recreational crab pots are deployed within South Carolina estuaries, yet there are currently no regulations in place to help mitigate the threat of entanglement.

LMMN also conducts land and boat-based surveys to better understand strand feeding, which is a unique foraging strategy utilized by a small number of dolphins in South Carolina. When dolphins strand feed, they herd and trap fish up onto mudbanks or shorelines. The dolphins chase after the fish, briefly stranding themselves as they try to catch them. It is an incredible behavior to witness and because of this, it has become highly publicized as a tourist activity. There are areas where the public can walk right up as dolphins are attempting to hunt and many instances of people trying to touch, feed, or otherwise harass the dolphins have been reported. I also conducted a small study where I used drones to identify human interferences towards dolphins strand feeding and found that boaters and kayakers were often approaching the animals too closely, following them, or speeding through the inlet when animals were present. The write up on that project can be found here. High levels of human disturbance towards dolphins strand feeding could lead individuals to abandon otherwise suitable habitat, causing them to expend more energy to look for food elsewhere.

To help mitigate threats to dolphins from entanglements, boat strikes, and illegal harassment, the LMMN team and I created an educational workshop called W.A.V.E., which stands for Wildlife Awareness and Viewing Etiquette. These half-day workshops are tailored to both recreational boaters/public and commercial tour operators and fishermen and cover topics ranging from the importance of marine mammals in our ecosystem, the Marine Mammal Protection Act, global and local threats, and ways we can view marine wildlife that reduce disturbance. It is my hope that with more education and awareness about how humans use our waterways and interact with wildlife in negative ways, it can lead to positive changes. For more information about LMMN’s W.A.V.E. Workshops, head to their website.

Image: Successful W.A.V.E. Workshop with local eco-tour operators. Photo credit: Lowcountry Marine Mammal Network

In addition to cumulative stressors from human interactions, I also began to contemplate the role of climate change as a threat to the lives of marine mammals during my master’s research on dolphin distribution within the Charleston Estuary System (CES). A main question I was investigating was if and why some dolphins travel into low salinity waters high in the estuarine system.  Bottlenose dolphins have evolved in marine and estuarine environments where salinity levels are typically ~30 parts per thousand (ppt). While dolphins can withstand short durations of exposure to low salinity (defined as 15 ppt), prolonged exposure to freshwater can result in negative health consequences, such as sloughing of skin and ulcerative lesions, changes in pathophysiology, and eventual mortality (Ewing et al., 2017). Over the past 20 years, many intermittent dolphin sightings and strandings occurred in riverine areas of the CES where salinity levels were below 10 ppt. To better understand how and why dolphins use this risky habitat, I conducted drone surveys across the CES for a year. I did find dolphin groups traveling and feeding in low salinity waters, however, the encounters were only during months with warmer water temperatures (Principe et al., 2023). We hypothesize that environmental conditions during those months may lead to decreased prey availability in the lower, more suitable parts of the estuary, forcing dolphins to travel further up the rivers to access higher abundances of prey (especially mullet). Other studies in different regions have found similar results of dolphins traveling into low salinity water during warmer months potentially in response to prey (Mintzer and Fazioli, 2021; Takeshita et al., 2021).

These results lead to questions as to how prey and dolphin movements will shift under future climate change scenarios. Increasing warm water temperatures may lead to further shifts in prey distribution, potentially driving more estuarine dolphins to utilize upper riverine habitats to find food. Just since 2022, four dolphins were observed in freshwater habitat for several weeks. Two were eventually found and confirmed deceased and two went missing and are presumed deceased. If more dolphins use and remain in these low salinity habitats for extended periods, negative health consequences could lead to population impacts and signal a need for more conservation and management actions.

It is quickly becoming evident that climate change is threatening marine mammals, at both local and global scales. More research is needed to better understand how changing environmental conditions is impacting the availability and quality of prey and how large marine predators are shifting in response. For my PhD, I am working with the GEMM Lab on the SAPPHIRE (Synthesis of Acoustics, Physiology, Prey, and Habitat in a Rapidly changing Environment) project, where we are researching how changing ocean conditions affect the availability of krill, and blue whale behavior, health, and reproduction in New Zealand. The South Taranaki Bight (STB) region experiences a productive coastal upwelling system that supports enhanced primary productivity (Chiswell et al. 2017) and dense aggregations of prey (Bradford-Grieve et al., 1993). Pygmy blue whales (Balaenoptera musculus brevicauda) in this region are not known to migrate and instead use the STB region year-round for foraging and reproduction (Torres, 2013; Barlow et al., 2022).  After a marine heatwave in the Tasman Sea in 2015-2016, there were less krill aggregations due to lessened upwelling (Barlow et al., 2020), which caused reduced foraging effort, and subsequently reduced reproductive activity by blue whales (Barlow et al. 2023). Continued field work and data analysis will help us to develop Species Health Models that will predict how these prey and predator populations will respond to future environmental change. 

Overall, it is clear that human activity is leading to direct and indirect impacts on marine mammal populations at many different scales, from an individual human harassing a foraging dolphin to global climate change impacts on blue whale population dynamics. Ongoing research is essential in understanding these impacts better and thus inform development of effective conservation strategies to protect both marine mammals and the environment.

References

Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG (2020) Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar Ecol Prog Ser 642:207–225.

Barlow DR, Klinck H, Ponirakis D, Branch TA, Torres LG (2023) Environmental conditions and marine heatwaves influence blue whale foraging and reproductive effort. Ecol Evol 13:e9770.

Barlow DR, Klinck H, Ponirakis D, Holt Colberg M, Torres LG (2022) Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring. J Mammal 104(1): 29–38.

Bradford-Grieve JM, Murdoch RC, Chapman BE (1993) Composition of macrozooplankton assemblages associated with the formation and decay of pulses within an upwelling plume in greater cook strait, New Zealand. New Zeal J Mar Freshw Res 27(1): 1–22.

Chiswell SM, Zeldis JR, Hadfield MG, Pinkerton MH (2017) Wind-driven upwelling and surface chlorophyll blooms in greater Cook Strait. New Zeal J Mar Fresw Res 51(4): 465–489.

Ewing RY, Mase-Guthrie B, McFee W, Townsend F, Manire CA, Walsh M,

Borkowski R, Bossart GD, Schaefer AM (2017). Evaluation of serum for pathophysiological effects of prolonged low salinity water exposure in displaced bottlenose dolphins (Tursiops truncatus). Front Vet Sci 4

Hornsby F, McDonald T, Balmer BC, Speakman T, Mullin K, Rosel P, Wells R, Telander A, Marcy P, Schwacke L (2017) Using salinity to identify common bottlenose dolphin habitat in Barataria Bay, Louisiana, USA. Endanger Species Res 33: 833–192.

Mintzer VJ, Fazioli KL (2021) Salinity and water temperature as predictors of bottlenose dolphin (Tursiops truncatus) encounter rates in upper Galveston Bay, Texas. Front Mar Sci 8

Principe N, McFee W, Levine N, Balmer B, Ballenger J (2023). Using Unoccupied Aerial Systems (UAS) to Determine the Distribution Patterns of Tamanend’s Bottlenose Dolphins (Tursiops erebennus) across Varying Salinities in Charleston, South Carolina. Drones 7(12): 10.3390/drones7120689. 

