Tag: foraging ecology

SST, EKE, SSH: Wading Through the Alphabet Soup of Oceanographic Parameters related to Deep-Dwelling Odontocetes

By: Marissa Garcia, PhD Student, Cornell University, Department of Natural Resources and the Environment, K. Lisa Yang Center for Conservation Bioacoustics

Predator-Prey Inference: A Tale as Old as Time

It’s a tale as old as time: where there’s prey, there’ll be predators.

As apex predators, cetaceans act as top-down regulators of ecosystem function. While baleen whales act as “ecosystem engineers,” facilitating nutrient cycling in the ocean (Roman et al., 2014), toothed whales, or “odontocetes,” can impart keystone-level effects — that is, they disproportionately control the marine community’s food-web structure (Valls, Coll, & Christensen, 2015). The menus of prey vary widely by species — ranging from mircronekton to fish to squid – and by extension, vary widely across trophic levels.

So, it naturally follows the old adage: where there’s an abundance of prey, there’ll be an abundance of cetaceans. Yet, creating models that accurately depict this predator-prey relationship is, perhaps unsurprisingly, not as straightforward.

Detecting the ‘Predator’ Half of the Equation

Scientists have successfully documented cetacean presence drawing upon a myriad of methods, each bearing its unique advantages and limitations.

Visual surveys — spanning viewpoints from land, boats, and air — can attain precise spatial data and species ID. However, this data can be constrained by “availability bias” — that is, scientists can only observe cetaceans visible at the surface, not those obscured by the ocean’s depths. Species that spend less time near the surface are more likely to elude the observer’s line of sight, thereby being missed in the data. Consequently, visual surveys have historically undersampled deep-diving species. For instance, since its discovery by western science in 1945, the Hubb’s beaked whale (Mesoplodon carlshubbi) has only been observed alive twice by OSU MMI’s very own Bob Pitman, once in 1994 and another time in 2021.

Scientists have also been increasingly conducting acoustic surveys to document cetacean presence. Acoustic recorders can “hear” each cetacean species at different ranges. Baleen whales, which bellow low-frequency calls, can be heard as far as across ocean basins (Munk et al., 1994). Toothed whales whistle, echolocate, and buzz at frequencies so high they’re considered ultrasonic. But it comes at a trade-off: high-frequency sounds have shorter wavelengths, meaning they are heard across smaller ranges. This high variability, which scientists refer to as “detection range,” translates to not always knowing where the vocalizing cetacean that was recorded is: as such, acoustic data can lack the high-resolution spatial precision often achieved by visual surveys. Nevertheless, acoustic data triumphs in temporal extent, sometimes managing to record continuously at six months at a time. Additionally, animals can elude visual detection in poor weather conditions or if they have a cryptic surface expression, but detected in acoustic surveys (e.g., North Atlantic right whales (Eubalaena glacialis) (Ganley, Brault, & Mayo, 2019; Clark et. al, 2010). Thus, acoustic surveys may be especially optimal for recording elusive deep-dwellers that occupy the often rough Oregon waters, such as beaked whales, the focus of my research in collaboration with the GEMM Lab.

Figure 1: HALO Project researchers Marissa Garcia (left; Yang Center via Cornell) and Imogen Lucciano (right; OSU MMI) among three Rockhopper acoustic recording units, ahead of deployment off the Oregon coast. Credit: Marissa Garcia.

Detecting the ‘Prey’ Half of the Equation

Prey can be measured by numerous methods. Most directly, prey can be measured “in-situ” — that is, prey is collected directly from the site where the cetaceans are detected or observed. A 2020 study combined fish trawls with a towed hydrophone array to identify which fish species odontocetes along the continental shelf of West Ireland (e.g., pilot whales, sperm whales, and Sowerby’s beaked whales) were feasting; the results found that odontocetes primarily fed upon mesopelagic fish and cephalopods (Breen et al., 2020). While trawls can glean species ID of prey, associating this prey data with depth and biomass can prove challenging.

Alternatively, prey can be detected via active acoustics. Echosounders release an acoustic signal that descends through the water column and then echoes back once it hits a sound-scattering organism. Beaked whales forage within deep scattering layers typically composed of myctophid fish and squid, both of which can echo back echosounder pings (Hazen et al., 2011). Thus, echosounder data can map prey density through the water column. When mapping prey density of beaked whales, Hazen et al. 2011 found a strong positive correlation among prey density, ocean vertical structure, and clicks primarily produced while foraging – suggesting beaked whales forage at depth when encountering large, multi-species aggregations of prey.

Figure 2: An example of prey mapping via a Simrad EK60 120 kHz split-beam echosounder. Credit: Rachel Kaplan (OSU MMI) via the HALO Project.

Most relevant to the HALO Project, prey is measured using proximate indices, which are more easily quantifiable metrics of ocean conditions, such as collected from ships via CTD casts or via satellite imagery, that are indirectly related to prey abundance. CTD data can provide information related to the water column structure, including depth and strength of the thermocline, depth of the mixed layer, depth of the euphotic zone, and total chlorophyll concentration in the euphotic zone (Redfern et al. 2006). Satellite imagery can characterize the dynamic patterns of the surface later, including sea surface temperature (SST), salinity, surface chlorophyll a, sea surface height (SSH), and sea surface currents (Virgili et al., 2022; Redfern et al., 2006). Ocean model data products can, such as the Regional Ocean Modeling System (ROMS) which models how an oceanic region of interest responds to physical processes, can provide water column variables related to eddy kinetic energy (EKE) and average temperature gradients (Virgili et al., 2022). In the case of my research with the HALO Project, we will be using oceanographic data collected through the Ocean Observatories Initiative to inform odontocete species distribution models.

Connecting the Dots: Linking Deep-Dwelling Top Predators and Prey

While scientists have made significant advances with collecting both cetacean and prey data, connecting the dots between the ecology of deep-dwelling odontocetes and the oceanographic parameters indicative of their prey still remains a challenge.

In the absence of in situ sampling, species distribution models of marine top predators often derive proxies for “prey data” from static bathymetric and dynamic surface water variables (Virgili et al., 2022). However, surface variables may be irrelevant to toothed whale prey inhabiting great depths (Virgili et al., 2022). Within the HALO Project, the deepest Rockhopper acoustic recording unit is recording odontocetes at nearly 3,000 m below the surface, putting into question the relevance of oceanographic parameters collected at the surface.

Figure 3: Schematic depicting the variation among different zones in the water column. Conditions at the surface may not represent conditions at depth. Credit: Barbara Ambrose, NOAA via NOAA Ocean Explorer.

In my research, I am setting out to estimate which oceanographic variables are optimal for explaining deep-dwelling odontocete presence. A 2022 study using visual survey data found that surface, subsurface, and static variables best explained beaked whale presence, whereas only surface and deep-water variables – not static – best explained sperm whale presence (Virgili et al., 2022). These results are associated with each species’ distinct foraging ecologies; beaked whales may truly only rely on organisms that live near the seabed, whereas sperm whales also feast upon meso-to-bathypelagic organisms, so they may be more sensitive to changes in water column conditions (Virgili et al., 2022). This study expanded the narrative: deep-water variables can also be key to predicting deep-dwelling odontocete presence. The oceanographic variables must be tailored to the ecology of each species of interest.

In the months ahead, I seek to build on this study by investigating which parameters best predict odontocete presence using an acoustic approach instead — I am looking forward to the results to come!

References

Breen, P., Pirotta, E., Allcock, L., Bennison, A., Boisseau, O., Bouch, P., Hearty, A., Jessopp, M., Kavanagh, A., Taite, M., & Rogan, E. (2020). Insights into the habitat of deep diving odontocetes around a canyon system in the northeast Atlantic ocean from a short multidisciplinary survey. Deep-Sea Research. Part I, Oceanographic Research Papers, 159, 103236. https://doi.org/10.1016/j.dsr.2020.103236

Clark, C.W., Brown, M.W., & Corkeron, P. (2010). Visual and acoustic surveys

for North Atlantic right whales, Eubalaena glacialis, in Cape Cod Bay, Massachusetts, 2001–2005: Management implications. Marine Mammal Science, 26(4), 837-854.

Ganley, L.C., Brault, S., & Mayo, C.A. (2019). What we see is not what there is: Estimating North Atlantic right whale Eubalaena glacialis local abundance. Endangered Species Research, 38, 101-113.

Hazen, E. L., Nowacek, D. P., St Laurent, L., Halpin, P. N., & Moretti, D. J. (2011). The relationship among oceanography, prey fields, and beaked whale foraging habitat in the Tongue of the Ocean. PloS One, 6(4), e19269–e19269.

