Tag: Blue whales

New GEMM Lab publication reveals how blue whale feeding and reproductive effort are related to environmental conditions

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

Learning by listening

Studying mobile marine animals that are only fleetingly visible from the water’s surface is challenging. However, many species including baleen whales rely on sound as a primary form of communication, producing different vocalizations related to their fundamental needs to feed and reproduce. Therefore, we can learn a lot about these elusive animals by monitoring the patterns of their calls. In the final chapter of my PhD, we set out to study blue whale ecology and life history by listening. I am excited to share our findings, recently published in Ecology and Evolution.

Blue whales produce two distinct types of vocalizations: song is produced by males and is hypothesized to play a role in breeding behavior, and D calls are a hypothesized social call produced by both sexes in association with feeding behavior. We analyzed how these different calls varied seasonally, and how they related to environmental conditions.

This paper is a collaborative study co-authored by Dr. Holger Klinck and Dimitri Ponirakis of the K. Lisa Yang Center for Conservation Bioacoustics, Dr. Trevor Branch of the University of Washington, and GEMM Lab PI Dr. Leigh Torres, and brings together multiple methods and data sources. Our findings shed light on blue whale habitat use patterns, and how climate change may impact both feeding and reproduction for this species of conservation concern.

The South Taranaki Bight: an ideal study system

Baleen whales typically migrate between high-latitude, productive feeding grounds and low-latitude breeding grounds. However, the New Zealand blue whale population is present in the South Taranaki Bight (STB) region year-round, which uniquely enabled us to monitor their behavior, ecology, and life history across seasons and years from a single location. We recorded blue whale vocalizations from Marine Autonomous Recording Units (MARUs) deployed at five locations in the STB for two full years (Fig. 1).

Figure 1. Study area map and blue whale call spectrograms. Left panel: map of the study area in the South Taranaki Bight region, with hydrophone (marine autonomous recording unit; MARU) locations denoted by the stars. Gray lines show bathymetry contours at 50 m depth increments, from 0 to 500 m. Location of the study area within New Zealand is indicated by the inset map. Right panels: example spectrograms of the two blue whale call types examined: the New Zealand song recorded on 31 May 2016 (top) and D calls recorded 20 September 2016 (bottom). Figure reproduced from Barlow et al. (2023).

We found that the two vocalization types had different seasonal occurrence patterns (Fig. 2). D calls were associated with upwelling conditions that indicate feeding opportunities, lending evidence for their function as a foraging-related call.

Figure 2. Average annual cycle in the song intensity index (dark blue) and D calls (green) per day of the year, computed across all hydrophone locations and the entire two-year recording period. Figure reproduced from Barlow et al. (2023).

In contrast, blue whale song showed a very clear seasonal peak in the fall and was less obviously correlated with environmental conditions. To investigate the hypothesized function of song as a breeding call, we turned to a perhaps unintuitive source of information: historical whaling records. Whenever a pregnant whale was killed during commercial whaling operations, the length of the fetus was measured. By looking at the seasonal pattern in these fetal lengths, we can presume that births occur around the time of year when fetal lengths are at their longest. The records indicated April-May. By back-calculating the 11-month gestation time for a blue whale, we can presume that mating occurs generally in May-June, which is the exact time of the peak in song intensity from our recordings (Fig. 3).

Figure 3. Annual song intensity and the breeding cycle. Top panel: average yearly cycle in song intensity index, computed across the five hydrophone locations and the entire recording period; dark blue line represents a loess smoothed fit. Bottom panel: fetal length measurements from whaling catch records for Antarctic blue whales (gray, measurements rounded to the nearest foot), pygmy blue whales in the southern hemisphere (blue, measurements rounded to the nearest centimeter). Measurements from blue whales caught within the established range of the New Zealand population are denoted by the dark red triangles. Calving presumably takes place around or shortly after fetal lengths are at their maximum (April–May), which implies that mating likely occurs around May–June, coincident with the peak song intensity. Figure reproduced from Barlow et al. (2023).

With this evidence for D calls as feeding-related calls and song as breeding-related calls, we had a host of new questions, we used this gained knowledge to explore how changing environmental conditions might impact multiple life history processes for New Zealand blue whales

Marine heatwaves impact multiple life history processes

Our study period between January 2016 and February 2018 spanned both typical upwelling conditions and dramatic marine heatwaves in the STB region. While we previously documented that the marine heatwave of 2016 affected blue whale distribution, the population-level impacts on feeding and reproductive effort remained unknown. In our recent study, we found that during marine heatwaves, D calls were dramatically reduced compared to during productive upwelling conditions. During the fall breeding peak, song intensity was likewise dramatically reduced following the marine heatwave. This relationship indicates that following poor feeding conditions, blue whales may invest less effort in reproduction. As marine heatwaves are projected to become more frequent and more intense under global climate change, our findings are perhaps a warning for what is to come as animal populations must contend with changing ocean conditions.

More than a decade of research on New Zealand blue whales

Ten years ago, Leigh first put forward a hypothesis that the STB region was an undocumented blue whale foraging ground based on multiple lines of evidence (Torres 2013). Despite pushback and numerous challenges, Leigh set out to prove her hypothesis through a comprehensive, multi-year data collection effort. I was lucky enough to join the team in 2016, first as a Masters’ student, and then as a PhD student. In the time since Leigh’s hypothesis, we not only documented the New Zealand blue whale population (Barlow et al. 2018), we learned a great deal about what drives blue whale feeding behavior (Torres et al. 2020) and habitat use patterns (Barlow et al. 2020, 2021), and developed forecast models to predict blue whale distribution for dynamic management of the STB (Barlow & Torres 2021). We also documented their unique, year-round presence in the STB, distinct from the migratory or vagrant presence of other blue whale populations (Barlow et al. 2022b). We now understand how marine heatwaves impact both feeding opportunities and reproductive effort (Barlow et al. 2023). We even analyzed blue whale skin condition (Barlow et al. 2019) and acoustic response to earthquakes (Barlow et al. 2022a) along the way. A decade later, it is humbling to reflect on how much we have learned about these whales. This paper is also the final chapter of my PhD, and as I reflect on how I have grown both personally and scientifically since I interviewed with Leigh as a wide-eyed undergraduate student in fall 2015, I am filled with gratitude for the opportunities for learning and growth that Leigh, these whales, and many mentors and collaborators have offered over the years. As is often the case in science, the more questions you ask, the more questions you end up with. We are already dreaming up future studies to further understand the ecology, health, and resilience of this blue whale population. I can only imagine what we might learn in another decade.

Figure 5. A blue whale mother and calf pair come up for air in the South Taranaki Bight. Photo by Dawn Barlow.

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References:

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

Barlow DR, Estrada Jorge M, Klinck H, Torres LG (2022a) Shaken, not stirred: blue whales show no acoustic response to earthquake events. R Soc Open Sci 9:220242.

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

Barlow DR, Klinck H, Ponirakis D, Garvey C, Torres LG (2021) Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep 11:1–10.

Barlow DR, Klinck H, Ponirakis D, Holt Colberg M, Torres LG (2022b) Temporal occurrence of three blue whale populations in New Zealand waters from passive acoustic monitoring. J Mammal.

Barlow DR, Pepper AL, Torres LG (2019) Skin deep: An assessment of New Zealand blue whale skin condition. Front Mar Sci 6:757.

Barlow DR, Torres LG (2021) Planning ahead: Dynamic models forecast blue whale distribution with applications for spatial management. J Appl Ecol 58:2493–2504.

Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.

Torres LG, Barlow DR, Chandler TE, Burnett JD (2020) Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ 8:e8906.

The many dimensions of a fat whale: Using drones to measure the body condition of baleen whales 

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

In my last blog, I discussed how to obtain morphological measurements from drone-based imagery of whales and the importance of calculating and considering uncertainty, as different drone platforms have varying levels of measurement uncertainty. But how does uncertainty scale and propagate when multiple measurements are combined, such as when measuring body condition of the whole animal? In this blog, I will discuss the different methods used for measuring body condition of baleen whales from drone-based imagery and how uncertainty differs between these metrics.

Body condition is defined as the energy stored in the body as a result of feeding and is assumed to indicate an animal’s overall health, as it reflects the balance between energy intake and investment toward growth, maintenance and reproduction (Peig and Green, 2009). Thus, body condition reflects the foraging success of an individual, as well as the potential for reproductive output and the quality of habitat. For example, female North American brown bears (Ursus arctos) in high quality habitats were in better body condition, produced larger litter sizes, and lived in greater population densities compared to females in lower quality habitats (Hilderbrand et al., 1999). As Dawn Barlow and Will Kennerley discussed in their recent blog, baleen whales are top predators and serve as ecosystem sentinels that shed light not only on the health of their population, but on the health of their ecosystem. As ocean climate conditions continue to change, monitoring the body condition of baleen whales is important to provide insight on how their population and ecosystem is responding. 

