Tag: Gray Whales

A Hundred and One Data Visualizations: What We Can Infer about Gray Whale Health Using Public Data

By Braden Adam Vigil, Oregon State University Undergraduate, GEMM Lab NSF REU Intern

Introduction

My name is Braden Vigil, and I am enjoying this summer with the company of Lisa Hildebrand and Dr. Leigh Torres as a National Science Foundation (NSF) Research Experience for Undergraduates (REU) intern. By slicing off a manageable chunk of the GRANITE project to focus on, I’ve had the chance to explore my passion for data visualization. My excitement for biological research was instilled in me by an impactful high school biology teacher (thank you Mr. Villalobos!) and was narrowed to marine biology research after a chance visit to Oregon State University’s Hatfield Marine Science Center. I’ve come all the way from Southern New Mexico to explore this passion of mine, and the REU program has been one of my first chances to get my feet wet. My advice for any students debating taking big leaps for the sake of passion is to do it – it’s scary, but I’d say there’s nothing better than living out what you want to do (and hopefully getting paid for it!). For this project, the GEMM Lab has saved me the trouble of collecting data – this summer, I’m all action. 

Where Gray Whales Are and Why It Matters

Just as you might find yourself at a grocery store to buy food or at a coffee shop catching up with an old friend, so too do whales have places to go and reasons for being there. Research concerning gray whale ecology – understanding the who, what, when, where, whys – should then have a lot to do with the “where?” and “why?” That’s what my project is about: investigating where the gray whales off the Oregon coast are, and what features of the environment are related to their presence and other aspects of the population. After all, distribution is considered the foundational unit for the biogeographical understanding of a population’s location and its interactions with other species. An example of an environmental driver may be phytoplankton and – subsequently – zooplankton abundance. It’s been shown that bottom-up trophic cascades based on primary productivity directly influence predator and prey populations in both terrestrial and marine ecosystems (Sinclair and Krebs 2002; Benoit-Bird and McManus 2012). This driver specifically could then inform something as significant as population abundance of a predator, though that’s out of the scope of my project. Instead, I’m studying how these environmental drivers, specifically sea water temperature, affects the variation of the thyroid hormone (tri-iodothyronine, T3) in gray whales, which the GEMM Lab quantifies from fecal samples that they non-invasively and opportunistically collect. In terrestrial mammals, T3 is believed to be associated with thermoregulation, yet it is unclear if T3 has the same function in baleen whales who use blubber insulation to thermoregulate. To estimate blubber insulation, we use a proxy, called body area index (BAI) collected via drone footage (Burnett et al. 2018), which you can read more about in Clara’s blog. Insights into variations in T3 hormone levels as related to changes in the environment may allow researchers to better understand thermoregulatory challenges whales face in warming oceans.

This Sounds Like a Lot of Data About the Environment, Where’s it Coming From?

Not only has the GEMM Lab relieved me of the hassle that data collection and fieldwork can be, so too has the Ocean Observatories Initiative (OOI). Starting in 2014, the OOI has set up several buoys off the U.S. West Coast, each equipped with numerous sensors and data-collecting devices. These have been extracting data from the nearby environment since then, including aspects such as dissolved oxygen, pH, and most important to this study, sea temperature. These buoys run deep too! Some devices reach as low as 25 m, which is where we often expect to see whales foraging during surveys. For our interest, there is one specific buoy that is within the GRANITE project’s survey region, the Oregon Inshore Surface Mooring.

Figure 1. Locations of OOI buoys. Blue dots represent buoys, while the yellow dot represents our buoy of interest, the Oregon Inshore Surface Mooring. 

Expectations

The OOI has published, and continues to publish, an unbelievable amount of data. There are many things that would be interesting to investigate, but until we know how much we can bite off versus how much we can chew, we’ve narrowed it down to a few hypotheses we’re currently investigating. 

Table 1. Hypotheses and Expected Results.

