1PhD 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
What do peanut butter m&ms, killer whales, affogatos, tired eyes, and puffins all have in common? They were all major features of the recent Northern California Current (NCC) ecosystem survey cruise.
The science party of the May 2022 Northern California Current ecosystem cruise.
We spent May 6–17 aboard the NOAA vessel Bell M. Shimada in northern California, Oregon, and Washington waters. This fabulously interdisciplinary cruise studies multiple aspects of the NCC ecosystem three times per year, and the GEMM lab has put marine mammal observers aboard since 2018.
This cruise was a bit different than usual for the GEMM lab: we had eyes on both the whales and their prey. While Dawn Barlow and Clara Bird observed from sunrise to sunset to sight and identify whales, Rachel Kaplan collected krill data via an echosounder and samples from net tows in order to learn about the preyscape the whales were experiencing.
From left, Rachel, Dawn, and Clara after enjoying some beautiful sunset sightings.
We sailed out of Richmond, California and went north, sampling as far north as La Push, Washington and up to 200 miles offshore. Despite several days of challenging conditions due to wind, rain, fog, and swell, the team conducted a successful marine mammal survey. When poor weather prevented work, we turned to our favorite hobbies of coding and snacking.
Rachel attends “Clara’s Beanbag Coding Academy”.
Cruise highlights included several fin whales, sperm whales, killer whales, foraging gray whales, fluke slapping and breaching humpbacks, and a visit by 60 pacific white-sided dolphins. While being stopped at an oceanographic sampling station typically means that we take a break from observing, having more time to watch the whales around us turned out to be quite fortunate on this cruise. We were able to identify two unidentified whales as sei whales after watching them swim near us while paused on station.
Marine mammal observation segments (black lines) and the sighting locations of marine mammal species observed during the cruise.
On one of our first survey days we also observed humpbacks surface lunge feeding close to the ship, which provided a valuable opportunity for our team to think about how to best collect concurrent prey and whale data. The opportunity to hone in on this predator-prey relationship presented itself in a new way when Dawn and Clara observed many apparently foraging humpbacks on the edge of Heceta Bank. At the same time, Rachel started observing concurrent prey aggregations on the echosounder. After a quick conversation with the chief scientist and the officers on the bridge, the ship turned around so that we could conduct a net tow in order to get a closer look at what exactly the whales were eating.
Success! Rachel collects krill samples collected in an area of foraging humpback whales.
This cruise captured an interesting moment in time: southerly winds were surprisingly common for this time of year, and the composition of the phytoplankton and zooplankton communities indicated that the seasonal process of upwelling had not yet been initiated. Upwelling brings deep, cold, nutrient-rich waters to the surface, generating a jolt of productivity that brings the ecosystem from winter into spring. It was fascinating to talk to all the other researchers on the ship about what they were seeing, and learn about the ways in which it was different from what they expected to see in May.
Experiencing these different conditions in the Northern California Current has given us a new perspective on an ecosystem that we’ve been observing and studying for years. We’re looking forward to digging into the data and seeing how it can help us understand this ecosystem more deeply, especially during a period of continued climate change.
The total number of each marine mammal species observed during the cruise.
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2MS Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Seabird Oceanography Lab
The marine environment is dynamic, and mobile animals must respond to the patchy and ephemeral availability of resource in order to make a living (Hyrenbach et al. 2000). Climate change is making ocean ecosystems increasingly unstable, yet these novel conditions can be difficult to document given the vast depth and remoteness of most ocean locations. Marine megafauna species such as marine mammals and seabirds integrate ecological processes that are often difficult to observe directly, by shifting patterns in their distribution, behavior, physiology, and life history in response to changes in their environment (Croll et al. 1998, Hazen et al. 2019). These mobile marine animals now face additional challenges as rising temperatures due to global climate change impact marine ecosystems worldwide (Hazen et al. 2013, Sydeman et al. 2015, Silber et al. 2017, Becker et al. 2019). Given their mobility, visibility, and integration of ocean processes across spatial and temporal scales, these marine predator species have earned the reputation as effective ecosystem sentinels. As sentinels, they have the capacity to shed light on ecosystem function, identify risks to human health, and even predict future changes (Hazen et al. 2019). So, let’s explore a few examples of how studying marine megafauna has revealed important new insights, pointing toward the importance of monitoring these sentinels in a rapidly changing ocean.
Cairns (1988) is often credited as first promoting seabirds as ecosystem sentinels and noted several key reasons why they were perfect for this role: (1) Seabirds are abundant, wide-ranging, and conspicuous, (2) although they feed at sea, they must return to land to nest, allowing easier observation and quantification of demographic responses, often at a fraction of the cost of traditional, ship-based oceanographic surveys, and therefore (3) parameters such as seabird reproductive success or activity budgets may respond to changing environmental conditions and provide researchers with metrics by which to assess the current state of that ecosystem.