As discussed in a previous blog, drones serve as a valuable tool for obtaining morphological measurements of baleen whales to estimate their body condition. Images are imported into photogrammetry software, such as MorphoMetriX (Torres and Bierlich, 2020), to measure the total length of an individual and that is then divided into perpendicular width segments (i.e., in 5 or 10% increments) down the body (Fig. 1). These total length and width measurements are then used to estimate body condition in either 1-, 2-, or 3-dimensions: a single width (1D), a projected dorsal surface area (2D), or a body volume measure (3D). These 1D, 2D, and 3D measurements of body condition can then be standardized by total length to produce a relative measure of an individual’s body condition to compare among individuals and populations. 

Figure 1. An example of a Pacific Coast Feeding Group (PCFG) gray whale measured in MorphoMetriX (Torres & Bierlich, 2020).

While several different studies have used each of these dimensions to assess whale body condition, it is unclear how these measurements compare amongst each other. Importantly, it is also unclear how measurement uncertainty scales across these multiple dimensions and influences inference, which can lead to misinterpretation of data. For example, the surface area and volume of two geometrically similar bodies of different sizes are not related to their linear dimensions in the same ratio, but rather to the second and third power, respectively (i.e., x2 vs. x3).  Similarly, uncertainty should not be expected to scale linearly across 1D, 2D, and 3D body condition measurements. 

The second chapter of my dissertation, which was recently published in Frontiers in Marine Science and includes Clara Bird and Leigh Torres as co-authors, compared the uncertainty associated with 1D, 2D, and 3D drone-based body condition measurements in three baleen whale species with different ranges in body sizes: blue, humpback, and Antarctic minke whales (Figure 2) (Bierlich et al., 2021). We used the same Bayesian model discussed in my last blog, to incorporate uncertainty associated with each 1D, 2D, and 3D estimate of body condition. 

Figure 2. An example of total length and perpendicular width (in 5% increments of total length) measurements of an individual blue, humpback and Antarctic minke whale. Each image measured using MorphoMetriX (Torres and Bierlich, 2020). 

We found that uncertainty does not scale linearly across multi-dimensional measurements, with 2D and 3D uncertainty increasing by a factor of 1.45 and 1.76 compared to 1D, respectively. This result means that there is an added cost of increased uncertainty when utilizing a multidimensional body condition measurement. Our finding is important to help researchers decide which body condition measurement best suits their scientific question,  particularly when using a drone platform that is susceptible to greater error – as discussed in my previous blog. However, a 1D measurement only relies on a single width measurement, which may be excluding other regions of an individual’s body condition that is important for energy storage. In these situations, a 2D or 3D measure may be more appropriate.

We found that when comparing relative measures of body condition (standardized by total length of the individual), each standardized metric was highly correlated with one another. This finding suggests that 1D, 2D, and 3D metrics will draw similar relative predictions of body condition for individuals, allowing researchers to be confident they will draw similar conclusions relating to the body condition of individuals, regardless of which standardized metric they use. However, when comparing the precision of each of these metrics, the body area index (BAI) – a 2D standardized metric – displayed the highest level of precision. This result highlights how BAI can advantageously detect small changes in body condition, which is useful for comparing individuals or even tracking the same individual over time.

BAI was developed by the GEMM Lab (Burnett et al., 2018) and was designed to be similar to body mass index (BMI) in humans [BMI = mass (kg)/(height (m))2], where BAI uses the calculated surface area as a surrogate for body mass. In humans, a healthy BMI range is generally considered 18.5–24.9, below 18.5 is considered underweight, above 24.9 is considered overweight, and above 30 is considered obese (Flegal et al., 2012). Identifying a healthy range in BAI for baleen whales is challenged by a limited knowledge of what a “healthy” body condition range is for a whale. We found strong evidence that a healthy range of BAI is species-specific, as each species displayed a distinctive range in BAI: blue whales: 11–16; AMW: 17–24; humpback whales: 23–32; humpback whale calves: 23–28 (Fig. 3). These differences in BAI ranges likely reflect differences in the body shape of each species (Fig. 4). For example, humpbacks have the widest range of BAI compared to these other two species, which was also reflected in their larger variation in perpendicular widths (Figs. 2-4). Thus, it seems that BAI offers conditionally “scalefree” comparisons between species, yet it is unreasonable to set a single, all-whale BAI threshold to determine “healthy” versus “unhealthy” body condition.  Collecting a large sample of body condition measurements across many individuals and demographic units over space and time with information on vital rates (e.g., reproductive capacity) will help elucidate a healthy BAI range for each species.

Figure 3. Body area index (BAI) for each species. AMW = Antarctic minke whale.  Figure from Bierlich et al. (2021).
Figure 4. A) Absolute widths (m) and B) relative widths, standardized by total length (TL) to help elucidate the different body shapes of Antarctic minke whales (AMW; n = 40), blue whales (n = 32), humpback whales (n = 40), and humpback whale calves (n = 15). Note how the peak in body width occurs at a different percent body width between species, demonstrating the natural variation in body shape between baleen whales. Figure from Bierlich et al. (2021).

Over the past six years, the GEMM Lab has been collecting drone images of Pacific Coast Feeding Group (PCFG) gray whales off the coast of Oregon to measure their BAI (see GRANITE Project blog). Many of the individuals we encounter are seen across years and throughout the foraging season, providing an opportunity to evaluate how an individual’s BAI is influenced by environmental variation, stress levels, maturity, and reproduction. These data will in turn help determine what the healthy range in BAI for gray whales is. For example, linking BAI to pregnancy – whether a whale is currently pregnant or becomes pregnant the following season – will help determine what BAI is needed to support calf production. We are currently analyzing hundreds of body condition measurements from 2016 – 2021, so stay tuned for upcoming results!

References

Bierlich, K. C., Hewitt, J., Bird, C. N., Schick, R. S., Friedlaender, A., Torres, L. G., … & Johnston, D. W. (2021). Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Frontiers in Marine Science, 1729.

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2018). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science, 35(1), 108–139.

Flegal, K. M., Carroll, M. D., Kit, B. K., & Ogden, C. L. (2012). Prevalence of Obesity and Trends in the Distribution of Body Mass Index Among US Adults, 1999-2010. JAMA, 307(5), 491. https://doi.org/10.1001/jama.2012.39

Hilderbrand, G. V, Schwartz, C. C., Robbins, C. T., Jacoby, M. E., Hanley, T. A., Arthur, S. M., & Servheen, C. (1999). The importance of meat, particularly salmon, to body size, population productivity, and conservation of North American brown bears. Canadian Journal of Zoology, 77(1), 132–138.

Peig, J., & Green, A. J. (2009). New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method. Oikos, 118(12), 1883–1891.

Torres, W., & Bierlich, K. C. (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), 1825–1826.

Different blue whale populations sing different songs

By Dawn Barlow, PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

In human cultures, how you sound is often an indicator of where you are from. Have you ever taken a linguistics quiz that tries to guess what part of the United States you grew up in? Questions about whether you pronounce the sugary sweet treat caramel as “carr-mul” or “care-a-mel”, whether you say “soda” or “pop”, or whether a certain type of intersection is called a “roundabout”, “rotary”, or “traffic circle” are used to make a guess at where in the country you were raised. I have spent time in the United States, Australia, and New Zealand, I was amused to learn that the shoes you might wear in summertime can be called flip flops, slippers, thongs, or jandals, depending on which English-speaking country you are in. We know that listening to how someone speaks can tell us about their heritage or culture. As it turns out, the same is true for blue whales. We can learn a lot about blue whales by listening to them.

A blue whale comes up for air in the South Taranaki Bight, New Zealand. We catch only a short glimpse of these ocean giants when they are at the surface. By listening to their vocalizations using acoustic recordings, we can gain a whole new perspective on their lives. Photo by D. Barlow.

Sound is an incredibly important sense to marine mammals, particularly since sound waves can efficiently transmit over long distances in the ocean where other senses, such as vision or smell, are limited. Therefore, passive acoustic monitoring—placing hydrophones underwater to listen for an extended period of time and record the sounds of animals and their environment—is a highly effective tool for studying marine mammals, including blue whales. Throughout the world, blue whales sing. In this case, “song” is defined as a limited number of sound types that are produced in succession to form a recognizable pattern (McDonald et al. 2006). These songs are presumed to be produced by males only, most likely used to maintain associations and mediate social interactions, and seem to play a role in reproduction (Oleson et al. 2007, Lewis et al. 2018). Furthermore, these songs are highly stereotyped, and stable over decadal scales (McDonald et al. 2006).

