Tag: biologging

Putting Fitbits on whales: How tag data allows for estimating calories burned by foraging PCFG gray whales

By: Kate Colson, MSc Student, University of British Columbia, Institute for the Oceans and Fisheries, Marine Mammal Research Unit

Hello! My name is Kate Colson and I am a master’s student at the University of British Columbia, co-supervised by Dr. Andrew Trites of the Marine Mammal Research Unit and Dr. Leigh Torres of the GEMM Lab. As part of my thesis work, I have had the opportunity to spend the summer field season with Leigh and the GEMM Lab team. 

For my master’s I am studying the foraging energetics of Pacific Coast Feeding Group (PCFG) gray whales as part of the much larger Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project. Quantifying the energy expenditure of PCFG gray whales during foraging can help establish a baseline for how disturbance impacts the ability of this unique population to meet their energy needs. Additionally, determining how many calories are burned during different PCFG foraging behaviors might help explain why some gray whales are in better body condition than others.

To understand how much energy different PCFG foraging behaviors cost, I am using data from suction cup tags we have temporarily applied on PCFG gray whales (Figure 1). You can read more about the why the GEMM Lab started using these tags in an earlier blog here. What I want to talk about in this blog is how exactly we can use this tag data to estimate energy expenditure of PCFG gray whales. 

Figure 1. The famous “Scarlett” with a suction cup tag just attached using a carbon fiber pole (seen on far right). This minimally invasive tag has many data sensors, all of which sample at high frequencies, that can allow for an estimation of energy expenditure for different gray whale behaviors. Source: GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678 

The suction cups tags used in this project have many data sensors that are useful for describing the movement of the tagged whale including accelerometers, magnetometers, gyroscopes, and pressure sensors, and all are sampling at high frequencies. For example, the accelerometer is taking 400 measurements per second! The accelerometer, magnetometer, and gyroscope take measurements in 3 dimensions along the X, Y, and Z-axes. The whale’s movement around the X-axis indicates roll (if the whale is swimming on its side), while movement around the Y-axis indicates pitch (if the whales head is oriented towards the surface or the sea floor). Changes in the whale’s movement around the Z-axis indicates if the whale is changing its swimming direction. Together, all of these sensors can describe the dive profile, body orientation, fluking behavior, and fine-scale body movements of the animal down to the second (Figure 2). This allows for the behavior of the tagged whale to be specifically described for the entirety of the tag deployment. 

Figure 2. An example of what the tag sensor data looks like. The top panels show the depth of the animal and can be used to determine the diving behavior of the whale. The middle panels show the body roll of the whale (the X axis) —a roll value close to 0 means the whale is swimming “normally” with no rotation to either side, while a higher roll value means the whale is positioned on its side. The bottom panels show the fluking behavior of the animal: each spike is the whale using its tail to propel itself through the water, with higher spikes indicating a stronger fluke stroke. Source: GEMM Lab, NMFS permit no. 21678

Although these suction cup tags are a great advancement in collecting fine-scale data, they do not have a sensor that actually measures the whale’s metabolism, or rate of calories burned by the whale. Thus, to use this fine-scale tag data as an estimate for energy expenditure, a summary metric must be calculated from the data and used as a proxy. The most common metric found in the literature is Overall Dynamic Body Acceleration (ODBA) and many papers have been published discussing the pros and cons of using ODBA as a proxy for energy expenditure (Brown et al., 2013; Gleiss et al., 2011; Halsey, 2017; Halsey et al., 2011; Wilson et al., 2020). The theory behind ODBA is that because an animal’s metabolic rate is primarily comprised of movement costs, then measuring the acceleration of the body is an effective way of determining energy expenditure. This theory might seem very abstract, but if you have ever worn a Fitbit or similar fitness tracking device to estimate how many calories you’ve burned during a workout, the same principle applies. Those fitness devices use accelerometers and other sensors, to measure the movement of your limbs and produce estimates of energy used. 

So now that we’ve established that the goal of my research is to essentially use these suction cup tags as Fitbits for PCFG gray whales, let’s look at how accelerometry data has been used to detect foraging behavior in large whales so far. Many accelerometry tagging studies have used rorquals as a focal species (see Shadwick et al. (2019) for a review). Well-known rorqual species include humpback, fin, and blue whales. These species forage by using lunges to bulk feed on dense prey patches in the water column. Foraging lunges are indicated by isolated periods of high acceleration that are easily detectable in the tag data (Figure 3; Cade et al., 2016; Izadi et al., 2022). 

Figure 3. Top image: A foraging blue whale performing a surface lunge (Photo credit: GEMM Lab). Note the dense aggregation of krill in the whale’s mouth. Bottom image: The signature acceleration signal for lunge feeding (adapted from Izadi et al., 2022). Each color represents one of the 3D axes of whale movement. The discrete periods of high acceleration represent lunges. 

