The whales keep coming and we keep learning: a wrap up of the eighth GRANITE field season.

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

As you may remember, last year’s field season was a remarkable summer for our team. We were pleasantly surprised to find an increased number of whales in our study area compared to previous years and were even more excited that many of them were old friends. As we started this field season, we were all curious to know if this year would be a repeat. And it’s my pleasure to report that this season was even better!

We started the season with an exciting day (6 known whales! see Lisa’s blog) and the excitement (and whales) just kept coming. This season we saw 71 individual whales across 215 sightings! Of those 71, 44 were whales we saw last year, and 10 were new to our catalog, meaning that we saw 17 whales this season that we had not seen in at least two years! There is something extra special about seeing a whale we have not seen in a while because it means that they are still alive, and the sighting gives us valuable data to continue studying health and survival. Another cool note is that 7 of our 12 new whales from last year came back this year, indicating recruitment to our study region.

Included in that group of 7 whales are the two calves from last year! Again, indicating good recruitment of new whales to our study area. We saw both Lunita and Manta (previously nick-named ‘Roly-poly’) throughout this season and we were always happy to see them back in our area and feeding on their own.

Drone image of Lunita from 2023
Drone image of Manta from 2023

We had an especially remarkable encounter with Lunita at the end of this season when we found this whale surface feeding on porcelain crab larvae (video 1)! This is a behavior that we rarely observe, and we’ve never seen a juvenile whale use this behavior before, inspiring questions around how Lunita knew how to perform this behavior.

Not only did we resight our one-year-old friends, but we found two new calves born to well-known mature females (Clouds and Spotlight). We had previously documented Clouds with a calf (Cheetah) in 2016 so it was exciting to see her with a new calf and to meet Cheetah’s sibling! Cheetah has become one of our regulars so we’re curious to see if this new calf joins the regular crew as well. We’re also hoping that Spotlight’s calf will stick around; and we’re optimistic since we observed it feeding alone later in the season.

Collage of new calves from 2023! Left: Clouds and her calf, Center: Spotlight and her calf, Right: Spotlight’s calf independently foraging

Of course, 71 whales means heaps of data! We spent 226 hours on the water, conducted 132 drone flights (a record!), and collected 61 fecal samples! Those 132 flights were over 64 individual whales, with Casper and Pacman tying for “best whale to fly over” with 10 flights each. We collected 61 fecal samples from 26 individual whales with a three-way tie for “best pooper” between Hummingbird, Scarlett, and Zorro with 6 fecal samples each. And we continued to collect valuable prey and habitat data through 80 GoPro drops and 79 zooplankton net tows.

And if you were about to ask, “but what about tagging?!”, fear not! We continued our suction cup tagging effort with a successful window in July where we were joined by collaborators John Calambokidis from Cascadia Research Collective and Dave Cade from Hopkins Marine Station and deployed four suction-cup tags.

It’s hard to believe all the work we’ve accomplished in the past five months, and I continue to be honored and proud to be on this incredible team. But as this season has come to a close, I have found myself reflecting on something else. Learning. Over the past several years we have learned so much about not only these whales in our study system but about how to conduct field work. And while learning is continuous, this season in particular has felt like an exciting time for both. In the past year our group has published work showing that we can detect pregnancy in gray whales using fecal samples and drone imagery (Fernandez Ajó et al., 2023), that PCFG gray whales are shorter and smaller than ENP whales (Bierlich et al., 2023), and that gray whales are consuming high levels of microplastics (Torres et al., 2023). We also have several manuscripts in review focused on our behavior work from drones and tags. While this information does not directly affect our field work, it does mean that while we’re observing these whales live, we better understand what we’re observing and we can come up with more specific, in-depth questions based on this foundation of knowledge that we’re building. I have enjoyed seeing our questions evolve each year based on our increasing knowledge and I know that our collaborative, inquisitive chats on the boat will only continue inspiring more exciting research.

On top of our gray whale knowledge, we have also learned so much about field work. When I think back to the early days compared to now, there is a stark difference in our knowledge and our confidence. We do a lot on our little boat! And so many steps that we once relied on written lists to remember to do are now just engrained in our minds and bodies. From loading the boat, to setting up at the dock, to the go pro drops, fecal collections, drone operations, photo taking, and photo ID, our team has become quite the well-oiled machine. We were also given the opportunity to reflect on everything we’ve learned over the past years when it was our turn to train our new team member, Nat! Nat is a new PhD student in the GEMM lab who is joining team GRANITE. Teaching her all the ins and outs of our fieldwork really emphasized how much we ourselves have learned.

On a personal note, this was my third season as a drone pilot, and honestly, I was pleasantly surprised by my experience this season. Since I started piloting, I have experienced pretty intense nerves every time I’ve flown the drone. From stress dreams, to mild nausea, and an elevated heart rate, flying the drone was something that I didn’t necessarily look forward to. Don’t get me wrong – it’s incredibly valuable data and a privilege to watch the whales from a bird’s eye view in real time. But the responsibility of collecting good data, while keeping the drone and my team members safe was something that I felt viscerally. And while I gained confidence with every flight, the nerves were still as present as ever and I was starting to accept that I would never be totally comfortable as a pilot. Until this season, when the nerves finally cleared, and piloting became as innate as all the other field work components. While there are still some stressful moments, the nerves don’t come roaring back. I have finally gone through enough stressful situations to not be fazed by new ones. And while I am fully aware that this is just how learning works, I write this reflection as a reminder to myself and anyone going through the process of learning any new skill to push through that fear. Remember there can be a disconnect between the time when you know how to do something well, or well-enough, and the time when you feel comfortable doing it. I am just as proud of myself for persevering as I am of the team for collecting so much incredible data. And as I look ahead to my next scary challenge (finishing my PhD!), this is a feeling that I am trying to hold on to. 

Stay tuned for updates from team GRANITE!

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References

Bierlich, K. C., Kane, A., Hildebrand, L., Bird, C. N., Fernandez Ajo, A., Stewart, J. D., Hewitt, J., Hildebrand, I., Sumich, J., & Torres, L. G. (2023). Downsized: Gray whales using an alternative foraging ground have smaller morphology. Biology Letters19(8), 20230043. https://doi.org/10.1098/rsbl.2023.0043

Fernandez Ajó, A., Pirotta, E., Bierlich, K. C., Hildebrand, L., Bird, C. N., Hunt, K. E., Buck, C. L., New, L., Dillon, D., & Torres, L. G. (2023). Assessment of a non-invasive approach to pregnancy diagnosis in gray whales through drone-based photogrammetry and faecal hormone analysis. Royal Society Open Science10(7), 230452. https://doi.org/10.1098/rsos.230452

Torres, L. G., Brander, S. M., Parker, J. I., Bloom, E. M., Norman, R., Van Brocklin, J. E., Lasdin, K. S., & Hildebrand, L. (2023). Zoop to poop: Assessment of microparticle loads in gray whale zooplankton prey and fecal matter reveal high daily consumption rates. Frontiers in Marine Science10. https://www.frontiersin.org/articles/10.3389/fmars.2023.1201078

Fantastic beasts and how to measure  them! 

Sagar Karki, Master’s student in the Computer Science Department at Oregon State University 

What beasts? Good question! We are talking about gray whales in this article but honestly we can tweak the system discussed in this blog a little and make it usable for other marine animals too.  

Understanding the morphology, such as body area and length, of wild animals and populations can provide important information on animal  behavior and health (check out postdoc Dr. KC Bierlich’s post on this topic). Since 2015, the GEMM Lab has been flying drones over whales to collect aerial imagery to allow for photogrammetric measurements to gain this important morphological data. This photogrammetry data has shed light on multiple important aspects of gray whale morphology, including the facts that the whales feeding off Oregon are skinnier [1] and shorter [2] than the gray whales that feed in the Arctic region.  But, these surprising conclusions overshadow the immense, time-consuming labor that takes place behind the scenes to move from aerial images to accurate measurements.  

