Tag: foraging ecology

Dive into Oregon’s underwater forests

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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References

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

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

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

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

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

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

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

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

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

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

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

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

What drives individual specialization?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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References

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

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

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

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

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

[6]       M. T. Tinker, M. Mangel, and J. A. Estes, “Learning to be different: acquired skills, social learning, frequency dependence, and environmental variation can cause behaviourally mediated foraging specializations,” Evol. Ecol. Res., vol. 11, pp. 841–869, 2009.

[7]       M. T. Tinker et al., “Structure and mechanism of diet specialisation: testing models of individual variation in resource use with sea otters,” Ecol. Lett., vol. 15, no. 5, pp. 475–483, 2012, doi: 10.1111/j.1461-0248.2012.01760.x.

[8]       S. Rossman et al., “Foraging habits in a generalist predator: Sex and age influence habitat selection and resource use among bottlenose dolphins (Tursiops truncatus),” Mar. Mammal Sci., vol. 31, no. 1, pp. 155–168, 2015, doi: https://doi.org/10.1111/mms.12143.

[9]       L. G. Torres, “A kaleidoscope of mammal , bird and fish : habitat use patterns of top predators and their prey in Florida Bay,” vol. 375, pp. 289–304, 2009, doi: 10.3354/meps07743.

[10]     M. S. Araújo et al., “Network Analysis Reveals Contrasting Effects of Intraspecific Competition on Individual Vs. Population Diets,” Ecology, vol. 89, no. 7, pp. 1981–1993, 2008, doi: 10.1890/07-0630.1.

[11]     R. Svanbäck and D. I. Bolnick, “Intraspecific competition drives increased resource use diversity within a natural population,” Proc. R. Soc. B Biol. Sci., vol. 274, no. 1611, pp. 839–844, Mar. 2007, doi: 10.1098/rspb.2006.0198.

[12]     D. E. Schindler, J. R. Hodgson, and J. F. Kitchell, “Density-dependent changes in individual foraging specialization of largemouth bass,” Oecologia, vol. 110, no. 4, pp. 592–600, May 1997, doi: 10.1007/s004420050200.

[13]     C. E. Sheppard et al., “Intragroup competition predicts individual foraging specialisation in a group-living mammal,” Ecol. Lett., vol. 21, no. 5, pp. 665–673, 2018, doi: 10.1111/ele.12933.

[14]     L. Kernaléguen, J. P. Y. Arnould, C. Guinet, and Y. Cherel, “Determinants of individual foraging specialization in large marine vertebrates, the Antarctic and subantarctic fur seals,” J. Anim. Ecol., vol. 84, no. 4, pp. 1081–1091, 2015, doi: 10.1111/1365-2656.12347.

[15]     E. M. Keen and K. M. Qualls, “Respiratory behaviors in sympatric rorqual whales: the influence of prey depth and implications for temporal access to prey,” J. Mammal., vol. 99, no. 1, pp. 27–40, Feb. 2018, doi: 10.1093/jmammal/gyx170.

[16]     R. H. MacArthur and E. R. Pianka, “On Optimal Use of a Patchy Environment,” Am. Nat., vol. 100, no. 916, pp. 603–609, 1966, doi: 10.1086/282454.

[17]     C. Fontaine, C. L. Collin, and I. Dajoz, “Generalist foraging of pollinators: diet expansion at high density,” J. Ecol., vol. 96, no. 5, pp. 1002–1010, 2008, doi: 10.1111/j.1365-2745.2008.01405.x.

[18]     R. Costa-Pereira, V. H. W. Rudolf, F. L. Souza, and M. S. Araújo, “Drivers of individual niche variation in coexisting species,” J. Anim. Ecol., vol. 87, no. 5, pp. 1452–1464, 2018, doi: 10.1111/1365-2656.12879.

[19]     M. M. Pires, P. R. Guimarães Jr, M. S. Araújo, A. A. Giaretta, J. C. L. Costa, and S. F. dos Reis, “The nested assembly of individual-resource networks,” J. Anim. Ecol., vol. 80, no. 4, pp. 896–903, 2011, doi: 10.1111/j.1365-2656.2011.01818.x.

[20]     M. S. Araújo, D. I. Bolnick, and C. A. Layman, “The ecological causes of individual specialisation,”Ecol. Lett., vol. 14, no. 9, pp. 948–958, 2011, doi: https://doi.org/10.1111/j.1461-0248.2011.01662.x.

[21]     C. E. Sheppard, R. Heaphy, M. A. Cant, and H. H. Marshall, “Individual foraging specialization in group-living species,” Anim. Behav., vol. 182, pp. 285–294, Dec. 2021, doi: 10.1016/j.anbehav.2021.10.011.

[22]     S. Wild, S. J. Allen, M. Krützen, S. L. King, L. Gerber, and W. J. E. Hoppitt, “Multi-network-based diffusion analysis reveals vertical cultural transmission of sponge tool use within dolphin matrilines,” Biol. Lett., vol. 15, no. 7, p. 20190227, Jul. 2019, doi: 10.1098/rsbl.2019.0227.