The unprecedented 2014-2016 North Pacific marine heatwave (“the Blob”) caused extreme ecosystem disruption over an immense swath of the ocean (Cavole et al. 2016). Seabirds offered an effective and morbid indication of the scale of this disruption: Common murres (Uria aalge), an abundant and widespread fish-eating seabird, experienced widespread breeding failure across the North Pacific. Poor reproductive performance suggested that there may have been fewer small forage fish around and that these changes occurred at a large geographic scale. The Blob reached such an extreme as to kill immense numbers of adult birds, which professional and community scientists found washed up on beach-surveys; researchers estimate that an incredible 1,200,000 murres may have died from starvation during this period (Piatt et al. 2020). While the average person along the Northeast Pacific Coast during this time likely didn’t notice any dramatic difference in the ocean, seabirds were shouting at us that something was terribly wrong.
Happily, living seabirds also act as superb ecosystem sentinels. Long-term research in the Gulf of Maine by U.S. and Canadian scientists monitors the prey species provisioned by adult seabirds to their chicks. Will has spent countless hours over five summers helping to conduct this research by watching terns (Sterna spp.) and Atlantic puffins (Fratercula arctica) bring food to their young on small islands off the Maine coast. After doing this work for multiple years, it’s easy to notice that what adults feed their chicks varies from year to year. It was soon realized that these data could offer insight into oceanographic conditions and could even help managers assess the size of regional fish stocks. One of the dominant prey species in this region is Atlantic herring (Clupea harengus), which also happens to be the focus of an economically important fishery. While the fishery targets four or five-year-old adult herring, the seabirds target smaller, younger herring. By looking at the relative amounts and sizes of young herring collected by these seabirds in the Gulf of Maine, these data can help predict herring recruitment and the relative number of adult herring that may be available to fishers several years in the future (Scopel et al. 2018). With some continued modelling, the work that we do on a seabird colony in Maine with just a pair of binoculars can support or maybe even replace at least some of the expensive ship-based trawl surveys that are now a popular means of assessing fish stocks.
A common tern (Sterna hirundo) with a young Atlantic herring from the Gulf of Maine, ready to feed its chick (Photo courtesy of the National Audubon Society’s Seabird Institute)
For more far-ranging and inaccessible marine predators such as whales, measuring things such as dietary shifts can be more challenging than it is for seabirds. Nevertheless, whales are valuable ecosystem sentinels as well. Changes in the distribution and migration phenology of specialist foragers such as blue whales (Balaenoptera musculus) and North Atlantic right whales (Eubalaena glacialis) can indicate relative changes in the distribution and abundance of their zooplankton prey and underlying ocean conditions (Hazen et al. 2019). In the case of the critically endangered North Atlantic right whale, their recent declines in reproductive success reflect a broader regime shift in climate and ocean conditions. Reduced copepod prey has resulted in fewer foraging opportunities and changing foraging grounds, which may be insufficient for whales to obtain necessary energetic stores to support calving (Gavrilchuk et al. 2021, Meyer-Gutbrod et al. 2021). These whales assimilate and showcase the broad-scale impacts of climate change on the ecosystem they inhabit.
Blue whales that feed in the rich upwelling system off the coast of California rely on the availability of their krill prey to support the population (Croll et al. 2005). A recent study used acoustic monitoring of blue whale song to examine the timing of annual population-level transition from foraging to breeding migration compared to oceanographic variation, and found that flexibility in timing may be a key adaptation to persistence of this endangered population facing pressures of rapid environmental change (Oestreich et al. 2022). Specifically, blue whales delayed the transition from foraging to breeding migration in years of the highest and most persistent biological productivity from upwelling, and therefore listening to the vocalizations of these whales may be valuable indicator of the state of productivity in the ecosystem.
Figure reproduced from Oestreich et al. 2022, showing relationships between blue whale life-history transition and oceanographic phenology of foraging habitat. Timing of the behavioral transition from foraging to migration (day of year on the y-axis) is compared to (a) the date of upwelling onset; (b) the date of peak upwelling; and (c) total upwelling accumulated from the spring transition to the end of the upwelling season.
In a similar vein, research by the GEMM Lab on blue whale ecology in New Zealand has linked their vocalizations known as D calls to upwelling conditions, demonstrating that these calls likely reflect blue whale foraging opportunities (Barlow et al. 2021). In ongoing analyses, we are finding that these foraging-related calls were drastically reduced during marine heatwave conditions, which we know altered blue whale distribution in the region (Barlow et al. 2020). Now, for the final component of Dawn’s PhD, she is linking year-round environmental conditions to the occurrence patterns of different blue whale vocalization types, hoping to shed light on ecosystem processes by listening to the signals of these ecosystem sentinels.
A blue whale comes up for air in the South Taranaki Bight of New Zealand. photo by L. Torres.
It is important to understand the widespread implications of the rapidly warming climate and changing ocean conditions on valuable and vulnerable marine ecosystems. The cases explored here in this blog exemplify the importance of monitoring these marine megafauna sentinel species, both now and into the future, as they reflect the health of the ecosystems they inhabit.