A Hundred and One Data Visualizations

As fun as I find testing correlations between variables and creating satisfying looking plots, I must admit that I’m not even halfway into this project and I’ve made a LOT of plots. Plots can be an easy way to understand big datasets and observations. Since not all of the data-collecting devices on the OOI data are continuously running, I first needed to get an idea of how much data we have to work with, and how much of that data overlaps in time with our annual gray whale survey period (June 1 – October 15). Some of these preliminary plots look like Figure 2. In addition, these plots grant us an idea of how variable sea surface temperatures have been in these past few years. Marine heatwaves have occurred recently in the Pacific Ocean and off the U.S. West Coast, and it is important to know if their effects continue to linger to the present. Other, unexplained peaks might also be worth investigating. 

Figure 2. Preliminary plot comparing sea surface temperature data over time, from around June 2016 to December 2021. Straight lines between December to June each year indicates no data, as we have removed these periods from our analysis. 

The goal here is to eventually compare the variables of sea temperature to the T3 hormone levels in gray whales foraging off the Oregon coast. Before this step, it is important to decide what depth of temperature readings are most appropriate to assess. I’ve made several correlation plots of sea  temperature between varying depths of 1 m, 7 m, and 25 m. One such plot is included below (Figure 3). This plot shows variation of temperature between different depths. If there is strong variation between the depths of 1 m and 25 m, then the water column may be well stratified, meaning that gray whale response to environmental temperature may be distinct between these distances, possibly even between 1 m and 7 m. 

Figure 3. Sea surface temperature at 1 m versus 25 m in degrees Celsius, with points color coded by year. 

Conclusion

As previously described, this study plays part into the larger GRANITE project with the goal to understand and make predictions about the ecology and physiology of the gray whale population off of the U.S. West Coast. This study will investigate the significance of sea temperature on aspects of whale health – so far including BAI and T3 hormone level. I will be pursuing a stronger grasp on the variation of these relationships through ongoing analysis. My results should be used to clarify nodes and the correlation between them in the web of dynamics encircling the population. This project has given me great insight into how raw data can be turned into meaningful understandings and subsequent impacts. The public OOI data is a scattershot of many different measurements using many different devices constantly. The answers/solutions to the conservation of species threatened by the Anthropocene are out there, all that’s required is that we harness them. 

References

Benoit-Bird, K. J., & McManus, M. A. (2012). Bottom-up regulation of a pelagic community through spatial aggregations. Biology Letters, 8(5), 813–816. https://doi.org/10.1098/rsbl.2012.0232

Burnett, J. D., & Wing, M. G. (2018). A low-cost near-infrared digital camera for fire detection and monitoring. International Journal of Remote Sensing, 39(3), 741–753. https://doi.org/10.1080/01431161.2017.1385109

Sinclair, A. R. E., & Krebs, C. J. (2002). Complex numerical responses to top–down and bottom–up processes in vertebrate populations. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 357(1425), 1221–1231.https://doi.org/10.1098/rstb.2002.1123.

Harbor porpoise and gray whale distribution over three decades: introducing the EMERALD project

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

Throughout the world, humans rely on coastal regions for shipping and commerce, fisheries, industrial development, and increasingly for the development of marine renewable energy such as wind and wave energy [1]. Nearshore environments, including the coastal waters of the Northern California Current (NCC), are therefore coupled social-ecological systems, at the intersection of human and biological productivity [2].

The NCC supports a diverse food web of ecologically and commercially important species [3]. The nearshore region of the NCC is further shaped by a rich mosaic of complex features including rocky reefs, kelp forests, and sloping sandy bottom substrate [4], creating habitat for numerous species of conservation interest, including invertebrates, fish, seabirds, and marine mammals [5]. Despite its importance, this realm poses significant challenges for vessel-based data collection, and therefore it remains relatively poorly monitored and understood.

The view from Cape Foulweather, showing the complex mosaic of nearshore habitat features. Photo: D. Barlow.

I am excited to introduce a new project focused on these important nearshore waters, in which we will be Examining Marine mammal Ecology through Region-wide Assessment of Long-term Data (EMERALD). Since 1992, standardized surveys have been conducted between San Francisco Bay, CA, and the Columbia River, OR, to monitor the abundance of marbled murrelets, a seabird of conservation concern. Each spring and summer, researchers have simultaneously been diligently documenting the locations of harbor porpoise and gray whale sightings—two iconic marine mammal species that rely on the nearshore waters of the NCC. This rich and extensive record is rare for marine mammal data, particularly in the challenging, turbulent nearshore environment. Furthermore, harbor porpoises are cryptic, making visual sampling particularly challenging, and gray whales can be sparsely distributed, yielding low sample sizes in the absence of long-term data collection.