[23]     L. M. Aplin, D. R. Farine, J. Morand-Ferron, A. Cockburn, A. Thornton, and B. C. Sheldon, “Experimentally induced innovations lead to persistent culture via conformity in wild birds,” Nature, vol. 518, no. 7540, pp. 538–541, Feb. 2015, doi: 10.1038/nature13998.

[24]     E. Van de Waal, C. Borgeaud, and A. Whiten, “Potent Social Learning and Conformity Shape a Wild Primate’s Foraging Decisions,” Science, Apr. 2013, doi: 10.1126/science.1232769.

[25]     D. I. Bolnick et al., “Why intraspecific trait variation matters in community ecology,” Trends Ecol. Evol., vol. 26, no. 4, pp. 183–192, Apr. 2011, doi: 10.1016/j.tree.2011.01.009.

Marine megafauna as ecosystem sentinels: What animals can tell us about changing oceans

By Dawn Barlow1 and Will Kennerley2

1PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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References

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

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

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

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

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

The right tool for the job: examining the links between animal behavior, morphology and habitat

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

In order to understand a species’ distribution, spatial ecologists assess which habitat characteristics are most often associated with a species’ presence. Incorporating behavior data can improve this analysis by revealing the functional use of each habitat type, which can help scientists and managers assign relative value to different habitat types. For example, habitat used for foraging is often more important than habitat that a species just travels through. Further complexity is added when we consider that some species, such as gray whales, employ a variety of foraging tactics on a variety of prey types that are associated with different habitats. If individual foraging tactic specialization is present, different foraging habitats could be valuable to specific subgroups that use each tactic. Consequently, for a population that uses a variety of foraging tactics, it’s important to study the associations between tactics and habitat characteristics.

Lukoschek and McCormick’s (2001) study investigating the spatial distribution of a benthic fish species’ foraging behavior is a great example of combining data on behavior, habitat, and morphology.  They collected data on the diet composition of individual fish categorized into different size classes (small, medium, and large) and what foraging tactics were used in which reef zones and habitat types. The foraging tactics ranged from feeding in the water column to digging (at a range of depths) in the benthic substrate. The results showed that an interesting combination of fish behavior and morphology explained the observed diet composition and spatial distribution patterns. Small fish foraged in shallower water, on smaller prey, and primarily employed the water column and shallow digging tactics. In contrast, large fish foraged in deep water, on larger prey, and primarily fed by digging deeper into the seafloor (Figure 1). This pattern is explained by both morphology and behavior. Morphologically, the size of the feeding apparatus (mouth gape size) affects the size of the prey that a fish can feed on. The gape of the small fish is not large enough to eat the larger prey that large fish are able to consume. Behaviorally, predation risk also affects habitat selection and tactic use. Small fish are at higher risk of being predated on, so they remain in shallow areas where they are more protected from predators and they don’t dig as deep to forage because they need to be able to keep an eye out for predators. Interestingly, while they found a relationship between the morphology of the fish and habitat use, they did not find an association between specific feeding tactics and habitat types.

Figure 1. Figure from Lukoschek and McCormick (2001) showing that small fish (black bar) were found in shallow habitat while large fish (white bar) were found in deep habitat.

Conversely, Torres and Read (2009) did find associations between theforaging tactics of bottlenose dolphins in Florida Bay, FL and habitat type. Dolphins in this bay employ three foraging tactics: herd and chase, mud ring feeding, and deep diving. Observations of the foraging tactics were linked to habitat characteristics and individual dolphins. The study found that these tactics are spatially structured by depth (Figure 2), with deep diving occurring in deep water whereas mud ring feeding occurrs in shallower water. They also found evidence of individual specialization! Individuals that were observed deep diving were not observed mud ring feeding and vice-versa. Furthermore, they found that individuals were found in the habitat type associated with their preferred tactic regardless of whether they were foraging or not. This result indicates that individual dolphins in this bay have a foraging tactic they prefer and tend to stay in the corresponding habitat type. These findings are really intriguing and raise interesting questions regarding how these tactics and specializations are developed or learned. These are questions that I am also interested in asking as part of my thesis.

Figure 2. Figure from Torres and Read (2009) showing that deep diving is associated with deeper habitat while mud ring feeding is associated with shallow habitat.

Both of these studies are cool examples that, combined, exemplify questions I am interested in examining using our study population of Pacific Coast Feeding Group (PCFG) gray whales. Like both studies, I am interested in assessing how specific foraging tactics are associated with habitat types. Our hypothesis is that different prey types live in different habitat types, so each tactic corresponds to the best way to feed on that prey type in that habitat. While predation risk doesn’t have as much of an effect on foraging gray whales as it does on small benthic fish, I do wonder how disturbance from boats could similarly affect tactic preference and spatial distribution. I am also curious to see if depth has an effect on tactic choice by using the morphology data from our drone-based photogrammetry. Given that these whales forage in water that is sometimes as deep as they are long, it stands to reason that maneuverability would affect tactic use. As described in a previous blog, I’m also looking for evidence of individual specialization. It will be fascinating to see how foraging preference relates to space use, habitat preference, and morphology.

