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. 

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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.

Navigating the Research Rollercoaster

By Amanda Rose Kent, College of Earth Ocean and Atmospheric Sciences, OSU, GEMM Lab/Krill Seeker undergraduate intern

If you asked me five years ago where I’d thought I’d be today, the answer I would give would not reflect where I am now. Back then, I was a customer service representative for a hazardous waste company, and I believed that going to university and participating in research was a straightforward experience. I learned soon after I left that career and began my journey at OSU in 2020 that I wasn’t even remotely aware of the process. I knew that as part of my oceanography degree I would need to become involved in some form of research, but I had no idea where to start.

I started looking through the Oregon State website and I eventually found an outdated flier from 2018 that advertised a lab that studied plankton in Antarctica, and that was when I first reached out to Dr. Kim Bernard. My journey took off from there. As an undergraduate researcher in the URSA Engage program working with Kim and one of her graduate students, Rachel, I conducted a literature review on the ecosystem services provided by two species of krill off the coast of Oregon, including their value to baleen whales. After learning all I could from the literature about krill and how important they were to the ocean, I knew that there was so much more to learn and that this was the topic I wanted to continue to pursue. After I completed the URSA program, I remained a member of Kim’s zooplankton ecology lab.

While continuing to work with Rachel, I was given the opportunity to join the GEMM Lab’s Project HALO for a daylong cruise conducting a whale survey along the Newport Hydrographic Line. I was initially brought on to learn how to use the echosounder to collect krill data but unfortunately, the device had technical difficulties and Rachel and I were no longer needed. We decided to go on the cruise anyway, and I was able to instead learn how to survey for marine mammals (it’s not as easy as it may seem, but still very fun!).

Figure 1. Enjoying the point of view from the crow’s nest on the R/V Pacific Storm, but also very cold.

Soon, another opportunity arose to apply for a brand-new program called ARC-Learn. This two-year research program focuses on studying the Arctic using publicly available data, and with the support of my mentors, I applied and was accepted. Initially I found that there were no mentors within the program that studied krill, so I found myself becoming immersed in a new topic: harmful algal blooms (HABs). Determined to incorporate krill into this research, I started looking through the literature trying to develop my hypothesis that HABs affected zooplankton in some way. There was evidence to potentially support my hypothesis, but I ended up encountering numerous data gaps in the region I was studying. After months of roadblocks, I eventually started feeling defeated and regretted applying for the program. Rachel was quick to remind me that all experiences are valuable experiences, and that I was still gaining new skills I could use in graduate school or my career.

As my undergraduate degree progressed, I continued supporting Rachel in her graduate research, spending some time during the summer processing krill samples by sorting, sexing, and drying them to crush them into pellets. Our goal was to process them in an instrument called a bomb calorimeter, which is used to quantify the caloric content of prey species and help us better understand the energy flux required for animals higher up the food chain (like whales) and the amount they need to eat. I was only able to do this for a few weeks before heading out on the experience of a lifetime, spending three weeks on a ship traveling around the Bering, Chukchi, and Beaufort Seas with one of my ARC-Learn mentors. It was a great opportunity for me to see the toxic phytoplankton (which can form HABs) I had been studying and learn about methods of sample collection and processing. If I could go back and do it again, I’d go in a heartbeat.

Figure 2. Pulling out all of the animal biomass out of the Arctic sediment.

At the beginning of my bachelor’s degree, I had expected to just work with Kim and conduct research within her lab. Instead, I have had opportunities I would never have expected five years ago. I have learned a vast amount from my graduate mentor, Rachel, which has helped influence my trajectory in my degree. I have had the privilege to not only meet giants in the field I’m interested in, but also work with them and learn from them, and to spend three weeks in the Arctic Ocean.  The experiences I have had throughout this roller-coaster helped me develop a project idea with new mentors that I eventually hope to pursue in my master’s degree. I wasn’t prepared for the number of adjustments I would make to find new experiences and start new projects, but all the experiences I had were necessary to learn about what I was interested in and what I wanted to pursue. Looking back on it all today, I have zero regrets.

Figure 3. A picture of the Norseman II, the ship I was on in the Arctic, taken by the Japanese ship JAMSTEC on a short rendezvous between the Chukchi and Beaufort Seas.
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SST, EKE, SSH: Wading Through the Alphabet Soup of Oceanographic Parameters related to Deep-Dwelling Odontocetes

By: Marissa Garcia, PhD Student, Cornell University, Department of Natural Resources and the Environment, K. Lisa Yang Center for Conservation Bioacoustics

Predator-Prey Inference: A Tale as Old as Time

It’s a tale as old as time: where there’s prey, there’ll be predators.

As apex predators, cetaceans act as top-down regulators of ecosystem function. While baleen whales act as “ecosystem engineers,” facilitating nutrient cycling in the ocean (Roman et al., 2014), toothed whales, or “odontocetes,” can impart keystone-level effects — that is, they disproportionately control the marine community’s food-web structure (Valls, Coll, & Christensen, 2015). The menus of prey vary widely by species — ranging from mircronekton to fish to squid – and by extension, vary widely across trophic levels.

So, it naturally follows the old adage: where there’s an abundance of prey, there’ll be an abundance of cetaceans. Yet, creating models that accurately depict this predator-prey relationship is, perhaps unsurprisingly, not as straightforward.

Detecting the ‘Predator’ Half of the Equation

Scientists have successfully documented cetacean presence drawing upon a myriad of methods, each bearing its unique advantages and limitations.

