Tag: conservation

Are Most Whale Entanglements “Out of Sight, Out of Mind?”

By Lindsay Wickman, Postdoctoral Scholar, Oregon State University Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Earlier this month, most of the GEMM Lab and I attended the 25th Biennial Conference on the Biology of Marine Mammals in Perth, Western Australia. This year’s theme, “Fishing for Change,” acknowledged that incidental entanglement in fishing gear is currently the most pervasive threat to marine mammals (e.g., Avila et al., 2018). While many presentations on the prevalence and impacts of entanglement on marine mammals were sobering, it was also inspiring to be surrounded by so many dedicated people working to address this urgent issue. For me, one of the most memorable anecdotes was an incredible whale disentanglement story shared by Paul Cottrell, a Marine Mammal Coordinator at the Department of Fisheries and Oceans Canada (DFO) in British Columbia.

An Incredible Story of Whale Disentanglement

During August 2024, DFO and a local NGO (Straitwatch) responded to a report of two humpback whales entangled in the same fishing gear near Quadra Island, B.C., Canada. Their photo-identification histories revealed that one whale had migrated from Hawaii, while the other had come from Mexico. Now tied together, the two whales’ fates became intertwined, forcing them to coordinate their movements. This situation obviously raised concerns about their welfare and survival, but I also had to silently wonder, “Did the two whales ever argue about where to migrate next? Would they choose Hawaii or Mexico?”

Thanks to the rescuers’ efforts, both whales were freed and able to make their own choice about where to spend the breeding season. As Paul explained, successfully disentangling one whale is challenging and dangerous, so freeing two was an impressive feat. After the rescue, a video showed the whales continuing to swim together synchronously, as if they did not realize they were no longer connected!

Most Entangled Whales are Out of Sight

The story above exemplifies a “confirmed” entanglement—these whales were seen dragging fishing gear and the event was reported by concerned citizens. However, most entanglement events are never witnessed, for several reasons.

When a whale becomes entangled in fishing gear, it rarely remains anchored in place. Instead, the whale often breaks part of the gear, dragging it behind as it swims. The likelihood of observing the entangled whale subsequently depends on both the chance of it being seen and the observer’s awareness and willingness to report the event (Robbins and Mattila, 2004).

An entangled humpback whale drags gear off of San Diego, California. Credit: Keith Yip, taken under NOAA Permit #18786.

Once entangled, many become a “dead whale swimming,” eventually succumbing to starvation and/or infections (Dolman and Moore, 2017). Many entanglements involve the mouth, severely impacting the whale’s ability to feed (Moore and van der Hoop, 2012). The additional drag imposed by entanglement is comparable to the energetic costs of migration or reproduction, causing a significant depletion in their energy reserves (van der Hoop et al. 2015). Serious injuries include amputations, hemorrhage, and infections (Cassoff et al., 2011).

Although some carcasses of entangled whales wash ashore, most are lost at sea and never recovered. For example, even with relatively intensive monitoring for North Atlantic right whale (NARW) carcasses, Pace et al. (2021) estimated that recovered carcasses represented just 36% of the total deaths. These recovered carcasses may also underestimate the toll of entanglement; entanglement accounted for 51% of mortality in the carcasses vs. 87% of serious injuries observed in living NARWs (Pace et al., 2021).

For whales that manage to dislodge the gear and survive, scars can provide clues to their past entanglement history. Injuries from the fishing lines can leave indentations where they cut through skin and blubber, and healed wounds often result in white pigmented scars that wrap around the body (especially the flukes and peduncle; e.g., Robbins and Matilla 2004). The widespread prevalence of these scars suggests that in many cases, whales can actually dislodge the gear on their own. For example, a study of entanglement scars on humpback whales in the Gulf of Maine revealed that 10% of adults and 30% of juveniles acquired new entanglement scars between 2009-2010. Without scarring analyses though, most of these entanglements would have been missed; just 7% of these individuals with entanglement-related scars were seen while entangled (Robbins 2012).

A humpback whale fluke and peduncle showing scarring likely caused by a past entanglement. Credit: GEMM lab, taken under NOAA Permit # 27426 issued to MMI.

Unfortunately, scars are not the only long-term consequence of non-lethal entanglement events. Previously entangled NARWs have lower survival rates than unaffected individuals (Robbins et al. 2015, Reed et al. 2024), and long-term stress responses can impact their future health and reproductive success (Pettis et al. 2004). It is tempting to assume that only severe entanglements affect future reproduction and survival, but a lack of extensive external injuries doesn’t necessarily mean that the impact of the entanglement event is more minor (Robbins and Matilla, 2004). For example, Reed et al. (2024) found that NARWs with entanglement injuries classified as minor were less likely to transition from a “non-breeder” to “breeder” status than those with severe injuries.

Tracking Unseen Entanglements: Project SLATE

Since reported entanglements and recovered carcasses reveal just a fraction of actual entanglements, researchers are continuing to innovate ways of documenting these “unseen” entanglement events.

As discussed in a previous blog post, photos of entanglement scars on the flukes and peduncles of humpback whales are being utilized in Project SLATE to detect trends in entanglement off the coast of Oregon, USA. Analyzing images of whales for signs of past entanglements is a meticulous process that may not seem as thrilling as responding to an actual disentanglement event. However, in areas with lower population densities, such as the Oregon coast, reported entanglements are undoubtedly an underestimate of the true number of events. Thus, tracking scarring rates can provide more comprehensive data on entanglement prevalence in Oregon than confirmed reports alone.

What to do if you see an entangled whale

If you happen to observe an entangled whale, please do not attempt to disentangle it yourself. Whale disentanglement is dangerous and complex, so best left to the experts! When well-meaning citizens attempt a disentanglement on their own, it can also result in an “incomplete disentanglement,” where some, but not all, gear is removed from the whale. Incomplete disentanglements just make it harder for responders to subsequently find and successfully rescue the whale.

Instead, report the entanglement by promptly calling:

  • Entanglement Reporting Hotline: 1-877-SOS-WHAL or 1-877-767-9425
  • or U.S. Coast Guard: VHF Ch. 16

Videos or photos showing the entangling gear is very helpful to trained responders, but remember to stay at least 100 yards from the whale, and beware of snagging your vessel in the lines. Visit NOAA Fisheries for more information.  

A NOAA-led team disentangle a humpback whale near Dutch Harbor, Alaska. Credit: Andy Dietrick/NOAA, taken under NOAA Permit #18786.


References:

Avila, I.C., Kaschner, K., Dormann, C.F.. (2018). Current global risks to marine mammals: Taking stock of the threats. Biol. Conserv. 221, 44–58.

Cassoff, R.M., Moore, K.M., McLellan, W.A., Barco, S.G., Rotstein, D.S., Moore, M.J. (2011). Lethal entanglement in baleen whales. Dis. Aquat. Organ. 96, 175–185.

Dolman, Sarah J., and Michael J. Moore. (2024). Chapter 4: Welfare implications of cetacean bycatch and entanglements. In A. Butterworth (Ed.) Marine Mammal Welfare: Human Induced Change in the Marine Environment and Its Impacts on Marine Mammal Welfare (pp. 41-65).

Moore, M. J., and van der Hoop, J. M. (2012). The painful side of trap and fixed net fisheries: chronic entanglement of large whales. Journal of Marine Sciences, 2012.

Pace III, R. M., Williams, R., Kraus, S. D., Knowlton, A. R., & Pettis, H. M. (2021). Cryptic mortality of North Atlantic right whales. Conservation Science and Practice, 3(2), e346

Reed, J., New, L., Corkeron, P., Harcourt, R. (2024). Disentangling the influence of entanglement on recruitment in North Atlantic right whales. Proc. R. Soc. B Biol. Sci. 291.

Robbins, J., Mattila, D. (2004). Estimating humpback whale (Megaptera novaeangliae) entanglement rates on the basis of scar evidence. Rep. to Northeast Fish. Sci. Center, Natl. Mar. Fish. Serv. 43EANF030121 22p.

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

van der Hoop JM, Corkeron P, Kenney J, Landry S, Morin D, Smith J, Moore MJ. (2015). Drag from fishing gear entangling North Atlantic right whales. Mar Mamm Sci 32(2):619–642.

