Grad school growing pains

Clara Bird, PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

“What if I’m wrong? What if I make a mistake?” When I began my career after completing my undergraduate degree, these questions echoed constantly in my head as the stakes were raised and my work was taken more seriously. Of course, this anxiety was not new. As a student, my worst fear had been poor performance in class. Post-undergrad, I was facing the possibility of making a mistake that could impact larger research projects and publications. 

Gaining greater responsibility and consequences is a fact of life and an intrinsic part of growing up. As I wrap up my third year of graduate school, I’ve been reflecting on how learning to take on this responsibility as a scientist has been a crucial part of my journey thus far.  

A scientist’s job is to ask, and try to answer, questions that no one knows the answer to – which is both terrifying and exciting. It feels a bit like realizing that grown-ups don’t have all the answers as a kid. Becoming comfortable with the fact that my work often involves making decisions that no one definitively can say are wrong or right has been one of my biggest challenges of grad school. The important thing to remember, I’ve learned, is that I’m not making wild guesses – I’m being trained to make the best, most informed decisions possible. And, hopefully, with more experience will come greater confidence. 

Through grad school I have learned to take on this responsibility both in the field and the lab, although each brings different experiences. In the field, the stakes can feel higher because the decisions we make affect not just the quality of the data, but the safety of the team (which is always the top priority). I felt this most acutely throughout my first summer as a drone pilot. As a pilot, I am responsible for the safety of the team, the drone, and the quality of the data. As a new pilot, I intensely felt this pressure and would come home feeling more exhausted than usual. Now, in my second field season in this role, I’ve become more comfortable and am slowly building confidence in my abilities as I gain more and more experience. 

Video 1 – Two gray whales foraging together off Newport, Oregon, USA. I recorded this footage during my first season as a pilot – a flight I’ll never forget! NOAA/NMFS permit #21678.

I have also had a similar experience in the lab. Once it’s time to work on the analysis of a project, I choose how to clean, analyze, and interpret the data. As a young scientist, every step of the process involves learning new skills and making decisions that I don’t feel entirely qualified to make.  When I started analysis for my first PhD chapter, I felt overwhelmed by deciding how to standardize my data, what kind of analysis to perform, and what indices to calculate. And, since it’s my first chapter, I felt further overwhelmed by the worry that any decision I made would become a later regret in a future part of my PhD. 

Recently, the most daunting decision has been how to standardize my data. For my first chapter, I am investigating individual specialization of gray whale foraging behavior. The results of this question are not only important for conservation, but for my subsequent work (check out these previous blogs from January 2021and April 2022 for more on this research question). While there is a wealth of literature to draw analysis inspiration from, most of these studies use discrete prey capture data, while I am working with continuous behavior data. So, to make my data points comparable to one another, I need to standardize the behavior observation time of each drone flight to account for the potential bias introduced by recording one individual for more time than another. After experiencing an internal roller coaster of having an idea, thinking it through, deciding it was terrible and restarting the cycle, I was reminded that turning to lab mates and collaborators is the best way to work through a problem.

Image 1 – Comic from phdcomics.com, source: https://phdcomics.com/comics/archive.php?comicid=2008

So, I had as many conversations as I could with my advisor, committee members, and peers. My thinking clarified with every conversation, and I gained confidence in the justification behind my decision. I cannot fully express the comfort that comes from hearing a trusted advisor say, “that makes ecological sense to me”. These conversations have also helped me remember that I am not alone in my worry and that I am not failing because I have these doubts.  While I may never be 100% convinced that I’ve made the right decision, I feel much better knowing that I’ve talked it through with the brilliant group of scientists around me. And as I enter an analysis-intensive phase of my PhD, I am extremely grateful to have this community around to challenge, advise, and support me. 

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Reflections from this year’s 27th Annual Markham Research Symposium

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

The 27th Annual Markham Research Symposium was hosted at the Hatfield Marine Science Center (HMSC) last week. During the event, students who have been awarded funds and scholarships through HMSC present their research via poster presentations or 5-minute “ignite” talks. Given how isolated and mostly remote academic events have been during the COVID pandemic, it was invigorating to have an in-person research event. The timing of the symposium was also strategically planned to occur during the first week of Hatfield’s REU (Research Experiences for Undergraduates) students’ arrival, and it felt special to have such a diversity of ages and career stages coming together to discuss science. While I was certainly expecting to have good conversations about research and receive feedback on my work, I was most surprised by how much this event inspired me to reflect on my first year as a graduate student. For this week’s blog I’d like to share some of these reflections I had while listening to the excellent keynote address and interacting with students during the poster session.

The symposium began with a keynote address by Dr. Elizabeth Perotti who identifies as a scientist, communicator, and a parent. Dr. Perotti works as the Education and Outreach Coordinator for NOAA’s Ocean Acidification Program (OAP). I was expecting to hear a 45-minute presentation on the latest ocean acidification efforts, but I was surprised and appreciated that Dr. Perotti spent her time mainly focused on discussing career development through the lens of her own winding career path. While I would have been equally excited to hear about her science communication and outreach work, I am glad she took the time to share her story and give advice based on her experiences. As someone who used to feel insecure about my non-linear path to science, it was validating and inspiring to hear about the variety of experiences that prepared her to take on her current position at NOAA. Dr. Perotti describes her career path as “clear as mud”, but acknowledged that there were several key mentors who helped her identify and shape her specific interests. 

One of those mentors was the late Dr. Marian Diamond, who is renowned for her work on brain plasticity research. She was the first female science professor at Cornell and is considered one of the founders of modern neuroscience. She and her team pioneered the idea that the brain can change, and even improve, with the right stimulation. Dr. Diamond was the first person to study Einstein’s brain in the hopes of uncovering the secret to his high intelligence. She found that Einstein’s brain had more glial cells (which are now sometimes called “genius cells”) than the average person. These glial cells are known to nourish strong neuron connections and build a more complex brain structure. Dr. Diamond hypothesized that Einstein’s brain had more of these cells due to the high stimulation he put on his neurons. From the synthesis of this study and other fascinating experiments during her life’s work, Dr. Diamond suggested five core things the brain needs to continue development, regardless of age: diet, exercise, challenges, newness, and love. A healthy diet fuels the brain, exercise builds better brain cells, challenges and newness stimulate brain function, and love enriches our lives  – each of these factors are shown to contribute to the neuroplasticity of our brains (Diamond, 2001). During the keynote, Dr. Perotti asked the audience to contemplate if they are pursuing a career that is fulfilling at least one of those core requirements. As I contemplated these “brain essentials”, I realized how my experience as a Master’s student in the GEMM lab actually fulfills each one of these, and I am excited by the science that suggests I may be producing more “genius cells” because of it! 

Figure 1: Illustration showing Dr. Diamond’s suggested 5 core essentials for a healthy brain. Taken from: ​​https://blog.stannah-stairlifts.com/society/marian-diamond-women-in-science/

First, the diet I’ve had over the past year has certainly been nurturing. During the field season in Port Orford, one of my favorite meals is when we are given locally-sourced and sustainably caught fish from Port Orford Sustainable Seafood in exchange for helping them process orders. When I am back in Newport and Corvallis, my lab mates and peers are always sharing homemade snacks and we frequently get together for meals (and when the weather is nice – picnics!)

Figures 2 & 3: To the left: Locally sourced salmon cooked by Lisa Hildebrand for one of the many 2021 Port Orford team dinners; To the right: Colorful plates on an impromptu sunny day picnic with Rachel Kaplan. 

For exercise – it almost goes without saying that the field season in Port Orford is physically demanding. During data collection we are constantly alert and on our feet on the cliff site, or paddling continuously to stay on station to obtain good zooplankton and oceanographic samples.

Figure 4: Lisa Hildebrand and A. Dawn enjoying one of the last days of kayak sampling for the 2021 Port Orford field season.

Challenges – there are a variety of challenges to face as a new graduate student. Not only are there difficult, yet exciting questions to tackle, and new analysis skills to learn, but as Dr. Perotti discussed in her talk, there are also soft skills (communication, time/conflict management, task prioritization) that I am sharpening, which are equally important to master. 

Newness – as a graduate student, almost everything feels new. I frequently feel I am out of my comfort zone. Especially during the past three terms, I find myself in the mental “growth zone” consistently. Between my coursework and getting to attend exciting seminars, I consistently learn something new on a daily basis. Despite having completed a field season last year, leading the team this year will also be new, and I anticipate a steep learning curve where I am excited to learn how to be a better scientist and mentor.

Lastly, the love I have experienced since starting my Master’s degree has been one of my most treasured aspects of my life here – love for my lab family and for the opportunity I have to be here. After the symposium I got together with a few lab mates and we journeyed to Nye Beach to watch the sunset. I appreciate that despite our busy schedules, we all make time to connect with each other and explore the beautiful coast we are privileged to call home.