Visual surveys — spanning viewpoints from land, boats, and air — can attain precise spatial data and species ID. However, this data can be constrained by “availability bias” — that is, scientists can only observe cetaceans visible at the surface, not those obscured by the ocean’s depths. Species that spend less time near the surface are more likely to elude the observer’s line of sight, thereby being missed in the data. Consequently, visual surveys have historically undersampled deep-diving species. For instance, since its discovery by western science in 1945, the Hubb’s beaked whale (Mesoplodon carlshubbi) has only been observed alive twice by OSU MMI’s very own Bob Pitman, once in 1994 and another time in 2021.

Scientists have also been increasingly conducting acoustic surveys to document cetacean presence. Acoustic recorders can “hear” each cetacean species at different ranges. Baleen whales, which bellow low-frequency calls, can be heard as far as across ocean basins (Munk et al., 1994). Toothed whales whistle, echolocate, and buzz at frequencies so high they’re considered ultrasonic. But it comes at a trade-off: high-frequency sounds have shorter wavelengths, meaning they are heard across smaller ranges. This high variability, which scientists refer to as “detection range,” translates to not always knowing where the vocalizing cetacean that was recorded is: as such, acoustic data can lack the high-resolution spatial precision often achieved by visual surveys. Nevertheless, acoustic data triumphs in temporal extent, sometimes managing to record continuously at six months at a time. Additionally, animals can elude visual detection in poor weather conditions or if they have a cryptic surface expression, but detected in acoustic surveys (e.g., North Atlantic right whales (Eubalaena glacialis) (Ganley, Brault, & Mayo, 2019; Clark et. al, 2010). Thus, acoustic surveys may be especially optimal for recording elusive deep-dwellers that occupy the often rough Oregon waters, such as beaked whales, the focus of my research in collaboration with the GEMM Lab.

Figure 1: HALO Project researchers Marissa Garcia (left; Yang Center via Cornell) and Imogen Lucciano (right; OSU MMI) among three Rockhopper acoustic recording units, ahead of deployment off the Oregon coast. Credit: Marissa Garcia.

Detecting the ‘Prey’ Half of the Equation

Prey can be measured by numerous methods. Most directly, prey can be measured “in-situ” — that is, prey is collected directly from the site where the cetaceans are detected or observed. A 2020 study combined fish trawls with a towed hydrophone array to identify which fish species odontocetes along the continental shelf of West Ireland (e.g., pilot whales, sperm whales, and Sowerby’s beaked whales) were feasting; the results found that odontocetes primarily fed upon mesopelagic fish and cephalopods (Breen et al., 2020). While trawls can glean species ID of prey, associating this prey data with depth and biomass can prove challenging.

Alternatively, prey can be detected via active acoustics. Echosounders release an acoustic signal that descends through the water column and then echoes back once it hits a sound-scattering organism. Beaked whales forage within deep scattering layers typically composed of myctophid fish and squid, both of which can echo back echosounder pings (Hazen et al., 2011). Thus, echosounder data can map prey density through the water column. When mapping prey density of beaked whales, Hazen et al. 2011 found a strong positive correlation among prey density, ocean vertical structure, and clicks primarily produced while foraging – suggesting beaked whales forage at depth when encountering large, multi-species aggregations of prey.

Figure 2: An example of prey mapping via a Simrad EK60 120 kHz split-beam echosounder. Credit: Rachel Kaplan (OSU MMI) via the HALO Project.

Most relevant to the HALO Project, prey is measured using proximate indices, which are more easily quantifiable metrics of ocean conditions, such as collected from ships via CTD casts or via satellite imagery, that are indirectly related to prey abundance. CTD data can provide information related to the water column structure, including depth and strength of the thermocline, depth of the mixed layer, depth of the euphotic zone, and total chlorophyll concentration in the euphotic zone (Redfern et al. 2006). Satellite imagery can characterize the dynamic patterns of the surface later, including sea surface temperature (SST), salinity, surface chlorophyll a, sea surface height (SSH), and sea surface currents (Virgili et al., 2022; Redfern et al., 2006). Ocean model data products can, such as the Regional Ocean Modeling System (ROMS) which models how an oceanic region of interest responds to physical processes, can provide water column variables related to eddy kinetic energy (EKE) and average temperature gradients (Virgili et al., 2022). In the case of my research with the HALO Project, we will be using oceanographic data collected through the Ocean Observatories Initiative to inform odontocete species distribution models.

Connecting the Dots: Linking Deep-Dwelling Top Predators and Prey

While scientists have made significant advances with collecting both cetacean and prey data, connecting the dots between the ecology of deep-dwelling odontocetes and the oceanographic parameters indicative of their prey still remains a challenge.

In the absence of in situ sampling, species distribution models of marine top predators often derive proxies for “prey data” from static bathymetric and dynamic surface water variables (Virgili et al., 2022). However, surface variables may be irrelevant to toothed whale prey inhabiting great depths (Virgili et al., 2022). Within the HALO Project, the deepest Rockhopper acoustic recording unit is recording odontocetes at nearly 3,000 m below the surface, putting into question the relevance of oceanographic parameters collected at the surface.

Figure 3: Schematic depicting the variation among different zones in the water column. Conditions at the surface may not represent conditions at depth. Credit: Barbara Ambrose, NOAA via NOAA Ocean Explorer.

In my research, I am setting out to estimate which oceanographic variables are optimal for explaining deep-dwelling odontocete presence. A 2022 study using visual survey data found that surface, subsurface, and static variables best explained beaked whale presence, whereas only surface and deep-water variables – not static – best explained sperm whale presence (Virgili et al., 2022). These results are associated with each species’ distinct foraging ecologies; beaked whales may truly only rely on organisms that live near the seabed, whereas sperm whales also feast upon meso-to-bathypelagic organisms, so they may be more sensitive to changes in water column conditions (Virgili et al., 2022). This study expanded the narrative: deep-water variables can also be key to predicting deep-dwelling odontocete presence. The oceanographic variables must be tailored to the ecology of each species of interest.