Left: The survey team collecting data; Right: Marbled murrelet floating on the water.

For the EMERALD project, we will investigate spatial and temporal distribution patterns of harbor porpoises and gray whales in relation to fluctuations in key environmental drivers. The primary goals of the project are to (1) Identify persistent hotspots in harbor porpoise and gray whale sightings over time, and (2) Examine the environmental drivers of sighting hotspots through spatial and temporal analyses.

A harbor porpoise surfacing off the central Oregon coast. Photo: L. Torres.

From a first look at the data, we are already excited by some emerging patterns. In total, the dataset contains sightings of 6,763 harbor porpoise (mean 233 per year) and 530 gray whales (mean 18 per year). Preliminary data exploration reveals that harbor porpoise sightings increased in 2011-2012, predominantly between Cape Blanco, OR, and Cape Mendocino, CA. Gray whale sightings appear to follow an oscillating, cyclical pattern with peaks approximately every three years, with notable disruption of this pattern during the marine heatwave of 2014-2015. What are the drivers of sighting hotspots and spatial and temporal fluctuations in sighting rates? Time—and a quantitative analytical approach involving density estimation, timeseries analysis, and species distribution modeling—will tell.

A gray whale forages in kelp forest habitat over a nearshore rocky reef. Photo: T. Chandler.

I recently completed my PhD on the ecology and distribution of blue whales in New Zealand (for more information, see the OBSIDIAN project). Now, I am excited to apply the spatial analysis skills have been honing to a new study system and two new study species as I take on a new role in the GEMM Lab as a Postdoctoral Scholar. The EMERALD project will turn my focus to the nearshore waters close to home that I have grown to love over the past six years as a resident of coastal Oregon. The surveys I will be working with began before I was born, and I am truly fortunate to inherit such a rich dataset—a rare treat for a marine mammal biologist, and an exciting prospect for a statistical ecologist.

Dawn and Quin the dog, enjoying views of Oregon’s complex and important nearshore waters. Both are thrilled to remain in Oregon for the EMERALD project. Photo: R. Kaplan.

So, stay tuned for our findings as the project unfolds. In the meantime, I want express gratitude to Craig Strong of Crescent Coastal Research who has led the dedicated survey effort for the marbled murrelet monitoring program, without whom none of the data would exist. This project is funded by the Oregon Gray Whale License Plate funds, and we thank the gray whale license plate holders for their support of marine mammal research.

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References:

1.        Jouffray, J.-B., Blasiak, R., Norström, A. V., Österblom, H., and Nyström, M. (2020). The Blue Acceleration: The Trajectory of Human Expansion into the Ocean. One Earth 2, 43–54.

2.        Sjostrom, A.J.C., Ciannelli, L., Conway, F., and Wakefield, W.W. (2021). Gathering local ecological knowledge to augment scientific and management understanding of a living coastal resource: The case of Oregon’s nearshore groundfish trawl fishery. Mar. Policy 131, 104617.

3.        Bograd, S.J., Schroeder, I., Sarkar, N., Qiu, X., Sydeman, W.J., and Schwing, F.B. (2009). Phenology of coastal upwelling in the California Current. Geophys. Res. Lett. 36, 1–5.

4.        Romsos, G., Goldfinger, C., Robison, R., Milstein, R., Chaytor, J., and Wakefield, W. (2007). Development of a regional seafloor surficial geologic habitat map for the continental margins of Oregon and Washington, USA. Mapp. Seafloor Habitat Charact. Geol. Assoc. Canada, Spec. Pap., 219–243.

5.        Oregon Department of Fish and Wildlife (2016). Oregon Nearshore Strategy. Available at: https://oregonconservationstrategy.org/oregon-nearshore-strategy/ [Accessed January 10, 2022].

Grad school growing pains

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

“What if I’m wrong? What if I make a mistake?” When I began my career after completing my undergraduate degree, these questions echoed constantly in my head as the stakes were raised and my work was taken more seriously. Of course, this anxiety was not new. As a student, my worst fear had been poor performance in class. Post-undergrad, I was facing the possibility of making a mistake that could impact larger research projects and publications. 

