Author: Celest Sorrentino

There she blows! Studying blow synchrony in blue and gray whale mother-calf pairs

Maddie Honomichl, CSUMB Undergraduate in Marine Science, 2025  NSF REU and TOPAZ/JASPER Intern

Hi everyone, my name is Maddie Honomichl, and I was one of the two NSF REU interns with the GEMM lab this summer. This fall will be my last semester at California State University, Monterey Bay (CSUMB), where I will receive my undergraduate degree in Marine Science. Growing up in the Arizona desert limited my exposure to large bodies of water but led to memorable family trips to San Diego. With each trip my adoration for the ocean and the vast marine ecosystem emerged, resulting in my choice to go to college at CSU Monterey Bay. During my time at CSUMB, I learned of what internships were and the magnitude of impact these experiences had on my peers. Naturally, I searched online for weeks for future summer internship openings available—eventually leading me to none other than the GEMM Lab’s TOPAZ/JASPER project.

Figure 1. A picture of me, Maddie Honomichl, in Redding, CA.

Celest Sorrentino, my amazing mentor, took me under her wing and helped me complete my very first research project: In sync? Studying blow synchrony in blue and gray whale mother-calf pairs using drone footage. The aim of my project is to understand more about calf development in gray and blue whales by investigating changes in mother-calf blow synchrony. But what is synchrony?

Synchrony can be defined as two individuals attempting to match each other’s behavior (Novotny & Bente 2022) and promote mimicry and learning. For example, humpback whales teaching their calves vocalizations and song patterns to communicate is an instance of synchrony and social learning (Anjara 2018). Another example of synchrony in wildlife is energetic transfer, where a whale calf will swim in alignment with its mother’s slipstream to expend less energy (Norris & Prescott 1961). Just like these behaviors, blow synchrony is a measure we can use in marine mammal mother-calf pairs to evaluate their relationship with each other.

Aside from the TOPAZ/JASPER project, you might already be familiar with two other incredible GEMM lab projects GRANITE and SAPPHIRE. During the years of 2016-2023, drone footage of the Pacific Coast Feeding Group of gray whales was collected along the Oregon coast for the GRANITE project. In 2016, 2017, 2024, and 2025, drone footage for the pygmy blue whales was collected in South Taranaki Bight, New Zealand, for the SAPPHIRE project. Large marine mammals, especially whales, are difficult to study for many reasons including their brief occurrences at the surface and the challenges of studying aquatic animals. However, the use of drones allows for a safer and non-invasive alternative method for marine mammal monitoring in their natural habitat (Álvarez-González et al., 2023).

Figure 2. Two still images taken from GRANITE drone footage. The left is of a mother gray whale blowing and the right is of a gray whale calf blowing

Baleen whale calves only have 6-8 months to learn everything they need to know before they wean and are sent off on their own (Lockyer 1984). I don’t know about you, but if my mom kicked me out after I turned 5 years old, I would be pretty lost. In American culture, the golden age for humans to be considered an adult is around 18 years old, when they finally leave the house and venture on their own. For humans, age is a cultural sign of independence, but not much is known about what factors influence what makes a calf ready to be independent. Blow synchrony between mother-calf pairs during the calf’s weaning period can be used as a metric for calf development, which is important to know more about as calf development and survival rates are critical factors to consider in population dynamics and management efforts.

When do whales eventually leave their mother? We frequently don’t know how old a calf is, so we use three different metrics as proxies of calf maturity. First, we use Total Length (TL), which is the length of the whale from rostrum to fluke (or nose to tail) (Pirotta et. al., 2023) and serves as an indicator of growth. Our next metric is Body Area Index (BAI), similar to BMI in humans, which is a score of body condition to understand how fat or skinny the whale calf is (Burnett et. al., 2019). Total Length and BAI measurements are derived from drone photogrammetry work conducted by the GEMM Lab and CODEX. Our last proxy is Day of Year (DOY), which is the day in the year we sighted the whales.

Figure 3. Demonstrating photogrammetry methods used to measure Total Length and Body Area Index (BAI). On the left is a drone image of a gray whale showing how we calculate Total Length (TL) from rostrum to fluke. This image is also divided into increments which are used to calculate Surface Area (SA), depicted by the green dashed box, and using the equation on the right with TL, BAI is calculated. 

The specific question I addressed was: Does mother-calf blow rate synchrony change as the calf’s Total Length and Body Area Index increase, and Day of Year increases? In other words, does synchrony change as calves become longer, healthier, and the year progresses. Meaning, as the calf grows in length, increases its body condition, and the day of year progresses, the calf will gain independence from its mother and become out of sync.

I analyzed blowhole rates of mother-calf gray and blue whales using  a program called BORIS (Friard & Gamba 2016). BORIS (Behavioral Observation Research Interactive Software) is an online free program where researchers can assign behavior states to animals in video. In BORIS, I watched the drone footage and marked a “blow” event for the mom and calf, recording a specific time stamp per event. I repeated this workflow for each video of both gray and blue whale mom-calf pairs. Once completed, I calculated the average difference of the calf’s timestamp from the mother’s timestamp per pair. The reason behind this approach is that  the larger the average difference, the more asynchronous the calf is with its mother, and the smaller the average difference the more synchronous they will become.

To evaluate the effect of our proxies for age, Total Length, BAI, and Day of Year, on mother-calf blow rate synchrony, I turned to my good friend RStudio. I created a scatterplot and regressions for these relationships (Figure 4). These results indicate that body condition (BAI) may be a better proxy of calf maturity and preparation for weaning in gray whales (p-value = 0.0064), whereas calf Total Length (TL) is more indicative of calf maturity in blue whales (p-value = 0.00097).

Figure 4. Scatterplot describing the relationship between the average difference in breath rate in seconds across our three proxies: (i) Average total length, (ii) Average BAI, (iii) Day of Year. The black line in the linear regression fit to the data produced by the linear model. The error bars around each point are the standard deviation or the variability in their blow synchrony. The bigger the error bars mean the more variation the mother and calf had in their blow rates, and the smaller the error bars means the less variation the mother and calf had in their blow rates. Ultimately to answer my question, for gray whales, blow synchrony between mother and calf decreases with increasing calf Body Area Index (BAI). For our blue whales, mother-calf blow synchrony decreases with increasing calf Total Length (TL).

As I end my 10-week internship summer filled with data collection and analysis, lots of laughs and inside jokes, I am proud to say I have learned so much about the research that goes into a project like mine. As someone who loves marine animals, especially whale sharks, I now have a newfound love for whales that will forever be in my heart. I am so incredibly grateful that I was able to work with the GEMM lab and the amazing team of researchers and scientists it encompasses. Being a first-generation college student comes with its challenges of learning how to navigate higher education without direct guidance of family who had been through the experience. But if there’s one thing I always tell myself, it’s that with a little bit of grit and hard work, you can do anything you put your mind to! Whatever my future holds for me, I hope it is filled with more research opportunities and the chance to work with marine mammals!

Figure 5. An image of Maddie Honomichl, presenting her research poster at the Hatfield summer coastal intern symposium remotely from Port Orford!

References:

Álvarez-González, M., Suarez-Bregua, P., Pierce, G. J., & Saavedra, C. (2023). Unmanned Aerial Vehicles (UAVs) in Marine Mammal Research: A Review of Current Applications and Challenges. Drones, 7(11), 667. https://doi.org/10.3390/drones7110667

Anjara Saloma. Humpback whales (Megaptera novaeangliae) mother-calf interactions. Vertebrate Zoology. Université Paris Saclay (COmUE); Université d’Antananarivo, 2018. English. ⟨NNT : 2018SACLS138⟩. ⟨tel-02869389⟩

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2019). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science, 35(1), 108–139. https://doi.org/10.1111/mms.12527

Friard, O., & Gamba, M. (2016). BORIS: A free, versatile open‐source event‐logging software for video/audio coding and live observations. Methods in Ecology and Evolution, 7(11), 1325–1330. https://doi.org/10.1111/2041-210X.12584

Huetz, C., Saloma, A., Adam, O., Andrianarimisa, A., & Charrier, I. (2022). Ontogeny and synchrony of diving behavior in Humpback whale mothers and calves on their breeding ground. Journal of Mammalogy, 103(3), 576–585. https://doi.org/10.1093/jmammal/gyac010

Lockyer, Christina. (1984). Review of Baleen Whale (Mysticeti) Reproduction and Implications for Management. Reproduction in whales, dolphins and porpoises. Proc. conference, La Jolla, CA, 1981. 6. 27-50.

Norris, K.S., & Prescott, J.H. (1961). Observations of Pacific cetaceans of Californian and Mexican waters. University of California Publications in Zoology, 63, 291- 402.

Novotny, E., & Bente, G. (2022). Identifying Signatures of Perceived Interpersonal Synchrony. Journal of nonverbal behavior, 46(4), 485–517. https://doi.org/10.1007/s10919-022-00410-9

Pirotta, E., Fernandez Ajó, A., Bierlich, K. C., Bird, C. N., Buck, C. L., Haver, S. M., Haxel, J. H., Hildebrand, L., Hunt, K. E., Lemos, L. S., New, L., & Torres, L. G. (2023). Assessing variation in faecal glucocorticoid co

Smultea, M. A., Fertl, D., Bacon, C. E., Moore, M. R., James, V. R., & Würsig, B. (2017). Cetacean mother-calf behavior observed from a small aircraft off Southern California. Animal Behavior and Cognition, 4(1), 1–23. https://doi.org/10.12966/abc.01.02.2017

Zoop Gone Missing: A Whale’s Dinner Dilemma

Dawson Mohney, TOPAZ/JASPER HS Intern, Pacific High School Graduate

My name is Dawson Mohney, I am a high school intern for the 2025 TOPAZ/JASPER team this field season. I first heard about the TOPAZ/JASPER internship from my friend Jonah Lewis, a previous intern from the 2023 field season. Coincidentally, Jonah and I both graduated this year from Pacific High School here on the coast—small world. I have called Port Orford my home for most of my life, and in recent years I discovered that a gray whale research project has been happening in my own backyard. Growing up less than a mile from the Oregon Coast, I’ve spent a lot of time looking out into the water. I always liked how, no matter what happened in my life, the ocean was always there. This interest is what encouraged me to apply for the internship with the hope of discovering more about the ocean, a substantial part of my home and family.