However, gray whales feed very differently from rorquals. Gray whales primarily suction feed on the benthos, using their head to dig into the sediment and filter prey out of the mud using their baleen. Yet,  PCFG gray whales often perform many other foraging behaviors such as headstanding and side-swimming (Torres et al., 2018). Additionally, PCFG gray whales tend to feed in water depths that are often shallower than their body length. This shallow depth makes it difficult to isolate signals of foraging in the accelerometry data from random variation in the data and separate the tag data into periods of foraging behaviors (Figure 4).

Figure 4. Top image: A foraging PCFG gray whale rolls on its side to feed on mysid prey. Bottom image: The graph shows the accelerometry data from our suction cup tags that can be used to calculate Overall Dynamic Body Acceleration (ODBA) as a way to estimate energy expenditure. Each color represents a different axis in the 3D motion of the whale. The X-axis is the horizontal axis shows forward and backward movement of the whale, the Y-axis shows the side-to-side movement of the whale, and the Z-axis shows the up-down motion of the whale. Note how there are no clear periods of high acceleration in all 3 axes simultaneously to indicate different foraging behaviors like is apparent during lunges of rorqual whales. However, there is a pattern showing that when acceleration in the Z-axis (blue line) is positive, the X- and Y-axes (red and green lines) are negative. Source: GEMM Lab; NMSF permit no. 21678

But there is still hope! Thanks to the GEMM Lab’s previous work describing the foraging behavior of the PCFG sub-group using drone footage, and the video footage available from the suction cup tags deployed on PCFG gray whales, the body orientation calculated from the tag data can be a useful indication of foraging. Specifically, high body roll is apparent in many foraging behaviors known to be used by the PCFG, and when the tag data indicates that the PCFG gray whale is rolled onto its sides, lots of sediment (and sometimes even swarms of mysid prey) is seen in the tag video footage. Therefore, I am busy isolating these high roll events in the collected tag data to identify specific foraging events. 

My next steps after isolating all the roll events will be to use other variables such as duration of the roll event and body pitch (i.e., if the whales head is angled down), to define different foraging behaviors present in the tag data. Then, I will use the accelerometry data to quantify the energetic cost of performing these behaviors, perhaps using ODBA. Hopefully when I visit the GEMM Lab again next summer, I will be ready to share which foraging behavior leads to PCFG gray whales burning the most calories!

References: 

Brown, D. D., Kays, R., Wikelski, M., Wilson, R., & Klimley, A. P. (2013). Observing the unwatchable through acceleration logging of animal behavior. Animal Biotelemetry, 1(1), 1–16. https://doi.org/10.1186/2050-3385-1-20

Cade, D. E., Friedlaender, A. S., Calambokidis, J., & Goldbogen, J. A. (2016). Kinematic diversity in rorqual whale feeding mechanisms. Current Biology, 26(19), 2617–2624. https://doi.org/10.1016/j.cub.2016.07.037

Duley, P. n.d. Fin whales feeding [photograph]. NOAA Northeast Fisheries Science Center Photo Gallery. https://apps-nefsc.fisheries.noaa.gov/rcb/photogallery/finback-whales.html

Gleiss, A. C., Wilson, R. P., & Shepard, E. L. C. (2011). Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure. Methods in Ecology and Evolution, 2(1), 23–33. https://doi.org/10.1111/j.2041-210X.2010.00057.x

Halsey, L. G. (2017). Relationships grow with time: A note of caution about energy expenditure-proxy correlations, focussing on accelerometry as an example. Functional Ecology, 31(6), 1176–1183. https://doi.org/10.1111/1365-2435.12822

Halsey, L. G., Shepard, E. L. C., & Wilson, R. P. (2011). Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comparative Biochemistry and Physiology – A Molecular and Integrative Physiology, 158(3), 305–314. https://doi.org/10.1016/j.cbpa.2010.09.002

Izadi, S., Aguilar de Soto, N., Constantine, R., & Johnson, M. (2022). Feeding tactics of resident Bryde’s whales in New Zealand. Marine Mammal Science, 1–14. https://doi.org/10.1111/mms.12918

Shadwick, R. E., Potvin, J., & Goldbogen, J. A. (2019). Lunge feeding in rorqual whales. Physiology, 34, 409–418. https://doi.org/10.1152/physiol.00010.2019

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 Science, 5, 1–14. https://doi.org/10.3389/fmars.2018.00319

Wilson, R. P., Börger, L., Holton, M. D., Scantlebury, D. M., Gómez-Laich, A., Quintana, F., Rosell, F., Graf, P. M., Williams, H., Gunner, R., Hopkins, L., Marks, N., Geraldi, N. R., Duarte, C. M., Scott, R., Strano, M. S., Robotka, H., Eizaguirre, C., Fahlman, A., & Shepard, E. L. C. (2020). Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. Journal of Animal Ecology, 89(1), 161–172. https://doi.org/10.1111/1365-2656.13040

Drivers of close encounters between albatross and fishing vessels

By Rachael Orben

In September of 2016, Leigh Torres, associate professor at Oregon State University, and I attended the 6th International Albatross and Petrel Conference. Somehow, amid all of the science that filled the week, Leigh first saw the Global Fishing Watch fishing map. She shouted with joy. She immediately envisioned a study to assess interactions between seabirds and fishing boats, and started considering a spatial overlap analysis between telemetry tracks of albatross with the Global Fishing Watch database. Such a study could help reduce bycatch, or the incidental catch of non-target species, like seabirds, in fisheries. Five years later, we executed that study in partnership with Global Fishing Watch, one of the first to look at fine-scale overlap between fishing vessels and marine life on the high seas (Orben et al. 2021).   

