The pathway to advancing knowledge of rorqual whale distribution off Oregon

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

In September 2020, I was hired as a postdoc in the GEMM Lab and was tasked to conduct the analyses necessary for the OPAL project. This research project has the ambitious, yet essential, goal to fill a knowledge gap hindering whale conservation efforts locally: where and when do whales occur off the Oregon coast? Understanding and predicting whale distribution based on changing environmental conditions is a key strategy to assess and reduce spatial conflicts with human activities, specifically the risk of entanglement in fixed fishing gear.

Starting a new project is always a little daunting. Learning about a new region and new species, in an alien research and conservation context, is a challenge. As I have specialized in data science over the last couple of years, I have been confronted many times with the prospect of working with massive datasets collected by others, from which I was asked to tease apart the biases and the ecological patterns. In fact, I have come to love that part of my job: diving down the data rabbit hole and making my way through it by collaborating with others. Craig Hayslip, faculty research assistant in MMI, was the observer who conducted the majority of the 102 helicopter surveys that were used for this study. During the analysis stage, his help was crucial to understand the data that had been collected and get a better grasp of the field work biases that I would later have to account for in my models. Similarly, it took hours of zoom discussions with Dawn Barlow, the GEMM lab’s latest Dr, to be able to clean and process the 75 days of survey effort conducted at sea, aboard the R/V Shimada and Oceanus.

Once the data is “clean”, then comes the time for modeling. Running hundreds of models, with different statistical approaches, different environmental predictors, different parameters etc. etc. That is when you realize what a blessing it is to work with a supervisor like Leigh Torres, head of the GEMM Lab. As an early career researcher, I really appreciate working with people who help me take a step back and see the bigger picture within which the whole data wrangling work is included. It is so important to have someone help you stay focused on your goals and the ecological questions you are trying to answer, as these may easily get pushed back to the background during the data analysis process.

And here we are today, with the first scientific publication from the OPAL project published, a little more than three years after Leigh and Craig started collecting data onboard the United States Coast Guard helicopters off the coast of Oregon in February 2019. Entitled “Seasonal, annual, and decadal distribution of three rorqual whale species relative to dynamic ocean conditions off Oregon, USA”, our study published in Frontiers in Marine Science presents modern and fine-scale predictions of rorqual whale distribution off Oregon, as well as a description of their phenology and a comparison to whale numbers observed across three decades in the region (Figure 1). This research focuses on three rorqual species sharing some ecological and biological traits, as well as similar conservation status: humpback whales (Megaptera novaeangliae), blue whales (Balaenoptera musculus musculus), and fin whales (Balaenoptera physallus); all of which migrate and feed over the US West coast (see a previous blog to learn more about these species here).

Figure 1: Graphical abstract of our latest paper published in Frontiers in Marine Science.

We demonstrate (1) an increase in rorqual numbers over the last three decades in Oregon waters, (2) differences in timing of migration and habitat preferences between humpback, blue, and fin whales, and (3) predictable relationships of rorqual whale distribution based on dynamic ocean conditions indicative of upwellings and frontal zones. Indeed, these ocean conditions are likely to provide suitable biological conditions triggering increased prey abundance. Three seasonal models covering the months of December-March (winter model), April-July (spring) and August-November (summer-fall) were generated to predict rorqual whale densities over the Oregon continental shelf (in waters up to 1,500 m deep). As a result, maps of whale densities can be produced on a weekly basis at a resolution of 5 km, which is a scale that will facilitate targeted management of human activities in Oregon. In addition, species-specific models were also produced over the period of high occurrence in the region;  that is humpback and blue whales between April and November, and fin whales between August and March. 

As we outline in our concluding remarks, this work is not to be considered an end-point, but rather a stepping stone to improve ecological knowledge and produce operational outputs that can be used effectively by managers and stakeholders to prevent spatial conflict between whales and human activities. As of today, the models of fin and blue whale densities are limited by the small number of observations of these two species over the Oregon continental shelf. Yet, we hope that continued data collection via fruitful research partnerships will allow us to improve the robustness of these species-specific predictions in the future. On the other hand, the rorqual models are considered sufficiently robust to continue into the next phase of the OPAL project that aims to assess overlap between whale distribution and Dungeness crab fishing gear to estimate entanglement risk. 

The curse (or perhaps the beauty?) of species distribution modeling is that it never ends. There are always new data to be added, new statistical approaches to be tested, and new predictions to be made. The OPAL models are no exception to this rule. They are meant to be improved in future years, thanks to continued helicopter and ship-based survey efforts, and to the addition of new environmental variables meant to better predict whale habitat selection. For instance, Rachel Kaplan’s PhD research specifically aims at understanding the distribution of whales in relation to krill. Her results will feed into the more general efforts to model and predict whale distribution to inform management in Oregon.

This first publication therefore paves the way for more exciting and impactful research!

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Derville, S., Barlow, D. R., Hayslip, C. E., and Torres, L. G. (2022). Seasonal, Annual, and Decadal Distribution of Three Rorqual Whale Species Relative to Dynamic Ocean Conditions Off Oregon, USA. Front. Mar. Sci. 9, 1–19. doi:10.3389/fmars.2022.868566.


We gratefully acknowledge the immense contribution of the United State Coast Guard sectors North Bend and Columbia River who facilitated and piloted our helicopter surveys. We would like to also thank NOAA Northwest Fisheries Science Center for the ship time aboard the R/V Bell M. Shimada. We thank the R/V Bell M. Shimada (chief scientists J. Fisher and S. Zeman) and R/V Oceanus crews, as well as the marine mammal observers F. Sullivan, C. Bird and R. Kaplan. We give special recognition and thanks to the late Alexa Kownacki who contributed so much in the field and to our lives. We also thank T. Buell and K. Corbett (ODFW) for their partnership over the OPAL project. We thank G. Green and J. Brueggeman (Minerals Management Service), J. Adams (US Geological Survey), J. Jahncke (Point blue Conservation), S. Benson (NOAA-South West Fisheries Science Center), and L. Ballance (Oregon State University) for sharing validation data. We thank J. Calambokidis (Cascadia Research Collective) for sharing validation data and for logistical support of the project. We thank A. Virgili for sharing advice and custom codes to produce detection functions.

New publication by GEMM Lab reveals sub-population health differences in gray whales 

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

In a previous blog, I discussed the importance of incorporating measurement uncertainty in drone-based photogrammetry, as drones with different sensors, focal length lenses, and altimeters will have varying levels of measurement accuracy. In my last blog, I discussed how to incorporate photogrammetric uncertainty when combining multiple measurements to estimate body condition of baleen whales. In this blog, I will highlight our recent publication in Frontiers in Marine Science ( led by GEMM Lab’s Dr. Leigh TorresClara Bird, and myself that used these methods in a collaborative study using imagery from four different drones to compare gray whale body condition on their breeding and feeding grounds (Torres et al., 2022).

Most Eastern North Pacific (ENP) gray whales migrate to their summer foraging grounds in Alaska and the Arctic, where they target benthic amphipods as prey. A subgroup of gray whales (~230 individuals) called the Pacific Coast Feeding Group (PCFG), instead truncates their migration and forages along the coastal habitats between Northern California and British Columbia, Canada (Fig. 1). Evidence from a recent study lead by GEMM Lab’s Lisa Hildebrand (see this blog) found that the caloric content of prey in the PCFG range is of equal or higher value than the main amphipod prey in the Arctic/sub-Arctic regions (Hildebrand et al., 2021). This implies that greater prey density and/or lower energetic costs of foraging in the Arctic/sub-Arctic may explain the greater number of whales foraging in that region compared to the PCFG range. Both groups of gray whales spend the winter months on their breeding and calving grounds in Baja California, Mexico. 

Figure 1. The GEMM Lab field team following a Pacific Coast Feeding Group (PCFG) gray whale swimming in a kelp bed along the Oregon Coast during the summer foraging season. 

In January 2019 an Unusual Mortality Event (UME) was declared for gray whales due to the elevated numbers of stranded gray whales between Mexico and the Arctic regions of Alaska. Most of the stranded whales were emaciated, indicating that reduced nutrition and starvation may have been the causal factor of death. It is estimated that the population dropped from ~27,000 individuals in 2016 to ~21,000 in 2020 (Stewart & Weller, 2021).

During this UME period, between 2017-2019, the GEMM Lab was using drones to monitor the body condition of PCFG gray whales on their Oregon coastal feeding grounds (Fig. 1), while Christiansen and colleagues (2020) was using drones to monitor gray whales on their breeding grounds in San Ignacio Lagoon (SIL) in Baja California, Mexico. We teamed up with Christiansen and colleagues to compare the body condition of gray whales in these two different areas leading up to the UME. Comparing the body condition between these two populations could help inform which population was most effected by the UME.