These studies demonstrate the complexity involved in studying a population’s relationship to its habitat. Such research involves considering the morphology and physiology of the animals, their social, individual, foraging, and predator-prey behaviors, and the relationship between their prey and the habitat. It’s a bit daunting but mostly really exciting because better understanding each puzzle piece improves our ability to estimate how these animals will react to changing environmental conditions.

While I don’t have any answers to these questions yet, I will be working with a National Science Foundation Research Experience for Undergraduates intern this summer to develop a habitat map of our study area that will be used in this analysis and potentially answer some preliminary questions about PCFG gray whale habitat use patterns. So, stay tuned to hear more about our work this summer!

References

Lukoschek, V., & McCormick, M. (2001). Ontogeny of diet changes in a tropical benthic carnivorous fish, Parupeneus barberinus (Mullidae): Relationship between foraging behaviour, habitat use, jaw size, and prey selection. Marine Biology, 138(6), 1099–1113. https://doi.org/10.1007/s002270000530

Torres, L. G., & Read, A. J. (2009). Where to catch a fish? The influence of foraging tactics on the ecology of bottlenose dolphins ( Tursiops truncatus ) in Florida Bay, Florida. Marine Mammal Science, 25(4), 797–815. https://doi.org/10.1111/j.1748-7692.2009.00297.x

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.

Are there picky eaters in the PCFG?

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

As anyone who has ever been, or raised, a picky eater knows, humans have a wide range of food preferences. The diversity of available cuisines is a testament to the fact that we have individual food preferences. While taste is certainly a primary influence, nutritional benefits and accessibility are other major factors that affect our eating choices. But we are not the only species to have food preferences. In cetacean research, it is common to study the prey types consumed by a population as a whole. Narrowing these prey preferences down to the individual level is rare. While the individual component is challenging to study and to incorporate into population models, it is important to consider what the effects of individual foraging specialization might be.

To understand the role and drivers of individual specialization in population ecology, it is important to first understand the concepts of niche variation and partitioning. An animal’s ecological niche describes its role in the ecosystem it inhabits (Hutchinson, 1957). A niche is multidimensional, with dimensions for different environmental conditions and resources that a species requires. One focus of my research pertains to the dimensions of the niche related to foraging. As discussed in a previous blog, niche partitioning occurs when ecological space is shared between competitors through access to resources varies across different dimensions such as prey type, foraging location, and time of day when foraging takes place. Niche partitioning is usually discussed on the scale of different species coexisting in an ecosystem. Pianka’s theory stating that niche partitioning will increase as prey availability decreases uses competing lizard species as the example (Pianka, 1974). Typically, niche partitioning theory considers inter-specific competition (competition between species), but niche partitioning can take place within a species in response to intra-specific competition (competition between individuals of the same species) through individual niche variation.

A species that consumes a multitude of prey types is considered a generalist while one with a specific prey type is considered a specialist. Gray whales are considered generalists (Nerini, 1984). However, we do not know if each individual gray whale is a generalist or if the generalist population is actually composed of individual specialists with different preferences. One way to test for the presence of individual specialization is to compare the niche width of the population to the niche width of each individual (Figure 1, Bolnick et al., 2003).  For example, if a population eats five different types of prey and each individual consumed those prey types, those individuals would be generalists. However, if each individual only consumed one of the prey types, then those individuals would be specialists within a generalist population.

Figure 1. Figure from Bolnick et al. 2003. The thick curve represents the total niche of the population and the thin curves represent individual niches. Note that in both panels the population has the same total niche. In panel A, the individual curves overlap and are all pretty wide. These curves represent individual generalists that make up a generalist population. In panel B, the thin curves are narrower and do not overlap as much as those in panel A. These curves represent individual specialists that make up a generalist population.

If individual specialization is present in a population the natural follow-up question is why? To answer this, we look for common characteristics between the individuals that are similarly specialized. What do all the individuals that feed on the same prey type have in common? Common characterizations that may be found include age, sex, or distinct morphology (such as different beak or body shapes) (Bolnick et al., 2003).

Woo et al. (2008) studied individual specialization in Brünnich’s guillemot, a generalist sea bird species, using diet and tagging data. They found individual specialization in both diet (prey type) and behavior (dive depth, shape, and flight time). Specialization occurred across multiple timescales but was higher over short-time scales. The authors found that it was more common for an individual to specialize in a prey-type/foraging tactic for a few days than for that specialization to continue across years, although a few individuals were specialists for the full 15-year period of the study. Based on reproductive success of the studies birds, the authors concluded that the generalist and specialist strategies were largely equivalent in terms of fitness and survival. The authors searched for common characteristics in the individuals with similar specialization and they found that the differences between sexes or age classes were so small that neither grouping explained the observed individual specialization. This is an interesting result because it suggests that there is some missing attribute, that of the authors did not examine, that might explain why individual specialists were present in the population.