Transparent data means opportunity for analysis

Despite knowing that bycatch from fisheries is a real, significant problem for many albatross populations, we have long struggled to know where birds go, where boats fish, and where the two interact in the vast ocean, especially in largely unregulated international waters. Albatross are long-lived seabirds and 15 out of the 22 species are threatened with extinction. Scientists have been tracking albatross for three decades, but assessing individual seabird encounters with vessels has traditionally been limited by a lack of transparency in fishing activity data. Some seabirds are attracted to fishing vessels because of the bait and offal, but we don’t know the whole story of why some birds approach vessels while others don’t.

When we first put our relatively large datasets together – 9,992 days of albatross tracking data from 150 birds and Global Fishing Watch fishing effort data from 2012-2016 – we weren’t sure what we would find. The ocean is a big place, and so finding where one bird and one vessel overlap is kind of like trying to find a needle in a haystack. Would we have enough encounters between birds and boats for an analysis? Would birds encounter fishing vessels as often as we think?

Measuring encounters between albatross and vessels

After overlaying the tracking data with a gridded daily layer of fishing effort, we identified potential encounters between birds and fishing boats. We identified when an albatross could detect a vessel, at a radius or 30 kilometers, and when an albatross had a close encounter with a vessel, within a radius of 3 kilometers (following methods developed in Collet Patrick & Weimerskirch, 2015). Then, we investigated factors that influenced the occurrence and duration of close encounters, considering the bird’s behavior, environmental conditions and habitat, fishing vessel and fisheries characteristics, and temporal variables, such as time of day and month.

Species variation of encounters

We conducted our analysis for three species of albatross that forage in the north Pacific ocean, Laysan albatross, black-footed albatross, and short-tailed albatross.

  • Adult black-footed albatrosses approached vessels for a close encounter 61.9 percent of the time they detected a fishing vessel. 
  • Adult Laysan albatross had close encounters with a fishing boat 35.7 percent of the time they detected a vessel. 
  • Juvenile short-tailed albatross had a lower frequency of close encounters (28.6 percent), 

Understanding close encounters and their duration

Due to a low sample size of encounters, we were unable to investigate the reason for close encounters or their duration for black-footed albatrosses. More tracking data is critical to understand factors influencing the impact of vessels on this vulnerable species.

Laysan albatross were more likely to approach fishing vessels when fishing effort was high, but fishing boat density was low. Laysan albatross also had close encounters with vessels more frequently while they were foraging. Due to sample size, we could not further investigate the reason for the duration of encounters for this species.

Short-tailed albatrosses were also more likely to approach fishing vessels when they were searching for prey, fishing effort was high, and fishing boat density was low. They were more likely to have close encounters with vessels during the day and in habitats with water depths from 75-1500 meters. 

Vessel attendance by short-tailed albatrosses was longer when sea surface temperatures were warmer and less productive, and during periods with lower wind speeds. 

A useful approach

The information available to fisheries managers in order to reduce bycatch is most often limited to data collected from the perspective of the fishing vessels. Our analysis provides an alternative view – an albatross’ view of when and where boats are encountered in the seascape. While our analysis didn’t specifically look at bycatch, our estimates of proximity between birds and boats can be considered a proxy for  increased bycatch risk.

For the endangered short-tailed albatross, bycatch events are few, but they come with high consequences for the bird population and fishing industry. Extending our study in a dynamic ocean management framework to provide an early warning system to predict when short-tailed albatross might make close and longer encounters with fishing vessels could be the next step. Furthermore, our analysis methods to assess when, where and why marine animals interact with fishing vessels can be applied to many other marine species in order to understand and reduce conflicts with fisheries. 

This blog was for the Global Fishing Watch blog at globalfishingwatch.org

References

Collet, J., Patrick, S. C., & Weimerskirch, H. (2015). Albatrosses redirect flight towards vessels at the limit of their visual range. Marine Ecology Progress Series, 526, 199–205. http://doi.org/10.3354/meps11233

Orben, RA J Adams, M Hester, SA Shaffer, R Suryan, T Deguchi, K Ozaki, F Sato, LC Young, C Clatterbuck, MG Conners, DA Kroodsma, LG Torres. 2021. Across borders: External factors and prior behavior influence North Pacific albatross associations with fishing vessels. Journal of Applied Ecology. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.13849