Fig 1: Picture fellow intern Maddie took of me (Dawson) during our trip to Natural Bridges.

A critical part of this project is understanding not only the magnificent gray whales but also the much less apparent zooplankton–after all, the whales need to eat a lot of zooplankton! Many different species of zooplankton—“zoop” for short—call the Oregon coast home. Each day, as we kayak to our 12 sample stations within the gray whale feeding grounds of Mill Rocks and Tichenor’s Cove, I find myself wondering which species of zoop I’ll get to identify later under the microscope.

Throughout the duration of this internship, our team has met to discuss a few research papers published by GEMM Lab members, including research produced from the TOPAZ/JASPER projects. Recently, I read, “Do Gray Whales Count Calories? Comparing Energetic Values of Gray Whale Prey Across Two Different Feeding Grounds in the Eastern North Pacific,” by Hildebrand et al. who describe the caloric content of different zooplankton species. Before reading this paper, I didn’t realize whale prey could vary in nutritional value – much like food for humans. This paper made it clear that each of the different species of zooplankton is just as important as the last, but consuming more of the higher caloric species such as the Neomysis rayii or the Dungeness crab larvae would certainly be a welcome meal. Seeing these “healthy” meals in the area makes me hopeful for the whales.

Fig 2: Image of a crab larvae in their megalopae stage.

From reading previous blog posts, the foraging habits of the whales this season appear to be unusual. In prior TOPAZ/JASPER field seasons, gray whales have often been tracked foraging near or around our Mill Rocks and Tichenor Cove study sites. This season, we haven’t tracked a single whale in Mill Rocks and only two in Tichenor Cove. Could there just not be enough good zoop?

Along with this lack of whales, there does seem to be a lack of these “high calorie zoop species”. Our team has most frequently collected samples primarily comprising of Atylus tridens, a lower calorie prey type. In fact, during one of our earlier kayak training days this field season we collected 2,019 individual A. tridens. However, since this day we have collected sparse amounts of zooplankton in our samples, ranging from zero to 121 in a given sample. Our total zoop count thus far is 2,524 zooplankton, a third of the total zooplankton collected last field season.

Fig 3: Image of an Atylus tridens under a microscope.

As for whale presence, we have been observing many whales blows near Hell’s Gate as mentioned in last week’s blog written by fellow intern Miranda Fowles. From our cliff site, it has been difficult to know whether these are gray whales or a different kind of whale, leading us to venture out to the Heads to get a better look. The persistence of whales in this area is certainly unusual, and perhaps it can be explained by a larger amount of higher calorie zooplankton species in the Hell’s Gate area.

Fig 4: Dawson tracking blows by Hell’s Gate with the theodolite.

Being part of the TOPAZ/JASPER project, I have become exposed to what the true meaning is behind “fieldwork,” including learning how to be flexible and adapt to new challenges every day. What I have most enjoyed is the team’s ability to overcome any new hurdle together as a unit.  My dad often says, “You learn something new every day,” and this internship couldn’t embody this quote more. In just these 5 weeks, it almost feels like my head is now a couple sizes bigger.

Before this experience, I never thought much about how one might track a whale or how different microscopic species could have such a profound impact on a whale’s decision to forage. Now I feel I understand just how important these less than obvious factors are and the effort which goes behind understanding these relationships. I can only hope future opportunities teach me as much as joining the TOPAZ/JASPER legacy has—it’s an experience that, even just a few days into the 2025 field season, I knew would be hard to match.

Fig 4: Dawson (navigator) and Miranda (sampler) during kayak training on their way to Mill Rocks.

Hildebrand, L., Bernard, K. S., & Torres, L. G. (2021). Do Gray Whales Count Calories? Comparing Energetic Values of Gray Whale Prey Across Two Different Feeding Grounds in the Eastern North Pacific. Frontiers in Marine Science, 8, 683634. https://doi.org/10.3389/fmars.2021.683634

Whales Off Course: Theodolite Tracking in an Unpredicted Area

Miranda Fowles, GEMM Lab TOPAZ/JASPER Intern, OSU Fisheries and Wildlife Undergraduate

Hello! My name is Miranda Fowles, and I am the OSU intern for the 2025 TOPAZ/JASPER project this summer! I recently earned my bachelor’s degree – almost, I have one more term, but I walked at commencement in June – from Oregon State University in Fisheries, Wildlife and Conservation Sciences and a minor in Spanish. My interest in whales began at a young age during a visit to SeaWorld. While I didn’t enjoy the killer whale shows for their entertainment aspect, this exposure allowed me to see a whale for the first time. From then on, I knew I wanted to contribute to understanding more about these animals, even if I wasn’t always sure how to make that happen. My decision to pursue Fisheries and Wildlife sciences was set from the beginning, however I wondered if there were actually opportunities to study whales.