Takeshita R, Balmer BC, Messina F, Zolman ES, Thomas L, Wells RS, Smith CR, Rowles TK, Schwacke LH (2021). High site-fidelity in common bottlenose dolphins despite low salinity exposure and associated indicators of compromised health. PLoS ONE, 16(9), e0258031.

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.

Two Leaders Wearing Two Hats: A wrap-up of the 2024 TOPAZ/JASPER Field Season

Celest Sorrentino, incoming master’s student, OSU Dept of Fisheries, Wildlife and Conservation Sciences, GEMM Lab

Allison Dawn, PhD student, Clemson University Dept of Forestry and Environmental Conservation, GEMM Lab Alum

Allison:

Celest and I were co-leaders this year, so it only feels fitting to co-write our wrap-up blog for the 2024 field season.

This was my first year training the project leader while also leading the field team. I have to say that I think I learned as much as Celest did throughout this process! This hand-off process requires the two team leaders to get comfortable wearing two different hats. For me, I not only made sure the whole team grasped every aspect of the project within the two training weeks, but also ensured Celest knew the reasoning behind those decisions AND got to exercise her own muscles in decision making according to the many moving parts that comprise a field season: shifts in weather, team needs, and of course the dynamics of shared space at a field site with many other teams. With the limited hours of any given day, this is no small task for either of us, and requires foresight to know where to fit these opportunities for the leader-in-training during our day-to-day tasks.

During this summer, I certainly gained even more respect for how Lisa Hildebrand juggled “Team Heck Yeah” in 2021 while she trained me as leader. Lisa made sure to take me aside in the afternoon to let me in on her thought process before the next days work. I brought this model forward for Team Protein this year, with the added bonus that Celest and I got to room together. By the end of the day, our brains would be buzzing with final thoughts, concerns, and excitement. I will treasure many memories from this season, including the memory of our end-of-day debriefs before bed. Overall, it was an incredibly special process to slowly pass the reins to Celest. I leave this project knowing it is ready for its new era, as Celest is full of positive energy, enthusiasm, and most importantly, just as much passion for this project as the preceding leaders.

Fig. 1: Two leaders wearing two (massive) hats. Field season means you have to be adaptable, flexible, and make the most out of any situation, including sometimes having to move your own bed! We had a blast using our muscles for this; we are Team Protein after all!

Celest:

As I sit down in the field station classroom to write this blog, I realize I am sitting in the same seat where just 12 hours ago a room full of community members laughed and divided delicious blueberry crumble with each other.

We kicked the morning of our final day together off with a Team Protein high powered breakfast in Bandon to have some delicious fuel and let the giggles all out before our presentation. When Dr. Torres arrived, the team got a chance to reflect on the field season and share ideas for next season. Finally, the moment we had all been waiting for:  at 5 PM Team Protein wrapped up our 2024 field station with our traditional Community Presentation.

Fig 2: Team breakfast at SunnySide Cafe in Bandon, which have delicious GF/DF options.

Within a month and a half, I transitioned from learning alongside each of the interns at the start of the season knowing only the basics of TOPAZ/JASPER, to eventually leading the team for the final stretch. The learning spurts were quite rapid and challenging, but I attribute my gained confidence to observing Allison lead. To say I have learned from Allison only the nitty-gritty whats and whys of TOPAZ/JASPER would not suffice, as in truth I observed the qualities needed to empower a team for 6 weeks. I have truly admired the genuine magnetic connection she established with each intern, and I hope to bring forth the same in future seasons to come.

Witnessing each intern (myself included!) begin the season completely new, to now explaining the significance of each task with ease to the very end was unlike any other. Presenting our field season recap to the Port Orford Community side-by-side with Sophia, Eden, Oceana, and Allison provided an incredible sense of pride and I am thrilled for the second TOPAZ/JASPER Decadel party in 2034 when we can uncover where this internship has taken us all.

…Until next season (:

Fig 3: Team Protein all together at the start of season all together.

Fig 4: Team Protein all smiles after wrapping up the season with the Community Presentation.

Fig 4: Our season by numbers for the 2024 TOPAZ/JASPER season!

Speeding Up, Slowing Down, and Choosing My Fig

Celest Sorrentino, incoming master’s student, OSU Dept of Fisheries, Wildlife, and Conservation Sciences, GEMM Lab

It’s late June, a week before I head back to the West Coast, and I’m working one of my last shifts as a server in New York. Summer had just turned on and the humidity was just getting started, but the sun brought about a liveliness in the air that was contagious. Our regulars traded the city heat for beaches in the Hamptons, so I stood by the door, watching the flow of hundreds upon hundreds of people fill the streets of Manhattan. My manager and I always chatted to pass the time between rushes, and he began to ask me how I felt to move across the country and start my master’s program so soon.

“I am so excited!” I beamed, “Also a bit nervous–”

Nervous? Why? 

Are you nervous you’ll become the person you’re meant to be?”

As a first-generation Hispanic student, I found solace in working in hospitality. Working in a restaurant for four years was a means to support myself to attain an undergraduate degree–but I’d be lying if I said I didn’t also love it. I found joy in orchestrating a unique experience for strangers, who themselves brought their own stories to share, each day bestowing opportunity for new friendships or new lessons. This industry requires you to be quick on your feet (never mess with a hungry person’s cacio e pepe), exuding a sense of finesse, continuously alert to your client’s needs and desires all the while always exhibiting a specific ambiance.

So why leave to start my master’s degree?

Fig 1: Me as a server with one of my regulars before his trip to Italy. You can never go wrong with Italian!

For anyone I have not had the pleasure yet to meet, my name is Celest Sorrentino, an incoming master’s student in the GEMM Lab this fall. I am currently writing to you from the Port Orford Field Station, located along the charming south coast of Oregon. Although I am new to the South Coast, my relationship with the GEMM Lab is not, but rather has been warmly cultivated ever since the day I first stepped onto the third floor of the Gladys Valley Building, as an NSF REU intern just two summers ago. Since that particular summer, I have gravitated back to the GEMM Lab every summer since: last summer as a research technician and this summer as a co-lead for the TOPAZ/JASPER Project, a program I will continue to spearhead the next two summers. (The GEMM Lab and me, we just have something– what can I say?)

 In the risk of cementing “cornball” to my identity, pursuing a life in whale research had always been my dream ever since I was a little girl. As I grew older, I found an inclination toward education, in particular a specific joy that could only be found when teaching others, whether that meant teaching the difference between “bottom-up” and “top-bottom” trophic cascades to my peers in college, teaching my 11 year old sister how to do fun braids for middle school, or teaching a room full of researchers how I used SLEAP A.I. to track gray whale mother-calf pairs in drone footage.

Onboarding to the TOPAZ/JASPER project was a new world to me, which required me to quickly learn the ins and outs of a program, and eventually being handed the reins of responsibility of the team, all within 1 month and a half. While the TOPAZ/JASPER 2024 team (aka Team Protein!) and I approach our 5th week of field season, to say we have learned “so much” is an understatement.

Our morning data collection commences at 6:30 AM, with each of us alternating daily between the cliff team and kayak team. 

For kayak team, its imperative to assemble all supplies swiftly given that we’re in a race against time, to outrun the inevitable windy/foggy weather conditions. However, diligence is required; if you forget your paddles back at lab or if you run out of charged batteries, that’s less time on the water to collect data and more time for the weather to gain in on you. We speed up against the weather, but also slow down for the details.

Fig 2: Throwback to our first kayak training day with Oceana (left), Sophia(middle), and Eden (right).

For cliff team, we have joined teams with time. At some point within the last few weeks, each of us on the cliff have had to uncover the dexterity within to become true marine mammal observers (for five or six hours straight). Here we survey for any slight shift in a sea of blue that could indicate the presence of a whale– and once we do… its go time. Once a whale blows, miles offshore, the individual manning the theodolite has just a few seconds to find and focus the reticle before the blow dissipates into the wind. If they miss it… its one less coordinate of that whale’s track. We speed up against the whale’s blow, but also slow down for the details. 

Fig 3: Cliff team tracking a whale out by Mill Rocks!

I have found the pattern of speeding up and slowing down are parallels outside of field work as well. In Port Orford specifically, slowing down has felt just as invigorating as the first breath one takes out of the water. For instance, the daily choice we make to squeeze 5 scientists into the world’s slowest elevator down to the lab every morning may not be practical in everyday life, but the extra minute looking at each other’s sleepy faces sets the foundation for our “go” mode. We also sit down after a day of fieldwork, as a team, eating our 5th version of pasta and meatballs while we continue our Hunger Games movie marathon from the night prior. And we chose our “off-day” to stroll among nature’s gentle giants, experiencing together the awe of the Redwoods trees.

Fig 4A & 4B: (A) Team Protein (Sophia, Oceana, Allison, Eden and I) slow morning elevator ride down to the lab. (B) Sophia hugging a tree at the Redwoods!

When my manager asked the above question, I couldn’t help but think upon an excerpt, popularly known as “The Fig Tree” by Sylvia Plath.

Fig 5: The Fig Tree excerpt by Sylvia Plath. Picture credits to @samefacecollective on Instagram.

For my fig tree, I imagine it as grandiose as those Redwood trees. What makes each of us choose one fig over the other is highly variable, just as our figs of possibilities, some of which we can’t make out quite yet. At some point along my life, the fig of owning a restaurant in the Big Apple propped up. But in that moment with my manager, I imagined my oldest fig, with little Celest sitting on the living room floor watching ocean documentaries and wanting nothing more than to conduct whale research, now winking at me as I start my master’s within the GEMM Lab. Your figs might be different from mine but what I believe we share in common is the alternating pace toward our fig. At times we need speeding up while other times we just need slowing down.

Then there’s that sweet spot in between where we can experience both, just as I have being a part of the 2024 TOPAZ/JASPER team.

Fig 6A and 6B: (A) My sister and I excited to go see some dolphins for the first time! (~2008). (B) Taking undergraduate graduation pics with my favorite whale plushy! (2023)

Fig 7: Team Protein takes on Port Orford Minimal Carnival, lots of needed booging after finishing field work!

Little bit of Kayaking, Lot a bit of Zoops

Eden Van Maren, Homeschool Student from Brookings, TOPAZ/JASPER High School Intern

Hey! I’m Eden Van Maren, an upcoming high school senior from Brookings. I am homeschooled and am taking electives at Brookings Harbor High School. 

Growing up in rural Oregon, the outdoors have always been more than just my backyard. It’s been both my classroom and my playground. When Oceana (the other high school intern) and I were homeschooled together as children, Fridays meant her mom would take us up the Chetco River. One Friday, we took our snorkels to observe mature salmon migrating upstream. I remember being so amazed by the size and quantity of the salmon, my young brain could not understand why such large fish would want to swim up to such a small area to lay eggs. The next year when we returned to try and see if the salmon would swim upstream again, we found only one salmon swimming around. This river became my classroom, planting my initial interest in science. 

However. Let’s be clear: Being outside in nature was never “all work, no play” – Definitely lots of play! Summers were filled with sunsets on the beach, some foggy day hikes, but most importantly kayaking on the river. I have many fun memories of waking up early on a weekend to pack food for a long day of kayaking in a tandem with my dad and a bunch of other friends. As I’ve gotten older, my passion for both the environment and science have only grown.

Fig 1: My dad and I kayaking with my dog on the Chetco River.

After going on a college tour at the University of Oregon in January, I suddenly started thinking that I should begin planning for college and future career options. On that tour, I met Ma’yet, the Youth Program Education Coordinator at Curry Watershed Partnership who had worked with Allison. Ma’yet was familiar with the TOPAZ/JASPER program run by the GEMM Lab and, while we were discussing possible summer opportunities in science, they suggested that I would be a great fit.  

In early March, when I discovered there would be someone from the program coming to present at school, I had already been scheduled to work a shift at Dutch Bros. I managed last minute to have one of my coworkers cover the last few hours of my shift so I would be able to get there. He arrived late, so I ran to get there on time, but I made it! Upon arriving, I sat down for the presentation, and, within minutes, Allison confirmed my desire to be a part of this program. I always knew that science is where I wanted to focus my studies. When I came across this program, I was very interested because it involved exciting outdoor activities while learning and experiencing scientific field work. I was thrilled to meet Allison in person to ask questions and share my enthusiasm about the project. 


Before working at the Port Orford field station, I had never given much thought to zooplankton. I had known they were the primary prey for whales, but other than that, I hadn’t considered that there was much else to think about. After starting the work associated with zooplankton on this project, I learned through Sophia how zooplankton can be affected by water temperature and kelp abundance, among other things. Along with learning more about zooplankton ecology, part of the program includes collecting zooplankton samples from 12 different stations (using a kayak) out along the Port Orford coastal area. On my very first training day of zooplankton sampling, I pulled up a ridiculous number of zooplankton in the net (much more than the last few seasons).

Fig 2: Me pulling up my first net of zoops! Look at all that zoop!

Once we return to the lab after a morning of zoop collection, we observe these samples under a microscope, identify their species, and count how many species we collected from each station. Just two weeks into our data collection, we have collected 4291 individual zoops, which already surpasses the total amount of zooplankton collected in 2023 and 2022 combined! That’s a lot of zoops! But how do we do it? 

In our team, I am considered the zoop expert, but I couldn’t do it without a handful of Welch’s fruit snacks and my playlist full of bangers. Zoop processing can be very tedious, but I really enjoy the peace that comes with finishing a giant sample by myself. I love being able to blast AJR in the background while ID’ing each zooplankton even though my team loves to tease me for it (but really, I’m totally putting them on). As I’ve gotten better at ID’ing zooplankton, I started brainstorming about what could help teach other interns in the future. Allison and Lisa, the previous TOPAZ/JASPER leaders, created very useful guides used to train me but I felt that there could be other interactive methods to help interns learn about zoop. Having used Quizlet in the past, I thought it would be a great resource to introduce the zooplankton basics to new interns, so I created an online Zooplankton Identification quiz!

Fig 3A & 3B: Me processing a giant sample of Atylus Tridens.