To give you a sense of this laborious process, here is a quick run through of the methods: First the 10 to 15 minute videos must be carefully watched to select the perfect frames of a whale (flat and straight at the surface) for measurement. The selected frames from the drone imagery are then imported into MorphoMetriX, which is a custom software developed for photogrammetry measurement [1]. MorphoMetriX is an interactive application that allows an analyst to manually measure the length by clicking points along the centerline of the whale’s body. Based on this line, the whale is divided into a set of sections perpendicular to the centerline, these are used to then measure widths along the body. The analyst then clicks border points at the edge of the whale’s body to delineate the widths following the whale’s body curve. MorphoMetriX then generates a file containing the lengths and widths of the whale in pixels for each measured image. The length and widths of whales are converted from pixels to metric units using a software called CollatriX [4] and this software also calculates metrics of body condition from the length and width measurements. 

While MorphoMetriX [3] and CollatriX [4] are both excellent platforms to facilitate these photogrammetry measurements, each measurement takes time, a keen eye, and attention to detail. Plus, if you mess up one step, such as an incorrect length or width measurement, you have to start from the first step. This process is a bottleneck in the process of obtaining important morphology data on animals. Can we speed this process up and still obtain reliable data? 

What if we can apply automation using computer vision to extract the frames we need and automatically obtain measurements that are as accurate as humans can obtain? Sounds pretty nice, huh? This is where I come into the picture. I am a Master’s student in the Computer Science Department at OSU, so I lack a solid background in marine science, but bring to the table my skills as a computer programmer. For my master’s project, I have been working in the GEMM Lab for the past year to develop automated methods to obtain accurate photogrammetry measurements of whales.  

We are not the first group to attempt to use computers and AI to speed up and improve the identification and detection of whales and dolphins in imagery. Researchers have used deep learning networks to speed up the time-intensive and precise process of photo-identification of  individual whales and dolphins [5], allowing us to more quickly determine animal location, movements and abundance. Millions of satellite images of the earth’s surface are collected daily and scientists are attempting to utilize these images to  benefit marine life by studying patterns of species occurrence, including detection of gray whales in satellite images using deep learning [6]. There has also been success using computer vision to identify whale species and segment out the body area of the whales  from drone imagery [7]. This process involves extracting segmentation masks of the whale’s body followed by length extraction from the mask. All this previous research shows promise for the application of computer vision and AI to assist with animal research and conservation. As discussed earlier, the automation of image extraction and photogrammetric measurement  from drone videos will help researchers collect vital data more quickly so that decisions that impact  the health of whales can be more responsive and effective.For instance,  photogrammetry data extracted from drone images can diagnose pregnancy of the whales [8], thus automation of this information could speed up our ability to understand population trends. 

Computer vision and natural language processing fields are growing exponentially. There are new foundation models like ChatGPT that can do most of the natural language understanding and processing tasks. Foundational models are also emerging for computer vision tasks, such as “the segment anything model” from Meta. Using these foundation models along with other existing research work in computer vision, we have developed and deployed a system that automates the manual and computational tasks of MorphoMetriX and CollatriX systems.  

This system is currently in its testing and monitoring phase, but we are rapidly moving toward a publication to disseminate all the tools developed, so stay tuned for the research paper that will explain in detail the methodologies followed on data processing, model training and test results. The following images give a sneak peak of results. Each image  illustrates a frame from a drone video that was  identified and extracted through automation, followed by another automation process that identified important points along the whale’s body and curvature.  The user interface of the system aims to make the user experience intuitive and easy to follow. The deployment is carefully designed to run on different hardwares, with easy monitoring and update options using the latest open source frameworks. The user has to do just two things. First, select the videos for analysis. The system then generates potential frames for photogrammetric analysis (you don’t need to watch 15 mins of drone footage!). Second, the user selects the frame of choice for photogrammetric analysis and waits for the system to give you measurements. Simple! Our goal is for these softwares to be a massive time-saver while  still providing vital, accurate body measurements  to the researchers in record time. Furthermore, an advantage of this approach is that researchers can follow the methods in our to-be-soon-published research paper to make  a few adjustments enabling the software to measure other marine species, thus expanding the impact of this work to many other life forms.  

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References 

  1. Torres LG, Bird CN, Rodríguez-González F, Christiansen F, Bejder L, Lemos L, Urban R J, Swartz S, Willoughby A, Hewitt J, Bierlich K (2022) Range-Wide Comparison of Gray Whale Body Condition Reveals Contrasting Sub-Population Health Characteristics and Vulnerability to Environmental Change. Front Mar Sci 910.3389/fmars.2022.867258 
  1. Bierlich KC, Kane A, Hildebrand L, Bird CN, Fernandez Ajo A, Stewart JD, Hewitt J, Hildebrand I, Sumich J, Torres LG (2023) Downsized: gray whales using an alternative foraging ground have smaller morphology. Biol Letters 19:20230043 doi:10.1098/rsbl.2023.0043 
  1. Torres et al., (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), 1825, https://doi.org/10.21105/joss.01825 
  1. Bird et al., (2020). CollatriX: A GUI to collate MorphoMetriX outputs. Journal of Open Source Software, 5(51), 2328, https://doi.org/10.21105/joss.02328 
  1. Patton, P. T., Cheeseman, T., Abe, K., Yamaguchi, T., Reade, W., Southerland, K., Howard, A., Oleson, E. M., Allen, J. B., Ashe, E., Athayde, A., Baird, R. W., Basran, C., Cabrera, E., Calambokidis, J., Cardoso, J., Carroll, E. L., Cesario, A., Cheney, B. J. … Bejder, L. (2023). A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species. Methods in Ecology and Evolution, 00, 1–15. https://doi.org/10.1111/2041-210X.14167 
  1. Green, K.M., Virdee, M.K., Cubaynes, H.C., Aviles-Rivero, A.I., Fretwell, P.T., Gray, P.C., Johnston, D.W., Schönlieb, C.-B., Torres, L.G. and Jackson, J.A. (2023), Gray whale detection in satellite imagery using deep learning. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.352 
  1. Gray, PC, Bierlich, KC, Mantell, SA, Friedlaender, AS, Goldbogen, JA, Johnston, DW. Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry. Methods Ecol Evol. 2019; 10: 1490–1500. https://doi.org/10.1111/2041-210X.13246 
  1. Fernandez Ajó A, Pirotta E, Bierlich KC, Hildebrand L, Bird CN, Hunt KE, Buck CL, New L, Dillon D, Torres LG (2023) Assessment of a non-invasive approach to pregnancy diagnosis in gray whales through drone-based photogrammetry and faecal hormone analysis. Royal Society Open Science 10:230452 

That’s so Real: Adult Beginners, Serial Podcast(s), and a whole lotta of Baja Gray Whale Video Analysis.

Celest Sorrentino, Research Technician, Geospatial Ecology of Marine Megafauna Lab

Hello again GEMM Lab family. I write to you exactly a year after (okay maybe 361 days after but who’s counting…) from my previous blog post describing my 2022 summer working in the GEMM Lab as an NSF REU intern. Since then, so much has changed, and I can’t wait to fill you in on it.

