There is something wonderful about time at sea, where your primary obligation is to observe the ocean from sunrise to sunset, day after day, scanning for signs of life. After hours of seemingly empty blue with only an occasional albatross gliding over the swells on broad wings, it is easy to question whether there is life in the expansive, blue, offshore desert. Splashes on the horizon catch your eye, and a group of dolphins rapidly approaches the ship in a flurry of activity. They play in the ship’s bow and wake, leaping out of the swells. Then, just as quickly as they came, they move on. Back to blue, for hours on end… until the next stirring on the horizon. A puff of exhaled air from a whale that first might seem like a whitecap or a smudge of sunscreen or salt spray on your sunglasses. It catches your eye again, and this time you see the dark body and distinctive dorsal fin of a humpback whale.
I have just returned from 10 days aboard the NOAA ship Bell M. Shimada, where I was the marine mammal observer on the Northern California Current (NCC) Cruise. These research cruises have sampled the NCC in the winter, spring, and fall for decades. As a result, a wealth of knowledge on the oceanography and plankton community in this dynamic ocean ecosystem has been assimilated by a dedicated team of scientists (find out more via the Newportal Blog). Members of the GEMM Lab have joined this research effort in the past two years, conducting marine mammal surveys during the transits between sampling stations (Fig. 2).
The fall 2019 NCC cruise was a resounding success. We were able to survey a large swath of the ecosystem between Crescent City, CA and La Push, WA, from inshore to 200 miles offshore. During that time, I observed nine different species of marine mammals (Table 1). As often as I use some version of the phrase “the marine environment is patchy and dynamic”, it never fails to sink in a little bit more every time I go to sea. On the map in Fig. 3, note how clustered the marine mammal sightings are. After nearly a full day of observing nothing but blue water, I would find myself scrambling to keep up with recording all the whales and dolphins we were suddenly in the midst of. What drives these clusters of sightings? What is it about the oceanography and prey community that makes any particular area a hotspot for marine mammals? We hope to get at these questions by utilizing the oceanographic data collected throughout the surveys to better understand environmental drivers of these distribution patterns.
Table 1. Summary of marine mammal
sightings from the September 2019 NCC Cruise.
Total # individuals
Northern Elephant Seal
Northern Fur Seal
Pacific White-sided Dolphin
Unidentified Baleen Whale
It was an auspicious time to survey the Northern California Current. Perhaps you have read recent news reports warning about the formation of another impending marine heatwave, much like the “warm blob” that plagued the North Pacific in 2015. We experienced it first-hand during the NCC cruise, with very warm surface waters off Newport extending out to 200 miles offshore (Fig. 4). A lot of energy input from strong winds would be required to mix that thick, warm layer and allow cool, nutrient-rich water to upwell along the coast. But it is already late September, and as the season shifts from summer to fall we are at the end of our typical upwelling season, and the north winds that would typically drive that mixing are less likely. Time will tell what is in store for the NCC ecosystem as we face the onset of another marine heatwave.
It was a joy to spend 10 days at sea with this team of scientists. Insight, collaboration, and innovation are born from interdisciplinary efforts like the NCC cruises. Beyond science, what a privilege it is to be on the ocean with a group of people you can work with and laugh with, from the dock to 200 miles offshore, south to north and back again.
In the GEMM Lab, our research focuses largely on the ecology of marine top predators. Inherent in our work are often assumptions that our study species—wide-ranging predators including whales, dolphins, otters, or seabirds—will distribute themselves relative to their prey. In order to make a living in the highly patchy and dynamic marine environment, predators must find ways to predictably locate and exploit prey resources.
But what about the prey? How do the prey structure themselves relative to their predators? This question is explored in depth in a paper titled “The Landscape of Fear: Ecological Implications of Being Afraid” (Laundre et al. 2010), which we discussed in our most recent lab meeting. When wolves were re-introduced in Yellowstone, the elk increased their vigilance and altered their grazing patterns. As a result, the plant community was altered to reflect this “landscape of fear” that the elk move through, where their distribution not only reflected opportunities for the elk to eat but also the risk of being eaten.
Translating the landscape of fear concept to the marine environment is tricky, but a fascinating exercise in ecological theory. We grappled with drawing parallels between the example system of wolves, elk, and vegetation and baleen whales, zooplankton, and phytoplankton. Relative to grazing mammals like elk, the cognitive abilities of zooplankton like krill, copepods, and mysid might pale in comparison. How could we possibly measure “fear” or “vigilance” in zooplankton? The swarming behavior of mysid and krill into dense patches is a defense mechanism—the strategy they have evolved to lessen the likelihood that any one of them will be eaten by a predator. I would posit that the diel vertical migration (DVM) of zooplankton is a manifestation of fear, at least on some level. DVM occurs over the course of each day, with plankton in pelagic ecosystems migrating vertically in the water column to avoid predators by hiding at depth during the daylight hours, and then swimming upward to feed on phytoplankton under the cover of darkness. I won’t speculate any further on the intelligence of zooplankton, but the need to survive predation has driven them to evolve this effective evolutionary strategy of hiding in the ocean’s twilight zone, swimming upward to feed only after dark so that they’re less likely to linger in spaces occupied by predators.
Laundre et al. (2010) present a visual representation of the landscape of fear (Fig. 1, reproduced below), where as an animal moves through space (represented as distance in meters or kilometers, for example), they also move through varying levels of predation risk. Environmental gradients (temperature, for example) tend to be much more stable across space in terrestrial ecosystems such as in the Yellowstone example from the paper. I wonder whether the same concept and visual depiction of a landscape of fear could be translated as risk across various environmental gradients, rather than geographic distances? In this proposed illustration, a landscape of fear would vary based on gradients of environmental conditions rather than geographic space. Such a shift in spatial reference —from geographic to environmental space—might make the model more applicable in the dynamic ocean ecosystems that we study.
