Author: Dawn Barlow

Roger that, we are currently enamored

Blog by Rachel Kaplan, PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Figures by Dawn Barlow, PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

Hello from the RV Bell M. Shimada! We are currently sampling at an inshore station on the Heceta Head Line, which begins just south of Newport and heads out 45 nautical miles west into the Pacific Ocean. We’ll spend 10 days total at sea, which have so far been full of great weather, long days of observing, and lots of whales.

Dawn and Rachel in matching, many-layered outfits, 125 miles offshore on the flying bridge of the RV Bell M. Shimada.

Run by NOAA, this Northern California Current (NCC) cruise takes place three times per year. It is fabulously interdisciplinary, with teams concurrently conducting research on phytoplankton, zooplankton, seabirds, and more. The GEMM Lab will use the whale survey, krill, and oceanographic data to fuel species distribution models as part of Project OPAL. I’ll be working with this data for my PhD, and it’s great to be getting to know the region, study system, and sampling processes.

I’ve been to sea a number of times and always really enjoyed it, but this is my first time as part of a marine mammal survey. The type and timing of this work is so different from the many other types of oceanographic science that take place on a typical research cruise. While everyone else is scurrying around, deploying instruments and collecting samples at a “station” (a geographic waypoint in the ocean that is sampled repeatedly over time), we – the marine mammal team – are taking a break because we can only survey when the boat is moving. While everyone else is sleeping or relaxing during a long transit between stations, we’re hard at work up on the flying bridge of the ship, scanning the horizon for animals.

Top left: marine mammal survey effort (black lines), and oceanographic sampling stations (red diamonds). Top right: humpback whale sighting locations. Bottom left: fin whale sighting locations. Bottom right: pacific white-sided dolphin sighting locations.

During each “on effort” survey period, Dawn and I cover separate quadrants of ocean, each manning either the port or starboard side. We continuously scan the horizon for signs of whale blows or bodies, alternating between our eyes and binoculars. During long transits, we work in chunks – forty minutes on effort, and twenty minutes off effort. Staring at the sea all day is surprisingly tiring, and so our breaks often involve “going to the eye spa,” which entails pulling a neck gaiter or hat over your eyes and basking in the darkness.  

Dawn has been joining these NCC cruises for the last four years, and her wealth of knowledge has been a great resource as I learn how to survey and identify marine mammals. Beyond learning the telltale signs of separate species, one of the biggest challenges has been learning how to read the sea better, to judge the difference between a frothy whitecap and a whale blow, or a distant dark wavelet and a dorsal fin. Other times, when conditions are amazing and it feels like we’re surrounded by whales, the trick is to try to predict the positions and trajectory of each whale so we don’t double-count them.

Over the last week, all our scanning has been amply rewarded. We’ve seen pods of dolphins play in our wake, and spotted Dall’s porpoises bounding alongside the ship. Here on the Heceta Line, we’ve seen a diversity of pinnipeds, including Northern fur seals, Stellar sea lions, and California sea lions. We’ve been surprised by several groups of fin whales, farther offshore than expected, and traveled alongside a pod of about 12 orcas for several minutes, which is exactly as magical as it sounds.

Killer whales traveling alongside the Bell M. Shimada, putting on a show for the NCC science team and ship crew. Photo by Dawn Barlow.

Notably, we’ve also seen dozens of humpbacks, including along what Dawn termed “the humpback highway” during our transit offshore of southern Oregon. One humpback put on a huge show just 200 meters from the ship, demonstrating fluke slapping behavior for several minutes. We wanted to be sure that everyone onboard could see the spectacle, so we radioed the news to the bridge, where the officers control the ship. They responded with my new favorite radio call ever: “Roger that, we are currently enamored.”

A group of humpbacks traveling along the humpback highway. Photo by Dawn Barlow.
A humpback whale fluke slapping. Photo by Dawn Barlow.

Even with long days and tired eyes, we are still constantly enamored as well. It has been such a rewarding cruise so far, and it’s hard to think of returning back to “real life” next week. For now, we’re wishing you the same things we’re enjoying – great weather, unlimited coffee, and lots of whales!

SpeciesNumber of sightingsTotal number observed
California Sea Lion26
Dall’s Porpoise325
Fin Whale1118
Humpback Whale140218
Killer Whale321
Northern Fur Seal99
Northern Right Whale Dolphin28
Pacific White-sided Dolphin13145
Steller Sea Lion33
Unidentified Baleen Whale104127
Unidentified Dolphin628
Unidentified Whale22

The Road to Oregon

By Imogen Lucciano, incoming graduate student, OSU Department of Fisheries, Wildlife and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

There are moments in our individual lifetimes that we can define as noteworthy and right now, as I prepare to start my graduate career within the Marine Mammal Institute (MMI) at OSU, I would say this is it for me. As I sit down to write this blog and document how surreal my future adventure is, I simultaneously feel this path is felicitous. After a year of being cooped up due to COVID, time presently seems to be going by at rocket speed. I am moving constantly in through my day to continue running my current life, while simultaneously arranging all that will encompass my new life. And while I answer questions to my 10-year-old daughter who is doing geometry homework in the living room, while hollering “That is not yours!” to the kitchen where the recently adopted feral dog is sticking his entire head under the trash can lid, while arranging our books in a cardboard box at the packing station I set up on the dining room table, I cannot deny a sense of serenity. This moment in my life, becoming a part of the GEMM Lab and MMI, and relocating to Corvallis is great.

This moment’s noteworthiness is emphasized by embarking on probably the most variable-heavy road trip I have planned to date. Since the age of 19, when I left my small mountain town on the Appalachian trail in Pennsylvania, I have transferred locations ~20 times. Due to extensive travel while serving in the Army (various Army trainings and overseas mission deployments), I have bounced around the US and to other countries often. Over time, one becomes acclimated to the hectic nature of this sort of lifestyle, and yet this new adventure holds significance. 

So here are the details of the adventure trip that lies ahead: I will drive my 2002 Jeep Grand Cherokee across the country; from Charlottesville, Virginia to Corvallis, Oregon. My projected route will extend 2,822 miles and take ~43 driving hours total. The route will fall within the boundaries of 11 states (see Figure 1.)

 Figure 1. Blue Line indicates route from Charlottesville to Corvallis (Google Maps)

Attached to the hitch of the Jeep will be a 6×12 rented cargo trailer containing our treasured books, furniture and things. Inside the Jeep will be three living variables: Mia (the 10-year-old), Angus (hyperactive border collie/ pit bull mix) and Mr. Gibbs (feral pirate dog); all three will need to be closely monitored for potential hiccups in the plan.

If we are going to make it to our destination hotel/Airbnb each night of the trip, I must be organized and calculate road time each day while factoring in breaks to the loo and fueling up. These calculations need to be precise, with little margin for error. I cannot play it too safely either, or it will take us too long to get across the country (I must start my graduate work after all). On the other hand, I cannot realistically expect too many road hours in a day. I think at this point I have got it worked out (Table 1.)

