Tag: Pacific Coast Feeding Group

Expand your rolodex and meet some more IndividuWhales!

In case you aren’t already aware, I want to remind you of a website called IndividuWhale we created about Pacific Coast Feeding Group (PCFG) gray whales we study as part of our GRANITE project. IndividuWhale features stories of some of the Oregon coast’s most iconic gray whales, as well as information about how we study them, stressors they experience in our waters, and even a game to test your gray whale identification skills. We also provide details about where to best spot gray whales along our coast and the different behaviors you might see gray whales displaying at different times of the year. Since launching the website in late 2021, we have made small tweaks and updates along the way, but now, after about 2.5 years, the time has come for a major content update as we are introducing you to three new individuals and their stories! Head over to IndividuWhale.com to check out the updates or continue reading for a preview of the content…

Lunita

Even though “Lunita” is only two years old (as of 2024), they (sex currently unknown!) have quickly become a star of our dataset and hearts. We documented Lunita as a calf with their mother “Luna” (hence the name Lunita, which means little Luna/moon) in 2022. We observed the mom-calf pair in our study area for almost two weeks during which it seemed like Lunita was a very attentive calf, always staying close to Luna and appearing to benthic feed alongside their mom. As is often the case when we document mom–calf pairs, we wonder whether we will see the calf again and how it will fair in an environment increasingly impacted by human activities. Much to our delight, we were reunited with Lunita later in the same summer when we saw them feeding independently, indicating that they had successfully weaned. We were even more delighted when we were reunited with Lunita again many times during the summer of 2023 as Lunita spent almost the entire feeding season along the central Oregon coast. This is yet another example, much like “Cheetah” and “Pacman,” of successful internal recruitment of calves born to PCFG females into the PCFG sub-population.

Lunita’s high site fidelity to our study area in 2023 meant that she was an excellent candidate for the suction-cup tagging we have been conducting in the last few years. During suction-cup tagging, we attach a device (or tag) via suction cups to a whale’s back. The tag contains a number of different sensors, including an accelerometer (to measure speed), a gyroscope (to measure direction), and a magnetometer (to measure magnetic field), as well as a high-definition video camera and hydrophone (or underwater microphone). These tags typically stay on for a maximum of 24 hours before they pop off the whale leaving no harm to the whale. Upon retrieval, we can recreate the whale’s dive path and see the environment and conditions that the whale experienced over several hours. We sometimes refer to tagging as giving the gray whales some temporary jewelry because the tags are a very flashy, bright orange color. From the video from Lunita’s tag shows how they soared through kelp forests feeding on mysids for many, many hours. Check out their profile here: https://www.individuwhale.com/whales/lunita/

Burned

There are many ways to assess the health of a whale. In our lab, we calculate body condition from drone images to determine how fat or skinny a whale is, examine different hormones from their poop, and assess growth rates via length measurements from drone images. Another health assessment metric that we explore in the lab is the skin and scarring on the individuals that we see in our central Oregon study area. By conducting a skin and scarring analysis, we can identify scarring patterns and lesions that may indicate interactions with human activities and track the progression of skin diseases that will help us understand the prevalence and impacts of pathogens on whales. One skin condition that we are particularly interested in tracking appears as a thick white or gray layer that can mask a gray whale’s natural pigmentation. An example of a whale that has experienced this skin condition is “Burned.”

Burned is a female who is at least 9 years old (as of 2024), as she was first documented in the PCFG range in 2015. We saw Burned for the first time in 2016. At the time, we noticed small, isolated, gray patches of the skin condition on both sides of Burned’s body. Throughout the years as we have continued to resight Burned, we noticed the skin condition spreading progressively across her body. We saw the skin condition at its maximum extent in 2022 when, at first glance, Burned was hardly recognizable. Luckily, we can identify gray whales using more than just their pigmentation patterns (learn more on our whale identification page). Interestingly, when we saw Burned in June 2024, it appeared that the skin condition completely disappeared! Burned is just one example of whales with this skin condition, leaving us with many questions about its origin and impact on the whales: What causes the skin condition (viral, fungal, bacterial?); How it is transmitted (via air or contact?); Is it harmful to the whale (weakened immune system?). Our research is aimed at addressing these questions to make this skin condition a little less mysterious. Check out her profile here: https://www.individuwhale.com/whales/burned/

Heart

“Heart,” who is also known as “Ginger,” is a very well known and popular whale in the Depoe Bay region. Heart is a female who is particularly famous for being a “tall fluker,” meaning that when she dives, she arches her tail fluke high in the air before it glides elegantly into the water. Heart was first documented as a calf in 2010, which means that she is 14 years old (as of 2024). At 14 years of age, we would expect for Heart to have had at least one, if not more, calves by now, as it is believed that gray whales reach sexual maturity at age 8 or 9. However, Heart has never been documented with a calf. Why?

While we cannot know for sure, we have a theory that it might be linked to her body length. Recent work in our lab has explored how growth of PCFG whales has changed over time. Using measurements of whales from our drone data, we  investigated how the asymptotic length (i.e. the final length reached once an individual stops growing) for the PCFG whales has changed since the 1980s. Shockingly, we found that starting in the year 2000 the asymptotic length of PCFG whales has declined at an average rate of 0.05–0.12 meters per year. Over time, this means that a whale born in 2020 is expected to reach an adult body length that is 13% shorter than a gray whale born prior to 2000. In Heart’s case specifically, when we last measured her length at 13 years old, she was 10.65 meters long. If she had been born prior to 2000, then she would be 12.04 meters long by now at the age of 13. That’s a whole 1.5 meters (or almost 5 feet) shorter!

You might be wondering how Heart’s length links back to her ability to have a calf. It takes a lot of energy to be pregnant and support the fetus, so by being smaller, Heart may not be able to store and allocate enough energy towards reproduction. Many of the whales we commonly see are shorter than expected based on their age (including “Zorro”), so we are monitoring the number and frequency of calves in the PCFG to see how this decline in length may impact the population. Check our her profile here: https://www.individuwhale.com/whales/heart/

Be sure to head over to IndividuWhale.com to explore all of the whale profiles and lots of other information that we have provided there about PCFG gray whales and how we study them here in Oregon waters!

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Who, where, when: Estimating individual space use patterns of PCFG gray whales

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

Understanding how baleen whales are affected by human activity is a central goal for many research projects in the GEMM Lab. The overarching goal of the GRANITE (Gray whale Response to Ambient Noise Informed by Technology and Ecology) project is to quantify baleen whale physiological response to different stressors (e.g., boat presence and noise) and model the subsequent impacts of these stressors on the population. We will achieve this goal by implementing our long-term, replicate dataset of Pacific Coast Feeding Group (PCFG) gray whales into a framework called population consequences of disturbance (PCoD). I will not go into the details of PCoD in this blog (but I wrote a post a few years ago that you can revisit). Instead, I will explain the approach I am taking to assess where and when individual whales spend time in our study area, which will form an essential component of PCoD and be one of the chapters of my PhD dissertation.

Individuals in a population are unlikely to be exposed to a stressor in a uniform way because they make decisions differently based on intrinsic (e.g., sex, age, reproductive status) and extrinsic (e.g., environment, prey, predators) factors (Erlinge & Sandell 1986). For example, a foraging female gray whale who is still nursing a calf will need to consider factors that are different to ones that an adult single male might need to consider when choosing a location to feed. These differences in decision-making exist across the whole population, which makes it important to understand where individuals are spending time and how they overlap with stressors in space and time before trying to quantify the impacts of stressors on the population as a whole (Pirotta et al. 2018). I am currently working on an analysis that will determine an individual’s exposure to a number of stressors based on their space use patterns. 

We can monitor space use patterns of individuals in a population through time using spatial capture-recapture techniques. As the name implies, a spatial capture-recapture technique involves capturing an individual in a marked location during a sampling period, releasing it back into the population, and then (hopefully) re-capturing it during another sampling period in the future, at either the same or a different location. With enough repeat sampling events, the method should build spatial capture histories of individuals through time to better understand an individual’s space use patterns (Borchers & Efford 2008). While the use of the word capture implies that the animal is being physically caught, this is not necessarily the case. Individuals can be “captured” in a number of non-invasive ways, including by being photographed, which is how we “capture” individual PCFG gray whales. These capture-recapture methods were first pioneered in terrestrial systems, where camera traps (i.e., cameras that take photos or videos when a motion sensor is triggered) are set up in a systematic grid across a study area (Figure 1; Royle et al. 2009, Gray 2018). Placing the cameras in a grid system ensures that there is an equal distribution of cameras throughout the study area, which means that an animal theoretically has a uniform chance of being captured. However, because we know that individuals within a population make space use decisions differently, we assume that individuals will distribute themselves differently across a landscape, which will manifest as individuals having different centers of their spatial activity. The probability of capturing an individual is highest when a camera trap is at that individual’s activity center, and the cameras furthest away from the individual’s activity center will have the lowest probability of capturing that individual (Efford 2004). By using this principle of probability, the data generated from spatial capture-recapture field methods can be modelled to estimate the activity centers and ranges for all individuals in a population. The overlap of an individual’s activity center and range can then be compared to the spatiotemporal distribution of stressors that an individual may be exposed to, allowing us to determine whether and how an individual has been exposed to each stressor. 