In June I walked across the commencement stage at UC Santa Barbara, earning my BS in Ecology, Evolution, and Marine Biology and my minor in Italian language. A week later, I packed my bags and headed straight back to the lukewarm beaches of Newport, Oregon as a Research Technician in the GEMM Lab. I am incredibly fortunate to have been invited back to the OSU Marine Mammal Institute to lend a hand analyzing drone footage of gray whales collected back in March 2023 when Leigh and Clara went down to Baja California, as mentioned previously in Clara’s blog

Fig. 1. View from the top! (of the bridge at Yaquina Bay Bridge in Newport, OR)

During my first meeting with Clara at the beginning of the summer we discussed that a primary goal of my position was to process all the drone footage collected in Baja so that the generated video clips could be later used in other analytical software such as BORIS and SLEAP A.I. Given my previous internships and past summer project, this video processing is familiar to me. My initial thoughts were:

Sweet! Watch drone footage, pop in some podcasts, note down when I see whales, let’s do this!*

Like any overly eager 23-year-old, I might have mentally cracked open a Celsius and kicked my feet up too soon. We added another layer to the goal: develop an ethogram – which requires me to identify and define the behaviors that the gray whales appear to be demonstrating within the videos (more on ethogram development in Clara’s previous blog.) This made me nervous. 

I don’t have any experience with behavior. How do I tell what is a real behavior or if the whale is just existing? What if I’m wrong and ruin the project? What if I totally mess this up?

Naturally, as any sane person, to resolve these thoughts I took to the Reddit search bar: “How to do a job you’ve never done before.” No dice. 

I pushed these thoughts aside and decided to just start the video analysis process. Clara provided me with the ethogram she is developing during her PhD as a point of reference (based on the published gray whale ethogram in Torres et al. 2018), I was surrounded by an insanely supportive lab, and I could Google anything at my fingertips. Fast-forward 6 weeks later: I had analyzed 128 drone videos of adult gray whales as well as mother-calf pairs, and developed an ethogram describing, 26 behaviors**. I named one of my favorite behaviors  a “Twirl” to describe when a gray whale lifts their head out of the water and performs a 360 turn. Reminds me of times when as a kid, sometimes all you really needed is a good spin!

Now I was ready to start a productive, open conversation with Leigh and Clara about this ethogram and my work. However, even walking up to that last meeting, remnants of those daunting, doubtful early summer thoughts persisted. Even after I double checked all the definitions I wrote, rewatched all videos with said behaviors, and had something to show for my work. What gives Brain?

A few days ago, as I sat on my family’s living room couch with my two younger sisters, Baylie and Cassey, Baylie wanted to watch some TikToks with me. One video that came up was of a group of adults taking a beginner dance class, having so much fun and radiating joy. The caption read, Being a beginner as an adult is such a fun and wild thing. Baylie and I watched the video at least 10x, repeating to each other phrases like, “Wow!” and “They’re so cool.” That caption and video has been on my mind since: 

Being a beginner as an adult is such a fun and wild thing.

Being a beginner as an adult is also scary. 

Having just graduated, I can no longer say I am undergraduate student. Now, I am a young adult. This was my first research technician job, as an adult. Don’t adults usually have everything figured out? Can adults be beginners too?

Yes. In fact, we’re beginners more than we realize. 

  • I was a beginner cooking my mother’s turkey recipe 3 years ago for my housemates during the pandemic (Even after having her on Facetime, I still managed to broil it a little too long.) 
  • I was a beginner driver 5 years ago in a rickety Jeep driving myself to school (Now, since I’ve been back home, I’ve been driving my little sisters to school.)
  • I was a beginner NSF REU intern just a year ago. (This summer I was the alumni on the panel for the current NSF REU interns at Hatfield.)
  • I was a beginner science communicator presenting my NSF REU project at Hatfield last summer. (This summer, I presented my research at the Animal Behavior Society Conference.) 
Fig 2A. Group Pic with the LABIRINTO Lab and GEMM Lab at the ABS Portland Conference!
Fig 2B. Clara Bird (left), Dr. Leigh Torres (middle), and I (right) at the ABS Portland Conference. 

I now recognize that during my time identifying and defining behaviors of gray whales in videos made me take on the seat of a “beginner video and behavioral analyst”. I could not rely on the automated computer vision lens I gained from previous internships, which felt familiar and secure. 

 Instead, I had to allow myself to be creative. Dig into the unfamiliar in an effort to complete a task or job I had never done before. Allowing myself to be imperfect, make mistakes, meanwhile unconsciously building a new skill. 

This is what makes being a beginner as an adult such a fun thing. 

I don’t think being a beginner is a wild thing, although it can definitely make you feel a wild range of emotions. Being a beginner means you’re allowing yourself to try something new. Being a beginner means you’re allowing yourself the chance to learn.

Whether you’re an adult beginner as you enter your 30s, adult beginner as you enter parenthood, adult beginner grabbing a drink with friends after a long day in lab, adult beginner as a dancer, or like me, a beginner of leaving behind my college student persona and entering a new identity of adulthood, being a beginner as an adult is such a fun and normal thing.

I am not sure what will be next, but I hope to write to you all again from this blog a year from now, as an adult beginner as a grad student in the GEMM Lab. For anyone approaching the question of “What’s next”, I encourage you to read “Never a straight Path” by GEMM Lab MSc alum Florence Sullivan, a blog that has brought me such solace in my new adult journey and advice that never gets old.

Being a beginner—that, is so real. 

Fig 3A. Kayaking as an adult beginner of the Port Orford Field Team!
Fig 3B “See you soon:” Wolftree evenings with the lab.
Fig 3C. GEMM Lab first BeReal!

*I listened to way too many podcasts to list them all, but I will include two that have been a GEMM Lab “gem” —-thanks to Lisa and Clara for looping me in and now, looping you in!)

**(subject to change)

References

Torres LG, Nieukirk SL, Lemos L, Chandler TE (2018) Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Front Mar Sci 510.3389/fmars.2018.00319

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Title: “Blown away”: measuring the blowholes of whales from drones

By Annie Doron, Undergraduate Intern, Oregon State University, GEMM Laboratory  

Hey up! My name is Annie Doron, and I am an undergraduate Environmental Science student from the University of Sheffield (UK) on my study year abroad. One of my main motivations for undertaking this year abroad was to gain experience working in a marine megafauna lab. Whales in particular have always captivated my interest, and I have been lucky enough to observe  humpback whales in Iceland and The Azores, and even encountered one whilst diving in Australia! For the past 10 months, I have had the unique opportunity to work in the GEMM Lab analyzing Pacific Coast Feeding Group (PCFG) gray whales off the Oregon Coast (Figure 1). I must admit, it has been simply wonderful! 

Figure 1. Aerial image of a PCFG gray whale off the Oregon Coast. 

How did I end up getting involved with the GEMM Lab? I was first accepted into Scarlett Arbuckle’s research-based class in fall term 2022, which is centered around partnering with a mentor for a research project. Having explored the various fields of research at HMSC, I contacted Leigh Torres with interest in getting involved in the GEMM Lab and to establish a research project suitable for a totally inexperienced, international, undergraduate student. Thankfully, Leigh forwarded my email to KC Bierlich who offered to be my mentor for the class, and the rest is history! I first began analyzing drone imagery to measure length and body condition of  PCFG gray whales, which provided an opportunity to get involved with the lab and gain experience using the photogrammetry software MorphoMetriX (Torres & Bierlich, 2020) (see KC’s blog), which is used to make morphometric measurements of whales. Viewing drone imagery of whales sparked my interest in how they use their blowholes (otherwise called ‘nares’) to replenish their oxygen stores; this led to us establishing a research project for the class where we tested if we could use MorphoMetriX to measure blowholes from drone imagery.

Extending this project into winter and spring terms (via research credits) has enabled me to continue working with Leigh and KC, as well as to collaborate with Clara Bird and Jim Sumich. Thanks to KC, who has patiently guided me through the ins and outs of working on a research project, I now feel more confident handling and manipulating large datasets, analyzing drone footage (i.e., differentiating between behavioral states, recording breathing sequences, detecting when a whale is exhaling vs inhaling, etc.), and speaking in public (although I still get pretty bad stage fright, but I think that is a typical conundrum undergrads face). Whatsmore, applying  R – a programming language used for statistical analysis and data visualization, which I have been trying to wrap my head around for years – to my own dataset has helped me greatly enhance my skills using it. 

So, what exciting things have we been working on this year? Given that we often cannot simply study a whale from inside a laboratory – due to size-related logistical implications – we must use proxies (i.e., a variable that is representative of an immeasurable variable). Since cetaceans must return to the surface to offload carbon dioxide and replenish their oxygen stores, measuring their breath frequency and magnitude is one way to study a whale’s oxygen consumption, in turn offering insight into its energy expenditure (Williams, 1999). Blowholes are one proxy we can use to study breath magnitude. Blowholes can be utilized in this way by measuring inhalation duration (the amount of time a whale is inhaling, which is based on a calculation developed by Jim Sumich) and blowhole area (the total area of a blowhole) to gauge variations in tidal volume (the amount of air flowing in and out of the lungs).

Measuring inhalation duration and blowhole area is important because a larger blowhole area (i.e., one that is more dilated) and a longer inhalation duration is indicative of higher oxygen intake, which can infer stress. For example, in this population, higher stress levels are associated with increased vessel traffic (Lemos et al., 2022), and skinnier whales have higher stress levels compared to chubby, healthy whales (Lemos, Olsen, et al., 2022). Hence, measuring the variation around blowholes could be utilized to predict challenges whales face from climate change and anthropogenic disturbance, including fishing (Scordino et al., 2017) and whale watching industry threats (Sullivan & Torres, 2018) (see Clara’s blog), as well as to inform effective management strategies. Furthermore, measuring the variables inhalation duration and blowhole area could help to identify whether whales are taking larger breaths associated with certain ‘gross behavior states’, otherwise known as ‘primary states’, which include: travel, forage, rest, social (Torres et al., 2018). This could enable us to assess the energetic costs of different foraging tactics (i.e., head standing, side-swimming, and bubble blasting (Torres et al., 2018), as well as consequences of disturbance events, on an individual and population health perspective. 

Inhalation duration has been explored in the past by using captive animals. For example, there have been studies on heart rate and breathing of bottlenose dolphins in human care facilities (Blawas et al., 2021; Fahlman et al., 2015). Recently, Nazario et al. (2022) was able to measure inhalation duration and blowhole area using suction-cup video tags. Her study led us to consider if it was possible to measure the parameters and variation around respiration by measuring blowhole area and inhalation duration of PCFGs from drone imagery. We employed MorphoMetriX to study the length, width, and area of a blowhole (Figure 2). Preliminary analyses verified that the areas of the left and right blowholes are very similar (Figure 3); this finding saved us a lot of time because from thereon we only measured either the left or right side. Interestingly, we see some variation in blowhole area within and across individuals (Figure 4). This variation changes within individuals based on primary state. For example, the whales “Glacier”, “Nimbus”, and “Rat” show very little variation whilst traveling but a large amount whilst foraging. Comparatively, “Dice” shows little variation whilst foraging and large variation whilst traveling. Whilst considering cross-individual comparisons, we can see that “Sole”, “Rat”, “Nimbus”, “Heart”, “Glacier”, “Dice”, and “Coal” each exhibit relatively large amounts of variation, yet “Mahalo”, “Luna”, “Harry”, “Hummingbird” and “Batman” exhibit very little. One potential reason for some individuals displaying higher levels of variation than others could be higher levels of exposure to disturbance events that we were unable to measure or evaluate in this study.

Figure 2. How we measured the length, width, and area of a blowhole using MorphoMetriX.

Figure 3. Data driven evidence that the left and the right blowhole areas are very similar. 

Figure 4. Variation in blowhole area amongst individual PCFG whales. The hollow circles represent the means, and the color represents the primary state the whale is exhibiting, foraging (purple) vs. traveling (blue), which will be further explored in Clara’s PhD.

Now, we are venturing into June and are at a stage where we (KC, Clara, Jim, Leigh, and I) are preparing to publish a manuscript! What a way to finish such a fantastic year! The transition from a 3-month-long pilot study to a much larger data analysis and eventual preparation for a manuscript has been a monumental learning experience. If anybody had told me a year ago that I would be involved in publishing a body of work – especially one that is so meaningful to me – I would simply not have believed them! We hope this established methodology for measuring blowholes will help other researchers carry out blowhole measurements using drone imagery across different populations and species. Further research is required to explore the differences in inhalation duration and blowhole area between different primary states, specifically across different foraging tactics.

It has been a great privilege working with the GEMM Lab these past months, and I was grateful to be included in their monthly lab meetings, during which members gave updates and we discussed recently published papers. Seeing such an enthusiastic, kind, and empathic group of people working together taught me what working in a supportive lab could look and feel like. In spite of relocating from Corvallis to Bend after my first term, I was happy to be able to continue working remotely for the lab for the remainder of my time (even though I was ~200 miles inland). I thoroughly enjoyed living in Corvallis, highlights of which were scuba diving adventures to the Puget Sound and coastal road trips with friends. The appeal to move arose from Bend’s reputation as an adventure hub – with unlimited opportunities for backcountry ski access – as well as its selection of wildlife ecology courses (with a focus on species specific to central Oregon). I moved into ‘Bunk & Brew’ (Bend’s only hostel, which is more like a big house of friends with occasional hostel guests) on January 1st after returning from spending Christmas with friends in my old home in Banff, Canada. I have since been enjoying this wonderful multifaceted lifestyle; working remotely in the GEMM Lab, attending in-person classes, working part-time at the hostel, as well as skiing volcanoes (Mount Hood, Middle and South Sister (Figure 5) or climbing at Smith Rock during my days off. Inevitably, I do miss the beautiful Oregon coast, and I will always be grateful for this ideal opportunity and hope this year marks the start of my marine megafauna career!

Figure 5. What I get up to when I’m not studying blowholes! (This was taken at 5am on the long approach to Middle and North Sister. North Sister is the peak featured in the backdrop).

References

Blawas, A. M., Nowacek, D. P., Allen, A. S., Rocho-Levine, J., & Fahlman, A. (2021). Respiratory sinus arrhythmia and submersion bradycardia in bottlenose dolphins (Tursiops truncatus). Journal of Experimental Biology, 224(1), jeb234096. https://doi.org/10.1242/jeb.234096

Fahlman, A., Loring, S. H., Levine, G., Rocho-Levine, J., Austin, T., & Brodsky, M. (2015). Lung mechanics and pulmonary function testing in cetaceans. Journal of Experimental Biology, 218(13), 2030–2038. https://doi.org/10.1242/jeb.119149

Lemos, L. S., Haxel, J. H., Olsen, A., Burnett, J. D., Smith, A., Chandler, T. E., Nieukirk, S. L., Larson, S. E., Hunt, K. E., & Torres, L. G. (2022). Effects of vessel traffic and ocean noise on gray whale stress hormones. Scientific Reports, 12(1), 18580. https://doi.org/10.1038/s41598-022-14510-5

Lemos, L. S., Olsen, A., Smith, A., Burnett, J. D., Chandler, T. E., Larson, S., Hunt, K. E., & Torres, L. G. (2022). Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Marine Mammal Science, 38(2), 801–811. https://doi.org/10.1111/mms.12877

Nazario, E. C., Cade, D. E., Bierlich, K. C., Czapanskiy, M. F., Goldbogen, J. A., Kahane-Rapport, S. R., van der Hoop, J. M., San Luis, M. T., & Friedlaender, A. S. (2022). Baleen whale inhalation variability revealed using animal-borne video tags. PeerJ, 10, e13724. https://doi.org/10.7717/peerj.13724

Scordino, J., Carretta, J., Cottrell, P., Greenman, J., Savage, K., & Scordino, J. (2017). Ship Strikes and Entanglements of Gray Whales in the North Pacific Ocean. Cambridge: International Whaling Commission, 1924–2015.