What about cases when the predators we study become prey? One example we discussed was gray whales migrating from breeding lagoons in Mexico to feeding grounds in the Bering Sea. Mother-calf pairs hug the coastline tightly, by no means taking the most direct route between locations and adding considerable travel distance to their migration. The leading hypothesis is that mother gray whales take the coastal route to minimize the risk that their calves will fall prey to killer whale attacks. Are there other cases where the predators we study operate in a seascape of fear that we do not yet understand? Likely so, and the predators’ own seascape of fear may account for cases when we cannot explain predator distribution simply by their prey and their environment. To take this a step further, it might be beneficial not only to think of predation risk as only the potential to be eaten, but expand our definition to include human disturbance. While humans may not directly prey on marine predators, the disturbance from human activity in the ocean likely creates a layer of fear which animals must navigate, even in the absence of actual predation.
Our lively lab meeting discussion prompted me to look into
how the landscape of fear model has been applied to the highly dynamic and
intricate marine environment. In a study examining predator-prey dynamics of
three species of marine mammals—bottlenose dolphins, harbor seals, and
dugongs—Wirsing et al. (2007) found that in all three cases, the study species
spent less time in more desirable prey patches or decreased riskier behavior in
the presence of predators. Most studies in marine ecology are observational, as
we rarely have the opportunity to manipulate our study system for experimental
design and hypothesis testing. However, a study of coral reefs in the Florida
Keys conducted by Catano et al. (2015) used fabricated predators—decoys of
black grouper, a predatory fish—to investigate the influence of fear of
predation on the reef system. What they found was that herbivorous fish
consumed significantly less and fed at a much faster rate in the presence of this
decoy predator. The grouper, even in decoy form, created a “reefscape of fear”,
altering patterns in herbivory with potential ramifications for the entire ecosystem.
My takeaway from our discussion and my musings in this
week’s blog post is that predator and prey distribution and behavior is highly
interconnected. While predators distribute themselves to maximize their ability
to find a meal, their prey respond accordingly by balancing finding a meal of
their own with minimizing the risk that they will be eaten. Ecology is the
study of an ecosystem, which means the questions we ask are complicated and
hierarchical, and must be considered from multiple angles, accounting for
biological, environmental, and behavioral elements to name a few. These
challenges of studying ecosystems are simultaneously what make ecology
fascinating, and exciting.
Laundré, J. W., Hernández, L., & Ripple, W. J. (2010).
The landscape of fear: ecological implications of being afraid. Open
Ecology Journal, 3, 1-7.
Catano, L. B., Rojas, M. C., Malossi, R. J., Peters, J. R.,
Heithaus, M. R., Fourqurean, J. W., & Burkepile, D. E. (2016). Reefscapes
of fear: predation risk and reef hetero‐geneity interact to shape herbivore
foraging behaviour. Journal of Animal Ecology, 85(1),
Wirsing, A. J., Heithaus, M. R., Frid, A., & Dill, L. M.
(2008). Seascapes of fear: evaluating sublethal predator effects experienced
and generated by marine mammals. Marine Mammal Science, 24(1),
By Christina Garvey, University of Maryland, GEMM Lab REU Intern
It is July 8th and it is my 4th week here in Hatfield as an REU intern for Dr. Leigh Torres. My name is Christina Garvey and this summer I am studying the spatial ecology of blue whales in the South Taranaki Bight, New Zealand. Coming from the east coast, Oregon has given me an experience of a lifetime – the rugged shorelines continue to take my breath away and watching sea lions in Yaquina Bay never gets old. However, working on my first research project has by far been the greatest opportunity and I have learned so much in so little time. When Dr. Torres asked me to contribute to this blog I was unsure of how I would write about my work thus far but I am excited to have the opportunity to share the knowledge I have gained with whoever reads this blog post.
The research project that I will be conducting this summer will use remotely sensed environmental data (information collected from satellites) to predict blue whale distribution in the South Taranaki Bight (STB), New Zealand. Those that have read previous blogs about this research may remember that the STB study area is created by a large indentation or “bight” on the southern end of the Northern Island. Based on multiple lines of evidence, Dr. Leigh Torres hypothesized the presence of an unrecognized blue whale foraging ground in the STB (Torres 2013). Dr. Torres and her team have since proved that blue whales frequent this region year-round; however, the STB is also very industrial making this space-use overlap a conservation concern (Barlow et al. 2018). The increasing presence of marine industrial activity in the STB is expected to put more pressure on blue whales in this region, whom are already vulnerable from the effects of past commercial whaling (Barlow et al. 2018) If you want to read more about blue whales in the STB check out previous blog posts that talk all about it!
The possibility of the STB as an important foraging ground for a resident population of blue whales poses management concerns as New Zealand will have to balance industrial growth with the protection and conservation of a critically endangered species. As a result of strong public support, there are political plans to implement a marine protected area (MPA) in the STB for the blue whales. The purpose of our research is to provide scientific knowledge and recommendations that will assist the New Zealand government in the creation of an effective MPA.
In order to create an MPA that would help conserve the blue whale population in the STB, we need to gather a deeper understanding of the relationship between blue whales and this marine environment. One way to gain knowledge of the oceanographic and ecological processes of the ocean is through remote sensing by satellites, which provides accessible and easy to use environmental data. In our study we propose remote sensing as a tool that can be used by managers for the design of MPAs (through spatial and temporal boundaries). Satellite imagery can provide information on sea surface temperature (SST), SST anomaly, as well as net primary productivity (NPP) – which are all measurements that can help describe oceanographic upwelling, a phenomena that is believed to be correlated to the presence of blue whales in the STB region.
Past studies in the STB showed evidence of a large upwelling event that occurs off the coast of Kahurangi Point (Fig. 2), on the northwest tip of the South Island (Shirtcliffe et al. 1990). In order to study the relationship of this upwelling to the distribution of blue whales, I plan to extract remotely sensed data (SST, SST anomaly, & NPP) off the coast of Kahurangi and compare it to data gathered from a centrally located site within the STB, which is close to oil rigs and so is of management interest. I will first study how decreases in sea surface temperature at the site of upwelling (Kahurangi) are related to changes in sea surface temperature at this central site in the STB, while accounting for any time differences between each occurrence. I expect that this relationship will be influenced by the wind patterns, and that there will be changes based on the season. I also predict that drops in temperature will be strongly related to increases in primary productivity, since upwelling brings nutrients important for photosynthesis up to the surface. These dips in SST are also expected to be correlated to blue whale occurrence within the bight, since blue whale prey (krill) eat the phytoplankton produced by the productivity.