Figure 5: Watching the sunset on Nye Beach never gets old, especially when you are with good friends. Photo credit: C. Bird.

Just as I incorrectly assumed the keynote would be solely research focused, I anticipated answering in-depth questions about my preliminary Master’s thesis analysis results at the poster session. While I did receive great questions and valuable feedback from mentors, which has already helped shape the next steps in my analysis, the interactions I had with the REU student cohort was very different. These budding scientists were more interested in my personal outlook on graduate school, and asked many questions that felt familiar to me. I let the undergraduates know that it was only a year ago that I graduated with my B.S., and shared many of those same, daunting questions about the next chapter of my career: “How do you know if a program is right for you?”, “How do you pick the right advisor?”, “What type of working environment should I be looking for?”. It was fulfilling to be able to echo the great advice Dr. Perotti gave during the keynote address, in which she encouraged students to find mentors, know their talents, learn how to communicate, and take a challenge.

Figure 6: Posing next to my Markam Symposium poster, excited to share my proposed research with peers and mentors. Photo credit: Lisa Hildebrand

I am extremely grateful to have received one of this year’s Mamie Markham awards, and for the opportunity to interact with younger career scientists who I can share my journey and experiences with. The symposium was good practice in communicating my work and stimulating food for thought as I move forward with my second year in graduate school.

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References

Diamond, Marian (2001) Successful Aging of the Healthy Brain. Conference of the American Society on Aging and The National Council on the Aging March 10, 2001, New Orleans, LA

The Rockhopper: Interesting birds and technological advancements in marine bioacoustics research.

Miranda Mayhall, Graduate student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab.

Rockhopper Penguin. https://www.forestandbird.org.nz/resources/researcher-reveals-climate-impacts-eastern-rockhopper-penguins

Pursuing a graduate degree as a member of the Marine Mammal Institute (MMI) comes with many advantages. Developing associations with curious, industrious researchers and working with advanced technological methods are certainly two of them. Particularly, as a member of the HALO project, I have the pleasure of working alongside not only the GEMM’s, but also acoustician Dr. Holger Klinck and his bioacoustics team at the K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab (CCB) who have made significant contributions to advance the field for marine mammal research.

When the HALO project kicked off in October, 2021, Holger and graduate student, Marissa Garcia, arrived for our initial voyage off the Oregon coast with three specialized acoustic recording devices, called Rockhoppers. We deployed each Rockhopper at their designated locations, where they will remain and be replaced every six months, to collect continuous passive acoustic data of cetacean vocalizations. These data are significant because they gather information on all vocalizing whales and dolphins within a detectable range of the Rockhoppers, supporting not only my thesis work concerning fin whale distribution in the Northern California Current (NCC) but has the potential to inform multiple other research projects as well.   

Figure 1. Craig Hayslip, Holger Klinck, and Marissa Garcia prepare a Rockhopper for deployment during the first HALO cruise off the Oregon coast.

Passive acoustic monitoring (PAM) is a non-invasive underwater method of recording acoustic output of cetaceans (Zimmer, 2011), and the Rockhopper is specialized for this task. The Rockhopper relatively small (each weighing ~90lbs.) and can be easily deployed with a minimal team from almost any vessel (Fig 1). The mooring is a simple system that anchors the Rockhopper to the sea floor after it sinks through the water column, tolerating depths up to 3,500 m (Klinck et al., 2020). The device can stay on the ocean floor for up to seven months continuously collecting high-frequency data (up to 197 kHz, 24 bits; Klinck et al., 2020). To recover the Rockhopper, the mooring system (Fig 2) includes an acoustic release; when the correct acoustic signal is transmitted by scientists from the vessel and received down at the seafloor, the Rockhopper is released. It’s positive buoyancy allows it to float to the surface where it is recovered. By developing the Rockhopper with these capabilities, the bioacoustics team at Cornell University have taken several steps to enhance cetacean research.     

According to one of it’s designers, David Winiarski, the Rockhopper development team, consisting of himself, Holger Klinck, Raymond Mack, Christopher Tessaglia-Hymes, Dmitri Ponirakis, Peter Dugan, Christopher Jones, and Haru Matsumoto, initiated it’s construction in 2015. Winiarski states that Jones developed the Rockhopper’s initial PAM electronics at Embedded Ocean Systems (EOS), Boston, MA and then the rest of the team developed the remainder of the device in 2017. The Rockhopper contains the electronic system and a 10.8 V Lithium battery pack in an oil-filled Vitrovex 43 cm glass sphere that is encased in hard polyethelene. Two 64 GB memory cards store the collected acoustic data. About every hour the internal processing unit moves the data to two 4 Terabyte solid-state drives in a process that ensures the data is not lost (Klinck et al., 2020). Winiarski attests that it was quite a hectic process to get six complete Rockhoppers ready for their initial deployment, however the team succeeded and in May 2018 they were deployed in the Gulf of Mexico. The Rockhoppers were recovered in 2019 after six months, returning an amazing 21,522 hours of continuous acoustic data (Klinck et al., 2020).

Learning this information about the acoustic devices that will be responsible for collecting my Master’s thesis data is encouraging. I am eager to see the fin whale energy captured within the Rockhopper records. The HALO team, along with myself, Holger, and Marissa, will head back out off the Oregon coast to retrieve our three HALO-designated Rockhoppers in early June (next month). We will then spend the summer at Cornell reading through our first six months of data.

So, why call this acoustic device, the “Rockhopper”? Winiarski explained that since the CCB is a subsect of the Cornell Lab of Ornithology their projects tend to be named after birds. The Rockhopper team thought that this device should respectively be named after a cool marine megafauna. Hence the rockhopper penguin was chosen. I do agree that such an outstanding device is well suited in relation with an equally remarkable marine species.    

Left: Rockhopper penguins on a New Zealand hillside. https://nzbirdsonline.org.nz/species/eastern-rockhopper-penguin Upper right: Chris Tessaglia-Hymes and David Winiarski with a Rockhopper acoustic device. Lower right: The first six complete Rockhopper acoustic devices developed at the Cornell Center of Bioacoustics in 2017.

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References

Klinck, H., Winiarski, D., Mack, R., Tessaglia-Hymes, C., Ponirakis, D., Dugan, P., Jones, C., Matsumoto, H. 2020. The Rockhopper: a compact and extensible marine autonomous passive acoustic recording system,” Global Oceans 2020: Singapore – U.S. Gulf Coast: 1-7. https://ieeexplore.ieee.org/document/9388970

Zimmer, W. 2011. Passive acoustic monitoring of cetaceans. Cambridge University Press, Cambridge, UK.

New publication by GEMM Lab reveals sub-population health differences in gray whales 

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

In a previous blog, I discussed the importance of incorporating measurement uncertainty in drone-based photogrammetry, as drones with different sensors, focal length lenses, and altimeters will have varying levels of measurement accuracy. In my last blog, I discussed how to incorporate photogrammetric uncertainty when combining multiple measurements to estimate body condition of baleen whales. In this blog, I will highlight our recent publication in Frontiers in Marine Science (https://doi.org/10.3389/fmars.2022.867258) led by GEMM Lab’s Dr. Leigh TorresClara Bird, and myself that used these methods in a collaborative study using imagery from four different drones to compare gray whale body condition on their breeding and feeding grounds (Torres et al., 2022).

Most Eastern North Pacific (ENP) gray whales migrate to their summer foraging grounds in Alaska and the Arctic, where they target benthic amphipods as prey. A subgroup of gray whales (~230 individuals) called the Pacific Coast Feeding Group (PCFG), instead truncates their migration and forages along the coastal habitats between Northern California and British Columbia, Canada (Fig. 1). Evidence from a recent study lead by GEMM Lab’s Lisa Hildebrand (see this blog) found that the caloric content of prey in the PCFG range is of equal or higher value than the main amphipod prey in the Arctic/sub-Arctic regions (Hildebrand et al., 2021). This implies that greater prey density and/or lower energetic costs of foraging in the Arctic/sub-Arctic may explain the greater number of whales foraging in that region compared to the PCFG range. Both groups of gray whales spend the winter months on their breeding and calving grounds in Baja California, Mexico. 

Figure 1. The GEMM Lab field team following a Pacific Coast Feeding Group (PCFG) gray whale swimming in a kelp bed along the Oregon Coast during the summer foraging season. 

In January 2019 an Unusual Mortality Event (UME) was declared for gray whales due to the elevated numbers of stranded gray whales between Mexico and the Arctic regions of Alaska. Most of the stranded whales were emaciated, indicating that reduced nutrition and starvation may have been the causal factor of death. It is estimated that the population dropped from ~27,000 individuals in 2016 to ~21,000 in 2020 (Stewart & Weller, 2021).

