Tag: Gray Whales

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.  

A sneak peek of the 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.

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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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Familiar flukes and flanks: The 9th GRANITE field season is underway

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

The winds are consistently (and sometimes aggressively) blowing from the north here on the Oregon coast, which can only mean one thing – summer has arrived! Since mid-May, the GRANITE (Gray whale Response to Ambient Noise Informed by Technology and Ecology) team has been looking for good weather windows to survey for gray whales and we have managed to get five great field work days already. In today’s blog post, I am going to share what (and who) we have seen so far.

On our first day of the field season, PI Leigh Torres, postdoc KC Bierlich and myself, were joined by a special guest: Dr. Andy Read. Andy is the director of the Duke University Marine Lab, where he also runs his own lab, which focuses on conservation biology and ecology of marine vertebrates. Andy was visiting the Hatfield Marine Science Center as part of the Lavern Weber Visiting Scientist program and was hosted here by Leigh. For those of you that do not know, Andy was Leigh’s graduate school advisor at Duke where she completed her Master’s and doctoral degrees. It felt very special to have Andy on board our RHIB Ruby for the day and to introduce him to some friends of ours. The first whale we encountered that day was “Pacman”. While we are always excited to re-sight an individual that we know, this sighting was especially mind-blowing given the fact that Leigh had “just” seen Pacman approximately two months earlier in Guerrero Negro, one of the gray whale breeding lagoons in Mexico (read this blog about Leigh and Clara’s pilot project there). Aside from Pacman, we saw five other individuals, all of which we had seen during last year’s field season. 

The first day of field work for the 2023 GRANITE field season! From left to right: Leigh Torres, Lisa Hildebrand, Andy Read, and KC Bierlich. Source: L. Torres.

Since that first day on the water, we have conducted field work on four additional days and so far, we have only encountered known individuals in our catalog. This fact is exciting because it highlights the strong site fidelity that Pacific Coast Feeding Group (PCFG) gray whales have to areas within their feeding range. In fact, I am examining the residency and space use of each individual whale we have observed in our GRANITE study for one of my PhD chapters to better understand the level of fidelity individuals have to the central Oregon coast. Furthermore, this site fidelity underpins the unique, replicate data set on individual gray whale health and ecology that the GRANITE project has been able to progressively build over the years. So far during this field season in 2023, we have seen 13 unique individuals, flown the drone over 10 of them and collected four fecal samples from two, which represent critical data points from early on in the feeding season.

Our sightings this year have not only highlighted the high site fidelity of whales to our study area but have also demonstrated the potential for internal recruitment of calves born to “PCFG mothers” into the PCFG. Recruitment to a population can occur in two ways: externally (individuals immigrate into a population from another population) or internally (calves born to females that are part of the population return to, or stay, within their mothers’ population). Three of the whales we have seen so far this year are documented calves from females that are known to consistently use the PCFG range, including our central Oregon coast study area. In fact, we documented one of these calves, “Lunita”, just last year with her mother (see Clara’s recap of the 2022 field season blog for more about Lunita). The average calf survival estimate between 1997-2017 for the PCFG was 0.55 (Calambokidis et al. 2019), though it varied annually and widely (range: 0.34-0.94). Considering that there have been years with calf survival estimates as low as ~30%, it is therefore all the more exciting when we re-sight a documented calf, alive and well!

“Lunita”, an example of successful internal recruitment

We have also been collecting data on the habitat and prey in our study system by deploying our paired GoPro/RBR sensor system. We use the GoPro to monitor the benthic substrate type and relative prey densities in areas where whales are feeding. The RBR sensor collects high-frequency, in-situ dissolved oxygen and temperature data, enabling us to relate environmental metrics to relative prey measurements. Furthermore, we also collect zooplankton samples with a net to assess prey community and quality. On our five field work days this year, we have predominantly collected mysid shrimp, including gravid (a.k.a. pregnant) individuals, however we have also caught some Dungeness and porcelain crab larvae. The GEMM Lab is also continuing our collaboration with Dr. Susanne Brander’s lab at OSU and her PhD student Lauren Kashiwabara, who plan on conducting microplastic lab experiments on wild-caught mysid shrimp. Their plan is to investigate the growth rates of mysid shrimp under different temperature, dissolved oxygen, and microplastic load conditions. However, before they can begin their experiments, they need to successfully culture the mysids in the lab, which is why we collect samples for them to use as their ‘starter culture’. Stay tuned to hear more about this project as it develops!

So, all in all, it has been an incredibly successful start to our field season, marked by the return of many familiar flukes and flanks! We are excited to continue collecting rock solid GRANITE data this summer to increase our efforts to understand gray whale ecology and physiology. 

References

Calambokidis, J., Laake, J., and Perez, A. (2019). Updated analyses of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2017. IWC, SC/A17/GW/05 for the Workshop on the Status of North Pacific Gray Whales. La Jolla: IWC.

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

As waters warm, what are “anomalous conditions” in the face of climate change?

By Dr. Dawn Barlow, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Recently, I had the opportunity to attend the Effects of Climate Change on the World’s Ocean (ECCWO) conference. This meeting brought together experts from around the world for one week in Bergen, Norway, to gather and share the latest information on how oceans are changing, what is at risk, responses that are underway, and strategies for increasing climate resilience, mitigation, and adaptation. I presented our recent findings from the EMERALD project, which examines gray whale and harbor porpoise distribution in the Northern California Current over the past three decades. Beyond sharing my postdoctoral research widely for the first time and receiving valuable feedback, the ECCWO conference was an incredibly fruitful learning experience. Marine mammals can be notoriously difficult to study, and often the latest methodological approaches or conceptual frameworks take some time to make their way into the marine mammal field. At ECCWO, I was part of discussions at the ground floor of how the scientific community can characterize the impacts of climate change on the ecosystems, species, and communities we study.