Sullivan, F. A., & Torres, L. G. (2018). Assessment of vessel disturbance to gray whales to inform sustainable ecotourism: Vessel Disturbance to Whales. The Journal of Wildlife Management, 82(5), 896–905. https://doi.org/10.1002/jwmg.21462

Sumich, J. L. (1994). Oxygen extraction in free-swimming gray whale caves. Marine Mammal Science, 10(2), 226–230. https://doi.org/10.1111/j.1748-7692.1994.tb00266.x

Torres, W., & Bierlich, K. (2020). MorphoMetriX: A photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), 1825. https://doi.org/10.21105/joss.01825

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Frontiers in Marine Science, 5, 319. https://doi.org/10.3389/fmars.2018.00319
Williams, T. M. (1999). The evolution of cost efficient swimming in marine mammals: Limits to energetic optimization. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 354(1380), 193–201. https://doi.org/10.1098/rstb.1999.0371

How do we study the impact of whale watching?

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

Since its start, the GEMM Lab has been interested in the effect of vessel disturbance on whales. From former student Florence’s masters project to Leila’s PhD work, this research has shown that gray whales on their foraging grounds have a behavioral response to vessel presence (Sullivan & Torres, 2018) and a physiological response to vessel noise (Lemos et al., 2022). Presently, our GRANITE project is continuing to investigate the effect of ambient noise on gray whales, with an emphasis on understanding how these effects might scale up to impact the population as a whole (Image 1).

To date, all this work has been focused on gray whales feeding off the coast of Oregon, but I’m excited to share that this is about to change! In just a few weeks, Leigh and I will be heading south for a pilot study looking at the effects of whale watching vessels on gray whale mom/calf pairs in the nursing lagoons of Baja California, Mexico.

Image 1. Infographic for the GRANITE project. Credit: Carrie Ekeroth

We are collaborating with a Fernanda Urrutia Osorio, a PhD candidate at Scripps Institute of Oceanography, to spend a week conducting fieldwork in one of the nursing lagoons. For this project we will be collecting drone footage of mom/calf pairs in both the presence and absence of whale watching vessels. Our goal is to see if we detect any differences in behavior when there are vessels around versus when there are not. Tourism regulations only allow the whale watching vessels to be on the water during specific hours, so we are hoping to use this regulated pattern of vessel presence and absence as a sort of experiment.

Image 2. A mom and calf pair.  NOAA/NMFS permit #21678.

The lagoons are a crucial place for mom/calf pairs, this is where calves nurse and grow before migration, and nursing is energetically costly for moms. So, it is important to study disturbance responses in this habitat since any change in behavior caused by vessels could affect both the calf’s energy intake and the mom’s energy expenditure. While this hasn’t yet been investigated for gray whales in the lagoons, similar studies have been carried out on other species in their nursing grounds.

Video 1. Footage of “likely nursing” behavior. NOAA/NMFS permit #21678.

We can use these past studies as blueprints for both data collection and processing. Disturbance studies such as these look for a wide variety of behavioral responses. These include (1) changes in activity budgets, meaning a change in the proportion of time spent in a behavior state, (2) changes in respiration rate, which would reflect a change in energy expenditure, (3) changes in path, which would indicate avoidance, (4) changes in inter-individual distance, and (5) changes in vocalizations. While it’s not necessarily possible to record all of these responses, a meta-analysis of research on the impact of whale watching vessels found that the most common responses were increases in the proportion of time spent travelling (a change in activity budget) and increased deviation in path, indicating an avoidance response (Senigaglia et al., 2016).

One of the key phrases in all these possible behavioral responses is “change in ___”. Without control data collected in the absence of whale watching vessels, it impossible to detect a difference. Some studies have conducted controlled exposures, using approaches with the research vessel as proxies for the whale watchers (Arranz et al., 2021; Sprogis et al., 2020), while others use the whale watching operators’ daily schedule and plan their data collection schedule around that (Sprogis et al., 2023). Just as ours will, all these studies collected data using drones to record whale behavior and made sure to collect footage before, during, and after exposure to the vessel(s).

One study focused on humpback mom/calf pairs found a decrease in the proportion of time spent resting and an increase in both respiration rate and swim speed during the exposure (Sprogis et al., 2020). Similarly, a study focused on short-finned pilot whale mom/calf pairs found a decrease in the mom’s resting time and the calf’s nursing time (Arranz et al., 2021). And, Sprogis et al.’s  study of Southern right whales found a decrease in resting behavior after the exposure, suggesting that the vessels’ affect lasted past their departure (Sprogis et al., 2023, Image 3). It is interesting that while these studies found changes in different response metrics, a common trend is that all these changes suggest an increase in energy expenditure caused by the disturbance.

However, it is important to note that these studies focused on short term responses. Long term impacts have not been thoroughly estimated yet. These studies provide many valuable insights, not only into the response of whales to whale watching, but also a look at the various methods used. As we prepare for our fieldwork, it’s useful to learn how other researchers have approached similar projects.

Image 3. Visual ethogram from Sprogis et al. 2023. This shows all the behaviors they identified from the footage.

I want to note that I don’t write this blog intending to condemn whale watching. I fully appreciate that offering the opportunity to view and interact with these incredible creatures is valuable. After all, it is one of the best parts of my job. But hopefully these disturbance studies can inform better regulations, such as minimum approach distances or maximum engine noise levels.

As these studies have done, our first step will be to establish an ethogram of behaviors (our list of defined behaviors that we will identify in the footage) using our pilot data. We can also record respiration and track line data. An additional response that I’m excited to add is the distance between the mom and her calf. Former GEMM Lab NSF REU intern Celest will be rejoining us to process the footage using the AI method she developed last summer (Image 4). As described in her blog, this method tracks a mom and calf pair across the video frames, and allows us to extract the distance between them. We look forward to adding this metric to the list and seeing what we can glean from the results.

Image 4. Example of a labelled frame from SLEAP, highlighting labels: rostrum, blowhole, dorsal, dorsal-knuckle, and tail. This labels are drawn to train the software to recognize the whales in unlabelled frames.

While we are just getting started, I am excited to see what we can learn about these whales and how best to study them. Stay tuned for updates from Baja!

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References

Arranz, P., Glarou, M., & Sprogis, K. R. (2021). Decreased resting and nursing in short-finned pilot whales when exposed to louder petrol engine noise of a hybrid whale-watch vessel. Scientific Reports, 11(1), 21195. https://doi.org/10.1038/s41598-021-00487-0

Lemos, L. S., Haxel, J. H., Olsen, A., Burnett, J. D., Smith, A., Chandler, T. E., Nieukirk, S. L., Larson, S. E., Hunt, K. E., & Torres, L. G. (2022). Effects of vessel traffic and ocean noise on gray whale stress hormones. Scientific Reports, 12(1), Article 1. https://doi.org/10.1038/s41598-022-14510-5

Senigaglia, V., Christiansen, F., Bejder, L., Gendron, D., Lundquist, D., Noren, D., Schaffar, A., Smith, J., Williams, R., Martinez, E., Stockin, K., & Lusseau, D. (2016). Meta-analyses of whale-watching impact studies: Comparisons of cetacean responses to disturbance. Marine Ecology Progress Series, 542, 251–263. https://doi.org/10.3354/meps11497

Sprogis, K. R., Holman, D., Arranz, P., & Christiansen, F. (2023). Effects of whale-watching activities on southern right whales in Encounter Bay, South Australia. Marine Policy, 150, 105525. https://doi.org/10.1016/j.marpol.2023.105525

Sprogis, K. R., Videsen, S., & Madsen, P. T. (2020). Vessel noise levels drive behavioural responses of humpback whales with implications for whale-watching. ELife, 9, e56760. https://doi.org/10.7554/eLife.56760

Sullivan, F. A., & Torres, L. G. (2018). Assessment of vessel disturbance to gray whales to inform sustainable ecotourism. Journal of Wildlife Management, 82(5), 896–905. https://doi.org/10.1002/jwmg.21462

Return of the whales: The GRANITE 2022 field season comes to a close

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

It’s hard to believe that it’s already been four and half months since we started the field season (check out Lisa’s blog for a recap of where we began), but as of this weekend the GRANITE project’s 8th field season has officially ended! As the gray whales wrap up their foraging season and start heading south for the winter, it’s time for us to put our gear into storage, settle into a new academic year, and start processing the data we spent so much time collecting.