The combined datasets consisted of four different drones used, thus different levels of photogrammetric uncertainty to consider. The GEMM Lab collected data using a DJI Phantom 3 Pro, DJI Phantom 4, and DJI Phantom 4 Pro, while Christiansen et al., (2020) used a DJI Inspire 1 Pro. By using the methodological approach described in my previous blog (here, also see Bierlich et al., 2021a for more details), we quantified photogrammetric uncertainty specific to each drone, allowing cross-comparison between these datasets. We also used Body Area Index (BAI), which is a standardized relative measure of body condition developed by the GEMM Lab (Burnett et al., 2018) that has low uncertainty with high precision, making it easier to detect smaller changes between individuals (see blog here, Bierlich et al., 2021b). 

While both PCFG and ENP gray whales visit San Ignacio Lagoon in the winter, we assume that the photogrammetry data collected in the lagoon is mostly of ENP whales based on their considerably higher population abundance. We also assume that gray whales incur low energetic cost during migration, as gray whale oxygen consumption rates and derived metabolic rates are much lower during migration than on foraging grounds (Sumich, 1983). 

Interestingly, we found that gray whale body condition on their wintering grounds in San Ignacio Lagoon deteriorated across the study years leading up to the UME (2017-2019), while the body condition of PCFG whales on their foraging grounds in Oregon concurrently increased. These contrasting trajectories in body condition between ENP and PCFG whales implies that dynamic oceanographic processes may be contributing to temporal variability of prey available in the Arctic/sub-Arctic and PCFG range. In other words, environmental conditions that control prey availability for gray whales are different in the two areas. For the ENP population, this declining nutritive gain may be associated with environmental changes in the Arctic/sub-Arctic region that impacted the predictability and availability of prey. For the PCFG population, the increase in body condition across years may reflect recovery of the NE Pacific Ocean from the marine heatwave event in 2014-2016 (referred to as “The Blob”) that resulted with a period of low prey availability. These findings also indicate that the ENP population was primarily impacted in the die-off from the UME. 

Surprisingly, the body condition of PCFG gray whales in Oregon was regularly and significantly lower than whales in San Ignacio Lagoon (Fig. 2). To further investigate this potential intrinsic difference in body condition between PCFG and ENP whales, we compared opportunistic photographs of gray whales feeding in the Northeastern Chukchi Sea (NCS) in the Arctic collected from airplane surveys. We found that the body condition of PCFG gray whales was significantly lower than whales in the NCS, further supporting our finding that PCFG whales overall have lower body condition than ENP whales that feed in the Arctic (Fig. 3). 

Figure 2. Boxplots showing the distribution of Body Area Index (BAI) values for gray whales imaged by drones in San Ignacio Lagoon (SIL), Mexico and Oregon, USA. The data is grouped by phenology group: End of summer feeding season (departure Oregon vs. arrival SIL) and End of wintering season (arrival Oregon vs. departure SIL). The group median (horizontal line), interquartile range (IQR, box), maximum and minimum 1.5*IQR (vertical lines), and outliers (dots) are depicted in the boxplots. The overlaid points represent the mean of the posterior predictive distribution for BAI of an individual and the bars represents the uncertainty (upper and lower bounds of the 95% HPD interval). Note how PCFG whales at then end of the feeding season (dark green) typically have lower body condition (as BAI) compared to ENP whales at the end of the feeding season when they arrive to SIL after migration (light brown).
Figure 3. Boxplots showing the distribution of Body Area Index (BAI) values of gray whales from opportunistic images collected from a plane in Northeaster Chukchi Sea (NCS) and from drones collected by the GEMM Lab in Oregon. The boxplots display the group median (horizontal line), interquartile range (IQR box), maximum and minimum 1.5*IQR (vertical lines), and outlies (dots). The overlaid points are the BAI values from each image. Note the significantly lower BAI of PCFG whales on Oregon feeding grounds compared to whales feeding in the Arctic region of the NCS.

This difference in body condition between PCFG and ENP gray whales raises some really interesting and prudent questions. Does the lower body condition of PCFG whales make them less resilient to changes in prey availability compared to ENP whales, and thus more vulnerable to climate change? If so, could this influence the reproductive capacity of PCFG whales? Or, are whales that recruit into the PCFG adapted to a smaller morphology, perhaps due to their specialized foraging tactics, which may be genetically inherited and enables them to survive with reduced energy stores?

These questions are on our minds here at the GEMM Lab as we prepare for our seventh consecutive field season using drones to collect data on PCFG gray whale body condition. As discussed in a previous blog by Dr. Alejandro Fernandez Ajo, we are combining our sightings history of individual whales, fecal hormone analyses, and photogrammetry-based body condition to better understand gray whales’ reproductive biology and help determine what the consequences are for these PCFG whales with lower body condition.

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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.

Bierlich, K. C., Schick, R. S., Hewitt, J., Dale, J., Goldbogen, J. A., Friedlaender, A.S., et al. (2021b). Bayesian Approach for Predicting Photogrammetric Uncertainty in Morphometric Measurements Derived From Drones. Mar. Ecol. Prog. Ser. 673, 193–210. doi: 10.3354/meps13814

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 Science35(1), 108–139.

Christiansen, F., Rodrı́guez-González, F., Martı́nez-Aguilar, S., Urbán, J., Swartz, S., Warick, H., et al. (2021). Poor Body Condition Associated With an Unusual Mortality Event in Gray Whales. Mar. Ecol. Prog. Ser. 658, 237–252. doi:10.3354/meps13585

Hildebrand, L., Bernard, K. S., and Torres, L. G. (2021). Do Gray Whales Count Calories? Comparing Energetic Values of Gray Whale Prey Across Two Different Feeding Grounds in the Eastern North Pacific. Front. Mar. Sci. 8. doi: 10.3389/fmars.2021.683634

Stewart, J. D., and Weller, D. (2021). Abundance of Eastern North Pacific Gray Whales 2019/2020 (San Diego, CA: NOAA/NMFS)

Sumich, J. L. (1983). Swimming Velocities, Breathing Patterns, and Estimated Costs of Locomotion in Migrating Gray Whales, Eschrichtius Robustus. Can. J. Zoology. 61, 647–652. doi: 10.1139/z83-086

Torres, L.G., Bird, C., Rodrigues-Gonzáles, F., Christiansen F., Bejder, L., Lemos, L., Urbán Ramírez, J., Swartz, S., Willoughby, A., Hewitt., J., Bierlich, K.C. (2022). Range-wide comparison of gray whale body condition reveals contrasting sub-population health characteristics and vulnerability to environmental change. Frontiers in Marine Science. 9:867258.

Dive into Oregon’s underwater forests

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

When I was younger, I aspired to be a marine mammal biologist. I thought it was purely about knowing as much about marine mammal species as possible. However, over time and with experience in this field, I have realized that in order to understand a species, you need to have a holistic understanding of its prey, habitat, and environment. When I first applied to be advised by Leigh in the GEMM Lab, I had no idea how much of my time I would spend looking at tiny zooplankton under a microscope, thinking about the different benefits of different habitat types, or reading about oceanographic processes. But these things have been incredibly vital to my research to date and as a result, I now refer to myself as a marine ecologist. This holistic understanding that I am gaining will only grow throughout my PhD as I am broadly looking at the habitat use, site fidelity, and population dynamics of the Pacific Coast Feeding Group (PCFG) of gray whales for my thesis research. 

The PCFG display many foraging tactics and occupy several habitat types along the Oregon coast while they spend their summer feeding seasons here (Torres et al. 2018). Here, I will focus on one of these habitats: kelp. When you hear the word kelp, you probably conjure an image of long, thick stalks that reach from the ocean floor to the surface, with billowing fronds waving around (Figure 1a). However, this type is only one of three basic morphologies (Filbee-Dexter & Scheibling 2014) and it is called canopy kelp, which often forms extensive forests. The other two morphologies are stipitate and prostrate kelps. The former forms midwater stands (Figure 1b) while the latter forms low-lying kelp beds (Figure 1c). All three of these morphologies exist on the Oregon coast and create a mosaic of understory and canopy kelp patches that dot our coastline.

Figure 1. Examples of the three different kelp morphologies. a: bull kelp (Nereocystis luetkeana) is a type of canopy kelp and the dominant kelp on the Oregon coast (Source: Oregon Coast Aquarium); b: sea palm (Postelsia palmaeformis) is a type of stipitate kelp that forms mid-water stands (Source: Oregon Conservation Strategy); c: sea cabbage (Saccharina sessilis) is a type of prostrate kelp that is stipeless and forms low-lying kelp beds (Source: Central Coast Biodiversity).