During this UME period, between 2017-2019, the GEMM Lab was using drones to monitor the body condition of PCFG gray whales on their Oregon coastal feeding grounds (Fig. 1), while Christiansen and colleagues (2020) was using drones to monitor gray whales on their breeding grounds in San Ignacio Lagoon (SIL) in Baja California, Mexico. We teamed up with Christiansen and colleagues to compare the body condition of gray whales in these two different areas leading up to the UME. Comparing the body condition between these two populations could help inform which population was most effected by the UME.

The combined datasets consisted of four different drones used, thus different levels of photogrammetric uncertainty to consider. The GEMM Lab collected data using a DJI Phantom 3 Pro, DJI Phantom 4, and DJI Phantom 4 Pro, while Christiansen et al., (2020) used a DJI Inspire 1 Pro. By using the methodological approach described in my previous blog (here, also see Bierlich et al., 2021a for more details), we quantified photogrammetric uncertainty specific to each drone, allowing cross-comparison between these datasets. We also used Body Area Index (BAI), which is a standardized relative measure of body condition developed by the GEMM Lab (Burnett et al., 2018) that has low uncertainty with high precision, making it easier to detect smaller changes between individuals (see blog here, Bierlich et al., 2021b). 

While both PCFG and ENP gray whales visit San Ignacio Lagoon in the winter, we assume that the photogrammetry data collected in the lagoon is mostly of ENP whales based on their considerably higher population abundance. We also assume that gray whales incur low energetic cost during migration, as gray whale oxygen consumption rates and derived metabolic rates are much lower during migration than on foraging grounds (Sumich, 1983). 

Interestingly, we found that gray whale body condition on their wintering grounds in San Ignacio Lagoon deteriorated across the study years leading up to the UME (2017-2019), while the body condition of PCFG whales on their foraging grounds in Oregon concurrently increased. These contrasting trajectories in body condition between ENP and PCFG whales implies that dynamic oceanographic processes may be contributing to temporal variability of prey available in the Arctic/sub-Arctic and PCFG range. In other words, environmental conditions that control prey availability for gray whales are different in the two areas. For the ENP population, this declining nutritive gain may be associated with environmental changes in the Arctic/sub-Arctic region that impacted the predictability and availability of prey. For the PCFG population, the increase in body condition across years may reflect recovery of the NE Pacific Ocean from the marine heatwave event in 2014-2016 (referred to as “The Blob”) that resulted with a period of low prey availability. These findings also indicate that the ENP population was primarily impacted in the die-off from the UME. 

Surprisingly, the body condition of PCFG gray whales in Oregon was regularly and significantly lower than whales in San Ignacio Lagoon (Fig. 2). To further investigate this potential intrinsic difference in body condition between PCFG and ENP whales, we compared opportunistic photographs of gray whales feeding in the Northeastern Chukchi Sea (NCS) in the Arctic collected from airplane surveys. We found that the body condition of PCFG gray whales was significantly lower than whales in the NCS, further supporting our finding that PCFG whales overall have lower body condition than ENP whales that feed in the Arctic (Fig. 3). 

Figure 2. Boxplots showing the distribution of Body Area Index (BAI) values for gray whales imaged by drones in San Ignacio Lagoon (SIL), Mexico and Oregon, USA. The data is grouped by phenology group: End of summer feeding season (departure Oregon vs. arrival SIL) and End of wintering season (arrival Oregon vs. departure SIL). The group median (horizontal line), interquartile range (IQR, box), maximum and minimum 1.5*IQR (vertical lines), and outliers (dots) are depicted in the boxplots. The overlaid points represent the mean of the posterior predictive distribution for BAI of an individual and the bars represents the uncertainty (upper and lower bounds of the 95% HPD interval). Note how PCFG whales at then end of the feeding season (dark green) typically have lower body condition (as BAI) compared to ENP whales at the end of the feeding season when they arrive to SIL after migration (light brown).
Figure 3. Boxplots showing the distribution of Body Area Index (BAI) values of gray whales from opportunistic images collected from a plane in Northeaster Chukchi Sea (NCS) and from drones collected by the GEMM Lab in Oregon. The boxplots display the group median (horizontal line), interquartile range (IQR box), maximum and minimum 1.5*IQR (vertical lines), and outlies (dots). The overlaid points are the BAI values from each image. Note the significantly lower BAI of PCFG whales on Oregon feeding grounds compared to whales feeding in the Arctic region of the NCS.

This difference in body condition between PCFG and ENP gray whales raises some really interesting and prudent questions. Does the lower body condition of PCFG whales make them less resilient to changes in prey availability compared to ENP whales, and thus more vulnerable to climate change? If so, could this influence the reproductive capacity of PCFG whales? Or, are whales that recruit into the PCFG adapted to a smaller morphology, perhaps due to their specialized foraging tactics, which may be genetically inherited and enables them to survive with reduced energy stores?

These questions are on our minds here at the GEMM Lab as we prepare for our seventh consecutive field season using drones to collect data on PCFG gray whale body condition. As discussed in a previous blog by Dr. Alejandro Fernandez Ajo, we are combining our sightings history of individual whales, fecal hormone analyses, and photogrammetry-based body condition to better understand gray whales’ reproductive biology and help determine what the consequences are for these PCFG whales with lower body condition.

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References

Bierlich, K. C., Hewitt, J., Bird, C. N., Schick, R. S., Friedlaender, A., Torres, L. G., … & Johnston, D. W. (2021). Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Frontiers in Marine Science, 1729.

Bierlich, K. C., Schick, R. S., Hewitt, J., Dale, J., Goldbogen, J. A., Friedlaender, A.S., et al. (2021b). Bayesian Approach for Predicting Photogrammetric Uncertainty in Morphometric Measurements Derived From Drones. Mar. Ecol. Prog. Ser. 673, 193–210. doi: 10.3354/meps13814

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2018). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science35(1), 108–139.

Christiansen, F., Rodrı́guez-González, F., Martı́nez-Aguilar, S., Urbán, J., Swartz, S., Warick, H., et al. (2021). Poor Body Condition Associated With an Unusual Mortality Event in Gray Whales. Mar. Ecol. Prog. Ser. 658, 237–252. doi:10.3354/meps13585

Hildebrand, L., Bernard, K. S., and 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. Front. Mar. Sci. 8. doi: 10.3389/fmars.2021.683634

Stewart, J. D., and Weller, D. (2021). Abundance of Eastern North Pacific Gray Whales 2019/2020 (San Diego, CA: NOAA/NMFS)

Sumich, J. L. (1983). Swimming Velocities, Breathing Patterns, and Estimated Costs of Locomotion in Migrating Gray Whales, Eschrichtius Robustus. Can. J. Zoology. 61, 647–652. doi: 10.1139/z83-086

Torres, L.G., Bird, C., Rodrigues-Gonzáles, F., Christiansen F., Bejder, L., Lemos, L., Urbán Ramírez, J., Swartz, S., Willoughby, A., Hewitt., J., Bierlich, K.C. (2022). Range-wide comparison of gray whale body condition reveals contrasting sub-population health characteristics and vulnerability to environmental change. Frontiers in Marine Science. 9:867258. https://doi.org/10.3389/fmars.2022.867258

Shifts in planktonic community composition due to marine heatwaves (MHWs)

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

As the first year of my Master’s is coming to an end, I am excited to have completed the first milestone of writing my research proposal. During the formation of my initial hypotheses, I have been thinking deeply about the potential drivers of zooplankton variability, and how these metrics relate to the Pacific Coast Feeding Group (PCFG) of gray whales foraging in Port Orford. One topic that continues to appear in the literature and throughout my coursework is that of the extreme marine heat wave (MHW) event (2013-2016) in the Pacific Ocean, otherwise known as the “warm blob”. In Dawn’s (now Dr. Barlow!) blog about this MHW, she discusses how whale habitat in California was compressed due to shifts in prey availability, and how this led to an increased number of whale entanglements (Santora et al., 2020). While sea surface temperature (SST) is only one of many factors that influence prey metrics, it is nevertheless an important factor to consider, especially as these heat waves are expected to increase in intensity and duration due to climate change (Joh and Di Lorenzo, 2017). As Lisa mentioned in her last blog, the “warm blob” exacerbated the loss of kelp and sea stars, which is now impacting multiple trophic levels in Port Orford. For my first thesis chapter, I plan to dive into how SST anomalies impact the mosaic of interactions at our study site in Port Orford, and ultimately try to better understand food availability for the PCFG whales.

Cavole et al., 2016 is one of the early comprehensive studies to discuss the impact of the blob on a variety of planktonic marine species. Their sea surface temperature anomaly figure (Figure 1) shows where the anomaly began in 2013 and how it migrated from the Northern Pacific to the Southern Pacific coast.

