Month: September 2025

Daily Diel Ins! Calling patterns of New Zealand blue whales

Alana Cary, St. Mary’s College of Maryland Undergraduate in Marine Science, 2026 NSF REU and GEMM Lab SAPPHIRE Project Intern

Hello everyone! My name is Alana Cary, and I was one of the NSF REU interns with the GEMM Lab this summer. I am currently in my senior year at St. Mary’s College of Maryland (SMCM), where I study Marine Science. Growing up along the Chesapeake Bay, I was always captivated by the connection between people and marine ecosystems. That curiosity led me to pursue marine mammal ecology, and this summer I had the amazing opportunity to travel across the country and dive into bioacoustics research with the GEMM Lab at Oregon State University’s Hatfield Marine Science Center.

My project, Daily Diel Ins: Investigating Diel and Seasonal Calling Patterns of New Zealand Blue Whales, explored how the world’s largest animals use sound over the course of the day and throughout the year. Working under the mentorship of Dawn Barlow and Leigh Torres, I spent ten weeks immersed in whale calls, coding, and a lot of matcha (shoutout to RISE cafe!).

Figure 1. Picture of me (Alana C) at Devils Punchbowl my first week in Oregon.

Why study whale voices?

Blue whales (Balaenoptera musculus) are record-breakers: the largest animals to have ever lived, producers of the loudest biological sounds, and yet, surprisingly elusive. They spend most of their lives far beneath the surface, only surfacing briefly to breathe. This lifestyle makes them difficult to study visually. But blue whales leave behind something extremely powerful — their voices.

Baleen whales rely heavily on low-frequency sounds to forage, navigate, and communicate across vast distances (Clark & Ellison 2004). New Zealand blue whales are unique since they produce a distinct regional song (Torres et al. 2013; Barlow et al. 2018), and their calls provide insight into both foraging and reproductive behavior.

Blue whales produce two main call types (Figure 3):

  • Songs – long, stereotyped sequences produced by males, likely used in reproduction and communication.
  • D calls – short, frequency-modulated downsweeps produced by both sexes, often linked to foraging or social behavior.

Previous research has described the seasonal calling patterns of New Zealand blue whales (Barlow et al. 2023), but until now, diel (day–night) patterns had not been studied. This is important because blue whales’ primary prey, krill, perform diel vertical migration (DVM) in many parts of the ocean — ascending at night and descending during the day. If whale calls are tied to foraging or social interactions, they might vary depending on these light-driven prey movements.

Listening to giants: methods and workflow

Figure 2. Map of Rockhopper hydrophone deployments in the South Taranaki Bight, New Zealand. Stars mark the Rockhopper east (RH-east) and west (RH-west) recording sites. Bathymetric contours are drawn at 100 m intervals.

My study focused on the South Taranaki Bight (STB), a productive upwelling region between New Zealand’s North and South Islands. The STB supports a unique population of blue whales year-round, providing foraging and reproductive opportunities (Torres et al. 2013; Barlow et al. 2018), but it is also an area with heavy human activity, including shipping, oil and gas, and fishing. Understanding when and how whales call here helps identify times of overlap with disturbance.

Here’s how I studied their voices:

  1. Hydrophones in the field – We used Rockhopper autonomous hydrophones, which are compact devices designed to record low-frequency sound for long periods (Klinck et al. 2020). These instruments collected a year of acoustic data in the STB (January 2024–January 2025).
  2. Manual annotation – From this massive dataset, one day was randomly selected from every two weeks (24 days total) and I manually annotated spectrograms of songs and D calls in Raven Pro. I identified songs in the 0–75 Hz band and D calls up to 150 Hz, following published criteria.
  3. Machine learning classification – These manual annotations were used to evaluate a BirdNET machine learning model (Kahl et al. 2021) trained to detect whale calls. The model then predicted calls across the entire year-long dataset.
  4. Validation – Because D calls are harder to classify, I manually reviewed all predicted D calls to remove false positives. Song detections, which had extremely high precision and recall, were kept without this extra step.
  5. Binning into diel phases – I aggregated call detections by hour and used the suncalc R package (Thieurmel et al. 2022) to assign diel phases (day or night). I also grouped calls by month and season to evaluate seasonal trends.
  6. Statistics in RStudio – To test whether month, diel phase, or their interaction influenced call rates, I ran two-way ANOVAs with post hoc Tukey’s HSD tests for pairwise comparisons
Figure 3. A spectrogram showing two distinct call types produced by New Zealand blue whales. The green box highlights the repetitive, low-frequency song, while the blue box highlights a short, frequency-modulated D call. Time is on the x-axis (hh:mm:ss) and frequency (Hz) on the y-axis