Last summer, I was a MACO intern and stayed at the Hatfield Marine Science Center where I met last year’s TOPAZ/JASPER REU student, Sophia Kormann, and she raved all about her experience, so I just had to apply for this year’s internship! I remember feeling so nervous for the interview, but Dr. Leigh Torres and Celest Sorrentino’s kindness and inspiration quickly put me to ease. When I found out I was offered the position, I was just more excited than I’d ever been!

My day-to-day life as a TOPAZ/JASPER intern here at the Port Orford Field Station looks one of two ways: either on the kayak or the cliff site. When we are ocean kayaking, we go to our 12 sampling sites in the Mill Rocks and Tichenor Cove study areas (Fig. 1), where we collect zooplankton samples (Fig. 2) and oceanographic data with our RBR (an oceanographic instrument), as well as GoPro footage. When on the cliff site, we keep our eyes peeled for any whales to take pictures of them and mark their location in the water with a theodolite.

Fig. 1: Map of our study sites (Tichenor Cove and Mill Rocks) and where we have been seeing gray whales (Hell’s Gate) circled in green, and our Cliff Site.
Fig. 2: Miranda Fowles out on the kayak pointing at her zooplankton samples.

A theodolite is an instrument that is used for mapping and engineering; in our case it is used to track where a gray whale blows and surfaces (For more info, please see this blog by previous intern Jonah Lewis). Each time a whale surfaces, we use the theodolite to create a point in space that marks its location. Once we have multiple points, we can draw lines between each point to establish the track of the whale. These tracklines can then be used to make assumptions of the whales’ behavior. For example, if the trackline is straight, and the individual is moving at a consistent speed and direction, we can assume the whale is transiting. Whereas if the trackline is going back and forth in one small area, the whale is likely searching or foraging for food (Hildebrand et al., 2022).

In last week’s blog my peer Nautika Brown showed how photo ID is a critical part in our field methods. When theodolite tracking, we assign a number with each new individual whale observation. If the whale is close enough, we also capture photographs of the whale (Fig. 3) and match it up to its given number, allowing us to link the trackline to an individual whale so we can understand more about individual behavior. Documenting individual specific behavior is important because previous research has shown that age, size and the individual ID of a whale can all influence different foraging tactic use (Bird et al., 2024). Therefore, each season as we collect more and more data, we establish a repertoire of recurring or new behaviors to sieve for trends and patterns.

Fig. 3: Photo of a gray whale surfacing captured from our cliff site.

I find animal behavior to be an integral role in many ecological studies, and I am intrigued to explore this topic more. As marine mammals that spend most of their time underwater, cetaceans are quite an inconspicuous species to study (Bird et al., 2024), but by studying their ecology through photo ID and theodolite tracking we get insight into who they are, how they behave, and where they go.

Up until this point in the season, we have theodolite tracked gray whales for 12 hours and 3 minutes (woohoo). Interestingly, most of these tracks of whales have been near an area called “Hell’s Gate”, which is located around large rocks toward the far west of our study site (Figs. 2 and 4). We can assume, but cannot be sure, that the whales are feeding here because they spend so much time in the area, and return day after day. According to Dr. Torres, the consistent use of this area near Hell’s Gate by gray whales is unusual. In the prior 10 years of the TOPAZ project, few whales have been tracked foraging in this area near Hell’s Gate, but rather most whales have foraged in the Mill Rocks and Tichenor Cove areas. It is interesting to think about why the whales are behaving differently this year. Maybe this is due to variations in prey availability at these different sites. In recent years, Port Orford has been affected by a surge in purple sea urchin density, which have overgrazed the once prominent kelp forests here. A high urchin density decreases the kelp condition, which then leads to less habitat for zooplankton, creating a decline in prey availability for gray whales (Hildebrand et al., 2024). Upon reflection of my time on the kayak, I have noticed minimal kelp and low zooplankton abundance when conducting our zooplankton drops in our Mill Rocks and Tichenor Cove study sites. Additionally, I have also noticed many purple sea urchins in our GoPro videos. With the effects of this trophic cascade in mind, not observing any gray whales in our traditional study sites is understandable. With these gray whales more commonly seen near Hell’s Gate this year, I am curious to know what prey is attracting them there. Perhaps it is a different type of prey species or one that is high in caloric value than what is in the Mill Rocks and Tichenor Cove areas.

Fig. 4: Intern Nautika Brown looking at Hell’s Gate through the binoculars. Hell’s Gate is the passage between the two large boulders in the distance.

From actively observing whales and learning from my mentor, Celest, I have started to understand that behavior is a critical piece to any form of studying gray whales (and all species). By integrating photo-ID and theodolite tracking, we can learn so much about whale behavior, from where they eat, who is spending time where, and how they may adjust their behavior in response to a changing environment. The TOPAZ/JASPER internship has allowed me to truly comprehend what field research is like, how studying the behaviors of an individual is important, and how detail and patience are extremely necessary when collecting data. As this summer is continuing, I wonder if we will continue to see gray whales primarily feeding in the Hell’s Gate area, or if we will start to observe them more in the Mill Rocks and Tichenor Cove sites like previous years. The thrill of seeing gray whales is unlike any other, and I am so ready to see more whales this season!