Figure reproduced from McDonald et al. (2006), illustrating the variation and in blue whale songs from different geographic regions, and their stability over time: Recordings from New Zealand (A), the Central North Pacific (B), Australia (C), the Northeast Pacific (D) and North Indian Ocean (E) illustrate the stable character of the blue whale song over long time periods. All song types for which long time spans of recordings are available show some frequency drift through time, but only minor change in character. These examples were chosen because recordings over a significant time span were available to the authors in raw form, and not because these song types are more stable than the others.

Fascinatingly, blue whale songs have acoustic characteristics that are distinct between geographic regions. A blue whale in the northeast Pacific sings a different song than a blue whale in the north Atlantic; the song heard around Australia is distinct from the one sung off the coast of Chile, and so on. Therefore, differences in blue whale songs between areas can be used as a provisional hypothesis about population structure (McDonald et al. 2006, Samaran et al. 2013, Balcazar et al. 2015). Vocalizations may evolve more rapidly than traditional markers such as genetics or morphology that are often used to delineate populations, particularly in long-lived mammalian species such as blue whales (McDonald et al. 2006).

Figure reproduced from McDonald et al. (2006): Blue whale residence and population divisions suggested from their song types. Arrows indicate the direction of seasonal movements.

Despite the general rule of thumb that population-specific blue whale songs occur in separate geographic regions, there are examples throughout the southern hemisphere where songs from different populations overlap and are recorded in the same location (Samaran et al. 2010, 2013, Tripovich et al. 2015, McCauley et al. 2018, Buchan et al. 2020, Leroy et al. 2021). However, these examples may be instances where the populations temporally or ecologically partition their use of the area. For example, there may be differences in the timing of peak occurrence so that overlap is minimized by alternating which population is predominantly present in different seasons (Leroy et al. 2018). Alternatively, whales from different populations may overlap in space and time, but occupy different ecological niches at the same site. In this case, an area may simultaneously be a migratory corridor for one population and a foraging ground for another (Tripovich et al. 2015).

Figure reproduced from Leroy et al. (2021): Distribution of the five blue whale acoustic populations of the Indian Ocean: the Sri Lankan—NIO (yellow); Madagascan—SWIO (orange); Australian—SEIO (blue); and Arabian Sea—NWIO (red) pygmy blue whales; the hypothesized Chagos pygmy blue whale (green); and the Antarctic blue whale (black dashed line). These distributions have been inferred from the acoustic recordings conducted in the area. The long-term recording sites used to infer these distribution areas are indicated by red stars. Blue whale illustration by Alicia Guerrero.

In the South Taranaki Bight (STB) region of New Zealand, where the GEMM lab has been studying blue whales for the past decade (Torres 2013), the New Zealand song type is recorded year-round (Barlow et al. 2018). New Zealand blue whales rely on a productive upwelling system in the STB that supports an important foraging ground (Barlow et al. 2020, 2021). Antarctic blue whales also seasonally pass through New Zealand waters, likely along their migratory pathway between polar feeding grounds and lower latitude areas (Warren et al. 2021). What does it mean in terms of population connectivity or separation when two different populations occasionally share the same waters? How do these different populations ecologically partition the space they occupy? What drives their differing occurrence patterns? These are the sorts of questions I am diving into as we continue to explore the depths of our acoustic recordings from the STB region. We still have a lot to learn about these blue whales, and there is a lot to be learned through listening.

References:

Balcazar NE, Tripovich JS, Klinck H, Nieukirk SL, Mellinger DK, Dziak RP, Rogers TL (2015) Calls reveal population structure of blue whales across the Southeast Indian Ocean and the Southwest Pacific Ocean. J Mammal 96:1184–1193.

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

Barlow DR, Klinck H, Ponirakis D, Garvey C, Torres LG (2021) Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep 11:1–10.

Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.

Buchan SJ, Balcazar-Cabrera N, Stafford KM (2020) Seasonal acoustic presence of blue, fin, and minke whales off the Juan Fernández Archipelago, Chile (2007–2016). Mar Biodivers 50:1–10.

Leroy EC, Royer JY, Alling A, Maslen B, Rogers TL (2021) Multiple pygmy blue whale acoustic populations in the Indian Ocean: whale song identifies a possible new population. Sci Rep 11:8762.

Leroy EC, Samaran F, Stafford KM, Bonnel J, Royer JY (2018) Broad-scale study of the seasonal and geographic occurrence of blue and fin whales in the Southern Indian Ocean. Endanger Species Res 37:289–300.

Lewis LA, Calambokidis J, Stimpert AK, Fahlbusch J, Friedlaender AS, Mckenna MF, Mesnick SL, Oleson EM, Southall BL, Szesciorka AR, Širović A (2018) Context-dependent variability in blue whale acoustic behaviour. R Soc Open Sci 5.

McCauley RD, Gavrilov AN, Jolli CD, Ward R, Gill PC (2018) Pygmy blue and Antarctic blue whale presence , distribution and population parameters in southern Australia based on passive acoustics. Deep Res Part II 158:154–168.

McDonald MA, Mesnick SL, Hildebrand JA (2006) Biogeographic characterisation of blue whale song worldwide: using song to identify populations. J Cetacean Res Manag 8:55–65.

Oleson EM, Wiggins SM, Hildebrand JA (2007) Temporal separation of blue whale call types on a southern California feeding ground. Anim Behav 74:881–894.

Samaran F, Adam O, Guinet C (2010) Discovery of a mid-latitude sympatric area for two Southern Hemisphere blue whale subspecies. Endanger Species Res 12:157–165.

Samaran F, Stafford KM, Branch TA, Gedamke J, Royer J, Dziak RP, Guinet C (2013) Seasonal and Geographic Variation of Southern Blue Whale Subspecies in the Indian Ocean. PLoS One 8:e71561.

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.

Tripovich JS, Klinck H, Nieukirk SL, Adams T, Mellinger DK, Balcazar NE, Klinck K, Hall EJS, Rogers TL (2015) Temporal Segregation of the Australian and Antarctic Blue Whale Call Types (Balaenoptera musculus spp.). J Mammal 96:603–610.

Warren VE, Širović A, McPherson C, Goetz KT, Radford CA, Constantine R (2021) Passive Acoustic Monitoring Reveals Spatio-Temporal Distributions of Antarctic and Pygmy Blue Whales Around Central New Zealand. Front Mar Sci 7:1–14.

Supporting marine life conservation as an outsider: Blue whales and earthquakes

By Mateo Estrada Jorge, Oregon State University undergraduate student, GEMM Lab REU Intern

Introduction

My name is Mateo Estrada and this past summer I had the pleasure of working with Dawn Barlow and Dr. Leigh Torres as a National Science Foundation (NSF) Research Experience for Undergraduates (REU) intern. I had the chance to proactively learn about the scientific method in the marine sciences by studying the acoustic behaviors of pygmy blue whales (Balaenoptera musculus brevicauda) that are documented residents of the South Taranaki Bight region in New Zealand (Torres 2013, Barlow et al. 2018). I’ve been interested in conducting scientific research since I began my undergraduate education at Oregon State University in 2015. Having the opportunity to apply the skills I gained through my education in this REU has been a blessing. I’m a physics and computer science major, but more than anything I’m a scientist and my passion has taken me in new, unexpected directions that I’m going to share in this blog post. My message for any students who feel like they haven’t found their path yet is: hang in there, sometimes it takes time for things to take shape. That has been my experience and I’m sure it’s been the experience of many people interested in the sciences. I’m a Physics and Computer Science student, so why am I studying blue whales, and more specifically, how can I be doing marine science research having only taken intro to biology 101?

My background

I decided to apply for the REU in the Spring 2021 because it was a chance to use my programming skills in the marine sciences. I’m also passionate about conservation and protecting the environment in a pragmatic way, so I decided to find a niche where I could put my technical skills to good use. Finally, I wanted to explore a scientific field outside of my area of expertise to grow as a student and to learn from other researchers. I was mostly inspired by anecdotal tales of Physicist Richard Feynman who would venture out of the physics department at Caltech and into other departments to learn about what other scientists were investigating to inspire his own work. This summer, I ventured into the world of marine science, and what I found in my project was fascinating.

Whale watching tour

Figure 1. Me standing on a boat on the Pacific Ocean off Long Beach, CA.

To get into the research mode, I decided to go on a whale watching tour with the Aquarium of the Pacific. The tour was two hours long and the sunburn was worth it because we got to see four blue whales off the Long Beach coast in California. I got to see the famous blue whale blow and their splashes. It was the first time I was on a big boat in the ocean, so naturally I got seasick (Fig 1). But it was exciting to get a chance to see blue whales in action (luckily, I didn’t actually hurl). The marine biologist onboard also gave a quick lecture on the relative size of blue whales and some of their behaviors. She also pointed out that they don’t use Sonar to locate whales as this has been shown to disturb their calling behaviors. Instead, we looked for a blow and splashing. The tour was a wonderful experience and I’m glad I got to see some whales out in nature. This experience also served as a reminder of the beauty of marine life and the responsibility I feel for trying to understand and help conserving it.