Despite having only completed three weeks of our data collection season so far, I have already learned so much! From waking up at 5:30 ten days in a row, to kayaking for four hours straight, to even counting 995 (not 1000!) zooplankton in one sitting, this internship has been amazing. It’s been a great introduction to working in the scientific field as many of the responsibilities we have been taught are completely new to me. I am excited to share this internship experience as I apply to colleges and add to my list of skills “Zoop expert.”

Fig 4: My favorite zooplankton! A Dungeness Crab Larvae.

“So, I hear you’re an expert in marine mammal ecology?”

Oceana Powers-Schmitz, Brookings-Harbor High School student, TOPAZ/JASPER GEMM Lab Project, MMI Oregon State University

Hi, I’m Oceana Powers-Schmitz and while I am not quite an expert in marine mammal ecology (yet!), I am quite the expert in bringing the team together through a clever game and a heartfelt laugh. One game I turned the team onto this summer during a team dinner was “So, I hear you’re an expert in ______.” Essentially, someone in your group provides you with a niche topic and someone else will have to then go on a spiel about it for at least a minute. One of the best ones I heard so far was when we were driving back from getting Langlois Market hot dogs (don’t knock it, till you’ve tried it). I tasked Eden with “So, I hear you’re an expert in the price of tea in China”. The most fun part of this game is how you have to think on your feet when the pressure is on. This skill is helpful during this internship because I have had to troubleshoot a lot in the field. One example of this was when the team had to rethink the mechanics of our zooplankton net because it was not collecting efficiently. We solved this problem by taking a trip to Gold Beach Lumber and attaching a washer to the bottle to weigh it down, allowing for more space to catch zooplankton within the bottle.

Fig 1: Zooplankton net (Left) with fishing weights in the bottle. Zooplankton net (Right) after removing weights and adding washer.

Although I’m definitely the best at this game, the team has shown some promise at getting better as we spend more and more time together. At the start of the internship, I watched the team make our share of mess ups (fortunately during training week!) such as not turning on equipment or losing the spare zooplankton net overboard. As the internship has progressed, it has been amusing to experience us getting a handle on all the new methods and protocols that come with the TOPAZ/JASPER project. For example, in the beginning, one of the most challenging methods to execute in the field is setting up the Theodolite on an unlevel cliff side. But now Celest and I have a competition to see who can set up and level the Theodolite the fastest. (If you ask her, we’re tied— but I’m obviously winning.)

Fig 2: Celest (right) and me (left) after I assembled the theodolite. (Celest thinks she’s winning.)

Afternoons in the lab are an enjoyable part of the day (for me at least) because it is a chance to relax after an eventful, physically demanding (and hopefully whale-filled) morning. After we break for lunch, I head to the kitchen to make my go-to: 4 slices of Oven-roasted Turkey, a slab of butter, pinch of pepper and salt (can’t be stingy with the salt) on two toasted slices of Buttermilk bread. Pro tip: food is best digested with a book; I’m on my third one.

Fig 3: The best post-field day lunch combo 🙂

The afternoon is also a chance to become closer as a team. From watching the GoPro bloopers to trying desperately to get Google Earth to work in order to check if we were “on station,” these afternoons are always fun. While this statement might be a hot take, I’ve really gravitated to data processing over zooplankton ID. For this project, data processing consists of inputting RBR data, visibility metrics, and going through each station’s GoPro footage into an excel sheet. This process is an important part of the long-term study of our Port Orford field site because researchers will be able to access and use this information. Using what we have collected, future studies may draw new conclusions or make important findings that can be published and add to our knowledge of the ecology of the local gray whales.

Though I am not yet an expert in the field, this internship has solidified the idea that I could become one in marine science. I’m glad for the hands-on experience this internship has provided and through this I feel confident in the fact that I would enjoy this career path. Over the next couple weeks, I can’t wait to introduce the team to more entertaining games to keep us on our toes while we wait for whales. I’m excited for each of us to return home saying “I’m an expert at GEMM Lab’s TOPAZ/JASPER Gray Whale Project.”

Fig 4: Locked in, listening to my favorite podcast, looking through RBR data.

*P.S. Here’s a great podcast to listen to when processing data, you can thank me later 🙂

Radio Lab Link

Getting to the Bottom of it

Sophia Kormann, NSF REU Intern in the GEMM Lab, St. Olaf College

Hello! My name is Sophia Kormann and I am an NSF REU intern this summer in the GEMM lab being mentored by PI Leigh Torres, Allison Dawn, and Clara Bird. I was introduced in last week’s blog as part of our awesome whale team (deemed “Team Protein”) working out of Port Orford. I am a rising senior at St. Olaf College where I am studying statistics and biology. One of my personal goals for this summer was to get to the bottom of what is next for me. A pretty small task if you ask me… I really want to figure out if research is the route I want to go within the intersection of these two subjects or if something else would be a better fit. When looking into internships I wanted to find something where I could analyze data and see how research works as a career first hand, but not be stuck at a desk all day. I pretty much struck gold with the GEMM lab.

This summer I get to participate in field work that involves ocean kayaking, tracking whales, and identifying zooplankton, while also conducting statistical analysis on data collected from the past two years of this decade-long project. In 2022, the TOPAZ project introduced a new sensor to the data collection procedures, the RBR concerto,which records for dissolved oxygen and temperature readings during a “cast” through the water column. My big task for the summer was to explore how temperature and dissolved oxygen affect zooplankton abundance data that were simultaneously collected via a GoPro during the 2022 and 2023 field seasons. 

Figure 1. Me (!) doing my first zooplankton sampling in our kayak R/V Robustus. 

My project involves modeling zooplankton abundance as a response to temperature and dissolved oxygen. The ultimate goal would be to be able to plug in the dissolved oxygen and temperature to an equation and get back an accurate prediction for the zooplankton abundance, but this is often tricky to do with data that has been collected from the field. I needed to get to the bottom of what causes a change in zooplankton abundance. After a lot of trial and error, I eventually determined that temperature and dissolved oxygen at the lowest depth of each cast has the best relationship with zooplankton abundance along the whole cast, and thus produce the most accurate predictions of zooplankton abundance from the model. I literally had to go to the bottom of the ocean to get to the bottom of the relationship. 

In hindsight, these relationships make a lot of sense for the Port Orford ecosystem. Ask anyone at the field station this summer and we can all tell you that it can be VERY windy here. This abundance of wind mixed with the shallow depths of the system make for very well mixed water, which means that there is little variation in the temperature and dissolved oxygen in the entire water column from the surface to the floor (Kämpf 2017).  The wind here causes an increase of upwelling, which is the process of moving surface water away from the coast and allowing for deeper water to replace it. This upwelling brings cold, nutrient dense water that is low on oxygen to the surface (Bograd 2023). Since this Port Orford ecosystem is so well mixed, the bottom is likely the most stable in terms of temperature and dissolved oxygen (Ni 2016). Therefore, it would make sense that this stability would then lead to a better prediction of zooplankton as it is less affected by other factors that could be affecting the zooplankton abundance such as wind speed, land temperature, turbidity and other variables that we did not take into account while modeling.