Table 1. Driving Hours and Miles Per Day

When I look back on my career, I had no idea that my not-so-smooth road would lead me to my dream goal of studying marine mammals. I took the Army placement tests at the age of 19, which led me to the field of “information operations” where I earned a great knowledge base in data analysis and encountered fantastic leaders whom I might not have known otherwise. I learned immensely on this path and it set me up very well for moving forward into research and collaboration in the sciences. I am so grateful that my life took this journey because working in the military provided me with the utmost respect for my opportunities and greater empathy for others. This route had many extreme obstacles and was intensely intimidating at times, but I am all the better for it. And I was never able to shake the dream of where I wanted to be (see Figures 2 & 3.) Timing is everything.

Figure 2 & 3. Two of the images of the Pacific coast I have hung up in my house. Keeping my eye on the prize, so to speak. 

It will feel great to cross over the Oregon state line. I cannot wait to meet GEMM Lab in-person and all the other wonderful researchers and staff at MMI and Hatfield Marine Science Center. I am eager to step onto the RV Pacific Storm and begin my thesis research on the magnificent cetaceans off the Oregon coast, and hopefully do some good in the end. As I evaluate the logistics of my trip from Charlottesville to Corvallis, I feel relieved rather than overwhelmed. We could attribute this relief to my not-so-smooth road to get to where I am. Looking ahead, of course, I see a road that will require focus, attention, passion, care, and lots of fuel. Even if this road is not completely smooth, I will have my hands on 10 and 2, and feel so grateful and ready to be on it.

Making predictions: A window into ecological forecast models

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

“What is the weather going to be like tomorrow?” “How long will it take to drive there, with traffic?” We all rely on forecasts to make decisions, such as whether to bring a rain jacket, when to get in the car to arrive at a certain destination on time, or any number of situations where we want a prediction of what will happen in the near future. Statistical models underpin many of these examples, using past data to inform future predictions.

Early on in graduate school, I was told that “all models are wrong, but some models work.” Any model is essentially a best approximation, using mathematical relationships, of how we understand a pattern. Models are powerful tools in ecology, enabling us to distill complex, dynamic, and interacting systems into terms and parameters that can be quantified. This ability can help us better understand our study systems and use that understanding to make predictions. We will never be able to describe every nuance of an ecosystem. Instead, the challenge is to collect enough information to build an informed model that can enhance our understanding, without over-simplifying or unnecessarily complicating the system we aim to describe. As Dr. Simon Levin stated in his 1989 seminal paper:

“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.”1

Species distribution models (SDMs) are the particular branch of models that underpin much of my PhD research on blue whale ecology and distribution in New Zealand. SDMs are mathematical algorithms that correlate observations of a species with environmental conditions at their observed locations to gain ecological insight and predict spatial distributions of the species (Fig. 1)2. The model is a best attempt to quantify and describe the relationships between predictors, e.g., temperature and the observed species distribution pattern. For example, blue whale occurrence is higher in areas of lower temperatures and greater krill availability, and these relationships can be described with models3. So, a model essentially takes all the data available, and synthesizes that information in terms of the relationships between the predictors (environment) and response (species occurrence). Then, we can look at the fitted relationships to ask what we would expect from the species distribution pattern when temperature, or krill availability, or any other predictor, is at a particular value. 

Figure 1. A schematic of a species distribution model (SDM) illustrating how the relationship between mapped species and environmental data (left) is compared to describe “environmental space” (center), and then map predictions from a model using only environmental predictors (right). Note that inter-site distances in geographic space might be quite different from those in environmental space—a and c are close geographically, but not environmentally. The patterning in the predictions reflects the spatial autocorrelation of the environmental predictors. Figure reproduced from Elith and Leathwick (2009).

So, if a model is simply a mathematical description of how terms interact to produce a particular outcome, how do predictions work? To make a spatial prediction, e.g., a map of the probability of a species being present, you need two things: a model describing the functional relationships between species presence and your environmental predictors, and the values of your predictor variables on the day you are interested in predicting to. For example, you may need to obtain a map of sea surface temperature, productivity, temperature anomaly, and surface currents on a day you want to know where whales are expected to be. Your model is the applied across that stack of spatial environmental layers and, based on the functional relationships derived by the model, you get an estimate of the probability of species occurrence based on the temperature, productivity, anomaly, and surface current values at each location. By applying the model over a range of values, you can obtain a continuous surface with the probability of presence, in the form of a map. These maps are typically for the past or present because that is when we can typically acquire spatial environmental layers. However, to make predictions for a future time of interest, we need to have spatial environmental layers for the future.

Forecasts are predictions for the future. Recent advances in technology and computing have led to an emergence of environmental and ecological forecasting tools that are being developed around the world to produce marine forecasts. These tools include predictions of the physical environment such as ocean temperatures or currents, and biological patterns such as where species will be distributed in space and timing of events like salmon spawning or lobster landings4. The ability to generate forecast of marine ecosystems is of particular interest to resource users and managers because it can allow them to be proactive rather than reactive. Forecasts enable us to anticipate events or patterns and prepare, rather than having to respond in real-time or after the fact.

The South Taranaki Bight region in New Zealand is an area where blue whale foraging habitat frequently coincides with industry pressures, including petroleum and mineral extraction, exploration for petroleum reserves using seismic airgun surveys, vessel traffic between ports, and even an ongoing proposal for seabed mining5. Static spatial restrictions to mitigate impacts from these activities on blue whales may be met with resistance from industry user groups, but dynamic spatial management6–8 of blue whale habitat could be more attractive and acceptable. The key for successful dynamic management is knowing where and when to put those boundaries; and this is where ecological forecast models can show their strength. If we can predict suitable blue whale habitat for the future, proactive regulations can be applied to enhance conservation management in the region. Can we develop reliable and useful ecological forecasts for the South Taranaki Bight? Well, given that we have already developed robust models of the relationships between blue whales and their habitat3 and have documented the spatial and temporal lags between wind, upwelling, and blue whales9, we feel confident that we can develop forecast models to predict where blue whales will be in the STB region. As we continue working hard toward this goal, we invite you to check back for our findings in the future. So, consider this blog post a forecast of sorts, and stay tuned!  

Figure 2. A blue whale surfaces in front of an oil extraction platform in the South Taranaki Bight, demonstrating the overlap between whales and industry in the region. Photo by D. Elvines.

References:

1.        Levin, S. A. The problem of pattern and scale. Ecology 73, 1943–1967 (1992).

2.        Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).

3.        Barlow, D. R., Bernard, K. S., Escobar-Flores, P., Palacios, D. M. & Torres, L. G. Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar. Ecol. Prog. Ser. 642, 207–225 (2020).

4.        Payne, M. R. et al. Lessons from the first generation of marine ecological forecast products. Front. Mar. Sci. 4, 1–15 (2017).

5.        Torres, L. G. Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal. J. Mar. Freshw. Res. 47, 235–248 (2013).

6.        Hyrenbach, K. D., Forney, K. A. & Dayton, P. K. Marine protected areas and ocean basin management. Aquat. Conserv. Mar. Freshw. Ecosyst. 10, 437–458 (2000).

7.        Maxwell, S. M. et al. Dynamic ocean management: Defining and conceptualizing real-time management of the ocean. Mar. Policy 58, 42–50 (2015).