The field season can be quite an intense time (40 days equaling over 255 hours on the water!), so we often don’t take a moment to reflect until the end. But this season has been nothing short of remarkable. As you may remember from past blogs, the past couple years (2020-21) have been a bit concerning, with lower whale numbers than previously observed. Since many of us started working on the project during this time, most of us were expecting another similar season. But we were wrong in the best way. From the very first day, we saw more whales than in previous years and we identified whales from our catalog that we hadn’t seen in several years.

Image 1: Collage of photos from our field season.

We identified friends – old and new!

This season we had 224 sightings of 63 individual whales. Of those 63, 51 were whales from our catalog (meaning we have seen them in a previous season). Of these 51 known whales, we only saw 20 of them last year! This observation brings up interesting questions such as, where did most of these whales forage last year? Why did they return to this area this year? And, the classic end of season question, what’s going to happen next year?

We also identified 12 whales that were not in our catalog, making them new to the GEMM lab. Two of our new whales are extra exciting because they are not just new to us but new to the population; we saw two calves this year! We were fortunate enough to observe two mom-calf pairs in July. One pair was of a “new” mom in our catalog and her calf. We nicknamed this calf “Roly-poly” because when we found this mom-calf pair, we recorded some incredible drone footage of “roly-poly” continuously performing body rolls while their mom was feeding nearby (video 1). 

Video 1: “Roly-poly” body rolling while their mom headstands. NOAA/NMFS permit #21678.

The other pair includes a known GEMM lab whale, Luna, and her calf (currently nicknamed “Lunita”). We recently found “Lunita” feeding on their own in early October (Image 2), meaning that they are now independent from its mom (for more on mom-calf behavior check out Celest’s recent blog). We’ll definitely be on the lookout for Roly-Poly and Lunita next year!

Image 2: (left) drone image of Luna and Lunita together in July and (right) drone image of Lunita on their own in October.  NOAA/NMFS permit #21678.

We flew, we scooped, we collected heaps of data!

From our previous blogs you probably know that in addition to photo-ID images, our other two most important forms of data collection are drone flights (for body condition and behavior data) and fecal samples (for hormone analysis). And this season was a success for both! 

We conducted 124 flights over 49 individual whales. The star of these flights was a local favorite Scarlett who we flew over 18 different times. These repeat samples are crucial data for us because we use them to gain insight into how an individual’s body condition changes throughout the season. We also recorded loads of behavior data, collecting footage of different foraging tactics like headstanding, side-swimming, and surfacing feeding on porcelain crab larvae (video 2)!

Video 2: Two whales surface feeding on porcelain crab larvae. NOAA/NMFS permit #21678.

We also collected 61 fecal samples from 26 individual whales (Image 3). The stars of that dataset were Soléand Peak who tied with 7 samples each. These hard-earned samples provide invaluable insight into the physiology and stress levels of these individuals and are a crucial dataset for the project.

Image 3: Photos of fecal sample collection. Left – a very heavy sample, center: Lisa and Enrico after collecting the first fecal sample of the season, right: Clara and Lisa celebrating a good fecal sample collection.

On top of all that amazing data collection we also collected acoustic data with our hydrophones, prey data from net tows, and biologging data from our tagging efforts. Our hydrophones were in the water all summer recording the sounds that the whales are exposed to, and they were successfully recovered just a few weeks ago (Image 4)! We also conducted 69 net tows to sample the prey near where the whales were feeding and identify which prey the whales might be eating (Image 5). Lastly, we had two very successful tagging weeks during which we deployed (and recovered!) a total of 9 tags, which collected over 30 hours of data (Image 6; check out Kate’s blog for more on that).

Image 4 – Photos from hydrophone recovery.
Image 5: Photos from zooplankton sampling.
Image 6: Collage of photos from our two tagging efforts this season.

Final thoughts

All in all, it’s been an incredible season. We’ve seen the return of old friends, collected lots of awesome data, and had some record-breaking days (28 whales in one day!). As we look toward the analysis phase of the year, we’re excited to dig into our eight-year dataset and work to understand what might explain the increase in whales this year.

To end on a personal note, looking through photos to put in this blog was the loveliest trip down memory lane (even though it only ended a few days ago) – I am so honored and proud to be a part of this team. The work we do is hard; we spend long hours on a small boat together and it can be a bit grueling at times. But, when I think back on this season, my first thoughts are not of the times I felt exhausted or grumpy, but of all the joy we felt together. From the incredible whale encounters to the revitalizing snacks to the off-key sing alongs, there is no other team I would rather do this work with, and I so look forward to seeing what next season brings. Stay tuned for more updates from team GRANITE!

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Putting Fitbits on whales: How tag data allows for estimating calories burned by foraging PCFG gray whales

By: Kate Colson, MSc Student, University of British Columbia, Institute for the Oceans and Fisheries, Marine Mammal Research Unit

Hello! My name is Kate Colson and I am a master’s student at the University of British Columbia, co-supervised by Dr. Andrew Trites of the Marine Mammal Research Unit and Dr. Leigh Torres of the GEMM Lab. As part of my thesis work, I have had the opportunity to spend the summer field season with Leigh and the GEMM Lab team. 

For my master’s I am studying the foraging energetics of Pacific Coast Feeding Group (PCFG) gray whales as part of the much larger Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project. Quantifying the energy expenditure of PCFG gray whales during foraging can help establish a baseline for how disturbance impacts the ability of this unique population to meet their energy needs. Additionally, determining how many calories are burned during different PCFG foraging behaviors might help explain why some gray whales are in better body condition than others.

To understand how much energy different PCFG foraging behaviors cost, I am using data from suction cup tags we have temporarily applied on PCFG gray whales (Figure 1). You can read more about the why the GEMM Lab started using these tags in an earlier blog here. What I want to talk about in this blog is how exactly we can use this tag data to estimate energy expenditure of PCFG gray whales. 

Figure 1. The famous “Scarlett” with a suction cup tag just attached using a carbon fiber pole (seen on far right). This minimally invasive tag has many data sensors, all of which sample at high frequencies, that can allow for an estimation of energy expenditure for different gray whale behaviors. Source: GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678 

The suction cups tags used in this project have many data sensors that are useful for describing the movement of the tagged whale including accelerometers, magnetometers, gyroscopes, and pressure sensors, and all are sampling at high frequencies. For example, the accelerometer is taking 400 measurements per second! The accelerometer, magnetometer, and gyroscope take measurements in 3 dimensions along the X, Y, and Z-axes. The whale’s movement around the X-axis indicates roll (if the whale is swimming on its side), while movement around the Y-axis indicates pitch (if the whales head is oriented towards the surface or the sea floor). Changes in the whale’s movement around the Z-axis indicates if the whale is changing its swimming direction. Together, all of these sensors can describe the dive profile, body orientation, fluking behavior, and fine-scale body movements of the animal down to the second (Figure 2). This allows for the behavior of the tagged whale to be specifically described for the entirety of the tag deployment. 

