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!


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 Science35(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. JAMA307(5), 491.

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 Zoology77(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. Oikos118(12), 1883–1891.

Torres, W., & Bierlich, K. C. (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software5(45), 1825–1826.

Robots are taking over the oceans

By Leila Lemos, PhD Student

In the past few weeks I read an article on the use of aquatic robots in the ocean for research. Since my PhD project uses technology, such as drones and GoPros, to monitor body condition of gray whales and availability of prey along the Oregon coast, I became really interested by the new perspective these robots could provide. Drones produce aerial images while GoPros generate an underwater-scape snapshot. The possible new perspective provided by a robot under the water could be amazing and potentially be used in many different applications.

The article was published on March 21st by The New York Times, and described a new finned robot named “SoFi” or “Sophie”, short for Soft Robotic Fish (Figure 1; The New York Times 2018). The aquatic robot was designed by scientists at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Lab, with the purpose of studying marine life in their natural habitats.

Figure 1: “SoFi”, a robotic fish designed by MIT scientists.
Source: The New York Times 2018.


SoFi’s  first swim trial occurred in a coral reef in Fiji, and the footage recorded can be seen in the following video:


SoFi can swim at depths up to 18 meters and at speeds up to half-its-body-length a second (average of 23.5 cm/s in a straight path; Katzschmann et al. 2018). Sofi can swim for up to ~40 minutes, as limited by battery time. The robot is also well-equipped (Figure 2). It has a compact buoyancy control mechanism and includes a wide-view video camera, a hydrophone, a battery, environmental sensors, and operating and communication systems. The operating and communication systems allow a diver to issue commands by using a controller that operates through sound waves.

Figure 2: “SoFi” system subcomponents overview.
Source: Katzschmann et al. 2018.


The robot designers highlight that while SoFi was swimming, fish didn’t seem to be bothered or get scared by SoFi’s presence. Some fish were seen swimming nearby the robot, suggesting that SoFi has the potential to integrate into the natural underwater environment and therefore record undisturbed behaviors. However, a limitation of this invention is that SoFi needs a diver on scene to control the robot. Therefore, SoFi’s study of marine life without human interference may be compromised until technology develops further.

Another potential impact of SoFi we might be concerned about is noise. Does this device produce noise levels that marine fauna can sense or maybe be stress by? Unfortunately, the answer is yes. Even if fish don’t seem to be bothered by SoFi’s presence, it might bother other animals with hearing sensitivity in the same frequency range of SoFi. Katzschmann and colleagues (2018) explained that they chose a frequency to operate SoFi that would minimally impact marine fauna. They studied the frequencies used by the aquatic animals and, since the hearing ranges of most aquatic species decays significantly above 10 KHz, they selected a frequency above this range (i.e., 36 KHz). However, this high frequency range can be sensed by some species of cetaceans and pinnipeds, but negative affects on these animals will be dependent on the sound amplitude that is produced.

Although not perfect (but what tool is?), SoFi can be seen as a great first step toward a future of underwater robots to assist research efforts.  Battery life, human disturbance, and noise disturbance are limitations, but through thoughtful application and continued innovation this fishy tool can be the start of something great.

The use of aquatic robots, such as SoFi, can help us advance our knowledge in underwater ecosystems. These robots could promote a better understanding of marine life in their natural habitat by studying behaviors, interactions and responses to threats. These robots may offer important new tools in the protection of animals against the effects caused by anthropogenic activities. Additionally, the use of aquatic robots in scientific research may substitute remote operated vehicles and submersibles in some circumstances, such as how drones are substituting for airplanes sometimes, thus providing a less expensive and better-tolerated way of monitoring wildlife.

Through continued multidisciplinary collaboration by robot designers, biologists, meteorologists, and more, innovation will continue allowing data collection with minimal to non-disturbance to the wildlife, providing lower costs and higher safety for the researchers.

It is impressive to see how technology efforts are expanding into the oceans. As drones are conquering our skies today and bringing so much valuable information on wildlife monitoring, I believe that the same will occur in our oceans in a near future, assisting in marine life conservation.




Katzschmann RK, DelPreto J, MacCurdy R, Rus D. 2018. Exploration of Underwater Life with an Acoustically Controlled Soft Robotic Fish. Sci. Robot. 3, eaar3449. DOI: 10.1126/scirobotics.aar3449.

The New York Times. 2018. Robotic Fish to Keep a Fishy Eye on the Health of the Oceans. Available at: