By Rachael Orben, Assistant Professor (Senior Research), Seabird Oceanography Lab
This February I had the opportunity to spend two weeks at Midway Atoll National Wildlife Refuge in the Papahānaumokuākea Marine National Monument. I was there to GPS track black-footed and Laysan albatross during their short chick-brooding foraging trips. Two weeks is just enough time since the albatross are taking short trips (3-5 days) to feed their rapidly growing chicks.
My first visit to Midway (2016 blog post) occurred right as the black-footed albatross chicks were hatching (quickly followed by the Laysan albatross chicks). This time, we arrived almost exactly when I had left off. The oldest chicks were just about two weeks old. This shift in phenology meant that, though subtle, each day offered new insights for me as I watched chicks transform into large aware and semi-mobile birds. By the time we left, unattended chicks were rapidly multiplying as the adults shifted to the chick-rearing stage. During chick rearing, both parents leave the chick unattended and take longer foraging trips.
Our research goal was to collect tracking data from both species that can be used to address a couple of research questions. First of all, winds can aid, or hinder albatross foraging and flight efficiency (particularly during the short brooding trips). In the North Pacific, the strength and direction of the winds are influenced by the ENSO (El Niño Southern Oscillation) cycles. The day after we left Midway, NOAA issued an El Niño advisory indicating weak El Nino conditions. We know from previous work at Tern Island (farther east and farther south at 23.87 N, -166.28 W) that El Niño improves foraging for Laysan albatrosses during chick brooding, while during La Niña reproductive success is lower (Thorne et al., 2016). However, since Midway is farther north, and farther west the scenario might be different there. Multiple years of GPS tracking data are needed to address this question and we hope to return to collect more data next year (especially if La Niña follows the El Niño as is often the case).
We will also overlap the tracking data with fishing boat locations from the Global Fishing Watch database to assess the potential for birds from Midway to interact with high seas fisheries during this time of year (project description, associated blog post). Finally, many of the tags we deployed incorporated a barometric pressure sensor and the data can be used to estimate flight heights relative to environmental conditions such as wind strength. This type of data is key to assessing the impact of offshore wind energy (Kelsey et al., 2018).
How to track an albatross
To track an albatross we use small GPS tags that we tape to the back feathers. After the bird returns from a foraging trip, we remove the tape from the feathers and take the datalogger off. Then we recharge the battery and download the data!
Catching a tagged albatross that just returned from its foraging trip. Photo V. Ternisien
Recording data. Photo V. Ternisien
Getting ready to remove the GPS datalogger. Photo V. Ternisien
The GPS datalogger was taped to the back feathers of the bird. To remove the tag we simply peel off the tape. Photo V. Ternisien
My previous visit to Midway occurred just after house mice were discovered attacking incubating adult albatrosses. Since then, a lot of thought and effort had gone into developing a plan to eradicate mice from Midway. You can find out more via Island Conservation’s Midway blogs and the USFWS.
Dancing.
There is always something happening in a seabird colony.
Red-tailed tropicbird
From left to right: A Laysan albatross, a hybrid, and a black-footed albatross.
The colony was never quiet. Even at night.
A GPS tagged Laysan albatross.
Habitat restoration.
A short-tailed albatross sitting next to his chick (the large black chick the his right).
References
Kelsey, E. C., Felis, J. J., Czapanskiy, M., Pereksta, D. M., & Adams, J. (2018). Collision and displacement vulnerability to offshore wind energy infrastructure among marine birds of the Pacific Outer Continental Shelf. Journal of Environmental Management, 227, 229–247. http://doi.org/10.1016/j.jenvman.2018.08.051
Thorne, L. H., Conners, M. G., Hazen, E. L., Bograd, S. J., Antolos, M., Costa, D. P., & Shaffer, S. A. (2016). Effects of El Niño-driven changes in wind patterns on North Pacific albatrosses. Journal of the Royal Society Interface, 13(119), 20160196. http://doi.org/10.1098/rsif.2016.0196
By Leila Lemos, PhD Candidate, Fisheries and Wildlife Department, Oregon State University
After three years of fieldwork and analyzing a large dataset, it is time to finally start compiling the results, create plots and see what the trends are. The first dataset I am analyzing is the photogrammetry data (more on our photogrammetry method here), which so far has been full of unexpected results.