One particular theme became increasingly apparent to me throughout the conference: as the oceans warm, what are “anomalous conditions”? There was an interesting dichotomy between presentations focusing on “extreme events,” “no-analog conditions,” or “non-stationary responses,” compared with discussions about the overall trend of increasing temperatures due to climate change. Essentially, the question that kept arising was, what is our frame of reference? When measuring change, how do we define the baseline?

Marine heatwaves have emerged as an increasingly prevalent phenomenon in recent years (see previous GEMM Lab blogs about marine heatwaves here and here). The currently accepted and typically applied definition of a marine heatwave is when water temperatures exceed a seasonal threshold (greater than the 90th percentile) for a given length of time (five consecutive days or longer) (Hobday et al. 2016). These marine heatwaves can have substantial ecosystem-wide impacts including changes in water column structure, primary production, species composition, distribution, and health, and fisheries management such as closures and quota changes (Cavole et al. 2016, Oliver et al. 2018). Through some of our own previous research, we documented that blue whales in Aotearoa New Zealand shifted their distribution (Barlow et al. 2020) and reduced their reproductive effort (Barlow et al. 2023) in response to marine heatwaves. Concerningly, recent projections anticipate an increase in the frequency, intensity, and duration of marine heatwaves under global climate change (Frölicher et al. 2018, Oliver et al. 2018).

However, as the oceans continue to warm, what baseline do we use to define anomalous events like marine heatwaves? Members of the US National Oceanic and Atmospheric Administration (NOAA) Marine Ecosystem Task Force recently put forward a comment article in Nature, proposing revised definitions for marine heatwaves under climate change, so that coastal communities have the clear information they need to adapt (Amaya et al. 2023). The authors posit that while a “fixed baseline” approach, which compares current conditions to an established period in the past and has been commonly used to-date (Hobday et al. 2016), may be useful in scenarios where a species’ physiological limit is concerned (e.g., coral bleaching), this definition does not incorporate the combined effect of overall warming due to climate change. A “shifting baseline” approach to defining marine heatwaves, in contrast, uses a moving window definition for what is considered “normal” conditions. Therefore, this shifting baseline approach would account for long-term warming, while also calculating anomalous conditions relative to the current state of the system.

An overview of two different definitions for marine heatwaves, relative to either fixed or shifting baselines. Reproduced from Amaya et al. 2023.

Why bother with these seemingly nuanced definitions and differences in terminology, such as fixed versus shifting baselines for defining marine heatwave events? The impacts of these events can be extreme, and potentially bear substantial consequences to ecosystems, species, and coastal communities that rely on marine resources. With the fixed baseline definition, we may be headed toward perpetual heatwave conditions (i.e., it’s almost always hotter than it used to be), at which point disentangling the overall warming trends from these short-term extremes becomes nearly impossible. What the shifting baseline definition means in practice, however, is that in the future temperatures would need to be substantially higher than the historical average in order to qualify as a marine heatwave, which could obscure public perception from the concerning reality of warming oceans. Yet, the authors of the Nature comment article claim, “If everything is extremely warm all of the time, then the term ‘extreme’ loses its meaning. The public might become desensitized to the real threat of marine heatwaves, potentially leading to inaction or a lack of preparedness.” Therefore, clear messaging surrounding both long-term warming and short-term anomalous conditions are critically important for adaptation and resource allocation in the face of rapid environmental change.

While the findings presented and discussed at an international climate change conference could be considered quite disheartening, I left the ECCWO conference feeling re-invigorated with hope. Crown Prince Haakon of Norway gave the opening plenary and articulated that “We need wise and concerned scientists in our search for truth”. Later in the week, I was a co-convenor of a session that gathered early-career ocean professionals, where we discussed themes such as how we deal with uncertainty in our own climate change-related ocean research, and importantly, how do we communicate our findings effectively. Throughout the meeting, I had formal and informal discussions about methods and analytical techniques, and also about what connects each of us to the work that we do. Interacting with driven and dedicated researchers across a broad range of disciplines and career stages gave me some renewed hope for a future of ocean science and marine conservation that is constructive, collaborative, and impactful.

Enjoying the ~anomalously~ sunny April weather in Bergen, Norway, during the ECCWO conference.

Now, as I am diving back in to understanding the impacts of environmental conditions on harbor porpoise and gray whale habitat use patterns through the EMERALD project, I am keeping these themes and takeaways from the ECCWO conference in mind. The EMERALD project draws on a dataset that is about as old as I am, which gives me some tangible perspective on how things have things changed in the Northern California Current during my lifetime. We are grappling with what “anomalous” conditions are in this dynamic upwelling system on our doorstep, whether these anomalies are even always bad, and how conditions continue to change in terms of cyclical oscillations, long-term trends, and short-term events. Stay tuned for what we’ll find, as we continue to disentangle these intertwined patterns of change.

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References

Amaya DJ, Jacox MG, Fewings MR, Saba VS, Stuecker MF, Rykaczewski RR, Ross AC, Stock CA, Capotondi A, Petrik CM, Bograd SJ, Alexander MA, Cheng W, Hermann AJ, Kearney KA, Powell BS (2023) Marine heatwaves need clear definitions so coastal communities can adapt. Nature 616:29–32.

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

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

Cavole LM, Demko AM, Diner RE, Giddings A, Koester I, Pagniello CMLS, Paulsen ML, Ramirez-Valdez A, Schwenck SM, Yen NK, Zill ME, Franks PJS (2016) Biological impacts of the 2013–2015 warm-water anomaly in the northeast Pacific: Winners, losers, and the future. Oceanography 29:273–285.

Frölicher TL, Fischer EM, Gruber N (2018) Marine heatwaves under global warming. Nature 560.

Hobday AJ, Alexander L V., Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Sen Gupta A, Wernberg T (2016) A hierarchical approach to defining marine heatwaves. Prog Oceanogr.