One of the most magnificent things about kelp is that it is not just a species itself, but it provides critical habitat, refuge, and food resources to a myriad of other species due to its high rates of primary production (Dayton 1985). Kelp is often referred to as a foundation species due to all of these critical services it provides. In Oregon, many species of rockfish, which are important commercial and recreational fisheries, use kelp as habitat throughout their life cycle, including as nursery grounds. Lingcod, another widely fished species, forages amongst kelp. A large number of macroinvertebrates can be found in Oregon kelp forests, including anemones, limpets, snails, sea urchins, sea stars, and abalone, to name a fraction of them. 

Kelps grow best in cold, nutrient-rich waters (Tegner et al. 1996) and their growth and distribution patterns are highly naturally variable on both temporal and spatial scales (Krumhansl et al. 2016). However, warm water, low nutrient or light conditions, intensive grazing by herbivores, and severe storm activity can lead to the erosion and defoliation of kelp beds (Krumhansl et al. 2016). While these events can occur naturally in cyclical patterns, the frequency of several of these events has increased in recent years, as a result of climate change and anthropogenic impacts. For example, Dawn’s blog discussed increasing marine heatwaves that represent an influx of warm water for a prolonged period of time. In fact, kelps can be useful sentinels of change as they tend to be highly responsive to changes in environmental conditions (e.g., Rogers-Bennet & Catton 2019) and their nearshore, coastal location directly exposes them to human activities, such as pollution, harvesting, and fishing (Bennett et al. 2016).

Due to its foundational role, changes or impacts to kelp can reverberate throughout the ecosystem and negatively affect many other species. As mentioned previously, kelp is naturally highly variable, and like many other ecological processes, undergoes boom and bust cycles. For over four decades, dense, productive kelp forests have been shown to transition to sea urchin barrens, and back again, in natural cycles (Sala et al. 1998; Pinnegar et al. 2000; Steneck et al. 2002; Figure 2). These transitions are called phase shifts. In a healthy, balanced kelp forest, sea urchins typically passively feed on detrital plant matter, such as broken off pieces of kelp fronds that fall to the seafloor. A phase shift occurs when the grazing intensity of sea urchins increases, resulting in them actively feeding on kelp stalks and fronds to a point where the kelp in an area can become greatly reduced, creating an urchin barren. Sea urchin grazing intensity can change for a number of reasons, including reduction in sea urchin predators (e.g., sea otters, sunflower sea stars) or poor kelp recruitment events (e.g., due to warm water temperature). Regardless of the reason, the phases tend to transition back and forth over time. However, there is concern that sea urchin barrens may become an alternative stable state of the subtidal ecosystem from which kelp in an area cannot recover (Filbee-Dexter & Scheibling 2014). 

Figure 2. Screenshots from GoPro videos from 2016 (left) and 2018 (right) at the same kayak sampling station in Port Orford showing the difference between a dense kelp forest and what appears to be an urchin barren. (Source: GEMM Lab).

For example, in 2014, bull kelp canopy cover in northern California was reduced by >90% and has not shown signs of recovery since (Rogers-Bennet & Catton 2019; Figure 3). This massive decline was attributed to two major events: 1) the onset of sea star wasting disease (SSWD) in 2013 and 2) the “warm blob” of 2014-2016. SSWD affected over 20 sea star species along the coast from Mexico to Alaska, with the predatory sunflower sea star, which consumes purple sea urchins, most affected, including population declines of 80-100% along the coast (Harvell et al. 2019). Following this SSWD outbreak, the “warm blob”, which was an extreme marine heatwave in the Pacific Ocean, caused ocean temperatures to spike. These two events allowed purple sea urchin populations to grow unchecked by their predators, and created nutrient-poor and warm water conditions, which limited kelp growth and productivity. Intense grazing on bull kelp by growing urchin populations resulted in the >90% reduction in bull kelp canopy cover and has left behind widespread urchin barrens instead (Rogers-Bennet & Catton 2019). Consequently, there have been ecological and economic impacts on the ecosystem and communities in northern California. Without bull kelp, red abalone and red sea urchin populations starved, leading to a subsequent loss of the recreational red abalone (estimated value of $44 million/year) and commercial red urchin fisheries in northern California (Rogers-Bennet & Catton 2019).

Figure 3. Surface kelp canopy area pre- and post-impact from sites in Sonoma and Mendocino counties, northern California from aerial surveys (2008, 2014-2016). Figure and figure caption taken from Rogers-Bennett & Catton (2019).

As I mentioned earlier, while phase shifts between kelp forests and urchin barrens are common cycles, the intensity of the events described above in northern California are an example of sea urchin barrens potentially becoming a stable state of the subtidal ecosystem (Filbee-Dexter & Scheibling 2014). Given that marine heatwaves are only expected to increase in intensity and frequency in the future (Frölicher et al. 2018), the events documented in northern California may not be an isolated incidence. 

Considering that parts of the Oregon coast, particularly the southern portion, are very similar to northern California biogeographically, and that it was not exempt from the “warm blob”, similar changes in kelp forests may be occurring along our coast. There are many individuals and groups that are actively working on this issue to examine potential impacts to kelp and the species that depend on the services it provides. For more information, check out the Oregon Kelp Alliance

Figure 4. A gray whale surfaces in a large kelp bed during a foraging bout along the Oregon coast. (Source: GEMM Lab).

So, what does all of this information have to do with gray whales? Given their affinity for kelp habitats (Figure 4) and their zooplankton prey that aggregates there, changes to kelp ecosystems may affect gray whale health and ecology. This aspect of the complex kelp trophic web has not been examined to date; thus one of my PhD chapters focuses on the response of gray whales to changing kelp ecosystems along the southern Oregon coast. To do this, I am examining 6 years of data collected during the TOPAZ/JASPER project in Port Orford, to look at the relationships between kelp health, sea urchin density, zooplankton abundance, and gray whale foraging effort over space and time. Documenting impacts of changing kelp forests on gray whales is important to assist management efforts as healthy and abundant kelp seems critical in providing ample food opportunities for these iconic Pacific Northwest marine predators.

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Bennett S, et al. The ‘Great Southern Reef’: Social, ecological and economic value of Australia’s neglected kelp forests. Marine and Freshwater Research 67:47-56.

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Frölicher TL, Fischer EM, Gruber N (2018) Marine heatwaves under global warming. Nature 560:360-364.

Harvell CD, et al. (2019) Disease epidemic and a marine heat wave are associated with the continental-scale collapse of a pivotal predator (Pycnopodia helianthoides). Science Advances 5(1) doi:10.1126/sciadv.aau7042.

Krumhansl KA, et al. (2016) Global patterns of kelp forest change over the past half-century. Proceedings of the National Academy of Sciences of the United States of America 113(48):13785-13790.

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Torres LG, Nieukirk SL, Lemos L, Chandler TE (2018) Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science doi:10.3389/fmars.2019.00319.

What drives individual specialization?

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

When I wrote my first blog on individual specialization well over a year ago, I just skimmed the surface of the literature on this topic and only started to recognize the importance of studying individual specialization. The question, “is there individual specialization in the PCFG of gray whales?” is the focus of my first thesis chapter and the results will affect all my subsequent work. Therefore, the literature and concepts of individual specialization are a focus of my literature review and studies.

In my previous blog I focused on common characteristics of individuals that are similarly specialized as an underlying driver of individual specialization. While these characteristics (often attributable to age, sex, or physical traits) are important to consider, I’ve learned that the list of drivers of individual specialization is long and that many variables are dynamic. Of all the drivers I’ve learned about, competition is among the most common.

Competition is a major driver of individual specialization, and a common driver of competition is resource availability. When resource availability decreases, whether caused by increasing population density or changing environmental conditions, competition for that resource increases. As competition increases, individuals have a choice. They can choose to engage in competition, either by racing, fighting, or sharing [1], which can be costly, or they can diffuse the competition by focusing on a different resource.  This second approach would be considered niche partitioning in the prey dimension. Niche partitioning is a way for individuals to share ecological space by using different resources. Essentially, individuals can share habitat without having to engage in direct competition by pursuing different prey types [2]. 

This switch to different prey types can change the degree of individual specialization present in the population (Figure 1). But the direction of the change is not constant. If all individuals were pursuing the same prey type under low competition conditions but then switched to different alternate prey types under high competition, then individual specialization would increase (Figure 1a). This direction has been observed across a range of species including sharks [3], otters [4]–[7], dolphins [8], [9], stickleback fish [10], [11], largemouth bass [12], banded mongoose [13], fur seals [14], and baleen whales [15].