Results: different rhythms for different calls

The results revealed that songs and D calls follow very different patterns.

Songs – Seasonal, weakly diel:

  • Songs showed strong seasonal variation, peaking in late summer and autumn (February–May).

  • Diel effects were weaker but significant: slightly more daytime calling, consistent across months.

  • This suggests that songs are driven mainly by reproductive cycles, not daily light cues.

Figure 4. Monthly variation in New Zealand blue whale song detections from January 2024 to January 2025, separated by diel phase (day vs. night). Songs peaked in late summer and autumn, consistent with reproductive timing. Diel differences were minimal, with slightly more daytime calling

D calls – Both seasonal and diel:

  • D calls showed highly significant effects of month, diel phase, and their interaction.

  • Calling rates peaked in spring and summer, with more D calls during daytime hours.

  • The diel effect varied by month: some months showed strong differences between day and night, while others showed none.

  • This suggests that D calls track foraging, responding to increased krill availability during spring and summer months, and krill’s diel vertical migration

Figure 5. Monthly variation in New Zealand blue whale D call detections from January 2024 to January 2025, separated by diel phase (day vs. night). Peaks occur during spring and summer, with more daytime calling during these seasons. This pattern suggests D calls are closely tied to foraging opportunities on krill.

Why does this matter?

Understanding when whales call is more than an academic exercise. The South Taranaki Bight is not only an important whale habitat, it’s also a busy human space. By identifying when whales are most acoustically active, we can better understand potential conflicts with anthropogenic noise from shipping or industrial activities. For example, the fact that daytime D calls peak in spring and summer, times of high foraging effort, means that increased vessel noise during these periods could disrupt critical feeding-related communication. This kind of fine-scale temporal knowledge can inform management strategies to reduce human impacts on a vulnerable whale population. These insights also feed into the GEMM Lab’s SAPPHIRE project goal: linking oceanography, prey dynamics, physiology, and acoustics to understand how blue whales respond to a rapidly changing ocean (Barlow & Torres, 2021; Barlow et al., 2023).

Reflections on my REU journey

When I first arrived at Hatfield, I had never worked with passive acoustic data before. At first, spectrograms just looked like fuzzy TV static. But every day I learned to trust the process. Soon, I was able to fly through annotations and recognize calls without even seeing them on the spectrogram— just listening. I remember creating my plots and seeing the clear diel patterns, I couldn’t help but grin.

Figure 6. Dawn, Leigh, and I at the Coastal Intern symposium after I finished presenting my final research poster.

There were some moments that were frustrating as well, like when my entire selection table would delete and I’d have to start over, or debugging the same error message with no hope of figuring it out. But these frustrations taught me to be patient, persistent, and to not be afraid to ask for help. Weekly check-ins with Dawn pushed me to put my critical thinking to the test to decipher what my results meant. My co-interns and lab mates provided laughter, support, and encouragement that made the work even more rewarding. Beyond the data, I also learned how science is built on community. Research isn’t just all about running tests and analyzing plots, it’s about sharing ideas, collaboration, and building on each other’s work.

Figure 7. Alana and Bennie the Banana Slug at Cape Perpetua after a hike.

As a first-generation college student, this opportunity showed me that I can belong in marine science. I leave this summer with new skills in bioacoustics, statistics, and science communication, but also with a deeper sense of confidence and belonging in research.