Context of blue whale calling

Sound plays a significant role in the marine environment and is a critical mode of communication for many marine animals including baleen whales. Blue whales produce different vocalizations, otherwise known as calls.  Blue whale song is theorized to be produced by males of the species as a form of reproductive behavior, similar to how male peacocks engage females by displaying their elongated upper tail covert feathers in iridescent colors as a courtship mechanism. Then there are “D calls” that are associated with social mechanisms while foraging, and these calls are made by both female and male blue whales (Lewis et al. 2018) (Fig. 2).

Figure 2. Spectrogram of Pygmy blue whale D calls manually (and automatically) selected, frequency 0-150 Hz.

Understanding research on blue whales

The most difficult part about coming into a project as an outsider is catching up. I learned how anthropogenetic (human made) noise affects blue whale communication. For example, it has been showing that Mid Frequency Active Sonar signals employed by the U.S. Navy affect blue whale D calling patterns (Melcón 2012). Furthermore, noise from seismic airguns used for oil and gas exploration has also impact blue whale calling behavior (Di Lorio, 2010). Understanding the environmental context in which the pygmy blue whales live and the anthropogenic pressures they face is essential in marine conservation. Protecting the areas in which they live is important so they can feed, reproduce and thrive effectively. What began as a slowly falling snowflake at the start of a snowstorm turned into a cascading avalanche of knowledge pouring into my mind in just two weeks.

Figure 3. The white stars show the hydrophone locations (n = 5). A bathymetric scale of the depth is also given.

The research question I set out to tackle in my internship was: do blue whales change their calling behavior in response to natural noise events from earthquake activity? To do this, I used acoustic recordings from five hydrophones deployed in the South Taranaki Bight (Fig. 3), paired with an existing dataset of all recorded earthquakes in New Zealand (GeoNet). I identified known earthquakes in our acoustic recordings, and then examined the blue whale D calls during 4 hours before and after each earthquake event to look for any change in the number of calls, call energy, entropy, or bandwidth.

A great mentor and lab team

The days kept passing and blending into each other, as they often do with remote work. I began to feel isolated from the people I was working with and the blue whales I was studying. The zoom calls, group chats, and working alongside other remote interns kept me afloat as I adapted to a work world fully online. Nevertheless, I was happy to continue working on this project because I felt like I was slowly becoming part of the GEMM Lab. I would meet with my mentor Dawn Barlow at least once a week and we would spend time talking about the project and sorting out the difficult details of data processing. She always encouraged my curiosity to ask questions. Even if they were silly questions, she was happy to ponder them because she is a curious scientist like myself.

What we learned

Pygmy blue whales from the South Taranaki Bight region do not change their acoustic behavior in response to earthquake activity. The energy of the earthquake, magnitude, depth, and distance to the origin all had no influence on the number of blue whale D calls, the energy of their calling, the entropy, and the bandwidth. A likely reason for why the blue whales would have no acoustic response to earthquakes (magnitude < 5) is that the STB region is a seismically active region due to the nearby interface of the Australian and Pacific plates. Because of the plate tectonics, the region averages about 20,000 recorded earthquakes per year (GeoNet: Earthquake Statistics). Given that pygmy blue whales are present in the STB region year-round (Barlow et al. 2018), the blue whales may have adapted to tolerate the earthquake activity (Fig 4).

Figure 4. Earthquake signal from MARU (1, 2, 3, 4, 5) and blue whale D calls, Frequency 0-150 Hz.

Looking at the future

I presented my work at the end of my REU internship program, which was a difficult challenge for me because I am often intimidated by public speaking (who isn’t?). Communicating science has always been a big interest of me. I love reading news articles about new breakthroughs and being a small part of that is a huge privilege for me. Finding my own voice and having new insights to contribute to the scientific world has always been my main objective. Now I will get to deliver a poster presentation of my REU work at the Association for the Sciences of Limnology and Oceanography (ASLO) Conference in March 2022. I am both excited and nervous to take on this new adventure of meeting seasoned professionals, communicating my results, and learning about the ocean sciences. I hope to gain new inspirations for my future academic and professional work.

References:

About Earthquake Drums – GeoNet. geonet.Org. Retrieved June 23, 2021, from https://www.geonet.org.nz/about/earthquake/drums

Barlow, D. R., Torres, L. G., Hodge, K. B., Steel, D., Scott Baker, C., 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

Di Iorio, L., & Clark, C. W. (2010). Exposure to seismic survey alters blue whale acoustic communication. Biology Letters, 6(3), 334–335. https://doi.org/10.1098/rsbl.2009.0967

Lewis, L. A., Calambokidis, J., Stimpert, A. K., Fahlbusch, J., Friedlaender, A. S., McKenna, M. F., Mesnick, S. L., Oleson, E. M., Southall, B. L., Szesciorka, A. R., & Sirović, A. (2018). Context-dependent variability in blue whale acoustic behaviour. Royal Society Open Science, 5(8). https://doi.org/10.1098/rsos.180241

Melcón, M. L., Cummins, A. J., Kerosky, S. M., Roche, L. K., Wiggins, S. M., & Hildebrand, J. A. (2012). Blue whales respond to anthropogenic noise. PLoS ONE, 7(2), 1–6. https://doi.org/10.1371/journal.pone.0032681

Torres LG. 2013 Evidence for an unrecognised blue whale foraging ground in New Zealand. NZ J. Mar. Freshwater Res. 47, 235–248. (doi:10. 1080/00288330.2013.773919)

Rorquals of the California Current

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

About 10 months have passed since I started working on OPAL, a project that aims to identify the co-occurrence between whales and fishing effort in Oregon to reduce entanglement risk. During this period, you would be surprised to know how little ecology I have actually done and how much time has been devoted to data processing! I compiled several million GPS trackline positions, processed hundreds of marine mammal observations, wrote several thousand lines of R code, downloaded and extracted a couple Gb of environmental data… before finally reaching the modeling phase of the OPAL project. And with it, finally comes the time to look more closely at the ecology and behavior of my species of interest. While the previous steps of the project were pretty much devoid of ecological reasoning, the literature homework now comes in handy to guide my choices regarding habitat use models, such as  selecting environmental predictors of whale occurrence, deciding on what seasons should be modeled, and choosing the spatio-temporal scale at which the data should be aggregated.

Whale diversity on the US west coast

The productive waters off the US west coast host a great diversity of cetaceans. Eight species of baleen whales are reported to occur there by NOAA fisheries: blue whales, Bryde’s whales, fin whales, gray whales, humpback whales, minke whales, North Pacific right whales and sei whales. Among them, no less than five are listed as Endangered under the Endangered Species Act. Whether they are only passing by or spending months feeding in the region, the timing and location where these animals are observed varies greatly by species and by population.

During the 113 hours of aerial survey effort and 264 hours of boat-based search conducted for the OPAL project, 563 groups of baleen whales have been observed to-date (up to mid-May 2021 to be exact… more data coming soon!). Among the observations where animals could be identified to the species level, humpback whales are preponderant, as they represent about half of the whale groups observed (n = 293). Blue (n = 41) and gray whales (n = 46) come next, the latter being observed in more nearshore waters. Finally, a few fin whale groups were observed (n = 28). The other baleen whale species reported by NOAA in the US west coast species list were very rarely or not observed at all during OPAL surveys.

The OPAL aerial surveys conducted in partnership with the United States Coast Guard (USCG) were specifically designed to study whales occurring on the continental shelf along the coast of Oregon. Hence, most of this survey effort is located in waters from 800 m to 30 m deep, which may explain the relatively low number of gray whales detected. Indeed, gray whales observed in Oregon may either be migrating along the coast to and from their breeding grounds in Baja California, or be part of the small Pacific Coast Feeding Group that forage in Oregon nearshore and shallow waters during the summer. This group of whales is one the main GEMM lab’s research focus, being at the core of no less than three ongoing research projects: AMBER, GRANITE, and TOPAZ.

So today, let’s turn our eyes to the sea horizon and talk about some other members of the baleen whale community: rorquals. Conveniently, the three species of baleen whales (gray whales aside) most commonly observed during OPAL surveys are all part of the rorqual family, a.k.a Balaenopteridae: humpback whales, blue whales and fin whales (Figure 1). They are morphologically characterized by the pleated throat grooves that allow them to engulf large quantities of food and water, for instance when lunge-feeding. Known cases of hybridization between these three species demonstrate their close relatedness (Jefferson et al., 2021)⁠. They all have worldwide distributions and display unequally understood migratory behaviors, seasonally traveling between warm tropical breeding grounds and temperate-polar feeding grounds. They occur in great numbers in productive waters such as the upwelling system of the California Current.