However, if individuals were pursuing different prey types under low competition conditions (maybe because of underlying differences such as age or sex) but then switched to the same alternate prey types under high competition, diet overlap would increase, and individual specialization would decrease (Figure 1b). Furthermore, an individual might not switch to an entirely new prey type but instead add prey items to its diet [16]. This diet expansion under competition would also decrease individual specialization. Fewer studies have reported this direction but it’s been found in the common bumblebee [17] and in several neotropical vertebrate species [18], [19].

Figure `1. Figure 3 from Araújo et al. 2011 [20]. Illustration of how ecological mechanisms may affect the degree of individual specialization. Arrows linking resources to individual consumers indicate resource consumption (relative thickness indicates proportional contribution). 
Horizontal arrows indicate the sign (positive or negative) of the effect on the degree of individual specialization. (a) Consumers share the same preferred resource (dark gray tangle) but have different alternative resources (white and light gray triangles). As the preferred resource becomes scarce, consumers switch to different alternatives, increasing the degree of individual specialization. (b) Alternatively, consumers have distinct preferred resources, so that as resources become scarce, individuals converge to the alternative resource (dark gray triangle), reducing diet variation.

Interestingly, its hypothesized that individual specialization driven by competition is one of the factors that facilitates the formation and existence of stable groups [21]. For example, a study on resident female dolphins in Sarasota Bay, FL, USA found that females with high spatial overlap used distinct foraging specializations [8](Figure 2). This study illustrates how partitioning prey enabled spatial and social coexistence. A study on banded mongooses reached a similar conclusion [13]. They found that specialization was highest in the biggest groups (with the most competition) and not explained by sex, age, or other inherent differences. They hypothesized that specialization increasing with competition reduced conflict and allowed the groups to remain stable. This study also highlighted the role of learning to determine an individual’s specialization.

Figure 2. A bottlenose dolphin.

Learning drives the distribution of knowledge throughout a population, which can lead to either specialization or generalization. ‘One-to-one’ learning, where one individual learns from one demonstrator, tends to promote individual specialization [21]. This form of transmission drives specialization because the individuals who learn the specialization tend to then carry on using, and eventually teaching, that specialization [6]. A common example of ‘one-to-one’ learning is vertical transmission from parent to offspring. It has been shown to transmit specializations in dolphins [22] and otters [6]. ‘One-to-one’ learning can occur outside of parent-offspring pairs; non-parent-offspring ‘one-to-one’ learning has been shown to drive specialization in banded mongooses [13](Figure 3).

However, other forms of social learning can promote more generalized foraging strategies. ‘Many-to-one’ or ‘one-to-many’ learning  can reduce the presence of specialization in species [13], [21] as can the presence of conformity in a group [23], [24].

Figure 3. A group of banded mongooses.

The multiple drivers of specialization and their dynamic quality means that it is important to contextualize specialization. For example, a study on four species of neotropical frogs found varying degrees of specialization across multiple populations of each species [18]. The degree of specialization was dependent on a variety of drivers including predation and both intra- and inter-specific competition. Notably, the direction of the relationship between degree of specialization and each driver was species specific. This study highlights that one species may not always be more specialized than another, but that a populations’ specialization is context dependent.

Therefore, it is important to not only be aware of the degree of specialization present in a population, but to also understand its dynamics and drivers. These relationships can then be used to understand how, and why, a population may react to competition from other species, predators, and changes in resource availability [20].  A population’s specialization can also affect the specialization of other populations and community dynamics [25], therefore, it’s important to consider and study individual specialization on both the population and community level. I am excited to start using our incredible six-year dataset to start investigating these questions for PCFG gray whales, so stay tuned for results!

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Marine megafauna as ecosystem sentinels: What animals can tell us about changing oceans

By Dawn Barlow1 and Will Kennerley2

1PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

2MS Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Seabird Oceanography Lab

The marine environment is dynamic, and mobile animals must respond to the patchy and ephemeral availability of resource in order to make a living (Hyrenbach et al. 2000). Climate change is making ocean ecosystems increasingly unstable, yet these novel conditions can be difficult to document given the vast depth and remoteness of most ocean locations. Marine megafauna species such as marine mammals and seabirds integrate ecological processes that are often difficult to observe directly, by shifting patterns in their distribution, behavior, physiology, and life history in response to changes in their environment (Croll et al. 1998, Hazen et al. 2019). These mobile marine animals now face additional challenges as rising temperatures due to global climate change impact marine ecosystems worldwide (Hazen et al. 2013, Sydeman et al. 2015, Silber et al. 2017, Becker et al. 2019). Given their mobility, visibility, and integration of ocean processes across spatial and temporal scales, these marine predator species have earned the reputation as effective ecosystem sentinels. As sentinels, they have the capacity to shed light on ecosystem function, identify risks to human health, and even predict future changes (Hazen et al. 2019). So, let’s explore a few examples of how studying marine megafauna has revealed important new insights, pointing toward the importance of monitoring these sentinels in a rapidly changing ocean.

Cairns (1988) is often credited as first promoting seabirds as ecosystem sentinels and noted several key reasons why they were perfect for this role: (1) Seabirds are abundant, wide-ranging, and conspicuous, (2) although they feed at sea, they must return to land to nest, allowing easier observation and quantification of demographic responses, often at a fraction of the cost of traditional, ship-based oceanographic surveys, and therefore (3) parameters such as seabird reproductive success or activity budgets may respond to changing environmental conditions and provide researchers with metrics by which to assess the current state of that ecosystem.

The unprecedented 2014-2016 North Pacific marine heatwave (“the Blob”) caused extreme ecosystem disruption over an immense swath of the ocean (Cavole et al. 2016). Seabirds offered an effective and morbid indication of the scale of this disruption: Common murres (Uria aalge), an abundant and widespread fish-eating seabird, experienced widespread breeding failure across the North Pacific. Poor reproductive performance suggested that there may have been fewer small forage fish around and that these changes occurred at a large geographic scale. The Blob reached such an extreme as to kill immense numbers of adult birds, which professional and community scientists found washed up on beach-surveys; researchers estimate that an incredible 1,200,000 murres may have died from starvation during this period (Piatt et al. 2020). While the average person along the Northeast Pacific Coast during this time likely didn’t notice any dramatic difference in the ocean, seabirds were shouting at us that something was terribly wrong.

Happily, living seabirds also act as superb ecosystem sentinels. Long-term research in the Gulf of Maine by U.S. and Canadian scientists monitors the prey species provisioned by adult seabirds to their chicks. Will has spent countless hours over five summers helping to conduct this research by watching terns (Sterna spp.) and Atlantic puffins (Fratercula arctica) bring food to their young on small islands off the Maine coast. After doing this work for multiple years, it’s easy to notice that what adults feed their chicks varies from year to year. It was soon realized that these data could offer insight into oceanographic conditions and could even help managers assess the size of regional fish stocks. One of the dominant prey species in this region is Atlantic herring (Clupea harengus), which also happens to be the focus of an economically important fishery.  While the fishery targets four or five-year-old adult herring, the seabirds target smaller, younger herring. By looking at the relative amounts and sizes of young herring collected by these seabirds in the Gulf of Maine, these data can help predict herring recruitment and the relative number of adult herring that may be available to fishers several years in the future (Scopel et al. 2018).  With some continued modelling, the work that we do on a seabird colony in Maine with just a pair of binoculars can support or maybe even replace at least some of the expensive ship-based trawl surveys that are now a popular means of assessing fish stocks.

A common tern (Sterna hirundo) with a young Atlantic herring from the Gulf of Maine, ready to feed its chick (Photo courtesy of the National Audubon Society’s Seabird Institute)

For more far-ranging and inaccessible marine predators such as whales, measuring things such as dietary shifts can be more challenging than it is for seabirds. Nevertheless, whales are valuable ecosystem sentinels as well. Changes in the distribution and migration phenology of specialist foragers such as blue whales (Balaenoptera musculus) and North Atlantic right whales (Eubalaena glacialis) can indicate relative changes in the distribution and abundance of their zooplankton prey and underlying ocean conditions (Hazen et al. 2019). In the case of the critically endangered North Atlantic right whale, their recent declines in reproductive success reflect a broader regime shift in climate and ocean conditions. Reduced copepod prey has resulted in fewer foraging opportunities and changing foraging grounds, which may be insufficient for whales to obtain necessary energetic stores to support calving (Gavrilchuk et al. 2021, Meyer-Gutbrod et al. 2021). These whales assimilate and showcase the broad-scale impacts of climate change on the ecosystem they inhabit.

Blue whales that feed in the rich upwelling system off the coast of California rely on the availability of their krill prey to support the population (Croll et al. 2005). A recent study used acoustic monitoring of blue whale song to examine the timing of annual population-level transition from foraging to breeding migration compared to oceanographic variation, and found that flexibility in timing may be a key adaptation to persistence of this endangered population facing pressures of rapid environmental change (Oestreich et al. 2022). Specifically, blue whales delayed the transition from foraging to breeding migration in years of the highest and most persistent biological productivity from upwelling, and therefore listening to the vocalizations of these whales may be valuable indicator of the state of productivity in the ecosystem.