Figure 2. Functional response curves produced from a general additive model for zooplankton abundance in response to bottom dissolved oxygen (top left), bottom temperature (top right), and station (bottom).

Table 1. Zooplankton abundance is significantly affected by bottom dissolved oxygen and bottom temperature.

At this point during my summer. I have made a lot of progress in completing the data analysis and I also have made a lot of progress in getting to the bottom of “what’s next?” for me. Thankfully, this effort did not involve going to the bottom of the ocean, although I aced my kayak safety and basic life safety training since being here, so I would definitely be able to self-rescue even if I did end up there. Anyhow, one thing that helped me with this process is that I had the privilege of attending the Decadal Celebration for the TOPAZ/JASPER project. I got the chance to interact with so many people that had been in my exact place as an intern on this project over the last nine years. We discussed gap years, masters programs, and just got to hear about so many different pathways to current roles. There truly is no one “right way” to go from here. 

This internship experience also taught me that I really enjoy sharing what I have discovered in this research. Whether answering the “What are you doing?” questions we get almost everyday from tourists while we are doing cliff work, or creating templates of my code for future researchers to use, or teaching Leigh Torres Gen-Z slang over dinner (ask her what “I’m dead” and “Let him cook” mean… she knows now!), I have found out that I love sharing information with others.

Figure 3. Me teaching the other interns and our new team lead how to analyze the GoPro footage. 

Part of what has drawn me to statistics is the ability to turn a long string of data into an easily digestible graph for the general public. Being a part of this opportunity has allowed me to really figure out my interests and I have discovered a very genuine passion for making sense of the unknown through data analysis. With this experience I know I will be happy with whatever comes next for me as long as there is someone to share results with and a challenging question for me to get to the bottom of.

We have four more weeks of work for this field season which means more time on the ocean and hopefully more time with whales! I am very excited to see what the near future holds for me and what more we will be able to uncover this summer. With our community presentation in front of us, I am excited to share our summer with those in Port Orford. I also get to present my own research in our REU poster symposium. I look back on the almost six weeks that have already flown by with gratefulness for all I’ve already been able to learn and look forward to the next four weeks with excitement for what’s yet to be discovered.

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly message when we post a new blog. Just add your name and email into the subscribe box below.

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References 

Bograd, SJ, Jacox, MG, Hazen, EL, Lovecchio, E, Montes, I, Pozo Buil, M, Shannon, LJ, Sydeman, WJ, Rykaczewski, RR (2023) Climate change impacts on eastern boundary upwelling systems. Annual Review of Marine Science 15

Kämpf, J (2017) Wind-driven overturning, mixing and upwelling in shallow water: A nonhydrostatic modeling study. Journal of Marine Science and Engineering, 5(4), 47. https://doi.org/10.3390/jmse5040047

Ni, X, Huang, D, Zeng, D, Zhang, T, Li, H, & Chen, J (2016) The impact of wind mixing on the variation of bottom dissolved oxygen off the Changjiang Estuary during summer. Journal of Marine Systems, 154, 122–130. https://doi.org/10.1016/j.jmarsys.2014.11.010 

Giving Ecologists Mega Muscles: Introducing the 2024 Port Orford Gray Whale Foraging Ecology Project Team, “Team Protein”!

Allison Dawn, Master’s alumni, OSU Department of Fisheries, Wildlife and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

In addition to honoring the decade-long legacy, this year is also special as I am co-leading “Team Protein” with Celest Sorrentino, incoming GEMM lab Master’s student. Now with four South Coast summers under my belt, I am beyond excited to get to share what I’ve learned with someone equally as passionate about immersive marine science education and mentorship. While I am teaching Celest how to prepare for weather-dependent fieldwork, lead a team of 5, shop on a budget, organize the lab, and more, I am also learning so much from her. I am especially grateful for her bright energy and unwavering positivity, which are skills that can rarely be taught yet have such a powerfully positive influence on the success of a field season. After just a week together I feel there is no one better suited for me to pass on the “GoPro/RBR torch” to and I know she will lead the project successfully into its next chapter.

Figure 1: Allison and Celest, on a particularly windy day, fully packed with gear and groceries, ready and excited to head to the South Coast Outpost!

That said, we still have 5 weeks before the 10th year has officially culminated, and it is my honor and pleasure to introduce you to the team who will be paddling us through this incredible milestone! Before I talk about each individual, I’d like to explain the inspiration behind our team name this year.

Every Port Orford field team gets to choose their team name, and we quickly settled on ours – “Team Protein”! After we spent a few days together, the five of us found we have at least two things in common: we all love exercise and to fuel up on protein. Between quick 2-3 mile evening runs, competitive pushups after dinner, yoga – on top of our kayaking and gear-carrying- and so much baked chicken, we are undoubtedly getting stronger together. 

In addition to this name describing the team well, we also have seen an increase in zooplankton abundance sampled during the first-week than in previous years. Because whale food seems to be prevalent this year, we all agree that this season’s whales will also be on “Team Protein”. We hope that means we will see strong, healthy PCFG whale visitors in the next several weeks!

Figure 2: Logo for “Team Protein”, created by NSF REU Sophia Kormann

So, who exactly are the brains and brawn behind Team Protein? First, we have team leader Allison (me!). I defended my master’s degree in June 2023 and loved the beauty and community of the South Coast so much I decided to stick around for one more adventure-filled year before moving on to begin my doctorate at Clemson University in South Carolina. There I will be implementing all the skills and lessons learned in the GEMM Lab into studying grassland bird habitat using remote sensing technologies. I am thrilled to get another year of leading this incredibly dynamic project, mentoring students, and obviously increasing my muscle mass before I move on from studying (gray) whales to (bobwhite) quails.

Figure 3: Allison stoked on great conditions for our first kayaking sampling training day

Next, we have our co-lead, Celest Sorrentino!

Figure 4: Celest, Allison, and Leigh grabbing a selfie before the awesome Decadal Party!

Name: Celest Sorrentino

School/year: Oregon State University, incoming master’s student 

What interested you in this project/what are you most excited for?

As an older sister of four, teaching and mentoring them has always been something I’ve loved to do and intended to hone my skills in as I pursued higher education. When the opportunity arose during a conversation with Dr. Torres last summer to be able to develop these valuable skills during my masters, I couldn’t be more excited. Now having completed just my first week here in Port Orford, I can totally understand the enamor Allison has shared for this project. I am excited to continue to learn from her as not only a lead for this project, but also from her own mentorship style that is both naturally impactful and unique. 

Our third team member is our NSF REU student Sophia Kormann. Stay tuned for her blog next week on the exciting project that she has been co-mentored by myself, Leigh and Clara.

Figure 5: Sophia enjoying the beautiful moonrise on the cliff site at the Decadal Party.

Name: Sophia Kormann

School/year: rising senior at St. Olaf College

What interested you in this project/what are you most excited for?

I was looking for something within biology research that would allow me to do a lot of analysis but wouldn’t just be sitting at a desk all day. And if you get the chance to whale watch everyday for the summer…you take it. I am the most excited to combine my interests in biology and statistics.

Next we have Eden Van Maren, who I met during a recruitment talk in Brookings. Eden immediately stood out as an enthusiastic and bright student.