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References

Barlow, D. R., Klinck, H., Ponirakis, D., Branch, T. A., & Torres, L. G. (2023). Environmental conditions and marine heatwaves influence blue whale foraging and reproductive effort. Ecology and Evolution, 13(2), e9770. https://doi.org/10.1002/ece3.9770

Barlow, D. R., & Torres, L. G. (2021). Planning ahead: Dynamic models forecast blue whale distribution with applications for spatial management. Journal of Applied Ecology, 58(11), 2493–2504. https://doi.org/10.1111/1365-2664.13992

Barlow, D. R., Torres, L. G., Hodge, K. B., Steel, D., Baker, C. S., Chandler, T. E., Bott, N., Constantine, R., Double, M. C., Gill, P., Glasgow, D., Hamner, R. M., Lilley, C., Ogle, M., Olson, P. A., Peters, C., Stockin, K. A., Tessaglia-Hymes, C. T., & Klinck, H. (2018). Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endangered Species Research, 36, 27–40. https://doi.org/10.3354/esr00891

Clark, C. W., & Ellison, W. T. (2004). Potential use of low-frequency sounds by baleen whales for probing the environment: Evidence from models and empirical measurements. In Echolocation in Bats and Dolphins (Vol. 1–1, pp. 564–589). Univeristy of Chicago Press.

Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021). BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61, 101236. https://doi.org/10.1016/j.ecoinf.2021.101236

Klinck, H., Winiarski, D., Mack, R. C., Tessaglia-Hymes, C. T., Ponirakis, D. W., Dugan, P. J., Jones, C., & Matsumoto, H. (2020). The Rockhopper: A compact and extensible marine autonomous passive acoustic recording system. Global Oceans 2020: Singapore – U.S. Gulf Coast, 1–7. https://doi.org/10.1109/IEEECONF38699.2020.9388970

Thieurmel, B., & Elmarhraoui, A. (2022). suncalc: Compute sun position, sunlight phases, moon position and lunar phase (Version 0.5.1) [R package]. Comprehensive R Archive Network (CRAN). https://CRAN.R-project.org/package=suncalc

Torres, L. (2013). Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zealand Journal of Marine and Freshwater Research, 47(2), 235–248. https://doi.org/10.1080/00288330.2013.773919

There she blows! Studying blow synchrony in blue and gray whale mother-calf pairs

Maddie Honomichl, CSUMB Undergraduate in Marine Science, 2025  NSF REU and TOPAZ/JASPER Intern

Hi everyone, my name is Maddie Honomichl, and I was one of the two NSF REU interns with the GEMM lab this summer. This fall will be my last semester at California State University, Monterey Bay (CSUMB), where I will receive my undergraduate degree in Marine Science. Growing up in the Arizona desert limited my exposure to large bodies of water but led to memorable family trips to San Diego. With each trip my adoration for the ocean and the vast marine ecosystem emerged, resulting in my choice to go to college at CSU Monterey Bay. During my time at CSUMB, I learned of what internships were and the magnitude of impact these experiences had on my peers. Naturally, I searched online for weeks for future summer internship openings available—eventually leading me to none other than the GEMM Lab’s TOPAZ/JASPER project.

Figure 1. A picture of me, Maddie Honomichl, in Redding, CA.

Celest Sorrentino, my amazing mentor, took me under her wing and helped me complete my very first research project: In sync? Studying blow synchrony in blue and gray whale mother-calf pairs using drone footage. The aim of my project is to understand more about calf development in gray and blue whales by investigating changes in mother-calf blow synchrony. But what is synchrony?

Synchrony can be defined as two individuals attempting to match each other’s behavior (Novotny & Bente 2022) and promote mimicry and learning. For example, humpback whales teaching their calves vocalizations and song patterns to communicate is an instance of synchrony and social learning (Anjara 2018). Another example of synchrony in wildlife is energetic transfer, where a whale calf will swim in alignment with its mother’s slipstream to expend less energy (Norris & Prescott 1961). Just like these behaviors, blow synchrony is a measure we can use in marine mammal mother-calf pairs to evaluate their relationship with each other.