Toward an enhanced understanding of large whale ecology: a standardized protocol to quantify hormones in whale blubber

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

Whales are exposed to an increasing number of human-induced stressors—ranging from pollution and bycatch to the impacts of climate change on prey quality and distribution. Understanding how these factors affect whale health is critical for their conservation. The use of alternative approaches (i.e., alternative to blood samples) for gathering physiological information on large whales using a variety of non-lethal and non to minimally invasive sample matrices (i.e., blubber biopsies, blow, and fecal samples) provides a window into their endocrine state, allowing researchers to assess how these animals respond to both short-term and long-term stressors, and assess their reproductive and nutritional status. However, a lack of standardized protocols might hinder the comparability of results across studies, making it difficult to draw broad conclusions about the health and reproductive parameters of different whale populations.

Dr. Logan Pallin and I organized a lab exchange, funded by The Company of Biologists, to start a new collaboration aimed at bridging this gap by validating and standardizing methods for endocrine assessments in whale blubber. This is not just a technical exercise; it is a foundational step towards building equity and capacity in laboratories worldwide to conduct reliable and comparable endocrine assessments, enhancing the opportunities for multi-lab collaborations. Through this exchange, we aim to consolidate a standardized approach that will yield consistent results between laboratories, enabling better comparisons across different large whale populations. Hosted by the University of California Santa Cruz Biotelemetry and Behavioral Ecology Lab (UCSC-BTBEL Lab) under the mentorship of Dr. Logan Pallin, this experience is instrumental in advancing my research on large whale ecology and conservation.

Dr. Logan Pallin and Alejandro Fernandez Ajó conducting hormone extractions from gray whale blubber samples (left). Preparing a microtiter assay plate for hormone quantification in blubber (right).

During this exchange at the BBE Lab, I had the privilege of working closely with Dr. Logan Pallin, whose expertise in large whale endocrinology (particularly analyzing blubber biopsies) has been instrumental in shaping modern approaches to whale research. The lab’s cutting-edge equipment and Logan’s extensive experience with hormone extraction and quantification methods provided an ideal setting for refining our protocols. Our work focused on the extraction and quantification of progesterone from gray whale blubber samples provided by the Oregon State University Marine Mammal Stranding Network, part of MMI. These large blubber sections allow for repeated sub-sampling to ensure that the selected immunoassays reliably detect and measure the hormones of interest, while also assessing potential sources of variability when applying a standardized protocol. We initially focused our tests and validations on progesterone, as it is the precursor of all major steroid hormones and serves as an indicator of reproductive state in females.

A fieldwork day off Monterrey Bay, California with Dr. Logan Pallin, and PhD candidate Haley Robb. Blubber. Blubber biopsies can be obtained from free swimming whales with minimally invasive methods. From each sample we can derive multiple information about the reproductive status, genetics and overall health of the individuals.

The broader impact of our work
The successful validation and standardization of these protocols represents a significant advancement in whale conservation physiology. Once these methods are established, we plan to acquire funds to apply them to a larger collection of blubber samples. We hope to expand our work to include other species and regions, building a broader network of researchers dedicated to studying large whales in a rapidly changing world, and to assess hormone profiles in relation to factors like reproductive success, body condition, and exposure to stressors such as vessel traffic and environmental changes.

During our fieldwork in Monterey Bay, we had fascinating encounters with Minke whales (Balaenoptera acutorostrata, top left), a large group of Risso’s dolphins (Grampus griseus, bottom left), playful Humpbacks (Megaptera novaeangliae, top right), and a Blue whale (Balaenoptera musculus, no photo).

As I conclude this lab exchange, I am filled with excitement for the future. The knowledge and skills gained during this experience will undoubtedly shape the next phase of my research, allowing me to contribute more effectively to the conservation of these incredible animals. I look forward to applying these standardized methods to ongoing and future projects, and to continuing this fruitful collaboration with the BBE Lab. This journey has reinforced the importance of collaboration, standardization, and innovation in the field of conservation physiology. By working together, we can better understand the complex lives of large whales and take meaningful steps towards their protection in an increasingly challenging environment.

Acknowledgments: This exchange was made possible by the support of The Company of Biologists Traveling Fellowship Grant. I would like to thank Dr. Ari Friedlaender (BBE Lab PI) for facilitating this exchange, and Dr. Leigh Torres (GEMM Lab PI) and Dr. Lisa Balance (MMI director) for their support in helping me expand my collaboration network and skillsets. Special thanks to PhD student Haley Robb for her assistance in the laboratory and fieldwork, and a heartfelt thank you to Dr. Logan Pallin for generously sharing his knowledge and time.

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Reflecting on a solitary journey surrounded by an incredible team

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

Graduate school is an odd phase of life, at least in my experience. You spend years hyperfocused on a project, learning countless new skills – and the journey is completely unique to you. Unlike high school or undergrad, you are on your own timeline. While you may have peers on similar timelines, at the end of day your major deadlines and milestone dates are your own. This has struck me throughout my time in grad school, and I’ve been thinking about it a lot lately as I approach my biggest, and final milestone – defending my PhD! 

I defend in just about two months, and to be honest, it’s very odd approaching a milestone like this alone. In high school and college, you count down to the end together. The feelings of anticipation, stress, excitement, and anticipatory grief that can accompany the lead-up to graduation are typically shared. This time, as I’m in an intense final push to the end while processing these emotions, most of the people around me are on their own unique timeline. At times grad school can feel quite lonely, but this journey would have been impossible without an incredible community of people.

A central contradiction of being a grad student is that your research is your own, but you need a variety of communities to successfully complete it. Your community of formal advisors, including your advisor and committee members, guide you along the way and provide feedback. Professors help you fill specific knowledge and skill gaps, while lab mates provide invaluable peer mentorship. Finally, fellow grad students share the experience and can celebrate and commiserate with you. I’ve also had the incredible fortune of having the community of the GRANITE team, and I’ve recently been reflecting on how special the experience has been.

To briefly recap, GRANITE stands for Gray whale Response to Ambient Noise Informed by Technology and Ecology (read this blog to learn more). This project is one of the GEMM lab’s long-running gray whale projects focused on studying gray whale behavior, physiology, and health to understand how whales respond to ocean noise. Given the many questions under this project, it takes a team of researchers to accomplish our goals. I have learned so much from being on the team. While we spend most of the year working on our own components, we have annual meetings that are always a highlight of the year. Our team is made up of ecologists, physiologists, and statisticians with backgrounds across a range of taxa and methodologies. These meetings are an incredible time to watch, and participate in, scientific collaboration in action. I have learned so much from watching experts critically think about questions and draw inspiration from their knowledge bases. It’s been a multi-year masterclass and a critically important piece of my PhD. 

The GRANITE team during our first in person meeting

These annual meetings have also served as markers of the passage of time. It’s been fascinating to observe how our discussions, questions, and ideas have evolved as the project progressed. In the early years, our presentations shared proposed research and our conversations focused on working out how on earth we were going to tackle the big questions we were posing. In parallel, it was so helpful to work out how I was going to accomplish my proposed PhD questions as part of this larger group effort. During the middle years, it was fun to hear progress updates and to learn from watching others go through their process too. In grad school, it’s easy to feel like your setbacks and stumbles are failures that reflect your own incompetence, but working alongside and learning from these scientists has helped remind me that setbacks and stumbles are just part of the process. Now, in the final phase, as results abound, it feels extra exciting to celebrate with this team that has watched the work, and me grow, from the beginning. 

The GRANITE team taking a beach walk after our second in person meeting.

We just wrapped up our last team meeting of the GRANITE project, and this year provided a learning experience in a phase of science that isn’t often emphasized in grad school. For graduate students, our work tends to end when we graduate. While we certainly think about follow-up questions to our studies, we rarely get the opportunity to follow through. In our final exams, we are often asked to think of next steps outside the constraints of funding or practicality, as a critical thinking exercise. But it’s a different skillset to dream up follow-up questions, and to then assess which of those questions are feasible and could come together to form a proposal. This last meeting felt like a cool full-story moment. From our earliest meetings determining how to answer our new questions, to now deciding what the next new questions are, I have learned countless lessons from watching this team operate. 

The GRANITE team after our third in person meeting.

There are a few overarching lessons I’ll take with me. First and foremost, the value of patience and kindness. As a young scientist stumbling up the learning curve of many skills all at once, I am so grateful for the patience and kindness I’ve been shown. Second, to keep an open mind and to draw inspiration from anything and everything. Studying whales is hard, and we often need to take ideas from studies on other animals. Which brings me to my third takeaway, to collaborate with scientists from a wide range of backgrounds who can combine their knowledges bases with yours, to generate better research questions and approaches to answering them.

I am so grateful to have worked with this team during my final sprint to the finish. Despite the pressure of the end nearing, I’m enjoying moments to reflect and be grateful. I am grateful for my teachers and peers and friends. And I can’t wait to share this project with everyone.