In the months ahead, I seek to build on this study by investigating which parameters best predict odontocete presence using an acoustic approach instead — I am looking forward to the results to come!

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References

Breen, P., Pirotta, E., Allcock, L., Bennison, A., Boisseau, O., Bouch, P., Hearty, A., Jessopp, M., Kavanagh, A., Taite, M., & Rogan, E. (2020). Insights into the habitat of deep diving odontocetes around a canyon system in the northeast Atlantic ocean from a short multidisciplinary survey. Deep-Sea Research. Part I, Oceanographic Research Papers, 159, 103236. https://doi.org/10.1016/j.dsr.2020.103236

Clark, C.W., Brown, M.W., & Corkeron, P. (2010). Visual and acoustic surveys

for North Atlantic right whales, Eubalaena glacialis, in Cape Cod Bay, Massachusetts, 2001–2005: Management implications. Marine Mammal Science, 26(4), 837-854.

Ganley, L.C., Brault, S., & Mayo, C.A. (2019). What we see is not what there is: Estimating North Atlantic right whale Eubalaena glacialis local abundance. Endangered Species Research, 38, 101-113.

Hazen, E. L., Nowacek, D. P., St Laurent, L., Halpin, P. N., & Moretti, D. J. (2011). The relationship among oceanography, prey fields, and beaked whale foraging habitat in the Tongue of the Ocean. PloS One, 6(4), e19269–e19269.

Munk, W. H., Spindel, R. C., Baggeroer, A., & Birdsall, T. G. (1994). The Heard Island Feasibility Test. The Journal of the Acoustical Society of America, 96(4), 2330–2342. https://doi.org/10.1121/1.410105

Redfern, J. V., Ferguson, M. C., Becker, E. A., Hyrenbach, K. D., Good, C., Barlow, J., Kaschner, K., Baumgartner, M. F., Forney, K. A., Ballance, L. T., Fauchald, P., Halpin, P., Hamazaki, T., Pershing, A. J., Qian, S. S., Read, A., Reilly, S. B., Torres, L., & Werner, F. (2006). Techniques for cetacean–habitat modeling. Marine Ecology. Progress Series (Halstenbek), 310, 271–295.

Roman, J., Estes, J. A., Morissette, L., Smith, C., Costa, D., McCarthy, J., Nation, J., Nicol, S., Pershing, A., & Smetacek, V. (2014). Whales as marine ecosystem engineers. Frontiers in Ecology and the Environment, 12(7), 377–385.

Valls, A., Coll, M., & Christensen, V. (2015). Keystone species: toward an operational concept for marine biodiversity conservation. Ecological Monographs, 85(1), 29–47.

Virgili, A., Teillard, V., Dorémus, G., Dunn, T. E., Laran, S., Lewis, M., Louzao, M., Martínez-Cedeira, J., Pettex, E., Ruiz, L., Saavedra, C., Santos, M. B., Van Canneyt, O., Vázquez Bonales, J. A., & Ridoux, V. (2022). Deep ocean drivers better explain habitat preferences of sperm whales Physeter macrocephalus than beaked whales in the Bay of Biscay. Scientific Reports, 12(1), 9620–9620.

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

Announcing our new project: SLATE – Scar-based Long-term Assessment of Trends in whale Entanglements

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

Filling the gaps

Reports of whale entanglements have been on the rise over the last decade on the US West Coast, with Dungeness crab fishing gear implicated in many cases (Feist et al., 2021; Samhouri et al., 2021; Santora et al., 2020). State agencies are responsible for managing this environmental issue that has implications both for the endangered whale sub-populations that are subject to entanglements, and for the fishing activities, which play an important social, cultural, and economic role for coastal communities. In Oregon, the Oregon Whale Entanglement Working Group (today the Oregon Entanglement Advisory Committee, facilitated by ODFW – Oregon Department of Fish and Wildlife) formed in 2017, tasked with developing options to reduce entanglement risk. The group members composed of managers, researchers and fishermen identified that a lack of information and understanding of whale distribution in Oregon waters was a significant knowledge gap of high priority.

In response, the GEMM Lab and its collaborators at ODFW developed the OPAL project (Overlap Predictions About Large whales, phase 1: 2018-2022). The first phase of the project (phase 1) was developed to 1) model and predict large whale distribution off the coast of Oregon in relation to dynamic environmental conditions, and 2) assess overlap with commercial crab fishing gear to inform conservation efforts. Although this first phase was extended up to June as a result of COVID, it is now coming to an end. As a postdoc in the GEMM Lab, I have been the main analyst working on this project. The habitat use models that I generated from several years of aerial and boat-based surveys provide improved knowledge about where and when rorqual whales (combining blue, humpback and fin) are most abundant (Derville et al., 2022). Moreover, we are about to publish an analysis of overlap between whale predicted densities and commercial Dungeness crab fishing effort. This analysis of co-occurrence over 10 years shows distinct spatio-temporal patterns in relation to climatic fluctuations affecting the northern California Current System (Derville et al., In review).

Although we are quite satisfied with the outputs of these four years of research, this is not the end of it! Project OPAL continues into a second phase (2022-2025; supported by NOAA Section 6 funding), during which models will be improved and refined via incorporation of new survey data (helicopter and boat-based) as well as prey data (krill and fish distribution). PhD student Rachel Kaplan is a key contributor to this research, and I will do my best to keep assisting her in this journey in the years to come.

Announcing SLATE!