Aside from the TOPAZ/JASPER project, you might already be familiar with two other incredible GEMM lab projects GRANITE and SAPPHIRE. During the years of 2016-2023, drone footage of the Pacific Coast Feeding Group of gray whales was collected along the Oregon coast for the GRANITE project. In 2016, 2017, 2024, and 2025, drone footage for the pygmy blue whales was collected in South Taranaki Bight, New Zealand, for the SAPPHIRE project. Large marine mammals, especially whales, are difficult to study for many reasons including their brief occurrences at the surface and the challenges of studying aquatic animals. However, the use of drones allows for a safer and non-invasive alternative method for marine mammal monitoring in their natural habitat (Álvarez-González et al., 2023).

Figure 2. Two still images taken from GRANITE drone footage. The left is of a mother gray whale blowing and the right is of a gray whale calf blowing

Baleen whale calves only have 6-8 months to learn everything they need to know before they wean and are sent off on their own (Lockyer 1984). I don’t know about you, but if my mom kicked me out after I turned 5 years old, I would be pretty lost. In American culture, the golden age for humans to be considered an adult is around 18 years old, when they finally leave the house and venture on their own. For humans, age is a cultural sign of independence, but not much is known about what factors influence what makes a calf ready to be independent. Blow synchrony between mother-calf pairs during the calf’s weaning period can be used as a metric for calf development, which is important to know more about as calf development and survival rates are critical factors to consider in population dynamics and management efforts.

When do whales eventually leave their mother? We frequently don’t know how old a calf is, so we use three different metrics as proxies of calf maturity. First, we use Total Length (TL), which is the length of the whale from rostrum to fluke (or nose to tail) (Pirotta et. al., 2023) and serves as an indicator of growth. Our next metric is Body Area Index (BAI), similar to BMI in humans, which is a score of body condition to understand how fat or skinny the whale calf is (Burnett et. al., 2019). Total Length and BAI measurements are derived from drone photogrammetry work conducted by the GEMM Lab and CODEX. Our last proxy is Day of Year (DOY), which is the day in the year we sighted the whales.

Figure 3. Demonstrating photogrammetry methods used to measure Total Length and Body Area Index (BAI). On the left is a drone image of a gray whale showing how we calculate Total Length (TL) from rostrum to fluke. This image is also divided into increments which are used to calculate Surface Area (SA), depicted by the green dashed box, and using the equation on the right with TL, BAI is calculated. 

The specific question I addressed was: Does mother-calf blow rate synchrony change as the calf’s Total Length and Body Area Index increase, and Day of Year increases? In other words, does synchrony change as calves become longer, healthier, and the year progresses. Meaning, as the calf grows in length, increases its body condition, and the day of year progresses, the calf will gain independence from its mother and become out of sync.

I analyzed blowhole rates of mother-calf gray and blue whales using  a program called BORIS (Friard & Gamba 2016). BORIS (Behavioral Observation Research Interactive Software) is an online free program where researchers can assign behavior states to animals in video. In BORIS, I watched the drone footage and marked a “blow” event for the mom and calf, recording a specific time stamp per event. I repeated this workflow for each video of both gray and blue whale mom-calf pairs. Once completed, I calculated the average difference of the calf’s timestamp from the mother’s timestamp per pair. The reason behind this approach is that  the larger the average difference, the more asynchronous the calf is with its mother, and the smaller the average difference the more synchronous they will become.

To evaluate the effect of our proxies for age, Total Length, BAI, and Day of Year, on mother-calf blow rate synchrony, I turned to my good friend RStudio. I created a scatterplot and regressions for these relationships (Figure 4). These results indicate that body condition (BAI) may be a better proxy of calf maturity and preparation for weaning in gray whales (p-value = 0.0064), whereas calf Total Length (TL) is more indicative of calf maturity in blue whales (p-value = 0.00097).