Oliver ECJ, Donat MG, Burrows MT, Moore PJ, Smale DA, Alexander L V., Benthuysen JA, Feng M, Sen Gupta A, Hobday AJ, Holbrook NJ, Perkins-Kirkpatrick SE, Scannell HA, Straub SC, Wernberg T (2018) Longer and more frequent marine heatwaves over the past century. Nat Commun 9:1–12.

A Gut Feeling: DNA Metabarcoding Gray Whale Diets

By Charles Nye, graduate student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Cetacean Conservation and Genomics Laboratory

Figure 1: An illustration (by me) of a feeding gray whale whose caudal end transitions into a DNA double helix.

Let’s consider how much stuff organisms shed daily. If you walk down a hallway, you’ll leave a microscopic trail of skin cells, evaporated sweat, and even more material if you so happen to sneeze or cough (as we’ve all learned). The residency of these bits and pieces in a given environment is on the order of days, give or take (Collins et al. 2018). These days, we can extract, amplify, and sequence DNA from leftover organismal material in environments (environmental DNA; eDNA), stomach contents (dietary DNA, dDNA), and other sources (Sousa et al. 2019; Chavez et al. 2021).

You might be familiar with genetic barcoding, where scientists are able to use documented and annotated pieces of a genome to identify a piece of DNA down to a species. Think of these as genetic fingerprints from a crime scene where all (described) species on Earth are prime suspects. With advancements in computing technology, we can barcode many species at the same time—a process known as metabarcoding. In short, you can now do an ecosystem-wide biodiversity survey without even needing to see your species of interest (Ficetola et al. 2008; Chavez et al. 2021).

(Before you ask: yes, people have tried sampling Loch Ness and came up with not a single strand of plesiosaur DNA (University of Otago, 2019).)

I received my crash course on metabarcoding when I was employed at the Monterey Bay Aquarium Research Institute (MBARI), right before grad school. There, I was employed to help refine eDNA survey field and laboratory methods (in addition to some cool robot stuff). Here at OSU, I use metabarcoding to research whale ecology, detection, and even a little bit of forensics  work. Cetacean species (or evidence thereof) I’ve worked on include North Atlantic right whales (Eubalaena glacialis), killer whales (Orcinus spp.), and gray whales (Eschrichtius robustus).

Long-time readers of the GEMM Lab Blog are probably quite knowledgeable about the summertime grays—the Pacific Coast Feeding Group (PCFG). All of us here at OSU’s Marine Mammal Institute (MMI) are keenly interested in understanding why these whales hang out in the Pacific Northwest during the summer months and what sets them apart from the rest of the Eastern North Pacific gray whale population. What interests me? Well, I want to double-check what they’re eating—genetically.

“What does my study species eat?” is a straightforward but underappreciated question. It’s also deceptively difficult to address. What if your species live somewhere remote or relatively inaccessible? You can imagine this is a common logistical issue for most research in marine sciences. How many observations do you need to make to account for seasonal or annual changes in prey availability? Do all individuals in your study population eat the same thing? I certainly like to mix and match my diet.

Gray whale foraging ecology has been studied comprehensively over the last several decades, including an in-depth stomach content evaluation by Mary Nerini in 1984 and GEMMer Lisa Hildebrand’s MSc research. PCFG whales seem to prefer shrimpy little creatures called mysids, along with Dungeness crab (Cancer magister) larvae, during their stay in the Pacific Northwest (PNW), most notably the mysid Neomysis rayii (Guerrero 1989; Hildebrand et al. 2021). Indeed, the average energetic values of common suspected prey species in PNW waters rival the caloric richness of Arctic amphipods (Hildebrand et al. 2021). However, despite our wealth of visual foraging observations, metabarcoding may add an additional layer of resolution. For example, the ocean sunfish (Mola mola) was believed to exclusively forage on gelatinous zooplankton, but a metabarcoding approach revealed a much higher diversity of prey items, including other bony fishes and arthropods (Sousa et al. 2016).

Given all this exposition, you may be wondering: “Charles—how do you intend on getting dDNA from gray whales? Are you going to cut them open?”

Figure 2: The battle station, a vacuum pump that I use to filter out all of the particulate matter from a gray whale dDNA sample. The filter is made of polycarbonate track etch material, which melts away in the DNA extraction process—quite handy, indeed!

No. I’m going to extract DNA from their poop.

Well, actually, I’ve been doing that for the last two years. My lab (Cetacean Conservation and Genomics Laboratory, CCGL) and GEMM Lab have been collaborating to make lemonade out of, er…whale poop. An archive of gray whale fecal samples (with ongoing collections every field season) originally collected for hormone analyses presented itself with new life—the genomics kind. In addition to community-level data, we are also able to recover informative DNA from the gray whales, including sex ID from “depositing” individuals, though the recovery rate isn’t perfect.

Because the GEMM Lab/MMI can non-invasively collect multiple samples from the same individuals over time, dDNA metabarcoding is a great way to repeatedly evaluate the diets of the PCFG, just shy of being at the right place at the right time with a GoPro or drone to witness a feeding event.  While we can get stomach contents and even usable dDNA from a naturally deceased whale, those data may not be ideal. How representative a stranded whale is of the population is dependent on the cause of death; an emaciated or critically injured individual, for example, is a strong outlier.

Figure 3: Presence/absence of the top 10 most-common taxonomic Families observed in the PCFG gray whale dDNA dataset (n = 20, randomly selected). Filled-in dots indicate at least one genetic read associated with that Family, and empty dots indicate none. Note the prey taxa: mysids (Mysidae), krill (Euphausiidae), and olive snails (Olividae).

Here’s a snapshot of progress to date for this dDNA metabarcoding project. I pulled out twenty random samples from my much larger working dataset (n = 82) for illustrative purposes (and legibility). After some bioinformatic wizardry, we can use a presence/absence approach to get an empirical glimpse at what passes through a PCFG gray whale. While I am able to recover species-level information, using higher-level taxonomic rankings summarizes the dataset in a cleaner fashion (and also, not every identifiable sequence resolves to species).