The three accomplices

Figure 1: Aerial view of three rorquals species: a humpback whale (left), a fin whale (center), and a blue whale (right). Photo credit: Leigh Torres and Craig Hayslip. Photos taken off the Oregon coast under NOAA/NMFS permit during USCG helicopter flights conducted as part of the OPAL project

Humpback whales (Megaptera novaeangliae) are easily differentiated from other rorquals because of their long pectoral fins (up to one third of their body length!), which inspired their scientific name, Megaptera, « big-winged » (Figure 1). Individuals observed in Oregon mostly belong to a mix of two Distinct Population Segments (DPS): the threatened Mexico and endangered Central American DPS. Although humpback whales from different DPS do not show any morphological differences, they are genetically distinct because they have been mating separately in distinct breeding grounds for generations and generations. This genetic differentiation has great implications in terms of conservation since the Central American DPS is recovering at a lesser rate than the Mexican and is therefore subject to different management measures (recovery plan, monitoring plan, designated critical habitats). Humpback whales migrate and feed off the US west coast, with a peak in abundance in the mid to late summer. Compared to other rorquals that are found in the open ocean, humpback whales are mostly observed on the continental shelf (Becker et al., 2019)⁠. They are considered to have a relatively generalist diet, as they feed on a mix of krill (Euphausiids) and fishes (e.g. anchovy, sardines) and are capable of switching their feeding behavior depending on relative prey availability (Fleming, Clark, Calambokidis, & Barlow, 2016; Fossette et al., 2017)⁠.

Blue whales (Balaenoptera musculus) are the largest animals ever known (max length 33 m, Jefferson et al., 2008), and sadly the most at risk of global extinction among our three species of interest (listed as « endangered » in the IUCN red list). They have a distinctive mottled blue and light gray skin, a slender body and a broad U-shaped head (or as some say « like a gothic arch », Figure 1). Blue whales tend to be open ocean animals, but they regroup seasonally to feed in highly productive nearshore areas such as the Southern California Bight (Becker et al. 2019, Abrahms et al. 2019). Blue whales migrating or feeding along the US west coast belong to the Eastern North Pacific stock and are subject to great research and conservation efforts. Contrary to their other rorqual counterparts, blue whales are quite picky eaters, as they exclusively feed on krill. This difference in diet leads to resource partitioning facilitating rorqual coexistence in the California Current (Fossette et al., 2017)⁠. These differences in feeding strategies have important implications for designing predictive models of habitat use.

Fin whales (Balaenoptera physalus) are nicknamed « greyhounds of the sea » due to their exceptional swim speed (max 46 km/h). They are a little smaller than blue whales (max length 27 m, Jefferson, Webber, & Pitman, 2008)⁠ but share a similar sleek and streamlined shape. Their coloration is their most distinctive feature: the left lower jaw being mostly dark while the right is white. V-shaped light-gray « chevrons » color their back, behind the head (Figure 1). The California/Oregon/Washington is one of the three stocks recognized in the North Pacific (NOAA Fisheries, 2018)⁠. Within this region, there is genetic evidence for a geographic separation north and south of Point Conception, CA (Archer et al., 2013)⁠. Like other rorquals, they are migratory, but their seasonal distribution is relatively less well understood as they appear to spend a lot of time in open oceans. For instance, a meta-analysis for the North Pacific found little evidence for fin whales using distinct calving areas (Mizroch, Rice, Zwiefelhofer, Waite, & Perryman, 2009)⁠. In the California Current System, satellite tracking has provided great insights into their space-use patterns. In the Southern California Bight, fin whales show year-round residency and seasonal shifts in habitat use as they move further offshore and north during the spring/summer (Scales et al., 2017)⁠. The Northern California Current offshore waters appeared to be used during the summer months by the whales tagged in the Southern California Bight. Yet, fin whales are observed year-round in Oregon (NOAA Fisheries, 2018)⁠.

Towards predictive models of rorqual distribution

Enough observations have now been collected as part of the OPAL project to be able to model the habitat use of some of these rorqual species. Based on 12 topographic (i.e., depth, slope, distance to canyons) and physical variables (temperature, chlorophyll-a, water column stratification, etc.), I have made my first attempt at predicting seasonal distribution patterns of humpback whales and blue whales in Oregon. These models will be improved in the coming months, with more data pouring in and refined parametrizations, but they already bring insights into the shared habitat use patterns of these species, as well as their specificities.

Across multiple cross-validations of the species-specific models, sea surface temperature, sea surface height and depth were recurrently selected among the most important variables influencing both humpback and blue whale distributions. Predicted densities of blue whales were relatively higher at less than 40 fathoms compared to humpback whales, although both species’ hotspots were located outside this newly implemented seasonal fishing limit (Figure 2). Higher densities were generally predicted off Newport and Port Orford, and north of North Bend.

Figure 2: Predicted densities of humpback and blue whales during the month of September 2018, 2019, and 2020 in Oregon waters (OPAL project). Core areas of use (predicted densities in the top 25%) are represented, with darker shades of blue and orange showing higher predicted densities. Dashed lines represent the tracklines followed by USCG monthly aerial surveys. The black line represents the 40 fathom isobath. Grey boxes overlayed on predictions delineate the areas of extrapolation where environmental conditions are non-analogous to the conditions in which the models were trained. Disclaimer: these model outputs are preliminary and should be interpreted with caution.

Once our rorqual models are finalized, we will work with our partners at the Oregon Department of Fisheries and Wildlife to overlay predicted whale hotspots with areas of high crab pot densities. This overlap analysis will help us understand the times and places where co-occurrence of suitable whale habitat and fishing activities put whales at risk of entanglement.

References

Archer, F. I., Morin, P. A., Hancock-Hanser, B. L., Robertson, K. M., Leslie, M. S., Bérubé, M., … Taylor, B. L. (2013). Mitogenomic Phylogenetics of Fin Whales (Balaenoptera physalus spp.): Genetic Evidence for Revision of Subspecies. PLoS ONE, 8(5). https://doi.org/10.1371/journal.pone.0063396

Becker, E. A., Forney, K. A., Redfern, J. V, Barlow, J., Jacox, M. G., Roberts, J. J., & Palacios, D. M. (2019). Predicting cetacean abundance and distribution in a changing climate. Diversity and Distributions, 25(4), 626–643. https://doi.org/10.1111/ddi.12867

Fleming, A. H., Clark, C. T., Calambokidis, J., & Barlow, J. (2016). Humpback whale diets respond to variance in ocean climate and ecosystem conditions in the California Current. Global Change Biology, 22, 1214–1224. https://doi.org/10.1111/gcb.13171

Fossette, S., Abrahms, B., Hazen, E. L., Bograd, S. J., Zilliacus, K. M., Calambokidis, J., … Croll, D. A. (2017). Resource partitioning facilitates coexistence in sympatric cetaceans in the California Current. Ecology and Evolution, 7, 9085–9097. https://doi.org/10.1002/ece3.3409

Jefferson, T. A., Palacios, D. M., Clambokidis, J., Baker, S. C., Hayslip, C. E., Jones, P. A., … Schulman-Janiger, A. (2021). Sightings and Satellite Tracking of a Blue / Fin Whale Hybrid in its Wintering and Summering Ranges in the Eastern North Pacific. Advances in Oceanography & Marine Biology, 2(4), 1–9. https://doi.org/10.33552/AOMB.2021.02.000545

Jefferson, T. A., Webber, M. A., & Pitman, R. L. (2008). Marine Mammals of the World. A comprehensive guide to their identification. Elsevier, London, UK.

Mizroch, S. A., Rice, D. W., Zwiefelhofer, D., Waite, J., & Perryman, W. L. (2009). Distribution and movements of fin whales in the North Pacific Ocean. Mammal Review, 39(3), 193–227. https://doi.org/10.1111/j.1365-2907.2009.00147.x

NOAA Fisheries. (2018). Fin whale stock assessment report ( Balaenoptera physalus physalus ): California / Oregon / Washington Stock.

Scales, K. L., Schorr, G. S., Hazen, E. L., Bograd, S. J., Miller, P. I., Andrews, R. D., … Falcone, E. A. (2017). Should I stay or should I go? Modelling year-round habitat suitability and drivers of residency for fin whales in the California Current. Diversity and Distributions, 23(10), 1204–1215. https://doi.org/10.1111/ddi.12611

Into the Krillscape: A Remote Expedition in Research and Mentorship

By Rachel Kaplan, PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

What are the most unexpected things you’ve done on Zoom in the last year? Since the pandemic dramatically changed all our lives in 2020, I think we’ve all been surprised by the diversity of things we’ve done remotely. I’ve baked bagels with a friend in Finland, done oceanography labs from my kitchen, had dance parties with people across the country, and conducted an award ceremony for my family’s Thanksgiving scavenger hunt – all on Zoom. Over the last several months, I’ve also mentored an Undergraduate Research, Scholarship, & the Arts (URSA) Engage student, named Amanda. Although we haven’t met in person yet, we’ve been connecting over Zoom since October. 