Figure 5: Eden’s first zooplankton net sample had more amphipods than Allison had ever seen in the net!

Name: Eden Van Maren

School/year: I am homeschooled doing electives at Brookings-Harbor High School. 

What interested you in this project/what are you most excited for?

I was interested in this opportunity because of the opportunity to do scientific field work while getting to kayak on the ocean. I’m excited to learn about how ocean conditions affect zooplankton and how that impacts whale foraging.

Last but not least, we have Oceana Powers-Schmitz. Oceana is a passionate bookworm and impressive history buff. In addition to taking on this fieldwork internship, she is also teaching herself Algebra 2 in order to test out of taking the class next year.

Figure 6: Oceana finds a red urchin at Nellie’s Cove!

Name: Oceana Powers-Schmitz

School/year: Brookings-Harbor High School

What interested you in this project/what are you most excited for?

Getting actual research/lab experience as well as using it to see what part of science I’m interested in, and to hopefully have a whale-filled summer. 

Well, as a surprise to no one, we’re off to do some yoga. Tune into our Instagram takeover by following @gemm_lab on instagram for more real-time updates from “Team Protein”!

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Measure faster! New tools for automatically obtaining body length and body condition of whales from drone videos

Dr. KC Bierlich, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Monitoring the body length and body condition of animals can help provide important information on the health status of individuals and their populations, and can even serve as early warning signs if a population is adapting to habitat changes or is at risk of collapse (Cerini et al., 2023). As discussed in previous blogs, drone-based photogrammetry provides a method for non-invasively collecting important size measurements of whales, such as for detecting differences in body condition and length between populations, and even diagnosing pregnancy. Thus, using drones to collect measurement data on the growth, body condition, and pregnancy rates of whales can help expedite population health assessments to elicit conservation and management actions.

However, it takes a long time to manually measure whales filmed in drone imagery. For every video collected, an analyst must carefully watch each video and manually select frames with whales in good positions for measuring (flat and straight at the surface). Once frames are selected, each image must then be ranked and filtered for quality before finally measuring using a photogrammetry software, such as MorphoMetriX. This entire manual processing pipeline ultimately delays results, which hinders the ability to rapidly assess population health. If only there was a way to automate this process of obtaining measurements…

Well now there is! Recently, a collaboration between researchers from the GEMM Lab, CODEX, and OSU’s Department of Engineering and Computer Science published a manuscript introducing automated methods for obtaining body length and body condition measurements (Bierlich et al., 2024). The manuscript describes two user-friendly models: 1) “DeteX”, which automatically detects whales in drone videos to output frames for measuring and 2) “XtraX”, which automatically extracts body length and body condition measurements from input frames (Figure 1). We found that using DeteX and XtraX produces measurements just as good as manual measurement (Coefficient of Variation < 5%), while substantially reducing the processing time by almost 90%. This increased efficiency not only saves hours (weeks!) of manual processing time, but enables more rapid assessments of populations’ health.

Future steps for DeteX and XtraX are to adapt the models so that measurements can be extracted from multiple whales in a single frame, which could be particularly useful for analyzing images containing mothers with their calf. We also look forward to adapting DeteX and XtraX to accommodate more species. While DeteX and XtraX was trained using only gray whale imagery, we were pleased to see that these models performed well when trialing on imagery of a blue whale (Figure 2). These results are encouraging because it shows that the models can be adapted to accommodate other species with different body shapes, such as belugas or beaked whales, with the inclusion of more training data.

We are excited to share these methods with the drone community and the rest of this blog walks through the features and steps for running DeteX and XtraX to make them even easier to use.

Figure 1. Overview of DeteX and XtraX for automatically obtaining body length and body condition measurements from drone-based videos.

Figure 2. Example comparing manual (MorphoMetriX) vs. automated (XtraX) measurements of a blue whale.

DeteX and XtraX walkthrough

Both DeteX and XtraX are web-based applications designed to be intuitive and user-friendly. Instructions to install and run DeteX and XtraX are available on the CODEX website. Once DeteX is launched, the default web-browser automatically opens the application where the user is asked to select 1) the folder containing the drone-based videos to analyze and 2) the folder to save output frames (Figure 3). Then, the user can select ‘start’ to begin. The default for DeteX is set to analyze the entire video from start to finish at one frame per second; if recording a video at 30 frames per second, the last (or 30th) frame is processed for each second in the video. There is also a “finetune” version of DeteX that offers users much more control, where they can change these default settings (Figure 4). For example, users can change the defaults to increase the number of frames processed per second (i.e., 10 instead of 1), to target a specific region in the video rather than the entire video, and adjust the “detection model threshold” to change the threshold of confidence the model has for detecting a whale. These specific features for enhanced control may be particularly helpful when there is a specific surfacing sequence that a user wants to have more flexibility in selecting specific frames for measuring.

Figure 3. A screenshot of the DeteX web-based application interface.

Figure 4. The DeteX “finetune” version provides more control for users to change the default settings to target a specific region in the video (here between 3 min 00 sec and 3 min 05 sec), change the number of frames per second to process (now 10 per second), and the detection threshold, or level of confidence for identifying a whale in the video (now a higher threshold at 0.9 instead of the default at 0.8).

Once output frames are generated by DeteX, the user can select which frames to input into XtraX to measure. Once XtraX is launched, the default web-browser automatically opens the application where the user is asked to select 1) the folder containing the frames to measure and 2) the folder to save the output measurements. If the input frames were generated using DeteX, the barometric altitude is automatically extracted from the file name (note, that altitudes collected from a LiDAR altimeter can be joined in the XtraX output .csv file to then calculate measurements using this altitude). The image width (pixels) is automatically extracted from the input frame metadata. Users can then input specific camera parameters, such as sensor width (mm) and the focal length of the camera (mm), the launch height of the drone (i.e., if launching from hand when on a boat), and the region along the body to measure body condition (Figure 5). This region along the body is called the Head-Tail range and is identified as the area where most lipid storage takes place to estimate body condition. To run, the user selects “start”. XtraX then will output a .png file of each frame showing the keypoints (used for the body length measurement) and the shaded region (used for the body condition estimate) along the body to help visual results so users can filter for quality (Figure 6). XtraX also outputs a single .csv containing all the measurements (in meters and pixels) with their associated metadata.

Figure 5. User interface for XtraX. The user specifies a folder containing the images to measure and a folder to save the outputs measurements, and then can enter in camera specifications, the launch height of the drone (to be added to the barometer altitude) and the range of body widths to include in the body condition measurement (in the case, 0.2 and 0.7 correspond to body region between widths 20% and 70% of the total length, respectively).

Figure 6. Example output from XtraX showing (red) keypoints along the body to measure body length and the (green) shaded region used for body condition.

We hope this walkthrough is helpful for researchers interested in using and adapting these tools for their projects. There is also a video tutorial available online. Happy (faster) measuring!

