By Hunter Warick, Research Technician, Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute
When monitoring the health of a capital breeding species, such as whales that store energy to support reproduction costs, it is important to understand what processes and factors drive the status of their body condition. Information gained will allow for better insight into their cost of reproduction and overall life history strategies.
For the past four years the GEMM Lab has utilized the perspective that Unoccupied Aerial Systems (UAS; or ‘drones’) provide for observations of marine mammals. This aerial perspective has documented gray whale behavior such as jaw snapping, drooling mud, and headstands, all of which shows or suggest foraging (Torres et al. 2018). However, UAS is limited to a bird’s eye view, allowing us to see WHAT whales are doing, but limited information about the reasons WHY. To overcome this hurdle, Leigh Torres and team have equipped their marine mammal research utility belts with the use of GoPro cameras. They developed a technique known as the “GoPro drop” where a GoPro camera mounted to a weighted pole is lowered off the side of the research vessel in waters < 20 m deep via a line to record video data. This technique allows the team to obtain fine-scale habitat and prey variation information, like what the whale experiences. Along with the context provided by the UAS, this dual camera perspective allows for deeper insight into gray whale foraging strategies and efficiency. Torres’s GoPro data analysis protocol examines kelp density, kelp health, benthic substrate, rock fish density, and mysid density. These characteristics are graded along a scale (Figure 1), allowing for relative comparisons of habitat and prey availability between where whales spend time and forage. These GoPro drops will also help create a fine-scale benthic habitat map of the Newport field area. So, why are these data on gray whale habitat and prey important to understand?
The foraging grounds are the first step in the life history domino chain reaction for many rorqual whales; if this step doesn’t go off cleanly then everything else fails to fall into place. Gray whales partake on a 15,000-20,000 km (round trip) migration, which is the longest of any known mammal (Swartz 1986). During this migration, whales spend around three months fasting in their breeding grounds (Highsmith & Coyle 1992), living only off the energy stores that they accumulated in their feeding grounds (Næss et al. 1998). These extreme conditions of existence for gray whales drive the need to be a successful forager and is why it is so crucial for them to forage in high prey density areas (Newell, C. 2009).
Mysids are a critical part of the gray whale diet in Oregon waters (Newell, C. 2009; Sullivan, F. 2017) and mysids have strong predator-prey relationships with both top-down and bottom-up control (Dunham & Duffus 2001; Newell & Cowles 2006). This unique tie illustrates the great dependency that gray whales have on mysids, further showing the benefit to looking at the density of mysids where gray whales are seen foraging. The quality of mysids may also be as important as quantity; with higher water temperatures resulting in lower lipid content in mysids (Mauchline 1980), suggesting density might not be the only factor for determining efficient whale foraging. The overall goal of gray whales on their foraging grounds is to get as fat as possible in order to reproduce as often as possible. But, this isn’t always as easy as it sounds. Gray whales typically have a two-year breeding interval but can be anywhere from 1-4 years (Blokhin 1984). The longer time it takes to build up adequate energy stores to support reproduction costs, the longer it will take to breed successfully. Building back up these energy stores can prove to be difficult, especially for lactating females (Figure 2).
Being able to track the health and behavior of gray whales on an individual level, including comparisons between variation in body condition, foraging behavior, and fine scale information on benthic communities gained through the use of GoPros, can provide a better understanding of the driving factors and impacts on their health and population trends (Figure 3).
Clara Bird, Masters Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
A big part of graduate school involves extensive reading to learn about the previous research conducted in the field you are joining and the embedded foundational theories. A firm understanding of this background literature is needed in order to establish where your research fits. Science is a constructive process; to advance our disciplines we must recognize and build upon previous work. Hence, I’ve been reading up on the central topic of my thesis: behavioral ecology. It is equally important to study the methods used in these studies as to understand the findings. As discussed in a previous blog, ethograms are a central component of the methodology for studying behavior. Ethograms are lists of defined behaviors that help us properly and consistently collect data in a standardized approach. It is especially important in a project that spans years to know that the data collected at the beginning was collected in the same way as the data collected at the end of the project.
While ethograms and standardized methods are commonly used within a study, I’ve noticed from reading through studies on cetaceans, a lack of standardization across studies. Not all behaviors that are named the same way have matching definitions, and not all behaviors with similar definitions have matching names. Of all the behaviors, “milling” may be the least standardized.
While milling is not in our ethogram (Leigh believes this term is a “cheat” for when behavior is actually “unknown”), we occasionally use “milling” in the field to describe when the gray whales are swimming around in an area, not foraging, but not in any other primary behavior state (travel, social, or rest). Sometimes we use when we think the whale may be searching, but we aren’t 100% sure yet. A recent conversation during a lab meeting on the confusing nature of the term “milling” inspired me to dig into the literature for this blog. I searched through the papers I’ve saved for my literature review and found 18 papers that used the term milling. It was fascinating to read how variably the term has been defined and used.
When milling was defined in these papers, it was most commonly described as numerous directional changes in movement within a restricted area 1–8. Milling often co-occurred with other behavior states. Five of these eight studies described milling as co-occurring with foraging behavior 3–6,8. In one case, milling was associated with foraging and slow movement 8. While another study described milling as passive, slow, nondirectional movement 9.
Eight studies used the term milling without defining the behavior 10–17. Of these, five described milling as being associated with other behavior states. Three studies described milling as co-occurring with foraging 10,14,16, one said that it co-occurred with social behavior 13, and another described milling as being associated with resting/slow movement 12.