Hoelzel et al. (1989) studied minke whale foraging specialization by observing the foraging behaviors of 23 minke whales over five years from a small boat. They identified two foraging tactics: lunge feeding and bird-associated feeding. Lunge feeding involved lunging up through the water with an open mouth to engulf a group of fish, while bird-associated feeding took advantage of a group of fish being preyed on by sea birds to attack the fish from below while they were already being attacked from above. They found that nine individuals used lunge feeding, and of those nine, six whales used this tactic exclusively. Five of those six whales were observed in at least two years. Seventeen whales were observed using bird-associated feeding, 14 exclusively. Of those 14, eight were observed in at least two years. Interestingly, like Woo et al. (2008), this study did not find any associations between foraging tactic use and sex, age, or size of whale. Through a comparison of dive durations and feeding rates, they hypothesized that lunge feeding was more energetically costly but resulted in more food, while bird-associated feeding was energetically cheaper but had a lower capture rate. This result means that these two strategies might have the similar energetic payoffs.

Both of these studies are examples of questions that I am excited to ask using our data on the PCFG gray whales feeding off the Oregon coast (especially after doing the research for this blog). We have excellent individual-specific data to address questions of specialization because the field teams for  this project always carefully link observed behaviors with individual whale ID.  Using these data, I am curious to find out if the whales in our study group are individual specialists or generalists (or some combination of the two). I am also interested in relating specific tactics to their energetic costs and benefits in order to assess the payoffs of each foraging tactic. I then hope to combine the results of both analyses to assess the payoffs of each individual whale’s strategy.

Figure 2. Example images of two foraging tactics, side swimming (left) and headstanding (right). Images captured under NOAA/NMFS permit #21678.

Studying individual specialization is important for conservation. Consider the earlier example of a generalist population that consumes five prey items but is composed of individual specialists. If the presence of individual specialization is not accounted for in management plans, then regulations may protect certain prey types or foraging tactics/areas of the whales and not others. Such a management plan could be a dangerous outcome for the whale population because only parts of the population would be protected, while other specialists are at risk, thus potentially losing genetic diversity, cultural behaviors, and ecological resilience in the population as a whole. A plan designed to maximize protection for all the specialists would be better for the population because populations with increased ecological resilience are more likely to persist through periods of rapid environmental change. Furthermore, understanding individual specialization could help us better predict how a population might be affected by environmental change. Environmental change does not affect all prey species in the same way. An individual specialization study could help identify which whales might be most affected by predicted environmental changes. Therefore, in addition to being a fascinating and exciting research question, it is important to test for individual specialization in order to improve management and our overall understanding of the PCFG gray whale population.

References

Bolnick, D. I., Svanbäck, R., Fordyce, J. A., Yang, L. H., Davis, J. M., Hulsey, C. D., & Forister, M. L. (2003). The ecology of individuals: Incidence and implications of individual specialization. American Naturalist, 161(1), 1–28. https://doi.org/10.1086/343878

Hoelzel, A. R., Dorsey, E. M., & Stern, S. J. (1989). The foraging specializations of individual minke whales. Animal Behaviour, 38(5), 786–794. https://doi.org/10.1016/S0003-3472(89)80111-3

Hutchinson, G. E. (1957). Concluding Remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22(0), 415–427. https://doi.org/10.1101/sqb.1957.022.01.039

Nerini, M. (1984). A Review of Gray Whale Feeding Ecology. In The Gray Whale: Eschrichtius Robustus (pp. 423–450). Elsevier Inc. https://doi.org/10.1016/B978-0-08-092372-7.50024-8

Pianka, E. R. (1974). Niche Overlap and Diffuse Competition. 71(5), 2141–2145.

Woo, K. J., Elliott, K. H., Davidson, M., Gaston, A. J., & Davoren, G. K. (2008). Individual specialization in diet by a generalist marine predator reflects specialization in foraging behaviour. Journal of Animal Ecology, 77(6), 1082–1091. https://doi.org/10.1111/j.1365-2656.2008.01429.x

Never a Straight Path

By Florence Sullivan, MSc, GEMM Lab Graduate

It’s rather amazing how, in a span of five years, the journey of your life can take twists and turns that you never expected. Long time blog readers may remember me as the graduate student who began this blog way back in February of 2015 with a recitation of our lab’s very first science outreach event. Since then, I completed my master’s thesis investigating gray whale foraging ecology (a project that just finished its 6th field season thanks to the excellent leadership of Lisa Hildebrand), fulfilled a dream of working as a marine mammal observer, and survived the existential crisis of graduation and job searching.