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References:
Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG (2020) 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.
Barlow DR, Klinck H, Ponirakis D, Garvey C, Torres LG (2021) Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep 11:1–10.
Becker EA, Forney KA, Redfern J V., Barlow J, Jacox MG, Roberts JJ, Palacios DM (2019) Predicting cetacean abundance and distribution in a changing climate. Divers Distrib 25:626–643.
Cairns DK (1988) Seabirds as indicators of marine food supplies. Biol Oceanogr 5:261–271.
Cavole LM, Demko AM, Diner RE, Giddings A, Koester I, Pagniello CMLS, Paulsen ML, Ramirez-Valdez A, Schwenck SM, Yen NK, Zill ME, Franks PJS (2016) Biological impacts of the 2013–2015 warm-water anomaly in the northeast Pacific: Winners, losers, and the future. Oceanography 29:273–285.
Croll DA, Marinovic B, Benson S, Chavez FP, Black N, Ternullo R, Tershy BR (2005) From wind to whales: Trophic links in a coastal upwelling system. Mar Ecol Prog Ser 289:117–130.
Croll DA, Tershy BR, Hewitt RP, Demer DA, Fiedler PC, Smith SE, Armstrong W, Popp JM, Kiekhefer T, Lopez VR, Urban J, Gendron D (1998) An integrated approch to the foraging ecology of marine birds and mammals. Deep Res Part II Top Stud Oceanogr.
Gavrilchuk K, Lesage V, Fortune SME, Trites AW, Plourde S (2021) Foraging habitat of North Atlantic right whales has declined in the Gulf of St. Lawrence, Canada, and may be insufficient for successful reproduction. Endanger Species Res 44:113–136.
Hazen EL, Abrahms B, Brodie S, Carroll G, Jacox MG, Savoca MS, Scales KL, Sydeman WJ, Bograd SJ (2019) Marine top predators as climate and ecosystem sentinels. Front Ecol Environ 17:565–574.
Hazen EL, Jorgensen S, Rykaczewski RR, Bograd SJ, Foley DG, Jonsen ID, Shaffer SA, Dunne JP, Costa DP, Crowder LB, Block BA (2013) Predicted habitat shifts of Pacific top predators in a changing climate. Nat Clim Chang 3:234–238.
Hyrenbach KD, Forney KA, Dayton PK (2000) Marine protected areas and ocean basin management. Aquat Conserv Mar Freshw Ecosyst 10:437–458.
Meyer-Gutbrod EL, Greene CH, Davies KTA, Johns DG (2021) Ocean regime shift is driving collapse of the north atlantic right whale population. Oceanography 34:22–31.
Oestreich WK, Abrahms B, Mckenna MF, Goldbogen JA, Crowder LB, Ryan JP (2022) Acoustic signature reveals blue whales tune life history transitions to oceanographic conditions. Funct Ecol.
Piatt JF, Parrish JK, Renner HM, Schoen SK, Jones TT, Arimitsu ML, Kuletz KJ, Bodenstein B, Garcia-Reyes M, Duerr RS, Corcoran RM, Kaler RSA, McChesney J, Golightly RT, Coletti HA, Suryan RM, Burgess HK, Lindsey J, Lindquist K, Warzybok PM, Jahncke J, Roletto J, Sydeman WJ (2020) Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016. PLoS One 15:e0226087.
Scopel LC, Diamond AW, Kress SW, Hards AR, Shannon P (2018) Seabird diets as bioindicators of atlantic herring recruitment and stock size: A new tool for ecosystem-based fisheries management. Can J Fish Aquat Sci.
Silber GK, Lettrich MD, Thomas PO, Baker JD, Baumgartner M, Becker EA, Boveng P, Dick DM, Fiechter J, Forcada J, Forney KA, Griffis RB, Hare JA, Hobday AJ, Howell D, Laidre KL, Mantua N, Quakenbush L, Santora JA, Stafford KM, Spencer P, Stock C, Sydeman W, Van Houtan K, Waples RS (2017) Projecting marine mammal distribution in a changing climate. Front Mar Sci 4:413.
Sydeman WJ, Poloczanska E, Reed TE, Thompson SA (2015) Climate change and marine vertebrates. Science 350:772–777.
In human cultures, how you sound is often an indicator of where you are from. Have you ever taken a linguistics quiz that tries to guess what part of the United States you grew up in? Questions about whether you pronounce the sugary sweet treat caramel as “carr-mul” or “care-a-mel”, whether you say “soda” or “pop”, or whether a certain type of intersection is called a “roundabout”, “rotary”, or “traffic circle” are used to make a guess at where in the country you were raised. I have spent time in the United States, Australia, and New Zealand, I was amused to learn that the shoes you might wear in summertime can be called flip flops, slippers, thongs, or jandals, depending on which English-speaking country you are in. We know that listening to how someone speaks can tell us about their heritage or culture. As it turns out, the same is true for blue whales. We can learn a lot about blue whales by listening to them.