Munk, W. H., Spindel, R. C., Baggeroer, A., & Birdsall, T. G. (1994). The Heard Island Feasibility Test. The Journal of the Acoustical Society of America, 96(4), 2330–2342. https://doi.org/10.1121/1.410105

Redfern, J. V., Ferguson, M. C., Becker, E. A., Hyrenbach, K. D., Good, C., Barlow, J., Kaschner, K., Baumgartner, M. F., Forney, K. A., Ballance, L. T., Fauchald, P., Halpin, P., Hamazaki, T., Pershing, A. J., Qian, S. S., Read, A., Reilly, S. B., Torres, L., & Werner, F. (2006). Techniques for cetacean–habitat modeling. Marine Ecology. Progress Series (Halstenbek), 310, 271–295.

Roman, J., Estes, J. A., Morissette, L., Smith, C., Costa, D., McCarthy, J., Nation, J., Nicol, S., Pershing, A., & Smetacek, V. (2014). Whales as marine ecosystem engineers. Frontiers in Ecology and the Environment, 12(7), 377–385.

Valls, A., Coll, M., & Christensen, V. (2015). Keystone species: toward an operational concept for marine biodiversity conservation. Ecological Monographs, 85(1), 29–47.

Virgili, A., Teillard, V., Dorémus, G., Dunn, T. E., Laran, S., Lewis, M., Louzao, M., Martínez-Cedeira, J., Pettex, E., Ruiz, L., Saavedra, C., Santos, M. B., Van Canneyt, O., Vázquez Bonales, J. A., & Ridoux, V. (2022). Deep ocean drivers better explain habitat preferences of sperm whales Physeter macrocephalus than beaked whales in the Bay of Biscay. Scientific Reports, 12(1), 9620–9620.

The road to candidacy is paved with knowledge

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

As I sat down to write this blog, I realized that it is the first post I have written in 2023! This is largely because I have spent the last seven weeks preparing for (and partly taking) my PhD qualifying exams, an academic milestone that involves written and oral exams prepared by each committee member for the student. The point of the qualifying exams is for the student’s committee to determine the student’s understanding of their major field, particularly where and what the limits of that understanding are, and to assess the student’s capability for research. How do you prepare for these exams? Reading. Lots of reading and synthesis of the collective materials assigned by each committee member. My dissertation research covers a broad range of Pacific Coast Feeding Group (PCFG) gray whale ecology, such as space use, oceanography, foraging theory and behavioral responses to anthropogenic activities. Accordingly, my assigned reading lists were equally broad and diverse. For today’s blog, I am going to share some of the papers that have stuck with me and muse about how these topics relate to my study system, the Pacific Coast Feeding Group (PCFG) of gray whales.

Space use & home range

For decades, ecologists have been interested in defining an animal’s use of space through time, often referred to as an animal’s home range. The seminal definition of a home range comes from Burt (1943) who outlined it as “the area traversed by an individual in its normal activities of food gathering, mating, and caring for young.”. I like this definition of a home range because it is biologically grounded and based on an animal’s requirements. However, quantifying an animal’s home range based on this definition is harder than it may sound. In an ideal world, it could be achieved if we were able to collect location data that is continuous (i.e., one location per second), long-term (i.e., at least half the lifespan of an animal) and precise (i.e., correct to the nearest meter) together with behavior for an individual. However, a device that could collect such data, particularly for a baleen whale, does not currently exist. Instead, we must use discontinuous (i.e., one location per hour, day or month) and/or short-term (i.e., <1 year) data with variable precision to calculate animal home ranges. A very common and simple analytical method that is used to calculate an animal’s home range is the minimum convex polygon (MCP). MCP draws the smallest polygon around points with all interior angles less than 180º. While this method is appealing and widely used, it often overestimates the home range by including areas not used by an animal at all (Figure 1).

Figure 1. (a) 10 point locations where an individual was observed; (b) the home range as determined by the minimum convex polygon method; (c) the red path shows the movements the animal actually took. Note the large white area in (c) where the animal never went even though it is considered part of the animal’s home range.

This example is just one of many where home range estimators inaccurately describe an animal’s space use. However, this does not mean that we should not attempt to make our best approximations of an animal’s home range using the tools and data we have at our disposal. Powell & Mitchell perfectly summarized this sentiment in their 2012 paper: “Understanding animal’s home ranges will be a messy, irregular, complex process and the results will be difficult to map. We must embrace this messiness as it simply represents the real behaviors of animals in complex and variable environments.”. For my second dissertation chapter, I am investigating individual PCFG gray whale space use patterns by calculating activity centers and ranges. The activity center is simply the geographic center of all points of observation (Hayne, 1949) and the range is the distance from the activity center to the most distant point of observations in either poleward direction. While the actual activity center is probably relatively meaningless to a whale, we hope that by calculating these metrics we can identify different strategies of space use that individuals employ to meet their energetic requirements (Figure 2).

Figure 2. Sightings of nine different PCFG individuals across our GRANITE study area. Each circle represents a location where an individual was sighted and circles are color-coded by year. Plotting the raw data of sighting histories of these individuals hints at patterns in space use by different individuals, which I will explore further in my second dissertation chapter.

Non-stationary responses to oceanography

Collecting spatiotemporally overlapping predator-prey datasets at the appropriate scales is notoriously challenging in the marine environment. As a result, marine ecologists often try to find patterns between marine species and oceanographic and/or environmental covariates, as these can sometimes be easier to sample and thus make marine species predictions simpler. This approach has been applied successfully in hundreds, if not thousands, of studies (e.g., Barlow et al., 2020; Derville et al., 2022). Unfortunately, these relationships are not always proving to be stable over time, a phenomenon called non-stationarity. For example, Schmidt et al. (2014) showed that the reproductive successes of Brandt’s cormorants and Cassin’s auklets on southeast Farallon Island were positively correlated with each other from 1975 to 1995 and were associated with negative El Niño-Southern Oscillation. However, around the mid-1990s this relationship broke down and by 2002, the reproductive successes of the two species were significantly negatively correlated (Figure 3). Furthermore, the relationships between reproductive success and most physical oceanographic conditions became highly variable from year to year and were non-stationary. Thus, if the authors continued to use the relationships defined early on in the study (1975-1995) to predict seabird reproductive success relative to ocean conditions from 2002-2012, their predictions would have been completely wrong. After reading this study, I thought a lot about what the oceanographic conditions have been since the GEMM Lab started studying PCFG gray whales vs. the years prior. Leigh launched the GRANITE project in 2016, right at the tail end of the record marine heatwave in the Pacific, known as “the Blob”. While we do not have as long of a dataset as the Schmidt et al. (2014) study, I wonder whether we might find non-stationary responses between PCFG gray whales and environmental and/or oceanographic variables, given how the effects of the Blob lingered for a long time and we may have captured the central Oregon coast environment shifting from ‘weird to normal’. Non-stationarity is something I will at least keep in mind when I am working on my third dissertation chapter which will investigate the environmental and oceanographic drivers of PCFG gray whale space use strategies.

Figure 3. Figure and caption taken from Schmidt et al. (2014).

There are so many more studies and musings that I could write about. I keep being told by others who have been through this qualifying exam process that this is the smartest I am ever going to be, and I finally understand what they mean. After spending almost two months in my own little study world, my research, and where it fits within the complex web of ecological knowledge, has snapped into hyperfocus. I can see clearly where past research will guide me and where I am blazing a new trail of things never attempted before. While I still have the oral portion of my exams before me (in fact, it’s tomorrow!), I am already giddy with excitement to switch back to analyzing data and making progress on my dissertation research.

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References

Barlow, D.R., Bernard, K.S., Escobar-Flores, P., Palacios, D.M., Torres, L.G. 2020. Links in the trophic chain: modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Marine Ecology Progress Series 642: 207−225. 

Burt, W.H. 1943. Territoriality and home range concepts as applied to mammals. Journal of Mammalogy 24(3): 346-352. https://doi.org/10.2307/1374834

Derville, S., Barlow, D.R., Hayslip, C., Torres, L.G. 2022. Seasonal, annual, and decadal distribution of three rorqual whale species relative to dynamic ocean conditions off Oregon, USA. Frontiers in Marine Science 9. https://doi.org/10.3389/fmars.2022.868566

Hayne, D.W. 1949. Calculation of size of home range. Journal of Mammalogy 30(1): 1-18. 