Figure 1. Example of camera trap grid in a study area. Figure taken from Gray (2018).

While capture-recapture methods were first developed in terrestrial systems, they have been adapted for application to marine populations, which is what I am doing for our GRANITE dataset of PCFG gray whales. Together with a team of committee members and GRANITE collaborators, I am developing a Bayesian spatial capture-recapture model to estimate individual space use patterns. In order to mimic the camera trap grid system, we have divided our central Oregon coast study area into latitudinal bins that are approximately 1 km long. Unfortunately, we do not have motion sensor activated cameras that automatically take photographs of gray whales in each of these latitudinal bins. Instead, we have eight years of boat-based survey effort with whale encounters where we collect photographs of many individual whales. However, as you now know, being able to calculate the probability of detection is important for estimating an individual’s activity center and range. Therefore, we calculated our spatial survey effort per latitudinal bin in each study year to account for our probability of detecting whales (i.e., the area of ocean in km2 that we surveyed). Next, we tallied up the number of times we observed every individual PCFG whale in each of those latitudinal bins per year, thus creating individual spatial capture histories for the population. Finally, using just those two data sets (the individual whale capture histories and our survey effort), we can build models to test a number of different hypotheses about individual gray whale space use patterns. There are many hypotheses that I want to test (and therefore many models that I need to run), with increasing complexity, but I will explain one here.

Over eight years of field work for the GRANITE project, consisting of over 40,000 km2 of ocean surveyed with 2,169 sightings of gray whales, our observations lead us to hypothesize that there are two broad space use strategies that whales use to optimize how they find enough prey to meet their energetic needs. For the moment, we are calling these strategies ‘home-body’ and ‘roamer’. As the name implies, a home-body is an individual that stays in a relatively small area and searches for food in this area consistently through time. A roamer, on the other hand, is an individual that travels and searches over a greater spatial area to find good pockets of food and does not generally tend to stay in just one place. In other words, we except a home-body to have a consistent activity center through time and a small activity range, while a roamer will have a much larger activity range and its activity center may vary more throughout the years (Figure 2). 

Figure 2. Schematic representing one of the hypotheses we will be testing with our Bayesian spatial capture-recapture models. The schematic shows the activity centers (the circles) and activity ranges (vertical lines attached to the circles) of two individuals (green and orange) across three years in our central Oregon study area. The green individual represents our hypothesized idea of a home-body, whereas the orange individuals represents our idea of a roamer.

While this hypothesis sounds straightforward, there are a lot of decisions that I need to make in the Bayesian modeling process that can ultimately impact the results. For example, do all home-bodies in a population have the same size activity range or can the size vary between different home-bodies? If it can vary, by how much can it vary? These same questions apply for the roamers too. I have a long list of questions just like these, which means a lot of decision-making on my part, and that long list of hypotheses I previously mentioned. Luckily, I have a fantastic team made up of Leigh, committee members, and GRANITE collaborators that are guiding me through this process. In just a few more months, I hope to reveal how PCFG individuals distribute themselves in space and time throughout our central Oregon study area, and hence describe their exposure to different stressors. Stay tuned! 

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References

Borchers DL, Efford MG (2008) Spatially explicit maximum likelihood methods for capture-recapture studies. Biometrics 64:377-385.

Efford M (2004) Density estimation in live-trapping studies. Oikos 106:598-610.

Erlinge S, Sandell M (1986) Seasonal changes in the social organization of male stoats, Mustela erminea: An effect of shifts between two decisive resources. Oikos 47:57-62.

Gray TNE (2018) Monitoring tropical forest ungulates using camera-trap data. Journal of Zoology 305:173-179.

Pirotta E, Booth CG, Costa DP, Fleishman E, Kraus SD, and others (2018) Understanding the population consequences of disturbance. Ecology and Evolution 8(19):9934–9946.Royle J, Nichols J, Karanth KU, Gopalaswamy AM (2009) A hierarchical model for estimating density in camera-trap studies. Journal of Applied Ecology 46:118-127.

The road to candidacy is paved with knowledge

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

As I sat down to write this blog, I realized that it is the first post I have written in 2023! This is largely because I have spent the last seven weeks preparing for (and partly taking) my PhD qualifying exams, an academic milestone that involves written and oral exams prepared by each committee member for the student. The point of the qualifying exams is for the student’s committee to determine the student’s understanding of their major field, particularly where and what the limits of that understanding are, and to assess the student’s capability for research. How do you prepare for these exams? Reading. Lots of reading and synthesis of the collective materials assigned by each committee member. My dissertation research covers a broad range of Pacific Coast Feeding Group (PCFG) gray whale ecology, such as space use, oceanography, foraging theory and behavioral responses to anthropogenic activities. Accordingly, my assigned reading lists were equally broad and diverse. For today’s blog, I am going to share some of the papers that have stuck with me and muse about how these topics relate to my study system, the Pacific Coast Feeding Group (PCFG) of gray whales.

Space use & home range

For decades, ecologists have been interested in defining an animal’s use of space through time, often referred to as an animal’s home range. The seminal definition of a home range comes from Burt (1943) who outlined it as “the area traversed by an individual in its normal activities of food gathering, mating, and caring for young.”. I like this definition of a home range because it is biologically grounded and based on an animal’s requirements. However, quantifying an animal’s home range based on this definition is harder than it may sound. In an ideal world, it could be achieved if we were able to collect location data that is continuous (i.e., one location per second), long-term (i.e., at least half the lifespan of an animal) and precise (i.e., correct to the nearest meter) together with behavior for an individual. However, a device that could collect such data, particularly for a baleen whale, does not currently exist. Instead, we must use discontinuous (i.e., one location per hour, day or month) and/or short-term (i.e., <1 year) data with variable precision to calculate animal home ranges. A very common and simple analytical method that is used to calculate an animal’s home range is the minimum convex polygon (MCP). MCP draws the smallest polygon around points with all interior angles less than 180º. While this method is appealing and widely used, it often overestimates the home range by including areas not used by an animal at all (Figure 1).

Figure 1. (a) 10 point locations where an individual was observed; (b) the home range as determined by the minimum convex polygon method; (c) the red path shows the movements the animal actually took. Note the large white area in (c) where the animal never went even though it is considered part of the animal’s home range.

This example is just one of many where home range estimators inaccurately describe an animal’s space use. However, this does not mean that we should not attempt to make our best approximations of an animal’s home range using the tools and data we have at our disposal. Powell & Mitchell perfectly summarized this sentiment in their 2012 paper: “Understanding animal’s home ranges will be a messy, irregular, complex process and the results will be difficult to map. We must embrace this messiness as it simply represents the real behaviors of animals in complex and variable environments.”. For my second dissertation chapter, I am investigating individual PCFG gray whale space use patterns by calculating activity centers and ranges. The activity center is simply the geographic center of all points of observation (Hayne, 1949) and the range is the distance from the activity center to the most distant point of observations in either poleward direction. While the actual activity center is probably relatively meaningless to a whale, we hope that by calculating these metrics we can identify different strategies of space use that individuals employ to meet their energetic requirements (Figure 2).

Figure 2. Sightings of nine different PCFG individuals across our GRANITE study area. Each circle represents a location where an individual was sighted and circles are color-coded by year. Plotting the raw data of sighting histories of these individuals hints at patterns in space use by different individuals, which I will explore further in my second dissertation chapter.

Non-stationary responses to oceanography

Collecting spatiotemporally overlapping predator-prey datasets at the appropriate scales is notoriously challenging in the marine environment. As a result, marine ecologists often try to find patterns between marine species and oceanographic and/or environmental covariates, as these can sometimes be easier to sample and thus make marine species predictions simpler. This approach has been applied successfully in hundreds, if not thousands, of studies (e.g., Barlow et al., 2020; Derville et al., 2022). Unfortunately, these relationships are not always proving to be stable over time, a phenomenon called non-stationarity. For example, Schmidt et al. (2014) showed that the reproductive successes of Brandt’s cormorants and Cassin’s auklets on southeast Farallon Island were positively correlated with each other from 1975 to 1995 and were associated with negative El Niño-Southern Oscillation. However, around the mid-1990s this relationship broke down and by 2002, the reproductive successes of the two species were significantly negatively correlated (Figure 3). Furthermore, the relationships between reproductive success and most physical oceanographic conditions became highly variable from year to year and were non-stationary. Thus, if the authors continued to use the relationships defined early on in the study (1975-1995) to predict seabird reproductive success relative to ocean conditions from 2002-2012, their predictions would have been completely wrong. After reading this study, I thought a lot about what the oceanographic conditions have been since the GEMM Lab started studying PCFG gray whales vs. the years prior. Leigh launched the GRANITE project in 2016, right at the tail end of the record marine heatwave in the Pacific, known as “the Blob”. While we do not have as long of a dataset as the Schmidt et al. (2014) study, I wonder whether we might find non-stationary responses between PCFG gray whales and environmental and/or oceanographic variables, given how the effects of the Blob lingered for a long time and we may have captured the central Oregon coast environment shifting from ‘weird to normal’. Non-stationarity is something I will at least keep in mind when I am working on my third dissertation chapter which will investigate the environmental and oceanographic drivers of PCFG gray whale space use strategies.