Amanda is an Ocean Sciences student working with me and Dr. Kim Bernard (CEOAS) to conduct a literature review about the two species of krill found off the coast of Oregon. Thysanoessa spinifera and Euphausia pacifica are an important food source for many of the animals that live off our coast — including blue, humpback, and fin whales. I am trying to learn how krill distributions shape those of humpback and blue whales as part of project OPAL, as well as which oceanographic factors drive krill abundances and distributions.

Thysanoessa spinifera (source: Scripps Institute of Oceanography). 

We’re also interested in T. spinifera and E. pacifica for the crucial roles they serve in ecosystems, beyond providing dinner for whales. Krill do many things that are beneficial to ecosystems and people, termed “ecosystem services.” These include facilitating carbon drawdown from the surface ocean to the deep, supporting lucrative fisheries species like salmon, flatfish, and rockfish, and feeding seabirds like auklets and shearwaters. We want to understand more fully the niche that T. spinifera and E. pacifica each fill off the coast of Oregon, which will help us anticipate how these important animals can be impacted by forces such as global climate change and marine management efforts.

Trying to understand the ecosystem services fulfilled by krill is inherently interdisciplinary, which means we have to learn a lot of new things, making this project a lot of fun. The questions Amanda and I have pursued together have ranged from intensely specific, to surprisingly broad. How many calories do blue whales need to eat in a day? How many krill do salmon need to eat? How big are krill fecal pellets, and how fast do they sink?

Trying to answer these questions has basically amounted to a heroic scouring of the internet’s krillscape by Amanda. She has hunted down papers dating back to the 1960s, pulled together findings from every corner of the world, and pursued what she refers to as “treasure troves” of data. In the process, she has also revealed the holes that exist in the literature, and given us new questions. This is the basis of the scientific process: understanding the current state of knowledge, identifying gaps in that knowledge, and developing the questions and methods needed to fill those gaps.

Euphausia pacifica (source: University of Irvine California, Peter J. Bryant).

Filling in knowledge gaps about T. spinifera and E. pacifica can help us better understand these animals, the ecosystems where they live, and the whales and other animals that depend on them for prey. It’s exciting to know that we will have the opportunity to help fill some of these gaps, as both Amanda and I continue this research over the course of our degrees.

Being able to engage in remote research and mentorship has been really rewarding, and it has shown me how far we’ve all come over the last year. Learning how to work together remotely has been crucial as we have adjusted to the funny new normal of the pandemic. As much as I miss working with people in person, I’ve learned that there’s a lot of great connection to be found even in remote collaboration – I’ve loved meeting Amanda’s pets on Zoom, learning about her career goals, and seeing her incredibly artistic representations of the carbon cycle held up to the camera.

Even though most of our conversations take place on Zoom from our homes, this research still feels plugged into a bigger community. Amanda and I also join Kim’s bigger Zooplankton Ecology Lab meetings, which include two other graduate students and eight undergraduate students, all of whom are working on zooplankton ecology questions that span from the Arctic to the Antarctic. Even though we’ve never met in person, a supportive and curious community has developed among all of us, which I know will persist when we can move back to in-person research and mentorship.

New Zealand blue whale research in the time of COVID

By Grace Hancock, Undergraduate Student at Kalamazoo College MI, GEMM Lab Intern (June 2020 to present)

It feels safe to say that everyone’s plans for the summer of 2020 went through a roller coaster of changes due to the pandemic. Instead of the summer research or travel plans that many undergraduate students, including myself, expected, many of us found ourselves at home, quarantining, and unsure of what to do with our time. Although it was unexpected, all that extra time brought me serendipitously to the virtual doorstep of the GEMM Lab. A few zoom calls and many, many emails later I am now lucky to be a part of the New Zealand Blue Whale photo-ID team. Under Leigh’s and Dawn’s guidance, I picked up the photo identification project where they had left it and am helping to advance this project to its next stage.

The skin of a blue whale is covered by distinct markings similar to a unique fingerprint. Thus, these whales can have a variety of markings that we use to identify them, including mottled pigmentation, pock marks (often caused by cookie cutter sharks), blisters, and even holes in the dorsal fins and flukes.

Figure 1. Examples of skin conditions that help in matching demonstrated on a photo of NZBW052 on the 10/9/2015

True blue blog fans may remember that in 2016 Dawn began the very difficult work of creating a photo ID catalog of all the blue whales that the GEMM Lab had encountered during field work in the South Taranaki Bight in New Zealand. Since that post, the catalog has grown and become an incredibly useful tool. When I came to the lab, I received a hard drive containing all the work Dawn had done to-date with the catalog, as well as two years of photos from various whale watching trips in the Hauraki Gulf of New Zealand. The goal of my internship was to integrate these photos into the GEMM catalog Dawn had created and, hopefully, identify some matches of whales between the two datasets.  If there were any matches – and if I found no matches – we would gain information about whale movement patterns and abundance in New Zealand waters.

Before we could dive into this exciting matching work, there was lots of data organization to be done. Most of the photos I analyzed were provided by the Auckland Whale and Dolphin Safari (AWADS), an eco-tourism company that does regular whale watching trips in the Hauraki Gulf, off the North Island of New Zealand. The photos I worked with were taken by people with no connection to the lab and, because of this, were often filled with pictures of seals, birds, and whatever else caught the whale watcher’s eye. This dataset led to hours of sorting, renaming, and removing photos. Next, I evaluated each photo of a whale to determine photo-quality (focus, angle to the camera, lighting) and then I used the high-quality photos where markings are visible to begin the actual matching of the whales.

Figure 2. The fluke of NZBW013 taken on 2/2/2016 with examples of unique nicks and markings that could be used to match

Blue whales are inarguably massive organisms. For this reason, it can be hard to know what part of the whale you’re looking at. To match the photos to the catalog, I found the clearest pictures that included the whale’s dorsal fin. For each whale I tried to find a photo from the left side, the right side, and (if possible) an image of its fluke. I could then compare these photos to the ones organized in the catalog developed by Dawn.

The results from my matching work are not complete yet, but there are a few interesting tidbits that I can share with our readers today. From the photos submitted by AWADS, I was able to identify twenty-two unique individual whales. We are in the process of matching these whales to the catalog and, once this is done, we will know how many of these twenty-two are whales we have seen before and how many are new individuals. One of the most exciting matches I made so far is of a whale known in our catalog as individual NZBW072. Part of what made this whale so exciting was the fact that it is the calf of NZBW031 who was spotted eight times from 2010-2017, in the Hauraki Gulf, off Kaikoura, and in the South Taranaki Bight. As it turns out, NZBW072 took after her mother and has been spotted a shocking nine times from 2010 to 2019, all in the Hauraki Gulf region. Many of the whales in our catalog have only been spotted once, so encountering two whales with this kind of sighting track record that also happen to be related is like hitting the jackpot.

Figure 3. NZBW072 photographed on 11/8/2010 (top photo taken by Rochelle Constantine in the Hauraki Gulf) and on 10/3/2019 (bottom photo taken by the Auckland Whale and Dolphin Safari) with marks circled in red or yellow to highlight the matched features.

Once I finish comparing and matching the rest of these photos, the catalog will be substantially more up-to-date. But that is not where the work stops. More photos of blue whales in New Zealand are frequently being captured, either by whale watchers in the Hauraki Gulf, fellow researchers on the water, keen workers on oil and gas rigs, or the GEMM Lab. Furthermore, the GEMM Lab contributes these catalog photos to the International Whaling Commission (IWC) Southern Hemisphere Blue Whale Catalog, which compiles all photos of blue whales in the Southern Ocean and enables interesting and critical conservation questions to be addressed, like “How many blue whales are there in the Southern Ocean?” Once I complete the matching of these 22 individuals, I will upload and submit them to this IWC collaborative database on behalf of the GEMM Lab. This contribution will expand the global knowledge of these whales and motivates me to continue this important photo ID work. I am so excited to be a part of this effort, through which I have learned important skills like the basics of science communication (through writing this blog post) and attention to detail (from working very closely with the photos I was matching). I know both of these skills, and everything else I have learned from this process, will help me greatly as I begin my career in the next few years. I can tell big things will come from this catalog and I will forever be grateful for the chance I have had to contribute to it.