Figure 2. An example of what the tag sensor data looks like. The top panels show the depth of the animal and can be used to determine the diving behavior of the whale. The middle panels show the body roll of the whale (the X axis) —a roll value close to 0 means the whale is swimming “normally” with no rotation to either side, while a higher roll value means the whale is positioned on its side. The bottom panels show the fluking behavior of the animal: each spike is the whale using its tail to propel itself through the water, with higher spikes indicating a stronger fluke stroke. Source: GEMM Lab, NMFS permit no. 21678

Although these suction cup tags are a great advancement in collecting fine-scale data, they do not have a sensor that actually measures the whale’s metabolism, or rate of calories burned by the whale. Thus, to use this fine-scale tag data as an estimate for energy expenditure, a summary metric must be calculated from the data and used as a proxy. The most common metric found in the literature is Overall Dynamic Body Acceleration (ODBA) and many papers have been published discussing the pros and cons of using ODBA as a proxy for energy expenditure (Brown et al., 2013; Gleiss et al., 2011; Halsey, 2017; Halsey et al., 2011; Wilson et al., 2020). The theory behind ODBA is that because an animal’s metabolic rate is primarily comprised of movement costs, then measuring the acceleration of the body is an effective way of determining energy expenditure. This theory might seem very abstract, but if you have ever worn a Fitbit or similar fitness tracking device to estimate how many calories you’ve burned during a workout, the same principle applies. Those fitness devices use accelerometers and other sensors, to measure the movement of your limbs and produce estimates of energy used. 

So now that we’ve established that the goal of my research is to essentially use these suction cup tags as Fitbits for PCFG gray whales, let’s look at how accelerometry data has been used to detect foraging behavior in large whales so far. Many accelerometry tagging studies have used rorquals as a focal species (see Shadwick et al. (2019) for a review). Well-known rorqual species include humpback, fin, and blue whales. These species forage by using lunges to bulk feed on dense prey patches in the water column. Foraging lunges are indicated by isolated periods of high acceleration that are easily detectable in the tag data (Figure 3; Cade et al., 2016; Izadi et al., 2022). 

Figure 3. Top image: A foraging blue whale performing a surface lunge (Photo credit: GEMM Lab). Note the dense aggregation of krill in the whale’s mouth. Bottom image: The signature acceleration signal for lunge feeding (adapted from Izadi et al., 2022). Each color represents one of the 3D axes of whale movement. The discrete periods of high acceleration represent lunges

However, gray whales feed very differently from rorquals. Gray whales primarily suction feed on the benthos, using their head to dig into the sediment and filter prey out of the mud using their baleen. Yet,  PCFG gray whales often perform many other foraging behaviors such as headstanding and side-swimming (Torres et al., 2018). Additionally, PCFG gray whales tend to feed in water depths that are often shallower than their body length. This shallow depth makes it difficult to isolate signals of foraging in the accelerometry data from random variation in the data and separate the tag data into periods of foraging behaviors (Figure 4).

Figure 4. Top image: A foraging PCFG gray whale rolls on its side to feed on mysid prey. Bottom image: The graph shows the accelerometry data from our suction cup tags that can be used to calculate Overall Dynamic Body Acceleration (ODBA) as a way to estimate energy expenditure. Each color represents a different axis in the 3D motion of the whale. The X-axis is the horizontal axis shows forward and backward movement of the whale, the Y-axis shows the side-to-side movement of the whale, and the Z-axis shows the up-down motion of the whale. Note how there are no clear periods of high acceleration in all 3 axes simultaneously to indicate different foraging behaviors like is apparent during lunges of rorqual whales. However, there is a pattern showing that when acceleration in the Z-axis (blue line) is positive, the X- and Y-axes (red and green lines) are negative. Source: GEMM Lab; NMSF permit no. 21678

But there is still hope! Thanks to the GEMM Lab’s previous work describing the foraging behavior of the PCFG sub-group using drone footage, and the video footage available from the suction cup tags deployed on PCFG gray whales, the body orientation calculated from the tag data can be a useful indication of foraging. Specifically, high body roll is apparent in many foraging behaviors known to be used by the PCFG, and when the tag data indicates that the PCFG gray whale is rolled onto its sides, lots of sediment (and sometimes even swarms of mysid prey) is seen in the tag video footage. Therefore, I am busy isolating these high roll events in the collected tag data to identify specific foraging events. 

My next steps after isolating all the roll events will be to use other variables such as duration of the roll event and body pitch (i.e., if the whales head is angled down), to define different foraging behaviors present in the tag data. Then, I will use the accelerometry data to quantify the energetic cost of performing these behaviors, perhaps using ODBA. Hopefully when I visit the GEMM Lab again next summer, I will be ready to share which foraging behavior leads to PCFG gray whales burning the most calories!

References

Brown, D. D., Kays, R., Wikelski, M., Wilson, R., & Klimley, A. P. (2013). Observing the unwatchable through acceleration logging of animal behavior. Animal Biotelemetry1(1), 1–16. https://doi.org/10.1186/2050-3385-1-20

Cade, D. E., Friedlaender, A. S., Calambokidis, J., & Goldbogen, J. A. (2016). Kinematic diversity in rorqual whale feeding mechanisms. Current Biology26(19), 2617–2624. https://doi.org/10.1016/j.cub.2016.07.037

Duley, P. n.d. Fin whales feeding [photograph]. NOAA Northeast Fisheries Science Center Photo Gallery. https://apps-nefsc.fisheries.noaa.gov/rcb/photogallery/finback-whales.html

Gleiss, A. C., Wilson, R. P., & Shepard, E. L. C. (2011). Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure. Methods in Ecology and Evolution2(1), 23–33. https://doi.org/10.1111/j.2041-210X.2010.00057.x

Halsey, L. G. (2017). Relationships grow with time: A note of caution about energy expenditure-proxy correlations, focussing on accelerometry as an example. Functional Ecology31(6), 1176–1183. https://doi.org/10.1111/1365-2435.12822

Halsey, L. G., Shepard, E. L. C., & Wilson, R. P. (2011). Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comparative Biochemistry and Physiology – A Molecular and Integrative Physiology158(3), 305–314. https://doi.org/10.1016/j.cbpa.2010.09.002

Izadi, S., Aguilar de Soto, N., Constantine, R., & Johnson, M. (2022). Feeding tactics of resident Bryde’s whales in New Zealand. Marine Mammal Science, 1–14. https://doi.org/10.1111/mms.12918

Shadwick, R. E., Potvin, J., & Goldbogen, J. A. (2019). Lunge feeding in rorqual whales. Physiology34, 409–418. https://doi.org/10.1152/physiol.00010.2019

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science5, 1–14. https://doi.org/10.3389/fmars.2018.00319

Wilson, R. P., Börger, L., Holton, M. D., Scantlebury, D. M., Gómez-Laich, A., Quintana, F., Rosell, F., Graf, P. M., Williams, H., Gunner, R., Hopkins, L., Marks, N., Geraldi, N. R., Duarte, C. M., Scott, R., Strano, M. S., Robotka, H., Eizaguirre, C., Fahlman, A., & Shepard, E. L. C. (2020). Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. Journal of Animal Ecology89(1), 161–172. https://doi.org/10.1111/1365-2656.13040

Do you lose SLEAP over video analysis of gray whale behavior? Not us in the GEMM Lab! 

Celest Sorrentino, University of California, Santa Barbara, Department of Ecological, Evolution, and Marine Biology, GEMM Lab NSF REU intern

Are you thinking “Did anyone proofread this blog beforehand? Don’t they know how to spell SLEEP?”  I completely understand this concern, but not to fear: the spelling of SLEAP is intentional! We’ll address that clickbait in just a moment. 

My name is Celest Sorrentino, a first-generation Latina undergrad who leaped at the opportunity to depart from the beaches of Santa Barbara, California to misty Newport, Oregon to learn and grow as a scientist under the influential guidance of Clara Bird, Dr. Leigh Torres and the powerhouse otherwise known as the GEMM lab. As a recent NSF REU (Research Experience for Undergraduates) intern in the GEMM Lab at Oregon State University, I am thrilled to have the chance to finally let you in on the project Clara, Leigh and I have been working on all summer. Ready for this?

Our project uses a deep-learning platform called SLEAP A.I. ( https://sleap.ai/) that can predict and track multiple animals in video to track gray whale mother calf pairs in drone footage. We also took this project a step further and explored how the distance between a gray whale mother and her calf, a proxy for calf independence, varied throughout the season and by different calf characteristics. 