The title of most commonly observed prey taxa belongs to our friends, the mysids (Mysidae). Surprisingly, crabs and amphipods are not as common in this dataset, instead losing to krill (Euphausiidae) and olive snails (Olividae). The latter has been found in association with gray whale foraging grounds but not documented in a prey study (Jenkinson 2001). We also get an appreciable amount of interference from non-prey taxa, most notably barnacles (Balanidae), with an honorable mention to hydrozoans (Clytiidae, Corynidae). While easy to dismiss as background environmental DNA, as gray whales do forage at the benthos, these taxa were physically present and identifiable in Nerini’s (1984) gray whale stomach content evaluation.

So—can we conclude that barnacles and hydrozoans are an important part of a gray whale’s diet, as much as mysids? From decades of previous observations, we might say…probably not. Gray whales are actively targeting patches of crabby, shrimpy zooplankton things, and even employ novel foraging strategies to do so (Newell & Cowles 2006; Torres et al. 2018). However, the sheer diversity of consumed species does present additional dimensionality to our understanding of gray whale ecology.

The whales are eating these ancillary organisms, whether they intend to or not, and this probably does influence population dynamics, recruitment, and succession in these nearshore benthic habitats. After all, the shallow pits that gray whales leave behind post-feeding provide a commensal trophic link with other predatory taxa, including seabirds and groundfish (Oliver & Slattery 1985). Perhaps the consumption of these collateral species affects gray whale energetics and reflects on their “performance”?

I hope to address all of this and more in some capacity with my published work and graduate chapters. I’m confident to declare that we can document diet composition of PCFG whales using dDNA metabarcoding, but what comes next is where one can get lost in the sea(weeds). How does the diet of individuals compare to one another? What about at differing time points? Age groups? How many calories are in a barnacle? No need to fret—this is where the fun begins!

References

Chavez F, Min M, Pitz K, Truelove N, Baker J, LaScala-Grunewald D, Blum M, Walz K,

Nye C, Djurhuus A, et al. 2021. Observing Life in the Sea Using Environmental

DNA Oceanog. 34(2):102–119. doi:10.5670/oceanog.2021.218.

Collins R, Wangensteen OS, O’Gorman EJ, Mariani S, Sims DW, Genner M. 2018. Persistence

of environmental DNA in marine systems. Comm Biol. 1(185).

https://doi.org/10.1038/s42003-018-0192-6

Ficetola GF, Miaud C, Pompanon F, Taberlet P. 2008. Species detection using

environmental DNA from water samples. Biol Lett. 4(4):423–425.

doi:10.1098/rsbl.2008.0118.

Hildebrand L, Bernard KS, Torres LG. 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. 8:683634.

doi:10.3389/fmars.2021.683634.

Jenkinson R. 2001. Gray whale (Eschrichtius robustus) prey availability and feeding ecology in

northern California, 1999-2000 [thesis]. California State Polytechnic University,

Humboldt. 81 p.

Newell CL, Cowles TJ. 2006. Unusual gray whale Eschrichtius robustus feeding in the summer

of 2005 off the central Oregon Coast. Geophys Res Lett. 33(22):L22S11.

doi:10.1029/2006GL027189.

Oliver JS, Slattery PN. 1985. Destruction and Opportunity on the Sea Floor: Effects of

Gray Whale Feeding. Ecology. 66(6):1965–1975. doi:10.2307/2937392.

Sousa LL, Silva SM, Xavier R. 2019. DNA metabarcoding in diet studies: Unveiling

ecological aspects in aquatic and terrestrial ecosystems. Environmental DNA.

1(3):199–214. doi:10.1002/edn3.27.

Sousa LL, Xavier R, Costa V, Humphries NE, Trueman C, Rosa R, Sims DW, Queiroz N.

2016. DNA barcoding identifies a cosmopolitan diet in the ocean sunfish. Sci

Rep. 6(1):28762. doi:10.1038/srep28762.

Torres LG, Nieukirk SL, Lemos L, Chandler TE. 2018. Drone Up! Quantifying Whale Behavior

From a New Perspective Improves Observational Capacity. Front Mar Sci. 5:319.

doi:10.3389/fmars.2018.00319.

University of Otago. 2019. First eDNA study of Loch Ness points to something fishy.

https://www.otago.ac.nz/news/news/otago717609.html. [accessed 2023 Apr 25]

The road to candidacy is paved with knowledge

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

As I sat down to write this blog, I realized that it is the first post I have written in 2023! This is largely because I have spent the last seven weeks preparing for (and partly taking) my PhD qualifying exams, an academic milestone that involves written and oral exams prepared by each committee member for the student. The point of the qualifying exams is for the student’s committee to determine the student’s understanding of their major field, particularly where and what the limits of that understanding are, and to assess the student’s capability for research. How do you prepare for these exams? Reading. Lots of reading and synthesis of the collective materials assigned by each committee member. My dissertation research covers a broad range of Pacific Coast Feeding Group (PCFG) gray whale ecology, such as space use, oceanography, foraging theory and behavioral responses to anthropogenic activities. Accordingly, my assigned reading lists were equally broad and diverse. For today’s blog, I am going to share some of the papers that have stuck with me and muse about how these topics relate to my study system, the Pacific Coast Feeding Group (PCFG) of gray whales.