References:

Bird, C. N., Pirotta, E., New, L., Bierlich, K. C., Donnelly, M., Hildebrand, L., Fernandez Ajó, A., & Torres, L. G. (2024). Growing into it: Evidence of an ontogenetic shift in grey whale use of foraging tactics. Animal Behaviour, 214, 121–135. https://doi.org/10.1016/j.anbehav.2024.06.004

Hildebrand, L., Derville, S., Hildebrand, I., & Torres, L. G. (2024). Exploring indirect effects of a classic trophic cascade between urchins and kelp on zooplankton and whales. Scientific Reports, 14(1), 9815. https://doi.org/10.1038/s41598-024-59964-x

Hildebrand, L, Sullivan, F. A., Orben, R. A., Derville. S., Torres L. G. (2022) Trade-offs in prey quantity and quality in gray whale foraging. Mar Ecol Prog Ser 695:189-201 https://doi-org.oregonstate.idm.oclc.org/10.3354/meps14115

A Nauti(k)al Journey with Photo ID  

Nautika Brown, GEMM Lab TOPAZ/JASPER Intern, recent Lake Roosevelt high school graduate 

Hi everyone! I’m Nautika Brown, a recent graduate at Lake Roosevelt High School in a small town on the Colville Indian Reservation in Washington.  

Growing up in beautiful Eastern Washington, I spent most all my days outside and, from the time I could swim, I was in the water. When I was little, I used to wish I was a fish so I could live underwater and swim every day of my life. And since then, I have always been fascinated by all animals that could live in and around water. This very fascination is what sparked the idea of becoming a marine biologist. Animals AND water, perfect! 

(Left): Nautika holding a fish she caught back home in Buffalo Lake.
(Right) Nautika with a new type of catch (purple sea urchin) while conducting a zooplankton drop at station MR 18.

Although, as you might assume, living on a reservation surrounded by wheat fields and a few lakes, there weren’t a lot of opportunities to explore my passion. Hence, when I came across a flyer for the 2025 TOPAZ/JASPER internship just a few days before the deadline, I submitted my application as soon as I could. I was so thrilled, I couldn’t imagine getting the chance to kayak with whales on the ocean! It was all I could talk about for weeks on end. 

Since starting my internship here in Port Orford, I have learned so many new things. During our first couple weeks at the field station, we went through a few different classes and trainings, one of them being a presentation on photo identification by GEMM Lab PhD candidate Lisa Hildebrand. Prior to this presentation, I had no idea photos were so important in marine mammal science. During this presentation, I learned about the many different identifiers of a whale and how you can apply them when looking at photos to identify a specific individual. For example, Lisa’s rule of three’s: to confidently ascertain an individual’s ID, at least 3 consistent characteristics between photos must be matched. At the end of this presentation, we even played a guessing game to test our new photo ID’ing skills. (I did pretty well – not to brag or anything.) 

Now with my new photo ID skills, I was excited to capture a photo of a gray whale. On our second day of training, we did spot a whale—but thanks to my newly learned photo-ID skills, I quickly realized it wasn’t the gray whale I was expecting. When the whale first surfaced, I noticed the lack of dorsal knuckles and its distinctly darker body—clear signs it wasn’t a gray whale, but a humpback whale! While it is common to see gray whales from shore along the Oregon coast as they feed in the very nearshore habitat, humpback whales are typically found in much deeper waters, further from shore. Over the last week we have seen a humpback whale within our study site across several days—and we’re not the only ones!  When chatting with the local fisherman pre and post kayak, a few have expressed their own excitement about seeing a humpback so close to shore as well. Throughout our conversations, the question of why a humpback would be so close to shore weighed on our minds, leading me to do my own online research.  

To investigate whether these humpback sightings have been of the same individual or multiple different whales, I decided to review the photos we have captured to try and determine a match. Once I conducted a first pass of the photos, I downloaded 10 of the most clear and definite shots and compared the photos using Lisa’s rule of threes. After reviewing the photos, I noticed that the humpback whale’s dorsal hump resembled one from a previous sighting, but I couldn’t find any other distinguishing markings on its body. While I couldn’t confirm we have been observing the same humpback whale, I gained a deeper understanding of the importance of clear, high-quality photos in photo-ID work.

(Left) Nautika getting ready to take pictures of whales with camera on our cliff site. 
(Right) Picture of humpback whale caught on camera on our 2nd day of training

After reading a few articles about humpback whale migration through Oregon, I found a few potential reasons behind this whale’s occurrence close to the shores of Port Orford. During the summer months, humpbacks travel to colder, more nutrient-dense places to feed, often near the shelf break (where the depth of the ocean suddenly gets deeper, around 200 m). Interestingly, the shelf break near Port Orford is not far from shore, and is a known hotspot for foraging humpback whales in the summer (Derville et al. 2022).  Humpback whales filter-feed on krill and small fish, so perhaps enough prey has moved into the waters near Port Orford to attract a humpback so close to shore. Another reason for this humpback to be close to shore could be the effects of climate change. As the waters warm, food distribution changes, causing multiple species, including humpbacks, to change their feeding grounds and migration routes (read more here).  Although the humpback sightings are outside the range of our kayak zooplankton sampling stations, it would be interesting to see what prey is in the water that is keeping them around.