A blue whale comes up for air in the South Taranaki Bight, New Zealand. We catch only a short glimpse of these ocean giants when they are at the surface. By listening to their vocalizations using acoustic recordings, we can gain a whole new perspective on their lives. Photo by D. Barlow.
Sound is an incredibly important sense to marine mammals, particularly since sound waves can efficiently transmit over long distances in the ocean where other senses, such as vision or smell, are limited. Therefore, passive acoustic monitoring—placing hydrophones underwater to listen for an extended period of time and record the sounds of animals and their environment—is a highly effective tool for studying marine mammals, including blue whales. Throughout the world, blue whales sing. In this case, “song” is defined as a limited number of sound types that are produced in succession to form a recognizable pattern (McDonald et al. 2006). These songs are presumed to be produced by males only, most likely used to maintain associations and mediate social interactions, and seem to play a role in reproduction (Oleson et al. 2007, Lewis et al. 2018). Furthermore, these songs are highly stereotyped, and stable over decadal scales (McDonald et al. 2006).
Figure reproduced from McDonald et al. (2006), illustrating the variation and in blue whale songs from different geographic regions, and their stability over time: Recordings from New Zealand (A), the Central North Pacific (B), Australia (C), the Northeast Pacific (D) and North Indian Ocean (E) illustrate the stable character of the blue whale song over long time periods. All song types for which long time spans of recordings are available show some frequency drift through time, but only minor change in character. These examples were chosen because recordings over a significant time span were available to the authors in raw form, and not because these song types are more stable than the others.
Fascinatingly, blue whale songs have acoustic characteristics that are distinct between geographic regions. A blue whale in the northeast Pacific sings a different song than a blue whale in the north Atlantic; the song heard around Australia is distinct from the one sung off the coast of Chile, and so on. Therefore, differences in blue whale songs between areas can be used as a provisional hypothesis about population structure (McDonald et al. 2006, Samaran et al. 2013, Balcazar et al. 2015). Vocalizations may evolve more rapidly than traditional markers such as genetics or morphology that are often used to delineate populations, particularly in long-lived mammalian species such as blue whales (McDonald et al. 2006).
Figure reproduced from McDonald et al. (2006): Blue whale residence and population divisions suggested from their song types. Arrows indicate the direction of seasonal movements.
Despite the general rule of thumb that population-specific blue whale songs occur in separate geographic regions, there are examples throughout the southern hemisphere where songs from different populations overlap and are recorded in the same location (Samaran et al. 2010, 2013, Tripovich et al. 2015, McCauley et al. 2018, Buchan et al. 2020, Leroy et al. 2021). However, these examples may be instances where the populations temporally or ecologically partition their use of the area. For example, there may be differences in the timing of peak occurrence so that overlap is minimized by alternating which population is predominantly present in different seasons (Leroy et al. 2018). Alternatively, whales from different populations may overlap in space and time, but occupy different ecological niches at the same site. In this case, an area may simultaneously be a migratory corridor for one population and a foraging ground for another (Tripovich et al. 2015).
Figure reproduced from Leroy et al. (2021): Distribution of the five blue whale acoustic populations of the Indian Ocean: the Sri Lankan—NIO (yellow); Madagascan—SWIO (orange); Australian—SEIO (blue); and Arabian Sea—NWIO (red) pygmy blue whales; the hypothesized Chagos pygmy blue whale (green); and the Antarctic blue whale (black dashed line). These distributions have been inferred from the acoustic recordings conducted in the area. The long-term recording sites used to infer these distribution areas are indicated by red stars. Blue whale illustration by Alicia Guerrero.
In the South Taranaki Bight (STB) region of New Zealand, where the GEMM lab has been studying blue whales for the past decade (Torres 2013), the New Zealand song type is recorded year-round (Barlow et al. 2018). New Zealand blue whales rely on a productive upwelling system in the STB that supports an important foraging ground (Barlow et al. 2020, 2021). Antarctic blue whales also seasonally pass through New Zealand waters, likely along their migratory pathway between polar feeding grounds and lower latitude areas (Warren et al. 2021). What does it mean in terms of population connectivity or separation when two different populations occasionally share the same waters? How do these different populations ecologically partition the space they occupy? What drives their differing occurrence patterns? These are the sorts of questions I am diving into as we continue to explore the depths of our acoustic recordings from the STB region. We still have a lot to learn about these blue whales, and there is a lot to be learned through listening.
References:
Balcazar NE, Tripovich JS, Klinck H, Nieukirk SL, Mellinger DK, Dziak RP, Rogers TL (2015) Calls reveal population structure of blue whales across the Southeast Indian Ocean and the Southwest Pacific Ocean. J Mammal 96:1184–1193.
Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG (2020) 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.
Barlow DR, Klinck H, Ponirakis D, Garvey C, Torres LG (2021) Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep 11:1–10.
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.
Buchan SJ, Balcazar-Cabrera N, Stafford KM (2020) Seasonal acoustic presence of blue, fin, and minke whales off the Juan Fernández Archipelago, Chile (2007–2016). Mar Biodivers 50:1–10.