Figure reproduced from Oestreich et al. 2022, showing relationships between blue whale life-history transition and oceanographic phenology of foraging habitat. Timing of the behavioral transition from foraging to migration (day of year on the y-axis) is compared to (a) the date of upwelling onset; (b) the date of peak upwelling; and (c) total upwelling accumulated from the spring transition to the end of the upwelling season.

In a similar vein, research by the GEMM Lab on blue whale ecology in New Zealand has linked their vocalizations known as D calls to upwelling conditions, demonstrating that these calls likely reflect blue whale foraging opportunities (Barlow et al. 2021). In ongoing analyses, we are finding that these foraging-related calls were drastically reduced during marine heatwave conditions, which we know altered blue whale distribution in the region (Barlow et al. 2020). Now, for the final component of Dawn’s PhD, she is linking year-round environmental conditions to the occurrence patterns of different blue whale vocalization types, hoping to shed light on ecosystem processes by listening to the signals of these ecosystem sentinels.

A blue whale comes up for air in the South Taranaki Bight of New Zealand. photo by L. Torres.

It is important to understand the widespread implications of the rapidly warming climate and changing ocean conditions on valuable and vulnerable marine ecosystems. The cases explored here in this blog exemplify the importance of monitoring these marine megafauna sentinel species, both now and into the future, as they reflect the health of the ecosystems they inhabit.

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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.

Becker EA, Forney KA, Redfern J V., Barlow J, Jacox MG, Roberts JJ, Palacios DM (2019) Predicting cetacean abundance and distribution in a changing climate. Divers Distrib 25:626–643.

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

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.

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Gavrilchuk K, Lesage V, Fortune SME, Trites AW, Plourde S (2021) Foraging habitat of North Atlantic right whales has declined in the Gulf of St. Lawrence, Canada, and may be insufficient for successful reproduction. Endanger Species Res 44:113–136.

Hazen EL, Abrahms B, Brodie S, Carroll G, Jacox MG, Savoca MS, Scales KL, Sydeman WJ, Bograd SJ (2019) Marine top predators as climate and ecosystem sentinels. Front Ecol Environ 17:565–574.

Hazen EL, Jorgensen S, Rykaczewski RR, Bograd SJ, Foley DG, Jonsen ID, Shaffer SA, Dunne JP, Costa DP, Crowder LB, Block BA (2013) Predicted habitat shifts of Pacific top predators in a changing climate. Nat Clim Chang 3:234–238.

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Oestreich WK, Abrahms B, Mckenna MF, Goldbogen JA, Crowder LB, Ryan JP (2022) Acoustic signature reveals blue whales tune life history transitions to oceanographic conditions. Funct Ecol.

Piatt JF, Parrish JK, Renner HM, Schoen SK, Jones TT, Arimitsu ML, Kuletz KJ, Bodenstein B, Garcia-Reyes M, Duerr RS, Corcoran RM, Kaler RSA, McChesney J, Golightly RT, Coletti HA, Suryan RM, Burgess HK, Lindsey J, Lindquist K, Warzybok PM, Jahncke J, Roletto J, Sydeman WJ (2020) Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016. PLoS One 15:e0226087.

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Sydeman WJ, Poloczanska E, Reed TE, Thompson SA (2015) Climate change and marine vertebrates. Science 350:772–777.

Wavelet analysis to describe biological cycles and signals of non-stationarity

By Allison Dawn, GEMM Lab Master’s student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab 

During my second term of graduate school, I have been preparing to write my research proposal. The last two months have been an inspiring process of deep literature dives and brainstorming sessions with my mentors. As I discussed in my last blog, I am interested in questions related to pattern and scale (fine vs. mesoscale) in the context of the Pacific Coast Feeding Group (PCFG) of gray whales, their zooplankton prey, and local environmental variables.

My work currently involves exploring which scales of pattern are important in these trophic relationships and whether the dominant scale of a pattern changes over time or space. I have researched which analysis tools would be most appropriate to analyze ecological time series data, like the impressive long-term dataset the GEMM lab has collected in Port Orford as part of the TOPAZ  project, where we have monitored the abundance of whales and zooplankton, as well as environmental variables since 2016. 

A useful analytical tool that I have come across in my recent coursework and literature review is called wavelet analysis. Importantly, wavelet analysis can handle non-stationarity and edge detection in time series data. Non-stationarity is when a dataset’s mean and/or variance can change over time or space, and edge detection is the identification of the change location (in time or space). For example, it is not just the cycles or “ups and downs” of zooplankton abundance I am interested in, but when in time or where in space these cycles of “ups and downs” might change in relation to what their previous values, or distances between values, were. Simply stated, non-stationarity is when what once was normal is no longer normal. Wavelet analysis has been applied across a broad range of fields, such as environmental engineering (Salas et al. 2020), climate science (Slater et al. 2021), and bio-acoustics (Buchan et al. 2021). It can be applied to any time series dataset that might violate the traditional statistical assumption of stationarity. 

In a recent review of climate science methodology, Slater et al. (2021) outlined the possible behavior of time series data. Using theoretical plots, the authors show that data can a) have the same mean and variance over time, or b) have non-stationarity that can be broken into three major groups – trend, step change, or shifts in variance. Figure 1 further demonstrates the difference between stationary vs. non-stationary data in relation to a given variable of interest over time. 

Figure 1. Plots showing the possible magnitude of a given variable across a time series: a) Stationary behavior, b) Non-stationary trend, step-change, and a shift in variance. [Taken from Slater et. al (2021)].

Traditional correlation statistics assumes stationarity, but it has been shown that ecological time series are often non-stationary at certain scales (Cazelles & Hales, 2006). In fact, ecological data rarely meets the requirements of a controlled experiment that traditional statistics require. This non-stationarity of ecological data means that while widely-used methods like generalized linear models and analyses of variances (ANOVAs) can be helpful to assess correlation, they are not always sufficient on their own to describe the complex natural phenomena ecologists seek to explain. Non-stationarity occurs frequently in ecological time series, so it is appropriate to consider analysis tools that will allow us to detect edges to further investigate the cause.

Wavelet analysis can also be conducted across a time series of multiple response variables to assess if these variables share high common power (correlation). When data is combined in this way it is called a cross-wavelet analysis. An interesting paper used cross-wavelet analysis to assess the seasonal response of zooplankton life history in relation to climate warming (Winder et. al 2009). Results from their cross-wavelet analysis showed that warming temperatures over the past two decades increased the voltinism (number of broods per year) of copepods. The authors show that where once annual recruitment followed a fairly stationary pattern, climate warming has contributed to a much more stochastic pattern of zooplankton abundance. From these results, the authors contribute to the hypothesis that climate change has had a temporal impact on zooplankton population dynamics, and recruitment has increasingly drifted out of phase from the original annual cycles. 

Figure 2. Cross-wavelet spectrum for immature and adult Leptodiaptomus ashlandi for 1965 through either 2000 or 2005. Plots show a) immatures and temperature, b) adults and temperature, c) immatures and phytoplankton, and d) adults and phytoplankton. Arrows indicate phase between combined time series. 0 degrees is in-phase and 180 degrees is anti-phase. Black contour lines show “cone of influence” or the 95% significance level, every value within the cone is considered significant. Left axis shows the temporal period, and the color legend shows wavelet frequency power, with low frequencies in blue and high frequencies in red. Plots show strong covariation of high common power at the 12-month period until the 1980s. This pattern is especially evident in plot c) and d). [Taken from (Winder et. al 2009)].

While wavelet and cross-wavelet analyses should not be the only tool used to explore data, due to its limitations with significance testing, it is still worth implementing to gain a better understanding of how time series variables relate to each other over multiple spatial and/or temporal scales. It is often helpful to combine multiple methods of analysis to get a larger sense of patterns in the data, especially in spatio-temporal research.

When conducting research within the context of climate change, where the concentration of CO2 in ppm in the atmosphere is a non-stationary time series itself (Figure 3), it is important to consider how our datasets might be impacted by climate change and wavelet analysis can help identify the scales of change. 

Figure 3. Plot showing the historic fluctuations of CO^2 and the recent deviation from normal levels. Source:

When considering our ecological time series of data in Port Orford, we want to evaluate how changing ocean conditions may be related to data trends. For example, has the annual mean or variance of zooplankton abundance changed over time, and where has that change occurred in time or space? These changes might have occurred at different scales and might be invisible at other scales. I am eager to see if wavelet analysis can detect these sorts of changes in the abundance of zooplankton across our time series of data, particularly during the seasons of intense heat waves or upwelling. 