To test the relationships I determine between remotely sensed data at different locations in the STB, I plan to use blue whale observations from marine mammal observers during a seismic survey conducted in 2013, as well as sightings recorded from the 2014, 2016, and 2017 field studies led by Dr. Leigh Torres. By studying the statistical relationships between all of these variables I hope to prove that remote sensing can be used as a tool to study and understand blue whale distribution.
I am very excited about this research, especially because the end goal of creating an MPA really gives me purpose. I feel very lucky to be part of a project that could make a positive impact on the world, if only in just a little corner of New Zealand. In the mean time I’ll be here in Hatfield doing the best I can to help make that happen.
Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.
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.
Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.
Just like that, I have wrapped up year 1 of my PhD in Wildlife Science. For my PhD, I am investigating the ecology and distribution of blue whales in New Zealand across multiple spatial and temporal scales. In a region where blue whales overlap with industrial activity, there is considerable interest from managers to be able to reliably forecast when and where blue whales are most likely to be in the area. In a series of five chapters and utilizing multiple different data sources (dedicated boat surveys, oceanographic data, acoustic recordings, remotely sensed environmental data, opportunistic blue whale sightings information), I will attempt to describe, quantify, and predict where blue whales are found in relation to their environment. Each chapter will evaluate the distribution of blue whales relative to the environment at different scales in space (ranging from 4 km to 25 km resolution) and time (ranging from daily to seasonal resolution). One overarching method I am using throughout my PhD is species distribution modeling. Having just completed my research review with my doctoral committee last week, I’ll share this aspect of my research proposal that I’ve particularly enjoyed reading, writing, and thinking about.
Species distribution models (SDMs), which are sometimes referred to as habitat models or ecological niche models, are mathematical algorithms that combine observations of a species with environmental conditions at their observed locations, to gain ecological insight and predict spatial distributions of the species (Elith and Leathwick, 2009; Redfern et al., 2006). Any model is just one description of what is occurring in the natural world. Just as there are many ways to describe something with words and many languages to do so, there are many options for modeling frameworks and approaches, with stark and nuanced differences. My labmate and friend Solene Derville has equated the number of choices one has for SDMs to the cracker section in an American grocery store. When navigating all of these choices and considerations, it is important to remember that no model will ever be completely correct—it is our best attempt at describing a complex natural system—and as an analyst we need to do the best that we can with the data available to address the ecological questions at hand. As it turns out, the dividing line between quantitative analysis and philosophy is thin at times. What may seem at first like a purely objective, statistical endeavor requires careful consideration and fundamental decision-making on the part of the analyst.
Ecosystems are multifaceted, complex, and hierarchical. They are comprised of multiple physical and biological components, which operate at multiple scales across space and time. As Dr. Simon Levin stated in at 1989 MacArthur Award lecture on the topic of scale in ecology:
“A good model does not attempt to reproduce every detail of the biological system; the system itself suffices for that purpose as the most detailed model of itself. Rather, the objective of a model should be to ask how much detail can be ignored without producing results that contradict specific sets of observations, on particular scales of interest” (Levin, 1992).
The question of scale is central to ecology. As many biology students learn in their first introductory classes, parsimony is “The principle that the most acceptable explanation of an occurrence, phenomenon, or event is the simplest, involving the fewest entities, assumptions, or changes” (Oxford Dictionary). In other words, the best explanation is the simplest one. One challenge in ecological modeling, including SDMs, is to select spatial and temporal scales as coarse as possible for the most parsimonious—the most straightforward—model, while still being fine enough to capture relevant patterns. Another critical consideration is the scale of the question you are interested in answering. The scale of the analysis must match the scale at which you want to make inferences about the ecology of a species.
Similarly, the issue of complexity is central to distribution modeling. Overly simple models may not be able to adequately describe the relationship between species occurrence and the environment. In contrast, highly complex models may have very high explanatory power, but risk ascribing an ecological pattern to noise in the data (Merow et al., 2014), in other words, finding patterns that aren’t real. Furthermore, highly complex models tend to have poorer predictive capacity than simpler models (Merow et al., 2014). There is a trade-off between descriptive and predictive power in SDMs (Derville et al., 2018). Therefore, a key component in the SDM process is establishing the end goal of the model with respect to the region of interest, scale, explanatory power, predictive capacity, and in many cases management need.
Finally, any model is ultimately limited by the data available and the scale at which it was collected (Elith and Leathwick, 2009; Guillera-Arroita et al., 2015; Redfern et al., 2006). Prior knowledge of what environmental features are important to the species of interest is often limited at the time of the data collection effort, and data collection is constrained by when it is logistically feasible to sample. For example, we collect detailed oceanographic data during the summer months when it is practical to get out on the water, satellite imagery of sea surface temperature might be unavailable during times of cloud cover, and people are more likely to report blue whale sightings in areas where there is more human activity. Therefore, useful SDMs that address both ecological and management needs typically balance the scale of analysis and model complexity with the limitations of the data.
Managers and politicians within the New Zealand government are interested in a tool to predict when and where blue whales are most likely to be, based on sound ecological analysis. This is one of the end-goals of my PhD, but in the meantime, I am grappling with the appropriate scales of analysis, and attempting to balance questions of model complexity, explanatory power, and predictive capacity. There is no single, correct answer, and so my process is in part quantitative analysis, part philosophy, and all with the goal of increased ecological understanding and conservation of a species.
Derville, S., Torres, L. G., Iovan, C., and Garrigue, C. (2018). Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Divers. Distrib. 24, 1657–1673. doi:10.1111/ddi.12782.