Gaining greater responsibility and consequences is a fact of life and an intrinsic part of growing up. As I wrap up my third year of graduate school, I’ve been reflecting on how learning to take on this responsibility as a scientist has been a crucial part of my journey thus far.  

A scientist’s job is to ask, and try to answer, questions that no one knows the answer to – which is both terrifying and exciting. It feels a bit like realizing that grown-ups don’t have all the answers as a kid. Becoming comfortable with the fact that my work often involves making decisions that no one definitively can say are wrong or right has been one of my biggest challenges of grad school. The important thing to remember, I’ve learned, is that I’m not making wild guesses – I’m being trained to make the best, most informed decisions possible. And, hopefully, with more experience will come greater confidence. 

Through grad school I have learned to take on this responsibility both in the field and the lab, although each brings different experiences. In the field, the stakes can feel higher because the decisions we make affect not just the quality of the data, but the safety of the team (which is always the top priority). I felt this most acutely throughout my first summer as a drone pilot. As a pilot, I am responsible for the safety of the team, the drone, and the quality of the data. As a new pilot, I intensely felt this pressure and would come home feeling more exhausted than usual. Now, in my second field season in this role, I’ve become more comfortable and am slowly building confidence in my abilities as I gain more and more experience. 

Video 1 – Two gray whales foraging together off Newport, Oregon, USA. I recorded this footage during my first season as a pilot – a flight I’ll never forget! NOAA/NMFS permit #21678.

I have also had a similar experience in the lab. Once it’s time to work on the analysis of a project, I choose how to clean, analyze, and interpret the data. As a young scientist, every step of the process involves learning new skills and making decisions that I don’t feel entirely qualified to make.  When I started analysis for my first PhD chapter, I felt overwhelmed by deciding how to standardize my data, what kind of analysis to perform, and what indices to calculate. And, since it’s my first chapter, I felt further overwhelmed by the worry that any decision I made would become a later regret in a future part of my PhD. 

Recently, the most daunting decision has been how to standardize my data. For my first chapter, I am investigating individual specialization of gray whale foraging behavior. The results of this question are not only important for conservation, but for my subsequent work (check out these previous blogs from January 2021and April 2022 for more on this research question). While there is a wealth of literature to draw analysis inspiration from, most of these studies use discrete prey capture data, while I am working with continuous behavior data. So, to make my data points comparable to one another, I need to standardize the behavior observation time of each drone flight to account for the potential bias introduced by recording one individual for more time than another. After experiencing an internal roller coaster of having an idea, thinking it through, deciding it was terrible and restarting the cycle, I was reminded that turning to lab mates and collaborators is the best way to work through a problem.

Image 1 – Comic from phdcomics.com, source: https://phdcomics.com/comics/archive.php?comicid=2008

So, I had as many conversations as I could with my advisor, committee members, and peers. My thinking clarified with every conversation, and I gained confidence in the justification behind my decision. I cannot fully express the comfort that comes from hearing a trusted advisor say, “that makes ecological sense to me”. These conversations have also helped me remember that I am not alone in my worry and that I am not failing because I have these doubts.  While I may never be 100% convinced that I’ve made the right decision, I feel much better knowing that I’ve talked it through with the brilliant group of scientists around me. And as I enter an analysis-intensive phase of my PhD, I am extremely grateful to have this community around to challenge, advise, and support me. 

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Reuniting with some old friends: The 8th GRANITE field season is underway

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

We are almost halfway through June which means summer has arrived! Although, here on the Oregon coast, it does not entirely feel like it. We have been swinging between hot, sunny days and cloudy, foggy, rainy days that are reminiscent of those in spring or even winter. Despite these weather pendulums, the GEMM Lab’s GRANITE project is off to a great start in its 8th field season! The field team has already ventured out onto the Pacific Ocean in our trusty RHIB Ruby on four separate days looking for gray whales and in this blog post, I am going to share what we have seen so far.

The core GRANITE field team before the May 24th “trial run”. From left to right: Leigh Torres, KC Bierlich, Clara Bird, Lisa Hildebrand, Alejandro Fernández Ajó. Source: L. Torres.