As this newly acquired knowledge leads to potentially new management measures in Oregon, it becomes essential for managers to evaluate their impacts on the entanglement issue. But how do we know exactly how many entanglements occur during any year within Oregon waters? Is recording reports of entanglements or signs of entanglements in stranded whales enough? The simple answer is no. Entanglements are notoriously under-detected and under-reported (Tackaberry et al., 2022). Over the US West Coast, entanglements are also relatively rare events that can easily go unnoticed in the immensity of the ocean. Moreover, entangled large whales are often able to carry the fishing gear for some time away from the initial gearset location, which makes it hard to locate the origin of the gear causing problems (van der Hoop et al., 2017).

Figure 1: Graphical representation of the SLATE project representing the different tasks described below. Work in progress…

Our approach to the challenge of assessing humpback whale entanglement rates in Oregon waters is to use scar analysis. Our new “SLATE” (Scar-based Long-term Assessment of Trends in whale Entanglements, Figure 1) project will be using scar-based methods as a proxy to detect unobserved entanglement events (e.g., Basran et al., 2019; Bradford et al., 2009; George et al., 2017; Knowlton et al., 2012; Robbins, 2012). Indeed, this approach has been effective to detect potential interactions with fishing gear at a much higher frequency than entanglement reports in the Atlantic Ocean (e.g., only 10% of entanglements of humpback whales in the Gulf of Maine were estimated to be reported; Robbins, 2012). We will be examining hundreds of photographs of humpback whales observed in Oregon waters to try to detect wrapping scars and notches that result from entanglement events. Based on this scar pattern, we will assign each whale a qualitative probability of prior entanglement (i.e., uncertain, low, high). We will specifically be looking at the caudal peduncle (the attachment point of the whale’s fluke, see Figure 2) following a methodology developed in the Gulf of Maine by Robbins & Mattila, (2001).

Figure 2: Examples of unhealed injuries interpreted as entanglement related in 2010 in the Gulf of Maine. Figure reproduced from (Robbins, 2012).

Data please?

While this approach is to-date the most applicable way to assess otherwise undetected entanglements, it is sometimes limited by sample size. Although we plan to collect more photos in the field in summer 2023 and 2024, this long-term analysis of scarring patterns would not be possible without the contribution of the Cascadia Research Collective (CRC) led by John Calambokidis. The CRC humpback whale catalogue will be crucial to assessing entanglement rates at the individual level over the last decade.

Moreover, as we have been contemplating the task ahead of us, we realized that the data collected through traditional scientific surveys might not be sufficient to achieve our goal. We need the help of the people who live off the ocean and encounter whales on a day-to-day basis: fishermen. That is why we decided to solicit interested fishermen to take photographs of whales while at sea. Starting this year, we will work with at least three self-selected fishermen who are interested in supporting this program and collecting data to support the research efforts. Participants will be provided a stipend, equipped with a high-quality camera, and trained to photograph whales while following National Oceanic and Atmospheric Administration (NOAA) Marine Mammal Protection Act (MMPA) guidelines.

And here come the statistics…

If we have some of my previous blogs (e.g., May 2022, June 2018), you know that I usually participate in projects that have a significant statistical modeling component. As part of the SLATE project, I will be trying out some new approaches that I never had the opportunity to work with before, which makes me feels both super excited and slightly apprehensive!

First, I will analyze humpback whale scarring at the population level. That means I will be using all available photos of whales in Oregon waters without considering individual identification, and I will model the probability of entanglement scars in relation to space and time. This model will help us answer questions such as: did whales have a higher chance of becoming entangled in certain years over others? Did whales observed in a certain zone in Oregon waters have a higher risk of getting entangled?

Second, I will analyze humpback whale scarring at the individual level. This time, we will only use encounters of a selected number of individuals that have a long recapture history, meaning that they were photo-identified and resighted several times throughout the last decade. Using a genetic database produced by the Cetacean Conservation and Genomic Laboratory (CCGL, Marine Mammal Institute), we will also be able to tell to which “Distinct Population Segment” (DPS) some of these individual whales belong. Down the line, this is an important piece of information because humpback whale DPS do not breed in the same areas, and these groups have different levels of population health. Then, we will use what is known as a “multi-event mark-recapture model” to estimate the probability of entanglement as a function of time and spatial residency or DPS assignment, while accounting for detection probability and survival.

Through these analyses, our goal is to produce a single indicator to help managers assess the effects of mandatory or voluntary changes in Oregon fishing practices. In the end, we hope that these models will provide a measurable and robust way of monitoring whale entanglements in fishing gear off the coast of Oregon.

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References

Basran, C. J., Bertulli, C. G., Cecchetti, A., Rasmussen, M. H., Whittaker, M., & Robbins, J. (2019). First estimates of entanglement rate of humpback whales Megaptera novaeangliae observed in coastal Icelandic waters. Endangered Species Research, 38(February), 67–77. https://doi.org/10.3354/ESR00936

Bradford, A. L., Weller, D. W., Ivashchenko, Y. v., Burdin, A. M., & Brownell, R. L. (2009). Anthropogenic scarring of western gray whales (Eschrichtius robustus). Marine Mammal Science, 25(1), 161–175. https://doi.org/10.1111/j.1748-7692.2008.00253.x

Derville, S., Barlow, D. R., Hayslip, C. E., & 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, 1–19. https://doi.org/10.3389/fmars.2022.868566

Derville, S., Buell, T., Corbett, K., Hayslip, C., & Torres, L. G. (n.d.). Exposure of whales to entanglement risk in Dungeness crab fish-ing gear in Oregon, USA, reveals distinctive spatio-temporal and climatic patterns. Biological Conservation.