Our first big expectation was to find a noticeable intra-year variation. Gray whales spend their winter in the warm waters of Baja California, Mexico, period while they are fasting. In the spring, they perform a big migration to higher latitudes. Only when they reach their summer feeding grounds, that extends from Northern California to the Bering and Chukchi seas, Alaska, do they start feeding and gaining enough calories to support their migration back to Mexico and subsequent fasting period.
Northeastern gray whale migration route along the NE Pacific Ocean. Source: https://journeynorth.org/tm/gwhale/annual/map.html
Thus, we expected to see whales arriving along the Oregon coast with a skinny body condition that would gradually improve over the months, during the feeding season. Some exceptions are reasonable, such as a lactating mother or a debilitated individual. However, datasets can be more complex than we expect most of the times, and many variables can influence the results. Our photogrammetry dataset is no different!
In addition, I need to decide what are the best plots to display the results and how to make them. For years now I’ve been hearing about the wonders of R, but I’ve been skeptical about learning a whole new programming/coding language “just to make plots”, as I first thought. I have always used statistical programs such as SPSS or Prism to do my plots and they were so easy to work with. However, there is a lot more we can do in R than “just plots”. Also, it is not just because something seems hard that you won’t even try. We need to expose ourselves sometimes. So, I decided to give it a try (and I am proud of myself I did), and here are some of the results:
Plot 1: Body Area Index (BAI) vs Day of the Year (DOY)
In this plot, we wanted to assess the annual Body Area Index (BAI) trends that describe how skinny (low number) or fat (higher number) a whale is. BAI is a simplified version of the BMI (Body Mass Index) used for humans. If you are interested about this method we have developed at our lab in collaboration with the Aerial Information Systems Laboratory/OSU, you can read more about it in our publication.
The plots above are three versions of the same data displayed in different ways. The first plot on the left shows all the data points by year, with polynomial best fit lines, and the confidence intervals (in gray). There are many overlapping observation points, so for the middle plot I tried to “clean up the plot” by reducing the size of the points and taking out the gray confidence interval range around the lines. In the last plot on the right, I used a linear regression best fit line, instead of polynomial.
We can see a general trend that the BAI was considerably higher in 2016 (red line), when compared to the following years, which makes us question the accuracy of the dataset for that year. In 2016, we also didn’t sample in the month of July, which is causing the 2016 polynomial line to show a sharp decrease in this month (DOY: ~200-230). But it is also interesting to note that the increasing slope of the linear regression line in all three years is very similar, indicating that the whales gained weight at about the same rate in all years.
Plot 2: Body Area Index (BAI) vs Body Condition Score (BCS)
In addition to the photogrammetry method of assessing whale body condition, we have also performed a body condition scoring method for all the photos we have taken in the field (based on the method described by Bradford et al. 2012). Thus, with this second set of plots, we wanted to compare both methods of assessing whale body condition in order to evaluate when the methods agree or not, and which method would be best and in which situation. Our hypothesis was that whales with a ‘fair’ body condition would have a lower BAI than whales with a ‘good’ body condition.
The plots above illustrate two versions of the same data, with data in the left plot grouped by year, and the data in the right plot grouped by month. In general, we see that no whales were observed with a poor body condition in the last analysis months (August to October), with both methods agreeing to this fact. Additionally, there were many whales that still had a fair body condition in August and September, but less whales in the month of October, indicating that most whales gained weight over the foraging seasons and were ready to start their Southbound migration and another fasting period. This result is important information regarding monitoring and conservation issues.
However, the 2016 dataset is still a concern, since the whales appear to have considerable higher body condition (BAI) when compared to other years.
Plot 3:Temporal Body Area Index (BAI) for individual whales
In this last group of plots, we wanted to visualize BAI trends over the season (using day of year – DOY) on the x-axis) for individuals we measured more than once. Here we can see the temporal patterns for the whales “Bit”, “Clouds”, “Pearl”, “Scarback, “Pointy”, and “White Hole”.
We expected to see an overall gradual increase in body condition (BAI) over the seasons, such as what we can observe for Pointy in 2018. However, some whales decreased their condition, such as Bit in 2018. Could this trend be accurate? Furthermore, what about BAI measurements that are different from the trend, such as Scarback in 2017, where the last observation point shows a lower BAI than past observation points? In addition, we still observe a high BAI in 2016 at this individual level, when compared to the other years.
My next step will be to check the whole dataset again and search for inconsistencies. There is something causing these 2016 values to possibly be wrong and I need to find out what it is. The overall quality of the measured photogrammetry images was good and in focus, but other variables could be influencing the quality and accuracy of the measurements.
For instance, when measuring images, I often struggled with glare, water splash, water turbidity, ocean swell, and shadows, as you can see in the photos below. All of these variables caused the borders of the whale body to not be clearly visible/identifiable, which may have caused measurements to be wrong.
Examples of bad conditions for performing photogrammetry: (1) glare and water splash, (2) water turbidity, (3) ocean swell, and (4) a shadow created in one of the sides of the whale body. Source: GEMM Lab. Taken under NMFS permit 16111 issued to John Calambokidis.
Thus, I will need to check all of these variables to identify the causes for bad measurements and “clean the dataset”. Only after this process will I be able to make these plots again to look at the trends (which will be easy since I already have my R code written!). Then I’ll move on to my next hypothesis that the BAI of individual whales varied by demographics including sex, age and reproductive state.
To carry out robust science that produces results we can trust, we can’t simply collect data, perform a basic analysis, create plots and believe everything we see. Data is often messy, especially when developing new methods like we have done here with drone based photogrammetry and the BAI. So, I need to spend some important time checking my data for accuracy and examining confounding variables that might affect the dataset. Science can be challenging, both when interpreting data or learning a new command language, but it is all worth it in the end when we produce results we know we can trust.
Paul Lask teaches writing at Oregon Coast Community College, and is a faculty fellow with Portland State University’s Institute for Sustainable Solutions. His writing can be found at prlask.com.
I pulled my kayak down to the beach, where a woman stood pointing toward the ocean. A fin rose from the water about a hundred yards offshore.
“It’s an orca,” she said.
“Naw,” the man beside her said. “That’s a gray.”
I recalled a documentary scene of a group of orcas spy-hopping near a seal marooned on an ice chunk. After their pogoing taunts, they left it alone. Another clip showed the orcas band together and charge forward, pushing a big wave over the ice and knocking the seal in.
I brought myself back to the beach. I wanted it to be a gray. It was one of my first solo ocean paddles, and I stood in my dry suit, PFD and helmet, having checked my weather and swell apps, having spent many hours in pools and bays learning rolls and rescues, and many dollars on courses, gear and guidebooks, now arguing a dubious fin into goodness.
It had to be a gray.
I dragged my boat to the water. Small dumping waves sucked back dark gravelly sand. The fin flopped over.
Aspiring rough water sea kayakers are trained in safety and rescue. We learn about dealing with battering surf, longshore currents, T-rescues and re-entry rolls. We don’t learn about sea life. I grew up in northern Illinois, where the nearest sea animal was a river dragon fashioned out of a downed tree that got painted annually, and TV specials on Loch Ness.
Paddling around rock near Cape Meares, Oregon.
I stuffed myself into my boat, suddenly remembering the shark story an instructor told me: They were out near Pacific City when the bad fin emerged. My instructor had a Go Pro on his helmet. His buddy dared him to roll to get a shot of their follower. My instructor declined.
Sealing my spray skirt over the cockpit, I focused on launch prep. I checked my radio. Made sure my extra paddle was secure. Confirmed I hadn’t sealed the skirt over my skeg rope. Here at North Fogarty Creek beach there was a gap between where the fin had been and a rock the size of a two story house. I waited for a set of waves to pass, then pushed off.
I saw the gray whale’s back split the water, heard the great sigh. A misty rainbow evaporated. I darted past the whale into the open sea. Other puffs dotted the horizon.