Powell, R.A., Mitchell, M.S. 2012. What is a home range? Journal of Mammalogy 93(4): 948-958. https://doi.org/10.1644/11-MAMM-S-177.1

Schmidt, A.E., Botsford, L.W., Eadie, J.M., Bradley, R.W., Di Lorenzo E., Jahncke, J. 2014. Non-stationary seabird responses reveal shifting ENSO dynamics in the northeast Pacific. Marine Ecology Progress Series 499: 249-258. https://doi.org/10.3354/meps10629

So big, but so small: why the smallest of the largest whales are not smaller

Dr. KC Bierlich, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab

Baleen whales are known for their gigantism and encompass a wide range in body sizes extending from blue whales that are the largest animals to live on earth (max length ~30 m) to minke whales (max length ~10 m) that are the smallest of baleen whales (Fig. 1). While all baleen whales are filter feeders, a group called the rorquals use a feeding strategy known as lunge feeding (or intermittent engulfment filtration), which involves engulfing large volumes of prey-laden water at high speeds and then filtering the water out of their mouth using their baleen as a “sieve”. There is positive allometry associated with this feeding technique and body size, meaning that as whales are larger, this feeding strategy becomes more efficient due to increased engulfment of water volume per each lunge feeding event. In other words, a bigger body size equates to a much larger mouthful of food. For example, a minke whale (body length ~7-10 m) will engulf water volume equivalent to ~42% of its body mass, while a blue whale (~21-24 m) engulfs ~135%. Thus, filter feeding enables gigantism through efficient exploitation of large, dense patches of prey. An interesting question then arises: what is the minimum body size at which filter feeding is still efficient? Or in other words, why are the smallest of the baleen whales, minke whales, not smaller? For this blog, I will highlight a study published today in Nature Ecology and Evolution titled “Minke whale feeding rate limitations suggest constraints on the minimum body size for engulfment filtration feeding” led by friend and collaborator of the GEMM Lab Dr. Dave Cade and included myself and other collaborators as co-authors from Stanford University, UC Santa Cruz, Cascadia Research Collective, Duke University, and University of Queensland.

Figure 1. Aerial imagery collected using drones of several baleen whales of various sizes. Each species shown is considered a rorqual whale, except for gray whales. Figure from Segre et al. (2022)

The largest animals of today are marine filter feeders, such as whale sharks, manta rays, and baleen whales, which all share parallel evolutionary histories in which their large body sizes and filter-feeding morphologies are derived from smaller-bodied ancestors that targeted single prey items. Changes in ocean productivity increased the concentrations of smaller prey in the oceans around 5 million years ago, enabling filter feeding as an efficient feeding strategy through capture of abundant aggregations of prey by filtering large volumes of water. It is interesting to note, that within these filter feeding lineages of animals, there are groups of animals that are single-prey foragers with smaller body sizes. For example, the whale shark is the only filter feeder amongst the carpet sharks and the manta ray is much larger than other rays that feed on single prey items. Amongst cetaceans, the smallest single-prey foragers, dolphins (~2-3 m) and porpoises (~1.4-1.9 m), are much smaller than the smallest of the filter feeding cetaceans, minke whales (~7-10 m). These common differences in body sizes and feeding strategies within lineages suggest that there may be minimum body size requirements for this filter feeding strategy to be efficient.

To investigate the limits on minimum body size for filter feeding, our study explored the foraging behavior of Antarctic minke whales, the smallest of the rorqual baleen whales, along the Western Antarctic Peninsula. Our team tagged a total of 23 individuals using non-invasive suction cup tags, like the ones we use for our tagging component in the GEMM Lab’s GRANITE project (see this blog for more details). One of my roles on the project was to obtain aerial imagery of the minke whales using drones to obtain body length measurements (sound familiar?) (Figs. 2-4). Flying drones in Antarctica over minke whales was an amazing experience. The minke whales were often found deep within the bays amongst ice floes and brash ice where they can be very tricky to spot, as they’ll often surface and then quickly disappear, hence their nickname “sneaky minkes”. They also appear “playful” and “athletic” as they are incredibly quick and maneuverable, doing barrel rolls and quick bank turns while they swim. Check out my past blog to read more on accounts of flying over these amazing whales.

Figure 2. Drone image of our team about to place a noninvasive suction cup biologging tag on an Antarctic minke whale. Photo credit: Duke University Marine Robotics and Remote Sensing Lab.
Figure 3. A drone image of a newly tagged and curious Antarctic minke whale approaching our research team. Photo credit: Duke University Marine Robotics and Remote Sensing Lab.
Figure 4. A drone image of a group of Antarctic minke whales swimming through the icy waters along the Antarctic Peninsula. Photo credit: Duke University Marine Robotics and Remote Sensing Lab.

In total, our team collected 437 hours of tag data consisting of day- and night-time foraging behaviors. While the proportion of time spent foraging and the number of lunges per dive (~3-4) was similar between day- and night-time foraging, daytime foraging was much deeper (~72 m) compared to nighttime foraging (~28 m) due to vertical migration of Antarctic krill, their main food source. Overall, nighttime foraging was much more intense than daytime foraging, with an average of 165 lunges per hour during the night compared to 53 lunges per hour during the day. These shallower nighttime dives enabled quicker surface sequences for replenishing oxygen reserves to then return to foraging, whereas the deeper dives during the day required longer surface recovery times before beginning another foraging dive. Thus, nighttime dives are a more efficient and critical component of minke whale foraging.

When it comes to body size, there was no relationship between dive depth and dive duration with body length, except for daytime deep dives, where longer minke whales dove for longer periods than smaller whales. These longer dive times also require longer surface times to replenish oxygen reserves. Longer minke whales can gulp larger amounts of food and thus need longer filtration times to process water from each engulfment. For example, a 9 m minke whale will take 50% longer to filter water through its baleen compared to a 5 m minke whale. In turn, smaller minke whales would need to feed more frequently than larger minke whales in order to maintain efficient foraging. This decreasing efficiency with smaller body size shines light on a broader trend for filter feeders that we refer to in our study as the minimum-size constraint (MSC) hypothesis: “while the maximum size of a filter-feeding body plan will be restricted by physical properties, the minimum size is restricted by the energetic efficiency of filter feeding and the time required to extract sufficient particles from the water” (Cade et al. 2023). When we examined the scaling of maximum feeding rates of minke whales, we found evidence of a minimum size constraint on efficiency at lengths around 5 m. Interestingly, the weaning length of minke whales is reported to be 4.5 – 5.5 m. Before weaning, newborn/yearling minke whales that are smaller than 4.5 ­– 5.5 m have a different foraging strategy where they are dependent on maternal milk. Thus, it is likely that the body size at weaning is influenced by the minimum size at which this specialized foraging technique of lunge feeding becomes efficient.

This study helps inform the evolutionary pathway for filter feeding whales and suggests that efficient filter feeding and gigantism likely co-evolved within the last 5 million years when ocean conditions changed to support larger prey patches suitable for lunge feeding. It is interesting to think about the MSC hypothesis for other baleen whale species that employ alternative filter feeding techniques, such as gray whales that generally use a form of filter feeding called suction feeding. Gray whales are estimated to have a birth length of ~4.6 m (Agbayani et al., 2020), and the body length of newly weaned calves that we have observed along the Oregon Coast from drone imagery seem to be ~8 – 9 m. Perhaps this is the minimum size of when suction feeding becomes efficient for a gray whale? This is something the GEMM Lab hopes to further explore as we continue to collect foraging data from suction cup tags and behavior and body size measurements from drone imagery.

References

Agbayani, S., Fortune, S. M., & Trites, A. W. (2020). Growth and development of North Pacific gray whales (Eschrichtius robustus). Journal of Mammalogy, 101(3), 742-754.

Cade, D.E., Kahane-Rapport, S.R., Gough, W.T., Bierlich, K.C., Linksy, J.M.J., Johnston, D.W., Goldbogen, J.A., Friedlaender, A.S. (2023). Ultra-high feeding rates of Antarctic minke whales imply a lower limit for body size in engulfment filtration feeders. Nature Ecology and Evolution. https://www.nature.com/articles/s41559-023-01993-2  

Paolo S. Segre, William T. Gough, Edward A. Roualdes, David E. Cade, Max F. Czapanskiy, James Fahlbusch, Shirel R. Kahane-Rapport, William K. Oestreich, Lars Bejder, K. C. Bierlich, Julia A. Burrows, John Calambokidis, Ellen M. Chenoweth, Jacopo di Clemente, John W. Durban, Holly Fearnbach, Frank E. Fish, Ari S. Friedlaender, Peter Hegelund, David W. Johnston, Douglas P. Nowacek, Machiel G. Oudejans, Gwenith S. Penry, Jean Potvin, Malene Simon, Andrew Stanworth, Janice M. Straley, Andrew Szabo, Simone K. A. Videsen, Fleur Visser, Caroline R. Weir, David N. Wiley, Jeremy A. Goldbogen; Scaling of maneuvering performance in baleen whales: larger whales outperform expectations. J Exp Biol 1 March 2022; 225 (5): jeb243224. doi: https://doi.org/10.1242/jeb.243224

A Matter of Time: Adaptively Managing the Timescales of Ocean Change and Human Response

By Rachel Kaplan, PhD student, OSU College of Earth, Ocean and Atmospheric Sciences and Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab 

Ocean ecosystems are complex and dynamic, shaped by the interconnected physical and biogeochemical processes that operate across a variety of timescales. A trip on the “ocean conveyer belt”, which transports water from the North Atlantic across the global ocean and back in a process called thermohaline circulation, takes about a thousand years to complete. Phytoplankton blooms, which cycle nutrients through the surface ocean and feed marine animals, often occur at the crucial, food-poor moment of spring, and last for weeks or months. The entanglement of a whale in fishing gear, a major anthropogenic threat to ocean life that drives the GEMM Lab’s Project OPAL, can happen in seconds.