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Buchan, S. J., Pérez-Santos, I., Narváez, D., Castro, L., Stafford, K. M., Baumgartner, M. F., … & Neira, S. (2021). Intraseasonal variation in southeast Pacific blue whale acoustic presence, zooplankton backscatter, and oceanographic variables on a feeding ground in Northern Chilean Patagonia. Progress in Oceanography, 199, 102709.

Cazelles, B., & Hales, S. (2006). Infectious diseases, climate influences, and nonstationarity. PLoS Medicine, 3(8), e328.

Salas, J. D., Anderson, M. L., Papalexiou, S. M., & Frances, F. (2020). PMP and climate variability and change: a review. Journal of Hydrologic Engineering, 25(12), 03120002.

Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., … & Wilby, R. L. (2021). Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management. Hydrology and Earth System Sciences, 25(7), 3897-3935.

Winder, M., Schindler, D. E., Essington, T. E., & Litt, A. H. (2009). Disrupted seasonal clockwork in the population dynamics of a freshwater copepod by climate warming. Limnology and Oceanography, 54(6part2), 2493-2505.

Drones with lasers: almost as cool as “sharks with laser beams attached to their heads”

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

The recent advancement in drones (or unoccupied aircraft systems, UAS) has greatly enhanced opportunities for scientists across a broad range of disciplines to collect high-resolution aerial imagery. Wildlife researchers in particular have utilized this technology to study large elusive animals, such as whales, to observe their behavior (see Clara Bird’s blog) and obtain morphological measurements via photogrammetry (see previous blog for a brief history on photogrammetry and drones). However, obtaining useful measurement data is not as easy as flying the drone and pressing record. For this blog, I will provide a brief overview on the basics of using photogrammetry to extract morphological measurements from images collected with drones, as well as the associated uncertainty from using different drone platforms. 

During my PhD at Duke University, I co-developed an open-source photogrammetry software called MorphoMetriX to measure whales in images I collected using drones (Fig. 1) (Torres and Bierlich, 2020) (see this blog for some fieldwork memoirs!). The software is designed to be flexible, simple to use, and customizable without knowledge of scripting languages. Using MorphoMetriX, measurements are made in pixels and then multiplied by the ground sampling distance (GSD) to convert to standard units (e.g., meters) (Fig. 2A). GSD represents the distance on the ground each pixel represents (i.e., the linear size of the pixel) and therefore sets the scale of the image (i.e., cm per pixel). Figure 2A describes how GSD is dependent on the camera sensor, focal length lens, and altitude. Thus, drones equipped with different cameras and focal length lenses will have inherent differences in GSD as altitude increases (Fig. 2B). A larger GSD increases the length each pixel represents in a photo and results in a lower resolution image, potentially obscuring important features in the photo and introducing measurement error.

Figure 1. An example of a Pacific Coast Feeding Group gray whale measured in MorphoMetriX (Torres & Bierlich, 2020).
Figure 2: Overview of photogrammetry methods and calculating ground sampling distance (GSD). A) Photogrammetry methods for how each image is scaled to convert measurements in pixels to standard units (e.g., meters). Altitude is the distance between the camera lens and whale (usually at the surface of the water). Figure from Torres and Bierlich (2020). B) The exact (not accounting for distortion or altitude error) ground sampling distance (GSD) for two drone platforms commonly used to obtain morphological measurements of cetaceans. The difference in GSD between the P4Pro and Inspire 1 is due to the difference in sensor width and focal lengths of the cameras used. Figure from Bierlich et al. (2021).

Obtaining accurate altitude information is a key component in obtaining accurate measurements. All drones are equipped with a barometer, which measures altitude from changes in pressure. In general, barometers usually yield low accuracy in the altitude recorded, particularly for low-cost sensors commonly found on small, off-the-shelf drones (Wei et al., 2016). Dawson et al. (2017) added a laser altimeter (i.e., LightWare SF11/C, to a drone, which yields higher accuracy in the altitude recorded. Since then, several studies have adopted use of a laser altimeter to study different species of baleen whales (i.e., Gough et al., 2019; Christiansen et al., 2018).

The first chapter of my dissertation, which was published last year in Marine Ecology Progress Series, compared the accuracy of several drones equipped with different camera sensors, focal length lenses, and a barometer vs. laser altimeter (Bierlich et al., 2021). We flew each drone over a known sized object floating at the surface and collected images at various altitudes (between 10 – 120 m). We used the known size of the floating object to determine the percent error of each measurement at each altitude. We found that 1) there is a lot of variation in measurement error across the different drones when using a barometer to measure altitude and 2) using a laser altimeter dramatically reduces measurement error for each drone (Fig. 3).

Figure 3. The % error for measurements from different drones. Black dashed line represents 0% error (true length = 1.48 m). The gray dashed lines represent under- and over-estimation of the true length by 5% (1.41 and 1.55 m, respectively).

These findings are important because if a study is analyzing measurements that are from more than one drone, the uncertainty associated with those measurements must be taken into account to know if measurements are reliable and comparable. For instance, let’s say we are comparing the body length of two different populations and found that population A is significantly longer than population B. From looking at Figure 3, that significant difference in length between population A and B could be unreliable as the difference may be due to the bias introduced by the type of drone, camera sensor, focal length lens, and whether a barometer or laser altimeter was used for recording altitude. In other words, without incorporating uncertainty associated with each measurement, how do you trust your measurement? 

Hence, the National Institute of Standards and Technology (NIST) states that a measurement is complete only when accompanied by a quantitative statement of its uncertainty (Taylor & Kuyatt, 1994). In our Bierlich et al. (2021) study, we develop a Bayesian statistical model where we use the measurements of the known-sized object floating at the surface (what was used for Fig. 3) as training data to predict the lengths of unknown-sized whales. This Bayesian approach views data and the underlying parameters that generated the data (such as the mean or standard deviation) as random, and thus can be described by a statistical distribution. Using Bayes’ Theorem, a model of the observed data (called the likelihood function), is combined with prior knowledge pertaining to the underlying parameters (called the prior probability distribution) to form the posterior probability distribution, which serves as updated knowledge about the underlying parameter. For example, if someone told me they saw a 75 ft blue whale, I would not be phased. But if someone told me they saw a 150 ft blue whale, I would be skeptical – I’m using prior knowledge to determine the probability of this statement being true. 

The posterior probability distribution produced by this Bayesian approach can also serve as new prior information for subsequent analyses. Following this framework, we used the known-sized objects to first estimate the posterior probability distribution for error for each drone. We then used that posterior probability distribution for error specific to each drone platform as prior information to form a posterior predictive distribution for length of unknown-sized whales. The length of an individual whale can then be described by the mean of this second posterior predictive distribution, and its uncertainty defined as the variance or an interval around the mean (Fig. 4). 

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

For over half a decade, the GEMM Lab has been collecting drone images of Pacific Coast Feeding Group (PCFG) of gray whales off the coast of Oregon to measure their morphology and body condition (see GRANITE Project Blog). We have been using several different types of drones equipped with different cameras, focal length lenses, barometers, and laser altimeters. These measurements from different drones will inherently have different levels of error associated with them, so adapting these methods for incorporating uncertainty will be key to ensure our measurements are comparable and analyses are robust. To do this, we fly over a known-sized board (1 m) at the start of each flight to use as training data to generate a posterior predictive distribution for length of the an unknown-sized PCFG gray whale that we fly over (Fig. 5). Likewise, we are working closely with several other collaborators who are also using different drones. Incorporating measurement uncertainty from drones used across research labs and in different environments will help ensure robust analyses and provide great opportunity for some interesting comparisons – such as differences in gray whale body condition on their feeding grounds in Oregon vs. their breeding grounds in Baja, Mexico, and morphological comparisons with other baleen whale species, such as blue and humpback whales. We are currently wrapping up measurement from thousands of boards (Fig. 5) and whales (Fig. 1) from 2016 – 2021, so stay tuned for the results!

Figure 5. An example of a known-sized object (1 m long board) used as training data to assess measurement uncertainty. 


Bierlich, K.C., Schick, R.S., Hewitt, J., Dale, J., Goldbogen, J.A., Friedlaender, A.S., Johnston D.J. (2021). A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS. Marine Ecology Progress Series. DOI:

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

Dawson SM, Bowman MH, Leunissen E, Sirguey P (2017) Inexpensive aerial photogrammetry for studies of whales and large marine animals. Front Mar Sci 4: 366

Gough, W.T., Segre, P.S., Bierlich, K.C., Cade, D.E., Potvin, J., Fish, F. E., Dale, J., di Clemente, J., Friedlaender, A.S., Johnston, D.W., Kahane-Rapport, S.R., Kennedy, J., Long, J.H., Oudejans, M., Penry, G., Savoca, M.S., Simon, M., Videsen, S.K.A., Visser, F., Wiley, D.N., Goldbogen, J.A. (2019). Scaling of swimming performance in baleen whales. Journal of Experimental Biology222(20).  