P.S. Interested in tuning into my defense seminar? Keep an eye on the GEMM lab Instagram (@gemm_lab) for the details and zoom link.

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Measure faster! New tools for automatically obtaining body length and body condition of whales from drone videos

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

Monitoring the body length and body condition of animals can help provide important information on the health status of individuals and their populations, and can even serve as early warning signs if a population is adapting to habitat changes or is at risk of collapse (Cerini et al., 2023). As discussed in previous blogs, drone-based photogrammetry provides a method for non-invasively collecting important size measurements of whales, such as for detecting differences in body condition and length between populations, and even diagnosing pregnancy. Thus, using drones to collect measurement data on the growth, body condition, and pregnancy rates of whales can help expedite population health assessments to elicit conservation and management actions.

However, it takes a long time to manually measure whales filmed in drone imagery. For every video collected, an analyst must carefully watch each video and manually select frames with whales in good positions for measuring (flat and straight at the surface). Once frames are selected, each image must then be ranked and filtered for quality before finally measuring using a photogrammetry software, such as MorphoMetriX. This entire manual processing pipeline ultimately delays results, which hinders the ability to rapidly assess population health. If only there was a way to automate this process of obtaining measurements…

Well now there is! Recently, a collaboration between researchers from the GEMM Lab, CODEX, and OSU’s Department of Engineering and Computer Science published a manuscript introducing automated methods for obtaining body length and body condition measurements (Bierlich et al., 2024). The manuscript describes two user-friendly models: 1) “DeteX”, which automatically detects whales in drone videos to output frames for measuring and 2) “XtraX”, which automatically extracts body length and body condition measurements from input frames (Figure 1). We found that using DeteX and XtraX produces measurements just as good as manual measurement (Coefficient of Variation < 5%), while substantially reducing the processing time by almost 90%. This increased efficiency not only saves hours (weeks!) of manual processing time, but enables more rapid assessments of populations’ health.

Future steps for DeteX and XtraX are to adapt the models so that measurements can be extracted from multiple whales in a single frame, which could be particularly useful for analyzing images containing mothers with their calf. We also look forward to adapting DeteX and XtraX to accommodate more species. While DeteX and XtraX was trained using only gray whale imagery, we were pleased to see that these models performed well when trialing on imagery of a blue whale (Figure 2). These results are encouraging because it shows that the models can be adapted to accommodate other species with different body shapes, such as belugas or beaked whales, with the inclusion of more training data.

We are excited to share these methods with the drone community and the rest of this blog walks through the features and steps for running DeteX and XtraX to make them even easier to use.

Figure 1. Overview of DeteX and XtraX for automatically obtaining body length and body condition measurements from drone-based videos.

Figure 2. Example comparing manual (MorphoMetriX) vs. automated (XtraX) measurements of a blue whale.

DeteX and XtraX walkthrough

Both DeteX and XtraX are web-based applications designed to be intuitive and user-friendly. Instructions to install and run DeteX and XtraX are available on the CODEX website. Once DeteX is launched, the default web-browser automatically opens the application where the user is asked to select 1) the folder containing the drone-based videos to analyze and 2) the folder to save output frames (Figure 3). Then, the user can select ‘start’ to begin. The default for DeteX is set to analyze the entire video from start to finish at one frame per second; if recording a video at 30 frames per second, the last (or 30th) frame is processed for each second in the video. There is also a “finetune” version of DeteX that offers users much more control, where they can change these default settings (Figure 4). For example, users can change the defaults to increase the number of frames processed per second (i.e., 10 instead of 1), to target a specific region in the video rather than the entire video, and adjust the “detection model threshold” to change the threshold of confidence the model has for detecting a whale. These specific features for enhanced control may be particularly helpful when there is a specific surfacing sequence that a user wants to have more flexibility in selecting specific frames for measuring.

Figure 3. A screenshot of the DeteX web-based application interface.

Figure 4. The DeteX “finetune” version provides more control for users to change the default settings to target a specific region in the video (here between 3 min 00 sec and 3 min 05 sec), change the number of frames per second to process (now 10 per second), and the detection threshold, or level of confidence for identifying a whale in the video (now a higher threshold at 0.9 instead of the default at 0.8).

Once output frames are generated by DeteX, the user can select which frames to input into XtraX to measure. Once XtraX is launched, the default web-browser automatically opens the application where the user is asked to select 1) the folder containing the frames to measure and 2) the folder to save the output measurements. If the input frames were generated using DeteX, the barometric altitude is automatically extracted from the file name (note, that altitudes collected from a LiDAR altimeter can be joined in the XtraX output .csv file to then calculate measurements using this altitude). The image width (pixels) is automatically extracted from the input frame metadata. Users can then input specific camera parameters, such as sensor width (mm) and the focal length of the camera (mm), the launch height of the drone (i.e., if launching from hand when on a boat), and the region along the body to measure body condition (Figure 5). This region along the body is called the Head-Tail range and is identified as the area where most lipid storage takes place to estimate body condition. To run, the user selects “start”. XtraX then will output a .png file of each frame showing the keypoints (used for the body length measurement) and the shaded region (used for the body condition estimate) along the body to help visual results so users can filter for quality (Figure 6). XtraX also outputs a single .csv containing all the measurements (in meters and pixels) with their associated metadata.

Figure 5. User interface for XtraX. The user specifies a folder containing the images to measure and a folder to save the outputs measurements, and then can enter in camera specifications, the launch height of the drone (to be added to the barometer altitude) and the range of body widths to include in the body condition measurement (in the case, 0.2 and 0.7 correspond to body region between widths 20% and 70% of the total length, respectively).

Figure 6. Example output from XtraX showing (red) keypoints along the body to measure body length and the (green) shaded region used for body condition.

We hope this walkthrough is helpful for researchers interested in using and adapting these tools for their projects. There is also a video tutorial available online. Happy (faster) measuring!

References

Bierlich, K. C., Karki, S., Bird, C. N., Fern, A., & Torres, L. G. (2024). Automated body length and body condition measurements of whales from drone videos for rapid assessment of population health. Marine Mammal Science, e13137. https://doi.org/10.1111/mms.13137

Cerini, F., Childs, D. Z., & Clements, C. F. (2023). A predictive timeline of wildlife population collapse. Nature Ecology & Evolution, 7(3), 320–331. https://doi.org/10.1038/s41559-023-01985-2

Disentangling the whys of whale entanglement

By Lindsay Wickman, Postdoctoral Scholar, Oregon State University Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Previously on our blog, we mentioned  the concerning rise of humpback whale (Megaptera novaeangliae) entanglement in fishing gear on the US West Coast (see here and here). Gaining an improved understanding of the rate of entanglement and risk factors of humpback whales in Oregon are primary aims of the GEMM Lab’s SLATE and OPAL projects. In this post, I will discuss some reasons why whales get entangled. With whales generally regarded as intelligent, it is understandable to wonder why whales are unable to avoid these underwater obstacles.

Figure 1. Wrapping scars like these at the base of the flukes indicate this humpback whale was previously entangled. Photo taken under NOAA/NMFS permit #21678 to John Calambokidis.

Fishing lines are hard to detect underwater

Water clarity, depth, and time of day can all influence how visible a fishing line is underwater.  Since baleen whales lack the ability to discriminate color (Levenson et al., 2000; Peichl et al. 2001), the brightly colored yellow and red ropes that make it easier for fishermen to find their gear make it harder for whales to see it underwater. White or black ropes may stand out better for whales (Kot et al., 2012), but there’s not enough evidence yet to suggest they reduce entanglement rates.

Whales have excellent hearing, but this may still not be enough to ensure detection of underwater ropes. Even if whales can hear water currents flowing over the rope, this noise can easily be masked by other sounds like weather, surf, and passing boats. Fishing gear also has a weak acoustic signature (Leatherwood et al., 1977), or it may be at a frequency not heard by whales. So even though whales produce and listen for sounds to help locate prey (Stimpert et al., 2007) and communicate, any sound produced by fishing lines may not be sufficient to alert whales to its presence.

There are very few studies that examine the behavior of whales around fishing gear, but a study of minke whales (Balaenoptera acutorostrata) by Kot et al. (2017) provides an exception. Researchers observed whales slowing down as they approached their test gear, and speeding up once they were past it (Kot et al., 2017). While the scope of the study was too small to generalize about whales’ ability to detect fishing gear, it does suggest whales can detect fishing gear, at least some of the time. There is also likely some individual variation in this skillset. Less experienced, juvenile humpback whales, for example, may be at a higher risk of entanglement than adults (Robbins, 2012).