Figure 3. Figure and caption taken from Schmidt et al. (2014).

There are so many more studies and musings that I could write about. I keep being told by others who have been through this qualifying exam process that this is the smartest I am ever going to be, and I finally understand what they mean. After spending almost two months in my own little study world, my research, and where it fits within the complex web of ecological knowledge, has snapped into hyperfocus. I can see clearly where past research will guide me and where I am blazing a new trail of things never attempted before. While I still have the oral portion of my exams before me (in fact, it’s tomorrow!), I am already giddy with excitement to switch back to analyzing data and making progress on my dissertation research.

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References

Barlow, D.R., Bernard, K.S., Escobar-Flores, P., Palacios, D.M., Torres, L.G. 2020. Links in the trophic chain: modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Marine Ecology Progress Series 642: 207−225. 

Burt, W.H. 1943. Territoriality and home range concepts as applied to mammals. Journal of Mammalogy 24(3): 346-352. https://doi.org/10.2307/1374834

Derville, S., Barlow, D.R., Hayslip, C., Torres, L.G. 2022. Seasonal, annual, and decadal distribution of three rorqual whale species relative to dynamic ocean conditions off Oregon, USA. Frontiers in Marine Science 9. https://doi.org/10.3389/fmars.2022.868566

Hayne, D.W. 1949. Calculation of size of home range. Journal of Mammalogy 30(1): 1-18. 

Powell, R.A., Mitchell, M.S. 2012. What is a home range? Journal of Mammalogy 93(4): 948-958. https://doi.org/10.1644/11-MAMM-S-177.1

Schmidt, A.E., Botsford, L.W., Eadie, J.M., Bradley, R.W., Di Lorenzo E., Jahncke, J. 2014. Non-stationary seabird responses reveal shifting ENSO dynamics in the northeast Pacific. Marine Ecology Progress Series 499: 249-258. https://doi.org/10.3354/meps10629

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 (https://doi.org/10.3389/fmars.2022.867258) led by GEMM Lab’s Dr. Leigh Torres, Clara 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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References

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

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 Science, 35(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. https://doi.org/10.3389/fmars.2022.867258

Do gray whales count calories? New GEMM Lab publication compares energetic values of prey available to gray whales on two feeding grounds in the eastern North Pacific

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

Predators have high energetic requirements that must be met to ensure reproductive success and population viability. For baleen whales, this task is particularly challenging since their foraging seasons are typically limited to short temporal windows during summer months when they migrate to productive high latitude environments. Foraging success is a balancing act whereby baleen whales must maximize the amount of energy they intake, while minimizing the amount of energy they expend to obtain food. Maximization of energy intake can be achieved by targeting the most beneficial prey. How beneficial a particular prey type (or prey patch) is can depend on a number of factors such as abundance, density, quality, size, and availability. Determining why baleen whales target particular prey types or patches is an important factor to enhance our understanding of their ecology and can ultimately aid in informing their management and conservation.

The GEMM Lab has several research projects in Newport and Port Orford, Oregon, on the Pacific Coast Feeding Group (PCFG), which is a sub-group of gray whales from the Eastern North Pacific (ENP) population. While ENP gray whales feed in the Bering, Chukchi, and Beaufort Seas (Arctic) in the summer months, the PCFG utilizes the range from northern California, USA to northern British Columbia, Canada. Our work to date has revealed a number of new findings about the PCFG including that they successfully gain weight during the summer on Oregon foraging grounds (Soledade Lemos et al. 2020). Furthermore, females that consistently use the PCFG range as their foraging grounds have successfully reproduced and given birth to calves (Calambokidis & Perez 2017). Yet, the abundance of the PCFG (~250 individuals; Calambokidis et al. 2017) is two orders of magnitude smaller than the ENP population (~20,000; Stewart & Weller 2021). So, why do more gray whales not use the PCFG range as their foraging grounds when it provides a shorter migration while also allowing whales to meet their high energetic requirements and ensure reproductive success? There are several hypotheses regarding this ecological mystery including that prey abundance, density, quality, and/or availability are higher in the Arctic than in the PCFG range, thus justifying the much larger number of gray whales that migrate further north for the summer feeding season. 

Figure 1. Locations of prey samples collected with a light trap (open circles) or opportunistic collections of surface swarms of crab larvae (black triangles) in Newport, along the Oregon coast in the Pacific Northwest coast of the United States.

Our recent paper in Frontiers in Marine Science addressed the hypothesis that prey quality in the Arctic is higher than that of PCFG prey. To test this hypothesis, we first determined the quality (energetic value) of nearshore Oregon zooplankton species that PCFG gray whales are assumed to feed on (based on observations of fine-scale spatial and temporal overlap of foraging gray whales and sampled zooplankton). We obtained prey samples from nearshore reefs along the Oregon coast (Figure 1) as part of the GRANITE project using a light trap, which is a modified water jug with a weight and two floats attached to it, allowing the trap to sit approximately 1 meter above the seafloor. The trap contained a light which attracted zooplankton and effectively captured epibenthic prey of gray whales. Traps were left to soak overnight in locations where gray whales had been observed feeding extensively and collected the following morning. After identifying each specimen to species level and sorting them into reproductive stages, we used a bomb calorimeter to determine the caloric content of each species by month, year, and reproductive stage. We then compared these values to the literature-derived caloric value of the predominant benthic amphipod species that  ENP gray whales feed on in the Arctic. These comparisons allowed us to extrapolate the caloric values gained from each prey type to estimated energetic requirements of pregnant and lactating female gray whales (Villegas-Amtmann et al. 2017). 

Figure 2. Median caloric content and interquartile ranges by (A) species, (B) reproductive stage, and (C) month. Sizes of the zooplankton images are scaled at actual ratios relative to one another.

So, what did we find? Our sampling along the Oregon coast revealed six predominant zooplankton species: two mysid shrimp (Neomysis rayii, Holmesimysis sculpta), two amphipods (Atylus tridens, Polycheria osborni), and two types of crab larvae (Dungeness crab megalopae, porcelain crab larvae). These six Oregon prey species showed significant differences in their caloric values, with N. rayii and Dungeness crab megalopae having significantly higher calories per gram than the other prey species (Figure 2), though Dungeness crab megalopae stood out as the caloric gold mines for feeding gray whales in the PCFG range. Furthermore, month and reproductive stage also influenced  the caloric content of some prey species, with gravid (aka pregnant) female mysid shrimp significantly increasing in calories throughout the summer (Figure 3).

Figure 3. Caloric content of different reproductive stages as a function of day of year (DOY; ranging from June to October) for the mysids Holmesimysis sculpta and Neomysis rayii, and the amphipod Atylus tridens. A. tridens is only represented on one panel due to small sample size of this species for the empty brood pouch and gravid reproductive stages. Asterisks indicate significant regressions (p<0.05).

The comparison of our Oregon prey caloric values to the predominant Arctic amphipod (Ampelisca macrocephala) proved our hypothesis wrong:  Arctic amphipods do not have higher caloric value than Oregon prey, which would have help to explain why many more gray whales feed in the Arctic. We found that two Oregon prey species (N. rayii and Dungeness crab megalopae) have higher caloric values than A. macrocephala. If we translate the caloric contents of these prey to gray whale energetic needs, these differences mean that lactating and pregnant gray whales feeding in the PCFG area would need between 0.7-1.03 and 0.22-0.33 metric tons of prey less per day if they fed on Dungeness crab megalopae or N. rayii, respectively, than a whale feeding on Arctic A. macrocephala (Figure 4). 

Figure 4. Daily prey requirements (A: metric tons; B: number of individuals) needed by pregnant and lactating female gray whales to meet their energetic requirements on the foraging ground. Energetic requirement estimates obtained from Villegas-Amtmann et al. (2017). Note the logarithmic scale of y-axis in panel (B).

If quality were the only prey metric that gray whales used to evaluate which food to eat, then it would make very little sense for so many gray whales to migrate to the Arctic when there are prey types of equal and greater quality available to them in the PCFG range. However, quality is not the only metric that influences gray whale foraging decisions. We therefore posit that the abundance, density, and availability of benthic amphipods in the Arctic are higher than the prey species found in the PCFG range. In fact, knowledge of the pulsed reproductive cycle of Dungeness and porcelain crabs allows us to conclude that the larvae of these two species are only available for a few weeks in the late spring and early summer on the Oregon coast. While mysid shrimp, such as N. rayii, are continuously available in the PCFG range throughout the summer, they may occur in less dense and more patchy aggregations than Arctic benthic amphipods. However, current estimates of prey density and abundance for either region are not available, and we do not have data on the energetic costs of the different foraging strategies. While there are still several unknowns, we have documented that higher prey quality in the Arctic is not the reason for the difference in gray whale foraging ground use in the eastern North Pacific.

References

Calambokidis, J., & Perez, A. 2017. Sightings and follow-up of mothers and calves in the PCFG and implications for internal recruitment. IWC Report SC/A17/GW/04 for the Workshop on the Status of North Pacific Gray Whales (La Jolla: IWC).