Feist, B. E., Samhouri, J. F., Forney, K. A., & Saez, L. E. (2021). Footprints of fixed-gear fisheries in relation to rising whale entanglements on the U.S. West Coast. Fisheries Management and Ecology, 28(3), 283–294. https://doi.org/10.1111/fme.12478

George, J. C., Sheffield, G., Reed, D. J., Tudor, B., Stimmelmayr, R., Person, B. T., Sformo, T., & Suydam, R. (2017). Frequency of injuries from line entanglements, killer whales, and ship strikes on bering-chukchi-beaufort seas bowhead whales. Arctic, 70(1), 37–46. https://doi.org/10.14430/arctic4631

Knowlton, A. R., Hamilton, P. K., Marx, M. K., Pettis, H. M., & Kraus, S. D. (2012). Monitoring North Atlantic right whale Eubalaena glacialis entanglement rates: A 30 yr retrospective. Marine Ecology Progress Series, 466(Kraus 1990), 293–302. https://doi.org/10.3354/meps09923

Robbins, J. (2012). Scar-Based Inference Into Gulf of Maine Humpback Whale Entanglement : 2010 (Issue January). Report to the Northeast Fisheries Science Center National Marine Fisheries Service, EA133F09CN0253 Item 0003AB, Task 3.

Robbins, J., & Mattila, D. K. (2001). Monitoring entanglements of humpback whales ( Megaptera novaeangliae ) in the Gulf of Maine on the basis of caudal peduncle scarring. SC/53/NAH25. Report to the Scientific Committee of the International Whaling Commission, 14, 1–12. http://www.ccbaymonitor.org/pdf/scarring.pdf

Samhouri, J. F., Feist, B. E., Fisher, M. C., Liu, O., Woodman, S. M., Abrahms, B., Forney, K. A., Hazen, E. L., Lawson, D., Redfern, J., & Saez, L. E. (2021). Marine heatwave challenges solutions to human-wildlife conflict. Proceedings of the Royal Society B: Biological Sciences, 288, 20211607. https://doi.org/10.1098/rspb.2021.1607

Santora, J. A., Mantua, N. J., Schroeder, I. D., Field, J. C., Hazen, E. L., Bograd, S. J., Sydeman, W. J., Wells, B. K., Calambokidis, J., Saez, L., Lawson, D., & Forney, K. A. (2020). Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nature Communications, 11, 536. https://doi.org/10.1038/s41467-019-14215-w

Tackaberry, J., Dobson, E., Flynn, K., Cheeseman, T., Calambokidis, J., & Wade, P. R. (2022). Low Resighting Rate of Entangled Humpback Whales Within the California , Oregon , and Washington Region Based on Photo-Identification and Long-Term Life History Data. Frontiers in Marine Science, 8(January), 1–13. https://doi.org/10.3389/fmars.2021.779448

van der Hoop, J., Corkeron, P., & Moore, M. (2017). Entanglement is a costly life-history stage in large whales. Ecology and Evolution, 7(1), 92–106. https://doi.org/10.1002/ece3.2615

A Matter of Time: Adaptively Managing the Timescales of Ocean Change and Human Response

By Rachel Kaplan, PhD student, OSU College of Earth, Ocean and Atmospheric Sciences and Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab 

Ocean ecosystems are complex and dynamic, shaped by the interconnected physical and biogeochemical processes that operate across a variety of timescales. A trip on the “ocean conveyer belt”, which transports water from the North Atlantic across the global ocean and back in a process called thermohaline circulation, takes about a thousand years to complete. Phytoplankton blooms, which cycle nutrients through the surface ocean and feed marine animals, often occur at the crucial, food-poor moment of spring, and last for weeks or months. The entanglement of a whale in fishing gear, a major anthropogenic threat to ocean life that drives the GEMM Lab’s Project OPAL, can happen in seconds.

Compounding this complexity, even the timescales that research has clarified are changing. Many processes in the ocean are shifting – and often accelerating – due to global climate change. Images of melting sea ice, calving glaciers, and coastal erosion all exemplify our natural world’s rapid reorganization, and even discrete events can have dramatic repercussions and leave their mark for years. For example, a marine heatwave that occurred in 2014-2015 raised temperatures up to 2.5° C warmer than usual, redistributed species northward along the United States’ West Coast, spurred harmful algal blooms, and shut down fisheries. The toxic blooms also caused marine mammal strandings, domoic acid poisoning in California sea lions, and seabird mass death events (McCabe et al., 2016).

Figure 1. Figures like this Stommel diagram reveal the broad temporal and spatial scales over which ocean phenomena occur. Source: Sloyan et al., 2019

As humans seek to manage ocean ecosystems and mitigate the effects of climate change, our political processes have their own time scales, interconnected cycles, and stochasticity, just like the ocean. At the federal level in the United States, the legislative process takes place over months to decades, sometimes punctuated by relatively quicker actions enacted through Executive Orders. In addition, just as plankton have their turnover times, so do governmental branches. Both the legislative branch and the executive branch change frequently, with new members of Congress coming in every two years, and the president and administration changing every four or eight years. Turnover in both of these branches may constitute a total regime shift, with new members seeking to redirect science policy efforts.

The friction between oceanic and political timescales has historically made crafting effective ocean conservation policy difficult. In recent years, the policy approach of “adaptive management” has sought to respond to the challenges at the tricky intersection of politics, climate change, and ocean ecosystems. The U.S. Department of the Interior’s Technical Guide to Adaptive Management highlights its capacity to deal with the uncertainty inherent to changing ecosystems, and its ability to accommodate progress made through research: “Adaptive management [is a decision process that] promotes flexible decision making that can be adjusted in the face of uncertainties as outcomes from management actions and other events become better understood. Careful monitoring of these outcomes both advances scientific understanding and helps adjust policies or operations as part of an iterative learning process” (Williams et al, 2009).