Distracted driving?

Just like distracted drivers are more likely to crash when texting or eating, whales may be more likely to get entangled when they are preoccupied with behaviors like feeding or socializing.

Evidence suggests feeding is especially risky for entanglement. An analysis of entanglements in the North Atlantic found that almost half (43%) of the humpback whales were entangled at the mouth, and the mouth was also the most common attachment point for North Atlantic right whales (Eubalaena glacialis, 77%; Johnson et al., 2005). In a study of minke whales in the East Sea of Korea, 80% of entangled whales had recently fed (Song et al, 2010). In many cases, entanglement at the mouth can severely restrict feeding ability, resulting in emaciation and/or death (Moore and van der Hoop, 2012).

Figure 2. A North Atlantic right whale with fishing gear attached at the mouth. Photo credit: NOAA Photo Library.

More whales, more heat waves, and more entanglements

On the US West Coast, the number of humpback whales has been increasing since the end of whaling (e.g., Barlow et al, 2011). With more whales in our waters, it makes sense that the number of entanglements will increase. Still, a larger population size is probably not the only reason for increasing entanglements.

Climate change, for example, may place whales in the areas with dense fishing gear much more often. A recent example of this was during 2014–2016, when a heatwave on the US West Coast led to a cascade of events that increased the likelihood of whale entanglements in California waters (Santora et al., 2020).

The increased temperatures led to a bloom of toxic diatoms, which delayed the commercial fishing season for Dungeness crabs in California. Unfortunately, the delay caused fishing to resume right as high numbers of whales were arriving from their annual migration from their breeding grounds. The wider ecosystem effects of the heat wave also meant humpback whales were feeding closer to shore — right where most crab pots are set. The combination of both the fisheries’ timing and the altered distribution of whales contributed to an unprecedented number of entanglements (Santora et al., 2020).

Whale entanglement is a concerning issue for fishermen, conservationists, and wildlife managers. By disentangling some of the whys of entanglement for humpback whales in Oregon, we hope our research can contribute to improved management plans that benefit both whales and the continuity of the Dungeness crab fishery. To learn more about these projects, visit the SLATE and OPAL pages, and subscribe to the blog for more updates.

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References

Barlow, J., Calambokidis, J., Falcone, E.A., Baker, C.S., Burdin, A.M., Clapham, P.J., Ford, J.K., Gabriele, C.M., LeDuc, R., Mattila, D.K. and Quinn, T.J. (2011). Humpback whale abundance in the North Pacific estimated by photographic capture‐recapture with bias correction from simulation studies. Marine Mammal Science, 27(4), 793-818.

Johnson, A., Salvador, G., Kenney, J., Robbins, J., Kraus, S., Landry, S., and Clapham, P. (2005). Fishing gear involved in entanglements of right and humpback whales. Marine Mammal Science, 21, 635–645.

Kot, B.W., Sears, R., Anis, A., Nowacek, D.P., Gedamke, J. and Marshall, C.D. (2012). Behavioral responses of minke whales (Balaenoptera acutorostrata) to experimental fishing gear in a coastal environment. Journal of Experimental Marine Biology and Ecology, 413, pp.13-20.

Leatherwood, J.S., Johnson, R.A., Ljungblad, D.K., and Evans, W.E. (1977). Broadband Measurements of Underwater Acoustic Target Strengths of Panels of Tuna Nets. Naval Oceans Systems Center, San Diego, CA Tech, Rep. 126.

Levenson, D.H., Dizon, A., and Ponganis, P.J. (2000). Identification of loss-of-function mutations within the short wave-length sensitive cone opsin genes of baleen and odontocete cetaceans. Investigative Ophthalmology & Visual Science, 41, S610.

Moore, M. J., and van der Hoop, J. M. (2012). The painful side of trap and fixed net fisheries: chronic entanglement of large whales. Journal of Marine Sciences, 2012.

Peichl, L., Behrmann, and G., Kröger, R.H.H. (2001). For whales and seals the ocean is not blue: a visual pigment loss in marine mammals. European Journal of Neuroscience, 13, 1520–1528.

Robbins J. (2012). Scar-based inference Into Gulf of Maine humpback whale entanglement: 2010. Report EA133F0 9CN0253 to the Northeast Fisheries Science Center, National Marine Fisheries Service. Center for Coastal Studies, Provincetown, MA.

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., and Forney, K. A. (2020). Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nature Communications, 11(1).

Song, K.-J., Kim, Z.G., Zhang, C.I., Kim, Y.H. (2010). Fishing gears involved in entanglements of minke whales (Balaenoptera acutorostrata) in the east sea of Korea. Marine Mammal Science, 26, 282–295.

Stimpert, A.K., Wiley, D.N., Au, W.W.L., Johnson, M.P., Arsenault, R. (2007). “Megapclicks”: acoustic click trains and buzzes produced during night-time foraging of humpback whales (Megaptera novaeangliae). Biology Letters, 3, 467–470.

First Flight

By Lindsay Wickman, Postdoc, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

I’ve had the privilege of attending several marine mammal surveys aboard ships at sea, but I had never surveyed for marine mammals from the air. So, when given the opportunity to participate in ongoing aerial surveys off the Oregon Coast with the US Coastguard’s helicopter fleet, I enthusiastically said yes. As Craig Hayslip, a Faculty Research Assistant with the Marine Mammal Institute, prepared me for my first helicopter survey, I was all excitement and no nerves. That is, until he explained the seating arrangement.

“There are two types of helicopters you’ll be flying on, and because of the seating arrangement in the Jayhawk, we fly with the door open when surveying for whales – it’s the only way to get a sufficient view,” Craig casually explained. I stared at the iPad I would use for recording data and imagined it flying through that open door and toward the sea, while I looked on flustered and helpless. Sensing my worry, Craig quickly showed me a set of straps that attached to the iPad, so it could be secured to one of my legs.

In addition to ensuring the iPad stayed in the aircraft, the straps also meant my hands would still be free to handle the camera (to aid in species identification), and a small tool called a geometer (developed by Pi Techology). By lining up the whale sighting in the sight of the geometer, the observer can record the angle between the aircraft and the sighting. Since we also know the height of the helicopter (we fly at a constant altitude of 500 feet), this angle can be used to calculate horizontal distance from the aircraft, allowing an accurate location to be estimated for each sighting.

My first flight was from Warrenton, Oregon, a four-hour drive north from the Hatfield Marine Science Center in Newport. Once at the airport, our first stop was to head to the flight operations office (a.k.a. “Ops”), who set us up with proper clothing and headgear for the flight. As we checked in, rock music played on a speaker while uniformed Coast Guardsmen serviced a helicopter in the hangar. I started to feel like a cool insider, until I clumsily donned the canvas flight suit and tried on several helmets. Suddenly several pounds heavier, all my movements became very awkward.

Lindsay outside the hangar wearing flight gear, in front of the survey’s helicopter. Photo by Craig Hayslip.

After my safety briefing, the entire crew gathered for a pre-flight meeting. We discussed weather conditions, did a wellness check, and discussed the flight’s mission. The conversation also included a brief overview of our scientific aims – why exactly were we looking for whales?

Craig briefly described the research project we were contributing to, titled Overlap Predictions About Large whales (OPAL). The main goal of this project is to better understand the overlap between whales and fisheries, with the aim of reducing entanglement risk. Fishing methods that use fixed, vertical lines in the water column, like the Dungeness crab fishery, can entangle whales as they migrate and feed along Oregon’s coastline. Since reports of whale entanglements have increased on the West Coast in the last 10 years, managing this threat is essential to ensure both the health of whale populations and the stability of Oregon’s crab fishery. Preventing these entanglements requires an understanding of where whales are distributed along the coast, as well as the times of year overlap with fisheries is most likely to occur. The OPAL project isn’t just mapping whale sightings, though. By using models to correlate whale sightings with oceanographic conditions, OPAL is also aiming to predict where whales are likely to occur.

After explaining the mission, the crew had to reach a consensus on both the level of “risk” in the mission and its level of “gain.” For a whale survey flight, risk was deemed low, with medium gain. While I initially felt mild offence that our scientific work was deemed to have just “medium” gain, I quickly reminded myself that when the crew is not flying scientists around, they are literally saving human lives. It was also a reminder that our whale surveys could easily be interrupted if necessary – Craig had mentioned several instances where flights were diverted to assist in rescue or medical emergencies.