Calambokidis, J., Laake, J., & Perez, A. 2017. Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2015. IWC Report SC/A17/GW/05 for the Workshop on the Status of North Pacific Gray Whales (La Jolla: IWC).

Soledade Lemos, L., Burnett, J. D., Chandler, T. E., Sumich, J. L., & Torres, L. G. 2020. Intra- and inter-annual variation in gray whale body condition on a foraging ground. Ecosphere 11(4):e03094.

Stewart, J. D., & Weller, D. W. 2021. Abundance of eastern North Pacific gray whales 2019/2020. Department of Commerce, NOAA Technical Memorandum NMFS-SWFSC-639. United States: NOAA. doi:10.25923/bmam-pe91.

Villegas-Amtmann, S., Schwarz, L. K., Gailey, G., Sychenko, O., & Costa, D. P. 2017. East or west: the energetic cost of being a gray whale and the consequence of losing energy to disturbance. Endangered Species Research 34:167-183.

Rock-solid GRANITE: Scaling the disturbance response of individual whales up to population level impacts

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

Since early May, much of the GEMM Lab has been consumed by the GRANITE project, which stands for Gray whale Response to Ambient Noise Informed by Technology and Ecology. Two weeks ago, PhD student Clara Bird discussed our field work preparations, and since May 20th we have conducted five successful days of field work (and one unsuccessful day due to fog). If you are now expecting a blog about the data we have collected so far and whales we encountered, I am sorry to disappoint you. Rather, I want to take a big step back and provide the context of the GRANITE project as a whole, explain why this project and data collection is so important, and discuss what it is that we hope to achieve with our ever-growing, multidisciplinary dataset and team.

We use the Pacific Coast Feeding Group (PCFG) of gray whales that forage off the Oregon coast as our study system to better understand the ecological and physiological response of baleen whales to multiple stressors. Our field methodology includes replicate physiological and ecological sampling of this accessible baleen whale population with synoptic measurement of multiple types of stressors. We collect fecal samples for hormone analysis, conduct drone overflights of whales to collect body condition and behavioral data, record the ambient soundscape through deployment of two hydrophones, and conduct whale photo-identification to link all data streams to each individual whale of known sex, estimated age, and reproductive status. We resample these data from multiple individuals within and between summer foraging seasons, while exposed to different potential stressors occurring at different intensities and temporal periods and durations. The hydrophones are strategically placed with one in a heavily boat-trafficked (and therefore noisy) area close to the Port of Newport, while the second is located in a relatively calm (and therefore quieter) spot near the Otter Rock Marine Reserve (Fig. 1). These hydrophones provide us with information about both natural (e.g. killer whales, wind, waves) and anthropogenic (e.g. boat traffic, seismic survey, marine construction associated with PacWave wave energy facility development) noise that may affect gray whales. During sightings with whales, we also drop GoPro cameras and sample for prey to better understand the habitats where whales forage and what they might be consuming.

Figure 1. Map of GRANITE study area from Seal Rock to Lincoln City with gray whale sightings (yellow circles) and and fecal samples collected (red triangles) from the 2020 field season. Green stars represent the two hydrophone locations. Source: L. Torres.

GEMM Lab PI Dr. Leigh Torres initiated this research project in 2015 and established partnerships with acoustician Dr. Joe Haxel and (then) PhD student Dr. Leila Lemos. Since then, the team working on this project has grown considerably to provide expertise in the various disciplines that the project integrates. Leigh is currently joined at the GRANITE helm by 4 co-PIs: Dr. Haxel, endocrinologist Dr. Kathleen Hunt, biological statistician Dr. Leslie New, and physiologist Dr. Loren Buck. Drs. Alejandro Fernandez Ajo, KC Bierlich and Enrico Pirotta are postdoctoral scholars who are working on the endocrinology, photogrammetry, and biostatistical modelling components, respectively. Finally, Clara and myself are partially funded through this project for our PhD research, with Clara focusing on the links between behavior, body condition, individualization, and habitat, while I am tackling questions about the recruitment and site fidelity of the PCFG (more about these topics below). 

Faculty Research Assistant Todd Chandler supervises PhD student Clara Bird during her maiden drone flight over a whale. Source: L. Torres.

The ultimate goal of this project is to use the PCFG as a case study to quantify baleen whale physiological response to different stressors and model the subsequent impacts on the population by implementing our long-term, replicate dataset into a framework called Population consequences of disturbance (PCoD; Fig. 2). PCoD is built upon the underlying concept that changes in behavior and/or physiology caused by disturbance (i.e. noise) affect the fitness of individuals by impacting their health and vital rates, such as survival, reproductive success, and growth rate (Pirotta et al. 2018). These impacts at the individual level may (or may not) affect the population as a whole, depending on what proportion of individuals in the population are affected by the disturbance and the intensity of the disturbance effect on each individual. The PCoD framework requires quantification of four stages: a) the physiological and/or behavioral changes that occur as a result of exposure to a stressor (i.e. noise), b) the acute effects of these physiological and/or behavioral responses on individual vital rates, and their chronic effects via individual health, c) the way in which changes in health may affect the vital rates of individuals, and d) how changes in individual vital rates may affect population dynamics (Fig. 2; Pirotta et al. 2018). While four stages may not sound like a lot, the amount and longevity of data needed to quantify each stage is immense. 

Figure 2. Conceptual framework of the population consequences of disturbance (PCoD). Letters (A-D) represent the four stages that require quantification in order for PCoD to be implemented. Each colored box represents external (ecological drivers, stressors) and internal (physiology, health, vital rates, behavior) factors that can change over time that are measured for each individual whale (dashed grey boundary line). The effects are then integrated across all individuals in the population to project their effects on the population’s dynamics. Figure and caption adapted from Pirotta et al. 2018.

The ability to detect a change in behavior or physiology often requires an understanding of what is “normal” for an individual, which we commonly refer to as a baseline. The best way to establish a baseline is to collect comprehensive data over a long time period. With our data collection efforts since 2015 of fecal samples, drone flights and photo identification, we have established useful baselines of behavioral and physiological data for PCFG gray whales. These baselines are particularly impressive since it is typically difficult to collect repeated measurements of hormones and body condition from the same individual baleen whale across multiple years. These repeated measurements are important because, like all mammals, hormones and body condition vary across life history phases (i.e., with pregnancy, injury, or age class) and across time (i.e., good or bad foraging conditions). To achieve these repeated measurements, GRANITE exploits the high degree of intra- and inter-annual site fidelity of the PCFG, their accessibility for study due to their affinity for nearshore habitat use, and the long-term sighting history of many whales that provides sex and approximate age information. Our work to-date has already established a few important baselines. We now know that the body condition of PCFG gray whales increases throughout a foraging season and can fluctuate considerably between years (Soledade Lemos et al. 2020). Furthermore, there are significant differences in body condition by reproductive state, with calves and pregnant females displaying higher body conditions (Soledade Lemos et al. 2020). Our dataset has also allowed us to validate and quantify fecal steroid and thyroid hormone metabolite concentrations, providing us with putative thresholds to identify a stressed vs. not stressed whale based on its hormone levels (Lemos et al. 2020).

PhD student Lisa Hildebrand and GRANITE co-PI Dr. Kathleen Hunt collecting a fecal sample. Source: L. Torres.

We continue to collect data to improve our understanding of baseline PCFG physiology and behavior, and to detect changes in their behavior and physiology due to disturbance events. All these data will be incorporated into a PCoD framework to scale from individual to population level understanding of impacts. However, more data is not the only thing we need to quantify each of the PCoD stages. The implementation of the PCoD framework also depends on understanding several aspects of the PCFG’s population dynamics. Specifically, we need to know whether recruitment to the PCFG population occurs internally (calves born from “PCFG mothers” return to the PCFG) or externally (immigrants from the larger Eastern North Pacific gray whale population joining the PCFG as adults). The degree of internal or external recruitment to the PCFG population should be included in the PCoD model as a parameter, as it will influence how much individual level disturbance effects impact the overall health and viability of the population. Furthermore, knowing residency times and home ranges of whales within the PCFG is essential to understand exposure durations to disturbance events. 

To assess both recruitment and residency patterns of the PCFG, I am undertaking a large photo-identification effort, which includes compiling sightings and photo data across many years, regions, and collaborators. Through this effort we aim to identify calves and their return rate to the population, the rate of new adult recruits to the population, and the spatial residency of individuals in our study system. Although photo-id is a basic, commonplace method in marine mammal science, its role is critical to tracking individuals over time to understand population dynamics (in a non-invasive manner, no less). A large portion of my PhD research will focus on the tedious yet rewarding task of photo-id data management and matching in order to address these pressing knowledge gaps on PCFG population dynamics needed to implement the PCoD model that is an ultimate goal of GRANITE. I am just beginning this journey and have already pinpointed many analytical and logistical hurdles that I need to overcome. I do not anticipate an easy path to addressing these questions, but I am extremely eager to dig into the data, reveal the patterns, and integrate the findings into our rock-solid GRANITE project.  

Funding for the GRANITE project comes from the Office of Naval Research, the Department of Energy, Oregon Sea Grant, the NOAA/NMFS Ocean Acoustics Program, and the OSU Marine Mammal Institute.

References

Lemos, L.S., Olsen, A., Smith, A., Chandler, T.E., Larson, S., Hunt, K., and L.G. Torres. 2020. Assessment of fecal steroid and thyroid hormone metabolites in eastern North Pacific gray whales. Conservation Physiology 8:coaa110.

Pirotta, E., Booth, C.G., Costa, D.P., Fleishman, E., Kraus, S.D., Lusseau, D., Moretti, D., New, L.F., Schick, R.S., Schwarz, L.K., Simmons, S.E., Thomas, L., Tyack, P.L., Weise, M.J., Wells, R.S., and J. Harwood. 2018. Understanding the population consequences of disturbance. Ecology and Evolution 8(19):9934-9946.

Soledade Lemos, L., Burnett, J.D., Chandler, T.E., Sumich, J.L., and L.G. Torres. 2020. Intra- and inter-annual variation in gray whale body condition on a foraging ground. Ecosphere 11(4):e03094.

Summaries, highlights, and musings – our 2020 gray whale field seasons at a glance

By Lisa Hildebrand, MSc student, OSU Department of Fisheries & Wildlife, Marine Mammal Institute, Geospatial Ecology of Marine Megafauna Lab

Fall has arrived in the Pacific Northwest. For humans, it means packing away the shorts and sandals, and getting the boots, raincoats and firewood ready. For gray whales, it means gulping down the last meal of zooplankton they will eat for several months and commencing the journey to warmer waters and sunnier skies in Mexico where they will spend the winter fasting, calving, and nursing. While the GEMM Lab may still squeeze in a day or two of field work this week, we are slowly wrapping up the 2020 field season as conditions get rougher and our beloved gray whales gradually depart our waters. This year marked the 6th year of data collection for both of our gray whale projects: the Newport project that investigates the impacts of multiple stressors on gray whale ecology and health, and the Port Orford project that explores fine-scale foraging ecology of gray whales and their zooplankton prey. Since it will be several months before the GEMM Lab heads back out onto the water again, I thought I would summarize our two field seasons, share some highlights, and muse about the drivers of our observations this summer.

Some snapshots of the field team hard at work this summer. Top left: Hunter Warwick operating the drone. Top right: PI Leigh Torres rocking the lab swag to protect against the sun. Bottom left: Lisa Hildebrand waiting for a whale to surface to get photos of it. Bottom right: Alejandro Fernandez Ajo transfers zooplankton from the light trap to sample jars.

Summaries

Our RHIB Ruby zipped around the central and southern Oregon coast on 33 different days. The summer started slow, with several days of field work where we encountered no whales despite surveying our entire study region. Our encounters picked up towards the end of June and by the end of the summer we totaled 107 sightings, encountering 46 unique individuals, 36 of which were resightings of known individuals we have identified in previous years. Our Newport star of the summer was Solé, a female gray whale we have seen every year since 2015, and we also saw many of our other regulars including Casper, Rafael, Spray, Bit, and Heart. None of these whales shone as bright as Solé though. We flew the drone over her 8 times and collected 7 fecal samples (one of which was the biggest whale fecal sample I have ever seen!). In total, we collected 30 fecal samples and flew the drone 88 times. These data will allow us to continue measuring body condition and hormone levels of Pacific Coast Feeding Group (PCFG) gray whales that use the Oregon coast.

Left: Our RHIB Ruby heading out for a day of field work in Port Orford, OR this summer. Right: Solé. Image captured under NOAA/NMFS permit #21678.

Our tandem research kayak Robustus may not be as zippy as Ruby (it is powered by human muscle rather than a powerful outboard engine after all), but it certainly continues to be a trusty vessel for the Port Orford team. The Port Orford research team, named the Theyodelers this year, collected 181 zooplankton samples and conducted 180 GoPro drops during the month of August from Robustus. Despite the many samples collected, the size of our prey samples remained relatively small throughout the whole season compared to previous years. The cliff team surveyed for a total of 117 hours, of which 15 were spent tracking whales with the theodolite and resulted in 40 different tracklines of whale movements. The whale situation in Port Orford was similar to the pattern of whale sightings in Newport, with low whale sightings at the start of the field season. Luckily, by the start of August (which marked the start of data collection for the Theyodelers), the number of whales using the Port Orford area, especially the two study sites, Mill Rocks & Tichenor Cove, had increased. Of the whales that came close enough to shore for us to identify using photo-id, we tracked 5 unique individuals, 3 of which we also saw in Newport this year. The Port Orford star of the summer was Smudge, with his tracklines making up a quarter of all of our tracklines collected. Smudge is also the whale we sighted most often last year in Port Orford. 

Top: Theyodelers Mattea Holt Colberg and Liz Kelly sampling atop the kayak Robustus. Bottom: Smudge. Image captured under NOAA/NMFS permit #21678.

Highlights

Many of you may be familiar with the whale Scarlett (formally known as Scarback). Scarlett is a female, at least 24 years old (she was first documented  in the PCFG range in 1996), who is well-known (and easily identified) by the large concave injury on her back that is covered in whale lice, or cyamids. No one knows for certain how Scarlett sustained this injury (though there are stories), however what we do know is that it has not prevented this female from reproducing and successfully raising several calves over her lifetime. The GEMM Lab last saw Scarlett with a calf (which we named Brown) in 2016. Since Scarlett is such a famous whale with a unique history, it shouldn’t be a surprise that one of our highlights this summer is the fact that Scarlett showed up with a new calf! In keeping with a “shades of red” theme, Leigh came up with the name Rose for the new calf. In July, the mom-calf pair put on quite a cute performance, with Rose rising up on Scarlett’s back, giving the team a glimpse of its face. The Scarlett-Rose highlight doesn’t end there though. Just last week, we had a very brief encounter in choppy, swelly waters with a small whale. The whale surfaced just twice allowing us to capture photo-id images, and as we were looking around to see where it would come up a third time, it suddenly breached approximately 20 m from the boat. Lo-and-behold, after comparing our photos of the whale to our catalogue, we realized that this elusive, breaching whale was Rose! I am excited to see whether Rose will return to the Oregon coast next summer and become a PCFG regular just like her mom.

Top: Scarlett, easily identified by the large concave injury on her back covered in cyamids. Bottom: Scarlett with her new calf Rose riding her back, giving us a glimpse of its face. Images captures under NOAA/NMFS permit #21678.

The highlight of the field season in Port Orford is the trial, failures and small successes of a new element to the project. There is still a lot that we do not know and understand about PCFG gray whales. One such thing is the way in which gray whales maneuver their large bodies in shallow rocky habitats, often riddled with kelp, and how exactly they capture their zooplankton prey in these environments. Using drones has certainly helped bring some light into this darkness and has led to the documentation of many novel foraging behaviors (Torres et al. 2018). However, the view from above is unable to provide the fine-scale interactions between whales, kelp, reefs, and zooplankton. Instead, we must somehow find a way to watch the whales underwater. Enter CamDo. CamDo is a technology company that designs specialty products to allow for GoPro cameras to be used for time-lapsed recordings over long periods of time in harsh environmental conditions. One of their products is a housing specifically designed for long-term filming underwater – exactly what we need! The journey was not as easy as simply purchasing the housing. We also needed to build a lander for the housing to sit on (thankfully our very own Todd Chandler designed and built something for us), and coordinate with divers and a vessel to deploy and retrieve the set-up, as well as undertake weekly battery and SD cards swaps (thankfully Dave Lacey of South Coast Tours and a very generous group of divers* donated their time and resources to make this happen). We unfortunately had some technological difficulties and bad visibility for the first 4 weeks (precisely why this CamDo effort was a pilot season this year), however we had some small success in the last 2 weeks of deployment that give us hope for the future. The camera recorded a lot of things: thick layers of mysids, countless rockfish and lingcod, several swimming and foraging murres, a handful of harbor seals, and two encounters of the species we were hoping to film – gray whales! While the footage is not the ‘money shot’ we are hoping to film (aka, a headstanding gray whale eating zooplankton right in front of the camera), the fact that we captured gray whales in the first place has showed us that this set-up is a promising investment of time, money and effort that will hopefully deliver next year.

Top left: CamDo atop its lander designed and built by Todd Chandler. Top right: Divers getting ready to get into the water for a battery and SD card swap. Bottom: Dave Lacey and two of our divers with the Black Pearl, South Coast Tour’s boat.

Musings

You may have picked up on the fact that we had slow starts to our field seasons in both Newport and Port Orford. Furthermore, while the number of whale sightings did increase in both locations throughout the field seasons, the number of sightings and whales per day were lower than they have been in previous years. For example, in 2018, we identified 15 different individuals in the month of August in Port Orford (compared to just 5 this year). In 2019, 63 unique whales were seen in Newport (compared to 46 this year). Interestingly, we had a greater diversity of encountered individuals at the start and end of the season in Newport, with a relatively small number of different individuals in July and August. While I cannot provide a definitive reason (or reasons) as to why patterns were observed (we will need to analyze several years of our data to try and understand why), I have some hypotheses I wish to share with you.

As I mentioned in a previous blog, this summer the coastal upwelling along the Oregon coast was delayed (Figure 1). Typically, peak upwelling occurs during the month of June or shortly thereafter, bringing nutrient-rich, deep waters to the surface and, when mixed with sunlight, a lot of productivity. This productivity sets off a chain of reactions — the input of nutrients leads to increased phytoplankton production, which in turn leads to increased zooplankton production, resulting in growth and development of larger organisms that consume zooplankton, such as rockfish and gray whales. If the timing of upwelling is delayed, then so too is this chain of reactions. As you can see from Figure 1, the red lines show that the peak upwelling this year occurred far later in the summer than any year in the last 10 years, with the exception of 2012. Gray whales may have cued into this delay and therefore also delayed their arrival to the PCFG feeding grounds, hence causing us to have low sighting rates at the start of our season. However, this is mostly speculative as we still do not understand the functional mechanisms by which cetaceans, such as gray whales, detect prey across different scales, and to what extent oceanographic conditions like upwelling may play a role in prey availability (Torres 2017). 

Figure 1. 10 year time series of the Coastal Upwelling Transport Index (CUTI). CUTI represents the amount of upwelling (positive numbers) or downwelling (negative numbers). The light-colored lines represent the CUTI at that point in time while the dark, bold line represents the long-term average. The vertical red lines represent the point of peak upwelling in that summer and the horizontal green line shows the peak level of upwelling in 2020 relative to all previous years.

Furthermore, the green line in Figure 1 shows that even after peak upwelling was reached this year, upwelling conditions were lower than all the other peaks in the previous 10 years. We know that weak upwelling is correlated to poor body condition of PCFG gray whales in subsequent years (Soledade Lemos et al. 2020). Upon arriving to the Oregon coast feeding grounds, gray whales may have noticed that it was shaping up to be a poor prey year (we certainly noticed it in Port Orford in the emptiness of our zooplankton net). Faced with this low resource availability, individuals had to make important decisions – risk staying in a currently prey-poor environment or continue the journey onward, searching for better prey conditions elsewhere. This conundrum is known as the marginal value theorem, whereby an individual must decide whether it should abandon the patch it is currently foraging on and move on to search for a new patch without knowing how far away the next patch may be or its value relative to the current patch (Charnov 1976). If we think of the Oregon coast as the ‘current patch’, then we can see how the marginal value theorem translates to the situation gray whales may have found themselves in at the start of the summer. 

Yet, an individual gray whale does not make these decisions in a vacuum. Instead, all gray whales in the same area are faced with the same conundrum. Seminal work by Pianka (1974) showed that when resources, such as food, are abundant, then competition between predators is low because there is enough food to go around. However, when resources dwindle, competition increases and the niches of predators begin to overlap more and more. With Charnov and Pianka’s theories in mind, we can see two groups of gray whales emerge from our 2020 field work observations: those that stayed in the ‘current patch’ (Oregon) and those that decided to seek out a new patch in hopes that it would be a better one. Solé certainly belongs in the first group. We saw her consistently throughout the whole summer. In fact, she was oftentimes so predictable that we would find her foraging on the same reef complex every time we went out to survey. Smudge may also belong in this group, however it is hard to say definitively since we only survey in Port Orford in late July and August. In contrast, I would place whales such as Spray and Heart in the second group since we saw them early in the summer and then not again until mid-to-late September. Where did they go in the interim? Did they go somewhere else in the PCFG range? Or did they venture all the way up to Alaska to the primary Eastern North Pacific (ENP) gray whale feeding grounds? Did their choice to search for food elsewhere pay off?  

As I said earlier, these are all just musings for now, but the GEMM Lab is already hard at work trying to answer these questions. Stay tuned to see what we find!

* Thanks to all the divers who assisted with the pilot CamDo season: Aaron Galloway, Ross Whippo, Svetlana Maslakova, Taylor Eaton, Cori Kane, Austin Williams, Justin Smith

References

Charnov, E.L. 1976. Optimal Foraging, the Marginal Value Theorem. Theoretical Population Biology 9(2):129-136.

Pianka, E.R. 1974. Niche Overlap and Diffuse Competition. PNAS 71(5):2141-2145.

Soledade Lemos, L., Burnett, J.D., Chandler, T.E., Sumich, J.L., and L.G. Torres. 2020. Intra- and inter-annual variation in gray whale body condition on a foraging ground. Ecosphere 11(4):e03094.

Torres, L.G. 2017. A sense of scale: Foraging cetaceans’ use of scale-dependent multimodal sensory systems. Marine Mammal Science 33(4):1170-1193.

Torres, L.G., Nieukirk, S.L., Lemos, L., and T.E. Chandler. 2018. Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Frontiers in Marine Science: https://doi.org/10.3389/fmars.2018.00319.

Do gray whales count calories?

By Lisa Hildebrand, MSc student, OSU Department of Fisheries & Wildlife, Marine Mammal Institute, Geospatial Ecology of Marine Megafauna Lab

When humans count calories it is typically to regulate and limit calorie intake. What I am wondering about is whether gray whales are aware of caloric differences in the prey that is available to them and whether they make foraging decisions based on those differences. In last week’s post, Dawn discussed what makes a good meal for a hungry blue whale. She discussed that total prey biomass of a patch, as well as how densely aggregated that patch is, are the important factors when a blue whale is picking its next meal. If these factors are important for blue whales, is it same for gray whales? Why even consider the caloric value of their prey?

Gray and blue whales are different in many ways; one way is that blue whales are krill specialists whereas gray whales are more flexible foragers. The Pacific Coast Feeding Group (PCFG) of gray whales in particular are known to pursue a more varied menu. Previous studies along the PCFG range have documented gray whales feeding on mysid shrimp (Darling et al. 1998; Newell 2009), amphipods (Oliver et al. 1984; Darling et al. 1998), cumacean shrimp (Jenkinson 2001; Moore et al. 2007; Gosho et al. 2011), and porcelain crab larvae (Dunham and Duffus 2002), to name a few. Based on our observations in the field and from our drone footage, we have observed gray whales feeding on reefs (likely on mysid shrimp), benthically (likely on burrowing amphipods), and at the surface on crab larvae (Fig. 1). Therefore, while both blue and PCFG whales must make decisions about prey patch quality based on biomass and density of the prey, gray whales have an extra decision to make based on prey type since their prey menu items occupy different habitats that require different feeding tactics and amount of energy to acquire them. In light of these reasons, I hypothesize that prey caloric value factors into their decision of prey patch selection. 

Figure 1. Gray whales use several feeding tactics to obtain a variety of coastal Oregon zooplankton prey including jaw snapping (0:12 of video), drooling mud (0:21), and head standing (0:32), to name a few.

This prey selection process is crucial since PCFG gray whales only have about 6 months to consume all the food they need to migrate and reproduce (even less for the Eastern North Pacific (ENP) gray whales since their journey to their Arctic feeding grounds is much longer). You may be asking, well if feeding is so important to gray whales, then why not eat everything they come across? Surely, if they ate every prey item they swam by, then they would be fine. The reason it isn’t quite this simple is because there are energetic costs to travel to, search for, and consume food. If an individual whale simply eats what is closest (a small, poor-quality prey patch) and uses up more energy than it gains, it may be missing out on a much more beneficial and rewarding prey patch that is a little further away (that patch may disperse or another whale may eat it by the time this whale gets there). Scientists have pondered this decision-making process in predators for a long time. These ponderances are best summed up by two central theories: the optimal foraging theory (MacArthur & Pianka 1966) and the marginal value theorem (Charnov 1976). If you are a frequent reader of the blog, you have probably heard these terms once or twice before as a lot of the questions we ask in the GEMM Lab can be traced back to these concepts.

Optimal foraging theory (OFT) states that a predator should pick the most beneficial resource for the lowest cost, thereby maximizing the net energy gained. So, a gray whale should pick a prey patch where it knows that it will gain more energy from consuming the prey in the patch than it will lose energy in the process of searching for and feeding on it. Marginal value theorem elaborates on this OFT concept by adding that the predator also needs to consider the cost of giving up a prey patch to search for a new one, which may or may not end up being more profitable or which may take a very long time to find (and therefore cost more energy). 

The second chapter of my thesis will investigate whether individual gray whales have foraging preferences by relating feeding location to prey quality (community composition) and quantity (relative density). However, in order to do that, I first must know about the quality of the individual prey species, which is why my first chapter explores the caloric content of common coastal zooplankton species in Oregon that may serve as gray whale prey. The lab work and analysis for that chapter are completed and I am in the process of writing it up for publication. Preliminary results (Fig. 2) show variation in caloric content between species (represented by different colors) and reproductive stages (represented by different shapes), with a potential increasing trend throughout the summer. These results suggest that some species and reproductive stages may be less profitable than others based solely on caloric content. 

Figure 2. Mean caloric content (J/mg) of coastal Oregon zooplankton (error bars represent standard deviation) from May-October in 2017-2018. Colors represent species and shapes represent reproductive stage.

Now that we have established that there may be bigger benefits to feeding on some species over others, we have to consider the availability of these zooplankton species to PCFG whales. Availability can be thought of in two ways: 1) is the prey species present and at high enough densities to make searching and foraging profitable, and 2) is the prey species in a habitat or depth that is accessible to the whale at a reasonable energetic cost? Some prey species, such as crab larvae, are not available at all times of the summer. Their reproductive cycles are pulsed (Roegner et al. 2007) and therefore these prey species are less available than species, such as mysid shrimp, that have more continuous reproduction (Mauchline 1980). Mysid shrimp appear to seek refuge on reefs in rock crevices and among kelp, whereas amphipods often burrow in soft sediment. Both of these habitat types present different challenges and energetic costs to a foraging gray whale; it may take more time and energy to dislodge mysids from a reef, but the payout will be bigger in terms of caloric gain than if the whale decides to sift through soft sediment on the seafloor to feed on amphipods. This benthic feeding tactic may potentially be a less costly foraging tactic for PCFG whales, but the reward is a less profitable prey item.  

My first chapter will extend our findings on the caloric content of Oregon coastal zooplankton to facilitate a comparison to the caloric values of the main ampeliscid amphipod prey of ENP gray whales feeding in the Arctic. Through this comparison I hope to assess the trade-offs of being a PCFG whale rather than an ENP whale that completes the full migration cycle to the primary summer feeding grounds in the Arctic. 

References

Charnov, E. L. 1976. Optimal foraging: the marginal value theorem. Theoretical Population Biology 9:129-136.

Darling, J. D., Keogh, K. E. and T. E. Steeves. 1998. Gray whale (Eschrichtius robustus) habitat utilization and prey species off Vancouver Island, B.C. Marine Mammal Science 14(4):692-720.

Dunham, J. S. and D. A. Duffus. 2002. Diet of gray whales (Eschrichtius robustus) in Clayoquot Sound, British Columbia, Canada. Marine Mammal Science 18(2):419-437.

Gosho, M., Gearin, P. J., Jenkinson, R. S., Laake, J. L., Mazzuca, L., Kubiak, D., Calambokidis, J. C., Megill, W. M., Gisborne, B., Goley, D., Tombach, C., Darling, J. D. and V. Deecke. 2011. SC/M11/AWMP2 submitted to International Whaling Commission Scientific Committee.

Jenkinson, R. S. 2001. Gray whale (Eschrichtius robustus) prey availability and feeding ecology in Northern California, 1999-2000. Master’s thesis, Humboldt State University.

MacArthur, R. H., and E. R. Pianka. 1966. On optimal use of a patchy environment. American Naturalist 100:603-609.

Mauchline, J. 1980. The larvae and reproduction in Blaxter, J. H. S., Russell, F. S., and M. Yonge, eds. Advances in Marine Biology vol. 18. Academic Press, London.

Moore, S. E., Wynne, K. M., Kinney, J. C., and C. M. Grebmeier. 2007. Gray whale occurrence and forage southeast of Kodiak Island, Alaska. Marine Mammal Science 23(2)419-428.

Newell, C. L. 2009. Ecological interrelationships between summer resident gray whales (Eschrichtius robustus) and their prey, mysid shrimp (Holmesimysis sculpta and Neomysis rayii) along the central Oregon coast. Master’s thesis, Oregon State University.

Oliver, J. S., Slattery, P. N., Silberstein, M. A., and E. F. O’Connor. 1984. Gray whale feeding on dense ampeliscid amphipod communities near Bamfield, British Columbia. Canadian Journal of Zoology 62:41-49.

Roegner, G. C., Armstrong, D. A., and A. L. Shanks. 2007. Wind and tidal influences on larval crab recruitment to an Oregon estuary. Marine Ecology Progress Series 351:177-188.

Makah Gray Whale Hunt Waiver – a long-time coming, but still premature?

By Lisa Hildebrand, MSc student, OSU Department of Fisheries & Wildlife, Marine Mammal Institute, Geospatial Ecology of Marine Megafauna Lab

Archaeological site of Ozette Village. Source: Makah Museum.

The Makah, an indigenous people of the Pacific Northwest Coast living in Washington State, have a long history with whaling. Deposits from a mudslide in the village of Ozette suggest that whaling may date back 2,000 years as archaeologists uncovered humpback and gray whale bones and barbs from harpoons (Kirk 1986). However, the history of Makah whaling is also quite recent. On January 29 of this year, the National Marine Fisheries Service (NMFS; informally known as NOAA Fisheries) announced a 45-day public comment period regarding a NMFS proposed waiver on the Marine Mammal Protection Act’s (MMPA) moratorium on the take of marine mammals to allow the Makah to take a limited number of eastern North Pacific gray whales (ENP). To understand how the process reached this point, we first must go back to 1855.

1855 marks the year in which the U.S. government and the Makah entered into the Treaty of Neah Bay (in Washington state). The Makah ceded thousands of acres of land to the U.S. government, and in return reserved their right to whale. Following the treaty, the Makah hunt of gray whales continued until the 1920s. At this point, commercial hunting had greatly reduced the ENP population, so much so that the Makah voluntarily ceased their whaling. The next seven decades brought about the formation of the International Whaling Commission (IWC), the enactment of the Whaling Convention Act, the listing of gray whales as endangered under the U.S. Endangered Species Act, and the enactment of the MMPA. For gray whales, these national and international measures were hugely successful, leading to the removal of the ENP from the Federal List of Endangered Wildlife in 1994 when it was determined that the population had recovered to near its estimated original population size.

One year later on May 5, 1995 (just one month after I was born!), the Makah asked the U.S. Department of Commerce to represent its interest to obtain a quota for gray whales from the IWC in order to resume their treaty right for ceremonial and subsistence harvest of the ENP. The U.S. government pursued this request at the next IWC meeting, and subsequently NMFS issued a final Environmental Assessment that found no significant impact to the ENP population if the hunt recommenced. The IWC set a catch limit and NMFS granted the Makah a quota in 1998. In 1999 the Makah hunted, struck and landed an ENP gray whale.

“Makahs cutting up whale, Neah Bay, ca. 1930. Photo by Asahel Curtis, Courtesy UW Special Collections (CUR767)”. Source and caption: History Link.

I will not go into detail about what happened between 1999 and now because frankly, a lot happened, particularly a lot of legal events including summary judgements, appeals, and a lot of other legal jargon that I do not quite understand. If you want to know the specifics of what happened in those two decades, I suggest you look at NMFS’ chronology of the Makah Tribal Whale Hunt. In short, cases brought against NMFS argued that they did not take a “hard [enough] look” at the National Environmental Policy Act when deciding that the Makah could resume the hunt. Consequently, the hunt was put on hold. Yet, in 2005 NMFS received a waiver request from the Makah on the MMPA’s take moratorium and NMFS published a notice of intent to review this request. A lot more happened between that event and now, including on January 29 of this year when NMFS announced the availability of transcripts from the Administrative Law Judge’s (ALJ) hearing (which happened from November 14-21, 2019) on the proposed regulations and waiver to allow the Makah to resume hunting the ENP. We are currently in the middle of the aforementioned 45-day public comment period on the formal rulemaking record. 

It has been 15 years since the Makah requested the waiver and while the decision has not yet been reached, we are likely nearing the end of this long process. This blog has turned into somewhat of a history lesson (not really my intention) but I feel it is important to understand the lengthy and complex history associated with the decision that is probably going to happen sometime this year. My actual intent for this blog is to ruminate on a few questions, some of which remain unanswered in my opinion, that are large and broad, and important to consider. Some of these questions point out gaps in our ecological knowledge regarding gray whales that I believe should be addressed for a truly informed decision to be made on NMFS’ proposed waiver now or anytime in the near future. 

1. Should the Pacific Coast Feeding Group (PCFG) of gray whales be recognized as its own stock?

Currently, the PCFG are considered a part of the ENP stock. This decision was published following a workshop held by a NMFS task force (Weller et al. 2013). The report concluded that based on photo-identification, genetics, tagging, and other data, there was a substantial level of uncertainty in the strength of the evidence to support the independence of the PCFG from the ENP. Nevertheless, mitochondrial genetic data have indicated a differentiation between the PCFG and the ENP, and the exchange rate between the two groups may be small enough for the two to be considered demographically independent (Frasier et al. 2011). Based on all currently available data, it seems that matrilineal fidelity plays a role in creating population structure within and between the PCFG and the ENP, however there has not been any evidence to suggest that whales from one feeding area (i.e. the PCFG range) are reproductively isolated from whales that utilize other feeding areas (i.e. the Arctic ENP feeding grounds) (Lang et al. 2011). Several PCFG researchers do argue that there needs to be recognition of the PCFG as an independent stock. It is clear that more research, especially efforts to link genetic and photo-identification data within and between groups, is required.

ENP gray whales foraging off the coast of Alaska on their main foraging grounds in the Bering Sea. Photo taken by ASAMM/AFSC. Funded by BOEM IAA No. M11PG00033. Source: NMFS.