Leroy EC, Royer JY, Alling A, Maslen B, Rogers TL (2021) Multiple pygmy blue whale acoustic populations in the Indian Ocean: whale song identifies a possible new population. Sci Rep 11:8762.
Leroy EC, Samaran F, Stafford KM, Bonnel J, Royer JY (2018) Broad-scale study of the seasonal and geographic occurrence of blue and fin whales in the Southern Indian Ocean. Endanger Species Res 37:289–300.
Lewis LA, Calambokidis J, Stimpert AK, Fahlbusch J, Friedlaender AS, Mckenna MF, Mesnick SL, Oleson EM, Southall BL, Szesciorka AR, Širović A (2018) Context-dependent variability in blue whale acoustic behaviour. R Soc Open Sci 5.
McCauley RD, Gavrilov AN, Jolli CD, Ward R, Gill PC (2018) Pygmy blue and Antarctic blue whale presence , distribution and population parameters in southern Australia based on passive acoustics. Deep Res Part II 158:154–168.
McDonald MA, Mesnick SL, Hildebrand JA (2006) Biogeographic characterisation of blue whale song worldwide: using song to identify populations. J Cetacean Res Manag 8:55–65.
Oleson EM, Wiggins SM, Hildebrand JA (2007) Temporal separation of blue whale call types on a southern California feeding ground. Anim Behav 74:881–894.
Samaran F, Adam O, Guinet C (2010) Discovery of a mid-latitude sympatric area for two Southern Hemisphere blue whale subspecies. Endanger Species Res 12:157–165.
Samaran F, Stafford KM, Branch TA, Gedamke J, Royer J, Dziak RP, Guinet C (2013) Seasonal and Geographic Variation of Southern Blue Whale Subspecies in the Indian Ocean. PLoS One 8:e71561.
Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.
Tripovich JS, Klinck H, Nieukirk SL, Adams T, Mellinger DK, Balcazar NE, Klinck K, Hall EJS, Rogers TL (2015) Temporal Segregation of the Australian and Antarctic Blue Whale Call Types (Balaenoptera musculus spp.). J Mammal 96:603–610.
Warren VE, Širović A, McPherson C, Goetz KT, Radford CA, Constantine R (2021) Passive Acoustic Monitoring Reveals Spatio-Temporal Distributions of Antarctic and Pygmy Blue Whales Around Central New Zealand. Front Mar Sci 7:1–14.
Dynamic forecast models predict environmental conditions and blue whale distribution up to three weeks into the future, with applications for spatial management. Founded on a robust understanding of ecological links and lags, a recent study by Barlow & Torres presents new tools for proactive conservation.
The ocean is dynamic. Resources are patchy, and animals move in response to the shifting and fluid marine environment. Therefore, protected areas bounded by rigid lines may not always be the most effective way to conserve marine biodiversity. If the animals we wish to protect are not within protected area boundaries, then ocean users pay a price without the conservation benefit. Management that is adaptive to current conditions may more effectively match the dynamic nature of the species and places of concern, but this approach is only feasible if we have the relevant ecological knowledge to implement it.
The South Taranaki Bight region of New Zealand is home to a foraging ground for a unique population of blue whales that are genetically distinct and present year-round. The area also sustains New Zealand’s most industrial marine region, including active petroleum exploration and extraction, and vessel traffic between ports.
To minimize overlap between blue whale habitat and human use of the area, we develop and test forecasts of oceanographic conditions and blue whale habitat. These tools enable managers to make decisions with up to three weeks lead time in order to minimize potential overlap between blue whales and other ocean users.
Overlap between blue whale habitat and industry presence in the South Taranaki Bight region. A blue whale surfaces in front of a floating production storage and offloading (FPSO) vessel, servicing the oil rigs in the area. Photo by Dawn Barlow.
Predicting the future
Knowing where animals were yesterday may not create effective management boundaries for tomorrow. Like the weather, our expectation of when and where to find species may be based on long-term averages of previous patterns, real-time descriptions based on recent data, and forecasts that predict the future using current conditions. Forecasts allow us to plan ahead and make informed decisions needed to produce effective management strategies for dynamic systems.
Just as weather forecasts help us make decisions about whether to wear a raincoat or pack sunscreen before leaving the house, ecological forecasts can enable managers to anticipate environmental conditions and species distribution patterns in advance of industrial activity that may pose risk in certain scenarios.
In our recent study, we develop and test models that do just that: forecast where blue whales are most likely to be, allowing informed decision making with up to three weeks lead time.
Harnessing accessible data for an applicable tool
We use readily accessible data gathered by satellites and shore-based weather stations and made publicly available online. While our understanding of the ecosystem dynamics in the South Taranaki Bight is founded on years of collecting data at-sea and ecological analyses, using remotely gathered data for our forecasting tool is critical for making this approach operational, sustainable, and useful both now and into the future.