Figure 1. Plots showing the SST anomalies as the “warm blob” migrated from the Northern Pacific to the Southern Pacific from 2013 until 2016.

Among many other impacts, this MHW caused a reduction in phytoplankton, the major food source for zooplankton. The decline of this food source subsequently caused significant changes in zooplankton populations. Specifically, studies on copepod diversity and biomass show that in a typical California Current System (CCS) there is a seasonal oscillation between warm-water with subtropical species and cold-water with subarctic species. In the winter, the CCS is characterized by a high diversity of subtropical species, due to a southern water source. In the spring, northern cold water advection brings low-diversity, subarctic copepods. While the timing of these shifts is subject to change due to changes in the Pacific Decadal Oscillation (PDO), it remains that these subtropical copepod species are known to be smaller and less nutritious than subarctic copepod species regardless of arrival time (Kintisch, 2015; Leising et al., 2015). However, in 2015, this shift to cold water copepod species did not occur, but rather coastal sampling along the Oregon coast saw subtropical copepod species prevail. Specifically, there were 17 main subtropical copepod species that dominated the species composition while the nutrient-rich arctic species were rare. This occurrence of major copepod shifts alone points to the overall concern for the ecosystem imbalance, to the detriment of top predators like marine mammals and seabirds (the “losers”), and others gaining advantage (the “winners”) (Figure 2).

Figure 2. Figure showing the “losers” (right column) and “winners” (left column) of MHW impacts. Species are organized by trophic level, with top predators at the bottom. Taken from Cavole et al., 2016.

More recent studies found that in certain areas, impacts from the “warm blob” outlived the duration of the larger scale anomaly. In fact, large, positive SST anomalies have lingered on the Oregon shelf until at least September 2017 (Peterson et al., 2017). During this time period, anomalously high abundances of nearshore larval North Pacific krill (Euphausia pacifica) were collected off of the Newport Hydrographic Station (Morgan et al., 2019). Additionally, Brodeur et al. (2019) demonstrate that while indicator species in the nearshore have consistent annual variability, there were substantial differences between community composition between 2011-2014 (low diversity) and 2015-2016 (high diversity). This work also documented the shift from crustacean species (like krill and mysids) to more low-quality gelatinous taxa. As the authors acknowledge, this change in prey community assemblage could have major negative impacts on trophic interactions. This is especially true in the context of whales, as they are not known to rely on gelatinous taxa for energy.

Just like our summer sampling in Port Orford, these studies only provide a “snapshot” of plankton species abundance and composition during a particular time of year. However, even a snapshot can reveal significant changes in prey variability, which then may help us understand the drivers of PCFG habitat utilization. We are actively investigating whether there have been significant changes in the variability of several zooplankton metrics (abundance, distribution, size class, composition) relative to SST changes in Port Orford over the last 6 years (2016-2021).

We will also consider multiple other static and dynamic factors that could influence zooplankton patterns (e.g., upwelling strength, kelp health, tidal height, topography); however, given these documented strong relationships between the zooplankton community and SST across the North Pacific, we hypothesize similar impacts in our Port Orford study region. For example, in certain sampling years, net tows seemed to be comprised of smaller size classes of zooplankton than usual. We will consider how size class availability has changed and if this was driven by SST variability. Gray whales are drawn to this area for enhanced feeding opportunities, and understanding the drivers of zooplankton, especially high quality prey, is a key step to understanding whale use of the area.

Please stay tuned for more updates as we continue working towards the answer to these pressing questions!

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References

Brodeur, R. D., Auth, T. D., & Phillips, A. J. (2019). Major shifts in pelagic micronekton and macrozooplankton community structure in an upwelling ecosystem related to an unprecedented marine heatwave. Frontiers in Marine Science, 6, 212.

Cavole, L. M., Demko, A. M., Diner, R. E., Giddings, A., Koester, I., Pagniello, C. M., … & Franks, P. J. (2016). Biological impacts of the 2013–2015 warm-water anomaly in the Northeast Pacific: winners, losers, and the future. Oceanography, 29(2), 273-285.

Joh, Y., & Di Lorenzo, E. (2017). Increasing coupling between NPGO and PDO leads to prolonged marine heatwaves in the Northeast Pacific. Geophysical Research Letters, 44(22), 11-663.

Kintisch, E. (2015). ‘The Blob’ invades Pacific, flummoxing climate experts.

​​Leising, A. W., Schroeder, I. D., Bograd, S. J., Abell, J., Durazo, R., Gaxiola-Castro, G., … & Warybok, P. (2015). State of the California Current 2014-15: Impacts of the Warm-Water” Blob”. California Cooperative Oceanic Fisheries Investigations Reports, 56.

Morgan, C. A., Beckman, B. R., Weitkamp, L. A., & Fresh, K. L. (2019). Recent ecosystem disturbance in the Northern California current. Fisheries, 44(10), 465-474.

NOAA Fisheries. 2015b. California Current Integrated Ecosystem Assessment (CCIEA) State of the California Current Report, 2015. NMFS Report 2.
Santora, J. A., Mantua, N. J., Schroeder, I. D., Field, J. C., Hazen, E. L., Bograd, S. J., … & Forney, K. A. (2020). Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nature communications, 11(1), 1-12.

A pregnancy test for whales?! Why and how?

Dr. Alejandro A. Fernández Ajó, Postdoctoral Scholar, Marine Mammal Institute – OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab.

I often receive two reactions when asked what I am currently working on; one is “Wow! That is a very cool job, it must be amazing to work with such incredible animals!”, the other is “How do you do that and why is that important?”. So, today I decided to blog about some of the reasons why it is important to develop a pregnancy test for gray whales and how we are doing this.

In a previous blogpost, I described the many ways in which whales play critical roles in sustaining marine ecosystem. Briefly, whales can enhance marine productivity by vertically and horizontally mixing of ocean waters, promoting primary production, and mitigating climate change by sequestering carbon with their large biomass and long life-span (1-3). Even after they die, their carcasses can contribute to biodiversity creating new habitat on the seafloor (4). But, over several decades, the whaling industry drastically removed whales around the globe, with some species and populations depleted to near extinction (5). Consequently, these depleted whale populations now play a diminished role in ocean ecosystem processes and their recovery is currently challenged by an increasing number of modern anthropogenic impacts. Hence, working towards whale conservation is essential for keeping a healthy marine ecosystem.

Working and designing effective strategies for conservation biology often involves gaining knowledge regarding the reproductive parameters of individual animals in wild populations. This information is critical for understanding population trends and the underlaying mechanisms that affect animal welfare and their potential for recovery. However, getting such information from free-living whales can be challenging (see Hunt et al. 2013). While we know that whales typically have long life-spans, lengthy generation times, extended parental care, and high survival rates, detailed knowledge on the life history and general reproductive biology of free-ranging whales is limited for the majority of the whale populations. In fact, much of what we do know about whale reproduction is derived from whaling records. Only recently, conservation physiology approaches (see our previous post here) have contributed alternative and non-invasive methods for monitoring key physiological processes that can help monitor a whale’s reproductive biology and determine reproductive parameters such as sexual maturity and pregnancy (6-9).

In this clip you can see an example of a fecal sample collection from a gray whale off the Oregon coast. We can look at hormones in the fecal samples which are useful indicators for endocrine assessments of free-swimming whales. Fecal sample and footage filmed under NOAA/NMFS permit #16111.

Gray whales (Eschrichtius robustus) in the Eastern North Pacific (ENP) typically undertake annual migrations between their lower latitude breeding grounds in the coastal waters of the Baja California Peninsula, Mexico, and the foraging grounds located on the Bering and Chukchi Seas (10). However, among the ENP whales a distinct subgroup of about 230 whales shorten their migration to feed in the coastal waters of Northern California, Oregon, and southeastern Alaska (11). This group of whales is known as the gray whale Pacific Coast Feeding Group (PCFG).

Since 2016, the GEMM Lab has monitored individual gray whales within the PCFG off the Oregon coast (check the GRANITE project). Gray whales have a distinct mottled skin; and each individual whale presents a unique pigmentation pattern that allows for the individual identification of whales. We can identify who is who among the whales who visit the Oregon coast. In this way, we can keep a detailed record of re-sightings of known individuals (visit our new web site to know more about the lives of individual whales that visit the Oregon coast).  We have high individual re-sighting rates, so this unique opportunity helps us keep a long-term data series for individual whales to monitor their health, body condition, and reproductive status over time, and thus further develop and advance our non-invasive study methods.

We are combining behavioral and feeding ecology with drone photogrammetry and endocrinology of the same individual whales to help us understand the relationships between natural and anthropogenic drivers with biological parameters. In this way, following individual whales, we are developing sensitive biomarkers to monitor and infer about the population health, population trends, and identify stressors that impact their recovery and welfare. In particular, we are now working to develop a noninvasive approach to detect pregnancy in gray whales based on fecal hormone analyses.