Inference, and the intersection of ecology and statistics

By Dawn Barlow, PhD student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Recently, I had the opportunity to attend the International Statistical Ecology Conference (ISEC), a biennial meeting of researchers at the interface of ecology and statistics. I am a marine ecologist, fascinated by the interactions between animals and the dynamic ocean environment they inhabit. If you had asked me five years ago whether I thought I would ever consider myself a statistician or a computer programmer, my answer would certainly have been “no”. Now, I find myself studying the ecology of blue whales in New Zealand using a variety of data streams and methodologies, but a central theme for my dissertation is species distribution modeling. Species distribution models (SDMs) are mathematical algorithms that correlate observations of a species with environmental conditions at their observed locations to gain ecological insight and predict spatial distributions of the species (Fig. 1; Elith and Leathwick 2009). I still can’t say I would identify as a statistician, but I have a growing appreciation for the role of statistics to gain inference in ecology.

Figure 1. A schematic of a species distribution model (SDM) illustrating how the relationship between mapped species and environmental data (left) is compared to describe “environmental space” (center), and then map predictions from a model using only environmental predictors (right). Note that inter-site distances in geographic space might be quite different from those in environmental space—a and c are close geographically, but not environmentally. The patterning in the predictions reflects the spatial autocorrelation of the environmental predictors. Figure reproduced from Elith and Leathwick (2009).

Before I continue, let’s take a look at just a few definitions from Merriam-Webster’s dictionary:

Statistics: a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data

Ecology: a branch of science concerned with the interrelationship of organisms and their environments

Inference: a conclusion or opinion that is formed because of known facts or evidence

Ecological data are notoriously noisy, messy, and complex. Statistical tests are meant to help us understand whether a pattern in the data is different from what we would expect through random chance. When we study how organisms interact with one another and their environment, it is impossible to completely capture all elements of the ecosystem. Therefore, ecology is a field ripe with challenges for statisticians. How do we quantify a meaningful biological signal amidst all the noise? How can we gain inference from ecological data to enhance knowledge, and how can we use that knowledge to make informed predictions? Marine mammals are notoriously difficult to study. They inhabit an environment that is relatively inaccessible and inhospitable to humans, they occur in low numbers, they are highly mobile, and they are rarely visible. All ecological data are difficult and noisy and riddled with small sample sizes, but counting trees presents fewer logistical challenges than counting moving whales in an ever-changing open-ocean setting. Therefore, new methodologies in areas like species distribution modeling are often developed using large, terrestrial datasets and eventually migrate to applications in the marine environment (Robinson et al. 2011).

Many presentations I attended at the conference were geared toward moving beyond correlative SDMs. SDMs were developed to correlate species occurrence patterns with features of the environment they inhabit (e.g. temperature, precipitation, terrain, etc.). However, those relationships do not actually explain the underlying mechanism of why a species is more likely to occur in one environment compared to another. Therefore, ecological statisticians are now using additional information and modeling approaches within SDMs to incorporate information such as species co-occurrence patterns, population demographic information, and physiological constraints. Building SDMs to include such process-explicit information allows us to make steps toward understanding not just when and where a species occurs, but why.

Machine learning is an area that continues to advance and open doors to new applications in ecology. Machine learning approaches differ fundamentally from classical statistics. In statistics, we formulate a hypothesis, select the appropriate model to test that hypothesis (for example, linear regression), then test how well the data fit the model (“Is the relationship linear?”), and test the strength of that inference (“Is the linear pattern different from what we would expect due to random chance?”). Machine learning, on the other hand, does not use a predetermined notion of relationships between variables. Rather, it tries to create an algorithm that fits the patterns in the data. Statistics asks how well the data fit a model, and machine learning asks how well a model fits the data.

Machine learning approaches allow for very complex relationships to be included in models and can be excellent for making predictions. However, sometimes the relationships fitted by a machine learning algorithm are so complex that it is not possible to infer any ecological meaning from them. As one ISEC presenter put it, in machine learning “the computer learns but the scientist does not”. The most important thing when selecting your methodology is to remember your question and your goal. Do you want to understand the mechanism of why an animal is where it is? Or do you not need to understand the driver, but rather want to make the best predictions of where an animal will be? In my case, the answer to that question differs from one of my PhD chapters to the next. We want to understand the functional relationships between oceanography, krill availability, and blue whale distribution (Barlow et al. 2020), and subsequently we want to develop forecasting models that can reliably predict blue whale distribution to inform conservation efforts (Fig. 2).

Figure 2. An example predictive map of where we expect blue whales to be distributed based on environmental conditions. Warmer colors represent areas with a higher probability of blue whale occurrence, and the blue crosses represent locations where blue whales were observed.

ISEC was an excellent opportunity for me to break out of my usual marine mammal-centered bubble and get a taste of what is happening on the leading edge of statistical ecology. I learned about the latest approaches and innovations in species distribution modeling, and in the process I also learned about trees, koalas, birds, and many other organisms from around the world. A fun bonus of attending a methods-focused conference is learning about completely new study species and systems. There are many ways of approaching an ecological question, gaining inference, and making predictions. I look forward to incorporating the knowledge I gained through ISEC into my own research, both in my doctoral work and in applications of new methods to future research projects.

Figure 3. The virtual conference photo of all who attended the biennial International Statistical Ecology Conference. Thank you to the organizers, who made it a truly excellent and engaging conference experience!

References

Barlow, D.R., Bernard, K.S., Escobar-Flores, P., Palacios, D.M., and Torres, L.G. 2020. Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar. Ecol. Prog. Ser. doi:https://doi.org/10.3354/meps13339.

Elith, J., and Leathwick, J.R. 2009. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40(1): 677–697. doi:10.1146/annurev.ecolsys.110308.120159.

Robinson, L.M., Elith, J., Hobday, A.J., Pearson, R.G., Kendall, B.E., Possingham, H.P., and Richardson, A.J. 2011. Pushing the limits in marine species distribution modelling: Lessons from the land present challenges and opportunities. doi:10.1111/j.1466-8238.2010.00636.x.

It all starts with the wind: The importance of upwelling

By Dawn Barlow, PhD student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

The focus of my PhD research is on the ecology and distribution of blue whales in New Zealand. However, it has been a long time since I’ve seen a blue whale, and much of my time recently has been spent thinking about wind. What does wind matter to a blue whale? It actually matters a whole lot, because the wind drives an important biological process in many coastal oceans called upwelling. Wind blowing along shore, paired with the rotation of the earth, leads to a net movement of surface waters offshore (Fig. 1). As the surface water is pushed away, it is replaced by cold, nutrient-rich water from much deeper. When those nutrients become exposed to sunlight, they provide sustenance for the little planktonic lifeforms in the ocean, which in turn provide food for much larger predators including marine mammals such as blue whales. This “wind-to-whales” trophic pathway was coined by Croll et al. (2005), who demonstrated that off the West Coast of the United States, aggregations of whales could be expected downstream of upwelling centers, in concert with high productivity and abundant krill prey.

Figure 1. Graphic of the upwelling process, illustrating that when the wind blows along shore, surface waters are replaced by deeper water that is cold and nutrient rich. Source: NOAA
Figure 2. Map of New Zealand, with the South Taranaki Bight region (STB) denoted by the black box.

Much of what is understood today about upwelling comes from decades of research on the California Current ecosystem off the West Coast of the United States. Yet, the focus of my research is on an upwelling system on the other side of the world, in the South Taranaki Bight region (STB) of New Zealand (Fig. 2). In the case of the STB, westerly winds over Kahurangi Shoals lead to decreased sea level nearshore, forcing cold, nutrient rich waters to rise to the surface. The wind, along with the persistence of the Westland Current, then pushes a cold and productive plume of upwelled waters around Cape Farewell and into the STB (Fig. 3; Shirtcliffe et al. 1990).

Figure 3. Satellite image of the cold water plume in the South Taranaki Bight, indicative of upwelling. The origin of the upwelling at Kahurangi Shoals, Cape Farewell, and the typical path of the upwelling plume are denoted.

Through research conducted by the GEMM Lab over the years, we have demonstrated that blue whales utilize the STB region for foraging (Torres 2013, Barlow et al. 2018). Recent research on the oceanography of the STB region has further illuminated the mechanisms of this upwelling system, including the path and persistence of the upwelling plume in the STB across years and seasons (Chiswell et al. 2017, Stevens et al. 2019). However, the wind-to-whales pathway has not yet been described for this part of the world, and that is where the next section of my PhD research comes in. The whole system does not respond instantaneously to wind; the pathway from wind to whales takes time. But how much time is required for each step? How long after a strong wind event can we expect aggregations of feeding blue whales? These are some of the questions I am trying to tackle. For example, we hypothesize that some of the mechanisms and their respective lag times can be sketched out as follows:

Figure 4. The wind-to-whales trophic pathway, and hypothesized lags between steps.