Space use & home range

For decades, ecologists have been interested in defining an animal’s use of space through time, often referred to as an animal’s home range. The seminal definition of a home range comes from Burt (1943) who outlined it as “the area traversed by an individual in its normal activities of food gathering, mating, and caring for young.”. I like this definition of a home range because it is biologically grounded and based on an animal’s requirements. However, quantifying an animal’s home range based on this definition is harder than it may sound. In an ideal world, it could be achieved if we were able to collect location data that is continuous (i.e., one location per second), long-term (i.e., at least half the lifespan of an animal) and precise (i.e., correct to the nearest meter) together with behavior for an individual. However, a device that could collect such data, particularly for a baleen whale, does not currently exist. Instead, we must use discontinuous (i.e., one location per hour, day or month) and/or short-term (i.e., <1 year) data with variable precision to calculate animal home ranges. A very common and simple analytical method that is used to calculate an animal’s home range is the minimum convex polygon (MCP). MCP draws the smallest polygon around points with all interior angles less than 180º. While this method is appealing and widely used, it often overestimates the home range by including areas not used by an animal at all (Figure 1).

Figure 1. (a) 10 point locations where an individual was observed; (b) the home range as determined by the minimum convex polygon method; (c) the red path shows the movements the animal actually took. Note the large white area in (c) where the animal never went even though it is considered part of the animal’s home range.

This example is just one of many where home range estimators inaccurately describe an animal’s space use. However, this does not mean that we should not attempt to make our best approximations of an animal’s home range using the tools and data we have at our disposal. Powell & Mitchell perfectly summarized this sentiment in their 2012 paper: “Understanding animal’s home ranges will be a messy, irregular, complex process and the results will be difficult to map. We must embrace this messiness as it simply represents the real behaviors of animals in complex and variable environments.”. For my second dissertation chapter, I am investigating individual PCFG gray whale space use patterns by calculating activity centers and ranges. The activity center is simply the geographic center of all points of observation (Hayne, 1949) and the range is the distance from the activity center to the most distant point of observations in either poleward direction. While the actual activity center is probably relatively meaningless to a whale, we hope that by calculating these metrics we can identify different strategies of space use that individuals employ to meet their energetic requirements (Figure 2).

Figure 2. Sightings of nine different PCFG individuals across our GRANITE study area. Each circle represents a location where an individual was sighted and circles are color-coded by year. Plotting the raw data of sighting histories of these individuals hints at patterns in space use by different individuals, which I will explore further in my second dissertation chapter.

Non-stationary responses to oceanography

Collecting spatiotemporally overlapping predator-prey datasets at the appropriate scales is notoriously challenging in the marine environment. As a result, marine ecologists often try to find patterns between marine species and oceanographic and/or environmental covariates, as these can sometimes be easier to sample and thus make marine species predictions simpler. This approach has been applied successfully in hundreds, if not thousands, of studies (e.g., Barlow et al., 2020; Derville et al., 2022). Unfortunately, these relationships are not always proving to be stable over time, a phenomenon called non-stationarity. For example, Schmidt et al. (2014) showed that the reproductive successes of Brandt’s cormorants and Cassin’s auklets on southeast Farallon Island were positively correlated with each other from 1975 to 1995 and were associated with negative El Niño-Southern Oscillation. However, around the mid-1990s this relationship broke down and by 2002, the reproductive successes of the two species were significantly negatively correlated (Figure 3). Furthermore, the relationships between reproductive success and most physical oceanographic conditions became highly variable from year to year and were non-stationary. Thus, if the authors continued to use the relationships defined early on in the study (1975-1995) to predict seabird reproductive success relative to ocean conditions from 2002-2012, their predictions would have been completely wrong. After reading this study, I thought a lot about what the oceanographic conditions have been since the GEMM Lab started studying PCFG gray whales vs. the years prior. Leigh launched the GRANITE project in 2016, right at the tail end of the record marine heatwave in the Pacific, known as “the Blob”. While we do not have as long of a dataset as the Schmidt et al. (2014) study, I wonder whether we might find non-stationary responses between PCFG gray whales and environmental and/or oceanographic variables, given how the effects of the Blob lingered for a long time and we may have captured the central Oregon coast environment shifting from ‘weird to normal’. Non-stationarity is something I will at least keep in mind when I am working on my third dissertation chapter which will investigate the environmental and oceanographic drivers of PCFG gray whale space use strategies.

Figure 3. Figure and caption taken from Schmidt et al. (2014).

There are so many more studies and musings that I could write about. I keep being told by others who have been through this qualifying exam process that this is the smartest I am ever going to be, and I finally understand what they mean. After spending almost two months in my own little study world, my research, and where it fits within the complex web of ecological knowledge, has snapped into hyperfocus. I can see clearly where past research will guide me and where I am blazing a new trail of things never attempted before. While I still have the oral portion of my exams before me (in fact, it’s tomorrow!), I am already giddy with excitement to switch back to analyzing data and making progress on my dissertation research.

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References

Barlow, D.R., Bernard, K.S., Escobar-Flores, P., Palacios, D.M., Torres, L.G. 2020. Links in the trophic chain: modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Marine Ecology Progress Series 642: 207−225. 

Burt, W.H. 1943. Territoriality and home range concepts as applied to mammals. Journal of Mammalogy 24(3): 346-352. https://doi.org/10.2307/1374834

Derville, S., Barlow, D.R., Hayslip, C., Torres, L.G. 2022. Seasonal, annual, and decadal distribution of three rorqual whale species relative to dynamic ocean conditions off Oregon, USA. Frontiers in Marine Science 9. https://doi.org/10.3389/fmars.2022.868566

Hayne, D.W. 1949. Calculation of size of home range. Journal of Mammalogy 30(1): 1-18. 

Powell, R.A., Mitchell, M.S. 2012. What is a home range? Journal of Mammalogy 93(4): 948-958. https://doi.org/10.1644/11-MAMM-S-177.1

Schmidt, A.E., Botsford, L.W., Eadie, J.M., Bradley, R.W., Di Lorenzo E., Jahncke, J. 2014. Non-stationary seabird responses reveal shifting ENSO dynamics in the northeast Pacific. Marine Ecology Progress Series 499: 249-258. https://doi.org/10.3354/meps10629

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

Keeping it simple: A lesson in model construction

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

Models can be extremely useful tools to describe biological systems and answer ecological questions, but they are often tricky to construct. If I have learned anything in my statistics classes, it is the importance of resisting the urge to throw everything but the kitchen sink into a model. However, this is usually much easier said than done, and model construction takes a lot of practice. The principle of simplicity is currently at the forefront of my thesis work, as I try to embody the famous quote by Albert Einstein:

 “Everything should be made as simple as possible, but no simpler.”