In this picture you can see “Rose”, a gray whale calf, on top of her mother “Scarlett”. Scarlett is one of the most recognizable whales from the PCFG, due to a large scar on the right side of her back (not visible in this picture). She has been observed along the Pacific NW coast since 1996, so she is at least 26 years old today. We know 3 of her calves. Following individual whales like Scarlett is helping us to better understand the gray whale reproductive biology. Photo by Alejandro Fernandez Ajo taken under NOAA/NMFS permit #21678.

In marine mammals, the progesterone hormone is secreted in the ovaries during the estrous cycle and gestation, and is the predominant hormone responsible for sustaining pregnancy (12). As the hormones are cleared from the blood into the gut, they are metabolized and eventually excreted in feces; fecal samples represent a cumulative and integrated concentration of hormone metabolites (13-14), which are useful indicators for endocrine assessments of free-swimming whales. Several studies show that changes in hormone concentration correlate in meaningful ways with exposure to stressors (15-16) and changes in reproductive status (17-19). We are using our long data series of fecal hormones and individual life histories to advance our understanding on the gray whales’ reproductive biology. We are close to developing a technique that will allow us to detect pregnancy in whales based in fecal hormones analyses and photogrammetry. Stay tuned for results from this pregnancy test!

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References:

1- Pershing AJ, Christensen LB, Record NR, Sherwood GD, Stetson PB (2010) The impact of whaling on the ocean carbon cycle: Why bigger was better. PLoS ONE 5(8): e12444.

2- Roman J and McCarthy JJ. 2010. The whale pump: marine mammals enhance primary productivity in a coastal basin. PLoS ONE. 5(10): e13255.

3- Morissette L, Kaschner K, and Gerber LR. 2010. “Whales eat fish”? Demystifying the myth in the Caribbean marine ecosystem. Fish Fish 11: 388–404.

4- Smith CR, Roman J, Nation JB. A metapopulation model for whale-fall specialists: The largest whales are essential to prevent species extinctions. J. Mar. Res. 77, 283–302 (2019).

5- Branch TA, Williams TM. Legacy of industrial whaling. Whales. Whal. Ocean Ecosyst. 2006, 262–278 (2006).

6- Kellar NM, Keliher J, Trego ML, Catelani KN, Hanns C, George JC, et al. Variation of bowhead whale progesterone concentrations across demographic groups and sample matrices. Endanger Species Res 2013; 22:61–72. https://doi.org/10.3354/esr00537.

7- Pallin L, Robbins J, Kellar N, Berube M, Friedlaender A. Validation of a blubber-based endocrine pregnancy test for humpback whales. Conserv Physiol 2018;6:1 11. https://doi.org/10.1093/conphys/coy031PMID:29942518.

8-Hunt KE, Robbins J, Buck CL, Bérubé M, Rolland RM (2019) Evaluation of fecal hormones for noninvasive research on reproduction and stress in humpback whales (Megaptera novaeangliae). Gen Comp Endocrinol 280: 24–34.

9-Melica, V., Atkinson, S., Calambokidis, J., Lang, A., Scordino, J., & Mueter, F. (2021). Application of endocrine biomarkers to update information on reproductive physiology in gray whale (Eschrichtius robustus). Plos one, 16(8), e0255368.

10-Swartz SL. Gray Whale. In: Wursig B, Thewissen JGM, Kovacs KM, editors. Encyclopedia of Marine Mammals (Third Edition). Elsevier;2018,p. 422–8.https://doi.org/10.1016/B978-0-12-804327-1.00140–0.

11-Calambokidis J, Darling JD, Deecke V, Gearin P, Gosho M, Megill W, et al. Abundance, range and movements of a feeding aggregation of gray whales (Eschrichtius robustus) from California to south-eastern Alaska in 1998. J Cetacean Res Manag 2002;4:267–76.

12- Bronson, F. H. (1989). Mammalian reproductive biology. University of Chicago Press.

13-Wasser SK, Hunt KE, Brown JL, Cooper K, Crockett CM, Bechert U, Millspaugh JJ, Larson S, Monfort SL (2000) A generalized fecal glucocorticoid assay for use in a diverse array of nondomestic mammalian and avian species. Gen Comp Endocrinol120:260–275.

14- Hunt, K.E., Rolland, R.M., Kraus, S.D., Wasser, S.K., 2006. Analysis of fecal glucocorticoids in the North Atlantic right whale (Eubalaena glacialis). Gen. Comp. Endocrinol. 148, 260–272. https://doi.org/10.1016/j.ygcen.2006.03.01215.

15- Lemos, L.S., Olsen, A., Smith, A., Burnett, J.D., Chandler, T.E., Larson, S., Hunt, K.E., Torres, L.G., 2021. Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Mar. Mammal Sci. 1–11. https://doi.org/10.1111/mms.12877

16- Rolland, R., McLellan, W., Moore, M., Harms, C., Burgess, E., Hunt, K., 2017. Fecal glucocorticoids and anthropogenic injury and mortality in North Atlantic right whales Eubalaena glacialis. Endanger. Species Res. 34, 417–429. https://doi.org/10.3354/esr00866.

17-Rolland, R.M., Hunt, K.E., Kraus, S.D., Wasser, S.K., 2005. Assessing reproductive status of right whales (Eubalaena glacialis) using fecal hormone metabolites. Gen. Comp. Endocrinol. 142, 308–317. https://doi.org/10.1016/j.ygcen.2005.02.002

18- Valenzuela Molina M, Atkinson S, Mashburn K, Gendron D, Brownell RL. Fecal steroid hormones reveal reproductive state in female blue whales sampled in the Gulf of California, Mexico. Gen Comp Endocrinol 2018;261:127–35.https://doi.org/10.1016/j.ygcen.2018.02.015 PMID:29476760.

19- Hunt, K. E., Robbins, J., Buck, C. L., Bérubé, M., & Rolland, R. M. (2019). Evaluation of fecal hormones for noninvasive research on reproduction and stress in humpback whales (Megaptera novaeangliae). General and Comparative Endocrinology, 280, 24-34.

The many dimensions of a fat whale: Using drones to measure the body condition of baleen whales 

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

In my last blog, I discussed how to obtain morphological measurements from drone-based imagery of whales and the importance of calculating and considering uncertainty, as different drone platforms have varying levels of measurement uncertainty. But how does uncertainty scale and propagate when multiple measurements are combined, such as when measuring body condition of the whole animal? In this blog, I will discuss the different methods used for measuring body condition of baleen whales from drone-based imagery and how uncertainty differs between these metrics.

Body condition is defined as the energy stored in the body as a result of feeding and is assumed to indicate an animal’s overall health, as it reflects the balance between energy intake and investment toward growth, maintenance and reproduction (Peig and Green, 2009). Thus, body condition reflects the foraging success of an individual, as well as the potential for reproductive output and the quality of habitat. For example, female North American brown bears (Ursus arctos) in high quality habitats were in better body condition, produced larger litter sizes, and lived in greater population densities compared to females in lower quality habitats (Hilderbrand et al., 1999). As Dawn Barlow and Will Kennerley discussed in their recent blog, baleen whales are top predators and serve as ecosystem sentinels that shed light not only on the health of their population, but on the health of their ecosystem. As ocean climate conditions continue to change, monitoring the body condition of baleen whales is important to provide insight on how their population and ecosystem is responding. 

As discussed in a previous blog, drones serve as a valuable tool for obtaining morphological measurements of baleen whales to estimate their body condition. Images are imported into photogrammetry software, such as MorphoMetriX (Torres and Bierlich, 2020), to measure the total length of an individual and that is then divided into perpendicular width segments (i.e., in 5 or 10% increments) down the body (Fig. 1). These total length and width measurements are then used to estimate body condition in either 1-, 2-, or 3-dimensions: a single width (1D), a projected dorsal surface area (2D), or a body volume measure (3D). These 1D, 2D, and 3D measurements of body condition can then be standardized by total length to produce a relative measure of an individual’s body condition to compare among individuals and populations. 

Figure 1. An example of a Pacific Coast Feeding Group (PCFG) gray whale measured in MorphoMetriX (Torres & Bierlich, 2020).

While several different studies have used each of these dimensions to assess whale body condition, it is unclear how these measurements compare amongst each other. Importantly, it is also unclear how measurement uncertainty scales across these multiple dimensions and influences inference, which can lead to misinterpretation of data. For example, the surface area and volume of two geometrically similar bodies of different sizes are not related to their linear dimensions in the same ratio, but rather to the second and third power, respectively (i.e., x2 vs. x3).  Similarly, uncertainty should not be expected to scale linearly across 1D, 2D, and 3D body condition measurements. 