Elith, J., and Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697. doi:10.1146/annurev.ecolsys.110308.120159.
Guillera-Arroita, G., Lahoz-Monfort, J. J., Elith, J., Gordon, A., Kujala, H., Lentini, P. E., et al. (2015). Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292. doi:10.1111/geb.12268.
Levin, S. A. (1992). The problem of pattern and scale. Ecology 73, 1943–1967.
Merow, C., Smith, M. J., Edwards, T. C., Guisan, A., Mcmahon, S. M., Normand, S., et al. (2014). What do we gain from simplicity versus complexity in species distribution models? Ecography (Cop.). 37, 1267–1281. doi:10.1111/ecog.00845.
Redfern, J. V., Ferguson, M. C., Becker, E. A., Hyrenbach, K. D., Good, C., Barlow, J., et al. (2006). Techniques for cetacean-habitat modeling. Mar. Ecol. Prog. Ser. 310, 271–295. doi:10.3354/meps310271.
There is an ancient Samoan legend that upon entry into a certain bay in Samoa, tuna would sacrifice pieces of their flesh to the community chief1. This was the explanation given for fish with circular shaped wounds where a plug of flesh had been removed. Similar round wounds are also observed on swordfish2, sharks3, and marine mammals including whales4,5, dolphins6, porpoises7, and pinnipeds8,9. In 1971, Everet C. Jones posited that the probable cause of these crater wounds was a small shark only 42-56 cm in length, Isistius brasiliensis1. The species was nicknamed “demon whale-biter” by Stewart Springer, who subsequently popularized the common name for the species, cookie cutter shark.
I am currently preparing a manuscript on blue whale skin condition. While this is only tangentially related to my doctoral research, it is an exciting side project that has encouraged me to stretch my comfort zone as an ecologist. This analysis of skin condition is part of a broader health assessment of blue whales in New Zealand, where we will be linking skin lesion severity with stress and reproductive hormone levels as well as body condition. Before I continue, I owe a major shout-out to Acacia Pepper, a senior undergraduate student at Oregon State University who has been working with me for nearly the past year through the Fisheries and Wildlife mentorship program. Acacia’s rigor in researching methodologies led us to develop a comprehensive protocol that can be applied widely to any cetacean photo-identification catalog. This method allows us to quantify prevalence and severity of different marking types in a standardized manner. Her passion for marine mammal science and interest in the subject matter is enough to excite this ecologist into fascination with wound morphology and blister concavity. Next thing you know, we are preparing a paper for publication together with P.I. Dr. Leigh Torres on a comprehensive skin condition assessment of blue whales that includes multiple markings and lesion types, but for the purpose of this blog post, I will share just a “bite-sized” piece of the story.
Back to the demon whale-biter. What do we know about cookie cutter sharks? Not a whole lot, it turns out. They are elusive, and are thought to live in deep (>1,000 m), offshore waters. They are considered to be both an ectoparasite and an ambush predator. Their distribution is tropical and sub-tropical. Much of what we know and assume about their distribution comes from the bite wounds they leave on their prey2.
In New Zealand where we study a unique population of blue whales10, the southernmost record of cookie cutter sharks is ~ 39⁰S11. We found that in our dataset of 148 photo-identified blue whales, 96% were affected by cookie cutter shark bites. Furthermore, 38% were categorized as having “severe” cookie cutter bite wounds or scars. The latitude of our blue whale sightings ranges from 29-48⁰S and blue whales are highly mobile, so any of the whales in our dataset could theoretically swim in and out of the known range of cookie cutter sharks. In our skin condition assessment, we also categorized cookie cutter bite “freshness” and phase of healing as follows:
We wanted to know if the freshness of cookie cutter shark bites was related in to the latitude at which the whales were photographed. Of the whales photographed north of 39⁰S (n=46), 76% had phase 1 or 2 cookie cutter shark bites present. In contrast, 57.1% of whales photographed south of 39⁰S (n=133) had phase 1 or 2 cookie cutter shark bites. It therefore appears that in New Zealand, the freshness of cookie cutter shark bites on blue whales is related to the latitude at which the whales were sighted, with fresher bites being more common at more northerly latitudes.
In the midst of a PhD on distribution modeling and habitat use of blue whales, I find myself reading about Samoan legends of tuna with missing flesh and descriptions of strange circular lesions from whaling records, and writing a paper about blue whale skin condition. Exciting “side projects” like this one emerge from rich datasets and good collaboration.
Jones, E. C. Isistius brasiliensis, a squaloid shark, the probable cause of crater wounds on fishes and cetaceans. Fish. Bull.69, 791–798 (1971).
Papastamatiou, Y. P., Wetherbee, B. M., O’Sullivan, J., Goodmanlowe, G. D. & Lowe, C. G. Foraging ecology of Cookiecutter Sharks (Isistius brasiliensis) on pelagic fishes in Hawaii, inferred from prey bite wounds. Environ. Biol. Fishes88, 361–368 (2010).
Hoyos-Padilla, M., Papastamatiou, Y. P., O’Sullivan, J. & Lowe, C. G. Observation of an Attack by a Cookiecutter Shark ( Isistius brasiliensis ) on a White Shark ( Carcharodon carcharias ) . Pacific Sci.67, 129–134 (2013).
Mackintosh, N. A. & Wheeler, J. F. G. Southern blue and fin whales. Discov. Reports1, 257–540 (1929).
Best, P. B. & Photopoulou, T. Identifying the ‘demon whale-biter’: Patterns of scarring on large whales attributed to a cookie-cutter shark Isistius sp. PLoS One11, (2016).
Heithaus, M. R. Predator-prey and competitive interactions between sharks (order Selachii) and dolphins (suborder Odontoceti): A review. J. Zool.253, 53–68 (2001).
Van Utrecht, W. L. Wounds And Scars In The Skin Of The Common Porpoise, Phocaena Phocaena (L.). Mammalia23, 100–122 (1959).