8.        Oestreich, W. K., Chapman, M. S. & Crowder, L. B. A comparative analysis of dynamic management in marine and terrestrial systems. Front. Ecol. Environ. 18, 496–504 (2020).

9.        Barlow, D. R., Klinck, H., Ponirakis, D., Garvey, C. & Torres, L. G. Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci. Rep. 11, (2021).

Why did I start sketchnoting?

By Solène Derville, Postdoc, OSU Department of Fisheries, Wildlife, and Conservation Science, Geospatial Ecology of Marine Megafauna Lab

Sketchnoting, also known as « visual notetaking » is a technique combining words with drawings, diagrams and typography to record ideas (Figure 1). This concept was invented by designer Michael Rohde in 2006 to combine tedious notetaking with doodling. He quickly discovered that adding drawings to his notes helped him concentrate and remember better. He would also be more likely to come back to his notes later on (something we must all admit is not so common). Similarly, after I followed a short online class by Magalie Le Gall (Sorbonne Université) I became convinced that sketchnoting shows  promise and can have a positive impact on my scientific work.

Figure 1 : What is sketchnoting ? By verbaltovisual.com

Draw to remember more

The impact of sketchnoting on memory is not without scientific backing. Back in 1971, Allan Paivio, an American professor of psychology, developed the dual-coding theory. It posits that visual and verbal information are mentally processed in two distinctive systems and have additive effects on cognitive operations such as memory. Numerous experiments have empirically confirmed that dual coding (images + words) improve learning and memory. In addition, converting what you hear or see into visually interconnected drawings and words helps you synthesize content. Personalizing ideas into your own symbols and images also lays a strong basis for remembering. The implications of sketchnoting for educational purposes are therefore huge!

Draw to stay focused

I have only started sketchnoting recently but the impact this method had on my concentration immediately struck me. In the constant stream of information that we experience nowadays, I found that synthesizing ideas on paper using symbols and diagrams helped me stay focused on what I am presently reading or hearing, instead of letting my thoughts drift in a thousand different directions. Again, this outcome can have big implications in the classroom or at your desk. Using very basic lettering, bullets, frames and connectors (Figure 2), sketchnoting appears to be a good didactic tool.

Figure 2 : A few drawing tips by sketchnoter Carol Anne McGuire.

Draw to create and appeal

Figure 3 (source: ASIDE 2013)

Mike Rohde’s motto is « ideas, not art » because a lot of people have an immediate reaction of fear of failure when they are asked to draw something. He emphasizes that sketchnoting is not necessarily meant to be pretty, as it mostly serves a personal purpose. However, if you have an artistic fiber (even slightly!), sketchnoting becomes a great communication tool and can help you convey ideas in posters, slides, blogs, etc. Even very simple drawings are appealing and fun. You can create your own visual libraries from a few basic shapes (Figure 3). Anything can be drawn with a few simple lines! You can also use drawing libraries such as quickdraw.withgoogle.com to find examples and eventually gain confidence… as you realize that the average people’s drawing skills are pretty low (the dolphin drawings on this website are worth a look)!

Now, the key to developing this new skill is clearly to practice! From now on, I have decided to record every one of our monthly GEMM lab meetings in a sketchnote to make sure I keep track of our great discussions. I will also definitely try to apply this approach when reading scientific literature, attending conferences, preparing drafts, teaching and so much more! And for a start, what could be better then to sketchnote the research project I currently working on (Figure 4)?

Figure 4 : My first attempt at sketchnoting! Illustration of the OPAL project that I am working on (credit : S. Derville).

References & resources:

Great intro to sketchnoting by Mike Rhode: https://www.youtube.com/watch?v=39Xq4tSQ31A

Training, tips, videos etc.: https://www.verbaltovisual.com/

Link to many ressources and websites: https://sites.google.com/site/ipadmultimediatools/sketchnote-tools

Paivio, A (1971). Imagery and verbal processes. New York: Holt, Rinehart, and Winston.

Cetacean strandings and unusual mortality events: Why do cetaceans beach?

By Alejandro Fernandez Ajo, PhD student in the Department of Biology, Northern Arizona University, visiting scientist in the GEMM Lab working on the gray whale physiology and ecology project  

When a cetacean (whales and dolphins) is ashore or trapped in nearshore waters and cannot return to the open waters, it is considered stranded. Frequently, the stranded animal is in distress, dying, or dead. Although rare, the stranded cetacean can be a healthy animal trapped due to changes in tide or disorientation. Every year many cetacean strandings are reported from along the coasts around the world, and likely many more stranding events go unnoticed when they occur in remote areas. In all cases, the question is: why do cetaceans beach?

Southern right whales stranded at the coast of Peninsula Valdés, Patagonia-Argentina. Photo: Matias DiMartino / Southern Right Whale Health Monitoring Program.

There may be different causes for whales and dolphins to strand on beaches, either dead or alive. Understanding and investigating the causes of cetaceans strandings is critical because they can be indicators of ocean health, can help identify anthropogenic sources of disturbance, and can give insights into larger environmental issues that may also have implications for human health (NOAA). In this context, when scientists are analyzing a stranding event, they consider both possibilities that the event was natural or human-caused and classify strandings according to specific characteristics to study the causes of these events.

Types of cetacean strandings:

Live or Dead Stranding:

A stranding can involve live animals or dead animals if the death occurs in the sea and the body is thrown ashore by wind or currents. In live strandings, when they occur near urbanized areas, usually significant efforts are made to rescue and return the animals to the water; with small odontocetes, sometimes there is success, and animals can be rescued. However, when large whales are beached alive, their own weight out of the water can compress their organs and can cause irreversible internal damage. Although not externally visible, such damage can sometimes cause the death of the animal even after returning to the sea.

According to the number of individuals:

Single strandings occur when only a single specimen is affected at the time. The cetaceans that most frequently strand individually are the baleen (or mysticete) whales, such as right and humpback whales, due to their often solitary habits.

Mass strandings comprise two or more specimens, and in some cases, it can involve tens or even a few hundred animals. The mass strandings are more frequently observed for the odontocetes, such as pilot whales, false killer whales, and sperm whales with more complex social structures and gregarious habits.

Left: Single southern right whale calf stranded at the coast of Peninsula Valdés, Patagonia-Argentina. Ph.: Mariano Sironi / ICB. Right: Mass stranding of common dolphins in Patagonia-Argentina. Photo: www.elpais.com

Unusual Mortality Events

The Marine Mammal Protection Act defines an unusual mortality event (UME) as a stranding event that is unexpected, involves a significant die-off of any marine mammal population, and demands immediate response. Seven criteria make a mortality event “unusual.” Source: https://www.fisheries.noaa.gov.

  1. A marked increase in the magnitude or a marked change in morbidity, mortality, or strandings when compared with prior records.
  2. A temporal change in morbidity, mortality, or strandings is occurring.
  3. A spatial change in morbidity, mortality, or strandings is occurring.
  4. The species, age, or sex composition of the affected animals is different than that of animals that are normally affected.
  5. Affected animals exhibit similar or unusual pathologic findings, behavior patterns, clinical signs, or general physical condition (e.g., blubber thickness).
  6. Potentially significant morbidity, mortality, or stranding is observed in species, stocks, or populations that are particularly vulnerable (e.g., listed as depleted, threatened, or endangered, or declining). For example, stranding of three or four right whales may be cause for great concern, whereas stranding of a similar number of fin whales may not.
  7. Morbidity is observed concurrent with or as part of an unexplained continual decline of a marine mammal population, stock, or species.