The second chapter of my dissertation, which was recently published in Frontiers in Marine Science and includes Clara Bird and Leigh Torres as co-authors, compared the uncertainty associated with 1D, 2D, and 3D drone-based body condition measurements in three baleen whale species with different ranges in body sizes: blue, humpback, and Antarctic minke whales (Figure 2) (Bierlich et al., 2021). We used the same Bayesian model discussed in my last blog, to incorporate uncertainty associated with each 1D, 2D, and 3D estimate of body condition. 

Figure 2. An example of total length and perpendicular width (in 5% increments of total length) measurements of an individual blue, humpback and Antarctic minke whale. Each image measured using MorphoMetriX (Torres and Bierlich, 2020). 

We found that uncertainty does not scale linearly across multi-dimensional measurements, with 2D and 3D uncertainty increasing by a factor of 1.45 and 1.76 compared to 1D, respectively. This result means that there is an added cost of increased uncertainty when utilizing a multidimensional body condition measurement. Our finding is important to help researchers decide which body condition measurement best suits their scientific question,  particularly when using a drone platform that is susceptible to greater error – as discussed in my previous blog. However, a 1D measurement only relies on a single width measurement, which may be excluding other regions of an individual’s body condition that is important for energy storage. In these situations, a 2D or 3D measure may be more appropriate.

We found that when comparing relative measures of body condition (standardized by total length of the individual), each standardized metric was highly correlated with one another. This finding suggests that 1D, 2D, and 3D metrics will draw similar relative predictions of body condition for individuals, allowing researchers to be confident they will draw similar conclusions relating to the body condition of individuals, regardless of which standardized metric they use. However, when comparing the precision of each of these metrics, the body area index (BAI) – a 2D standardized metric – displayed the highest level of precision. This result highlights how BAI can advantageously detect small changes in body condition, which is useful for comparing individuals or even tracking the same individual over time.

BAI was developed by the GEMM Lab (Burnett et al., 2018) and was designed to be similar to body mass index (BMI) in humans [BMI = mass (kg)/(height (m))2], where BAI uses the calculated surface area as a surrogate for body mass. In humans, a healthy BMI range is generally considered 18.5–24.9, below 18.5 is considered underweight, above 24.9 is considered overweight, and above 30 is considered obese (Flegal et al., 2012). Identifying a healthy range in BAI for baleen whales is challenged by a limited knowledge of what a “healthy” body condition range is for a whale. We found strong evidence that a healthy range of BAI is species-specific, as each species displayed a distinctive range in BAI: blue whales: 11–16; AMW: 17–24; humpback whales: 23–32; humpback whale calves: 23–28 (Fig. 3). These differences in BAI ranges likely reflect differences in the body shape of each species (Fig. 4). For example, humpbacks have the widest range of BAI compared to these other two species, which was also reflected in their larger variation in perpendicular widths (Figs. 2-4). Thus, it seems that BAI offers conditionally “scalefree” comparisons between species, yet it is unreasonable to set a single, all-whale BAI threshold to determine “healthy” versus “unhealthy” body condition.  Collecting a large sample of body condition measurements across many individuals and demographic units over space and time with information on vital rates (e.g., reproductive capacity) will help elucidate a healthy BAI range for each species.

Figure 3. Body area index (BAI) for each species. AMW = Antarctic minke whale.  Figure from Bierlich et al. (2021).
Figure 4. A) Absolute widths (m) and B) relative widths, standardized by total length (TL) to help elucidate the different body shapes of Antarctic minke whales (AMW; n = 40), blue whales (n = 32), humpback whales (n = 40), and humpback whale calves (n = 15). Note how the peak in body width occurs at a different percent body width between species, demonstrating the natural variation in body shape between baleen whales. Figure from Bierlich et al. (2021).

Over the past six years, the GEMM Lab has been collecting drone images of Pacific Coast Feeding Group (PCFG) gray whales off the coast of Oregon to measure their BAI (see GRANITE Project blog). Many of the individuals we encounter are seen across years and throughout the foraging season, providing an opportunity to evaluate how an individual’s BAI is influenced by environmental variation, stress levels, maturity, and reproduction. These data will in turn help determine what the healthy range in BAI for gray whales is. For example, linking BAI to pregnancy – whether a whale is currently pregnant or becomes pregnant the following season – will help determine what BAI is needed to support calf production. We are currently analyzing hundreds of body condition measurements from 2016 – 2021, so stay tuned for upcoming results!

References

Bierlich, K. C., Hewitt, J., Bird, C. N., Schick, R. S., Friedlaender, A., Torres, L. G., … & Johnston, D. W. (2021). Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Frontiers in Marine Science, 1729.

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2018). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science35(1), 108–139.

Flegal, K. M., Carroll, M. D., Kit, B. K., & Ogden, C. L. (2012). Prevalence of Obesity and Trends in the Distribution of Body Mass Index Among US Adults, 1999-2010. JAMA307(5), 491. https://doi.org/10.1001/jama.2012.39

Hilderbrand, G. V, Schwartz, C. C., Robbins, C. T., Jacoby, M. E., Hanley, T. A., Arthur, S. M., & Servheen, C. (1999). The importance of meat, particularly salmon, to body size, population productivity, and conservation of North American brown bears. Canadian Journal of Zoology77(1), 132–138.

Peig, J., & Green, A. J. (2009). New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method. Oikos118(12), 1883–1891.

Torres, W., & Bierlich, K. C. (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software5(45), 1825–1826.

Hope lies in cooperation: the story of a happy whale!

By Solène Derville, Postdoc, OSU Department of Fisheries, Wildlife, and Conservation Science, Geospatial Ecology of Marine Megafauna Lab

I wrote my last blogpost in the midst of winter and feeling overwhelmed as I was trying to fly to the US at the peak of the omicron pandemic… Since then, morale has improved exponentially. I have spent two months in the company of my delightful GEMM lab friends, nerding over statistics, sharing scientific conversations, drinking (good!) beer and enjoying the company of this great group of people. During that stay, I was able to focus on my OPAL project more than I have ever been able to, as I set myself the goal of not getting distracted by anything else during my stay in Newport.

The only one distraction that I do not regret is a post I read one morning on the Cetal Fauna Facebook page, a group of cetacean experts and lovers who share news, opinions, photos… anything cetacean related! Someone was posting a photo of a humpback whale stranded in the 1990s’ on Peregian beach, on the east coast of Australia, which is known as a major humpback whale migratory corridor. The story said that (probably with considerable effort) the whale was refloated by many different individuals and organizations present at the beach on that day, specifically Sea World Research, Rescue & Conservation.

I felt very touched by this story and the photo that illustrated it (Figure 1). Seeing all these people come together in this risky operation to save this sea giant is quite something. And the fact that they succeeded was even more impressive! Indeed, baleen whales strand less commonly than toothed whales but their chances of survival when they do so are minimal. In addition to the actual potential damages that might have caused the whale to strand in the first place (entanglements, collisions, diseases etc.), the beaching itself is likely to hurt the animal in a permanent way as their body collapses under their own weight usually causing a cardiovascular failure (e.g., Fernández et al., 2005)⁠. The rescue of baleen whales is also simply impaired by the sheer size and weight of these animals. Compared to smaller toothed whales such as pilot whales and false killer whales that happen to strand quite frequently over some coastlines, baleen whales are almost impossible to move off the beach and getting close to them when beached can be very dangerous for responders. For these reasons, I found very few reports and publications mentioning successful rescues of beached baleen whales (e.g., Priddel and Wheeler, 1997; Neves et al., 2020).⁠

Figure 1: Stranded humpback whale in Peregian Beach, East Australia, on Aug 16th 1991. Look at the size of the fluke compared to the men who are trying to rescue her! Luckily, that risky operation ended well. Credit: Sea World Research, Rescue and Conservation. Photo posted by P. Garbett on https://www.facebook.com/groups/CetalFauna – February 26, 2022)

Now the story gets even better… the following day I received an email from Ted Cheeseman, director and co-founder of Happywhale, a collaborative citizen science tool to share and match photographes of cetaceans (initially only humpback whales but has extended to other species) to recognize individuals based on the unique patterns of the their fluke or dorsal fin. The fluke of the whale stranded in Australia in 1991 had one and only match within the Happywhale immense dataset… and that match was to a whale seen in New Caledonia (Figure 2). “HNC338” was the one!