Gallo‐Reynoso, J. ‐P & Figueroa‐Carranza, A. ‐L. A COOKIECUTTER SHARK WOUND ON A GUADALUPE FUR SEAL MALE. Mar. Mammal Sci.8, 428–430 (1992).
Le Boeuf, B. J., McCosker, J. E. & Hewitt, J. Crater wounds on northern elephant seals: the cookiecutter shark strikes again. Fish. Bull.85, 387–392 (1987).
Barlow, D. R. et al. Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res.36, 27–40 (2018).
Dwyer, S. L. & Visser, I. N. Cookie cutter shark (Isistius sp.) bites on cetaceans, with particular reference to killer whales (Orca) (Orcinus orca). Aquat. Mamm.37, 111–138 (2011).
As you may have read in previous blog posts, my research focuses on the ecology of blue whales in New Zealand. Through my MS research and years of work by a dedicated team, we were able to document and describe a population of around 700 blue whales that are unique to New Zealand, present year-round, and genetically distinct from all other known populations . While this is a very exciting discovery, documenting this population has also unlocked a myriad of further questions about these whales. Can we predict when and where the whales are most likely to be? How does their distribution change seasonally? How often do they overlap with anthropogenic activity? My PhD research will aim to answer these questions through models of blue whale distribution patterns relative to their environment at multiple spatial and temporal scales.
Because time at sea for vessel-based surveys is cost-limited and difficult to come by, it is in any scientist’s best interest to collect as many concurrent streams of data as possible while in the field. When Dr. Leigh Torres designed our blue whale surveys that were conducted in 2014, 2016, and 2017, she really did a miraculous job of maximizing time on the water. With more data, more questions can be asked. These complimentary datasets have led to the pursuit of many “side projects”. I am lucky enough to work on these questions in parallel with what will form the bulk of my PhD, and collaborate with a number of people in the process. In this blog post, I’ll give you some short teasers of these “side projects”!
Surface lunge feeding as a foraging strategy for New Zealand blue whales
Most of what we know about blue whale foraging behavior comes from studies conducted off the coast of Southern California[2,3] using suction cup accelerometer tags. While these studies in the California Current ecosystem have led to insights and breakthroughs in our understanding of these elusive marine predators and their prey, they have also led us to adopt the paradigm that krill patches are denser at depth, and blue whales are most likely to target these deep prey patches when they feed. We have combined our prey data with blue whale behavioral data observed via a drone to investigate blue whale foraging in New Zealand, with a particular emphasis on surface feeding as a strategy. In our recent analyses, we are finding that in New Zealand, lunge feeding at the surface may be more than just “snacking”. Rather, it may be an energetically efficient strategy that blue whales have evolved in the region with unique implications for conservation.
Combining multiple data streams for a comprehensive health assessment
In the field, we collected photographs, blubber biopsy samples, fecal samples, and conducted unmanned aerial system (UAS, a.k.a. “drone”) flights over blue whales. The blubber and fecal samples can be analyzed for stress and reproductive hormone levels; UAS imagery allows us to quantify a whale’s body condition; and photographs can be used to evaluate skin condition for abnormalities. By pulling together these multiple data streams, this project aims to establish a baseline understanding of the variability in stress and reproductive hormone levels, body condition, and skin condition for the population. Because our study period spans multiple years, we also have the ability to look at temporal patterns and individual changes over time. From our preliminary results, we have evidence for multiple pregnant females from elevated pregnancy and stress hormones, as well as apparent pregnancy from the body condition analysis. Additionally, a large proportion of the population appear to be affected by blistering and cookie cutter shark bites.
Comparing body shape and morphology between species
The GEMM Lab uses UAS to quantitatively study behavior and health of large whales. From various projects in different parts of the world we have now assimilated UAS data on blue, gray, and humpback whales. We will measure these images to investigate differences in body shape and morphology among these species. We plan to explore how form follows function across baleen whales, based on their different life histories, foraging strategies, and ecological roles.
So it goes—my dissertation will contain a series of chapters that build on one another to explore blue whale distribution patterns at increasing scales, as well as a growing number of appendices for these “side projects”. Explorations and collaborations like I’ve described here allow me to broaden my perspectives and diversify my analytical skills, as well as work with many excellent teams of scientists. The more data we collect, the more questions we are able to ask. The more questions we ask, the more we seem to uncover that is yet to be understood. So stay tuned for some exciting forthcoming results from all of these analyses, as well as plenty of new questions, waiting to be posed.
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)
Hazen EL, Friedlaender AS, Goldbogen JA. 2015 Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Sci. Adv.1, e1500469–e1500469. (doi:10.1126/sciadv.1500469)
Goldbogen JA, Calambokidis J, Oleson E, Potvin J, Pyenson ND, Schorr G, Shadwick RE. 2011 Mechanics, hydrodynamics and energetics of blue whale lunge feeding: efficiency dependence on krill density. J. Exp. Biol.214, 131–146. (doi:10.1242/jeb.048157)
Burnett JD, Lemos L, Barlow DR, Wing MG, Chandler TE, Torres LG. 2018 Estimating morphometric attributes on baleen whales using small UAS photogrammetry: A case study with blue and gray whales. Mar. Mammal Sci. (doi:10.1111/mms.12527)
Torres LG, Nieukirk SL, Lemos L, Chandler TE. 2018 Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Front. Mar. Sci.5. (doi:10.3389/fmars.2018.00319)
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 , leading to an abundance of krill , the desired blue whale prey .
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.
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):
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:
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:
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.
By Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
In what is the longest government shutdown in the history of the United States, many people are impacted. Speaking from a scientist’s point of view, I acknowledge the scientific community is one of many groups that is being majorly obstructed. Here at the GEMM Laboratory, all of us are feeling the frustrations of the federal government grinding to a halt in different ways. Although our research spans great distances—from Dawn’s work on New Zealand blue whales that utilizes environmental data managed by our federal government, to new projects that cannot get federal permit approvals to state data collection, to many of Leigh’s projects on the Oregon coast of the USA that are funded and collaborate with federal agencies—we all recognize that our science is affected by the shutdown. My research on common bottlenose dolphins is no exception; my academic funding is through the US Department of Defense, my collaborators are NOAA employees who contribute NOAA data; I use publicly-available data for additional variables that are government-maintained; and I am part of a federally-funded public university. Ironically, my previous blog post about the intersection of science and politics seems to have become even more relevant in the past few weeks.