In this story, we’ve got a little bit for everyone: the dynamic duo of computer vision and machine learning for my data scientist friends, and ecological inquest for my cetacean researcher friends. 

About the Author

Before we begin, I’d like to share that I am not a data scientist. I’ve only ever taken one coding class. I also do not have years of gray whale expertise under my belt (not yet at least!). I’m entering my 5th year at University of California, Santa Barbara as a double major in Ecology and Evolution (BS) as well as Italian Studies (BA). I am sharing this information to convey the feasibility of learning how to use machine-learning as a solution to streamline the laborious task of video analysis, which would permit more time towards answering your own ecological question, as we did here.

Essential Background

Hundreds of Hours of Drone footage

Since 2016, the GEMM Lab has been collecting drone footage of gray whales off the Oregon Coast to observe gray whale behavior in more detail (Torres et al. 2018). Drones have been shown to increase observational time of gray whales by a three-fold (Torres et al. 2018), including the opportunity to revisit the video with fresh eyes at any time one pleases. The GEMM Lab has flow over 500 flights in the past 6 years, including limited footage of gray whale mother-calf pairs. Little is known about gray whale mother-calf dynamics and even less about factors that influence calf development. As we cannot interview hundreds of gray-whale mother-calf pairs to develop a baseline for this information, we explore potential proxies for calf development instead (similar to how developmental benchmarks are used for human growth). 

Distance and Development

During our own life journey, each of us became less and less dependent on our parents to survive on our own. Formulating our first words so that we can talk for ourselves, cracking an egg for our parents so that we can one day cook for ourselves, or even letting go of their hand when crossing the street. For humans, we spend many years with our kin preparing for these moments, but gray whale mother-calf pairs only have a few months after birth until they separate. Gray whale calves are born on their wintering grounds in Baja California, Mexico (around February), migrate north with their mothers to the foraging grounds, and are then weaned during the foraging season (we think around August). This short time with their mother means that they have to become independent pretty quickly (about 6 months!).

Distance between mother and calf can be considered a measure of independence because we would expect increased distance between the pair as calf independence increases. In a study by Nielson et al (2019), distance between Southern Right Whale mother-calf pairs was found to increase as the calf grew, indicating that it can serve as a good proxy for independence. The moment a mother-calf pair separates has not been documented, but the GEMM lab has footage of calves during the foraging season pre-weaning that can be used to investigate this process.  However, video analysis is no easy feat: video analysis can range from post-processing, diligent evaluation, and video documentation (Torres et al. 2018). Although the use of UAS has become a popular method for many researchers, the extensive time required for video analysis is a limitation. As mentioned in Clara’s blog, the choice to pursue different avenues to streamline this process, such as automation through machine learning, is highly dependent on the purpose and the kind of questions a project intends to answer.

SLEAP A.I.

 In a world where modern technology is constantly evolving to cater towards making everyday tasks easier, machine learning leads the charge with its ability for a machine to perform human tasks. Deep learning is a subset of machine learning in which the model learns and adapts the ability to perform a task given a dataset. SLEAP (Social LEAP Estimation of Animal Poses) A.I. is an open-source deep-learning framework created to be able to track multiple subjects, specifically animals, throughout a variety of environmental conditions and social dynamics. In previous cases, SLEAP has tracked animals with distinct morphologies and conditions such as mice interactions, fruit flies engaging in courtship, and bee behavior in a petri dish (Pereira 2020). While these studies show that SLEAP could help make video analysis more efficient, these experiments were all conducted on small animals and in controlled environments. However, large megafauna, such as gray whales, cannot be cultivated and observed in a controlled Petri dish. Could SLEAP learn and adapt to predict and track gray whales in an uncontrolled environment, where conditions are never the same (ocean visibility, sunlight, obstructions)? 

Methods

In order to establish a model within SLEAP, we split our mother-calf drone video dataset into training (n=9) and unseen/testing (n=3) videos. Training involves teaching the model to recognize gray whales, and necessitated me to label every four frames using the following labels (anatomical features): rostrum, blowhole, dorsal, dorsal-knuckle, and tail (Fig. 1). Once SLEAP was trained and able to successfully detect gray whales, we ran the model on unseen video. The purpose of using unseen video was to evaluate whether the model could adapt and perform on video it had never seen before, eliminating the need for a labeler to retrain it. 

We then extracted the pixel coordinates for the mom and calf, calculated the distance between their respective dorsal knuckles, and converted the distance to meters using photogrammetry (see KC’s blog  for a great explanation of these methods).  The distance between each pair was then summarized on a daily scale as the average distance and the standard deviation. Standard deviation was explored to understand how variable the distance between mother-calf pair was throughout the day. We then looked at how distance and the standard deviation of distance varied by day of year, calf Total Length (TL), and calf Body Area Index (BAI; a measure of body condition). We hypothesized that these three metrics may be drivers of calf independence (i.e., as the calf gets longer or fatter it becomes more independent from its mother).  

Fig 1. Example of a labelled frame from SLEAP, highlighting labels: rostrum, blowhole, dorsal, dorsal-knuckle, and tail. 

Results

SLEAP A.I. was able to successfully detect and track gray whale mother-calf pairs across all videos (that’s a total of 1318 frames!). When evaluating how the average distance changed across Day of Year, calf Total length, and calf BAI, the plots did not demonstrate the positive relationship we anticipated (Fig 2A). However, when evaluating the standard deviation of distance across Day of Year, calf Total Length, and calf BAI, we did notice that there does appear to be an increase in variability of distance with an increase in Day of Year and calf Total length (Fig 2B)

Fig 2A: Relationship between average distance(m) between each mother and calf (colors represent different named calves) relative to Day of Year, Total length of calf, and BAI of calf. 
Fig 2B: Relationship between standard deviation of  distance(m) between each mother and calf (colors represent different named calves) relative to Day of Year, Total length of calf, and BAI of calf.

Concluding thoughts

These results are monumental! We demonstrated the feasibility to use AI to create a model that can track gray whale pairs in drone footage, which is a fantastic tool that can be applied to updated datasets in the future. As more footage of gray whale mother-calf pairs are collected, this video can be quickly uploaded to SLEAP for model evaluation, predictions can be exported, and results subsequently included in the distance analysis to update our plots and increase our understanding. Our data currently provide a preliminary understanding of how the distance between mother-calf pairs changes with Day of Year, Total length, and BAI, but we are now able to continue updating our dataset as we collect more drone footage. 

I suppose you can say I did mislead you a bit with my title, as I have lost some SLEEP recently. But, not over video analysis per say but rather in the form of inspiration. Inspiration toward expanding my understanding of machine learning so that it can be applied toward answering pressing ecological questions. This project has only propelled me to dig my heels in and investigate further the potential of machine learning to analyze dense datasets for huge knowledge gains.

Fig 3A: Snapshot of Celest working in SLEAP GUI.

Acknowledgements

This project was made possible in partnership by the continuous support by Clara Bird, Dr. Leigh Torres, KC Bierlich, and the entire GEMM Lab!

References

Nielsen, M., Sprogis, K., Bejder, L., Madsen, P., & Christiansen, F. (2019). Behavioural development in southern right whale calves. Marine Ecology Progress Series629, 219–234. https://doi.org/10.3354/meps13125

Pereira, Talmo D., Nathaniel Tabris, Junyu Li, Shruthi Ravindranath, Eleni S. Papadoyannis, Z. Yan Wang, David M. Turner, et al. “SLEAP: Multi-Animal Pose Tracking.” Preprint. Animal Behavior and Cognition, September 2, 2020. https://doi.org/10.1101/2020.08.31.276246.

Torres, Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Frontiers in Marine Science, 5. https://doi.org/10.3389/fmars.2018.00319

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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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