With the briefing over, each of us had to consent to the flight plan by saying, “I accept this mission.” I’d heard this phrase from secret agents and soldiers in movies, but never from a marine scientist. I felt out of place saying them at first, but the words undeniably helped me establish a self-assured confidence I would give the survey my 100%.

Finally, it was time to head out of the hangar and to the aircraft. With both a pair of earplugs and my flight helmet on, the whirring of the blades was just a soft hum. I couldn’t hear speech, so we all relied on hand signals to communicate until our headsets were connected to the aircraft. The crew helped make sure I correctly put on my seatbelt harness, which had not just one, but five buckles. While I still felt some mild concern for the iPad strapped to my leg, at least I knew I wouldn’t fall out.

Preparing for takeoff. Photo by Craig Hayslip.
Lindsay holds up the geometer during the flight. Photo by Craig Hayslip.

Craig helped ensure I had all the equipment set up properly: the iPad’s survey program, the GPS tracking, and the computer recording the geometer’s measurements. Soon after, the helicopter slowly rose, hovering above the runway, before turning and heading towards the coast at speed. My stomach dropped slightly, my ears popped, and cold air rushed through the open door. I looked out at the Columbia River as it stretched toward the coastline and out to sea, and I couldn’t stop smiling.

A rainbow mid-air. Photo by Craig Hayslip.

As we approached the ocean, my attention shifted back to the mission, and I started scanning the surface for whale blows. With the large helmet on, I noticed the camera and geometer were much more difficult to use, so I also made “practice sightings” of passing boats and buoys. It didn’t take long before my first real whale sighting though – two gray whales (Eschrichtius robustus). Over the next two hours, I saw four more gray whales, and six more whales I was unable to identify due to distance. With each sighting, I had to act fast to make each geometer recording. The helicopter travels at a speed of 90 knots and whales can disappear soon after surfacing.

Two hours of flying with the door open meant my nose was running and my typing skills were worsening due to cold fingers. As exciting as it was to spot whales from the air, I was a little relieved when we arrived back at the airport and I could warm back up. Luckily, my nightmare of losing an iPad from the helicopter did not come true, and I was returning home with another survey to add to over 200 (and counting!) helicopter surveys completed for the OPAL project. Four different flights covering different parts of the Oregon coast are completed each month, so I know I have more flights to look forward to. After a successful first mission, I feel ready to take on my next flight.

The four flight routes completed monthly for the OPAL project. Helicopter flights are enabled through a partnership with the US Coastguard.

If you’d like to learn more about the OPAL research project, check out these past blog posts:

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

The pathway to advancing knowledge of rorqual whale distribution off Oregon

From land, sea,… and space: searching for whales in the vast ocean

The ups and downs of the ocean

Recent publications presenting findings from the first two years of OPAL include:

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

Derville, S., Buell, T. v., Corbett, K. C., Hayslip, C., & Torres, L. G. (2023). Exposure of whales to entanglement risk in Dungeness crab fishing gear in Oregon, USA, reveals distinctive spatio-temporal and climatic patterns. Biological Conservation, 281. https://doi.org/10.1016/j.biocon.2023.10998

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Baleen analyses reveals patterns in foraging ecology and stress physiology in gray whales prior to death.

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

The Eastern North Pacific (ENP) gray whale population has experienced at least two recorded Unusual Mortality Events (UMEs), from 1999–2000 and from 2019 to 2024, during which many gray whales stranded along the Pacific coast from northern Mexico to the Alaskan Arctic, USA (Martínez-Aguilar et al., 2019; Urbán, 2020). Several factors have been considered as possible causes for the high number of gray whale’s strandings, including variation in Arctic prey availability and the duration of their feeding season caused by the timing of sea ice formation and breakup (Stewart et al., 2023), starvation, anthropogenically derived toxicants, biotoxins, infectious diseases, parasites, fisheries interactions, and ship strikes (F. Gulland et al., 2005). In the most recent UME, many of the stranded whales showed signs of emaciation, indicating malnutrition as a causal factor of death (Christiansen et al., 2021; Torres et al., 2022). While the poor condition of many of the stranded whales supports the idea of starvation as a cause for these mortalities, the underlying causes of malnutrition are unknown, and it is also unclear whether the whales’ decline in body condition was rapid or gradual.

Figure 1. Gray whale with baleen exposed. Photo: GEMM Lab  NOAA/NMFS permit #16111.

Large whales face a multitude of stressors in their environment, ranging from ocean noise to contaminants, climate change, and prey shifts. Understanding how individual whales respond to these disturbances is crucial for assessing potential impacts on the population as a whole. However, monitoring the health parameters and vital rates of whales presents significant challenges due to their large size, mobility, and the vast ranges of their marine habitat. Studying stranded whales can provide valuable insights into health risks, disease susceptibility, and the impacts of pollutants and other stressors on whale populations, thus informing conservation strategies (see post). Nonetheless, the quality of information obtained from necropsies heavily relies on the timeliness of stranding reports, as decomposition begins immediately after death, limiting detailed investigations into the cause of death. Therefore, establishing a robust network capable of promptly reporting and addressing stranding events is essential (Gulland & Stockin, 2020). An effective network involves having well-trained staff, proper infrastructure, sufficient funding, and the expertise and tools necessary to gather and analyze data and samples to infer their health and causes of mortality.

During my doctoral dissertation, I worked to develop and ground truth the endocrine analyses of whale baleen as a novel sample type that can be used for retrospective assessments of the whale’s physiology (see my previous post & post). Baleen, the filter-feeding apparatus of mysticetes whales (Figure 1), consists of long fringed plates of keratinized tissue that grow continuously and slowly downward from the whale’s upper jaw. These plates are routinely collected at necropsies; and unlike other tissue types, they are durable and have minimum storage requirements; they can be preserved dry at room temperature, allowing for the analysis of both historical and current whale populations. Moreover, while most sample types used for studying whale health and physiology provide a single time-point measure of current circulating hormone levels (e.g., skin or respiratory vapor) or hold integrated information from the previous few hours or days (e.g., urine and feces), baleen tissue provides a unique opportunity for retrospective and longitudinal analyses of multiple biological parameters of the individual during the time that the tissue was grown, i.e., months to years prior to death, helping to describe the whale’s physiology, migration patterns, and exposure to pollutants (see my previous post).

In our recent study, “A longitudinal study of endocrinology and foraging ecology of subadult gray whales prior to death based on baleen analysis”, published in the journal General and Comparative Endocrinology, we examine isotope and hormone levels in the baleen of five young gray whales stranded in central Oregon during the most recent UME. Our primary objectives were to retrospectively examine the hormone and isotopic profiles of the individual whales prior to mortality, assess potential factors contributing to death, and identify the timing for the onset of chronic illness leading to mortality. Our analysis included tracing longitudinal changes in (1) stable isotope values in baleen (δ13C and δ15N), which allowed us to infer the baleen growth rate and assess the seasonal changes in diet and foraging location in large whales (Figure 2), along with the quantification of (2) two adrenal glucocorticoid steroids that are biomarkers for the whale’s stress response, (3) one thyroid hormone (triiodothyronine, T3) as an indicator of nutritional state, and (4) two sex hormones, progesterone and testosterone, to infer about reproductive status and sexual maturity. By integrating isotopic and hormonal methodologies, our study demonstrates how baleen analysis offers a comprehensive narrative of the endocrine and trophic ecology of individual whales over time.

Figure 2. Gray whales, like other large marine mammals that rely on built-up energy reserves, exhibit distinct seasonal shifts in their feeding habits. During summer, these whales feed at the ocean’s bottom, consuming organisms lower in the food chain, which is reflected in lower nitrogen values in their baleen (summer foraging). In winter, however, they must rely on their own fat reserves, causing an increase in nitrogen values (wintering). In this plot we can observe the oscillations in δ15N over time; this information allows us to estimate the baleen growth rate. Our results suggest that gray whale baleen holds a record of around 1.3 years of stable isotopes and hormone data prior to the time of death (Fernandez Ajo et al. 2024). The red cross in the X-axis, indicate the time of death. Gray whale illustration https://www.fisheries.noaa.gov/species/gray-whale

Our endocrine assessments revealed detailed profiles of stress-related hormones (glucocorticoids, cortisol) and thyroid hormones along the lengths of the baleen. We found increased levels of cortisol in whales that died from unknown causes, starting about eight months prior to their deaths. This suggests these whales were under prolonged stress before dying. In contrast, in the case of a whale killed acutely by a killer whale, cortisol levels were low and constant prior to death, indicating this individual was likely in good health prior to the sudden attack. In terms of thyroid activity, indicated by T3 hormone levels, we found a gradual increase over several months in the whales that died of unknown causes. This pattern is not typically expected, as stress usually suppresses thyroid function. This anomaly could suggest an adaptive response to maintain body temperature and metabolism in potentially malnourished whales. Regarding the sex hormones, as expected for this age class, we found no significant fluctuations or spikes that would indicate sexual maturity in these young whales (Figure 3).