References

Bierlich, K. C., Karki, S., Bird, C. N., Fern, A., & Torres, L. G. (2024). Automated body length and body condition measurements of whales from drone videos for rapid assessment of population health. Marine Mammal Science, e13137. https://doi.org/10.1111/mms.13137

Cerini, F., Childs, D. Z., & Clements, C. F. (2023). A predictive timeline of wildlife population collapse. Nature Ecology & Evolution, 7(3), 320–331. https://doi.org/10.1038/s41559-023-01985-2

Disentangling the whys of whale entanglement

By Lindsay Wickman, Postdoctoral Scholar, Oregon State University Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Previously on our blog, we mentioned  the concerning rise of humpback whale (Megaptera novaeangliae) entanglement in fishing gear on the US West Coast (see here and here). Gaining an improved understanding of the rate of entanglement and risk factors of humpback whales in Oregon are primary aims of the GEMM Lab’s SLATE and OPAL projects. In this post, I will discuss some reasons why whales get entangled. With whales generally regarded as intelligent, it is understandable to wonder why whales are unable to avoid these underwater obstacles.

Figure 1. Wrapping scars like these at the base of the flukes indicate this humpback whale was previously entangled. Photo taken under NOAA/NMFS permit #21678 to John Calambokidis.

Fishing lines are hard to detect underwater

Water clarity, depth, and time of day can all influence how visible a fishing line is underwater.  Since baleen whales lack the ability to discriminate color (Levenson et al., 2000; Peichl et al. 2001), the brightly colored yellow and red ropes that make it easier for fishermen to find their gear make it harder for whales to see it underwater. White or black ropes may stand out better for whales (Kot et al., 2012), but there’s not enough evidence yet to suggest they reduce entanglement rates.

Whales have excellent hearing, but this may still not be enough to ensure detection of underwater ropes. Even if whales can hear water currents flowing over the rope, this noise can easily be masked by other sounds like weather, surf, and passing boats. Fishing gear also has a weak acoustic signature (Leatherwood et al., 1977), or it may be at a frequency not heard by whales. So even though whales produce and listen for sounds to help locate prey (Stimpert et al., 2007) and communicate, any sound produced by fishing lines may not be sufficient to alert whales to its presence.

There are very few studies that examine the behavior of whales around fishing gear, but a study of minke whales (Balaenoptera acutorostrata) by Kot et al. (2017) provides an exception. Researchers observed whales slowing down as they approached their test gear, and speeding up once they were past it (Kot et al., 2017). While the scope of the study was too small to generalize about whales’ ability to detect fishing gear, it does suggest whales can detect fishing gear, at least some of the time. There is also likely some individual variation in this skillset. Less experienced, juvenile humpback whales, for example, may be at a higher risk of entanglement than adults (Robbins, 2012).

Distracted driving?

Just like distracted drivers are more likely to crash when texting or eating, whales may be more likely to get entangled when they are preoccupied with behaviors like feeding or socializing.

Evidence suggests feeding is especially risky for entanglement. An analysis of entanglements in the North Atlantic found that almost half (43%) of the humpback whales were entangled at the mouth, and the mouth was also the most common attachment point for North Atlantic right whales (Eubalaena glacialis, 77%; Johnson et al., 2005). In a study of minke whales in the East Sea of Korea, 80% of entangled whales had recently fed (Song et al, 2010). In many cases, entanglement at the mouth can severely restrict feeding ability, resulting in emaciation and/or death (Moore and van der Hoop, 2012).

Figure 2. A North Atlantic right whale with fishing gear attached at the mouth. Photo credit: NOAA Photo Library.

More whales, more heat waves, and more entanglements

On the US West Coast, the number of humpback whales has been increasing since the end of whaling (e.g., Barlow et al, 2011). With more whales in our waters, it makes sense that the number of entanglements will increase. Still, a larger population size is probably not the only reason for increasing entanglements.

Climate change, for example, may place whales in the areas with dense fishing gear much more often. A recent example of this was during 2014–2016, when a heatwave on the US West Coast led to a cascade of events that increased the likelihood of whale entanglements in California waters (Santora et al., 2020).

The increased temperatures led to a bloom of toxic diatoms, which delayed the commercial fishing season for Dungeness crabs in California. Unfortunately, the delay caused fishing to resume right as high numbers of whales were arriving from their annual migration from their breeding grounds. The wider ecosystem effects of the heat wave also meant humpback whales were feeding closer to shore — right where most crab pots are set. The combination of both the fisheries’ timing and the altered distribution of whales contributed to an unprecedented number of entanglements (Santora et al., 2020).

Whale entanglement is a concerning issue for fishermen, conservationists, and wildlife managers. By disentangling some of the whys of entanglement for humpback whales in Oregon, we hope our research can contribute to improved management plans that benefit both whales and the continuity of the Dungeness crab fishery. To learn more about these projects, visit the SLATE and OPAL pages, and subscribe to the blog for more updates.

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly message when we post a new blog. Just add your name and email into the subscribe box below.

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References

Barlow, J., Calambokidis, J., Falcone, E.A., Baker, C.S., Burdin, A.M., Clapham, P.J., Ford, J.K., Gabriele, C.M., LeDuc, R., Mattila, D.K. and Quinn, T.J. (2011). Humpback whale abundance in the North Pacific estimated by photographic capture‐recapture with bias correction from simulation studies. Marine Mammal Science, 27(4), 793-818.

Johnson, A., Salvador, G., Kenney, J., Robbins, J., Kraus, S., Landry, S., and Clapham, P. (2005). Fishing gear involved in entanglements of right and humpback whales. Marine Mammal Science, 21, 635–645.

Kot, B.W., Sears, R., Anis, A., Nowacek, D.P., Gedamke, J. and Marshall, C.D. (2012). Behavioral responses of minke whales (Balaenoptera acutorostrata) to experimental fishing gear in a coastal environment. Journal of Experimental Marine Biology and Ecology, 413, pp.13-20.

Leatherwood, J.S., Johnson, R.A., Ljungblad, D.K., and Evans, W.E. (1977). Broadband Measurements of Underwater Acoustic Target Strengths of Panels of Tuna Nets. Naval Oceans Systems Center, San Diego, CA Tech, Rep. 126.

Levenson, D.H., Dizon, A., and Ponganis, P.J. (2000). Identification of loss-of-function mutations within the short wave-length sensitive cone opsin genes of baleen and odontocete cetaceans. Investigative Ophthalmology & Visual Science, 41, S610.

Moore, M. J., and van der Hoop, J. M. (2012). The painful side of trap and fixed net fisheries: chronic entanglement of large whales. Journal of Marine Sciences, 2012.

Peichl, L., Behrmann, and G., Kröger, R.H.H. (2001). For whales and seals the ocean is not blue: a visual pigment loss in marine mammals. European Journal of Neuroscience, 13, 1520–1528.

Robbins J. (2012). Scar-based inference Into Gulf of Maine humpback whale entanglement: 2010. Report EA133F0 9CN0253 to the Northeast Fisheries Science Center, National Marine Fisheries Service. Center for Coastal Studies, Provincetown, MA.

Santora, J. A., Mantua, N. J., Schroeder, I. D., Field, J. C., Hazen, E. L., Bograd, S. J., Sydeman, W. J., Wells, B. K., Calambokidis, J., Saez, L., Lawson, D., and Forney, K. A. (2020). Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nature Communications, 11(1).