As you might remember from my earlier blog, the goal of my thesis is to use biologging data to define different foraging behaviors of Pacific Coast Feeding Group (PCFG) gray whales, and then calculate the energetic cost of those behaviors. I am defining PCFG foraging behaviors at two scales: (1) dives that represent different behavior states (e.g., travelling vs foraging), and (2) roll events, which are periods during dives where the whale is rolled onto their side, that represent different foraging tactics (e.g., headstanding vs side-swimming).

Initially, I was planning to use a clustering analysis to define these different foraging behaviors at both the dive and roll event scale, as this method has been used to successfully classify different foraging strategies for Galapagos sea lions (Schwarz et al., 2021). In short, this clustering analysis uses summary variables from events of interest to group events based on their similarity. These can be any metric that describes the event such as duration and depth, or body positioning variables like median pitch or roll. The output of the clustering analysis method results in groups of events that can each be used to define a different behavior.

However, while this method works for defining the foraging tactics of PCFG gray whales, my discussions with other scientists have suggested that there is a better method available for defining foraging behavior at the dive scale: Hidden Markov Models (HMMs). HMMs are similar to the clustering method described above in that they use summary variables at discrete time scales to define behavior states, but HMMs take into account the bias inherent to time series data – events that occur closer together in time are more likely to be more similar. This bias of time can confound clustering analyses, making HMMs a better tool for classifying a series of dives into different behavior states.

Like many analytical methods, the HMM framework was first proposed in a terrestrial system where it was used to classify the movement of translocated elk (Morales et al., 2004). The initial framework proposed using the step length, or the spatial distance between the animal’s locations at the start of subsequent time intervals, and the corresponding turning angle, to isolate “encamped” from “exploratory” behaviors in each elk’s movement path (Figure 1, from Morales et al., 2004). “Encamped” behaviors are those with short step lengths and high turning angles that show the individual is moving within a small area, and they can be associated with foraging behavior. On the other hand, “exploratory” behaviors are those with long step lengths and low turning angles that show the individual is moving in a relatively straight path and covering a lot of ground, which is likely associated with travelling behavior.

Figure 1. The difference between “encamped” and “exploratory” behavior states from a simple Hidden Markov Model (HMM) in a translocated elk equipped with a GPS collar (Fig. 1 in Morales et al., 2004). The top rose plots show the turning angles while the bottom histograms show the step lengths as a daily movement rate. The “encamped” state has short step lengths (low daily movement rate) and high turning angles while the “exploratory” state has long step lengths (high daily movement rate) and low turning angle. These behavior states from the HMM can then be interpollated to elk behavior, as the low daily movement and tight turns of the “encamped” behavior state likely indicates foraging while the high daily movement and direct path of the “exploratory” behavior state likely indicates traveling. Thus, it is important to keep the biological relevance of the study system in mind while constructing and interpreting the model.

In the two decades following this initial framework proposed by Morales et al. (2004), the use of HMMs in anlaysis has been greatly expanded. One example of this expansion has been the development of mutlivariate HMMs that include additional data streams to supplement the step length and turning angle classification of “encamped” vs “exploratory” states in order to define more behaviors in movement data. For instance, a multivariate HMM was used to determine the impact of acoustic disturbance on blue whales (DeRuiter et al., 2017). In addition to step length and turning angle, dive duration and maximum depth, the duration of time spent at the surface following the dive, the number of feeding lunges in the dive, and the variability of the compass direction the whale was facing during the dive were all used to classify behavior states of the whales. This not only allowed for more behavior states to be identified (three instead of two as determined in the elk model), but also the differences in behavior states between individual animals included in the study, and the differences in the occurrence of behavior states due to changes in environmental noise.

The mutlivariate HMM used by DeRuiter et al. (2017) is a model I would ideally like to emulate with the biologging data from the PCFG gray whales. However, incorporating more variables invites more questions during the model construction process. For example, how many variables should be incorporated in the HMM? How should these variables be modeled? How many behavior states can be identified when including additional variables? These questions illustrate how easy it is to unnecessarily overcomplicate models and violate the principle of simiplicity toted by Albert Einstein, or to be overwhelmed by the complexity of these analytical tools.

Figure 2. Example of expected output of Hidden Markov Model (HMM) for the PCFG gray whale biologging data (GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678). The figure shows the movement track the whale swam during the deployment of the biologger, with each point representing the start of a dive. The axes show “Easting” and “Northing” rather than map coordinates because this is the relative path the whale took rather than GPS coordinates of the whale’s location. Each color represents a different behavior state—blue has short step lengths and high turning angles (likely foraging), red has intermediate step lengths and turning angles (likely searching), and black has long step lengths and low turning angles (likely transiting). These results will be refined as I construct the multivariate HMM that will be used in my thesis.  

Luckily, I can draw on the support of Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project collaborators Dr. Leslie New and Dr. Enrico Pirotta to guide my HMM model construction and assist in interpreting the outputs (Figure 2). With their help, I have been learning the importance of always asking if the change I am making to my model is biologically relevent to the PCFG gray whales, and if it will help give me more insight into the whales’ behavior. Even though using complex tools, such as Hidden Markov Models, has a steep learning curve, I know that this approach is not only placing this data analysis at the cutting edge of the field, but helping me practice fundamental skills, like model construction, that will pay off down the line in my career.