Figure 3. Longitudinal hormone profiles in an individual gray whale that died due to unknown causes (left) and one that died acutely due to orca predation (right). Note the pronounced elevations in cortisol levels (indicative of stress) and T3 prior to death in the case of unknown cause of death, while hormone levels remained low and constant prior to death in the whale acutely killed. Sex hormones do not present any clear oscillations, indicating that these whales were likely sexually immature. The red cross in the X-axis, indicate the time of death. Killer whale (Orcinus orca) illustration https://www.fisheries.noaa.gov/species/ killer-whale

Although commercial whaling is currently banned and several whale populations show evidence of recovery, today’s whales are exposed to a variety of other human stressors that cause significant lethal and non-lethal impacts (e.g., entanglement in fishing gear, vessel strikes, shipping noise, climate change, etc.; reviewed in Thomas et al., 2016). The recovery and conservation of large whale populations is particularly important to the oceanic environment due to their key ecological role and unique biological traits (See my previous post). Our research demonstrates the strengths of using baleen as a tool for the retrospective assessments of whale endocrinology and trophic ecology. As the Eastern North Pacific gray whale population faces recurring challenges, indicated by fluctuating numbers and unusual mortality events, innovative techniques like the baleen analysis presented here, are essential to investigate the causes of mortality and inform management, helping us understand not only the immediate causes of death but also broader environmental and ecological changes affecting their survival. Broadly implementing this approach with a greater sample size of baleen collected across a larger spatial and temporal range could significantly improve our strategies for conservation and management of baleen whales.

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conservation? Subscribe to our blog and get a weekly message when we post a new
blog. Just add your name and email into the subscribe box below.

References

Christiansen, F., Rodríguez-González, F., Martínez-Aguilar, S., Urbán, J., Swartz, S., Warick, H., Vivier, F., & Bejder, L. (2021). Poor body condition associated with an unusual mortality event in gray whales. Marine Ecology Progress Series, 658, 237–252. https://doi.org/10.3354/meps13585

Gulland, F. M. D., & Stockin, K. A. (2020). Harmonizing global strandings response. European Cetacean Society Special Publication Series.

Gulland, F., Pérez-Cortés, H., Urbán, J. R., Rojas-Bracho, L., Ylitalo, G., Weir, J., Norman, S., Muto, M., Rugh, D., Kreuder, C., & Rowles, T. (2005). Eastern North Pacific gray whale (Eschrichtius robustus) unusual mortality event, 1999-2000. U.S. Department of Commerce. NOAA Technical Memorandum. NMFS-AFSC-150., March, 33 pp. http://www.afsc.noaa.gov/publications/AFSC-TM/NOAA-TM-AFSC-150.pdf

Martínez-Aguilar, S., Mariano-Meléndez, E., López-Paz, N., Castillo-Romero, F., Zaragoza-aguilar, G. A., Rivera-Rodriguez, J., Zaragoza-Aguilar, A., Swartz, S., Viloria-Gómora, L., & Urbán, J. R. (2019). Gray whale (Eschrichtius robustus) stranding records in Mexico during the winter breeding season in 2019. Report of the International Whaling Commission. Document SC/68A/CMP/14, May.

Stewart, J. D., Joyce, T. W., Durban, J. W., Calambokidis, J., Fauquier, D., Fearnbach, H., Grebmeier, J. M., Lynn, M., Manizza, M., Perryman, W. L., Tinker, M. T., & Weller, D. W. (2023). Boom-bust cycles in gray whales associated with dynamic and changing Arctic conditions. Science, 382(6667), 207–211. https://doi.org/10.1126/science.adi1847

Torres, L. G., Bird, C. N., Rodríguez-González, F., Christiansen, F., Bejder, L., Lemos, L., Urban R, J., Swartz, S., Willoughby, A., Hewitt, J., & Bierlich, KC. (2022). Range-Wide Comparison of Gray Whale Body Condition Reveals Contrasting Sub-Population Health Characteristics and Vulnerability to Environmental Change. Frontiers in Marine Science, 9. https://doi.org/10.3389/fmars.2022.867258

Urbán, R. (2020). Gray whale stranding records in Mexico during the 2020 winter breeding season. Unpublished Paper SC/68B/CMP/13 Presented to the IWC Scientific Committee, Cambridge.

An ‘X’travaganza! Introducing the Marine Mammal Institute’s Center of Drone Excellence (CODEX)

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

Drones are becoming more and more prevalent in marine mammal research, particularly for non-invasively obtaining morphological measurements of cetaceans via photogrammetry to identify important health metrics (see this and this previous blog). For example, the GEMM Lab uses drones for the GRANITE Project to study Pacific Coast Feeding Group (PCFG) gray whales and we have found that PCFG whales are skinnier and morphologically shorter with smaller skulls and flukes compared to the larger Eastern North Pacific (ENP) population. The GEMM Lab has also used drones to document variation in body condition across years and within a season, to diagnose pregnancy, and even measure blowholes.

While drone-based photogrammetry can provide major insight into cetacean ecology, several drone systems and protocols are used across the scientific community in these efforts, and no consistent method or centralized framework is established for quantifying and incorporating measurement uncertainty associated with these different drones. This lack of standardization restricts comparability across datasets, thus hindering our ability to effectively monitor populations and understand the drivers of variation (e.g., pollution, climate change, injury, noise).

We are excited to announce the Marine Mammal Institute’s (MMI) Center of Drone Excellence (CODEX), which focuses on developing analytical methods for using drones to non-invasively monitor marine mammal populations. CODEX is led by GEMM Lab member’s KC Bierlich, Leigh Torres, and Clara Bird and consists of other team members within and outside OSU. We draw from many years of trials, errors, headaches, and effort working with drones to study cetacean ecology in a variety of habitats and conditions on many different species.

Already CODEX has developed several open-source hardware and software tools. We developed, produced, and published LidarBoX (Bierlich et al., 2023), which is a 3D printed enclosure for a LiDAR altimeter system that can be easily attached and swapped between commercially available drones (i.e., DJI Inspire, DJI Mavic, and DJI Phantom) (Figure 1). Having a LidarBoX installed helps researchers obtain altitude readings with greater accuracy, yielding morphological measurements with less uncertainty. Since we developed LidarBoX, we have received over 35 orders to build this unit for other labs in national and international universities.

Figure 1. A ‘LidarBoX’ attached to a DJI Inspire 2. The LidarBoX is a 3D printed enclosure containing a LiDAR altimeter to help obtain more accurate altitude readings.

Additionally, CODEX recently released MorphoMetriX version 2 (v2), an easy-to-use photogrammetry software that provides users with the flexibility to obtain custom morphological measurements of megafauna in imagery with no knowledge of any scripting language (Torres and Bierlich, 2020). CollatriX is a user-friendly software for collating multiple MorphoMetriX outputs into a single dataframe and linking important metadata to photogrammetric measurements, such as altitude measured with a LidarBoX (Bird and Bierlich, 2020). CollatriX also automatically calculates several body condition metrics based on measurements from MorphoMetriX v2. CollatriX v2 is currently in beta-testing and scheduled to be released late Spring 2024. 

Figure 2. An example of a Pygmy blue whale imported into MorphoMetriX v2, open-source photogrammetry software. 

CODEX also recently developed two automated tools to help speed up the laborious manual processing of drone videos for obtaining morphological measurements (Bierlich & Karki et al., in revision). DeteX is a graphical user interface (GUI) that uses a deep learning model for automated detection of cetaceans in drone-based videos. Researchers can input their drone-based videos and DeteX will output frames containing whales at the surface. Users can then select which frames they want to use for measuring individual whales and then input these selected frames into XtraX, which is a GUI that uses a deep learning model to automatically extract body length and body condition measurements of cetaceans (Figure 4). We found automated measurements from XtraX to be similar (within 5%) of manual measurements. Importantly, using DeteX and XtraX takes about 10% of the time it would take to manually process the same videos, demonstrating how these tools greatly speed up obtaining key morphological data while maintaining accuracy, which is critical for effectively monitoring population health.