Song, K.-J., Kim, Z.G., Zhang, C.I., Kim, Y.H. (2010). Fishing gears involved in entanglements of minke whales (Balaenoptera acutorostrata) in the east sea of Korea. Marine Mammal Science, 26, 282–295.

Stimpert, A.K., Wiley, D.N., Au, W.W.L., Johnson, M.P., Arsenault, R. (2007). “Megapclicks”: acoustic click trains and buzzes produced during night-time foraging of humpback whales (Megaptera novaeangliae). Biology Letters, 3, 467–470.

From Bytes to Behaviors: How AI is Used to Study Whales

By Natalie Chazal, PhD student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

In today’s media, artificial intelligence, or AI, has captured headlines that can stir up strong emotions and opinions. From promises of seemingly impossible breakthroughs to warnings of job displacement and ethical dilemmas, there is a lot of discourse surrounding AI. 

But what actually is artificial intelligence? The term artificial intelligence (or AI) was defined as “the science and engineering of making intelligent machines” and can generally describe a suite of methods used to simulate human information processing. 

AI actually began in the 1950s with puzzle solving robots and networks that identified shapes. But because the computational power required to run these complex networks was too high and funding cuts, there was an “AI winter” for the following decades. In the 1990’s there was a boom in advancement following renewed interest in AI, advancements in machine learning algorithms, and improved computational power. The 2010’s saw a resurgence of deep learning (a subfield of AI) designed because of the availability of large datasets and optimization algorithm improvements. Currently, AI is being used in extremely diverse ways because of its ability to handle large quantities of unstructured data.

Figure 1. An intuitive visualization of the nested relationship between AI, machine learning, and deep learning as subdomains (Rubbens et al. 2023)

To place AI in a better context, we should clarify some of the buzz words I’ve mentioned: artificial intelligence (AI), machine learning, and deep learning. There are a few schools of thought, but one that is generally accepted is that AI is a broad category of methods and techniques of systems that function to mimic human intelligence. Machine learning falls under this AI category but rather than using explicitly programmed rules to make decisions, we “train” these systems so that they are essentially learning from the data that we provide. Lastly, deep learning falls under machine learning because it uses the principles of “learning” from the data to build neural networks.

While AI is generally rooted in computer science, statistics provides the foundation for AI techniques. In particular, statistical learning is a combined field that adopts machine learning methods for more statistics based settings. Trevor Hastie, a leader in statistical learning, defines the field as “a set of tools for modeling and understanding complex datasets” (Hastie et al. 2009) and is used to explore patterns in data but within a statistical framework. 

Continuously improving methods like statistical learning and AI provide us with very powerful tools to collect data, automate processing, handle large datasets, and understand complex processes. 

How do marine mammal ecologists employ AI?

Even on small scales, marine mammal research often involves vast amounts of data collected from tons of different sources, including drone and satellite imagery, acoustic recordings, boat surveys, buoys, and many more. New deep learning tools, such as neural networks, are able to perform tasks with remarkable precision and speed that we traditionally needed to painstakingly do manually. For example, researchers spend hours poring over thousands of drone images and videos to understand the behavior and health of whales. In the GEMM Lab, postdoc KC Bierlich is leading the development of AI models to automatically measure important whale metrics from the images. These advancements streamline the process of understanding whale ecology and makes it easier to identify stressors that may be affecting these animals.

For photographic analyses, we can leverage Convolutional Neural Networks for tasks like feature extraction, where we can automatically get morphological measurements like body length and body area indices from drone imagery to understand the health of whales. This can provide valuable insight into the stressors placed on these animals. 

We can also identify whale species from boat and aerial imagery (Patton et al. 2023). Projects like Flukebook and Happywhale have even been able to identify individual humpback whales with techniques like this one. 

Figure 2. Flukebook neural networks can use the edges of flukes to identify individuals by mapping marks to a library of known individuals (Flukebook)

AI also excels at prediction especially with non-linear responses. Ecology is filled with thresholds, stepwise changes, and chaos that may not be captured by linear models. But being able to predict these responses is particularly important when we want to look at how whale populations respond to different facets of their environment. Ensemble machine learning algorithms like Random Forests or Gradient Boosting Machines are very common to model species-habitat relationships and can predict how whale distributions will change in response to changes in things like sea surface temperature or ocean currents (Viquerat et al. 2022). 

Even spatial data, which can be tricky to work with analytically, can be used in a machine learning framework. Data from satellite and acoustic tags can be analyzed from hidden Markov models and Gaussian mixture models. The results of these could potentially identify diving behaviors, habitat preferences, identify migration corridors, and aid in marine spatial planning (Quick et al. 2017; Lennox et al. 2019). 

While all of these projects and methods are very exciting, AI is not a panacea. We have to take into account the amount of data that AI models rely on. Some of these methods require very high resolutions of data and without adequate quantity to train the models, results can be biased or produce inaccurate predictions. Data deficiency can be especially problematic for rare, elusive, and quiet animals. Methods that utilize complex architectures and non-linear transformations can often be viewed as “black box” and difficult to interpret at first. However, there are some methods that can be used to retrace the steps of the model and create a pathway of understanding for the results that can help interpretability. AI also requires supervision. While AI methods can operate autonomously, oversight and evaluation are always necessary to validate their reliability in their application.  Lastly, there are also concerns about the use of AI (particularly Large Language Models) in scientific writing, but that’s a whole separate beast. 

With careful consideration, AI can be a powerful method for addressing the unique and challenging problems in marine mammal research. 

Using AI to find dinner

Last fall, I wrote a blog post to introduce my project that involves looking at echograms from the past 8 years of GRANITE effort to characterize prey availability within our study region of the Oregon coast. To automate the process of finding zooplankton swarms in 8 years of echosounder data, I’m planning to utilize deep learning methods to look for structures in our echogram that look like mysid swarms. Instead of reviewing over 500 hours of echosounder data to manually identify mysid swarms (which may produce biased or inaccurate results from human error), I can apply AI methods to process the echogram data with speed and consistent rules. I’ll specifically be using image segmentation, which can fall under any of the AI, machine learning, or deep learning umbrellas depending on the specific algorithms used. 

Another way AI can come into my project is after I gather the mysid swarm data from the image segmentation. While the exact structure of this resulting relative zooplankton abundance data will influence how I can use it, I could combine these prey data at a given place and time with a suite of environmental parameters to make predictions about the health and behavior of PCFG gray whales. This type of analysis could involve models that fall within AI and machine learning similar to the Boosted Regression Trees used by GEMM Labs postdoc, Dawn Barlow. Barlow et al. (2020) used Boosted Regression Trees to test the predictive relationships between oceanographic variables, relative krill abundance, and blue whale presence. Based on that work, Barlow et al. (2022) was able to develop a forecasting model based on these relationships to predict where blue whales will be in New Zealand’s South Taranaki Bight (read more about this conservation tool here!).

Hopefully by now you’ve gained a better sense of what AI actually is and its application in marine mammal ecology. AI is a powerful tool and has its value, but is not always a substitute for more established methods. By carefully integrating AI methodologies with other techniques, we can leverage the strengths of both and enhance existing approaches. The GEMM Lab aims to use AI methods to observe and understand the intricacies of whale ecology more accurately and efficiently to ultimately support effective conservation strategies.

References

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