Compounding this complexity, even the timescales that research has clarified are changing. Many processes in the ocean are shifting – and often accelerating – due to global climate change. Images of melting sea ice, calving glaciers, and coastal erosion all exemplify our natural world’s rapid reorganization, and even discrete events can have dramatic repercussions and leave their mark for years. For example, a marine heatwave that occurred in 2014-2015 raised temperatures up to 2.5° C warmer than usual, redistributed species northward along the United States’ West Coast, spurred harmful algal blooms, and shut down fisheries. The toxic blooms also caused marine mammal strandings, domoic acid poisoning in California sea lions, and seabird mass death events (McCabe et al., 2016).

Figure 1. Figures like this Stommel diagram reveal the broad temporal and spatial scales over which ocean phenomena occur. Source: Sloyan et al., 2019

As humans seek to manage ocean ecosystems and mitigate the effects of climate change, our political processes have their own time scales, interconnected cycles, and stochasticity, just like the ocean. At the federal level in the United States, the legislative process takes place over months to decades, sometimes punctuated by relatively quicker actions enacted through Executive Orders. In addition, just as plankton have their turnover times, so do governmental branches. Both the legislative branch and the executive branch change frequently, with new members of Congress coming in every two years, and the president and administration changing every four or eight years. Turnover in both of these branches may constitute a total regime shift, with new members seeking to redirect science policy efforts.

The friction between oceanic and political timescales has historically made crafting effective ocean conservation policy difficult. In recent years, the policy approach of “adaptive management” has sought to respond to the challenges at the tricky intersection of politics, climate change, and ocean ecosystems. The U.S. Department of the Interior’s Technical Guide to Adaptive Management highlights its capacity to deal with the uncertainty inherent to changing ecosystems, and its ability to accommodate progress made through research: “Adaptive management [is a decision process that] promotes flexible decision making that can be adjusted in the face of uncertainties as outcomes from management actions and other events become better understood. Careful monitoring of these outcomes both advances scientific understanding and helps adjust policies or operations as part of an iterative learning process” (Williams et al, 2009).

Over the last several years, adaptive management policy approaches have been key as resource managers along the West Coast have responded to the problem of whale entanglement in fishing gear. When the 2014-2015 marine heatwave event caused anomalously low krill abundance in the central California Current region, humpback whales used a tactic called “prey-switching”, and fed on inshore anchovy schools rather than offshore krill patches. The resulting habitat compression fueled an increase in humpback whale entanglement events in Dungeness crab fishing gear (Santora et al, 2020). 

This sudden uptick in whale entanglements necessitated strategic management responses along the West Coast. In 2017, the California Dungeness Crab Fishing Gear Working Group developed the Risk Assessment and Mitigation Program (RAMP) to analyze real-time whale distribution and ocean condition data during the fishing season, and provide contemporaneous assessments of entanglement risk to the state’s Department of Fish and Wildlife. The Oregon Whale Entanglement Working Group (OWEWG) formed in 2017, tasked with developing options to reduce risk. Oregon Department of Fish and Wildlife (ODFW) has guided whale entanglement reduction efforts by identifying four areas of ongoing work: accountability, risk reduction, best management practices, and research – with regular, scheduled reviews of the regulations and opportunities to update and adjust them.

Figure 2. Entanglement in fishing gear can occur in seconds and may negatively impact whales for years. Source Scott Benson/NOAA

The need for research to support the best possible policy is where the GEMM Lab comes in. ODFW has established partnerships with Oregon State University and Oregon Sea Grant in order to improve understanding of whale distributions along the coast that can inform management efforts. Being involved in this cooperative “iterative learning process” is exactly why I’m so glad to be part of Project OPAL. Initial results from this work have already shaped ODFW’s regulations, and the framework of adaptive management and assessment means that regulations can continue being updated as we learn more through our research.

Ecosystem management will always be complex, just like ecosystems themselves. Today, the pace at which the climate is changing causes many people concern and even despair (Bryndum-Buchholz, 2022). Building adaptive approaches into marine policymaking, like the ones in use off the West Coast, introduces a new timescale into the U.S. policy cycle – one more in line with the rapid changes that are occurring within our dynamic ocean.

References

Williams, B. L., Szaro, R. C., and Shapiro, C. D. 2009. Adaptive management: the U.S. Department of the Interior Technical Guide. Adaptive Management Working Group, v pp.

Bryndum-Buchholz, A. (2022). Keeping up hope as an early career climate-impact scientist. ICES Journal of Marine Science, 79(9), 2345–2350. https://doi.org/10.1093/icesjms/fsac180

McCabe, R. M., Hickey, B. M., Kudela, R. M., Lefebvre, K. A., Adams, N. G., Bill, B. D., Gulland, F. M., Thomson, R. E., Cochlan, W. P., & Trainer, V. L. (2016). An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys Res Lett, 43(19), 10366–10376. https://doi.org/10.1002/2016GL070023

Santora, J. A., Sydeman, W. J., Schroeder, I. D., Wells, B. K., & Field, J. C. (2011). Mesoscale structure and oceanographic determinants of krill hotspots in the California Current: Implications for trophic transfer and conservation. Progress in Oceanography, 91(4), 397–409. https://doi.org/10.1016/j.pocean.2011.04.002

Sloyan, B. M., Wilkin, J., Hill, K. L., Chidichimo, M. P., Cronin, M. F., Johannessen, J. A., Karstensen, J., Krug, M., Lee, T., Oka, E., Palmer, M. D., Rabe, B., Speich, S., von Schuckmann, K., Weller, R. A., & Yu, W. (2019). Evolving the Physical Global Ocean Observing System for Research and Application Services Through International Coordination. Frontiers in Marine Science, 6, 449. https://doi.org/10.3389/fmars.2019.00449

Keeping it simple: A lesson in model construction

By: Kate Colson, MSc Student, University of British Columbia, Institute for the Oceans and Fisheries, Marine Mammal Research Unit

Models can be extremely useful tools to describe biological systems and answer ecological questions, but they are often tricky to construct. If I have learned anything in my statistics classes, it is the importance of resisting the urge to throw everything but the kitchen sink into a model. However, this is usually much easier said than done, and model construction takes a lot of practice. The principle of simplicity is currently at the forefront of my thesis work, as I try to embody the famous quote by Albert Einstein:

 “Everything should be made as simple as possible, but no simpler.”

As you might remember from my earlier blog, the goal of my thesis is to use biologging data to define different foraging behaviors of Pacific Coast Feeding Group (PCFG) gray whales, and then calculate the energetic cost of those behaviors. I am defining PCFG foraging behaviors at two scales: (1) dives that represent different behavior states (e.g., travelling vs foraging), and (2) roll events, which are periods during dives where the whale is rolled onto their side, that represent different foraging tactics (e.g., headstanding vs side-swimming).

Initially, I was planning to use a clustering analysis to define these different foraging behaviors at both the dive and roll event scale, as this method has been used to successfully classify different foraging strategies for Galapagos sea lions (Schwarz et al., 2021). In short, this clustering analysis uses summary variables from events of interest to group events based on their similarity. These can be any metric that describes the event such as duration and depth, or body positioning variables like median pitch or roll. The output of the clustering analysis method results in groups of events that can each be used to define a different behavior.

However, while this method works for defining the foraging tactics of PCFG gray whales, my discussions with other scientists have suggested that there is a better method available for defining foraging behavior at the dive scale: Hidden Markov Models (HMMs). HMMs are similar to the clustering method described above in that they use summary variables at discrete time scales to define behavior states, but HMMs take into account the bias inherent to time series data – events that occur closer together in time are more likely to be more similar. This bias of time can confound clustering analyses, making HMMs a better tool for classifying a series of dives into different behavior states.