PI Leigh, PhD candidate Clara and I headed out for a “trial run” on May 24th. While the intention for the day was to make sure all our gear was running smoothly and we still remembered how to complete the many tasks associated with our field work (boat loading and trailering, drone flying and catching, poop scooping, data download, to name a few), we could not resist surveying our entire study range given the excellent conditions. It was a day that all marine field scientists hope for – low winds (< 5 kt all day) and a 3 ft swell over a long period. Despite surveying between Waldport and Depoe Bay, we only encountered one whale, but it was a whale that put a smile on each of our faces. After “just” 252 days, we reunited with Solé, the star of our GRANITE dataset, with record numbers of fecal samples and drone flights collected. This record is due to what seems to be a strong habitat or foraging tactic preference by Solé to remain in a relatively small spatial area off the Oregon coast for most of the summer, rather than traveling great swaths of the coast in search for food. Honest truth, on May 24th we found her exactly where we expected to find her. While we did not collect a fecal sample from her on that day, we did perform a drone flight, allowing us to collect a critical early feeding season data point on body condition. We hope that Solé has a summer full of mysids on the Oregon coast and that we will be seeing her often, getting rounder each time!

Our superstar whale Solé. Her identifying features are a small white line on her left side (green box) and a white dot in front of her dorsal hump on the right side (red circle). Source: GEMM Lab. Photograph captured under NOAA/NMFS permit #21678

Just a week after this trial day, we had our official start to the field season with back-to-back days on the water. On our first day, postdoc Alejandro, Clara and I were joined by St. Andrews University Research Fellow Enrico Pirotta, who is another member of the GRANITE team. Enrico’s role in the GRANITE project is to implement our long-term, replicate dataset into a framework called Population consequences of disturbance (PCoD; you can read all about it in a previous blog). We were thrilled that Enrico was able to join us on the water to get a sense for the species and system that he has spent the last several months trying to understand and model quantitatively from a computer halfway across the world. Luckily, the whales sure showed up for Enrico, as we saw a total of seven whales, all of which were known individuals to us! Several of the whales were feeding in water about 20 m deep and surfacing quite erratically, making it hard to get photos of them at times. Our on-board fish finder suggested that there was a mid-water column prey layer that was between 5-7 m thick. Given the flat, sandy substrate the whales were in, we predicted that these layers were composed of porcelain crab larvae. Luckily, we were able to confirm our hypothesis immediately by dropping a zooplankton net to collect a sample of many porcelain crab larvae. Porcelain crab larvae have some of the lowest caloric values of the nearshore zooplankton species that gray whales likely feed on (Hildebrand et al. 2021). Yet, the density of larvae in these thick layers probably made them a very profitable meal, which is likely the reason that we saw another five whales the next day feeding on porcelain crab larvae once again.

Porcelain crab larvae. Source: GEMM Lab.
A happy captain Ale! Source: GEMM Lab.
Enrico (right) and myself after collecting a large fecal sample. Source: GEMM Lab.

On our most recent field work day, we only encountered Solé, suggesting that the porcelain crab swarms had dissipated (or had been excessively munched on by gray whales), and many whales went in search for food elsewhere. We have done a number of zooplankton net tows across our study area and while we did collect a good amount of mysid shrimp already, they were all relatively small. My prediction is that once these mysids grow to a more profitable size in a few days or weeks, we will start seeing more whales again.

The GRANITE team from above, waiting & watching for whales, as we will be doing for the rest of the summer! Source: GEMM Lab.

So far we have seen nine unique individuals, flown the drone over eight of them, collected fecal samples from five individuals, conducted 10 zooplankton net tows and seven GoPro drops in just four days of field work! We are certainly off to a strong start and we are excited to continue collecting rock solid GRANITE data this summer to continue our efforts to understand gray whale ecology and physiology.

Literature cited

Hildebrand L, Bernard KS, Torres LGT. 2021. Do gray whales count calories? Comparing energetic values of gray whale prey across two different feeding grounds in the Eastern North Pacific. Frontiers in Marine Science 8. doi: 10.3389/fmars.2021.683634

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

Dive into Oregon’s underwater forests

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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References

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

Dayton PK (1985) Ecology of kelp communities. Annual Review of Ecology and Systematics 16:215-245.