The purpose of the classification of a mortality event as a UME is to activate an emergency response that aims to minimize deaths, determine the event cause, or causes, determine the effect of the event on the population, and identify the role of environmental parameters in the event. Such classification authorizes a federal investigation that is led by the expertise of the Working Group on Marine Mammal Unusual Mortality Events to investigate the event. This working group is comprised of experts from scientific and academic institutions, conservation organizations, and state and federal agencies, all of whom work closely with stranding networks and have a wide variety of experience in biology, toxicology, pathology, ecology, and epidemiology.

Southern right whale necropsy and external measurements. Source: Southern Right Whale Health Monitoring Program / ICB.

What can be learned from strandings and UMEs?

Examining stranded marine mammals can provide valuable insight into marine mammal health and identify environmental factors leading to strandings. Through forensic examinations, the aim is to identify possible risks to whales’ health and evaluate their susceptibility to diseases, pollutants, and other stressors. This information can contribute to cetacean conservation through informed management strategies. However, the quality of the data derived from a necropsy (the postmortem examination of carcasses) is highly contingent upon how early the stranding event is reported. As soon as the animal is deceased, decomposition starts, hindering the possibilities of detailed investigations of the cause of death.

Therefore, a solid network that can report and respond quickly to a stranding event is fundamental; this includes trained personnel, infrastructure, funding, and expertise to respond in a manner that provides for animal welfare (in the case of live strandings) and obtains data on marine mammal health and causes of death. Moreover, a coordinated international organization that integrates national marine mammal stranding networks has also been identifying as a critical aspect to enable adequate response to such mortality events. In many locations and countries around the world, funding, logistical support, and training remain challenging to stranding response.

In response to these concerns and needs, at the last World Marine Mammal Conference, which took place in Barcelona in December of 2019, The Global Stranding Network was founded to “enhance and strengthen international collaboration to (1) ensure consistent, high-quality response to stranded marine mammals globally, and (2) support conservation efforts for species under threat of extinction.” Monitoring marine mammal health worldwide can guide conservation and help identify priority areas for management (Gulland and Stockin, 2020).

What to do in case of finding a whale or dolphin on the beach?

When strandings occur, it is essential to know how to act. Unfortunately, untrained people, often with good intentions, can worsen the situation of stress and injury to the animal or can put themselves at risk of injury or exposure to pathogens. If you find a cetacean alive or dead on the beach, the most important things to do are:

  1. Record information about the location and the animal´s characteristics (the species, if known; the animal’s approximate size; and status (alive or dead)).
  2. Give immediate notice to the responsible authorities so that specialized help arrives as soon as possible. Report a Stranded or Injured Marine Animal.
  3. Keep at a safe distance: the animal may appear dead to the naked eye and not be. It is important to remember that cetaceans are wild animals and that in stressful situations such as strandings, they can try to defend themselves.
  4. Do not touch the animal: one of the causes of strandings is diseases; therefore, it is advisable not to contact the individuals to avoid exposure to potential pathogens.
  5. If the animal is alive, keep a distance from the animal, especially from its head and tail. Prevent children or dogs from approaching the animal.
  6. Keep calm and do not make noise that could disturb the stranded animal.
  7. Do not take the animal out of the water if it is on the shore or return it to the sea if it is on the beach: Such movement could cause serious injuries, or even death.
  8. Do not feed the animal or give it water: keep the blowhole clear because it is where they breathe.

Source: Whale Conservation Institute of Argentina

Important contacts in case of reporting a Stranded or injured Marine Mammal:

  1. National Oceanic and Atmospheric Administration
  2. Oregon Marine Mammal Stranding Network

References:

https://www.fisheries.noaa.gov/national/marine-mammal-protection/marine-mammal-unusual-mortality-events

https://www.fisheries.noaa.gov/insight/understanding-marine-mammal-unusual-mortality-events#what_criteria_define_an_ume?

https://ballenas.org.ar/programa-de-monitoreo-sanitario-ballena-franca-austral-pmsbfa/

https://globalstrandingnetwork.com/about

https://iwc.int/strandings

Proceedings of the workshop “Harmonizing Global Stranding Response.” (2020) World marine mammal Conference Barcelona, Catalonia, Spain. Editors: Gulland F and Stockin K; Ecs Special Publication Series No. 62.

Mazzariol S., Siebert U., Scheinin A., Deaville R., Brownlow A., Uhart M.., Marcondes M., Hernandez G., Stimmelmayr R., Rowles T., Moore K., Gulland F., Meyer M., Grover D., Lindsay P., Chansue N., Stockin K. (2020). Summary of Unusual Cetaceans Strandings Events worldwide (2018-2020). SC-68B/E/09 Rev1.

Fashionably late: New GEMM Lab publication measures lags between wind, upwelling, and blue whale occurrence

By Dawn Barlow, PhD Candidate, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

To understand the complex dynamics of an ecosystem, we need to examine how physical forcing drives biological response, and how organisms interact with their environment and one another. The largest animal on the planet relies on the wind. Throughout the world, blue whales feed areas where winds bring cold water to the surface and spur productivity—a process known as upwelling. In New Zealand’s South Taranaki Bight region (STB), westerly winds instigate a plume of cold, nutrient-rich waters that support aggregations of krill, and ultimately lead to foraging opportunities for blue whales. This pathway, beginning with wind input and culminating in blue whale occurrence, does not take place instantaneously, however. Along each link in this chain of events, there is some lag time.

Figure 1. A blue whale comes up for air in New Zealand’s South Taranaki Bight. Photo: L. Torres.

Our recent paper published in Scientific Reports examines the lags between wind, upwelling, and blue whale occurrence patterns. While marine ecologists have long acknowledged that lag plays a role in what drives species distribution patterns, lags are rarely measured, tested, and incorporated into studies of marine predators such as whales. Understanding lags has the potential to greatly improve our ability to predict when and where animals will be under variable environmental conditions. In our study, we used timeseries analysis to quantify lag between different metrics (wind speed, sea surface temperature, blue whale vocalizations) at different locations. While our methods are developed and implemented for the STB ecosystem, they are transferable to other upwelling systems to inform, assess, and improve predictions of marine predator distributions by incorporating lag into our understanding of dynamic marine ecosystems.

So, what did we find? It all starts with the wind. Wind instigates upwelling over an area off the northwest coast of the South Island of New Zealand called Kahurangi Shoals. This wind forcing spurs upwelling, leading to the formation of a cold water plume that propagates into the STB region, between the North and South Islands, with a lag of 1-2 weeks. Finally, we measured the density of blue whale vocalizations—sounds known as D calls, which are produced in a social context, and associated with foraging behavior—recorded at a hydrophone downstream along the upwelling plume’s path. D call density increased 3 weeks after increased wind speeds near the upwelling source. Furthermore, we looked at the lag time between wind events and aggregations in blue whale sightings. Blue whale aggregations followed wind events with a mean lag of 2.09 ± 0.43 weeks, which fits within our findings from the timeseries analysis. However, lag time between wind and whales is variable. Sometimes it takes many weeks following a wind event for an aggregation to form, other times mere days. The variability in lag can be explained by the amount of prior wind input in the system. If it has recently been windy, the water column is more likely to already be well-mixed and productive, and so whale aggregations will follow wind events with a shorter lag time than if there has been a long period without wind and the water column is stratified.