Figure 3. An example of an automated body length (top) and body condition (bottom) measurement of a gray whale using XtraX (Bierlich & Karki et al., in revision).

CODEX is also in the process of developing Xcertainty, an R package that uses a Bayesian statistical model to quantify and incorporate uncertainty associated with measurements from different drones (see this blog). Xcertainty is based on the Bayesian statistical model developed by Bierlich et al., (2021b; 2021a), which has been utilized by many studies with several different drones to compare body condition and body morphology across individuals and populations  (Bierlich et al., 2022; Torres et al., 2022; Barlow et al., 2023). Rather than a single point-estimate of a length measurement for an individual, Xcertainty produces a distribution of length measurements for an individual so that the length of a whale can be described by the mean of this distribution, and its uncertainty as the the variance or an interval around the mean (Figure 4). These outputs ensure measurements are robust and comparable across different drones because they provide a measure of the uncertainty around each measurement. For instance, a measurement with more uncertainty will have a wider distribution. The uncertainty associated with each measurement can be incorporated into analyses, which is key when detecting important differences or changes in individuals or populations, such as changes in body condition (blog).

Figure 4. An example of a posterior predictive distribution for total length of an individual blue whale produced by the ‘Xcertainty’ R package. The black bars represent the uncertainty around the mean value (the black dot) – the longer black bars represent the 95% highest posterior density (HPD) interval, and the shorter black bars represent the 65% HPD interval. 

CODEX has integrated all these lessons learned, open-source tools, and analytical approaches into a single framework of suggested best practices to help researchers enhance the quality, speed, and accuracy of obtaining important morphological measurements to manage vulnerable populations. These tools and frameworks are designed to be accommodating and accessible to researchers on various budgets and to facilitate cross-lab collaborations. CODEX plans to host workshops to educate and train researchers using drones on how to apply these tools within this framework within their own research practices. Potential future directions for CODEX include developing a system for using drones to drop suction-cup tags on whales and to collect thermal imagery of whales for health assessments. Stay up to date with all the CODEX ‘X’travaganza here: https://mmi.oregonstate.edu/centers-excellence/codex.  

Huge shout out to Suzie Winquist for designing the artwork for CODEX!

References

Barlow, D.R., Bierlich, K.C., Oestreich, W.K., Chiang, G., Durban, J.W., Goldbogen, J.A., Johnston, D.W., Leslie, M.S., Moore, M.J., Ryan, J.P. and Torres, L.G., 2023. Shaped by Their Environment: Variation in Blue Whale Morphology across Three Productive Coastal Ecosystems. Integrative Organismal Biology, [online] 5(1). https://doi.org/10.1093/iob/obad039.

Bierlich, K., Karki, S., Bird, C.N., Fern, A. and Torres, L.G., n.d. Automated body length and condition measurements of whales from drone videos for rapid assessment of population health. Marine Mammal Science.

Bierlich, K.C., Hewitt, J., Bird, C.N., Schick, R.S., Friedlaender, A., Torres, L.G., Dale, J., Goldbogen, J., Read, A.J., Calambokidis, J. and Johnston, D.W., 2021a. Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.749943.

Bierlich, K.C., Hewitt, J., Schick, R.S., Pallin, L., Dale, J., Friedlaender, A.S., Christiansen, F., Sprogis, K.R., Dawn, A.H., Bird, C.N., Larsen, G.D., Nichols, R., Shero, M.R., Goldbogen, J., Read, A.J. and Johnston, D.W., 2022. Seasonal gain in body condition of foraging humpback whales along the Western Antarctic Peninsula. Frontiers in Marine Science, 9(1036860), pp.1–16. https://doi.org/10.3389/fmars.2022.1036860.

Bierlich, K.C., Schick, R.S., Hewitt, J., Dale, J., Goldbogen, J.A., Friedlaender, A.S. and Johnston, D.W., 2021b. Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones. Marine Ecology Progress Series, 673, pp.193–210. https://doi.org/10.3354/meps13814.

Bird, C. and Bierlich, K.C., 2020. CollatriX: A GUI to collate MorphoMetriX outputs. Journal of Open Source Software, 5(51), pp.2323–2328. https://doi.org/10.21105/joss.02328.

Torres, L.G., Bird, C.N., Rodríguez-González, F., Christiansen, F., Bejder, L., Lemos, L., Urban R, J., Swartz, S., Willoughby, A., Hewitt, J. and Bierlich, K.C., 2022. Range-Wide Comparison of Gray Whale Body Condition Reveals Contrasting Sub-Population Health Characteristics and Vulnerability to Environmental Change. Frontiers in Marine Science, 9(April), pp.1–13. https://doi.org/10.3389/fmars.2022.867258.

Torres, W. and Bierlich, K.C., 2020. MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), pp.1825–1826. https://doi.org/10.21105/joss.01825.

Every breath [a whale] takes: How and why we study cetacean respiration

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

We need energy to function and survive. For animals in the wild who may have limited food availability, knowing how they spend their energy is a critical question for many scientists because it fundamentally informs how we understand their decisions about where they go and what they do. The entire field of foraging theory is founded on the concept that animals optimize their ratio of energy in and energy out so that they have enough energy to survive, reproduce (pass on their genes), watch out for threats, if need be, and rest. And, if we understand an animal’s ‘typical’ energy budget, we can then try to predict how disturbance or environmental change will affect their actual energy budgets as a consequence of that change. But how do we measure energy expenditure?

The most commonly measured energy currency is oxygen. Since our cells use oxygen to produce energy (this is why we need oxygen to live), we can measure oxygen consumption as a metric of energy expenditure. The more oxygen we consume, the more energy we’re expending. In ideal lab settings, oxygen consumption can be accurately measured by placing the subject in a chamber where the oxygen flow can be controlled (Speakman, 1999). However, you can probably see how that approach is problematic for measuring oxygen consumption in most large free-living animals, especially cetaceans. It isn’t exactly feasible to put a whale in a box.

Image 1. A great tit in a metabolic chamber. Figure 1 from Broggi et al., 2009

Fortunately, a tool called a spirometer was developed to measure oxygen consumption in restrained cetaceans. A spirometer is a device that can be placed over the blowhole(s) of an individual to accurately measure the amount of air that is exhaled and inhaled (Figure 1).  For trained cetaceans in captivity (e.g., dolphins), spirometers can be used to quantify how respiration changes after the animal performs certain behaviors (Fahlman et al., 2019). The breathing patterns of diving mammals are particularly interesting because they cannot breathe during most of their exercise (energy expenditure) as they are underwater. So, their breathing patterns after a dive tell us a lot about how much energy they spent during that dive. For example, Fahlman et al. (2019) used spirometer data from dolphins in captivity to study how their breathing patterns changed while recovering from dives of different durations. Interestingly, they found that after longer dives, dolphins took larger breaths (i.e., inhaled more air) while recovering but did not change the number of breaths. This finding is particularly relevant to the work we are conducting in the GEMM lab, where we utilize breathing patterns to quantify the energy expenditure of cetaceans in the wild, where spirometers cannot be used.

Figure 1. Figure 1 from Sumich et al. (2023). Left: a spirometer being held over the blow holes of JJ, a gray whale calf at sea world in 1997; one of the rare times that a large baleen whale was in captivity and available for these measurements. Right: example of a plot created using the data from a spirometer over JJ’s blow holes. The duration of a “blow” (exhale followed by immediate inhale) is on the x-axis, the flow rate (in liters per second) is on the y-axis. The positive curve during the exhale shows that the whale strongly exhales a lot of air very quickly, then the negative curve shows the whale inhaling a lot of air very quickly.

In a previous blog, I described how inter-breath intervals (the time between consecutive blows) are useful for estimating energy expenditure in free-living cetaceans. Essentially, a shorter interval indicates that the whale was just engaged in an energetically demanding activity. When you’re recovering from a sprint, you breathe faster (i.e., with shorter inter-breath intervals), than when you’re recovering from a walk. However, a big assumption in using inter-breath intervals as a proxy for energy expenditure is that every breath is equal. But as Fahlman et al. emphasize in their 2016 paper, every blow is not equal (Fahlman et al., 2016). In addition to varying the time between breaths, an animal can vary the intensity of each breath (e.g., Fahlman et al., 2019), the duration of each breath (Sumich et al., 2023), the number of breaths, and even the expansion of their nostrils (Nazario et al., 2022; check out this blog for more).