Figure 2: Happy whale page showing the match of HNC338 between East Australia and New Caledonia. https://happywhale.com/individual/78069;enc=284364?fbclid=IwAR1QEG_6JkpH_k2UrF-qp-9qrOboHYakKjlTj0lLbDFygjN5JugkkKVeMQw

Since I conducted my PhD on humpback whale spatial ecology in New Caledonia, I have continued working on a number of topics along with my former PhD supervisor, Dr Claire Garrigue, in New Caledonia. Although I do not remember each and every whale from her catalogue (composed of more than 1600 humpback whales as of today), I do love a good “whale tale” and I was eager to know who this HNC338 was. I quickly looked into Claire’s humpback whale database and sure enough I found it there: encountered at the end of the 2006 breeding season on September 12th, at a position of 22°26.283’S and 167°01.991’E and followed for an hour. Field notes reported a shy animal that kept the boat at a distance. But most of all, HNC338 was genetically identified as a female and was accompanied by a calf during that season! The calf was particularly big, as expected at this time of the season. What an inspiring thing to think that this whale, stranded in 1991, was resighted 15 years later in a neighboring breeding ground, apparently healthy and raising a calf of her own.

As genetic paternity analysis have been conducted on many New Caledonia calf biopsy samples as part of the Sexy Singing project conducted with our colleagues from St Andrews University in Scotland, we might be able to identify the calf’s father in this breeding stock. Thanks to the great amount of data shared and collected through Happywhale, we are discovering more and more about whale migratory patterns and behavior. It might as well be that this calf’s father was one of those whales that seem to roam over several different breeding grounds (New Caledonia and East Australia). This story is far from finished…

Figure 3: A (pretty bad!) photo of HNC338’s fluke. Luckily the Happywhale matching algorithm is very efficient and was able to detect the similarities of the fluke’s trailing edge compared to figure 1 (Cheeseman et al., 2021)⁠. Also of note, see that small dorsal fin popping out of the waters behind big mama’s fluke? That’s her calf!

From the people who pulled this whale back into the water in 1991, to the scientists and cetacean enthusiasts who shared their data and whale photos online, this story once again shows us that hope lies in cooperation! Happywhale was only created in 2015 but since then it has brought together the general public and the scientists to contribute over 465,000 photos allowing the identification of 75,000 different individuals around the globe. In New Caledonia, in Oregon and elsewhere, I hope that these collective initiatives grow more and more in the future, to the benefit of biodiversity and people.

Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get weekly updates and more! Just add your name into the subscribe box on the left panel. 

References

Cheeseman, T., Southerland, K., Park, J., Olio, M., Flynn, K., Calambokidis, J., et al. (2021). Advanced image recognition: a fully automated, high-accuracy photo-identification matching system for humpback whales. Mamm. Biol. doi:10.1007/s42991-021-00180-9.

Fernández, A., Edwards, J. F., Rodríguez, F., Espinosa De Los Monteros, A., Herráez, P., Castro, P., et al. (2005). “Gas and fat embolic syndrome” involving a mass stranding of beaked whales (Family Ziphiidae) exposed to anthropogenic sonar signals. Vet. Pathol. 42, 446–457. doi:10.1354/vp.42-4-446.

Neves, M. C., Neto, H. G., Cypriano-Souza, A. L., da Silva, B. M. G., de Souza, S. P., Marcondes, M. C. C., et al. (2020). Humpback whale (megaptera novaeangliae) resighted eight years after stranding. Aquat. Mamm. 46, 483–487. doi:10.1578/AM.46.5.2020.483.

Priddel, D., and Wheeler, R. (1997). Rescue of a Bryde’s whale Balaenoptera edeni entrapped in the Manning River, New South Wales: Unmitigated success or unwarranted intervention? Aust. Zool. 30, 261–271. doi:10.7882/AZ.1997.002.

Cross-taxa collaborations: a look at the value of human and cetacean partnerships.

Miranda Mayhall, Graduate student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab.

For marine science to be successful and impactful, it is crucial for collected data and results of analyses to be shared as widely as possible. This sharing should occur with the research community itself (which of course saves time and helps ignite the big, impactful ideas), and also amongst the public, in government, the fishing industry, big energy businesses, the military, and shipping industries as well. All these entities can relate in some way to the use of the oceans. Our increased collective knowledge can help us make conscious and intelligent management choices that will promote healthy oceans and in turn provide more resources to humans as well.

Though I am only just breaking the ice in my marine science education, I am already experiencing my first tastes of what this collaboration can look like. My graduate thesis focuses on the acoustic and observational detections of fin whales, an endangered species, as they relate to environmental characteristics in the NE Pacific. I am still in the early stages collecting data with the HALO project, but for now it is important to get started reviewing what’s currently available in the field. GEMM lab’s OPAL project, led by Dr. Leigh Torres and Dr. Solene Derville, was quick to provide me with their fin whale sightings data collected over the past few years, as well as share some of their great fin whale photos (Fig. 1). Clearly, I am already becoming rich through this association.

Figure 1. Two fin whales surface off the Oregon coast. Photographed by Leigh Torres during an OPAL helicopter survey in September 2021 under NMFS permit # 21678.

My career interests revolve around filling knowledge gaps of cetacean behaviors, so I often find myself associating what’s happening in my life to what I am reading currently as it relates to this field of research. My most recent blog, highlighted my need to relax occasionally with play and prompted me to consider how play is defined in cetacean behavior. So, with the ignition of my graduate research and this first aforementioned taste of scientific collaboration, I synaptically thought about a recent study of interspecies collaborative hunting between dolphins and humans that was co-authored by the Marine Mammal Institute’s Dr. Mauricio Cantor. Here, bottlenose dolphins who have learned to herd fish to shore, stick together and use their skills to move schools of fish toward local fishermen standing by with nets. The dolphins then provide a signal to the fishermen, the nets are cast at just the right time, and the dolphins forage on the fish trapped between the fishermen and the nets (Daura-Jorge, Cantor, et al., 2012). Both the dolphins and the fishermen greatly benefit by working together. I found this study thought-provoking; I have not seen anything quite like this interspecies association.

National Geographic video provides close perspective of the Laguna, Brazil fishermen working together with dolphins to net fish. https://www.youtube.com/watch?v=8kMGJ8T3-Pg.

In the interest of potentially finding more cross-taxa cetacean relationships, I dug into the literature and found a few more interspecies associations to note. The first article that took me aback was a 2017 report detailing humpback whales defending other marine mammal species by interfering with the hunting practices of transient killer whales (Pitman et al., 2017). Killer whales are apex predators who hunt marine mammals, to include pinnipeds, adult baleen whales and often the calves of baleen whales. Slow, rotund baleen whales (right whales, gray whales, and humpbacks) are known to use their immense size and large appendages to fight off killer whales. What is unique with this study is that humpback whales were observed not only protecting their own calves from predation but also using a mobbing tactic to protect other cetacean species (minke whales, gray whales, Dall’s porpoises, and others) and pinnipeds (Steller sea lions, California sea lions, Weddell seals, and others; Fig. 2) as well, showing acts of potential altruism in cetaceans (Pitman et al., 2017).

Figure 2. Humpback whale moving in to interfere with a killer whale hunting a seal. Photo credit: Robert Pitman, https://whalescientists.com/humpback-whales-altruism/.

The next interspecies association catching my eye came from studies detailing the two largest marine mammals, blue and fin whales, reproducing together. Though the two species are relatively alike in having large sleek physiques, they are very different in their known migratory and acoustic behaviors, so it doesn’t seem obvious or likely the two would mate. However, following the genetic testing of a whale near Iceland that displayed an unusual phenotype, researchers were able to determine that the whale did in fact contain the DNA of both species (Pampoulie et al., 2020). These blue/fin hybrids have been spotted in several locations worldwide and they are even found to be fertile. A recent study of a successfully tagged and observed blue/fin hybrid called, “Flue” (Fig. 3), co-authored by Dr. Daniel Palacios of MMI’s WHET Lab, found that though the animal possessed a phenotype mostly descriptive of fin whale, Flue appeared to follow blue whale migratory behavior (moving farther north along the California coast to forage in the summer and then moving to southern breeding ground waters along the coast of Mexico). These researchers suggest that blue/fin hybrid whales are common and postulate whether these animals are the source of an unmatched 52 Hz whale call sometimes recorded in the North Pacific (Jefferson et al., 2021).

Figure 3. Highly observed and documented blue/fin whale hybrid, called “Flue”, spotted off the coast of Santa Barbara, CA, USA. Photo credit: Adam Ernster, Condor Express Media, https://www.youtube.com/watch?v=4LjH2-naRPE&feature=youtu.be&app=desktop.

Lastly (and perhaps my favorite of the papers of the collection), there is a report published in 2019 detailing a closely followed bottlenose dolphin female who adopted a young melon-headed whale calf near French Polynesia in the South Pacific (Fig. 4). Though cetaceans have been known to participate in allonursing, a form of alloparental care in which adult females will nurse another’s offspring of the same species, an interspecific adoption has rarely been reported. This mother-calf interspecies pair were observed together just after the adoptive mother gave birth to another calf, so it was impossible that the adopted calf was a potential hybrid. Furthermore, the two species have overlapping populations in this area of the South Pacific and thus it was concluded that the female dolphin had accepted a lost calf as her own (Carzon et al., 2019). Lactation is energetically costly, and considering the dolphin already had another calf to feed, the fact that she accepted the adopted calf, was observed nursing it, and developed a lengthy bond with it is remarkable.