Like many analytical methods, the HMM framework was first proposed in a terrestrial system where it was used to classify the movement of translocated elk (Morales et al., 2004). The initial framework proposed using the step length, or the spatial distance between the animal’s locations at the start of subsequent time intervals, and the corresponding turning angle, to isolate “encamped” from “exploratory” behaviors in each elk’s movement path (Figure 1, from Morales et al., 2004). “Encamped” behaviors are those with short step lengths and high turning angles that show the individual is moving within a small area, and they can be associated with foraging behavior. On the other hand, “exploratory” behaviors are those with long step lengths and low turning angles that show the individual is moving in a relatively straight path and covering a lot of ground, which is likely associated with travelling behavior.

Figure 1. The difference between “encamped” and “exploratory” behavior states from a simple Hidden Markov Model (HMM) in a translocated elk equipped with a GPS collar (Fig. 1 in Morales et al., 2004). The top rose plots show the turning angles while the bottom histograms show the step lengths as a daily movement rate. The “encamped” state has short step lengths (low daily movement rate) and high turning angles while the “exploratory” state has long step lengths (high daily movement rate) and low turning angle. These behavior states from the HMM can then be interpollated to elk behavior, as the low daily movement and tight turns of the “encamped” behavior state likely indicates foraging while the high daily movement and direct path of the “exploratory” behavior state likely indicates traveling. Thus, it is important to keep the biological relevance of the study system in mind while constructing and interpreting the model.

In the two decades following this initial framework proposed by Morales et al. (2004), the use of HMMs in anlaysis has been greatly expanded. One example of this expansion has been the development of mutlivariate HMMs that include additional data streams to supplement the step length and turning angle classification of “encamped” vs “exploratory” states in order to define more behaviors in movement data. For instance, a multivariate HMM was used to determine the impact of acoustic disturbance on blue whales (DeRuiter et al., 2017). In addition to step length and turning angle, dive duration and maximum depth, the duration of time spent at the surface following the dive, the number of feeding lunges in the dive, and the variability of the compass direction the whale was facing during the dive were all used to classify behavior states of the whales. This not only allowed for more behavior states to be identified (three instead of two as determined in the elk model), but also the differences in behavior states between individual animals included in the study, and the differences in the occurrence of behavior states due to changes in environmental noise.

The mutlivariate HMM used by DeRuiter et al. (2017) is a model I would ideally like to emulate with the biologging data from the PCFG gray whales. However, incorporating more variables invites more questions during the model construction process. For example, how many variables should be incorporated in the HMM? How should these variables be modeled? How many behavior states can be identified when including additional variables? These questions illustrate how easy it is to unnecessarily overcomplicate models and violate the principle of simiplicity toted by Albert Einstein, or to be overwhelmed by the complexity of these analytical tools.

Figure 2. Example of expected output of Hidden Markov Model (HMM) for the PCFG gray whale biologging data (GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678). The figure shows the movement track the whale swam during the deployment of the biologger, with each point representing the start of a dive. The axes show “Easting” and “Northing” rather than map coordinates because this is the relative path the whale took rather than GPS coordinates of the whale’s location. Each color represents a different behavior state—blue has short step lengths and high turning angles (likely foraging), red has intermediate step lengths and turning angles (likely searching), and black has long step lengths and low turning angles (likely transiting). These results will be refined as I construct the multivariate HMM that will be used in my thesis.  

Luckily, I can draw on the support of Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project collaborators Dr. Leslie New and Dr. Enrico Pirotta to guide my HMM model construction and assist in interpreting the outputs (Figure 2). With their help, I have been learning the importance of always asking if the change I am making to my model is biologically relevent to the PCFG gray whales, and if it will help give me more insight into the whales’ behavior. Even though using complex tools, such as Hidden Markov Models, has a steep learning curve, I know that this approach is not only placing this data analysis at the cutting edge of the field, but helping me practice fundamental skills, like model construction, that will pay off down the line in my career.

Sources

DeRuiter, S. L., Langrock, R., Skirbutas, T., Goldbogen, J. A., Calambokidis, J., Friedlaender, A. S., & Southall, B. L. (2017). A multivariate mixed Hidden Markov Model for blue whale behaviour and responses to sound exposure. Annals of Applied Statistics, 11(1), 362–392. https://doi.org/10.1214/16-AOAS1008

Morales, J. M., Haydon, D. T., Frair, J., Holsinger, K. E., & Fryxell, J. M. (2004). Extracting more out of relocation data: Building movement models as mixtures of random walks. Ecology, 85(9), 2436–2445. https://doi.org/10.1890/03-0269

Schwarz, J. F. L., Mews, S., DeRango, E. J., Langrock, R., Piedrahita, P., Páez-Rosas, D., & Krüger, O. (2021). Individuality counts: A new comprehensive approach to foraging strategies of a tropical marine predator. Oecologia, 195(2), 313–325. https://doi.org/10.1007/s00442-021-04850-w

Return of the whales: The GRANITE 2022 field season comes to a close

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

It’s hard to believe that it’s already been four and half months since we started the field season (check out Lisa’s blog for a recap of where we began), but as of this weekend the GRANITE project’s 8th field season has officially ended! As the gray whales wrap up their foraging season and start heading south for the winter, it’s time for us to put our gear into storage, settle into a new academic year, and start processing the data we spent so much time collecting.

The field season can be quite an intense time (40 days equaling over 255 hours on the water!), so we often don’t take a moment to reflect until the end. But this season has been nothing short of remarkable. As you may remember from past blogs, the past couple years (2020-21) have been a bit concerning, with lower whale numbers than previously observed. Since many of us started working on the project during this time, most of us were expecting another similar season. But we were wrong in the best way. From the very first day, we saw more whales than in previous years and we identified whales from our catalog that we hadn’t seen in several years.

Image 1: Collage of photos from our field season.

We identified friends – old and new!

This season we had 224 sightings of 63 individual whales. Of those 63, 51 were whales from our catalog (meaning we have seen them in a previous season). Of these 51 known whales, we only saw 20 of them last year! This observation brings up interesting questions such as, where did most of these whales forage last year? Why did they return to this area this year? And, the classic end of season question, what’s going to happen next year?

We also identified 12 whales that were not in our catalog, making them new to the GEMM lab. Two of our new whales are extra exciting because they are not just new to us but new to the population; we saw two calves this year! We were fortunate enough to observe two mom-calf pairs in July. One pair was of a “new” mom in our catalog and her calf. We nicknamed this calf “Roly-poly” because when we found this mom-calf pair, we recorded some incredible drone footage of “roly-poly” continuously performing body rolls while their mom was feeding nearby (video 1). 

Video 1: “Roly-poly” body rolling while their mom headstands. NOAA/NMFS permit #21678.

The other pair includes a known GEMM lab whale, Luna, and her calf (currently nicknamed “Lunita”). We recently found “Lunita” feeding on their own in early October (Image 2), meaning that they are now independent from its mom (for more on mom-calf behavior check out Celest’s recent blog). We’ll definitely be on the lookout for Roly-Poly and Lunita next year!

Image 2: (left) drone image of Luna and Lunita together in July and (right) drone image of Lunita on their own in October.  NOAA/NMFS permit #21678.

We flew, we scooped, we collected heaps of data!

From our previous blogs you probably know that in addition to photo-ID images, our other two most important forms of data collection are drone flights (for body condition and behavior data) and fecal samples (for hormone analysis). And this season was a success for both! 

We conducted 124 flights over 49 individual whales. The star of these flights was a local favorite Scarlett who we flew over 18 different times. These repeat samples are crucial data for us because we use them to gain insight into how an individual’s body condition changes throughout the season. We also recorded loads of behavior data, collecting footage of different foraging tactics like headstanding, side-swimming, and surfacing feeding on porcelain crab larvae (video 2)!

Video 2: Two whales surface feeding on porcelain crab larvae. NOAA/NMFS permit #21678.

We also collected 61 fecal samples from 26 individual whales (Image 3). The stars of that dataset were Soléand Peak who tied with 7 samples each. These hard-earned samples provide invaluable insight into the physiology and stress levels of these individuals and are a crucial dataset for the project.

Image 3: Photos of fecal sample collection. Left – a very heavy sample, center: Lisa and Enrico after collecting the first fecal sample of the season, right: Clara and Lisa celebrating a good fecal sample collection.