In time I would learn the kelp forest I had just paddled through hosted galaxies of tiny shrimp-like zooplankton. The gray was “sharking,” a foraging behavior in shallow water wherein it lays on its side with half its tail sticking out. Of the 20,000 gray whales that annually migrate from Mexico to Alaska, about 200 mysteriously break away and feed nearshore in Oregon. Scientists don’t know[i] for sure why this occurs, but the abundance of those shrimp-like animals is one theory.
Gray whale landing after a breach off Newport, Oregon. Taken under NMFS permit 16111 by Leigh Torres.
The mavericks are good for the tourism industry. From late spring through summer Depoe Bay is a frenzy of camera clicks and selfie sticks. A gauntlet of vehicles cram both sides of Hwy 101. Whale watching boats enter and exit the “world’s smallest harbor” through a bottleneck I’ve heard can be sketchy for kayakers.
As I paddled I toyed with wishful thinking—because I was a non-motorized vessel, the whales might better appreciate my presence. I was not there to photograph them. I just liked being in the sway of the water. “No cradle is so comfortable,” Rudyard Kipling wrote, “as the long, rocking swell of the Pacific.”[ii] Especially on an uncharacteristically calm day like this.
I have met paddlers who are indifferent to our resident grays. One referred to them as squirrels. Another claimed he got too near a spout, and was covered in the slime geyser, which he’d found disgusting. Others want to get close. A friend is interested in bringing snorkeling gear out next season, and a non-paddling acquaintance wants to get a kayak so he can sneak up and swim with one.
Dr. Roger Payne, the biologist famous for discovering that humpbacks sing, discusses Baja’s “‘friendly gray whale phenomenon’, wherein gray whales come so close to whale-watching boats that the tourists can reach out and pat them.”[iii] Grays weren’t always treated like housecats. When whaling was in full swing, Dr. Payne continues, they were referred to as “devil fish” by whalers in Scammon’s Lagoon in Baja. The whales were being routinely harpooned, so they fought back, earning a fierce reputation. Their numbers plummeted. Federal protections helped them recover, and in 1994 eastern Pacific gray whales were removed from the U.S. Endangered Species List.
Paddling under arch at Three Arch Rocks.
U.S. federal law requires people keep a hundred yards away from whales. Natural law supports this precaution. Once paddling through my shark and orca anxiety, I developed an ambivalence about my proximity to the grays. It was not fear of aggression, but indifference. I was sneaking around the living room of 35-ton animals. Despite their boxcar bulk, they moved with quick snaky grace; regardless of my attempts at putting a football field between us, what was to keep one from accidentally rolling over me or smashing me with its tail?
With shipwrecks in mind, Herman Melville pondered the power of a whale fluke: “But as if this vast local power in the tendinous tail were not enough, the whole bulk of the leviathan is knit over with a warp and woof of muscular fibers and filaments, which passing on either side of the loins and running down into the flukes, insensibly blend with them, and largely contribute to their might; so that in the tail the confluent measureless force of the whole whale seems concentrated to a point. Could annihilation occur to matter, this were the thing to do it.”[iv]
Whale-caused shipwrecks didn’t end in the nineteenth century. Contemplating how his sloop went down, Steven Callahan, a sailor lost at sea for 76 days, recalls how his nineteen-ton, forty-three-foot schooner and a heavy cruiser were both sunk by whales in the 1970s.[v] Dr. Payne also has boat breaching stories. “There’s a woman who works in my laboratory who had a whale breach directly on top of her boat. Not a glancing blow, but a direct hit across the bow. The boat was totaled…”
In 2015, a 33-ton humpback breached onto a tandem kayak in Monterey Bay, California. Reanalyzing video footage, Tom Mustill, one of the struck kayakers, believes he can see the whale “sticking its eyes out and taking a look at us while he’s in the air.” He speculates that the whale may have calculated its landing so as to avoid full body impact. Mustill is currently making a BBC2 documentary about the incident titled “Humpback Whales: A Detective Story.”