Filbee-Dexter K, Scheibling RE (2014) Sea uechin barrens as alternative stable states of collapsed kelp ecosystems. Marine Ecology Progress Series 495:1-25.

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

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

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

Pinnegar JK, et al. (2000) Trophic cascades in benthic marine ecosystems: lessons for fisheries and protected-area management. Environmental Conservation 27:179-200.

Rogers-Bennett L, Catton CA (2019) Marine heat wave and multiple stressors tip bull kelp forest to sea urchin barrens. Scientific Reports 9:15050.

Sala E, Boudouresque CF, Harmelin-Vivien M (1998) Fishing, trophic cascades and the structure of algal assemblages; evaluation of an old but untested paradigm. Oikos 82:425-439.

Steneck RS, et al. (2002) Kelp forest ecosystems: biodiversity, stability, resilience and future. Environmental Conservation 29:436-459.

Tegner MJ, Dayton PK, Edwards PB, Riser KL (1996) Is there evidence for the long-term climatic change in southern California kelp forests? California Cooperative Oceanic Fisheries Investigations Report 37:111-126.

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

A pregnancy test for whales?! Why and how?

Dr. Alejandro A. Fernández Ajó, Postdoctoral Scholar, Marine Mammal Institute – OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab.

I often receive two reactions when asked what I am currently working on; one is “Wow! That is a very cool job, it must be amazing to work with such incredible animals!”, the other is “How do you do that and why is that important?”. So, today I decided to blog about some of the reasons why it is important to develop a pregnancy test for gray whales and how we are doing this.

In a previous blogpost, I described the many ways in which whales play critical roles in sustaining marine ecosystem. Briefly, whales can enhance marine productivity by vertically and horizontally mixing of ocean waters, promoting primary production, and mitigating climate change by sequestering carbon with their large biomass and long life-span (1-3). Even after they die, their carcasses can contribute to biodiversity creating new habitat on the seafloor (4). But, over several decades, the whaling industry drastically removed whales around the globe, with some species and populations depleted to near extinction (5). Consequently, these depleted whale populations now play a diminished role in ocean ecosystem processes and their recovery is currently challenged by an increasing number of modern anthropogenic impacts. Hence, working towards whale conservation is essential for keeping a healthy marine ecosystem.

Working and designing effective strategies for conservation biology often involves gaining knowledge regarding the reproductive parameters of individual animals in wild populations. This information is critical for understanding population trends and the underlaying mechanisms that affect animal welfare and their potential for recovery. However, getting such information from free-living whales can be challenging (see Hunt et al. 2013). While we know that whales typically have long life-spans, lengthy generation times, extended parental care, and high survival rates, detailed knowledge on the life history and general reproductive biology of free-ranging whales is limited for the majority of the whale populations. In fact, much of what we do know about whale reproduction is derived from whaling records. Only recently, conservation physiology approaches (see our previous post here) have contributed alternative and non-invasive methods for monitoring key physiological processes that can help monitor a whale’s reproductive biology and determine reproductive parameters such as sexual maturity and pregnancy (6-9).

In this clip you can see an example of a fecal sample collection from a gray whale off the Oregon coast. We can look at hormones in the fecal samples which are useful indicators for endocrine assessments of free-swimming whales. Fecal sample and footage filmed under NOAA/NMFS permit #16111.

Gray whales (Eschrichtius robustus) in the Eastern North Pacific (ENP) typically undertake annual migrations between their lower latitude breeding grounds in the coastal waters of the Baja California Peninsula, Mexico, and the foraging grounds located on the Bering and Chukchi Seas (10). However, among the ENP whales a distinct subgroup of about 230 whales shorten their migration to feed in the coastal waters of Northern California, Oregon, and southeastern Alaska (11). This group of whales is known as the gray whale Pacific Coast Feeding Group (PCFG).

Since 2016, the GEMM Lab has monitored individual gray whales within the PCFG off the Oregon coast (check the GRANITE project). Gray whales have a distinct mottled skin; and each individual whale presents a unique pigmentation pattern that allows for the individual identification of whales. We can identify who is who among the whales who visit the Oregon coast. In this way, we can keep a detailed record of re-sightings of known individuals (visit our new web site to know more about the lives of individual whales that visit the Oregon coast).  We have high individual re-sighting rates, so this unique opportunity helps us keep a long-term data series for individual whales to monitor their health, body condition, and reproductive status over time, and thus further develop and advance our non-invasive study methods.