None of the species we study in this lab forage in straight lines.  If we consider a job (and salary) as the mechanism by which most of us put food on the table (i.e. foraging) why should our path be any less complex than theirs? By April 2018, I had moved back in with my parents in Seattle and was thoroughly heartsick about how long my job search was taking, so I jumped on the first field opportunity that came my way.  The project was billed as an attempt to pair discrete killer whale behaviors to specific call sequences, with collaborators from a variety of countries and backgrounds. In my enthusiasm, I ignored some red flags, and paid for it with a field season where I (1) experienced my first person-overboard situation, (2) witnessed steady verbal harassment, (3) was injured when our live-aboard trimaran was run aground on a clearly marked reef, and (4) ended up committing mutiny and leaving the project early.  There have been encouraging discussions in the marine science community recently about the barriers that women & early career scientists face while in the field, particularly with regard to accessibility, equity, and unpaid/underpaid internships. I will add some learned lessons to the list of things one should consider before embarking on a new research endeavor:

  • If someone says they are affiliated with a university, but will not give you a project or lab website because ‘the project is quite delicate, we don’t want other folks stealing our work’, check that they actually do have university affiliations and aren’t misrepresenting their connections. Do some homework.
  • Don’t cross a border without a contract, and when repeated attempts to secure a description of your responsibilities and payment are put off until later, consider that this might be a pattern of behavior.
  • If you were told that you would work under a senior bioacoustician, and you show up to find that your new colleague had been told the same thing, but neither of you has more than a MSc degree or much experience with acoustics, add this to your tally of red flags.
  • If basic safety standards (like wearing a life jacket on deck) are being ignored, hold yourself to a higher standard, and lead by example.
  • If sustained verbal and emotional abuse is occurring, you still have not been paid, and you’ve been asked to keep working after being injured in an accident caused by negligence, it is ok to break faith and leave a project.

Entering this project, I was very keen to learn new skills in acoustics, study a new species, and build partnerships with international researchers. Instead, I learned about interpersonal conflict and resolution strategies.  So, time for a new plan & another bend in my path. Thankfully, I have the immense privilege of a capable, employed husband who was able to support me while I recovered and began a new job search.

A pod of northern resident orcas resting during the rain, British Columbia. Photo credit: F. Sullivan
As salmon migrate upriver, their bodies undergo drastic changes in coloration, and once they spawn, they die. Their decomposing carcasses provide a critical influx of nutrients to the river and forest ecosystems of the Pacific Northwest. Photo credit: F. Sullivan

In the year that followed, I joined the team at the Environmental Science Center and taught 3rd -10th graders how to be “Salmon Heroes”. I explained salmon ecology, taking them on field experiences where we dissected salmon, measured oxygen and nitrogen levels in salmon streams, assessed habitat quality, observed migration and spawning behaviors (when fish cooperated), and brainstormed ways to protect these special (and delicious) fish. 

When salmon season came to an end, we transitioned to the “Beach Hero” program, targeted at K-3rd grade, where I became part of the classroom team, teaching intertidal ecology before bringing the kids to the beach where many of them experienced low tide for the first time.  In keeping with the education theme, I also worked with South Sound Nature School to provide kids with a forest-based after school program and was a summer camp counselor at Camp Long for several weeks. Still, I continued to try to find my way back to research and a data-driven career.

This is a sea star, it turns its stomach inside out to eat mussels! (we had awesome puppets for demonstration). Photo credit: Kharli Rose

Another bend in my ‘foraging’ job search happened when I stumbled across a short term data contract at my local election office while searching the state and county job boards. Washington State is a vote-by-mail state, and with a record turn-out in the 2018 mid-term elections they needed help updating everyone’s contact information & verifying signatures.  Let me tell you, staring at a computer screen, deciphering people’s handwriting to add emails and phone numbers to their voter registration for 8 hours a day for 6 weeks was not particularly fun. Yet, it gave me a little more experience in government databases, and gave me a lot of confidence in my election office for how transparent they are about every step of the voting process. I can’t speak for anywhere else, but in King County (Washington), you can go visit the election office & give yourself a self-guided walking tour of the whole ballot counting process from arrival to sorting to signature verification and opening to tallying. (There’s a hallway with massive windows surrounding the giant open concept floor space where everything happens, so you can observe without interfering). I’d never thought about what happens to my ballot after I mail it before, and its rather fascinating. Speaking of which, Please Vote!

Frustrated by a job search that failed to yield anything with health benefits or more than part-time hours, my Dad suggested that I apply to the University of Washington Continuing Education program, and enroll in a professional certificate to add another explicit skill to my resume. When enough pressure is applied to the system, something has to give eventually. The month where I was accepted to the UW Certificate in Statistical Analysis with R Programming was also the month I started interviewing for the Research Analyst Position at the Pacific Whale Foundation.  Partially because I could prove my data management experience, and that I was serious about continuing to hone my skiIls, I was offered, and accepted the position! Hilariously or stressfully, however you want to look at it, I moved to Maui, began my new position, and started my statistics with R programming coursework all in the same week – the learning curve was STEEP.

Amazingly, Leila (another GEMM Lab alumn) was visiting Maui the week I moved, and we were able to have dinner together my first night on-island! Photo credit: Leila Lemos

I completed my certificate in June, and hit my one-year work anniversary last month! I’m responsible for a good portion of our database management, and use R coding on a daily basis to pull data requests, tidy historical data, and add new information.  I’d never been to Maui before moving here, but now I’ve experienced the glory of Humpback whale breeding & calving season and heard whale song underwater. I’ve helped collect important life history data for false killer whales, spotted, spinner & bottlenose dolphins, and I’m looking forward to encountering more new-to-me odontocete species. It took months before I felt like I was past the ‘onboarding’ information stage, but now I’m collaborating with my colleagues on my first data analysis project (rather than simple data management) and loving my team despite the wrenches that the pandemic has thrown in our work. My job search = cetacean foraging analogy breaks down a little at this point, but my story still stands. I acknowledge my privilege of a good education and supportive husband, but I have this suggestion for job seekers: Don’t be afraid to get creative while you search for the right position, because you never know what you might stumble across and learn along the way. In the process, do your best to catch red flags, and keep yourself out of dangerous positions.  My job search hasn’t been a straight path, but that doesn’t mean it wasn’t full of small victories, and it did ultimately lead to a successful “prey patch”.

A pod of northern resident orcas traveling along the outer coast of British Columbia.

What makes a good meal for a hungry whale?

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

In the vast and dynamic marine environment, food is notoriously patchy and ephemeral [1]. Predators such as marine mammals and seabirds must make a living in this dynamic environment by locating and capturing those prey patches. Baleen whales such as blue and humpback whales have a feeding strategy called “lunge feeding”, whereby they accelerate forward and open their massive jaws, engulf prey-laden water in their buccal pouch that expands like an accordion, and filter the water out through baleen plates so that they are left with a mouthful of food (Fig. 1) [2]. This approach is only efficient if whales can locate and target dense prey patches that compensate for the energetic costs of diving and lunging [3]. Therefore, not only do these large predators need to locate enough food to survive in the expansive and ever-changing ocean, they need to locate food that is dense enough to feed on, otherwise they actually lose more energy by lunging than they gain from the prey they engulf.

Figure 1. Schematic of a humpback whale lunge feeding on a school of fish. Illustration by Alex Boersma.

Why do baleen whales rely on such a costly feeding approach? Interestingly, this tactic emerged after the evolution of schooling behavior of prey such as zooplankton and forage fish (e.g., herring, anchovy, sand lance) [4]. Only because the prey aggregate in dense patches can these large predators take advantage of them by lunge feeding, and by engulfing a whole large patch they efficiently exploit these prey patches. Off the coast of California, where krill aggregations are denser in deeper water, blue whales regularly dive to depths of 100-300 m in order to access the densest krill patches and get the most bang for their buck with every lunge [5]. In New Zealand, we have found that blue whales exploit the dense krill patches near the surface to maximize their energetic gain [6], and have documented a blue whale bypassing smaller krill patches that presumably were not worth the effort to feed on.

By now hopefully I have convinced you of the importance of dense prey patches to large whales looking for a meal. It is not necessarily only a matter of total prey biomass in an area that is important to a whale, it is whether that prey biomass is densely aggregated. What makes for a dense prey patch? Recent work has shown that forage species, namely krill and anchovies, swarm in response to coastal upwelling [7]. While upwelling events do not necessarily change the total biomass of prey available to a whale over a spatial area, they may aggregate prey to a critical density to where feeding by predators becomes worthwhile. Forage species like zooplankton and small fish may school because of enhanced food resources, for predator avoidance, or reproductive grouping. While the exact behavioral reason for the aggregation of prey may still only be partially understood, the existence of these dense patches allows the largest animals on the planet to survive.

Another big question is, how do whales actually find their food? In the vast, seemingly featureless, and ever-changing ocean environment, how does a whale know where to find a meal, and how do they know it will be worthwhile before they take a lunge? In a review paper written by GEMM Lab PI Dr. Leigh Torres, she suggests it is all a matter of scale [8]. On a very large scale, baleen whales likely rely on oceanographic stimuli to home in on areas where prey are more likely to be found. Additionally, recent work has demonstrated that migrating blue whales return to areas where foraging conditions were best in previous years, indicating a reliance on memory [9,10]. On a very fine scale, visual cues may inform how a blue whale chooses to lunge [6,8,11].

What does it matter what a blue whale’s favorite type of meal is? Besides my interest in foundational research in ecology such as predator-prey dynamics, these questions are fundamental to developing effective management approaches for reducing impacts of human activities on whales. In the first chapter of my PhD, I examined how oceanographic features of the water column structure krill aggregations, and how blue whale distribution is influenced by oceanography and krill availability [12]. Currently, I am deep into my second chapter, analyzing the pathway from wind to upwelling to krill to blue whales in order to better understand the links and time lags between each step. Understanding the time lags will allow us to make more informed models to forecast blue whale distribution in my third chapter. Environmental managers in New Zealand plan to establish a protected area to conserve the population of blue whales that I study [13] on their foraging grounds. Understanding where blue whales will be distributed, and consequently how their distribution patterns might shift with environmental conditions or overlap with human activities, comes down the fundamental question I started this blog post with: What makes a good meal for a hungry whale?

References

1.        Hyrenbach KD, Forney KA, Dayton PK. 2000 Marine protected areas and ocean basin management. Aquat. Conserv. Mar. Freshw. Ecosyst. 10, 437–458. (doi:10.1002/1099-0755(200011/12)10:6<437::AID-AQC425>3.0.CO;2-Q)

2.        Goldbogen JA, Cade DE, Calambokidis J, Friedlaender AS, Potvin J, Segre PS, Werth AJ. 2017 How Baleen Whales Feed: The Biomechanics of Engulfment and Filtration. Ann. Rev. Mar. Sci. 9, 367–386. (doi:10.1146/annurev-marine-122414-033905)

3.        Goldbogen JA, Calambokidis J, Oleson E, Potvin J, Pyenson ND, Schorr G, Shadwick RE. 2011 Mechanics, hydrodynamics and energetics of blue whale lunge feeding: efficiency dependence on krill density. J. Exp. Biol. 214, 131–146. (doi:10.1242/jeb.048157)

4.        Cade DE, Carey N, Domenici P, Potvin J, Goldbogen JA. 2020 Predator-informed looming stimulus experiments reveal how large filter feeding whales capture highly maneuverable forage fish. Proc. Natl. Acad. Sci. U. S. A. (doi:10.1073/pnas.1911099116)

5.        Hazen EL, Friedlaender AS, Goldbogen JA. 2015 Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Sci. Adv. 1, e1500469–e1500469. (doi:10.1126/sciadv.1500469)

6.        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 (doi:10.7717/peerj.8906)

7.        Benoit-Bird KJ, Waluk CM, Ryan JP. 2019 Forage Species Swarm in Response to Coastal Upwelling. Geophys. Res. Lett. 46, 1537–1546. (doi:10.1029/2018GL081603)

8.        Torres LG. 2017 A sense of scale: Foraging cetaceans’ use of scale-dependent multimodal sensory systems. Mar. Mammal Sci. 33, 1170–1193. (doi:10.1111/mms.12426)

9.        Abrahms B et al. 2019 Memory and resource tracking drive blue whale migrations. Proc. Natl. Acad. Sci. U. S. A. (doi:10.1073/pnas.1819031116)

10.      Szesciorka AR, Ballance LT, Širovi A, Rice A, Ohman MD, Hildebrand JA, Franks PJS. 2020 Timing is everything: Drivers of interannual variability in blue whale migration. Sci. Rep. 10, 1–9. (doi:10.1038/s41598-020-64855-y)

11.      Friedlaender AS, Herbert-Read JE, Hazen EL, Cade DE, Calambokidis J, Southall BL, Stimpert AK, Goldbogen JA. 2017 Context-dependent lateralized feeding strategies in blue whales. Curr. Biol. (doi:10.1016/j.cub.2017.10.023)

12.      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. (doi:https://doi.org/10.3354/meps13339)

13.      Barlow DR et al. 2018 Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res. 36, 27–40. (doi:https://doi.org/10.3354/esr00891)

Connecting Research Questions

Clara Bird, Masters Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

The field season can be quite a hectic time of year. Between long days out on the water, trouble-shooting technology issues, organizing/processing the data as it comes in, and keeping up with our other projects/responsibilities, it can be quite overwhelming and exhausting.

But despite all of that, it’s an incredible and exciting time of year. Outside of the field season, we spend most of our time staring at our computers analyzing the data that we spend a relatively short amount of time collecting. When going through that process it can be easy to lose sight of why we do what we do, and to feel disconnected from the species we are studying. Oftentimes the analysis problems we encounter involve more hours of digging through coding discussion boards than learning about the animals themselves. So, as busy as it is, I find that the field season can be pretty inspiring. I have recently been looking through our most recent drone footage of gray whales and feeling renewed excitement for my thesis.

At the moment, my thesis has four central questions: (1) Are there associations between habitat type and gray whale foraging tactic? (2) Is there evidence of individualization? (3) What is the relationship between behavior and body condition? (4) Do we see evidence of learning in the behavior of mom and calf pairs? As I’ve been organizing my thoughts, what’s become quite clear is how interconnected these questions are. So, I thought I’d take this blog to describe the potential relationships.