Sources

DeRuiter, S. L., Langrock, R., Skirbutas, T., Goldbogen, J. A., Calambokidis, J., Friedlaender, A. S., & Southall, B. L. (2017). A multivariate mixed Hidden Markov Model for blue whale behaviour and responses to sound exposure. Annals of Applied Statistics, 11(1), 362–392. https://doi.org/10.1214/16-AOAS1008

Morales, J. M., Haydon, D. T., Frair, J., Holsinger, K. E., & Fryxell, J. M. (2004). Extracting more out of relocation data: Building movement models as mixtures of random walks. Ecology, 85(9), 2436–2445. https://doi.org/10.1890/03-0269

Schwarz, J. F. L., Mews, S., DeRango, E. J., Langrock, R., Piedrahita, P., Páez-Rosas, D., & Krüger, O. (2021). Individuality counts: A new comprehensive approach to foraging strategies of a tropical marine predator. Oecologia, 195(2), 313–325. https://doi.org/10.1007/s00442-021-04850-w

How fat do baleen whales get? Recent publication shows how humpback whales increase their body condition over the foraging season. 

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

Traveling across oceans takes a lot of energy. Most baleen whales use stored energy acquired on their summer foraging grounds to support the costs of migration to and reproduction on their winter breeding grounds. Since little, if any, feeding takes place during the migration and winter season, it is essential that baleen whales obtain enough food to increase their fat reserves to support reproduction. As such, baleen whales are voracious feeders, and they typically depart the foraging grounds much fatter than when they had arrived. 

So, how fat do baleen whales typically get by the end of the foraging season, and how does this differ across reproductive classes, such as a juvenile female vs. a pregnant female? Understanding these questions is key for identifying what a typical “healthy” whale looks like, information which can then help scientists and managers monitor potential impacts from environmental and anthropogenic stressors. In this blog, I will discuss a recent publication in Frontiers in Marine Science (https://doi.org/10.3389/fmars.2022.1036860) that is from my PhD dissertation with the Duke University Marine Robotics and Remote Sensing (MaRRS) Lab, and also includes GEMM lab members Allison Dawn and Clara Bird. In this study, we analyzed how humpback whales (Megaptera novaeangliae) along the Western Antarctic Peninsula (WAP) increase their fat reserves throughout the austral summer foraging season (Bierlich et al., 2022). This work also helps provide insight to the GEMM Lab’s GRANITE project (Gray whale Response to Ambient Noise Informed by Technology and Ecology), where we are interested in how Pacific Coast Feeding Group (PCFG) gray whales increase their energy reserves in response to environmental variability and increasing human activities. 

Eastern South Pacific humpback whales, identified as Stock G by the International Whaling Commission, travel over 16,000 km between summer foraging grounds along the WAP and winter breeding grounds between Ecuador and Costa Rica (Fig. 1). Like most baleen whales, Stock G humpback whales were heavily exploited by 20th century commercial whaling. Recent evidence suggests that this population is recovering, with an estimated increase in population size of ~7,000 individuals in 2000 to ~19,107 in 2020 (Johannessen et al., 2022). 

However, there are long-term concerns for this population. The WAP is one of the fastest warming regions on the planet, and regional populations of krill, an important food source for humpback whales, have declined steeply over the past half-century. Additionally, the WAP has seen a rapid expansion of human activities, such as tourism and krill fishing. Specifically, the WAP has experienced an increase in tourism from a total of 6,700 visitors from 59 voyages in 1990 to 73,000 visitors from 408 voyages in 2020, which may be causing increased stress levels amongst Stock G (Pallin et al., 2022). Furthermore, the krill fishery has increased harvest activities in key foraging areas for humpback whales (Reisinger et al., 2022). Understanding how humpback whales increase their energy reserves over the course of the foraging season can help researchers establish a baseline to monitor future impacts from climate change and human activities. This work also provides an opportunity for comparisons to other baleen whale populations that are also exposed to multiple stressors, such as the PCFG gray whales off the Newport Coast who are constantly exposed to vessel traffic and at risk of entanglement from fishing gear. 

Figure 1. The migration route of the Stock G humpback whale population. Figure adapted from Whales of the Antarctic Peninsula Report, WWF 2018.

To understand how humpback whales increase their energy reserves throughout the foraging season, we collected drone imagery of whales along the WAP between November and June, 2017-2019 (Fig. 2). We used these images to measure the length and width of the whale to estimate body condition, which represents an animal’s relative energy reserve and can reflect foraging success (see previous blog). We collected drone imagery from a combination of research stations (Palmer Station), research vessels (Laurence M. Gould), and tour ships (One Ocean Expeditions). We used several different drones types and accounted for measurement uncertainty associated with the camera, focal length lens, altitude, and altimeter (barometer/LiDAR) from each drone (see previous blog and Bierlich et al., 2021a, 2021b). We also took biopsy samples to identify the sex of each individual and to determine if females were pregnant or not. 

Figure 2. Two humpbacks gracefully swimming in the chilly water along the Western Antarctic Peninsula. Photo taken by KC Bierlich & the Duke University Marine Robotics and Remote Sensing (MaRRS) Lab.

Our final dataset included body condition measurements for 228 total individuals. We found that body condition increased linearly between November and June for each reproductive class, which included calves, juvenile females, juvenile whales of unknown sex, lactating females, mature whales of unknown sex, and non-pregnant females (Fig. 3). This was an interesting finding because a recent publication analyzing tagged whales from the same population found that humpback whales have high foraging rates in early season that then significantly decrease by February and March (Nichols et al., 2022). So, despite these reduced foraging rates throughout the season, humpback whales continue to gain substantial mass into the late season. This continued increase in body condition implies a change in krill abundance and/or quality into the late season, which may compensate for the lower feeding rates. For example, krill density and biomass increases by over an order of magnitude across the season (Reiss et al., 2017) and their lipid content increases by ~4x (Hagen et al., 1996). Thus, humpback whales likely compensate for their lower feeding rates by feeding on denser and higher quality krill, ultimately increasing their efficiency in energy deposition. 