Taylor, B. N., and Kuyatt, C. E. (1994). Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results. Washington, DC: National Institute of Standards and Technology. 1–25.

Torres, W.I., & Bierlich, K.C. (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software5(45), 1825.  

Wei S, Dan G, Chen H (2016) Altitude data fusion utilizing differential measurement and complementary filter. IET Sci Meas Technol (Singap) 10: 874−879

The costs and benefits of automated behavior classification

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

“Why don’t you just automate it?” This is a question I am frequently asked when I tell someone about my work. My thesis involves watching many hours of drone footage of gray whales and meticulously coding behaviors, and there are plenty of days when I have asked myself that very same question. Streamlining my process is certainly appealing and given how wide-spread and effective machine learning methods have become, it is a tempting option to pursue. That said, machine learning is only appropriate for certain research questions and scales, and it’s important to consider these before investing in using a new tool.

The application of machine learning methods to behavioral ecology is called computational ethology (Anderson & Perona, 2014). To identify behaviors from videos, the model tracks individuals across video frames and identifies patterns of movement that form a behavior. This concept is similar to the way we identify a whale as traveling if it’s moving in a straight line and as foraging if it’s swimming in circles within a small area (Mayo & Marx, 1990, check out this blog to learn more). The level of behavioral detail that the model is able to track  depends on the chosen method (Figure 1, Pereira et al., 2020). These methods range from tracking each animal as a simple single point (called a centroid) to tracking the animal’s body positioning in 3D (this method is called pose estimation), which range from providing less detailed to more detailed behavior definitions. For example, tracking an individual as a centroid could be used to classify traveling and foraging behaviors, while pose estimation could identify specific foraging tactics. 

Figure 1. Figure from Pereira et al. (2020) illustrating the different methods of animal behavior tracking that are possible using machine learning.

Pose estimation involves training the machine learning algorithm to track individual anatomical features of an individual (e.g., the head, legs, and tail of a rat), meaning that it can define behaviors in great detail. A behavior state could be defined as a combination of the angle between the tail and the head, and the stride length. 

For example, Mearns et al. (2020) used pose estimation to study how zebrafish larvae in a lab captured their prey. They tracked the tail movements of individual larvae when presented with prey and classified these movements into separate behaviors that allowed them to associate specific behaviors with prey capture (Figure 2). The authors found that these behaviors occurred in a specific sequence, that the behaviors kept the prey within the larvae’s line of sight, and that the sequence was triggered by visual cues.  In fact, when they removed the visual cue of the prey, the larvae terminated the behavior sequence, meaning that the larvae are continually choosing to do each behavior in the sequence, rather than the sequence being one long behavior event that is triggered only by the initial visual cue. This study is a good example of the applicability of machine learning models for questions aimed at kinematics and fine-scale movements. Pose estimation has also been used to study the role of facial expression and body language in rat social communication (Ebbesen & Froemke, 2021). 

Figure 2. Excerpt from figure 1 of Mearns et al. (2020) illustrating (A) the camera set up for their experiment, (B) how the model tracked the eye angles and tail of the larvae fish, (C) the kinematics extracted from the footage. In panel (C) the top plot shows how the eyes converged on the same object (the prey) during prey capture event, the middle plot shows when the tail was curved to the left or the right, and the bottom plot shows the angle of the tail tip relative to the body.

While previous machine learning methods to track animal movements required individuals to be physically marked, the current methods can perform markerless tracking (Pereira et al., 2020). This improvement has broadened the kinds of studies that are possible. For example, Bozek et al., (2021) developed a model that tracked individuals throughout an entire honeybee colony and showed that certain individual behaviors were spatially distributed within the colony (Figure 3). Machine learning enabled the researchers to track over 1000 individual bees over several months, a task that would be infeasible for someone to do by hand. 

Figure 3. Excerpt from figure 1 of Bozek et al., (2021) showing how individual bees and their trajectories were tracked.

These studies highlight that the potential benefits of using machine learning when studying fine scale behaviors (like kinematics) or when tracking large groups of individuals. Furthermore, once it’s trained, the model can process large quantities of data in a standardized way to free up time for the scientists to focus on other tasks.

While machine learning is an exciting and enticing tool, automating behavior detection via machine learning could be its own PhD dissertation. Like most things in life, there are costs and benefits to using this technique. It is a technically difficult tool, and while applications exist to make it more accessible, knowledge of the computer science behind it is necessary to apply it effectively and correctly. Secondly, it can be tedious and time consuming to create a training dataset for the model to “learn” what each behavior looks like, as this step involves manually labeling examples for the model to use. 

As I’ve mentioned in a previous blog, I came quite close to trying to study the kinematics of gray whale foraging behaviors but ultimately decided that counting fluke beats wasn’t necessary to answer my behavioral research questions. It was important to consider the scale of my questions (as described in Allison’s blog) and I think that diving into more fine-scale kinematics questions could be a fascinating follow-up to the questions I’m asking in my PhD. 

For instance, it would be interesting to quantify how gray whales use their flukes for different behavior tactics. Do gray whales in better body condition beat their flukes more frequently while headstanding? Does the size of the fluke affect how efficiently they can perform certain tactics? While these analyses would help quantify the energetic costs of different behaviors in better detail, they aren’t necessary for my broad scale questions. Consequently, taking the time to develop and train a pose estimation machine learning model is not the best use of my time.

That being said, I am interested in applying machine learning methods to a specific subset of my dataset. In social behavior, it is not only useful to quantify the behaviors exhibited by each individual but also the distance between them. For example, the distance between a mom and her calf can be indicative of the calves’ dependence on its mom (Nielsen et al., 2019). However, continuously measuring the distance between two individuals throughout a video is tedious and time intensive, so training a machine learning model could be an effective use of time. I plan to work with an intern this summer to develop a machine learning model to track the distance between pairs of gray whales in our drone footage and then relate this distance data with the manually coded behaviors to examine patterns in social behavior (Figure 4).  Stay tuned to learn more about our progress!

Figure 4. A mom and calf pair surfacing together. Image collected under NOAA/NMFS permit #21678

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Anderson, D. J., & Perona, P. (2014). Toward a Science of Computational Ethology. Neuron84(1), 18–31.

Bozek, K., Hebert, L., Portugal, Y., Mikheyev, A. S., & Stephens, G. J. (2021). Markerless tracking of an entire honey bee colony. Nature Communications12(1), 1733.

Ebbesen, C. L., & Froemke, R. C. (2021). Body language signals for rodent social communication. Current Opinion in Neurobiology68, 91–106.

Mayo, C. A., & Marx, M. K. (1990). Surface foraging behaviour of the North Atlantic right whale, Eubalaena glacialis , and associated zooplankton characteristics. Canadian Journal of Zoology68(10), 2214–2220.

Mearns, D. S., Donovan, J. C., Fernandes, A. M., Semmelhack, J. L., & Baier, H. (2020). Deconstructing Hunting Behavior Reveals a Tightly Coupled Stimulus-Response Loop. Current Biology30(1), 54-69.e9.

Nielsen, M., Sprogis, K., Bejder, L., Madsen, P., & Christiansen, F. (2019). Behavioural development in southern right whale calves. Marine Ecology Progress Series629, 219–234.

Pereira, T. D., Shaevitz, J. W., & Murthy, M. (2020). Quantifying behavior to understand the brain. Nature Neuroscience23(12), 1537–1549.

It Takes a Village to Raise a PhD Student

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

This year in late February is the 2022 Ocean Sciences Meeting, an interdisciplinary bonanza of ocean scientists from all over the world. The conference will be held online this year as a precaution against Covid-19, and a week of virtual talks and poster sessions will cover new research in diverse topics from microbial ecology to ocean technology to whale vocalizations.

The meeting will also include my first poster presentation at a major conference, and so I have the typical grad student jitters that accompany each new thing I do (read more about the common experience of “imposter syndrome” here). This poster is the first time since starting graduate school and joining Project OPAL that I’m trying to craft a full science story that connects whales, their prey, and oceanographic conditions.

Learning how to do the analyses to assess and quantify these connections has involved plenty of head-scratching and periodic frustration on my part, but it has also offered a surprisingly joyful and even moving experience. In my efforts to troubleshoot a problem with my prey analysis, I’ve reached out to nearly everyone who works with krill acoustic data on the West Coast. Every single person has been incredibly welcoming and ready to help me, and excited to learn about my work in return. This experience has made me realize how many people I have on my team, and that even strangers are willing to support me on the whacky journey that is a PhD.