Many graduate students like me are feeling the crunch as federal agencies close their doors and operations. Most people have seen the headlines that allude to such funding-related issues. However, it’s important to understand what the funding in question is actually doing. Whether we see it or not, the daily operations of the United States Federal government helps science progress on a multitude of levels.
Federal research in the United States is critical. Most governmental branches support research with the most well-known agencies for doing so being the National Science Foundation (NSF), the US Department of Agriculture (USDA), the National Oceanic and Atmospheric Administration (NOAA), and the National Aeronautics and Space Administration. There are 137 executive agencies in the USA (cei.org). On a finer scale, NSF alone receives approximately 40,000 scientific proposals each year (nsf.gov).
If I play a word association game and I am given the word “science”, my response would be “data”. Data—even absence data—informs science. The largest aggregate of metadata with open resources lives in the centralized website, data.gov, which is maintained by the federal government and is no longer accessible and directs you to this message:Here are a few more examples of science that has stopped in its track from lesser-known research entities operated by the federal government:
It is important to remember that science happens outside of laboratories and field sites; it happens at meetings and conferences where collaborations with other great minds brainstorm and discover the best solutions to challenging questions. The shutdown has stopped most federal travel. The annual American Meteorological Society Meeting and American Astronomical Society meeting were two of the scientific conferences in the USA that attract federal employees and took place during the shutdown. Conferences like these are crucial opportunities with lasting impacts on science. Think of all the impressive science that could have sparked at those meetings. Instead, many sessions were cancelled, and most major agencies had zero representation (https://spacenews.com/ams-2019-overview/). Topics like lidar data applications—which are used in geospatial research, such as what the GEMM Laboratory uses in some its projects, could not be discussed. The cascade effects of the shutdown prove that science is interconnected and without advancement, everyone’s research suffers.
It should be noted, that early-career scientists are thought to be the most negatively impacted by this shutdown because of financial instability and job security—as well as casting a dark cloud on their futures in science: largely unknown if they can support themselves, their families, and their research. (https://eos.org/articles/federal-government-shutdown-stings-scientists-and-science). Graduate students, young professors, and new professionals are all in feeling the pressure. Our lives are based on our research. When the funds that cover our basic research requirements and human needs do not come through as promised, we naturally become stressed.
So, yes, funding—or the lack thereof—is hurting many of us. Federally-funded individuals are selling possessions to pay for rent, research projects are at a standstill, and people are at greater health and safety risks. But, also, science, with the hope for bettering the world and answering questions and using higher thinking, is going backwards. Every day without progress puts us two days behind. At first glance, you may not think that my research on bottlenose dolphins is imperative to you or that the implications of the shutdown on this project are important. But, consider this: my study aims to quantify contaminants in common bottlenose dolphins that either live in nearshore or offshore waters. Furthermore, I study the short-term and long-term impacts of contaminants and other health markers on dolphin hormone levels. The nearshore common bottlenose dolphin stocks inhabit the highly-populated coastlines that many of us utilize for fishing and recreation. Dolphins are mammals, that respond to stress and environmental hazards, in similar ways to humans. So, those blubber hormone levels and contamination results, might be more connected to your health and livelihood than at first glance. The fact that I cannot download data from ERDDAP, reach my collaborators, or even access my data (that starts in the early 1980s), does impact you. Nearly everyone’s research is connected to each other’s at some level, and that, in turn has lasting impacts on all people—scientists or not. As the shutdown persists, I continue to question how to work through these research hurdles. If anything, it has been a learning experience that I hope will end soon for many reasons—one being: for science.
By Dawn Barlow, PhD student, Department of Fisheries & Wildlife, Geospatial Ecology of Marine Megafauna Lab
As 2018 draws to a close, it is gratifying to step back and appreciate the accomplishments of the past year. For all members of the GEMM Lab, 2018 has certainly been one for the books! Here are some of our highlights for your holiday enjoyment.
We conducted fieldwork to collect new data in multiple seasons, multiple hemispheres, and across oceans. For the first time, GEMM Lab members joined the Northern California Current Ecosystem cruises aboard NOAA ship Bell M. Shimada as marine mammal observers—Florence in February, Alexa in May, and me in September.
Common dolphins (Delphinus delphis). Photo: Dawn Barlow.
Alexa on-effort using binoculars to estimate the distance and bearing of a marine mammal sighted off the starboard bow. Image source: Alexa K.
Dawn Barlow scans for marine mammals from the flying bridge of NOAA ship Bell M. Shimada. Photo: Jess O’Loughlin.
An image of the NOAA ship Bell M. Shimada transiting between stations. Multiple members of the GEMM Lab conducted surveys from this NOAA vessel in 2018. (Image source: Alexa Kownacki)
Summertime in the Pacific Northwest brings the gray whales to the Oregon Coast. The drone-flying, poop-scooping, plankton-trapping team of Leigh, Todd, Leila, Joe, and Sharon took to the water for the third year to investigate the health of this gray whale population. It was a successful field season, ending with 72 fecal samples collected! Visiting students joined our experienced members to shadow the gray whale fieldwork—Julia Stepanuk and Alejandro Fernandez Ajo came from across the country to hop on board with us for a bit. Friendship and collaboration were built quickly in a little boat chasing after whale poop, bonding over peanut butter and jelly sandwiches.
Launching the drone. Photo by Alejandro Fernandez Ajo.
The gray whale team aboard R/V Ruby. Photo by Alejandro Fernandez Ajo.
Gray whale photo-identification. Photo by Dawn Barlow.