Figure 2. Top panel: Map of the study region within the South Taranaki Bight (STB) of New Zealand, with location denoted by the white rectangle on inset map in the upper right panel. All spatial sampling locations for sea surface temperature implemented in our timeseries analyses are denoted by the boxes, with the four focal boxes shown in white that represent the typical path of the upwelling plume originating off Kahurangi shoals and moving north and east into the STB. The purple triangle represents the Farewell Spit weather station where wind measurements were acquired. The location of the focal hydrophone (MARU2) where blue whale D calls were recorded is shown by the green star. (Reproduced from Barlow et al. 2021). Bottom panel: Results of the timeseries cross-correlation analyses, illustrating the lag between some of the metrics and locations examined.

This publication forms the second chapter of my PhD dissertation. However, in reality it is the culmination of a team effort. Just as whale aggregations lag wind events, publications lag years of hard work. The GEMM Lab has been studying New Zealand blue whales since Leigh first hypothesized that the STB was an undocumented foraging ground in 2013. I was fortunate enough to join the research effort in 2016, first as a Masters student and now as a PhD Candidate. I remember standing on the flying bridge of R/V Star Keys in New Zealand in 2017, when early in our field season we saw very few blue whales. Leigh and I were discussing this, with some frustration. Exclamations of “This is cold, upwelled water! Where are the whales?!” were followed by musings of “There must be a lag… It has to take some time for the whales to respond.” In summer 2019, Christina Garvey came to the GEMM Lab as an intern through the NSF Research Experience for Undergraduates program. She did an outstanding job of wrangling remote sensing and blue whale sighting data, and together we took on learning and understanding timeseries analysis to quantify lag. In a meeting with my PhD committee last spring where I presented preliminary results, Holger Klinck chimed in with “These results are interesting, but why haven’t you incorporated the acoustic data? That is a whale timeseries right there and would really add to your analysis”. Dimitri Ponirakis expertly computed the detection area of our hydrophone so we could adequately estimate the density of blue whale calls. Piecing everything together, and with advice and feedback from my PhD committee and many others, we now have a compelling and quantitative understanding of the upwelling dynamics in the STB ecosystem, and have thoroughly described the pathway from wind to whales in the region.

Figure 3. Dawn and Leigh on the flying bridge of R/V Star Keys on a windy day in New Zealand during the 2017 field season. Photo: T. Chandler.

Our findings are exciting, and perhaps even more exciting are the implications. Understanding the typical patterns that follow a wind event and how the upwelling plume propagates enables us to anticipate what will happen one, two, or up to three weeks in the future based on current conditions. These spatial and temporal lags between wind, upwelling, productivity, and blue whale foraging opportunities can be harnessed to generate informed forecasts of blue whale distribution in the region. I am thrilled to see this work in print, and equally thrilled to build on these findings to predict blue whale occurrence patterns.

Reference: Barlow, D.R., Klinck, H., Ponirakis, D., Garvey, C., Torres, L.G. Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep 11, 6915 (2021). https://doi.org/10.1038/s41598-021-86403-y

From land, sea,… and space: searching for whales in the vast ocean

By Solène Derville, Postdoc, OSU Department of Fisheries, Wildlife, and Conservation Science, Geospatial Ecology of Marine Megafauna Lab

The ocean is vast.

What I mean is that the vastness of the ocean is very hard to mentally visualize. When facing a conservation issue such as increased whale entanglement along the US West Coast (see OPAL project ), a tempting solution may  be to suggest « let’s go see where the whales are and report their location to the fishermen?! ». But, it only takes a little calculation to realize how impractical this idea is.

Let’s roll out the numbers. The US West Coast exclusive economic zone (EEZ) stretches from the coast out to 200 nautical miles offshore, as prescribed by the 1982 United Nations Convention on the Law of the Sea. It covers an area of 825,549 km² (Figure 1). Now, imagine that you wish to survey this area for marine mammals. Using a vessel such as the R/V Bell M. Shimada that is used for the Northern California Current Ecosystem surveys cruises (NCC cruises, see Dawn and Rachel’s last blog), we may detect whales at a distance of roughly 6 km (based on my preliminary results). This distance of detection depends on the height of the observer, hence the height of the flying bridge where she/he is standing (the observer’s height may also be accounted for, but unless she/he is a professional basket-ball player, I think it can be neglected here). The Shimada is quite a large ship and it’s flying bridge is 13 meters above the water. Two observers may survey the water on each side of the trackline.

Considering that the vessel is moving at 8 knots (~15 km/h), we may expect to be effectively surveying 180 km² per hour (6x2x15). That’s not too bad, right?

Again, perspective is the key. If we divide the West Coast EEZ surface by 180 km² we can estimate that it would take 2,752 hours to survey this entire region. With an average of 12 hours of daylight, this takes us to…

382 DAYS OF SURVEY, searching for marine mammals over the US West Coast. Considering that observations cannot be undertaken on days with bad weather (fog, heavy rain, strong winds…), it might take more than a year and a half to complete the survey! And what would the marine mammals have done in the meantime? Move…

This little math exercise proves that exhaustively searching for the needle in the haystack from a vessel is not the way to go if we are to describe whale distribution and help mitigate the risk of entanglement. And using another platform of observation is not necessarily the solution. The OPAL project has relied on a great collaboration with the United States Coast Guard to survey Oregon waters. The USCG helicopters travel fast compared to a vessel, about 90 knots (167 km/h). As a result, more ground is covered but the speed at which it is traveling prevents the observer from detecting whales that are very far away. Based on the last analysis I ran for the OPAL project, whales are usually detected up to 3 km from the helicopter (only 5 % of sightings exceed that distance). In addition, the helicopter generally only has capacity for one observer at a time.

If we replicate the survey time calculation from above for the USCG helicopter, we realize that even with a fast-moving aerial survey platform it would still take 137 days to cover the West Coast EEZ.

Figure 1. What is the best survey method to document marine mammal occurrence in the US West Coast Exclusive Economic zone (EEZ)?

First, we can model and extrapolate. This approach is the path we are taking with the OPAL project: we survey Oregon waters in 4 different areas along the coast each month, then model observed whale densities as a function of topographic and oceanographic variables, and then predict whale probability of presence over the entire region. These predictions are based on the assumption that our survey design effectively sampled the variety of environmental conditions experienced by whales over the study region, which it certainly did considering that all sites are surveyed year-round.