Let’s start with the first question: are there associations between habitat types and gray whale foraging tactics? This question is central because it relates foraging behavior to habitat, which is ultimately associated with prey. This relationship is the foundation of all other questions involving foraging tactics because food is necessary for the whales to have the energy and nutrients they need to survive. It’s reasonable to think that the whales are flexible and use different foraging tactics to eat different prey that live in different habitats. But, if different prey types have different nutritional value (this is something that Lisa is studying right now; check out the COZI project to learn more), then not all whales may be getting the same nutrients.

The next question relates to the first question but is not necessarily dependent on it. It’s the question of individualization, a topic Lisa also explored in a past blog. Within our Oregon field sites we have documented a variety of gray whale foraging tactics (Torres et al. 2018; Video 1) but we do not know if all gray whales use all the tactics or if different individuals only use certain tactics. While I think it’s unlikely that one whale only uses one tactic all the time, I think we could see an individual use one tactic more often than the others. I reason that there could be two reasons for this pattern. First, it could be a response to resource availability; certain tactics are more efficient than others, this could be because the tactic involves capturing the more nutritious prey or because the behavior is less energetically demanding. Second, foraging tactics are socially learned as calves from their mothers, and hence individuals use those learned tactics more frequently. This pattern of maternally inherited foraging tactics has been documented in other marine mammals (Mann and Sargeant 2009; Estes et al. 2003). These questions between foraging tactic, habitat and individualization also tie into the remaining two questions.

My third question is about the relationship between behavior and body condition. As I’ve discussed in a previous blog, I am interested in assessing the relative energetic costs and benefits of the different foraging tactics. Is one foraging tactic more cost-effective than another (less energy out per energy in)? Ever since our lab’s cetacean behavioral ecology class, I’ve been thinking about how my work relates to niche partitioning theory (Pianka 1974).This theory states that when there is low prey availability, niche partitioning will increase. Niche partitioning can occur across several different dimensions: for instance, prey type, foraging location, and time of day when active. If gray whales partition across the prey type dimension, then different whales would feed on different kinds of prey. If whales partition resources across the foraging location dimension, individuals would feed in different areas. Lastly, if whales partition resources across the time axis, individuals would feed at different times of day. Using different foraging tactics to feed on different prey would be an example of partitioning across the prey type dimension. If there is a more preferable prey type, then maybe in years of high prey availability, we would see most of the gray whales using the same tactics to feed on the same prey type. However, in years of low prey availability we might expect to see a greater variety of foraging tactics being used. The question then becomes, does any whale end up using the less beneficial foraging tactic? If so, which whales use the less beneficial tactic? Do the same individuals always switch to the less beneficial tactic? Is there a common characteristic among the individuals that switched, like sex, age, size, or reproductive status? Lemos et al. (2020) hypothesized that the decline in body condition observed from 2016 to 2017 might be a carryover effect from low prey availability in 2016. Could it be that the whales that use the less beneficial tactic exhibit poor body condition the following year?

My fourth, and final, question asks if foraging tactics are passed down from moms to their calves. We have some footage of a mom foraging with her calf nearby, and occasionally it looks like the calf could be copying its mother. Reviewing this footage spiked my interest in seeing if there are similarities between the behavior tactics used by moms and those used by their calves after they have been weaned. While this question clearly relates to the question of individualization, it is also related to body condition: what if the foraging tactics used by the mom is influenced by her body condition at the time?

I hope to answer some of these fascinating questions using the data we have collected during our long field days over the past 6 years. In all likelihood, the story that comes together during my thesis research will be different from what I envision now and will likely lead to more questions. That being said, I’m excited to see how the story unfolds and I look forward to sharing the evolving ideas and plot lines with all of you.

References

Estes, J A, M L Riedman, M M Staedler, M T Tinker, and B E Lyon. 2003. “Individual Variation in Prey Selection by Sea Otters: Patterns, Causes and Implications.” Source: Journal of Animal Ecology. Vol. 72.

Mann, Janet, and Brooke Sargeant. 2009. “ Like Mother, like Calf: The Ontogeny of Foraging Traditions in Wild Indian Ocean Bottlenose Dolphins ( Tursiops Sp.) .” In The Biology of Traditions, 236–66. Cambridge University Press. https://doi.org/10.1017/cbo9780511584022.010.

Pianka, Eric R. 1974. “Niche Overlap and Diffuse Competition” 71 (5): 2141–45.

Soledade Lemos, Leila, Jonathan D Burnett, Todd E Chandler, James L Sumich, and Leigh G. Torres. 2020. “Intra‐ and Inter‐annual Variation in Gray Whale Body Condition on a Foraging Ground.” Ecosphere 11 (4). https://doi.org/10.1002/ecs2.3094.

Torres, Leigh G., Sharon L. Nieukirk, Leila Lemos, and Todd E. Chandler. 2018. “Drone up! Quantifying Whale Behavior from a New Perspective Improves Observational Capacity.” Frontiers in Marine Science 5 (SEP). https://doi.org/10.3389/fmars.2018.00319.