Figure 3. Body condition, here measured as Body Area Index (BAI), increases linearly for each reproductive class across the austral summer foraging season (Nov – June) for humpback whales along the Western Antarctic Peninsula. The shading represents the uncertainty around the estimated relationship. The colors represent the month of data collection.

We found that body condition increase varied amongst reproductive classes. For example, lactating females had the poorest measures of body condition across the season, reflecting the high energetic demands of nursing their calves (Fig. 3). Conversely, non-pregnant females had the highest body condition at the start of the season compared to all the other classes, likely reflecting the energy saved and recovered by skipping breeding that year.  Calves, juvenile whales, and mature whales all reached similar levels of body condition by the end of the season, though mature whales will likely invest most of their energy stores toward reproduction, whereas calves and juveniles likely invest toward growth. We also found a positive relationship between the total length of lactating females and their calves, suggesting that bigger moms have bigger calves (Fig. 4). A similar trend has also been observed in other baleen whale species including southern and North Atlantic right whales (Christiansen et al., 2018; Stewart et al., 2022).

Figure 4. Big mothers have big calves. Total length (TL) measurement between mother-calf pairs. The bars around each point represents the uncertainty (95% highest posterior density intervals). The colors represent the month of data collection. The blue line represents the best fit from a Deming regression, which incorporate measurement uncertainty in both the independent (mother’s TL) and dependent variable (calf’s TL).

The results from the humpback study provide insight for my current work exploring how PCFG gray whales increase their energy reserves in relation to environmental variability and increasing human activities. Over the past seven years, the GEMM Lab has been collecting drone images of PCFG gray whales off the coast of Oregon to measure their body condition (see this 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 body condition is influenced by environmental variation, stress levels, maturity, and reproduction. For example, we had nine total body condition measurements of a female PCFG whale named “Sole”, who had a curvilinear increase in body condition throughout the summer foraging season – a rapid increase in early season that slowed as the season progressed (Fig. 5). This raises many questions for us: is this how most PCFG whales typically increase their body condition during the summer? Is this increase different for pregnant or lactating females? How is this increase impacted by environmental variability or anthropogenic stressors? Repeated measurements of individuals, in addition to Sole, in different reproductive classes across different years will help us determine what body condition is considered a healthy range for gray whales. This is particularly important for monitoring any potential health consequences from anthropogenic stressors, such as vessel noise and traffic (see recent blog by GEMM Lab alum Leila Lemos). We are currently analyzing body condition measurements between 2016 – 2022, so stay tuned for upcoming results!

Figure 6. Body condition, here measured as Body Area Index (BAI), increases curvilinearly for “Sole”, a mature female Pacific Coat Feeding Group gray whale, imaged nine times along the Oregon coast in 2021. The colors represent the month of data collection. 

References

Bierlich, K. C., Hewitt, J., Bird, C. N., Schick, R. S., Friedlaender, A., Torres, L. G., et al. (2021a). Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Front. Mar. Sci. 8, 1–16. doi:10.3389/fmars.2021.749943.

Bierlich, K. C., Hewitt, J., Schick, R. S., Pallin, L., Dale, J., Friedlaender, A. S., et al. (2022). Seasonal gain in body condition of foraging humpback whales along the Western Antarctic Peninsula. Front. Mar. Sci. 9, 1–16. doi:10.3389/fmars.2022.1036860.

Bierlich, K., Schick, R., Hewitt, J., Dale, J., Goldbogen, J., Friedlaender, A., 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.

Christiansen, F., Vivier, F., Charlton, C., Ward, R., Amerson, A., Burnell, S., et al. (2018). Maternal body size and condition determine calf growth rates in southern right whales. Mar. Ecol. Prog. Ser. 592, 267–281.

Hagen, W., Van Vleet, E. S., and Kattner, G. (1996). Seasonal lipid storage as overwintering strategy of Antarctic krill. Mar. Ecol. Prog. Ser. 134, 85–89. doi:10.3354/meps134085.

Johannessen, J. E. D., Biuw, M., Lindstrøm, U., Ollus, V. M. S., Martín López, L. M., Gkikopoulou, K. C., et al. (2022). Intra-season variations in distribution and abundance of humpback whales in the West Antarctic Peninsula using cruise vessels as opportunistic platforms. Ecol. Evol. 12, 1–13. doi:10.1002/ece3.8571.

Nichols, R., Cade, D. E., Kahane-Rapport, S., Goldbogen, J., Simpert, A., Nowacek, D., et al. (2022). Intra-seasonal variation in feeding rates and diel foraging behavior in a seasonally fasting mammal, the humpback whale. Open Sci. 9, 211674.

Pallin, L. J., Botero-Acosta, N., Steel, D., Baker, C. S., Casey, C., Costa, D. P., et al. (2022). Variation in blubber cortisol levels in a recovering humpback whale population inhabiting a rapidly changing environment. Sci. Rep. 12, 1–13. doi:10.1038/s41598-022-24704-6.

Reisinger, R., Trathan, P. N., Johnson, C. M., Joyce, T. W., Durban, J. W., Pitman, R. L., et al. (2022). Spatiotemporal overlap of baleen whales and krill fisheries in the Antarctic Peninsula region. Front. Mar. Sci. doi:doi: 10.3389/fmars.2022.914726.

Reiss, C. S., Cossio, A., Santora, J. A., Dietrich, K. S., Murray, A., Greg Mitchell, B., et al. (2017). Overwinter habitat selection by Antarctic krill under varying sea-ice conditions: Implications for top predators and fishery management. Mar. Ecol. Prog. Ser. 568, 1–16. doi:10.3354/meps12099.

Stewart, J. D., Durban, J. W., Europe, H., Fearnbach, H., Hamilton, P. K., Knowlton, A. R., et al. (2022). Larger females have more calves : influence of maternal body length on fecundity in North Atlantic right whales. Mar. Ecol. Prog. Ser. 689, 179–189. doi:10.3354/meps14040.