An alternative approach that has been recently discussed in the GEMM Llab, is the use of satellite images to detect whales along the coast. A communication entitled « The Potential of Satellite Imagery for Surveying Whales » was published last month in the Sensors Journal (Höschle et al., 2021) and presents the opportunities offered by this relatively new technology. The WorldView-3 satellite, owned by the company Digitalglobe and launched in 2016, has made it possible to commercialize imagery with a resolution never reached before, of the order of 30 cm per pixel. These very high resolution (VHR) satellite images make it possible to identify several species of large whales (Cubaynes et al. al., 2019) and to estimate their density (Bamford et al., 2020). Furthermore, machine learning algorithms, such as Neural Networks, have proved quite efficient at automatically detecting whales in satellite images (Guirado et al., 2019, Figure 2). While several new ultra-high resolution imaging satellites are expected to be launched in 2021 (by Maxar Technologies and Airbus), this “remote” approach looks like a promising avenue to detect whales over vast regions while drinking a cup of coffee at the office.

Figure 2. Illustration of a whale detection algorithm working on a gridded satellite image (DigitalGlobe). Source: Guirado et al., 2019.

But like any other data collection method, satellites have their drawbacks. We recently discovered that these VHR satellites are routinely switched off while passing above the ocean. Specific inquiries would need to be made to acquire data over our study areas, which would be at great expense. One of the cheapest provider I found is the Soar platform, that provides images at 50 cm resolution in partnership with the Chinese Aerospace Science and Technology Corporation. They advertise daily images anywhere on earth at $10 USD per km². This might sound cheap at first glance, but circling back to our US West Coast EEZ area calculations, we estimate that surveying this region entirely with satellite imagery would cost more than $8 million USD.

Yet, we have to look forward. The use of satellite imagery is likely to broaden and increase in the coming years, with a possible decrease in cost. Quoting Höschle et al. (2021) ‘To protect our world’s oceans, we need a global effort and we need to create opportunities for that to happen’.

Will satellites soon save whales?

References

Bamford, C. C. G. et al. A comparison of baleen whale density estimates derived from overlapping satellite imagery and a shipborne survey. Sci. Rep. 10, 1–12 (2020).

Cubaynes, H. C., Fretwell, P. T., Bamford, C., Gerrish, L. & Jackson, J. A. Whales from space: Four mysticete species described using new VHR satellite imagery. Mar. Mammal Sci. 35, 466–491 (2019).

Guirado, E., Tabik, S., Rivas, M. L., Alcaraz-Segura, D. & Herrera, F. Whale counting in satellite and aerial images with deep learning. Sci. Rep. 9, 1–12 (2019).

Höschle, C., Cubaynes, H. C., Clarke, P. J., Humphries, G. & Borowicz, A. The potential of satellite imagery for surveying whales. Sensors 21, 1–6 (2021).

Lessons learned from (not) going to sea

By Rachel Kaplan1 and Dawn Barlow2

1PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

2PhD Candidate, Oregon State University Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

“Hurry up and wait.” A familiar phrase to anyone who has conducted field research. A flurry of preparations, followed by a waiting game—waiting for the weather, waiting for the right conditions, waiting for unforeseen hiccups to be resolved. We do our best to minimize unknowns and unexpected challenges, but there is always uncertainty associated with any endeavor to collect data at sea. We cannot control the whims of the ocean; only respond as best we can.

On 15 February 2021, we were scheduled to board the NOAA Ship Bell M. Shimada as marine mammal observers for the Northern California Current (NCC) ecosystem survey, a recurring research cruise that takes place several times each year. The GEMM Lab has participated in this multidisciplinary data collection effort since 2018, and we are amassing a rich dataset of marine mammal distribution in the region that is incorporated into the OPAL project. February is the middle of wintertime in the North Pacific, making survey conditions challenging. For an illustration of this, look no further than at the distribution of sightings made during the February 2018 cruise (Fig. 1), when rough sea conditions meant only a few whales were spotted.

Figure 1. (A) Map of marine mammal survey effort (gray tracklines) and baleen whale sightings recorded onboard the NOAA ship R/V Shimada during each of the NCC research cruises to-date and (B) number of individuals sighted per cruise since 2018. Note the amount of survey effort conducted in February 2018 (top left panel) compared to the very low number of whales sighted. Data summary and figures courtesy of Solene Derville.

Now, this is February 2021 and the world is still in the midst of navigating the global coronavirus pandemic that has affected every aspect of our lives. The September 2020 NCC cruise was the first NOAA fisheries cruise to set sail since the pandemic began, and all scientists and crew followed a strict shelter-in-place protocol among other COVID risk mitigation measures. Similarly, we sheltered in place in preparation for the February 2021 cruise. But here’s where the weather comes in yet again. Not only did we have to worry about winter weather at sea, but the inclement conditions across the country meant our COVID tests were delayed in transit—and we could not board the ship until everyone tested negative. By the time our results were in, the marine forecast was foreboding, and the Captain determined that the weather window for our planned return to port had closed.

So, we are still on shore. The ship never left the dock, and NCC February 2021 will go on the record as “NAs” rather than sightings of marine mammal presence or absence. So it goes. We can dedicate all our energy to studying the ocean and these spectacularly dynamic systems, but we cannot control them. It is an important and humbling reminder. But as we have continued to learn over the past year, there are always silver linings to be found.

Even though we never made it to the ship, it turns out there’s a lot you can get done onshore. Dawn has sailed on several NCC cruises before, and one of the goals this time was to train Rachel for her first stint at marine mammal survey work. This began at Dawn’s house in Newport, where we sheltered in place together for the week prior to our departure date.

We walked through the iPad program we use to enter data, looked through field guides, and talked over how to respond in different scenarios we might encounter while surveying for marine mammals at sea. We also joined Solene, a postdoc working on the OPAL project, for a Zoom meeting to edit the distance sampling protocol document. It was great training to discuss the finer points of data collection together, with respect to how that data will ultimately be worked into our species distribution models.

The February NCC cruise is famously rough, and a tough time to sight whales (Fig. 1). This low sighting rate arises from a combination of factors: baleen whales typically spend the winter months on their breeding grounds in lower latitudes so their density in Oregon waters is lower, and the notorious winter sea state makes sighting conditions difficult. Solene signed off our Zoom call with, “Go collect that high-quality absence data, girls!” It was a good reminder that not seeing whales is just as important scientifically as seeing them—though sometimes, of course, it’s not possible to even get out where you can’t see them. Furthermore, all absence data is not created equal. The quality of the absence data we can collect deteriorates along with the weather conditions. When we ultimately use these survey data to fuel species distribution models, it’s important to account for our confidence in the periods with no whale sightings.

In addition to the training we were able to conduct on land, the biggest silver lining came just from sheltering in place together. We had only met over Zoom previously, and spending this time together gave us the opportunity to get to know each other in real life and become friends. The week involved a lot of fabulous cooking, rainy walks, and an ungodly number of peanut butter cups. Even though the cruise couldn’t happen, it was such a rich week. The NCC cruises take place several times each year, and the next one is scheduled for May 2021. We’ll keep our fingers crossed for fair winds and negative COVID tests in May!

Figure 2. Dawn’s dog Quin was a great shelter in place buddy. She was not sad that the cruise was canceled.