Figure 4. Bottlenose dolphin female with her adopted melon-headed whale calf near French Polynesia in the South Pacific (Carzon et al., 2019).

I admit it was more fun than work to dig into these interspecies associations this week, because they depict how rich our world can be when animals (including humans) evoke positive associations across taxa. Reverting into my fin whale research, I cannot wait to see where my analysis will lead. I am eager to share my results, begin collaborations with other researchers and eventually present it to the public with the hopes of developing positive associations between humans and the marine world.

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Literature Cited

Carzon, P., Delfour, F., Dudzinski, K. et al. 2019. Cross-genus adoption in delphinids: One example with taxonomic discussion. Ethology: Behavioral Notes, 125: 669-676.

Daura-Jorge, F., Cantor, M., Ingram, S. et al. 2012. The structure of a bottlenose dolphin society is coupled to a unique foraging cooperation with artisanal fisherman. Biology Letters, 8: 702-705.

Jefferson, T., Palacios, D., Calambokidis, J. et al. 2021. Sightings and satellite tracking of a blue/fin whale hybrid in its wintering and summering ranges in the eastern north pacific. Advances in Oceanography & Marine Biology, 2 (4). http://dx.doi.org/10.33552/AOMB.2021.02.000545.  

Pampoulie, C., Gislason, D., Olafsdottir, G. et al. 2020. Evidence of unidirectional hybridization and second-generation adult hybrid between the two largest animals on Earth, the fin and blue whales. Evolutionary Applications, 14: 314-321.

Pitman, R., Deecke, V., Gabriele, C., et al. 2016. Humpback whales interfering when mammal-eating killer whales attack other species: Mobbing behavior and interspecific altruism? Marine Mammal Science, 33 (1): 7-58. https://doi.org/10.1111/mms.12343.

Social turmoil due to the approval of an offshore oil exploration project off the coast of Argentina.

Dr. Alejandro A. Fernández Ajó, Postdoctoral Scholar, Marine Mammal Institute – OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab.

I just returned to my home country, Argentina, after over 2 years without leaving the USA due to COVID-19 travel restrictions. Being back with my family, my friends, my culture, and speaking my native language feels great and relaxing. However, I returned to a country struggling to rebound from the coronavirus pandemic. I am afraid this post pandemic scenario places Argentina in a vulnerable situation. The need for economic growth could result in decisions or policies that, in the long term, hurt the country, leaving environmental damage for potential economic growth.

Argentina holds extensive oil and gas deposits, including the world’s second largest gas formation, Vaca Muerta. Although offshore (i.e., in the ocean) oil exploration and exploitation are not yet extensively developed, the intention of offshore gas and oil drilling is on the agenda. In July 2021, a public hearing was held with the purpose to consider the environmental impact assessment for carrying out seismic exploration in the North Argentinian basin off the southern coast of the Buenos Aires province. Over 90% of the participants, including scientists, researchers, technicians from various institutions, non-governmental organizations and representatives of the fishing sector spoke against the project and highlighted the negative impacts that such activity can generate on marine life, and to other socioeconomic activities such as tourism and fishing, not only in Argentina but at the regional level.

Thousands of people marched along the beaches and the main coastal cities of Argentina to protest against the approval for a seismic explorations project in the Argentinian basin. Photo source: prensaobrera.com

Seismic prospections are usually done with the purpose for oil and gas exploitation and less frequently for research purposes. In seismic prospections, ships carry out explosions with airguns, whose sound waves reach the seabed, bounce back and are captured by receivers on the ships to map the petroleum deposits in seafloor and identify potential areas for hydrocarbon extractions. The sound emitted by the seismic airguns can reach extremely loud levels of sounds that travel for thousands of miles underwater. Such extreme high levels of sound can alter the behavior of many marine species, from the smallest planktonic species, to the largest marine mammals, masking their communication, causing physical and physiological stress, interfering with their vital functions, and reducing the local availability of prey (Di Iorio & Clark, 2010; Hildebrand, 2009; Weilgart, 2018).

Here you can listen to a short audio clip of a seismic airgun firing every ~8 seconds, a typical pattern. Close your eyes and imagine you are a whale living in this environment. Now, put the clip on loop and play it for three months straight. This would be the soundscape that whales living in a region of oil and gas exploration hear, as seismic surveys often last 1-4 months (see our previous post “Hearing is believing” for more details).

Despite the public rejection and the mounting evidence about the negative impacts and environmental risks associated with such activities, the government approved the initiation of the seismic prospection off the southern coast of Buenos Aires. In response, thousands of people marched along the beaches and the main coastal cities of Argentina to protest against the oil exploration project. The areas where the seismic surveys will be carried out overlap largely with the southern right whale’s migration routes and feeding areas during their spring and summer (Figure 1). Likewise, the area overlaps with highly productive areas in the ocean that hosts great biodiversity of species of ecological and commercial importance, including the feeding areas of seabirds, turtles and other marine mammals. Additionally, the seismic activity will endanger the health of the beaches of the coast of Buenos Aires and Uruguay where thousands of tourists spend the summer to escape from the large cities.

Figure 1. The map on the left is showing (light blue squares CAN_100, CAN_108, and CAN_114) the areas where seismic prospections are proposes. The map on the right is showing the individual satellite track lines for eleven individual southern right whales (SRW) during the feeding season. You can observe that the proposed area for seismic explorations overlaps with critical feeding habitat for the SRW. Source: Whale Conservation Institute of Argentina (ICB).

The impacts of these activities to marine wildlife are difficult to control and monitor (Elliott et al. 2019, Gordon et al, 2003), especially for large whales that are a very challenging taxa to study (Hunt et al. 2013). We know that the ability to perceive biologically important sounds is critical to marine mammals, and acoustic disturbance through human-generated noise can interfere with their natural functions. Sounds from seismic surveys are intense and have peak frequency bands overlapping those used by baleen whales (Di Lorio & Clark, 2010); however, evidence of interference with baleen whale acoustic communication, and the effects on their health and physiology are sparse. In this context, the GEMM Lab project GRANITE (Gray Whale Response to Ambient Noise Informed by Technology and Ecology), plans to generate information to fulfill these knowledge gaps and provide tools to aid conservation and management decisions in terms of allowable noise level in whale habitats. I am hopeful such information will reach decision makers and influence their decisions, however, sometimes it is frustrating to see how evidence-based information generated with high quality standards are often ignored.

The recent approval of the seismic exploration in Argentina is an example of my frustration. There is no way that the oil industry can guarantee a low-risk of impact on biodiversity and the environment. There are too many examples of environmental catastrophes related to the oil industries at sea that speak for themselves. Moreover, the promotion of such activities goes against the compromises assumed by the country to work to mitigate the effects of Climate Change, and to achieve the reductions of the greenhouse gas emissions to comply with the Paris Agreement. Decades of research help recognized the areas that would be impacted by these seismic activities as key habitat for the life cycle of whales, penguins, seals and more. But, apparently all these scientific data are ignored at the time of weighing the tradeoffs between “economic development” and environmental impacts. As a conservation biologist, I am questioning what can be done in order to be heard and significantly influence such decisions.

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References:

  • Di Iorio, L., & Clark, C. W. (2010). Exposure to seismic survey alters blue whale acoustic communication. Biology Letters, 6(1), 51–54. https://doi.org/10.1098/rsbl.2009.0651
  • Weilgart, L. (2018). The impact of ocean noise pollution on fish and invertebrates. Report for OceanCare, Switzerland.
  • Elliott, B. W., Read, A. J., Godley, B. J., Nelms, S. E., & Nowacek, D. P. (2019). Critical information gaps remain in understanding impacts of industrial seismic surveys on marine vertebrates. In Endangered Species Research (Vol. 39, pp. 247–254). Inter-Research. https://doi.org/10.3354/esr00968
  • Gordon, J., Gillespie, D., Potter, J., Frantzis, A., Simmonds, M. P., Swift, R., & Thompson, D. (2003). A review of the effects of seismic surveys on marine mammals. Marine Technology Society Journal37(4), 16-34.
  • Hunt, K. E., Moore, M. J., Rolland, R. M., Kellar, N. M., Hall, A. J., Kershaw, J., Raverty, S. A., Davis, C. E., Yeates, L. C., Fauquier, D. A., Rowles, T. K., & Kraus, S. D. (2013). Overcoming the challenges of studying conservation physiology in large whales: a review of available methods. Conservation Physiology, cot006–cot006. https://doi.org/10.1093/conphys/cot006