All of these questions involve integrating oceanography, satellite imagery, wind data, and lag times, leading me to delve into many different analytical approaches including time series analysis and predictive modeling. If we are able to understand the lag times along this series of events leading to blue whale feeding opportunities, then we may be able to forecast blue whale occurrence in the STB based on the current wind and upwelling conditions. Forecasting with some amount of lead time could be a very powerful management tool, allowing for protection measures that are dynamic in space and time and therefore more effective in conserving this blue whale population and balancing human impacts.

Figure 5. A blue whale lunges on a patch of krill. The end of the wind-to-whales pathway. Drone piloted by Todd Chandler.

References:

Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.

Chiswell SM, Zeldis JR, Hadfield MG, Pinkerton MH (2017) Wind-driven upwelling and surface chlorophyll blooms in Greater Cook Strait. New Zeal J Mar Freshw Res.

Croll DA, Marinovic B, Benson S, Chavez FP, Black N, Ternullo R, Tershy BR (2005) From wind to whales: Trophic links in a coastal upwelling system. Mar Ecol Prog Ser 289:117–130.

Shirtcliffe TGL, Moore MI, Cole AG, Viner AB, Baldwin R, Chapman B (1990) Dynamics of the Cape Farewell upwelling plume, New Zealand. New Zeal J Mar Freshw Res 24:555–568.

Stevens CL, O’Callaghan JM, Chiswell SM, Hadfield MG (2019) Physical oceanography of New Zealand/Aotearoa shelf seas–a review. New Zeal J Mar Freshw Res.

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.

Detecting blue whales from acoustic data

By Dawn Barlow, PhD student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

In January of 2016, five underwater recording units were dropped to the seafloor in New Zealand to listen for blue whales (Fig. 1). These hydrophones sat listening for two years, brought to the surface only briefly every six months to swap out batteries and offload the data. Through all seasons and conditions when scientists couldn’t be on the water, they recorded the soundscape, generating a wealth of acoustic data with the potential to greatly expand our knowledge of blue whale ecology. 

Figure 1. Locations of the five Marine Autonomous Recording Units (MARUs) in the South Taranaki Bight region of New Zealand.

We have established that blue whales are present in New Zealand waters year-round 1. However, many questions remain regarding their distribution across daily, seasonal, and yearly scales. Our two-year acoustic dataset from five hydrophones throughout the STB region is a goldmine of information on blue whale occurrence patterns and the soundscape they inhabit. Having year-round occurrence data will allow us to examine what environmental and anthropogenic factors may influence blue whale distribution patterns. The hydrophones were listening for whales around the clock, every day, while we were on the other side of the world awaiting the recovery of the data to answer our questions.

Before any questions of seasonal distribution or anthropogenic impacts and noise can be addressed, however, we need to know something far more basic: when and where did we record blue whale vocalizations? This may seem like a simple, stepping-stone question, but it is actually quite involved, and the reason I spent the last month working with a team of acousticians at Cornell University’s Center for Conservation Bioacoustics. The expert research group here at Cornell, led by Dr. Holger Klinck, have been instrumental in our New Zealand blue whale research, including developing and building the recording units, hydrophone deployment and recovery, data processing, analysis, and advice. I am thrilled to work with all of them, and had an incredibly productive month of learning about acoustics from the best.

Blue whales produce multiple vocalizations that we are interested in documenting. The New Zealand song (Fig. 2A) is highly stereotyped and unique to the Southwest Pacific Ocean 2,3. Low-frequency downsweeps, or “D calls” (Fig. 2B), are far more variable and produced by blue whale populations around the world 4. Furthermore, Antarctic blue whales produce a highly-stereotyped “Z call” (Fig. 2C) and are known to be present in New Zealand waters occasionally 5.

Figure 2. Spectrograms of (A) the New Zealand blue whale song, (B), D calls, and (C) Antarctic Z calls.

One way to determine when blue whales were vocalizing is for an analyst to manually review the entirety of the two years of sound recordings for each of the five hydrophones by hand to scan for and select individual vocalizations. An alternative approach is to develop a detector algorithm to locate calls in the data based on their stereotypical characteristics. Over the past month I built, tested, and ran detectors for each blue whale call type using what is called a data template detector. This technique uses example signals from the data that the analyst selects as templates. The templates should be clear signals, and representative of the variation in calls contained in the dataset. Then, by comparing pixel characteristics between the template spectrograms and the spectrogram of the recording of interest using certain matching criteria (e.g. threshold for spectrogram correlation, detection frequency range), the algorithm searches for other signals like the templates in the full dataset. For example, in Fig. 3 you can see units of blue whale song I selected as templates for my detector.

Figure 3. Spectrogram of selected sound clips of New Zealand blue whale song, with units used as templates for a detector shown inside the teal boxes.

Testing the performance of a detector algorithm is critical. Therefore, a dataset is needed where calls were identified by an analyst and then used as the “ground truth”, to which the detector results are compared. For my ground truth dataset, I took a subset of 52 days and hand-browsed the spectrograms to identify and log New Zealand blue whale song, D calls, and Antarctic Z calls. In evaluating detector performance, there are three important metrics that need to be weighed: precision (the proportion of detections that are true), recall (the proportion of true calls identified by the detector), and false alarm rate (the number of false positive detections per hour). Ideally, the detector should be optimized to maximize precision and recall and minimize the false positives.

The STB region is highly industrial, and our two-year acoustic dataset contains periods of pervasive seismic airgun noise from oil and gas exploration. Ideally, a detector would be able to identify blue whale vocalizations even in the presence of airgun operations that dominate the soundscape for months. For blue whale song, the detector did quite well! With a precision of 0.91 and recall of 0.93, the detector could pick out song units over airgun noise (Fig. 4). A false alarm rate of 8 false positives per hour is a sacrifice worth making to identify song during seismic operations (and the false positives will be removed in a subsequent step). For D calls, seismic survey activity presented a different challenge. While the detector did well at identifying D calls during airgun operation, the first several detector attempts also logged every single airgun blast as a blue whale vocalization—clearly problematic. Through an iterative process of selecting template signals, and adjusting the number of templates used and the correlation threshold, I was able to come up with a detector which selected D calls and missed most airgun blasts. This success felt like a victory.

Figure 4. An example of spectrograms of simultaneous recordings from the five hydrophones illustrating seismic airgun noise (strong broadband signals that appear as repetitive black, vertical lines) overlapping New Zealand blue whale song. The red boxes are detection events selected by the detector, demonstrating its ability to capture song even during airgun operation.

After this detector development and validation process, I ran each detector on the full two-year acoustic dataset for all five recording units. This step was a good exercise in patience as I eagerly awaited the outputs for the many hours they took to run. The next step in the process will be for me to go through and validate each detector event to eliminate any false positives. However, running the detectors on the full dataset has allowed for exciting preliminary examinations of seasonal blue whale acoustic patterns, which need to be refined and expanded upon as the analysis continues. For example, sometimes the New Zealand song dominates the recordings on all hydrophones (Fig. 5), whereas other times of year song is less common. Similarly, there appear to be seasonal patterns in D calls and Antarctic Z calls, with peaks and dips in detections during different times of year.

Figure 5. An example spectrogram of simultaneous recordings from all five hydrophones during a time when New Zealand blue whale song dominated the recordings, with numerous, overlapping calls.

As with many things, the more questions you ask, the more questions you come up with. From preliminary explorations of the acoustic data my head is buzzing with ideas for further analysis and with new questions I hadn’t thought to ask of the data before. My curiosity has been fueled by scrolling through spectrograms, looking, and listening, and I am as excited as ever to continue researching blue whale ecology. I would like to thank the team at the Center for Conservation Bioacoustics for their support and guidance over the past month, and I look forward to digging deeper into the stories being told in the acoustic data!

Figure 6. A pair of blue whales observed in February 2017 in the South Taranaki Bight. Photo: L. Torres.

References

1.          Barlow, D. R. et al. Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res. 36, 27–40 (2018).

2.          McDonald, M. A., Mesnick, S. L. & Hildebrand, J. A. Biogeographic characterisation of blue whale song worldwide: using song to identify populations. J. Cetacean Res. Manag. 8, 55–65 (2006).

3.          Balcazar, N. E. et al. Calls reveal population structure of blue whales across the Southeast Indian Ocean and the Southwest Pacific Ocean. J. Mammal. 96, 1184–1193 (2015).

4.          Oleson, E. M. et al. Behavioral context of call production by eastern North Pacific blue whales. Mar. Ecol. Prog. Ser. 330, 269–284 (2007).

5.          McDonald, M. A. An acoustic survey of baleen whales off Great Barrier Island, New Zealand. New Zeal. J. Mar. Freshw. Res. 40, 519–529 (2006).