The ups and downs of the ocean

By Solène Derville, Postdoc, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

As a GEMM lab post-doc working on the OPAL project, my main goal for 2021 will be to produce accurate predictive models of baleen whale distribution off the Oregon coast to reduce entanglement risk. For the past months, I have been compiling, cleaning, and processing about two years of data collected by Leigh Torres and Craig Hayslip during monthly repeat surveys conducted onboard United States Coast Guard (USCG) helicopters. These standardized surveys record where and when whales are observed off the Oregon coast. These presence and absence data may now be modeled in relation to habitat, while accounting for effort and detection (as several parameters, such as weather and sea state, can affect the capacity of observers to detect whales at the surface). Considering that several baleen whale species (namely, humpback, fin, blue and gray whales) are known to feed in the area, prey availability is expected to be a major driver of their distribution.

As prey distribution data are frequently the lacking component in the habitat model equation, whale ecologists often resort to using environmental proxies. Variables such as topography (e.g., the depth or slope of the seafloor), water physical and chemical characteristics (e.g., temperature, salinity, oxygen concentration) or ocean circulation (e.g., currents, turbulence) have proved to be good predictors for fish or krill distribution, and in turn potential predictors for whale suitable habitats. In my search for such environmental variables to be tested in our future OPAL models, I have been focusing my research on a fascinating ocean feature: sea height.

Sea height varies both temporally and spatially under the influence of multiple factors, from internal mass of the solid Earth to the orbital revolution of the moon. After reading this blog you will realize that the flatness of the horizon at sea is a deceiving perspective (Figure 1) …

Figure 1: Flat? Really? (source: Pixabay)

Gravity and the geoid

We all know of Newton’s s discovery of gravity: the attraction force exerted by any object with a given mass on its surroundings. Yet, it is puzzling to think that the rate of acceleration of the apple falling on Newton’s head would have been different if Newton had been anywhere else on Earth.

Why is that and what does it have to do with sea height? On Earth, the standard gravity g is set at 9.80665 m/s2. This constant is called a “standard” because in fact, gravity varies at the surface of our planet, even if estimated at a fixed altitude. Indeed, as gravity is caused by mass, any change in relief or rock composition results in a change in gravity. For instance, magmatic activity in the upper mantle of the Earth and the crust causes a change in rock density and results in a change in gravity measured at the surface.

Gravity therefore is the first reason why the ocean surface is not flat. Gravity shapes an irregular surface called the “geoid”. This hypothetical ocean surface has equal gravitational potential anywhere on Earth and differs from the ellipsoid of reference by as much as 100 m! So to the question whether Earth is round or flat, I would say it is potato shaped (Figure 2)!

Figure 2: Exaggerated view of the gravitational potential of Earth. View a video animation here. (credit: European Space Agency)

The geoid is an essential reference for understanding ocean currents and monitoring changes in sea-level. Hypothetically, if ocean water had equal density everywhere and at any depth, the sea surface should match with the geoid… but that’s not the case. Let’s see why.

Ocean dynamic topography

Not unlike the hills and valleys covering landscapes, the ocean surface also has its highs and lows. Except that in the ocean, the surface topography is ever changing. Sea surface height (SSH) measures the average height difference between the observed sea level and the ellipsoid of reference (Figure 3). SSH is mostly affected by ocean circulation and may vary by as much as ±1 m. Indeed, just like the rocks inside the Earth, the water in the ocean varies in density. The vertical and horizontal physical structuring of the ocean was extensively discussed by Dawn last November while she was preparing for her PhD Qualifying Exams. Temperature clearly is at the core of the processes. As thermal expansion increases the space between warming water particles, the volume of a given amount of liquid water increases with increasing temperature. Warmer waters therefore take up “more space” than cooler waters, resulting in an elevated SSH.

Figure 3: Overview of the different fields used in altimetry (credit: CLS, https://duacs.cls.fr/)

SSH may therefore be used as an indicator of oceanographic phenomena such as upwellings, where warm surface waters are replaced by deep, cooler, and nutrient-rich waters moving upwards. The California Current that moves southwards along the North American coast is known as one of the world’s major currents affiliated with strong upwelling zones, which often triggers increased biological productivity. Several studies conducted in the California Current system have found a link between the variations in SSH and whale abundance or foraging activity (Abrahms et al. 2019; Pardo et al. 2015; Becker et al. 2016; Hazen et al. 2016).⁠

SSH is measured by altimeter satellites and is made freely available by the European Space Agency and the US National Aeronautics and Space Administration. Lucky me! Numerous variables are derived from SSH, as shown in Figure 3. Among other things, I was able to download the daily maps of Sea Surface Height Anomaly (SSHa, also referred to as Sea Level Anomaly: SLA) over the Oregon coast from February 2019 to December 2020. SSHa is the difference between observed SSH at a specific time and place from the mean SSH field of reference calculated over a long period of time. Negative values of SSHa potentially suggest upwellings of cooler waters that could be associated with higher prey availability. Figure 4 shows an example of environmental data mining as I try to match SSHa with whale observations made during OPAL surveys. Figure 4B suggests increased whale occurrence where/when SSHa is lower.

Figure 4: Preliminary exploration of the relationship between sea surface height anomaly (SSHa) and baleen whales (blue, fin, humpback, unidentified) observed during OPAL surveys off Oregon, USA, between February 2019 and December 2020. A) Example covering 3 months of survey during summer 2019. Sightings were grouped over 5-km segments of surveyed trackline and segments with at least one sighting were mapped with colored circles. Dotted grey lines are the repeated survey tracklines for each of the labeled study areas (NB = North Bend). Sightings are symbolized by area (color)
and group size (circle size). Monthly averages of SSHa are represented with a colored gradient. B) Monthly averages of SSHa measured over 5-km segments where whales were detected (presence) or not (absence).

Although encouraging, these preliminary insights are just the tip of the modeling iceberg. Many more testing and modeling steps will be required to determine confounding factors and relevant spatio-temporal scales at which these oceanographic variables may be influencing whale distribution off the Oregon coast. I am only at the start of a long road…

References

Abrahms, Briana, Heather Welch, Stephanie Brodie, Michael G. Jacox, Elizabeth A. Becker, Steven J. Bograd, Ladd M. Irvine, Daniel M. Palacios, Bruce R. Mate, and Elliott L. Hazen. 2019. “Dynamic Ensemble Models to Predict Distributions and Anthropogenic Risk Exposure for Highly Mobile Species.” Diversity and Distributions, no. December 2018: 1–12. https://doi.org/10.1111/ddi.12940.

Becker, Elizabeth, Karin Forney, Paul Fiedler, Jay Barlow, Susan Chivers, Christopher Edwards, Andrew Moore, and Jessica Redfern. 2016. “Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?” Remote Sensing 8 (2): 149. https://doi.org/10.3390/rs8020149.

Hazen, Elliott L, Daniel M Palacios, Karin A Forney, Evan A Howell, Elizabeth Becker, Aimee L Hoover, Ladd Irvine, et al. 2016. “WhaleWatch : A Dynamic Management Tool for Predicting Blue Whale Density in the California Current.” Journal of Applied Ecology 54 (5): 1415–28. https://doi.org/10.1111/1365-2664.12820.

Pardo, Mario A., Tim Gerrodette, Emilio Beier, Diane Gendron, Karin A. Forney, Susan J. Chivers, Jay Barlow, and Daniel M. Palacios. 2015. “Inferring Cetacean Population Densities from the Absolute Dynamic Topography of the Ocean in a Hierarchical Bayesian Framework.” PLOS One 10 (3): 1–23. https://doi.org/10.1371/journal.pone.0120727.