2. Is emigration/immigration driving PCFG population growth, or is it births/deaths?

It is unclear whether the current PCFG population growth is a consequence of births and deaths that occur within the group (internal dynamics) or whether it is due to immigration and emigration (external dynamics). Likely, it is a combination of the two, however which of the two has more of an effect or is more prevalent? This question is important to answer because if population growth is driven more by external dynamics, then potential losses to the PCFG population due to the Makah hunt may not be as detrimental to the group as a whole. However, if internal dynamics play a bigger role, then the loss of just a few females could have long-term ramifications for the PCFG (Schubert 2019). NMFS has taken precautions to try and avoid such effects. In their proposed waiver, of the cumulative limit of 16 strikes of PCFG whales over the 10-year waiver period, no more than 8 of the strikes may be PCFG females (Yates 2019a). While a great step, it still begs the question how the loss of 8 females, admittedly over a rather long period of time, may affect population dynamics since we do not know what ultimately drives recruitment. Especially when taken together with potential non-lethal effects on whales (further discussed in question 5 below).

“Scarlet” is a PCFG female who has had multiple calves in the decades that researchers have seen her in the PCFG range. Image captured under NOAA/NMFS permit #21678. Source: L Hildebrand.

3. How important are individual patterns within the PCFG, and how might the loss of these individuals affect the population? 

The hunt will be restricted to the Makah Usual & Accustomed fishing area (U&A), which is off the Washington coast. It has been shown that site fidelity among PCFG individuals is strong. In fact, based on the 143 PCFG gray whales observed in nine or more years from 1996 to 2015, 94.4% were seen in at least one of nine different PCFG regions during six or more of the years they were seen (Calambokidis et al. 2017). While high site-fidelity seems to be common for some PCFG individuals in certain regions, interestingly, an analysis of sighting histories of all individuals that utilized the Makah U&A from 1985-2011 revealed that most PCFG whales do not have strong site fidelity to the Makah U&A (Scordino et al. 2017). Only about 20% of the whales were observed in six or more years of the total 26 years of data analyzed. Since high individual site fidelity does not appear to be strong in this area, perhaps a loss of genetic diversity, cultural knowledge, and behavioral individualism is not of great concern.

“Buttons” seems to have a preference for the southern Oregon coast as in the last 5 years the GEMM Lab has conducted research, he has only been sighted in 1 year in Newport but in all 5 years in Port Orford. However, perhaps such preferences are not common among all PCFG whales. Source: F. Sullivan.

4. How has the current UME affected the situation?

The ENP has experienced two Unusual Mortality Events (UMEs) in the past 20 years; one from 1999-2000 and the second began in May 2019. Many questions arise when thinking about the Makah hunt in light of the UME. 

  • What impacts will the current UME have on ENP and PCFG birth rates in subsequent years? 
  • Could the UME lead to shifts in feeding behavior of ENP whales and result in greater use of PCFG range by more individuals?
  • What caused the UME? Shifting prey availability and a changing climate? Or has the ENP reached carrying capacity? 
  • Will UMEs become more frequent in the future with continued warming of the Arctic? 
  • What is the added impact of such periodic UMEs on population trends?
“A gray whale found dead off Point Reyes National Seashore in northern California [during the 2019 UME]. Photo by M. Flannery, California Academy of Sciences.” Source and caption: NMFS.

A key assumption of the model developed by NMFS (Moore 2019) to forecast PCFG population size for the period 2016-2028, is that the population processes underlying the data from 2002-2015 (population size estimates developed by Calambokidis et al. 2017) will be the same during the forecasted period. In other words, it is assuming that PCFG gray whales will experience similar environmental conditions (with similar variation) during the next decade as the previous one, and that there will be no catastrophic events that could drastically affect population dynamics. The UME that is still ongoing could arguably affect population dynamics enough such that they are drastically different to effects on the population dynamics during the previous decade. The cause of  the 1999/2000 UME remains undetermined and the results of the investigation of the current UME will possibly not be available for several years (Yates 2019b). Even though the ENP did rebound following the 1999/2000 UME and the abundance of the PCFG increased during and subsequent to that UME, much has changed in the 20 years since then. Increased noise due to increased vessel traffic and other anthropogenic activities (seismic surveys, pile driving, construction to name a few) as well as increased coastal recreational and commercial fishing, have all contributed to a very different oceanscape than the ENP and PCFG encountered 20 years ago. Furthermore, the climate has changed considerably since then too, which likely has caused changes in the spatial distribution of habitat and quantity, quality, and predictability of prey. All of these factors make it difficult to predict what impact the UME will have now. If such events were to become more frequent in the future or the impacts of such events are greater than anticipated, then the PCFG population forecasts will not have accounted for this change. 

5. What impacts will the hunt and associated training exercises have on energy and stress levels of whales?

The proposed waiver would allow hunts to occur in the following manner: in even-years, the hunting period is from December 1 of an odd-numbered year through May 31 of the following even-numbered year. While in odd-years, the hunt is limited from July to October.

In the even-years, the hunt coincides with the northbound migration toward the foraging grounds for ENP whales and with the arrival of PCFG whales to their foraging grounds near the Makah U&A. During the northbound migration, gray whales are at their most nutritionally stressed state as they have been fasting for several months. They are therefore most vulnerable to energy losses due to disturbance at this point (Villegas-Amtmann 2019). Attempted strikes and training exercises would certainly cause some level of disturbance and stress to the whales. Furthermore, the timing of even-year hunts, means that hunters would likely encounter pregnant females, as they are the first to arrive at foraging grounds. A loss of just ~4% of a pregnant female’s energy budget could cause them to abort the fetus or not produce a calf that year (Villegas-Amtmann 2019).

In odd-years, the Makah hunt will most certainly target PCFG whales as the Makah U&A forms one of the nine PCFG regions where PCFG individuals will be feeding during those months. However, NMFS’ waiver limits the number of strikes during odd-years to 2 (Yates 2019a), which certainly protects the PCFG population.

Stress is a difficult response to quantify in baleen whales and research on stress through hormone analysis is still relatively novel. It is unlikely that a single boat training approach of a gray whale will have an adverse effect on the individual. However, a whale is never just experiencing one disturbance at a time. There are typically many confounding factors that influence a whale’s state. In an ideal world, we would know what all of these factors are and how to recognize these effects. Yet, this is virtually impossible. Therefore, while precautions will be taken to try to minimize harm and stress to the gray whales, there may very well still be unanticipated impacts that we cannot anticipate. 

Gray whale fluke. Image captured under NOAA/NMFS permit #21678. Photo: L Hildebrand.

Final thoughts

Many unknowns still remain about the ENP and PCFG gray whale populations. During the ALJ hearing, both sides tried to deal with these unknowns. After reading testimony from both sides, it is clear to me that some of the unknowns still have not been reconciled. Ultimately, a lot of the questions circle back to the first one I posed above: Are the PCFG an independent stock? If there is independent population structure, then the proposed waiver put forth by NMFS would likely change. While NMFS has certainly taken the PCFG into account during the declarations of several experts at the ALJ hearing and has aired on the side of caution, the fact that the PCFG is considered part of the ENP might underestimate the impact that a resumption of the Makah hunt may have on the PCFG. As you can see, there are still many questions that should be addressed to make fully informed decisions on such an important ruling. While this research may take several years to obtain results, the data are within reach through synthesis and collaboration that will fill these critical knowledge gaps. 

Literature cited

Calambokidis, J. C., J. Laake, and A. Pérez. 2017. Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1996-2015. International Whaling Commission SC/A17/GW/05.

Frasier, T. R., S. M. Koroscil, B. N. White, and J. D. Darling. 2011. Assessment of population substructure in relation to summer feeding ground use in eastern North Pacific gray whale. Endangered Species Research 14:39-48.

Kirk, Ruth. 1986. Tradition and change on the Northwest Coast: the Makah, Nuu-chah-nulth, southern Kwakiutl and Nuxalk. University of Washington Press, Seattle.

Lang, A. R., D. W. Weller, R. LeDuc, A. M. Burdin, V. L. Pease, D. Litovka, V. Burkanov, and R. L. Brownell, Jr. 2011. Genetic analysis of stock structure and movements of gray whales in the eastern and western North Pacific. SC/63/BRG10.

Moore, J. E. 2019. Declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

Schubert, D. J. 2019. Rebuttal testimony in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

Scordino, J. J., M. Gosho, P. J. Gearin, A. Akmajian, J. Calambokidis, and N. Wright. 2017. Individual gray whale use of coastal waters off northwest Washington during the feeding season 1984-2011: Implications for management. Journal of Cetacean Research and Management 16:57-69.

Villegas-Amtmann, S. 2019. Declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001.

Weller, D. W., S. Bettridge, R. L. Brownell, Jr., J. L. Laake, J. E. Moore, P. E. Rosel, B. L. Taylor, and P. R. Wade. 2013. Report of the National Marine Fisheries Service Gray Whale Stock Identification Workshop. NOAA-TM-NMFS-SWFSC-507. 

Yates, C. 2019a. Declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.

Yates, C. 2019b. Fifth declaration in re: ‘Proposed Waiver and Regulations Governing the Taking of Eastern North Pacific Gray Whales by the Makah Indian Tribe’. Administrative Law Judge, Hon. George J. Jordan. Docket No. 19-NMFS-0001. RINs: 0648-BI58; 0648-XG584.