We are combining behavioral and feeding ecology with drone photogrammetry and endocrinology of the same individual whales to help us understand the relationships between natural and anthropogenic drivers with biological parameters. In this way, following individual whales, we are developing sensitive biomarkers to monitor and infer about the population health, population trends, and identify stressors that impact their recovery and welfare. In particular, we are now working to develop a noninvasive approach to detect pregnancy in gray whales based on fecal hormone analyses.

In this picture you can see “Rose”, a gray whale calf, on top of her mother “Scarlett”. Scarlett is one of the most recognizable whales from the PCFG, due to a large scar on the right side of her back (not visible in this picture). She has been observed along the Pacific NW coast since 1996, so she is at least 26 years old today. We know 3 of her calves. Following individual whales like Scarlett is helping us to better understand the gray whale reproductive biology. Photo by Alejandro Fernandez Ajo taken under NOAA/NMFS permit #21678.

In marine mammals, the progesterone hormone is secreted in the ovaries during the estrous cycle and gestation, and is the predominant hormone responsible for sustaining pregnancy (12). As the hormones are cleared from the blood into the gut, they are metabolized and eventually excreted in feces; fecal samples represent a cumulative and integrated concentration of hormone metabolites (13-14), which are useful indicators for endocrine assessments of free-swimming whales. Several studies show that changes in hormone concentration correlate in meaningful ways with exposure to stressors (15-16) and changes in reproductive status (17-19). We are using our long data series of fecal hormones and individual life histories to advance our understanding on the gray whales’ reproductive biology. We are close to developing a technique that will allow us to detect pregnancy in whales based in fecal hormones analyses and photogrammetry. Stay tuned for results from this pregnancy test!

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References:

1- Pershing AJ, Christensen LB, Record NR, Sherwood GD, Stetson PB (2010) The impact of whaling on the ocean carbon cycle: Why bigger was better. PLoS ONE 5(8): e12444.

2- Roman J and McCarthy JJ. 2010. The whale pump: marine mammals enhance primary productivity in a coastal basin. PLoS ONE. 5(10): e13255.

3- Morissette L, Kaschner K, and Gerber LR. 2010. “Whales eat fish”? Demystifying the myth in the Caribbean marine ecosystem. Fish Fish 11: 388–404.

4- Smith CR, Roman J, Nation JB. A metapopulation model for whale-fall specialists: The largest whales are essential to prevent species extinctions. J. Mar. Res. 77, 283–302 (2019).

5- Branch TA, Williams TM. Legacy of industrial whaling. Whales. Whal. Ocean Ecosyst. 2006, 262–278 (2006).

6- Kellar NM, Keliher J, Trego ML, Catelani KN, Hanns C, George JC, et al. Variation of bowhead whale progesterone concentrations across demographic groups and sample matrices. Endanger Species Res 2013; 22:61–72. https://doi.org/10.3354/esr00537.

7- Pallin L, Robbins J, Kellar N, Berube M, Friedlaender A. Validation of a blubber-based endocrine pregnancy test for humpback whales. Conserv Physiol 2018;6:1 11. https://doi.org/10.1093/conphys/coy031PMID:29942518.

8-Hunt KE, Robbins J, Buck CL, Bérubé M, Rolland RM (2019) Evaluation of fecal hormones for noninvasive research on reproduction and stress in humpback whales (Megaptera novaeangliae). Gen Comp Endocrinol 280: 24–34.

9-Melica, V., Atkinson, S., Calambokidis, J., Lang, A., Scordino, J., & Mueter, F. (2021). Application of endocrine biomarkers to update information on reproductive physiology in gray whale (Eschrichtius robustus). Plos one, 16(8), e0255368.

10-Swartz SL. Gray Whale. In: Wursig B, Thewissen JGM, Kovacs KM, editors. Encyclopedia of Marine Mammals (Third Edition). Elsevier;2018,p. 422–8.https://doi.org/10.1016/B978-0-12-804327-1.00140–0.