The ecologist and the economist: Exploring parallels between disciplines

By Dawn Barlow1 and Johanna Rayl2

1PhD Candidate, Oregon State University Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

2PhD Student, Northwestern University Department of Economics

The Greek word “oikos” refers to the household and serves as the root of the words ecology and economics. Although perhaps surprising, the common origin reflects a shared set of basic questions and some shared theoretical foundations related to the study of how lifeforms on earth use scarce resources and find equilibrium in their respective “households”. Early ecological and economic theoretical texts drew inspiration from one another in many instances. Paul Samuelson, fondly referred to as “the father of modern economics,” observed in his defining work Foundations of Economic Analysis that the moving equilibrium in a market with supply and demand is “essentially identical with the moving equilibrium of a biological or chemical system undergoing slow change.” Likewise, early theoretical ecologists recognized the strength of drawing on theories previously established in economics (Real et al. 1991). Similar broad questions are central to researchers in both fields; in a large and dynamic system (termed “macro” in economics) scale, ecologists and economists alike work to understand where competitive forces find equilibrium, and an in individual (or micro) scale, they ask how individuals make behavior choices to maximize success given constraints like time, energy, wealth, or physical resources.

The central model economists have in mind when trying to understand human choices involves “constrained optimization”: what decision will maximize a person, family, firm, or other agent’s objectives given their limitations? For example, someone that enjoys relaxing but also seeks a livable income must choose how much time to devote to working versus relaxing, given the constraint of having just 24 hours in the day, and given the wage they receive from working. An economist studying this decision may want to learn about how changes in the wage will affect that person’s choice of working hours, or how much they dislike working relative to relaxing. Along similar lines, early ecologists theorized that organisms could be selected for one of two optimization strategies: minimizing the time spent acquiring a given amount of energy (i.e., calories from food), or maximizing total energy acquisition per unit of time (Real et al. 1991). Foundational work in the field of economics clarified numerous technical details about formulating and solving such optimization problems. Returning to the example of the leisure time decision, economic theory asks: does it matter if we model this decision as maximizing income given wages and limited time, or as minimizing hours spent working given a desired lifetime income?; can we formulate a “utility function” that  describes how well-off someone is with a given income and amount of leisure?; can we solve for the optimal amount of leisure with pen and paper? The toolkit arising from this work serves as a jumping off point for all contemporary economic research, and the kinds of choices understood under this framework is vast, from, where should a child attend school?; to, how should a government allocate its budget across public resources?

Early work in ecology drew from foundational concepts in economics, following the realization that the strategies by which organisms exploit resources most efficiently also involve optimization. This parallel was articulated by MacArthur and Pianka in their foundational 1966 paper Optimal Use of a Patchy Environment, in which they state: “In this paper we undertake to determine in which patches a species would feed and which items would form its diet if the species acted in the most economical fashion. Hopefully, natural selection will often have achieved such optimal allocation of time and energy expenditures.” Subsequently, this idea was refined into what is known in ecology as the marginal value theorem, which states that an animal should remain in a prey patch until the rate of energy gain drops below the expected energy gain in all remaining available patches (Charnov 1976). In other words, if it is more profitable to switch prey patches than to stay, an animal should move on. These optimization models therefore allow ecologists to pose specific evolutionary and behavioral hypotheses, such as examining energy acquisition over time to understand selective forces on foraging behavior.

As the largest animals on the planet, blue whales have massive prey requirements to meet energy demands. However, they must balance their need to feed with costs such as oxygen consumption during breath-holding, the travel time it takes to reach prey patches at depth, the physiological constraints of diving, and the necessary recuperation time at the surface. It has been demonstrated that blue whales forage selectively to optimize this energetic budget. Therefore, blue whales should only feed on krill aggregations when the energetic gain outweighs the cost (Fig. 1), and this pattern has been empirically demonstrated for blue whale populations in the Gulf of St. Lawrence, Canada (Doniol-Valcroze et al. 2011), in the California Current, (Hazen et al. 2015) and in New Zealand (Torres et al. 2020).

Figure 1. Figure reprinted from Hazen et al. 2015, illustrating how a blue whale should theoretically optimize foraging success in two scenarios. Energy gained from feeding is shown by the blue lines, whereas the cost of foraging in terms of declining oxygen stores during a dive is illustrated by the red lines. On the left (panel B), the whale maximizes its energy gain by increasing the number of feeding lunges (shown by black circles) at the expense of declining oxygen stores when prey density is high. On the right (panel C), the whale minimizes oxygen use by reducing the number of feeding lunges when prey density is low.

The notion of the marginal value theorem is likewise at work in countless economic settings. Economic theory predicts that a farmer cultivating two crops would allocate resources into each crop such that the returns to adding more resources into each crop are the same. If not, she should move resources from the less productive crop to the one where marginal gains are larger. A fisherman, according to this notion, continues to fish longer into the season until the marginal value of one additional day at sea equals the marginal cost of their time, effort, and expenses. These predictions are intuitive by the same logic as the blue whale choosing where to forage, and derive from the mathematics of constrained and unconstrained optimization. Reassuringly, empirical work finds evidence of such profit-maximizing behavior in many settings. In a recent working paper, Burlig, Preonas, and Woerman explore how farmers’ water use in California responds to changes in the price of electricity, which effectively makes groundwater irrigation more expensive due to electric pumping. They find that farmers are very responsive to these changes in marginal cost. Farmers achieve this reduction in water use predominantly by switching to less water-intensive crops and fallowing their land (Burlig, Preonas, and Woerman 2020).

Undoubtedly there are fundamental differences between an ecosystem with interacting biotic and abiotic components and the human-economic environment with its many social and political structures. But for certain types of questions, the parallels across the shared optimization problems are striking. The foundational theories discussed here have paved the way for subsequent advances in both disciplines. For example, the field of behavioral ecology explores how competition and cooperation between and within species affects fitness of populations. Reflecting on early seminal work lends some perspective on how an area of research has evolved. Likewise, exploring parallels between disciplines sheds light on common threads, in turn revealing insights into each discipline individually.

References:

Burlig, Fiona, Louis Preonas, and Matt Woerman (2020). Groundwater, energy, and crop choice. Working Paper.

Charnov EL (1976) Optimal foraging: The marginal value theorem. Theoretical Population Biology 9:129–136.

Doniol-Valcroze T, Lesage V, Giard J, Michaud R (2011) Optimal foraging theory predicts diving and feeding strategies of the largest marine predator. Behavioral Ecology 22:880–888.

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. Science Advces 1:e1500469–e1500469.

MacArthur RH, Pianka ER (1966) On optimal use of a patchy environment. The American Naturalist 100:603–609.

Real LA, Levin SA, Brown JH (1991) Part 2: Theoretical advances: the role of theory in the rise of modern ecology. In: Foundations of ecology: classic papers with commentaries.

Samuelson, Paul (1947). Foundations of Economic Analysis. Harvard University Press.

Torres LG, Barlow DR, Chandler TE, Burnett JD (2020) Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ 8:e8906.