Measurements of conditions such as wind speed and ocean temperature anomaly are paired with known measurements of the lag times between wind input, upwelling, productivity, and blue whale foraging opportunities to produce forecasted environmental conditions.
Example environmental forecast maps, illustrating the predicted sea surface temperature and productivity in the South Taranaki Bight region, which can be forecasted by the models with up to three weeks lead time.
The forecasted environmental layers are then implemented in species distribution models to predict suitable blue whale habitat in the region, generating a blue whale forecast map. This map can be used to evaluate overlap between blue whale habitat and human uses, guiding management decisions regarding potential threats to the whales.
Example forecast of suitable blue whale habitat, with areas of higher probability of blue whale occurrence shown by the warmer colors and the area classified as “suitable habitat” denoted by the white boundaries. This habitat suitability map can be produced for any day in the past 10 years or for any day up to three weeks in the future.
Dynamic ecosystems, dynamic management
These forecasts of whale distribution can be effectively applied for dynamic spatial management because our models are founded on carefully measured links and lags between physical forcing (e.g., wind drives cold water upwelling) and biological responses (e.g., krill aggregations create feeding opportunities for blue whales). The models produce outputs that are dynamic and update as conditions change, matching the dynamic nature of the ecosystem.
A blue whale raises its majestic fluke on a deep foraging dive in the South Taranaki Bight. Photo by Leigh Torres.
Engagement with stakeholders—including managers, scientists, industry representatives, and environmental organizations—has been critical through the creation and implementation of this forecasting tool, which is currently in development as a user-friendly desktop application.
Our forecast tool provides managers with lead time for decision making and allows flexibility based on management objectives. Through trial, error, success, and feedback, these tools will continue to improve as new knowledge and feedback are received.
The people behind the science, from data collection to conservation application. Left: Dawn Barlow and Dr. Leigh Torres aboard a research vessel in New Zealand in 2017, collecting data on blue whale distribution patterns that contributed to the findings in this study. Right: Dr. Leigh Torres and Dawn Barlow at the Parliament buildings in Wellington, New Zealand, where they discussed research findings with politicians and managers, gathered feedback on barriers to implementation, and subsequently incorporated feedback into the development and implementation of the forecasting tools.
Reference: Barlow, D. R., & Torres, L. G. (2021). Planning ahead: Dynamic models forecast blue whale distribution with applications for spatial management. Journal of Applied Ecology, 00, 1–12. https://doi.org/10.1111/1365-2664.13992
By Mateo Estrada Jorge, Oregon State University undergraduate student, GEMM Lab REU Intern
Introduction
My name is Mateo Estrada and this past summer I had the pleasure of working with Dawn Barlow and Dr. Leigh Torres as a National Science Foundation (NSF) Research Experience for Undergraduates (REU) intern. I had the chance to proactively learn about the scientific method in the marine sciences by studying the acoustic behaviors of pygmy blue whales (Balaenoptera musculus brevicauda) that are documented residents of the South Taranaki Bight region in New Zealand (Torres 2013, Barlow et al. 2018). I’ve been interested in conducting scientific research since I began my undergraduate education at Oregon State University in 2015. Having the opportunity to apply the skills I gained through my education in this REU has been a blessing. I’m a physics and computer science major, but more than anything I’m a scientist and my passion has taken me in new, unexpected directions that I’m going to share in this blog post. My message for any students who feel like they haven’t found their path yet is: hang in there, sometimes it takes time for things to take shape. That has been my experience and I’m sure it’s been the experience of many people interested in the sciences. I’m a Physics and Computer Science student, so why am I studying blue whales, and more specifically, how can I be doing marine science research having only taken intro to biology 101?
My background
I decided to apply for the REU in the Spring 2021 because it was a chance to use my programming skills in the marine sciences. I’m also passionate about conservation and protecting the environment in a pragmatic way, so I decided to find a niche where I could put my technical skills to good use. Finally, I wanted to explore a scientific field outside of my area of expertise to grow as a student and to learn from other researchers. I was mostly inspired by anecdotal tales of Physicist Richard Feynman who would venture out of the physics department at Caltech and into other departments to learn about what other scientists were investigating to inspire his own work. This summer, I ventured into the world of marine science, and what I found in my project was fascinating.
Whale watching tour
Figure 1. Me standing on a boat on the Pacific Ocean off Long Beach, CA.
To get into the research mode, I decided to go on a whale watching tour with the Aquarium of the Pacific. The tour was two hours long and the sunburn was worth it because we got to see four blue whales off the Long Beach coast in California. I got to see the famous blue whale blow and their splashes. It was the first time I was on a big boat in the ocean, so naturally I got seasick (Fig 1). But it was exciting to get a chance to see blue whales in action (luckily, I didn’t actually hurl). The marine biologist onboard also gave a quick lecture on the relative size of blue whales and some of their behaviors. She also pointed out that they don’t use Sonar to locate whales as this has been shown to disturb their calling behaviors. Instead, we looked for a blow and splashing. The tour was a wonderful experience and I’m glad I got to see some whales out in nature. This experience also served as a reminder of the beauty of marine life and the responsibility I feel for trying to understand and help conserving it.