Over the last several years, adaptive management policy approaches have been key as resource managers along the West Coast have responded to the problem of whale entanglement in fishing gear. When the 2014-2015 marine heatwave event caused anomalously low krill abundance in the central California Current region, humpback whales used a tactic called “prey-switching”, and fed on inshore anchovy schools rather than offshore krill patches. The resulting habitat compression fueled an increase in humpback whale entanglement events in Dungeness crab fishing gear (Santora et al, 2020). 

This sudden uptick in whale entanglements necessitated strategic management responses along the West Coast. In 2017, the California Dungeness Crab Fishing Gear Working Group developed the Risk Assessment and Mitigation Program (RAMP) to analyze real-time whale distribution and ocean condition data during the fishing season, and provide contemporaneous assessments of entanglement risk to the state’s Department of Fish and Wildlife. The Oregon Whale Entanglement Working Group (OWEWG) formed in 2017, tasked with developing options to reduce risk. Oregon Department of Fish and Wildlife (ODFW) has guided whale entanglement reduction efforts by identifying four areas of ongoing work: accountability, risk reduction, best management practices, and research – with regular, scheduled reviews of the regulations and opportunities to update and adjust them.

Figure 2. Entanglement in fishing gear can occur in seconds and may negatively impact whales for years. Source Scott Benson/NOAA

The need for research to support the best possible policy is where the GEMM Lab comes in. ODFW has established partnerships with Oregon State University and Oregon Sea Grant in order to improve understanding of whale distributions along the coast that can inform management efforts. Being involved in this cooperative “iterative learning process” is exactly why I’m so glad to be part of Project OPAL. Initial results from this work have already shaped ODFW’s regulations, and the framework of adaptive management and assessment means that regulations can continue being updated as we learn more through our research.

Ecosystem management will always be complex, just like ecosystems themselves. Today, the pace at which the climate is changing causes many people concern and even despair (Bryndum-Buchholz, 2022). Building adaptive approaches into marine policymaking, like the ones in use off the West Coast, introduces a new timescale into the U.S. policy cycle – one more in line with the rapid changes that are occurring within our dynamic ocean.

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References

Williams, B. L., Szaro, R. C., and Shapiro, C. D. 2009. Adaptive management: the U.S. Department of the Interior Technical Guide. Adaptive Management Working Group, v pp.

Bryndum-Buchholz, A. (2022). Keeping up hope as an early career climate-impact scientist. ICES Journal of Marine Science, 79(9), 2345–2350. https://doi.org/10.1093/icesjms/fsac180

McCabe, R. M., Hickey, B. M., Kudela, R. M., Lefebvre, K. A., Adams, N. G., Bill, B. D., Gulland, F. M., Thomson, R. E., Cochlan, W. P., & Trainer, V. L. (2016). An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys Res Lett, 43(19), 10366–10376. https://doi.org/10.1002/2016GL070023

Santora, J. A., Sydeman, W. J., Schroeder, I. D., Wells, B. K., & Field, J. C. (2011). Mesoscale structure and oceanographic determinants of krill hotspots in the California Current: Implications for trophic transfer and conservation. Progress in Oceanography, 91(4), 397–409. https://doi.org/10.1016/j.pocean.2011.04.002

Sloyan, B. M., Wilkin, J., Hill, K. L., Chidichimo, M. P., Cronin, M. F., Johannessen, J. A., Karstensen, J., Krug, M., Lee, T., Oka, E., Palmer, M. D., Rabe, B., Speich, S., von Schuckmann, K., Weller, R. A., & Yu, W. (2019). Evolving the Physical Global Ocean Observing System for Research and Application Services Through International Coordination. Frontiers in Marine Science, 6, 449. https://doi.org/10.3389/fmars.2019.00449

Clicks, buzzes, and rasps: How the MMPA has spurred what we know about beaked whale acoustic repertoire

By Marissa Garcia, PhD Student, Cornell University, Department of Natural Resources and the Environment, K. Lisa Yang Center for Conservation Bioacoustics

In October 1972, the tides turned for U.S. environmental politics: the Marine Mammal Protection Act (MMPA) was passed. Its creation ushered in a new flavor of conservation and management. With phrases like “optimum sustainable population” baked into its statutory language, it marked among the first times that ecosystem-based management — an approach which directly calls upon knowledge of ecology to inform action — was required by law (Ray and Potter 2022). Transitioning from reductionist, species-siloed policies, the MMPA instead placed the interdependency of species at the core of ecosystem function and management. 

Beyond deepening the role of science on Capitol Hill, the MMPA’s greatest influence may have been spurred by the language that prohibited “the taking and importation of marine mammals” (16 U.S.C. 1361). Because the word “taking” is multivalent, it carries on its back many interpretations. “Taking” a marine mammal is not limited to intentionally hunting or killing them, or even accidental bycatch. “Taking” also includes carelessly operating a boat when a marine mammal is present, feeding a marine mammal in the wild, or tagging a marine mammal without the appropriate scientific permit. “Taking” a marine mammal can also extend to the fatal consequences caused by noise pollution — not intent, but incident (16 U.S.C. 1362).

The latter circumstances remain reverberant for the U.S. Navy. To comply with the MMPA, they are granted “incidental, but not intentional, taking of small numbers of marine mammals….[when] engag[ing] in a specified activity (other than commercial fishing)” (87 FR 33113). So, if the sonar activities required for national security exercises adversely impact marine mammals, the Navy has a bit of leeway but is still expected to minimize this impact. To further mitigate this potential harm, the Navy thus invests heavily in marine mammal research. (If you are interested in learning more about how the Navy has influenced the trajectory of oceanographic research more broadly, you may find this book interesting.) 