On top of all that amazing data collection we also collected acoustic data with our hydrophones, prey data from net tows, and biologging data from our tagging efforts. Our hydrophones were in the water all summer recording the sounds that the whales are exposed to, and they were successfully recovered just a few weeks ago (Image 4)! We also conducted 69 net tows to sample the prey near where the whales were feeding and identify which prey the whales might be eating (Image 5). Lastly, we had two very successful tagging weeks during which we deployed (and recovered!) a total of 9 tags, which collected over 30 hours of data (Image 6; check out Kate’s blog for more on that).

Image 4 – Photos from hydrophone recovery.
Image 5: Photos from zooplankton sampling.
Image 6: Collage of photos from our two tagging efforts this season.

Final thoughts

All in all, it’s been an incredible season. We’ve seen the return of old friends, collected lots of awesome data, and had some record-breaking days (28 whales in one day!). As we look toward the analysis phase of the year, we’re excited to dig into our eight-year dataset and work to understand what might explain the increase in whales this year.

To end on a personal note, looking through photos to put in this blog was the loveliest trip down memory lane (even though it only ended a few days ago) – I am so honored and proud to be a part of this team. The work we do is hard; we spend long hours on a small boat together and it can be a bit grueling at times. But, when I think back on this season, my first thoughts are not of the times I felt exhausted or grumpy, but of all the joy we felt together. From the incredible whale encounters to the revitalizing snacks to the off-key sing alongs, there is no other team I would rather do this work with, and I so look forward to seeing what next season brings. Stay tuned for more updates from team GRANITE!

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Surprises at Sea

By Rachel Kaplan, PhD student, OSU College of Earth, Ocean and Atmospheric Sciences and Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

By Renee Albertson, Senior Instructor and Research Associate, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Marine Mammal Institute

Going to sea is always full of surprises, and the most recent Northern California Current (NCC) cruise was no different. We had surprises both logistical and scientific, disappointing and delightful. By the end, what stood out clearly is that with a great team of people like the one aboard the R/V Bell M. Shimada, any challenging situation is made the best of, and any exciting moment is only more so.

Our great science party enjoys the Seattle skyline at the end of the September 2022 NCC cruise.

A few days into the cruise, engine trouble caused the Commanding Officer to decide that we needed to cut the trip short, halt instrument deployment operations, and head in to port. Lucky for us, this new plan included 30 hours of transit to Seattle, and long transits are exactly when we collect marine mammal observations. We were able to keep surveying as we moved up the coast and through the Strait of Juan de Fuca into Seattle. There were many surprises here too – we did not find whales in areas where we have previously sighted many, and overall made fewer sightings than is typical.

For example, we expected to see many whales on the Heceta Head Line (south of Newport), whose shallow depth makes the region a rich underwater garden that supports prey and attracts whales. Instead, we saw hardly any whales in this area. Perhaps they simply weren’t present, or perhaps we missed spotting some whales due to the heavy fog, which makes sighting animals that are not near the ship difficult to impossible. This dearth of animals led us to have to interesting conversations with other researchers as we speculated about what might be going on. The scientists on board these NCC cruises collectively research a wide range of oceanographic fields, including ocean chemistry, phytoplankton, zooplankton, fish, seabirds, and marine mammals. Bringing these data together can provide a better understanding of how the ecosystem is changing over time and help contextualize observations in the moment.

Though we often think about how the distributions of prey structure those of foraging whales, we started to wonder whether a lower trophic level could be at play here. Interestingly, in situ phytoplankton analyses showed a type of diatom called Pseudo-nitzchia along much of our cruise track, with the highest concentration off Cape Meares. In stressful conditions, these diatoms sometimes produce the toxin domoic acid, and we wondered whether this could possibly be related to the low whale counts.

Cells of Pseudo-nitzschia, a genus of microalgae that includes several species that make the neurotoxin domoic acid. NOAA photo courtesy of Vera Trainer.

Along the northern Oregon coast and near the Columbia River, the number of whales we observed increased dramatically. The vast majority were humpbacks, some of which were quite active, breaching and tail slapping the surface of the water. On our best day, we turned into the Strait of Juan de Fuca and sighted about 20 whales in quick succession, as well as a sea otter, and both Steller and California sea lions.

Simultaneously as we surveyed for whales, we were able to continue collecting concurrent echosounder data, which reveals the presence of nearby prey like krill and forage fish. Early in the trip, other researchers also collected krill samples that we could bring back to shore and analyze for their caloric content. Even with a shorter time at sea, we felt lucky to be able to fulfill these scientific goals.

Research cruises always center around two things: science and people. Discussing the scientific surprises we observed with other researchers aboard was inspirational, and left us with interesting questions to pursue. Navigating changes to the cruise plan highlighted the importance of the people aboard even more. Everyone worked together to refine our plans with cooperation and positivity, and we all marveled at what a great group it was, often saying, “Good thing we like each other!”

The cruise ended by transiting under the Fremont Bridge into Lake Union.

On the last day of the cruise, we transited into Seattle, moving through the Ballard Locks and into Lake Union. It was an incredible experience to see the city from the water, and an amazing way to cap off the trip. With the next NCC cruise ahead in a few months, we are excited to get back out to sea together soon and tackle whatever surprises come our way.

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Bombs Away! A Summer of Bomb Calorimetry

By Hadley Robinson, undergraduate student, OSU College of Earth, Ocean, and Atmospheric Sciences and School of Language, Culture, and Society

My name is Hadley Robinson and I am a sophomore undergraduate at OSU, double majoring in Environmental Science and Spanish. This summer, I had the privilege of working with Rachel on her PhD research project involving bomb calorimetry, a technique that allows you to quantify the caloric content of organisms like the zooplankton krill.

Hadley preparing the bomb calorimetry machine to run a sample (photo by Rachel Kaplan).

Prior to this internship, I had never worked in a lab before, and as an environmental science major, I had no previous exposure to oceanography. The connection that Rachel made between our labwork and the broader goal of helping decrease whale entanglement events sparked my interest in this project. Our work this summer aimed to process a set of krill samples collected off the coast of Oregon and Washington, so that we could find the number of calories in single krill, and then look at patterns in krill caloric content based on their species, sex, and other characteristics. 

We first identified the krill by species and sex (this was my favorite part of the experiment!). I not only loved looking at them under the microscope, but I also loved how it became a collaborative process. We quickly began getting each other’s opinions on whether or not a krill was Euphausia pacifica, Thysanoessa spinifera, male, female, sexless, gravid (carrying eggs), and much more.

Female Thysanoessa spinifera krill (photo by Abby Tomita).

After identification, we weighed and dried the krill, and finally turned them into small pellets that could fit in an instrument called a bomb calorimeter. These pellets were placed individually into in a “bomb cell” that could then be filled with oxygen and receive a shock from a metal wire. When the machine sent an electric pulse through the wire and combusted the krill pellet, the water surrounding the bomb cell warmed very slightly. The instrument measures this minute temperature change and uses it to calculate the amount of energy in the combusted material. With this information, we were able to quantify how many calories each krill sample contained. Eventually, this data could be used to create a seasonal caloric map of the ocean. Assuming that foraging whales seek out regions with calorically dense prey, such a map could play a crucial role in predicting whale distributions. 

Working with Rachel taught me how dynamic the world of research really is. There were many variables that we had to control and factor into our process, such as the possibility of high-calorie lipids being lost if the samples became too warm during the identification process, the risk of a dried krill becoming rehumidified if it sat out in the open air, and even the tiny amount of krill powder inevitably lost in the pelletization process. This made me realize that we cannot control everything! Grappling with these realities taught me to think quickly, adapt, and most importantly, realize that it is okay to refine the process of research as it is being conducted. 

Intern Abby (left) pressing the krill powder into a pellet and Hadley (right) prepping the bomb (photo by Rachel Kaplan).

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Decisions, decisions: New GEMM Lab publication reveals trade-offs in prey quantity and quality in gray whale foraging

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

Obtaining enough food is crucial for predators to ensure adequate energy gain for maintenance of vital functions and support for energetically costly life history events (e.g., reproduction). Foraging involves decisions at every step of the process, including prey selection, capture, and consumption, all of which should be as efficient as possible. Making poor foraging decisions can have long-term repercussions on reproductive success and population dynamics (Harris et al. 2007, 2008, Grémillet et al. 2008), and for marine predators that rely on prey that is spatially and temporally dynamic and notoriously patchy (Hyrenbach et al. 2000), these decisions can be especially challenging. Prey abundance and density are frequently used as predictors of marine predator distribution, movement, and foraging effort, with predators often selecting highly abundant or dense prey patches (e.g., Goldbogen et al. 2011, Torres et al. 2020). However, there is increased recognition that prey quality is also an important factor to consider when assessing a predator’s ecology and habitat use (Spitz et al. 2012), and marine predators do show a preference for higher quality prey items (e.g., Haug et al. 2002, Cade et al. 2022). Moreover, negative impacts of low-quality prey on the health and breeding success of some marine mammals (Rosen & Trites 2000, Trites & Donnelly 2003) have been documented. Therefore, examining multiple prey metrics, such as prey quantity and quality, in predator ecology studies is critical.