Context of blue whale calling
Sound plays a significant role in the marine environment and is a critical mode of communication for many marine animals including baleen whales. Blue whales produce different vocalizations, otherwise known as calls. Blue whale song is theorized to be produced by males of the species as a form of reproductive behavior, similar to how male peacocks engage females by displaying their elongated upper tail covert feathers in iridescent colors as a courtship mechanism. Then there are “D calls” that are associated with social mechanisms while foraging, and these calls are made by both female and male blue whales (Lewis et al. 2018) (Fig. 2).
Figure 2. Spectrogram of Pygmy blue whale D calls manually (and automatically) selected, frequency 0-150 Hz.
Understanding research on blue whales
The most difficult part about coming into a project as an outsider is catching up. I learned how anthropogenetic (human made) noise affects blue whale communication. For example, it has been showing that Mid Frequency Active Sonar signals employed by the U.S. Navy affect blue whale D calling patterns (Melcón 2012). Furthermore, noise from seismic airguns used for oil and gas exploration has also impact blue whale calling behavior (Di Lorio, 2010). Understanding the environmental context in which the pygmy blue whales live and the anthropogenic pressures they face is essential in marine conservation. Protecting the areas in which they live is important so they can feed, reproduce and thrive effectively. What began as a slowly falling snowflake at the start of a snowstorm turned into a cascading avalanche of knowledge pouring into my mind in just two weeks.
Figure 3. The white stars show the hydrophone locations (n = 5). A bathymetric scale of the depth is also given.
The research question I set out to tackle in my internship was: do blue whales change their calling behavior in response to natural noise events from earthquake activity? To do this, I used acoustic recordings from five hydrophones deployed in the South Taranaki Bight (Fig. 3), paired with an existing dataset of all recorded earthquakes in New Zealand (GeoNet). I identified known earthquakes in our acoustic recordings, and then examined the blue whale D calls during 4 hours before and after each earthquake event to look for any change in the number of calls, call energy, entropy, or bandwidth.
A great mentor and lab team
The days kept passing and blending into each other, as they often do with remote work. I began to feel isolated from the people I was working with and the blue whales I was studying. The zoom calls, group chats, and working alongside other remote interns kept me afloat as I adapted to a work world fully online. Nevertheless, I was happy to continue working on this project because I felt like I was slowly becoming part of the GEMM Lab. I would meet with my mentor Dawn Barlow at least once a week and we would spend time talking about the project and sorting out the difficult details of data processing. She always encouraged my curiosity to ask questions. Even if they were silly questions, she was happy to ponder them because she is a curious scientist like myself.
What we learned
Pygmy blue whales from the South Taranaki Bight region do not change their acoustic behavior in response to earthquake activity. The energy of the earthquake, magnitude, depth, and distance to the origin all had no influence on the number of blue whale D calls, the energy of their calling, the entropy, and the bandwidth. A likely reason for why the blue whales would have no acoustic response to earthquakes (magnitude < 5) is that the STB region is a seismically active region due to the nearby interface of the Australian and Pacific plates. Because of the plate tectonics, the region averages about 20,000 recorded earthquakes per year (GeoNet: Earthquake Statistics). Given that pygmy blue whales are present in the STB region year-round (Barlow et al. 2018), the blue whales may have adapted to tolerate the earthquake activity (Fig 4).
Figure 4. Earthquake signal from MARU (1, 2, 3, 4, 5) and blue whale D calls, Frequency 0-150 Hz.
Looking at the future
I presented my work at the end of my REU internship program, which was a difficult challenge for me because I am often intimidated by public speaking (who isn’t?). Communicating science has always been a big interest of me. I love reading news articles about new breakthroughs and being a small part of that is a huge privilege for me. Finding my own voice and having new insights to contribute to the scientific world has always been my main objective. Now I will get to deliver a poster presentation of my REU work at the Association for the Sciences of Limnology and Oceanography (ASLO) Conference in March 2022. I am both excited and nervous to take on this new adventure of meeting seasoned professionals, communicating my results, and learning about the ocean sciences. I hope to gain new inspirations for my future academic and professional work.
Barlow, D. R., Torres, L. G., Hodge, K. B., Steel, D., Scott Baker, C., 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. https://doi.org/10.3354/esr00891
Di Iorio, L., & Clark, C. W. (2010). Exposure to seismic survey alters blue whale acoustic communication. Biology Letters, 6(3), 334–335. https://doi.org/10.1098/rsbl.2009.0967
Lewis, L. A., Calambokidis, J., Stimpert, A. K., Fahlbusch, J., Friedlaender, A. S., McKenna, M. F., Mesnick, S. L., Oleson, E. M., Southall, B. L., Szesciorka, A. R., & Sirović, A. (2018). Context-dependent variability in blue whale acoustic behaviour. Royal Society Open Science, 5(8). https://doi.org/10.1098/rsos.180241
Melcón, M. L., Cummins, A. J., Kerosky, S. M., Roche, L. K., Wiggins, S. M., & Hildebrand, J. A. (2012). Blue whales respond to anthropogenic noise. PLoS ONE, 7(2), 1–6. https://doi.org/10.1371/journal.pone.0032681
Torres LG. 2013 Evidence for an unrecognised blue whale foraging ground in New Zealand. NZ J. Mar. Freshwater Res. 47, 235–248. (doi:10. 1080/00288330.2013.773919)
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!