So far, I have learned the importance of photo identification in marine mammal science and the many ways it can be used. I’m especially grateful for Lisa’s fun and insightful presentation at the start of the season and even more surprised by how quickly I was able to put those photo-ID skills into practice. With three weeks left in the field season, I’m excited to keep building on what I’ve learned and to keep growing my skills. And speaking of building, I’m also curious to see how my “kayak muscles” are shaping up by the end of this amazing TOPAZ/JASPER internship!  

  (Left) Nautika and Celest on kayak heading Mill Rocks stations. 
(Right) Miranda and Nautika wrapping up kayak training with a celebratory team dab

Derville, S., D.R. Barlow, C. Hayslip, and L.G. Torres, Seasonal, Annual, and Decadal Distribution of Three Rorqual Whale Species Relative to Dynamic Ocean Conditions Off Oregon, USA. Frontiers in Marine Science, 2022. 9: p. 868566.

A pinch of salty, silly, and science-y: meet Team Dabwich

Celest Sorrentino, GEMM Lab Master’s student, OSU Department of Fisheries, Wildlife and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab 

As a loyal and trusted GEMM Lab blog reader, I am sure you know just what time of year it is: the beginning of the 11th annual TOPAZ/JASPER field season where we study whales and their prey while also training the next generation of scientists. The start of the season has been kicked into high tail already and we have many updates to share. Fear not, dear reader, as I am here to release you from relentlessly refreshing your inbox for the long-awaited introduction of the TOPAZ/JASPER team that is taking the project into their second decade.

But first, to appreciate the present milestone, it’s worth revisiting the legacy of those who guided us to this moment. The TOPAZ/JASPER  projects began in 2015, with PI. Dr. Leigh Torres and master’s student Florence Sullivan (2015-2018), and continued forward with Lisa Hildebrand (2018-2021), and Allison Dawn (2022-2024). Now, as a new droplet in this stream of brilliant leaders before me, I feel immense gratitude to be the master’s student leading the TOPAZ/JASPER team this summer. Having been trained by Allison Dawn with Team Protein in 2024, and full unwavering support from Leigh and each leader before me, I enter this new role with confidence and excitement for the next six gray-whale-and-zooplankton filled weeks of data collection. Now, let’s meet the young scientist interns for 2025!

(Left picture) Maddie (right) with Nautika (top) and Celest (left) during their kayak training.
(Right picture) Photo Maddie took of a humpback in the Port Orford Bay.

Madison (Maddie) Honomichl is a senior wrapping up her last  semester of undergrad at CSU Monterrey Bay this fall to gain a degree in Marine Science. As the GEMM Lab’s REU intern this summer, Maddie began her internship in June by joining me in Newport to learn more about gray whale and pymgy blue whale mother-calf relationships. Without spoiling too much (you’ll hear more from her in her blog post in just a few weeks!) her project focuses on capturing mother-calf blow synchrony of gray and blue whales in drone footage. Now in Port Orford, her gifted talent for photography has been excellent in helping capture photos of traveling whales on the cliff.

(Left picture) Nautika finding a purple urchin after a successful zooplankton drop at our station MR 18.
(Right picture) Miranda(front) and Nautika(rear) after their first kayak training, where Nautika accidentally fell into the water but got back on the kayak in record breaking time, still in good spirits to dab!

Nautika Brown is one of our high school interns from Coulee Dam, Washington. Having just graduated, Nautika’s ambition and passion for studying wildlife lead her to apply to our TOPAZ/JASPER project and we are so happy she did. Accidentally hilarious, she has made everything from kayak training to zooplankton identification that much more enjoyable—reminding the team to have some fun while still getting the job done.

(Left picture) Dawson leading the team with the heavy theodolite stand up to the cliff.
(Right picture) The team hyper locked in on tracking a humpback whale in the bay, working together to describe the position of the whale for Dawson on the theodolite.

Dawson Mohney is our Port Orford local, having recently graduated from Pacific High School in May. Though he might not know the best spots around town, Dawson’s demeanor mirrors that of Port Orford itself: kind, welcoming, and always helpful. Always up for any task, he is the first to ask if anyone needs help with carrying equipment up to the cliff or cooking a ground beef refried beans mash for team dinner. Come fall Dawson is excited to start his first semester at Southwestern Oregon Community college.

(Left picture) Miranda enjoying an outdoor stroll of Port Orford beaches.
(Right picture) Miranda stoked on catching so many atylus tridens for her first kayak training day!

Miranda Fowles is a recent graduate at Oregon State University having completed her major in Fisheries, Wildlife, and Conservation Sciences with a minor in Spanish. Originally from Seattle, her childhood memories include kayaking with her family, so ocean kayaking has come naturally. Miranda’s genuine curiosity shines through in her eagerness to ask questions about whale life histories and their social dynamics. She’s expressed a clear passion for continuing her journey in marine science and academia.