Rachael observed seabirds from Yaquina Head in May and June, where the colony of common murres had the highest reproductive success in 10 years! Then she left the summertime in July to travel to the other end of the world, braving winter in the remote South Atlantic to study South American fur seals in the Falkland Islands.
Beyond fieldwork, our members have been busily disseminating our findings. In July, Leigh and I traveled to Wellington to present our latest findings on New Zealand blue whales to scientists, managers, politicians, industry representatives, and advocacy groups. Because of our documentation of a unique New Zealand blue whale population, which was published earlier this year, the New Zealand government has proposed to create a Marine Mammal Sanctuary for the protection of blue whales. This is quite a feat, considering blue whales were classified as only “migrant” in New Zealand waters prior to our work. Fueled by flat whites in wintery Wellington, we navigated government buildings, discussing blue whale distribution patterns, overlap with the oil and gas industry, what we now know based on our latest analyses, and what we consider to be the most pressing gaps in our knowledge.
Alexa spent the summer and fall in San Diego, where she collaborated with researchers at NOAA Southwest Fisheries Science Center on her study of about the health of bottlenose dolphins off the California coast. Her time down south has been productive and we look forward to having her back in Oregon with us to round out the second year of her PhD program.
In the fall, Dom and Leigh participated in the first ever Oregon Sea Otter Status of Knowledge Symposium. With growing interest in a potential sea otter reintroduction, the symposium brought together a range of experts – including scientists, managers, and tribes – to discuss what we currently know about sea otters in other regions and how this knowledge could be applied to an Oregon reintroduction effort. Dom was one of many speakers at this event, and gave a well-received talk on Oregon’s previous sea otter reintroduction attempt and brief discussion on his thesis research. Over the next year, Dom not only plans to finish his thesis, but also to join an interdisciplinary research team to further investigate other social, genetic, and ecological implications of a potential sea otter reintroduction.
Several GEMM Lab members reached academic milestones this year. Rachael was promoted to Assistant Professor in the spring! She now leads the Seabird Oceanography Lab, and remains involved in multiple projects studying seabirds and pinnipeds all over the world. Leila passed her PhD qualifying exams and advanced to candidacy in the spring, a major accomplishment toward completing her doctoral degree. I successfully defended my MS degree in June, and my photo was added to our wall gallery of GEMM Lab graduates. I won’t be leaving the GEMM Lab anytime soon, however, as I will be continuing my research on New Zealand blue whales as a PhD student. The GEMM Lab welcomed a new MS student in the summer—Lisa Hildebrand will be studying gray whale foraging ecology on the Oregon Coast. Welcome, Lisa! In early December, Solene successfully defended her PhD, officially becoming Dr. Derville. Congratulations to all on these milestones, and congratulations to Leigh for continuing to grow such a successful lab and guiding us all toward these accomplishments.
Perhaps you’re looking to do some reading over the holidays? The GEMM Lab has been publishing up a storm this year! The bulletin board outside our lab is overflowing with new papers. Summarizing our work and sharing our findings with the scientific community is a critical piece of what we do. The 21 new publications this year in 14 scientific journals include contributions from Leigh (13), Rachael (3), Solene (3), Leila (6), Florence (1), Amanda (1), Erin (1), Courtney (1), Theresa (1), and myself (3). Scroll down to the end of this post to see the complete list!
If you are reading this, thank you for your support of our lab, our members, and our work. Our successes come not only from our individual determination, but more importantly from our support of one another and the support of our communities. We look forward to what’s ahead in 2019. Happy holidays from the GEMM Lab!
Barlow, D. R., Torres, L. G., Hodge, K. B., Steel, D., Baker, C. S., Chandler, T. E., Bott, N., Constantine, R., Double, M. C., Gill, P., Glasgow, D., Hamner, R. M., Lilley, C., Ogle, M., Olson, P. A., Peters, C., Stockin, K. A., Tessaglia-Hymes, C. T., & Klinck, H. (2018). Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endangered Species Research, 36, 27-40.
Barlow, D. R., Fournet, M., & Sharpe, F. (2018). Incorporating tides into the acoustic ecology of humpback whales. Marine Mammal Science.
Baylis, A. M., Tierney, M., Orben, R. A., Staniland, I. J., & Brickle, P. (2018). Geographic variation in the foraging behaviour of South American fur seals. Marine Ecology Progress Series, 596, 233-245.
Bishop, A., Brown, C., Rehberg, M., Torres, L., & Horning, M. (2018). Juvenile Steller sea lion (Eumetopias jubatus) utilization distributions in the Gulf of Alaska. Movement ecology, 6(1), 6.
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.
Cardoso, M. D., Lemos, L. S., Roges, E. M., de Moura, J. F., Tavares, D. C., Matias, C. A. R., … & Siciliano, S. (2018). A comprehensive survey of Aeromonas sp. and Vibrio sp. in seabirds from southeastern Brazil: outcomes for public health. Journal of applied microbiology, 124(5), 1283-1293.
Derville, S., Torres, L. G., Iovan, C., & Garrigue, C. (2018). Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Diversity and Distributions, 24(11), 1657-1673.
Derville, S., Torres, L. G., & Garrigue, C. (2018). Social segregation of humpback whales in contrasted coastal and oceanic breeding habitats. Journal of Mammalogy, 99(1), 41-54.
Hann, C. H., Stelle, L. L., Szabo, A., & Torres, L. G. (2018). Obstacles and Opportunities of Using a Mobile App for Marine Mammal Research. ISPRS International Journal of Geo-Information, 7(5), 169.
Holdman, A. K., Haxel, J. H., Klinck, H., & Torres, L. G. (2018). Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, USA. Marine Mammal Science.
Kirchner, T., Wiley, D. N., Hazen, E. L., Parks, S. E., Torres, L. G., & Friedlaender, A. S. (2018). Hierarchical foraging movement of humpback whales relative to the structure of their prey. Marine Ecology Progress Series, 607, 237-250.