Through these collaborations, I am learning to analyze the acoustic signal of krill, small animals that are important food for whales foraging off the coast of Oregon and beyond. As part of Project OPAL, we plan to compare krill swarms with whale survey data to learn about the types of aggregations that whales are drawn to. From the perspective of a hungry whale, not all krill are created equal.

Analysis of a layer of krill in the upper ocean. The blue color in the top panel indicates scattering of acoustic signal by the krill, and the outline in the bottom panel shows the results of an algorithm programmed to detect krill aggregations.

In addition to developing great remote relationships through this work, the ability to meet in person as we continue adapting to life during the pandemic has absolutely not lost its thrill. After over a year of meetings and collaborating on Zoom, I was delighted to meet GEMM Lab postdoc Solène Derville this January, after she journeyed from her home in New Caledonia to Oregon. It was so exciting to see her in real life (we’re more similar in height than I knew!) and a few minutes into our first lunch together she was already helping me refine my analysis plans and think of new approaches.

Our interaction also made me think about how impressive the GEMM Lab is. The first two people Solène saw upon her arrival in Oregon were me and fellow GEMM Lab student Allison Dawn, two newer members who joined the lab after her last trip to Oregon. Without a moment of hesitation, Allison stepped up to give Solène a ride to Newport from Corvallis to finish her long journey. The connection our lab has developed and maintained during a pandemic, across borders and time zones, is special.

Hiking on gorgeous days is just one of the many benefits of being in the same place! This adventure included spotting a whale blow off the coast and a lot of GEMM excitement.

As I look out at the next few weeks until the Ocean Sciences meeting, and out towards the rest of my PhD, I inevitably feel worried about all I need to accomplish. But, I know that the dynamics in our lab and the other collaborative relationships I’m forming are what will carry me through. Every meeting and new connection reminds me that I’m not doing this alone. I’m grateful that there’s a team of people who are ready and willing to help me muddle my way through my first Principal Components Analysis, puzzle over algorithm errors, and celebrate with me as we make progress.

Of snakes and whales: How food availability and body condition affect reproduction

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

Over six field seasons the GEMM lab team has conducted nearly 500 drone flights over gray whales, equaling over 100 hours of footage. These hours of footage are the central dataset for my PhD dissertation, so it’s up to me to process them all. This process can be challenging, tedious, and daunting, but it is also quite fun and a privilege to be the one person who gets to watch all the footage. It’s fascinating to get to know the whales and their behaviors and pick up on patterns. It motivates me to get through this video processing step and start doing the data analysis. Recently, it’s been especially fun to notice patterns that I’ve seen mentioned in the literature. One example is adult social behavior. 

There are two categories of social behavior that I’m interested in studying: maternal behavior, defined as interactions between a mom and its calf, and general social behaviors, defined as social interactions between non-mom/calf pairs. In this blog I’ll focus on general social behaviors, but if you’re interested in maternal behavior check out this blog. General social behavior, which I’ll refer to as social behavior moving forward, includes tactile interactions and promiscuous behaviors (Torres et al. 2018; Clip 1). While gray whales in the PCFG range are primarily foraging, researchers have observed increases in social behavior towards the end of the foraging season (Stelle et al., 2008; Torres et al., 2018). We think that this indicates that the whales are starting to focus less on feeding and more on breeding. This tradeoff of foraging vs. socializing time is interesting because it comes at an energetic cost.

Clip 1. Example of social interaction between a male and female gray whale off the coast of Oregon, USA. Collected under NOAA/NMFS permit #21678

Broadly, animals need to balance the energetic demands of survival with those of reproduction. They need to reproduce to pass on their genes, but reproduction is energetically demanding, and animals also need to survive and grow to be able to reproduce. The decision to reproduce is costly because reproduction requires energetic investment and time investment since animals do not forage (gaining energy) when they are socializing. Consequently, only animals with sufficient energy reserves (i.e., body condition) to invest in reproduction actually engage in reproduction. Given these costs associated with reproduction, we expect to see a relationship between social behavior and body condition (Green, 2001) with mainly animals in good body condition engaging in social behavior because these animals have sufficient reserves to sustain the cost. Furthermore, since body condition is an indicator of foraging success and prey availability, environmental conditions can also affect social behavior and reproduction through this pathway. 

Rahman et al. (2014) used a lab experiment to study the relationship between nutritional stress and male guppy courtship behavior (Figure 1). In their experiment they tested for the effects of both decreased diet quantity and quality on the frequency of male courtship behaviors. Rahman et al (2014) found that individuals in the low-quantity group were significantly smaller than those in the high-quality group and that diet quantity had a significant effect on the frequency of courtship behaviors. Males fed a low-quantity diet performed fewer courtship behaviors. Interestingly, there was no significant effect of diet quality on courtships behavior, although there was some evidence of an interaction effect, which suggests that within the low-quantity group, males fed with high-quality food performed more courtship behaviors that those fed with low-quality food. This study is interesting because it shows how foraging success (diet quantity and quality) can affect courting behavior. 

Figure 1. A guppy (Rahman et al., 2013)

However, guppies are not the ideal species for comparison to gray whales because gray whales and guppies have quite different life history traits. A more fitting comparison would be with an example species with more in common with gray whales, such as viviparous capital breeders. Viviparous animals develop the embryo inside the body and give live birth. Capital breeders forage to build energy reserves and then rely on those energy reserves during reproduction. Surprisingly, I found asp vipers to be a good example species for comparison to gray whales.

Asp vipers (Figure 2) are viviparous snakes who are considered capital breeders because they forage prior to hibernation, and then begin reproduction immediately following hibernation without additional foraging. Naulleau & Bonnet (1996) conducted a field study on female asp vipers to determine if there was a difference in body condition at the start of the breeding season between females who reproduced or not during that season. To do this they marked individuals and measured their body condition at the start of the breeding season and then recaptured those individuals at the end of the breeding season and recorded whether the individual had reproduced. Interestingly, they found that there was a strongly significant difference in body condition between females that did and did not reproduce. In fact, they discovered that no female below a certain body condition value reproduced, meaning that they found a body condition threshold for reproduction. 

Figure 2. An asp viper

Additionally, a study on water pythons found that their body condition threshold for reproduction shifted over time in response to prey availability (Madsen & Shine, 1999). These authors found that females lowered their threshold after several consecutive years of poor prey availability. These studies are really exciting to me because they address questions that the GRANITE project team is interested in tackling.

Understanding the relationship between body condition and reproduction in gray whales is an important puzzle piece for our work. The aim of the GRANITE project is to understand how the effects of stressors on individual whales scales up to population level impacts (read Lisa’s blog to learn more). Reproduction rates play a big role in population dynamics, so it is important to understand what factors affect reproduction. Since we’re studying these whales on their foraging grounds, assessing body condition provides an important link between foraging behavior and reproduction. 

For example, if an individual’s response to a stressor is to forage less, that may lead to poorer body condition, meaning that they may be less likely to reproduce. While reduced reproduction in one individual may not have a big effect on the population, the same response from multiple individuals could impact the population’s dynamics (i.e., increasing or decreasing abundance). Understanding these different relationships between behavior, body condition, and reproduction rates is a big undertaking, but it’s exciting to be a member of the GRANITE team as this strong group of scientists works to bring together different data streams to work on this big picture question. We’re all deep into data processing right now so stay tuned over the next few years to learn more about gray whale social behavior and to find out if fat whales are more social than skinny whales. 

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Green, A. J. (2001). Mass/Length Residuals: Measures of Body Condition or Generators of Spurious Results? Ecology82(5), 1473–1483.[1473:MLRMOB]2.0.CO;2

Madsen, T., & Shine, R. (1999). The adjustment of reproductive threshold to prey abundance in a capital breeder. Journal of Animal Ecology68(3), 571–580.

Naulleau, G., & Bonnet, X. (1996). Body Condition Threshold for Breeding in a Viviparous Snake. Oecologia107(3), 301–306.

Rahman, M. M., Kelley, J. L., & Evans, J. P. (2013). Condition-dependent expression of pre- and postcopulatory sexual traits in guppies. Ecology and Evolution3(7), 2197–2213.

Rahman, M. M., Turchini, G. M., Gasparini, C., Norambuena, F., & Evans, J. P. (2014). The Expression of Pre- and Postcopulatory Sexually Selected Traits Reflects Levels of Dietary Stress in Guppies. PLOS ONE9(8), e105856.

Stelle, L. L., Megill, W. M., & Kinzel, M. R. (2008). Activity budget and diving behavior of gray whales (Eschrichtius robustus) in feeding grounds off coastal British Columbia. Marine Mammal Science24(3), 462–478.

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science5(SEP).