Figure 1. Site map of the Port Orford TOPAZ/JASPER integrated projects. Blue squares represent the location of the 12 sampling stations within the 2 study sites (site boundaries demarcated with black lines). Brown dot represents the cliff-top observation site where theodolite tracking occurred.

Our integrated TOPAZ/JASPER projects in Port Orford do just this! We collect both prey quantity and quality data from a tandem research kayak, while we track Pacific Coast Feeding Group (PCFG) gray whales from shore. The prey and whale sampling overlap spatially (and often temporally within the same day). This kind of concurrent predator-prey sampling at similar scales is often logistically challenging to achieve, yet because PCFG gray whales have an affinity for nearshore, coastal habitats, it is something we have been able to achieve in Port Orford. Since 2016, a field team comprised of graduate, undergraduate, and high school students has collected data during the month of August to investigate gray whale foraging decisions relative to prey. Every day, a kayak team collects GoPro videos (to assess relative prey abundance; AKA: quantity) and zooplankton samples using a tow net (to assess prey community composition; AKA: quality through caloric content of different species) (Figure 1). At the same time, a cliff team surveys for gray whales from shore and tracks them using a theodolite, which provides us with tracklines of individual whales; We categorize each location of a whale into three broad behavior states (feeding, searching, transiting) based on movement patterns. Over the years, the various students who have participated in the TOPAZ/JASPER projects have written many blog posts, which I encourage you to read here (particularly to get more detailed information about the field methods). 

Figure 2. An example daily layer of relative prey abundance (increasing color darkness corresponds with increasing abundance) in one study site with a whale theodolite trackline recorded on the same day overlaid and color-coded by behavioral state.

Several years of data are needed to conduct a robust analysis for our ecological questions about prey choice, but after seven years, we finally had the data and I am excited to share the results, which are due to the many years of hard work from many students! Our recent paper in Marine Ecology Progress Series aimed to determine whether PCFG gray whale foraging decisions are driven by prey quantity (abundance) or quality (caloric content of species) at a scale of 20 m (which is slightly less than 2 adult gray whale body lengths). In this study, we built upon results from my previous Master’s publication, which revealed that there are significant differences in the caloric content between the six common nearshore zooplankton prey species that PCFG gray whales feed on (Hildebrand et al. 2021). Therefore, in this study we addressed the hypothesis that individual whales will select areas where the prey community is dominated by the mysid shrimp Neomysis rayii, since it is significantly higher in caloric content than the other two prey species we identified, Holmesimysis sculpta (a medium quality mysid shrimp species) and Atylus tridens (a low quality amphipod species) (Hildebrand et al. 2021). We used spatial statistics and model to make daily maps of prey abundance and quality that we compared to our whale tracks and behavior from the same day. Please read our paper for the details on our novel methods that produced a dizzying amount of prey layers, which allowed us to tease apart whether gray whales target prey quantity, quality, or a mixture of both when they forage. 

Figure 3. Figure shows the probability of gray whale foraging relative to prey abundance (color-coded by prey species). Dark grey vertical line represents the mean threshold for the H. sculpta curves (12.0); light grey vertical lines: minimum (7.2) and maximum (15.3) thresholds for the H. sculpta curves. Inflection points could not be calculated for the N. rayii curves

So, what did we find? The models proved our hypothesis wrong: foraging probability was significantly correlated with the quantity and quality of the mysid H. sculpta, which has significantly lower calories than N. rayii. This result puzzled us, until we started looking at the overall quantity of these two prey types in the study area and realized that the amount of calorically-rich N. rayii never reached a threshold where it was beneficial for gray whales to forage. But, there was a lot of H. sculpta, which likely made for an energetic gain for the whales despite not being as calorically rich as N. rayii. We determined a threshold of H. sculpta relative abundance that is required to initiate the gray whale foraging behavior, and the abundance of N. rayii never came close to this level (Figure 3). Despite not having the highest quality, H. sculpta did have the highest abundance and showed a significant positive relationship with foraging behavior, unlike the other prey items. Interestingly, whales never selected areas dominated by the low-calorie species A. tridens. These results demonstrate trade-off choices by whales for this abundant, medium-quality prey.

To our knowledge, individual baleen whale foraging decisions relative to available prey quantity and quality have not been addressed previously at this very fine-scale. Interestingly, this trade-off between prey quantity and quality has also been detected in humpback whales foraging in Antarctica at depths deeper than where the densest krill patches occur; while the whales are exploiting less dense krill patches, these krill composed of larger, gravid, higher-quality krill (Cade et al. 2022). While it is unclear how baleen whales differentiate between prey species or reproductive stages, several mechanisms have been suggested, including visual and auditory identification (Torres 2017). We assume here that gray whales, and other baleen whale species, can differentiate between prey species. Thus, our results showcase the importance of knowing the quality (such as caloric content) of prey items available to predators to understand their foraging ecology (Spitz et al. 2012). 

References

Cade DE, Kahane-Rapport SR, Wallis B, Goldbogen JA, Friedlaender AS (2022) Evidence for size-selective pre- dation by Antarctic humpback whales. Front Mar Sci 9:747788

Goldbogen JA, Calambokidis J, Oleson E, Potvin J, Pyenson ND, Schorr G, Shadwick RE (2011) Mechanics, hydrody- namics and energetics of blue whale lunge feeding: effi- ciency dependence on krill density. J Exp Biol 214:131−146

Grémillet D, Pichegru L, Kuntz G, Woakes AG, Wilkinson S, Crawford RJM, Ryan PG (2008) A junk-food hypothesis for gannets feeding on fishery waste. Proc R Soc B 275: 1149−1156

Harris MP, Beare D, Toresen R, Nøttestad L, and others (2007) A major increase in snake pipefish (Entelurus aequoreus) in northern European seas since 2003: poten- tial implications for seabird breeding success. Mar Biol 151:973−983

Harris MP, Newell M, Daunt F, Speakman JR, Wanless S (2008) Snake pipefish Entelurus aequoreus are poor food for seabirds. Ibis 150:413−415

Haug T, Lindstrøm U, Nilssen KT (2002) Variations in minke whale (Balaenoptera acutorostrata) diet and body condi- tion in response to ecosystem changes in the Barents Sea. Sarsia 87:409−422

Hildebrand L, Bernard KS, Torres LG (2021) Do gray whales count calories? Comparing energetic values of gray whale prey across two different feeding grounds in the eastern North Pacific. Front Mar Sci 8:1008

Hyrenbach KD, Forney KA, Dayton PK (2000) Marine pro- tected areas and ocean basin management. Aquat Con- serv 10:437−458

Rosen DAS, Trites AW (2000) Pollock and the decline of Steller sea lions: testing the junk-food hypothesis. Can J Zool 78:1243−1250

Spitz J, Trites AW, Becquet V, Brind’Amour A, Cherel Y, Galois R, Ridoux V (2012) Cost of living dictates what whales, dolphins and porpoises eat: the importance of prey quality on predator foraging strategies. PLOS ONE 7:e50096

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

Torres LG (2017) A sense of scale: foraging cetaceans’ use of scale-dependent multimodal sensory systems. Mar Mamm Sci 33:1170−1193

Trites AW, Donnelly CP (2003) The decline of Steller sea lions Eumetopias jubatus in Alaska: a review of the nutri- tional stress hypothesis. Mammal Rev 33:3−28

Putting Fitbits on whales: How tag data allows for estimating calories burned by foraging PCFG gray whales

By: Kate Colson, MSc Student, University of British Columbia, Institute for the Oceans and Fisheries, Marine Mammal Research Unit

Hello! My name is Kate Colson and I am a master’s student at the University of British Columbia, co-supervised by Dr. Andrew Trites of the Marine Mammal Research Unit and Dr. Leigh Torres of the GEMM Lab. As part of my thesis work, I have had the opportunity to spend the summer field season with Leigh and the GEMM Lab team. 

For my master’s I am studying the foraging energetics of Pacific Coast Feeding Group (PCFG) gray whales as part of the much larger Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) project. Quantifying the energy expenditure of PCFG gray whales during foraging can help establish a baseline for how disturbance impacts the ability of this unique population to meet their energy needs. Additionally, determining how many calories are burned during different PCFG foraging behaviors might help explain why some gray whales are in better body condition than others.