How whales behave around vessels is still an open scientific question. OSU whale mammologist Dr. Leigh Torres asks: “Are there behavior differences based on boat traffic and composition? Whales might react to some boats, but perhaps not others based on speed, approach, motor type, etc.”[vi] The ocean is also getting noisier. One study shows that over the last sixty years ambient noise in the ocean has increased about three to five decibels per decade.[vii] To what extent is this noise stressing out whales, and what kind of reactions will we begin to see?
***
Dr. Torres told me whales were like a gateway drug for getting people hooked on marine ecology. Since that tricky fin at Fogarty Creek I’ve given them a good amount of thought. It’s partially their size that inspires awe and reflection. Writer Julia Whitty gets at their enormity by thinking about their deaths, comparing whales to old growth trees. She describes whalefall beautifully:
“…the downward journey takes place in the slow motion of the underwater world, as the processes of decomposition produce buoyant gases that duel with the force of gravity in such a way that the carcass rides a gentle elevator up and down on its way down” (178). Once the body hits the ocean floor it provides a “nutritional bonanza of a magnitude that might otherwise take thousands of years to accumulate from the background flow of small detritus from the surface.” A gray takes a year and a half to be “stripped to the bone by the scalpels and stomachs of the deep.” A blue whale can take as long as eleven years. [viii]
But I don’t think it’s just their size that hooks us. They’re mammals, nurse their young, sing to one another. “Flowing like breathing planets,” Gary Snyder writes,[ix] we can only wonder what a whale might know.
As I continue exploring our coast by kayak, I occasionally talk to whales. It no longer seems strange to want to hug one. I attempt to maintain the lawful distance, though now and then one rises close enough to see the individual barnacles studded among old scratches and scribbles. This wordless poetry is like a map into deep time. I realize I want to keep being humbled and a little afraid. I realize I’m hooked.
Author paddling near Three Arch Rocks. Photo by Bruce Moreira.
References
[i] Oregon State University. (2015, August 4). Researchers studying Oregon’s “resident population” of gray whales. Retrieved from https://today.oregonstate.edu/archives/2015/aug/researchers-studying-oregon’s-“resident-population”-gray-whales
[ii] Kipling, R. (1914). The Jungle Book (p. 145). New York, NY: Double Day. Retrieved from https://play.google.com/store/books/details?id=LO88AQAAIAAJ&rdid=book-LO88AQAAIAAJ&rdot=1
[iii] White, J. (2016). Talking on the Water (pp. 25-26). San Antonio, TX: Trinity University Press.
[iv] Friends of the Earth. (1970). Wake of the Whale (p. 71). San Francisco, CA: Friends of the Earth, Inc.
[v] Steven, C. (2002). Adrift (p. 37). New York, NY: First Mariner Books.
[vi]Oregon State University. (2015, August 4). Researchers studying Oregon’s “resident population” of gray whales. Retrieved from
[vii] Lemos, L. (2016, April 6). Does ocean noise stress-out whales?. In Geospatial Ecology of Marine Megafauna Laboratory. Retrieved from http://blogs.oregonstate.edu/gemmlab/2016/04/06/does-ocean-noise-stress-out-whales/
[viii] Whitty, J. (2010). Deep Blue Home (pp. 178-181). New York, NY: Houghton Mifflin Harcourt.
[ix] Snyder, G. (1974). Turtle Island. New York, NY: New Directions Publishing Group. Retrieved from https://www.poets.org/poetsorg/poem/mother-earth-her-whales-0
I have the privilege of studying the largest animals on the planet: blue whales (Balaenoptera musculus). However, in order to understand the ecology, distribution, and habitat use patterns of these ocean giants, I have dedicated the past several months to studying something much smaller: krill (Nyctiphanes australis). New Zealand’s South Taranaki Bight region (“STB”, Figure 1) is an important foraging ground for a unique population of blue whales [1,2]. A wind-driven upwelling system off of Kahurangi Point (the “X” in Figure 1) generates productivity in the region [3], leading to an abundance of krill [4], the desired blue whale prey [5].
Figure 1. A map of New Zealand, with the South Taranaki Bight (STB) region delineated by the black box. Cape Farewell is denoted by a star, and Kahurangi point is denoted by an X.