Image 2. Gray whale blow. Source: https://www.lajollalight.com/sdljl-natural-la-jolla-winter-wildlife-2015jan08-story.html

Altogether, this means that it’s important to measure every breath and that no one metric tells the complete story. This also means my research question focused on comparing the energetic costs of different tactics is more complicated than I originally thought. If we go back to the first blog I wrote on this topic, I was planning ons only using inter-breath intervals to estimate energy expenditure. Fast forward four years, with all my new knowledge gained on respiration variability, I’ve modified my plan and now I’m working to first understand how all these different metrics of breathing relate to each other. Then, I’ll compare how breathing varies between different foraging tactics, which is an important follow up to my questions around individual specialization of foraging tactics. If different whales are using different foraging behaviors, does that mean they’re spending different amounts of energy? If so, are certain behaviors more advantageous than others? Of course, these answers are incomplete without understanding the prey the whales are eating, but that’s something that PhD student Nat Chazal is working to understand (check out her recent blog here).  For now, I’m working on bringing integrating all the measures of breathing, then we will start putting the story together and finding some answers to our pressing questions. 

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References

Broggi, J., Hohtola, E., Koivula, K., Orell, M., & Nilsson, J. (2009). Long‐term repeatability of winter basal metabolic rate and mass in a wild passerine. Functional Ecology, 23(4), 768–773. https://doi.org/10.1111/j.1365-2435.2009.01561.x

Fahlman, A., Brodsky, M., Miedler, S., Dennison, S., Ivančić, M., Levine, G., Rocho-Levine, J., Manley, M., Rocabert, J., & Borque-Espinosa, A. (2019). Ventilation and gas exchange before and after voluntary static surface breath-holds in clinically healthy bottlenose dolphins, Tursiops truncatus. Journal of Experimental Biology, 222(5), jeb192211. https://doi.org/10.1242/jeb.192211

Fahlman, A., van der Hoop, J., Moore, M. J., Levine, G., Rocho-Levine, J., & Brodsky, M. (2016). Estimating energetics in cetaceans from respiratory frequency: Why we need to understand physiology. Biology Open,5(4), 436–442. https://doi.org/10.1242/bio.017251

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

Speakman, J. R. (1999). The Cost of Living: Field Metabolic Rates of Small Mammals. In A. H. Fitter & D. G. Raffaelli (Eds.), Advances in Ecological Research (Vol. 30, pp. 177–297). Academic Press. https://doi.org/10.1016/S0065-2504(08)60019-7

Sumich, J. L., Albertson, R., Torres, L. G., Bird, C. N., Bierlich, K. C., & Harris, C. (2023). Using audio and UAS-based video for estimating tidal lung volumes of resting and active adult gray whales (Eschrichtius robustus). Marine Mammal Science, 1(8). https://doi.org/10.1111/mms.13081

How big, how blue, how beautiful! Studying the impacts of climate change on big, (and beautiful) blue whales

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

The SAPPHIRE Project is in full swing, as we spend our days aboard the R/V Star Keys searching for krill and blue whales (Figure 1) in the South Taranaki Bight (STB) region of Aotearoa New Zealand. We are investigating how changing ocean conditions impact krill availability and quality, and how this in turn impacts blue whale behavior, health, and reproduction. Understanding the link between changing environmental conditions on prey species and predators is key to understanding the larger implications of climate change on ocean food webs and each populations’ resiliency. 

Figure 1. The SAPPHIRE team searching for blue whales. Top left) KC Bierlich, top right) Dawn Barlow, bottom left) Dawn Barlow, Kim Bernard (left to right), bottom right) KC Bierlich, Dawn Barlow, Leigh Torres, Mike Ogle (left to right).  

One of the many components of the SAPPHIRE Project is to understand how foraging success of blue whales is influenced by environmental variation (see this recent blog written by Dr. Dawn Barlow introducing each component of the project). When you cannot go to a grocery store or restaurant any time you are hungry, you must rely on stored energy from previous feeds to fuel energy needs. Body condition reflects an individual’s stored energy in the body as a result of feeding and thus represents the foraging success of an individual, which can then affect its potential for reproductive output and the individual’s overall health (see this previous blog). As discussed in a previous blog, drones serve as a valuable tool for obtaining morphological measurements of whales to estimate their body condition. We are using drones to collect aerial imagery of pygmy blue whales to obtain body condition measurements late in the foraging season between years 2024 and 2026 of the SAPPHIRE Project (Figure 2). We are quantifying body condition as Body Area Index (BAI), which is a relative measure standardized by the total length of the whale and well suited for comparing individuals and populations (Figure 3). 

The GEMM Lab recently published an article led by Dr. Dawn Barlow where we investigated the differences in BAI between three blue whale populations: Eastern North Pacific blue whales feeding in Monterey Bay, California; Chilean blue whales feeding in the Corcovado Gulf; and New Zealand Pygmy blue whales feeding in the STB (Barlow et al., 2023). These three populations are interesting to compare since blue whales that feed in Monterey Bay and Corcovado Gulf migrate to and from these seasonally productive feeding grounds, while the Pygmy blue whales stay in Aotearoa New Zealand year-round. Interestingly, the Pygmy blue whales had higher BAI (were fatter) compared to the other two regions despite relatively lower productivity in their foraging grounds. This difference in body condition may be due to different life history strategies where the non-migratory Pygmy blue whales may be able to feed as opportunities arrive, while the migratory strategies of the Eastern North Pacific and Chilean blue whales require good timing to access high abundant prey. Another interesting and unexpected result from our blue whale comparison was that Pygmy blue whales are not so “pygmy”; they are actually the same size as Eastern North Pacific and Chilean blue whales, with an average size around 22 m. Our findings from this blue whale comparison leads us to more questions about how environmental conditions that vary from year to year influence body condition and reproduction of these “not so pygmy” blue whales. 

Figure 2. An aerial image of a Pygmy blue whale in the South Taranaki Bight region of Aotearoa New Zealand collected during the SAPPHIRE 2024 field season using a DJI Inspire 2 drone. 
Figure 3. A drone image of a Pygmy blue whale and the length and body width measurements used to estimate Body Area Index (BAI), represented by the shaded blue region. Width measurements will also be used to help identify pregnant individuals.

The GEMM Lab has been studying this population of Pygmy blue whales in the STB since 2013 and found that years designated as a marine heatwave resulted with a reduction in blue whale feeding activity. Interestingly, breeding activity is also reduced during marine heatwaves in the following season when compared to the breeding season following a more productive, typical foraging season. These findings indicate that fluctuations in the environment, such as marine heatwaves, may affect not only foraging success, but also reproduction in Pygmy blue whales. 

To help us better understand reproductive patterns across years, we will use body width measurements from drone images paired with hormone concentrations collected from fecal and biopsy samples to identify pregnant individuals. Progesterone is a hormone secreted in the ovaries of mammals during the estrous cycle and gestation, making it the predominant hormone responsible for sustaining pregnancy. Recently, the GEMM Lab’s Dr. Alejandro Fernandez-Ajo wrote a blog discussing his publication identifying pregnant individual gray whales using drone-based body width measurements and progesterone concentrations from fecal samples (Fernandez et al., 2023). While individuals that were pregnant had higher levels of progesterone compared to when they were not pregnant, the body width at 50% of the body length served as a more reliable method for detecting pregnancy in gray whales. We will use similar methods to help identify pregnancy in Pygmy blue whales for the SAPPHIRE Project where will we examine body width measurement paired with progesterone concentrations collected from fecal and biopsy samples to identify pregnant individuals. We hope our work will help to better understand how climate change will influence Pygmy blue whale body condition and reproduction, and thus the overall health and resiliency of the population. Stay tuned! 

References

Barlow, D. R., Bierlich, K. C., Oestreich, W. K., Chiang, G., Durban, J. W., Goldbogen, J. A., Johnston, D. W., Leslie, M. S., Moore, M. J., Ryan, J. P., & Torres, L. G. (2023). Shaped by Their Environment: Variation in Blue Whale Morphology across Three Productive Coastal Ecosystems. Integrative Organismal Biology, 5(1). https://doi.org/10.1093/iob/obad039

Fernandez Ajó, A., Pirotta, E., Bierlich, K. C., Hildebrand, L., Bird, C. N., Hunt, K. E., Buck, C. L., New, L., Dillon, D., & Torres, L. G. (2023). Assessment of a non-invasive approach to pregnancy diagnosis in gray whales through drone-based photogrammetry and faecal hormone analysis. Royal Society Open Science, 10(7), 230452. https://doi.org/10.1098/rsos.230452