11-Calambokidis J, Darling JD, Deecke V, Gearin P, Gosho M, Megill W, et al. Abundance, range and movements of a feeding aggregation of gray whales (Eschrichtius robustus) from California to south-eastern Alaska in 1998. J Cetacean Res Manag 2002;4:267–76.

12- Bronson, F. H. (1989). Mammalian reproductive biology. University of Chicago Press.

13-Wasser SK, Hunt KE, Brown JL, Cooper K, Crockett CM, Bechert U, Millspaugh JJ, Larson S, Monfort SL (2000) A generalized fecal glucocorticoid assay for use in a diverse array of nondomestic mammalian and avian species. Gen Comp Endocrinol120:260–275.

14- Hunt, K.E., Rolland, R.M., Kraus, S.D., Wasser, S.K., 2006. Analysis of fecal glucocorticoids in the North Atlantic right whale (Eubalaena glacialis). Gen. Comp. Endocrinol. 148, 260–272. https://doi.org/10.1016/j.ygcen.2006.03.01215.

15- Lemos, L.S., Olsen, A., Smith, A., Burnett, J.D., Chandler, T.E., Larson, S., Hunt, K.E., Torres, L.G., 2021. Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Mar. Mammal Sci. 1–11. https://doi.org/10.1111/mms.12877

16- Rolland, R., McLellan, W., Moore, M., Harms, C., Burgess, E., Hunt, K., 2017. Fecal glucocorticoids and anthropogenic injury and mortality in North Atlantic right whales Eubalaena glacialis. Endanger. Species Res. 34, 417–429. https://doi.org/10.3354/esr00866.

17-Rolland, R.M., Hunt, K.E., Kraus, S.D., Wasser, S.K., 2005. Assessing reproductive status of right whales (Eubalaena glacialis) using fecal hormone metabolites. Gen. Comp. Endocrinol. 142, 308–317. https://doi.org/10.1016/j.ygcen.2005.02.002

18- Valenzuela Molina M, Atkinson S, Mashburn K, Gendron D, Brownell RL. Fecal steroid hormones reveal reproductive state in female blue whales sampled in the Gulf of California, Mexico. Gen Comp Endocrinol 2018;261:127–35.https://doi.org/10.1016/j.ygcen.2018.02.015 PMID:29476760.

19- Hunt, K. E., Robbins, J., Buck, C. L., Bérubé, M., & Rolland, R. M. (2019). Evaluation of fecal hormones for noninvasive research on reproduction and stress in humpback whales (Megaptera novaeangliae). General and Comparative Endocrinology, 280, 24-34.

What drives individual specialization?

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

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

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

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

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

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

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

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

Figure 2. A bottlenose dolphin.
Source: https://sarasotadolphin.org

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

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

Figure 3. A group of banded mongooses.
Source: http://socialisresearch.org/about-the-banded-mongoose-project/

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

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

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References

[1]       M. Taborsky, M. A. Cant, and J. Komdeur, The Evolution of Social Behaviour. Cambridge: Cambridge University Press, 2021. doi: 10.1017/9780511894794.

[2]       E. R. Pianka, “Niche Overlap and Diffuse Competition,” vol. 71, no. 5, pp. 2141–2145, 1974.

[3]       P. Matich et al., “Ecological niche partitioning within a large predator guild in a nutrient-limited estuary,” Limnol. Oceanogr., vol. 62, no. 3, pp. 934–953, 2017, doi: https://doi.org/10.1002/lno.10477.

[4]       S. D. Newsome et al., “The interaction of intraspecific competition and habitat on individual diet specialization: a near range-wide examination of sea otters,” Oecologia, vol. 178, no. 1, pp. 45–59, May 2015, doi: 10.1007/s00442-015-3223-8.

[5]       M. T. Tinker, G. Bentall, and J. A. Estes, “Food limitation leads to behavioral diversification and dietary specialization in sea otters,” Proc. Natl. Acad. Sci., vol. 105, no. 2, pp. 560–565, Jan. 2008, doi: 10.1073/pnas.0709263105.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Figure 3. Plot showing the historic fluctuations of CO^2 and the recent deviation from normal levels. Source: https://globalclimate.ucr.edu/resources.html

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

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References

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

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

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

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

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