Figure 4. A blue whale lunges on an aggregation of krill. UAS piloted by Todd Chandler.
Krill (Nyctiphanes australis), blue whale prey in New Zealand’s South Taranaki Bight region. Image: Te Papa.
Our blue whale research team collected a multitude of datastreams in three different years, including hydroacoustic data to map krill distribution throughout our study region. The summers of 2014 and 2017 were characterized by what could be considered “typical” conditions: A plume of cold, upwelled water curving its way around Cape Farewell (marked with the star in Figure 1) and entering the South Taranaki Bight, spurring a cascade of productivity in the region. The 2016 season, however, was different. The surface water temperatures were hot, and the whales were not where we expected to find them.
Figure 2. Sea surface temperature maps of the South Taranaki Bight region in each of our three study years. The white circles indicate where most blue whale sightings were made in each year. Note the very warm temperatures in 2016, and more westerly location of blue whale sightings.
What happened to the blue whales’ food source under these different conditions in 2016? Before I share some preliminary findings from my recent analyses, it is important to note that there are many possible ways to measure krill availability. For example, the number of krill aggregations, as well as how deep, thick, and dense those aggregations are in an area will all factor into how “desirable” krill patches are to a blue whale. While there may not be “more” or “less” krill from one year to the next, it may be more or less accessible to a blue whale due to energetic costs of capturing it. Here is a taste of what I’ve found so far:
In 2016, when surface waters were warm, the krill aggregations were significantly deeper than in the “typical” years (ANOVA, F=7.94, p <0.001):
Figute 3. Boxplots comparing the median krill aggregation depth in each of our three survey years.
The number of aggregations was not significantly different between years, but as you can see in the plot below (Figure 4) the krill were distributed differently in space:
Figure 4. Map of the South Taranaki Bight region with the number of aggregations per 4 km^2, standardized by vessel survey effort. The darker colors represent areas with a higher density of krill aggregations.
While the bulk of the krill aggregations were located north of Cape Farewell under typical conditions (2014 and 2017), in the warm year (2016) the krill were not in this area. Rather, the area with the most aggregations was offshore, in the western portion of our study region. Now, take a look at the same figure, overlaid with our blue whale sighting locations:
Figure 5. Map of standardized number of krill aggregations, overlaid with blue whale sighting locations in red stars.
Where did we find the whales? In each year, most whale encounters were in the locations where the most krill aggregations were found! Not only that, but in 2016 the whales responded to the difference in krill distribution by shifting their distribution patterns so that they were virtually absent north of Cape Farewell, where most sightings were made in the typical years.
The above figures demonstrate the importance of studying an ecosystem. We could puzzle and speculate over why the blue whales were further west in the warm year, but the story that is emerging in the krill data may be a key link in our understanding of how the ecosystem responds to warm conditions. While the focus of my dissertation research is blue whales, they do not live in isolation. It is through understanding the ecosystem-scale story that we can better understand blue whale ecology in the STB. As I continue modeling the relationships between oceanography, krill, and blue whales in warm and typical years, we are beginning to scratch the surface of how blue whales may be responding to their environment.
Torres LG. 2013 Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal. J. Mar. Freshw. Res.47, 235–248. (doi:10.1080/00288330.2013.773919)
Barlow DR et al. 2018 Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res.36, 27–40. (doi:https://doi.org/10.3354/esr00891)
Shirtcliffe TGL, Moore MI, Cole AG, Viner AB, Baldwin R, Chapman B. 1990 Dynamics of the Cape Farewell upwelling plume, New Zealand. New Zeal. J. Mar. Freshw. Res.24, 555–568. (doi:10.1080/00288330.1990.9516446)
Bradford-Grieve JM, Murdoch RC, Chapman BE. 1993 Composition of macrozooplankton assemblages associated with the formation and decay of pulses within an upwelling plume in greater cook strait, New Zealand. New Zeal. J. Mar. Freshw. Res.27, 1–22. (doi:10.1080/00288330.1993.9516541)
Gill P. 2002 A blue whale (Balaenoptera musculus) feeding ground in a southern Australian coastal upwelling zone. J. Cetacean Res. Manag.4, 179–184.