Beaked whales are an example of a marine mammal we know much about due to the MMPA’s call for research when incidental take occurs. Three decades ago, many beaked whales stranded ashore following a series of U.S. Navy sonar exercises. Since then, the Navy has flooded research dollars toward better understanding beaked whale hearing, vocal behavior, and movements (e.g., Klinck et al. 2012). Through these efforts, a deluge of research charged with developing effective tools to acoustically monitor and conserve beaked whales has emerged.  

These studies have laid the foundation for my Ph.D. research, which is dedicated to the Holistic Assessment of Living marine resources off Oregon (HALO) project. Through both visual and acoustic surveys, the HALO project’s mission is to understand how changes in ocean conditions — driven by global climate change — influence living marine resources in Oregon waters. 

In my research specifically, I aim to learn more about beaked whales off the Oregon coast. Beaked whales represent nearly a fourth of cetacean species alive today, with at least 21 species recorded to date (Roman et al. 2013). Even so, 90% of beaked whales are considered data deficient: we lack enough information about them to confidently describe the state of their populations or decide upon effective conservation action. 

Much remains to be learned about beaked whales, and I aim to do so by eavesdropping on them. By referring to the “acoustic repertoire” of beaked whales — that is, their vocalizations and corresponding behaviors — I aim to tease out their vocalizations from the broader ocean soundscape and understand how their presence in Oregon waters varies over time. 

Beaked whales are notoriously cryptic, elusive to many visual survey efforts like those aboard HALO cruises. In fact, some species have only been identified via carcasses that have washed ashore (Moore and Barlow 2013). Acoustic studies have elucidated ecological information (beaked whales forage at night at seamounts summits; Johnston et al. 2008) and have also introduced promising population-level monitoring efforts (beaked whales have been acoustically detected in areas with a historical scarcity of sightings; Kowarski et al. 2018). Their deep-diving nature often renders them inconspicuous, and they forage at depths between 1,000 and 2,000 m, on dives as long as 90 minutes (Moore and Barlow 2013; Klinck et al. 2012). Their echolocation clicks are produced at frequencies within the hearing range of killer whales, and previous studies have suggested that Blainville’s beaked whales are only vocally active during deep foraging dives and not at the surface, possibly to prevent being acoustically detected by predatory killer whales. Researchers refer to this phenomenon as “acoustic crypsis,” or when vocally-active marine mammals are strategically silent to avoid being found by potential predators (Aguilar de Soto et al. 2012).

We expect to see evidence of Blainville’s beaked whales in Oregon waters, as well as Baird’s, Cuvier’s, Stejneger’s, Hubb’s, and other beaked whale species. Species-specific echolocation clicks were comprehensively described a decade ago in Baumann-Pickering et al. 2013 (Figure 1). While this study laid the groundwork for species-level beaked whale acoustic detection, much more work is still needed to describe their acoustic repertoire with higher resolution detail. For example, though Hubb’s beaked whales live in Oregon waters, their vocal behavior remains scantly defined.

Figure 1: Baird’s, Blainville’s, Cuvier’s, and Stejneger’s beaked whales are among the most comprehensively acoustically described beaked whales inhabiting central Oregon waters, though more work would improve accuracy in species-specific acoustic detection. Credit: Marissa Garcia. Infographic draws upon beaked whale imagery from NOAA Fisheries and spectrograms and acoustical statistics published in Baumann-Pickering et al. 2013.

The HALO project seeks to add a biological dimension to the historical oceanographic studies conducted along the Newport Hydrographic (NH) line ever since the 1960s (Figure 2). Rockhopper acoustic recording units are deployed at sites NH 25, NH 45, and NH 65. The Rockhopper located at site NH 65 is actively recording on the seafloor about 2,800 m below the surface. Because beaked whales tend to be most vocally active at these deep depths, we will first dive into the acoustic data on NH 65, our deepest unit, in hopes of finding beaked whale recordings there.

Figure 2: The HALO project team conducts quarterly visual surveys along the NH line, spanning between NH 25 and NH 65. Rockhopper acoustic recording units continuously record at the NH 25, NH 45, and NH 65 sites. Credit: Leigh Torres.

Beaked whales’ acoustic repertoire can be broadly split into four primary categories: burst pulses (aka “search clicks”), whistles, buzz clicks, and rasps. Beaked whale search clicks, which are regarded as burst pulses when produced in succession, have distinct qualities: their upswept frequency modulation (meaning the frequency gets higher within the click), their long duration especially when compared to other delphinid clicks, and a consistent interpulse interval  which is the time of silence between signals (Baumann-Pickering et al. 2013). Acoustic analysts can identify different species based on how the frequency changes in different burst pulse sequences (Baumann-Pickering et al. 2013; Figure 1). For this reason, when I conduct my HALO analyses, I intend to automatically detect beaked whale species using burst pulses, as they are the best documented beaked whale signal, with unique signatures for each species. 

In the landscape of beaked whale acoustics, the acoustic repertoire of Blainville’s beaked whales (Mesoplodon densirostris) — a species of focus in my HALO analyses — is especially well defined. Blainville’s beaked whale whistles have been recorded up to 900 m deep, representing the deepest whistle recorded for any marine mammal to date in the literature (Aguilar de Soto et al. 2012). While Blainville’s beaked whales only spend 40% of their time at depths below 170 m, two key vocalizations occur at these depths: whistles and rasps. While they remain surprisingly silent near the surface, beaked whales produce whistles and rasps at depths up to 900 m. The beaked whales dive together in synchrony, and right before they separate from each other, they produce the most whistles and rasps, further indicating that these vocalizations are used to enhance foraging success (Aguilar de Soto et al. 2006). As beaked whales transition to foraging on their own, they predominantly produce frequently modulated clicks and buzzes. Beaked whales produce buzzes in the final stages of prey capture to receive up-to-date information about their prey’s location. The buzzes’ high repetition enables the whale to achieve 300+ updates on their intended prey’s location in the last 3 m before seizing their feast (Johnson et al. 2006; Figure 3). 