To understand how much energy different PCFG foraging behaviors cost, I am using data from suction cup tags we have temporarily applied on PCFG gray whales (Figure 1). You can read more about the why the GEMM Lab started using these tags in an earlier blog here. What I want to talk about in this blog is how exactly we can use this tag data to estimate energy expenditure of PCFG gray whales. 

Figure 1. The famous “Scarlett” with a suction cup tag just attached using a carbon fiber pole (seen on far right). This minimally invasive tag has many data sensors, all of which sample at high frequencies, that can allow for an estimation of energy expenditure for different gray whale behaviors. Source: GEMM Lab; National Marine Fisheries Service (NMFS) permit no. 21678 

The suction cups tags used in this project have many data sensors that are useful for describing the movement of the tagged whale including accelerometers, magnetometers, gyroscopes, and pressure sensors, and all are sampling at high frequencies. For example, the accelerometer is taking 400 measurements per second! The accelerometer, magnetometer, and gyroscope take measurements in 3 dimensions along the X, Y, and Z-axes. The whale’s movement around the X-axis indicates roll (if the whale is swimming on its side), while movement around the Y-axis indicates pitch (if the whales head is oriented towards the surface or the sea floor). Changes in the whale’s movement around the Z-axis indicates if the whale is changing its swimming direction. Together, all of these sensors can describe the dive profile, body orientation, fluking behavior, and fine-scale body movements of the animal down to the second (Figure 2). This allows for the behavior of the tagged whale to be specifically described for the entirety of the tag deployment. 

Figure 2. An example of what the tag sensor data looks like. The top panels show the depth of the animal and can be used to determine the diving behavior of the whale. The middle panels show the body roll of the whale (the X axis) —a roll value close to 0 means the whale is swimming “normally” with no rotation to either side, while a higher roll value means the whale is positioned on its side. The bottom panels show the fluking behavior of the animal: each spike is the whale using its tail to propel itself through the water, with higher spikes indicating a stronger fluke stroke. Source: GEMM Lab, NMFS permit no. 21678

Although these suction cup tags are a great advancement in collecting fine-scale data, they do not have a sensor that actually measures the whale’s metabolism, or rate of calories burned by the whale. Thus, to use this fine-scale tag data as an estimate for energy expenditure, a summary metric must be calculated from the data and used as a proxy. The most common metric found in the literature is Overall Dynamic Body Acceleration (ODBA) and many papers have been published discussing the pros and cons of using ODBA as a proxy for energy expenditure (Brown et al., 2013; Gleiss et al., 2011; Halsey, 2017; Halsey et al., 2011; Wilson et al., 2020). The theory behind ODBA is that because an animal’s metabolic rate is primarily comprised of movement costs, then measuring the acceleration of the body is an effective way of determining energy expenditure. This theory might seem very abstract, but if you have ever worn a Fitbit or similar fitness tracking device to estimate how many calories you’ve burned during a workout, the same principle applies. Those fitness devices use accelerometers and other sensors, to measure the movement of your limbs and produce estimates of energy used. 

So now that we’ve established that the goal of my research is to essentially use these suction cup tags as Fitbits for PCFG gray whales, let’s look at how accelerometry data has been used to detect foraging behavior in large whales so far. Many accelerometry tagging studies have used rorquals as a focal species (see Shadwick et al. (2019) for a review). Well-known rorqual species include humpback, fin, and blue whales. These species forage by using lunges to bulk feed on dense prey patches in the water column. Foraging lunges are indicated by isolated periods of high acceleration that are easily detectable in the tag data (Figure 3; Cade et al., 2016; Izadi et al., 2022). 

Figure 3. Top image: A foraging blue whale performing a surface lunge (Photo credit: GEMM Lab). Note the dense aggregation of krill in the whale’s mouth. Bottom image: The signature acceleration signal for lunge feeding (adapted from Izadi et al., 2022). Each color represents one of the 3D axes of whale movement. The discrete periods of high acceleration represent lunges. 

However, gray whales feed very differently from rorquals. Gray whales primarily suction feed on the benthos, using their head to dig into the sediment and filter prey out of the mud using their baleen. Yet,  PCFG gray whales often perform many other foraging behaviors such as headstanding and side-swimming (Torres et al., 2018). Additionally, PCFG gray whales tend to feed in water depths that are often shallower than their body length. This shallow depth makes it difficult to isolate signals of foraging in the accelerometry data from random variation in the data and separate the tag data into periods of foraging behaviors (Figure 4).

Figure 4. Top image: A foraging PCFG gray whale rolls on its side to feed on mysid prey. Bottom image: The graph shows the accelerometry data from our suction cup tags that can be used to calculate Overall Dynamic Body Acceleration (ODBA) as a way to estimate energy expenditure. Each color represents a different axis in the 3D motion of the whale. The X-axis is the horizontal axis shows forward and backward movement of the whale, the Y-axis shows the side-to-side movement of the whale, and the Z-axis shows the up-down motion of the whale. Note how there are no clear periods of high acceleration in all 3 axes simultaneously to indicate different foraging behaviors like is apparent during lunges of rorqual whales. However, there is a pattern showing that when acceleration in the Z-axis (blue line) is positive, the X- and Y-axes (red and green lines) are negative. Source: GEMM Lab; NMSF permit no. 21678

But there is still hope! Thanks to the GEMM Lab’s previous work describing the foraging behavior of the PCFG sub-group using drone footage, and the video footage available from the suction cup tags deployed on PCFG gray whales, the body orientation calculated from the tag data can be a useful indication of foraging. Specifically, high body roll is apparent in many foraging behaviors known to be used by the PCFG, and when the tag data indicates that the PCFG gray whale is rolled onto its sides, lots of sediment (and sometimes even swarms of mysid prey) is seen in the tag video footage. Therefore, I am busy isolating these high roll events in the collected tag data to identify specific foraging events. 

My next steps after isolating all the roll events will be to use other variables such as duration of the roll event and body pitch (i.e., if the whales head is angled down), to define different foraging behaviors present in the tag data. Then, I will use the accelerometry data to quantify the energetic cost of performing these behaviors, perhaps using ODBA. Hopefully when I visit the GEMM Lab again next summer, I will be ready to share which foraging behavior leads to PCFG gray whales burning the most calories!

References: 

Brown, D. D., Kays, R., Wikelski, M., Wilson, R., & Klimley, A. P. (2013). Observing the unwatchable through acceleration logging of animal behavior. Animal Biotelemetry, 1(1), 1–16. https://doi.org/10.1186/2050-3385-1-20

Cade, D. E., Friedlaender, A. S., Calambokidis, J., & Goldbogen, J. A. (2016). Kinematic diversity in rorqual whale feeding mechanisms. Current Biology, 26(19), 2617–2624. https://doi.org/10.1016/j.cub.2016.07.037

Duley, P. n.d. Fin whales feeding [photograph]. NOAA Northeast Fisheries Science Center Photo Gallery. https://apps-nefsc.fisheries.noaa.gov/rcb/photogallery/finback-whales.html

Gleiss, A. C., Wilson, R. P., & Shepard, E. L. C. (2011). Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure. Methods in Ecology and Evolution, 2(1), 23–33. https://doi.org/10.1111/j.2041-210X.2010.00057.x

Halsey, L. G. (2017). Relationships grow with time: A note of caution about energy expenditure-proxy correlations, focussing on accelerometry as an example. Functional Ecology, 31(6), 1176–1183. https://doi.org/10.1111/1365-2435.12822

Halsey, L. G., Shepard, E. L. C., & Wilson, R. P. (2011). Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comparative Biochemistry and Physiology – A Molecular and Integrative Physiology, 158(3), 305–314. https://doi.org/10.1016/j.cbpa.2010.09.002

Izadi, S., Aguilar de Soto, N., Constantine, R., & Johnson, M. (2022). Feeding tactics of resident Bryde’s whales in New Zealand. Marine Mammal Science, 1–14. https://doi.org/10.1111/mms.12918

Shadwick, R. E., Potvin, J., & Goldbogen, J. A. (2019). Lunge feeding in rorqual whales. Physiology, 34, 409–418. https://doi.org/10.1152/physiol.00010.2019

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science, 5, 1–14. https://doi.org/10.3389/fmars.2018.00319

Wilson, R. P., Börger, L., Holton, M. D., Scantlebury, D. M., Gómez-Laich, A., Quintana, F., Rosell, F., Graf, P. M., Williams, H., Gunner, R., Hopkins, L., Marks, N., Geraldi, N. R., Duarte, C. M., Scott, R., Strano, M. S., Robotka, H., Eizaguirre, C., Fahlman, A., & Shepard, E. L. C. (2020). Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. Journal of Animal Ecology, 89(1), 161–172. https://doi.org/10.1111/1365-2656.13040