We are now T-minus 2 days until the last of the team’s training period, and we couldn’t be more thrilled for the 4 more weeks to come. Through unexpected wildlife sightings and spontaneous team jokes, our team has only grown stronger and more connected. For all of the interns, this experience is not only their first experience with marine fieldwork, but also their longest. Training days have been both rewarding and physically strengthening; we’ve watched harbor seals lounging between Mill Rocks and tracked a particularly active humpback whale that keeps surfacing in the bay—all while developing what we now call our “ultimate kayak muscles.” By the time lunch rolls around, it feels like an ultimate power recharge, to continue forward with data processing. As any marine field scientist will tell you: there’s something deeply satisfying about coming back to shore and sinking your teeth into a handmade sandwich.

And speaking of our absolute craving for sandwiches, this team has unexpectedly brought back the 2010s dab—with such enthusiasm that it was only right to fuse “dab” with our love for chips-in-sandwiches. With this, I share with your our new, very official team name:

Team Dabwich.

With the right amount of salty, silly, and scienc-y, Team Dabwich is ready to crush the 11th TOPAZ/JASPER field season.

Team Dabwich dabbing right before a successful kayak training
ヽ(⌐_⌐ゞ)!

Demystifying AI: a brief overview of Image-Pre-Processing and a Machine Learning Workflow

Celest Sorrentino, MSc student, OSU Dept of Fisheries, Wildlife and Conservation Sciences, GEMM Lab

The first memory I have of A.I. (Artificial Intelligence) stems from one of my favorite movies growing up: I, Robot (2004). Shifting focus from the sci-fi thriller plot, the distinguished notion of a machine integrating into normal everyday life to perform human tasks, such as converse and automate labor, sparks intrigue. In 2014, my own realization of sci-fi fantasy turned reality initiated with the advertisements of self-driving cars by TESLA. But how does one go from a standard tool, like a vehicle, to an automated machine?

Fig 1: Tesla Self-Driving car, image by Bloomberg.com

For my first thesis chapter, I am applying a machine learning model to our lab’s drone video dataset to understand whale mother-calf associations, which is in continuation of my previous internship in 2022. A.I. has absolutely skyrocketed in marine science and hundreds of papers have confirmed the advantage in using machine learning models, such as in species abundance estimates (Boulent et al 2023), whale morphometrics (Bierlich et al 2024), and even animal tracking (Periera et al 2022). Specifically, Dr. KC Bierlich recently led a publication on an incredible A.I. model that can extract still images from drone footage to be subsequently used for body morphometric analysis. Earlier this year my lab mate Nat wrote an insightful blog introducing the history of A.I. and how she uses A.I. for image segmentation to quantify mysid swarms. For those of us who study animal behavior and utilize video-based tools for observation, A.I. is a sweet treat we’ve been craving to speed up and improve our analyses —but where do we start?

With a Venn Diagram and definitions of course!

Figure 1: Venn diagram demonstrating the relationships of 4 subsets of AI (Machine learning, Deep-learning, Computer Vision, and Natural Language Processing) and how they relate to one another.

Good terms to know:

Artificial Intelligence: a machine/model built to mimic human intelligence.

Machine Learning: a subset of A.I. that uses statistical algorithms to recognize patterns and form predictions, usually requiring human intervention for correction.

Deep-learning: a specific form of machine learning that is meant to mimic human neural networks through artificial neural networks (ANN) by recognizing hierarchal patterns with minimal to no human-intervention to correct.

Computer Vision: a type of machine learning that enables a machine/model to gather and retain information from images, video, etc.

Natural Language Processing: a subset of machine learning in which a machine/model to identify, understand, and create text and speech.

(Still a bit confused? A great example of the difference between machine learning and deep-learning can be found here)

So, you have a dataset, what’s the pipeline?

Figure 2: How to go from your research question and use your dataset to using an A.I. model.

First, we must consider what type of data we have and our question. In fact, you might find these two questions are complimentary: What type of questions does our dataset inspire and/or what type of dataset is needed to answer our question?

Responses to these questions can guide whether A.I. is beneficial to invest in and which type to pursue. In my case, we have an imagery dataset (i.e., drone videos) and our question explores the relationship of mom-calf proximity as an indicator of calf-independence. Therefore, a model that employs Computer Vision is a sensible decision because we need a model that extracts information from imagery. From that decision, I then selected SLEAP A.I. as the deep-learning model I’ll use to identify and track animals in video (Pereira et al 2022).

Figure 3: A broad schematic of the workflow utilizing a computer vision* model. As detailed above, a computer vision model is a machine learning model that uses images/videos as a dataset to retain information.

Why is image pre-processing important?

Image pre-processing is an essential step in “cleaning” the imagery data into a functional and insightful format for the machine learning model to extract information. Although tedious to some, I find this to be an exciting yet challenging step to push my ability to reframe my own perspective into another, a trait I believe all researchers share.