Figure 3: Blainville’s beaked whales generally have four categories within their acoustic repertoire, including burst pulses, whistles, buzz clicks, and rasps. Credit: Marissa Garcia.

All of this knowledge about beaked whale acoustics can be linked back to the MMPA, which has also achieved broader success. Since the MMPA’s implementation, marine mammal population numbers have risen across the board. For marine mammal populations with sufficient data, approximately 65% of these stocks are increasing and 17% are stable (Roman et al. 2013). 

Nevertheless, perhaps much of the MMPA’s true success lies in the research it has indirectly fueled, by virtue of the required compliance of governmental bodies such as the U.S. Navy. And the response has proven to be a boon to knowledge: if the U.S. Navy has been the benefactor of marine mammal research, beaked whale acoustics has certainly been the beneficiary. We hope the beaked whale acoustic analyses stemming from the HALO Project can further this expanse of what we know.

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References

Aguilar de Soto, N., Madsen, P. T., Tyack, P., Arranz, P., Marrero, J., Fais, A., Revelli, E., & Johnson, M. (2012). No shallow talk: Cryptic strategy in the vocal communication of Blainville’s beaked whales. Marine Mammal Science, 28(2), E75–E92. https://doi.org/10.1111/j.1748-7692.2011.00495.x

Baumann-Pickering, S., McDonald, M. A., Simonis, A. E., Solsona Berga, A., Merkens, K. P. B., Oleson, E. M., Roch, M. A., Wiggins, S. M., Rankin, S., Yack, T. M., & Hildebrand, J. A. (2013). Species-specific beaked whale echolocation signals. The Journal of the Acoustical Society of America, 134(3), 2293–2301. https://doi.org/10.1121/1.4817832

Dawson, S., Barlow, J., & Ljungblad, D. (1998). SOUNDS RECORDED FROM BAIRD’S BEAKED WHALE, BERARDIUS BAIRDIL. Marine Mammal Science, 14(2), 335–344. https://doi.org/10.1111/j.1748-7692.1998.tb00724.x

Johnston, D. W., McDonald, M., Polovina, J., Domokos, R., Wiggins, S., & Hildebrand, J. (2008). Temporal patterns in the acoustic signals of beaked whales at Cross Seamount. Biology Letters (2005), 4(2), 208–211. https://doi.org/10.1098/rsbl.2007.0614

Johnson, M., Madsen, P. T., Zimmer, W. M. X., de Soto, N. A., & Tyack, P. L. (2004). Beaked whales echolocate on prey. Proceedings of the Royal Society. B, Biological Sciences, 271(Suppl 6), S383–S386. https://doi.org/10.1098/rsbl.2004.0208

Johnson, M., Madsen, P. T., Zimmer, W. M. X., de Soto, N. A., & Tyack, P. L. (2006). Foraging Blainville’s beaked whales (Mesoplodon densirostris) produce distinct click types matched to different phases of echolocation. Journal of Experimental Biology, 209(Pt 24), 5038–5050. https://doi.org/10.1242/jeb.02596

Klinck, H., Mellinger, D. K., Klinck, K., Bogue, N. M., Luby, J. C., Jump, W. A., Shilling, G. B., Litchendorf, T., Wood, A. S., Schorr, G. S., & Baird, R. W. (2012). Near-real-time acoustic monitoring of beaked whales and other cetaceans using a Seaglider. PloS One, 7(5), e36128. https://doi.org/10.1371/annotation/57ad0b82-87c4-472d-b90b-b9c6f84947f8

Kowarski, K., Delarue, J., Martin, B., O’Brien, J., Meade, R., Ó Cadhla, O., & Berrow, S. (2018). Signals from the deep: Spatial and temporal acoustic occurrence of beaked whales off western Ireland. PloS One, 13(6), e0199431–e0199431. https://doi.org/10.1371/journal.pone.0199431

Madsen, P. T.,  Johnson, M., de Soto, N. A., Zimmer, W. M. X., & Tyack, P. (2005). Biosonar performance of foraging beaked whales (Mesoplodon densirostris). Journal of Experimental Biology, 208(Pt 2), 181–194. https://doi.org/10.1242/jeb.01327

McCullough, J. L. K., Wren, J. L. K., Oleson, E. M., Allen, A. N., Siders, Z. A., & Norris, E. S. (2021). An Acoustic Survey of Beaked Whales and Kogia spp. in the Mariana Archipelago Using Drifting Recorders. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.664292

Moore, J. E. & Barlow, J. P. (2013). Declining abundance of beaked whales (family Ziphiidae) in the California Current large marine ecosystem. PloS One, 8(1), e52770–e52770. https://doi.org/10.1371/journal.pone.0052770

Ray, G. C. & Potter, F. M. (2011). The Making of the Marine Mammal Protection Act of 1972. Aquatic Mammals, 37(4), 522.

Roman, J., Altman, I., Dunphy-Daly, M. M., Campbell, C., Jasny, M., & Read, A. J. (2013). The Marine Mammal Protection Act at 40: status, recovery, and future of U.S. marine mammals. Annals of the New York Academy of Sciences, 1286(1), 29–49. https://doi.org/10.1111/nyas.12040

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.

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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