Moura, J. F., Tavares, D. C., Lemos, L. S., Acevedo-Trejos, E., Saint’Pierre, T. D., Siciliano, S., & Merico, A. (2018). Interspecific variation of essential and non-essential trace elements in sympatric seabirds. Environmental pollution, 242, 470-479.
Moura, J. F., Tavares, D. C., Lemos, L. S., Silveira, V. V. B., Siciliano, S., & Hauser-Davis, R. A. (2018). Variation in mercury concentration in juvenile Magellanic penguins during their migration path along the Southwest Atlantic Ocean. Environmental Pollution, 238, 397-403.
Orben, R. A., Kokubun, N., Fleishman, A. B., Will, A. P., Yamamoto, T., Shaffer, S. A., Takahashi, A., & Kitaysky, A. S. (2018). Persistent annual migration patterns of a specialist seabird. Marine Ecology Progress Series, 593, 231-245.
Orben, R. A., Connor, A. J., Suryan, R. M., Ozaki, K., Sato, F., & Deguchi, T. (2018). Ontogenetic changes in at-sea distributions of immature short-tailed albatrosses Phoebastria albatrus. Endangered Species Research, 35, 23-37.
Pickett, E. P., Fraser, W. R., Patterson‐Fraser, D. L., Cimino, M. A., Torres, L. G., & Friedlaender, A. S. (2018). Spatial niche partitioning may promote coexistence of Pygoscelis penguins as climate‐induced sympatry occurs. Ecology and Evolution, 8(19), 9764-9778.
Siciliano, S., Moura, J. F., Tavares, D. C., Kehrig, H. A., Hauser-Davis, R. A., Moreira, I., Lavandier, R., Lemos, L. S., & Quinete, N. S. (2018). Legacy Contamination in Estuarine Dolphin Species From the South American Coast. In Marine Mammal Ecotoxicology (pp. 95-116). Academic Press.
Sullivan, F. A., & Torres, L. G. (2018). Assessment of vessel disturbance to gray whales to inform sustainable ecotourism. The Journal of Wildlife Management, 82(5), 896-905.
Sztukowski, L. A., Cotton, P. A., Weimerskirch, H., Thompson, D. R., Torres, L. G., Sagar, P. M., Knights, A. M., Fayet, A. L., & Votier, S. C. (2018). Sex differences in individual foraging site fidelity of Campbell albatross. Marine Ecology Progress Series, 601, 227-238.
Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science, 5.
Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., … & Sequeira, A. M. M. (2018). Outstanding challenges in the transferability of ecological models. Trends in ecology & evolution.
By Dawn Barlow, PhD student, Department of Fisheries & Wildlife, Geospatial Ecology of Marine Megafauna Lab
When people hear that I study blue whales, they often ask me questions about what it’s like to be close to the largest animal on the planet, where we do fieldwork, and what data we are interested in collecting. While I love time at sea, my view on a daily basis is rarely like this:
More often than not, it looks something like this:
In my application letter to Dr. Leigh Torres, I wrote something along the lines of “while I relish remote fieldwork, I also find great satisfaction in the analysis process.” This statement is increasingly true for me as I grow more proficient in statistical modeling and computer programming. When excitedly telling my family about how I am trying to model relationships between oceanography, krill, whales, and satellite imagery, I was asked what I meant by “model”. Put simply, a model is a formula or equation that we can use to describe a pattern. I have been told, “all models are wrong, but some models work.” What does this mean? While we may never know exactly every pattern of whale feeding behavior, we can use the data we have to describe some of the important relationships. If our model performance is very good, then we have likely described most of what drives the patterns we see. If model performance is poor, then there is more to the pattern that we have not yet captured in either our data collection or in our analytical methods. Another common saying about models is, “A model is only ever as good as the data you put into it.” While we worked hard during field seasons to collect a myriad of data about what could be influencing blue whale distribution patterns, we inevitably could not capture everything, nor do we know everything that should be measured.
So, how do you go about finding the ‘best’ model? This question is what I’ve been grappling with over the last several weeks. My goal is to describe the patterns in the krill that drive patterns in whale distribution, the patterns in oceanography that drive patterns in the krill, and the patterns in the oceanography that drive patterns in whale distribution. The thing is, we have many metrics to describe oceanographic patterns (surface temperature, mixed layer depth, strength of the thermocline, integral of fluorescence, to name just a few), as well as several metrics to describe the krill (number of aggregations, aggregation density, depth, and thickness). When I multiplied out how many possible combinations of predictor variables and parameters we’re interested in modeling, I realized this meant running nearly 300 models in order to settle on the best ten. This is where programming comes in, I told myself, and caught my breath.
I’ve always loved languages. When I was much younger, I thought I might want to study linguistics. As a graduate student in wildlife science, the language I’ve spent the most time learning, and come to love, is the statistical programming language R. Just like any other language, R has syntax and structure. Like any other language, there are many ways in which to articulate something, to make a particular point or reach a particular end goal. Well-written code is sometimes described as “elegant”, much like a well-articulated piece of writing. While I certainly do not consider myself “fluent” in R, it is a language I love learning. I like to think that the R scripts I write are an attempt to eloquently uncover and describe ecological patterns.
Rather than running 300 models one by one, I wrote an R script to run many models at a time, and then sort the outputs by model performance. I may look at the five best models of 32 options in order to select one. But this is where Leigh reminds me to step back from the programming for a minute and put my ecologist hat back on. Insight on the part of the modeler is needed in order to discern between what are real ecological relationships and what are spurious correlations in the data. It may not be quite as simple as choosing the model with the highest explanatory power when my goal is to make ecological inferences.
So, where does this leave me? Hundreds of models later, I am still not entirely sure which ones are best, although I’ve narrowed it down considerably. My programming proficiency and confidence continue to grow, but that only goes so far in ecology. Knowledge of my study system is equally important. So my workflow lately goes something like this: write code, try to interpret model outputs, consider what I know about the oceanography of my study region, re-write code, re-interpret the revised results, and so on. Hopefully this iterative process is bringing us gradually closer to an understanding of the ecology of blue whales on a foraging ground… stay tuned.