Figure 4. Scatterplot describing the relationship between the average difference in breath rate in seconds across our three proxies: (i) Average total length, (ii) Average BAI, (iii) Day of Year. The black line in the linear regression fit to the data produced by the linear model. The error bars around each point are the standard deviation or the variability in their blow synchrony. The bigger the error bars mean the more variation the mother and calf had in their blow rates, and the smaller the error bars means the less variation the mother and calf had in their blow rates. Ultimately to answer my question, for gray whales, blow synchrony between mother and calf decreases with increasing calf Body Area Index (BAI). For our blue whales, mother-calf blow synchrony decreases with increasing calf Total Length (TL).

As I end my 10-week internship summer filled with data collection and analysis, lots of laughs and inside jokes, I am proud to say I have learned so much about the research that goes into a project like mine. As someone who loves marine animals, especially whale sharks, I now have a newfound love for whales that will forever be in my heart. I am so incredibly grateful that I was able to work with the GEMM lab and the amazing team of researchers and scientists it encompasses. Being a first-generation college student comes with its challenges of learning how to navigate higher education without direct guidance of family who had been through the experience. But if there’s one thing I always tell myself, it’s that with a little bit of grit and hard work, you can do anything you put your mind to! Whatever my future holds for me, I hope it is filled with more research opportunities and the chance to work with marine mammals!

Figure 5. An image of Maddie Honomichl, presenting her research poster at the Hatfield summer coastal intern symposium remotely from Port Orford!

References:

Álvarez-González, M., Suarez-Bregua, P., Pierce, G. J., & Saavedra, C. (2023). Unmanned Aerial Vehicles (UAVs) in Marine Mammal Research: A Review of Current Applications and Challenges. Drones, 7(11), 667. https://doi.org/10.3390/drones7110667

Anjara Saloma. Humpback whales (Megaptera novaeangliae) mother-calf interactions. Vertebrate Zoology. Université Paris Saclay (COmUE); Université d’Antananarivo, 2018. English. ⟨NNT : 2018SACLS138⟩. ⟨tel-02869389⟩

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2019). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science, 35(1), 108–139. https://doi.org/10.1111/mms.12527

Friard, O., & Gamba, M. (2016). BORIS: A free, versatile open‐source event‐logging software for video/audio coding and live observations. Methods in Ecology and Evolution, 7(11), 1325–1330. https://doi.org/10.1111/2041-210X.12584

Huetz, C., Saloma, A., Adam, O., Andrianarimisa, A., & Charrier, I. (2022). Ontogeny and synchrony of diving behavior in Humpback whale mothers and calves on their breeding ground. Journal of Mammalogy, 103(3), 576–585. https://doi.org/10.1093/jmammal/gyac010

Lockyer, Christina. (1984). Review of Baleen Whale (Mysticeti) Reproduction and Implications for Management. Reproduction in whales, dolphins and porpoises. Proc. conference, La Jolla, CA, 1981. 6. 27-50.

Norris, K.S., & Prescott, J.H. (1961). Observations of Pacific cetaceans of Californian and Mexican waters. University of California Publications in Zoology, 63, 291- 402.

Novotny, E., & Bente, G. (2022). Identifying Signatures of Perceived Interpersonal Synchrony. Journal of nonverbal behavior, 46(4), 485–517. https://doi.org/10.1007/s10919-022-00410-9

Pirotta, E., Fernandez Ajó, A., Bierlich, K. C., Bird, C. N., Buck, C. L., Haver, S. M., Haxel, J. H., Hildebrand, L., Hunt, K. E., Lemos, L. S., New, L., & Torres, L. G. (2023). Assessing variation in faecal glucocorticoid co

Smultea, M. A., Fertl, D., Bacon, C. E., Moore, M. R., James, V. R., & Würsig, B. (2017). Cetacean mother-calf behavior observed from a small aircraft off Southern California. Animal Behavior and Cognition, 4(1), 1–23. https://doi.org/10.12966/abc.01.02.2017