A few methods for image/video preprocessing include Resizing, Grayscaling, Noise Reduction, Normalization, Binarization, and Contrast enhancement. I found the following definitions and Python code by Maahi Patel to be incredibly concise and helpful. (Medium.com)

• Resizing: Resizing images to a uniform size is important for machine learning algorithms to function properly. We can use OpenCV’s resize() method to resize images.
• Grayscaling: Converting color images to grayscale can simplify your image data and reduce computational needs for some algorithms. The cvtColor() method can be used to convert RGB to grayscale.
• Noise reduction: Smoothing, blurring, and filtering techniques can be applied to remove unwanted noise from images. The GaussianBlur () and medianBlur () methods are commonly used for this.
• Normalization: Normalization adjusts the intensity values of pixels to a desired range, often between 0 to 1. This step can improve the performance of machine learning models. Normalize () from scikit-image can be used for this.
• Binarization: Binarization converts grayscale images to black and white by thresholding. The threshold () method is used to binarize images in OpenCV.
• Contrast enhancement: The contrast of images can be adjusted using histogram equalization. The equalizeHist () method enhances the contrast of images.

When deciding which between these techniques is best to apply to a dataset, it can be useful to think ahead about how you ultimately intend to deploy this model.

Image/Video Pre-Processing Re-framing

Notice the deliberate selection of the word “mimic” in the above definition for A.I. Living in an expeditiously tech-hungry world, losing sight of A.I. as a mimicry of human intelligence, not a replica, is inevitable. However, our own human intelligence derives from years of experience and exposure, constantly evolving – a machine learning model** does not have this same basis. As a child, we began with phonetics, which lead to simple words, subsequently achieving strings of sentences to ultimately formulate conversations. In a sense, you might consider these steps as “training” as we had more exposure to “data.” Therefore, when approaching image-preprocessing for the initial training dataset for an A.I. model, it’s integral to recognize the image from the lens of a computer, not as a human researcher. With each image, reminding ourselves: What is and isn’t necessary in this image? What is extra “noise”? Do all the features within this image contribute to getting closer to my question?

Model Workflow: What’s Next?

Now that we have our question, model, and “cleaned” dataset, the next few steps are: (II) Image/Video Processing, (III) Labeling, (IV) Model Training, (V) Model Predictions, and (VI) Model Corrections, which leads us to the ultimate step of (VII) A.I. Model Deployment. Labeling is the act of annotating images/videos with classifications the annotator (me or you) deems important for the model to recognize. Next, Model Training, Model Predictions, and Model Corrections can be considered an integrated part of the workflow broken down into steps. Model Training takes place once all labeling is complete, which begins the process for the model to perform the task assigned (i.e., object detection, image segmentation, pose estimation, etc.). After training, we provide the model with new data to test its performance, entering the stage of Model Predictions. Once Predictions have been made, the annotator reviews these attempts and corrects any misidentifications or mistakes, resulting in another round of Model Training. Finally, once satisfied with the model’s Performance, Model Deployment begins, which integrates the model into a “real-world” application.

In the ceaselessly advancing field of A.I., sometimes it can feel like the learning never ends. However, I encourage you to welcome the uncharted territory with a curious mind. Just like with any field of science, errors can happen but, with the right amount of persistence, so can success. I hope this blog has helped as a step forward toward understanding A.I. as an asset and how you can utilize it too!

**granted you are using a machine learning model that is not a foundation model. A foundation model is one that has been pre-trained on a large diverse dataset that one can use as a basis (or foundation) to perform specialized tasks. (i.e. Open A.I. ChatGPT).

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, 40(4). https://doi.org/10.1111/mms.13137

Boulent, J., Charry, B., Kennedy, M. M., Tissier, E., Fan, R., Marcoux, M., Watt, C. A., & Gagné-Turcotte, A. (2023). Scaling whale monitoring using deep learning: A human-in-the-loop solution for analyzing aerial datasets. Frontiers in Marine Science, 10. https://doi.org/10.3389/fmars.2023.1099479

Deep Learning vs Machine Learning: The Ultimate Battle. (2022, May 2). https://www.turing.com/kb/ultimate-battle-between-deep-learning-and-machine-learning

Jain, P. (2024, November 28). Breakdown: Simplify AI, ML, NLP, deep learning, Computer vision. Medium. https://medium.com/@jainpalak9509/breakdown-simplify-ai-ml-nlp-deep-learning-computer-vision-c76cd982f1e4

Pereira, T.D., Tabris, N., Matsliah, A. et al. SLEAP: A deep learning system for multi-animal pose tracking. Nat Methods 19, 486–495 (2022). https://doi.org/10.1038/s41592-022-01426-1

Patel, M. (2023, October 23). The Complete Guide to Image Preprocessing Techniques in Python. Medium. https://medium.com/@maahip1304/the-complete-guide-to-image-preprocessing-techniques-in-python-dca30804550c

Team, I. D. and A. (2024, November 25). AI vs. Machine learning vs. Deep learning vs. Neural networks. IBM/Think. https://www.ibm.com/think/topics/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks

The Tesla Advantage: 1.3 Billion Miles of Data. (2016). Bloomberg.Com. https://www.bloomberg.com/news/articles/2016-12-20/the-tesla-advantage-1-3-billion-miles-of-data?embedded-checkout=true

Wolfewicz, A. (2024, September 30). Deep Learning vs. Machine Learning – What’s The Difference? https://levity.ai/blog/difference-machine-learning-deep-learning

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!