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
1PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and 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 2021and 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.
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.
The ocean is vast, ever-changing, and at first glance, seemingly featureless. Yet, we know that the warm, blue tropics differ from icy polar waters, and that temperate kelp forests are different from coral reefs. In the connected fluid environment of the global oceans, how do such different habitats exist, and what separates them? On a smaller scale, you may observe a current mixing line at the ocean surface, or dive down from the surface and feel the temperature drop sharply. In a featureless ocean, what boundaries exist, and how can we delineate between different environments?
These questions have been on my mind recently as I study for my PhD Qualifying Exams, an academic milestone that involves written and oral exams prepared by each committee member for the student. The subject matter spans many different areas, including ecological theory, underwater acoustics, oceanography, zooplankton dynamics, climate change and marine heatwaves, and protected area design. Yet, in my recent studying, I was struck by a realization: since when did my PhD involve so much physics? Atmospheric pressure differences generate wind, which drive global ocean circulation patterns. Density properties of seawater create structure in the ocean, and these physical features influence productivity and aggregate prey for predators such as whales. Sound propagates through the fluid ocean as a pressure wave, and its transmission is influenced by physical characteristics of the sound and the medium it moves through. Many of these examples can be distilled and described with equations rooted in physics. Physics doesn’t behave, it simply… is. In considering the vast and dynamic ocean, there is something quite satisfying in that simple notion.
Circling back to boundaries in the ocean, there are changes in physical properties of the oceans that create boundaries, some stark and some nuanced. These physical features structure and partition the marine environment through differences in properties such as temperature, salinity, density, and pressure. Geographic partitions can occur in both horizontal and vertical dimensions of the water column, and on scales ranging from less than a kilometer to thousands of kilometers [1,2].
In the horizontal dimension, currents, fronts, and eddies mark transition zones between environments. In the time of industrial whaling, observations of temperature and salinity were made at the surface from factory whaling ships and examined to understand where the most whales were available for hunting. These early measurements identified temperature contour lines, or isotherms, and led to observations that whales were found in areas of stark temperature change and places where isotherms bent into “tongues” of interacting water masses [3,4] (Fig. 1). These areas where water masses of different properties meet are often areas of high productivity. Today, we understand that shelf break fronts, river plumes, tidal fronts, and eddies are important horizontal structures that drive elevated nutrient availability, phytoplankton production, and prey availability for mobile marine predators, including whales.
Figure 1. Surface temperature and salinity contour lines from measurements taken aboard a factory whaling ship in the Antarctic, reproduced from Nasu (1959).
In the vertical dimension, the water column is also structured into distinct layers. Surface waters are warmed by the sunlight and are often lower in salinity due to freshwater input from rain and runoff. Below this distinct surface portion of the water column, the temperature drops sharply in a layer known as the thermocline, and below which pressure and density increase with depth. The surface layer is subject to mixing from wind input, which can draw nutrients from below up into the photic zone and spur productivity. The alternation between stratification—a water column with distinctive layers—and mixing drives optimal conditions for entire food webs to thrive [1,2].
While I began this blog post by writing about boundaries that partition different ocean environments, I have continued to learn that those boundary zones are often critically important in their own right. I started by thinking about boundaries in terms of their importance for separation, but now understand that the leaky points between them actually spur ocean productivity. Features such as fronts, currents, mixed layers, and eddies separate water masses of different properties. However, they are not truly complete and rigid boundaries, and precisely for that reason they are uniquely important in promoting productive marine ecosystems.
Figure 2. Left: Some of the materials I am studying for my qualifying exams. Right: A blue whale surfaces in New Zealand’s South Taranaki Bight, the subject of my PhD and the lens through which I consider the concepts I am reading about (photo by L. Torres).
Many thanks to my PhD Committee members who continue to guide me through this degree and who I am lucky to learn from. In particular, the contents of this blog post were inspired by materials recommended by, and discussions with, Dr. Daniel Palacios.
References:
1. Mann, K.H., and Lazier, J.R.N. (2006). Dynamics of Marine Ecosystems 3rd ed. (Blackwell Publishing).
2. Longhurst, A.R. (2007). Ecological Geography of the Sea 2nd ed. (Academic Press).
3. Nasu, K. (1959). Surface water conditions in the Antarctic whaling pacific area in 1956-57.
4. Machida, S. (1974). Surface temperature fields in the Crozet and Kerguelen whaling grounds. Sci. Reports Whales Res. Inst. 26, 271–287.