In addition to this variety of definitions and behavior associations, there were also inconsistencies with the placement of “milling” within ethograms. In nine studies, milling was listed as a primary state 1,2,4,7–9,15,17,18. But, in two studies that mentioned milling and used an ethogram, milling was not included in the ethogram 6,14.
Diving into the associations between milling and foraging reveal how varied the use of milling has been within the cetacean literature. For example, two studies simply described milling as occurring near foraging in time 10,16. While another two studies explained that milling was applied in situations where there was evidence of feeding without feeding being directly observed 8,14. Bobkov et al. (2019) described milling as occurring between feeding cycles along with breathing. Lastly, two studies describe milling as a behavior within the foraging primary state 3,5, while another study described feeding as a behavior within milling 4.
It’s all rather confusing, huh? Across these studies, milling has been defined, mentioned without being defined, included in ethograms as a primary state, included in ethograms as a sub-behavior, and excluded from ethograms. Milling has also been associated with multiple primary behavior states (foraging, resting, and socializing). It has been described as both passive 9 and slow 12, and strong 16 and active 5.
It appears that milling is often used to describe behaviors that the observer cannot distinctly classify or describe its function. I have also struggled to define these times when a whale is in between behavior states; I often end up calling it “just being a whale”, which includes time spent breathing at the surface, or just swimming around.
As I’ve said above, Leigh thinks that this term is a “cheat” for when a behavior is actually “unknown”. I think we have trouble equating “milling” with “unknown” because it seems like “unknown” should refer to a behavior where we can’t quite tell what the whale is doing. However, during milling, we can see that the whale is swimming at the surface. But here’s the thing, while we can see what the whale is doing, the function of the behavior is still unknown. Instead of using an indistinct term, we should use a term that better describes the behavior. If it’s swimming at the surface, name the behavior “swimming at the surface”. If we can’t tell what the whale is doing because we can’t quite see what it’s doing, then name the behavior “unknown-partially visible”. Instead of using vague terminology, we should use clear names for behaviors and embrace using the term “unknown”.
I am most certainly not criticizing these studies as they all provided valuable contributions and interesting results. The studies that asked questions about behavioral ecology defined milling. The term was mentioned without being defined in studies focused on other topics. So, defining behaviors mentioned was less important.
With this exploration into the use of “milling” in studies, I am not implying that all behavioral ecologists need to agree on the use of the same behavior terms. However, I have learned clear definitions are critical. This lesson is also important outside of behavioral ecology. Different labs, and different people, use different terms for the same things. As I dig into my thesis, I am keeping a list of terminology I use and how I define those terms, because as I learn more, my terminology evolves and changes. For example, at the beginning of my thesis I used “sub-behavior” to refer to behaviors within the primary state categories. But, now after chatting with Leigh and learning more, I’ve decided to use the term “tactic” instead as these are often processes or events that contribute to the broader behavior state. My running list of terminology helps me remember what I meant when I used a certain word, so that when I read my notes from three months ago, I can know what I meant. Digging into the literature for this blog reminded me of the importance of clearly defining all terminology and never assuming that everyone uses the same term in the same way.
Check out these videos to see some of the behaviors we observe:
1. Mallonee, J. S. Behaviour of gray whales (Eschrichtius robustus) summering off the northern California coast, from Patrick’s Point to Crescent City. Can. J. Zool.69, 681–690 (1991).
2. Clarke, J. T., Moore, S. E. & Ljungblad, D. K. Observations on gray whale (Eschrichtius robustus) utilization patterns in the northeastern Chukchi Sea. Can. J. Zool67, (1988).
3. Ingram, S. N., Walshe, L., Johnston, D. & Rogan, E. Habitat partitioning and the influence of benthic topography and oceanography on the distribution of fin and minke whales in the Bay of Fundy, Canada. J. Mar. Biol. Assoc. United Kingdom87, 149–156 (2007).
4. Lomac-MacNair, K. & Smultea, M. A. Blue Whale (Balaenoptera musculus) Behavior and Group Dynamics as Observed from an Aircraft off Southern California. Anim. Behav. Cogn.3, 1–21 (2016).
5. Lusseau, D., Bain, D. E., Williams, R. & Smith, J. C. Vessel traffic disrupts the foraging behavior of southern resident killer whales Orcinus orca. Endanger. Species Res.6, 211–221 (2009).
6. Bobkov, A. V., Vladimirov, V. A. & Vertyankin, V. V. Some features of the bottom activity of gray whales (Eschrichtius robustus) off the northeastern coast of Sakhalin Island. 1, 46–58 (2019).
7. Howe, M. et al. Beluga, Delphinapterus leucas, ethogram: A tool for cook inlet beluga conservation? Mar. Fish. Rev.77, 32–40 (2015).
8. Clarke, J. T., Christman, C. L., Brower, A. A. & Ferguson, M. C. Distribution and Relative Abundance of Marine Mammals in the northeastern Chukchi and western Beaufort Seas, 2012. Annu. Report, OCS Study BOEM117, 96349–98115 (2013).
9. Barendse, J. & Best, P. B. Shore-based observations of seasonality, movements, and group behavior of southern right whales in a nonnursery area on the South African west coast. Mar. Mammal Sci.30, 1358–1382 (2014).
10. Le Boeuf, B. J., M., H. P.-C., R., J. U. & U., B. R. M. and F. O. High gray whale mortality and low recruitment in 1999: Potential causes and implications. (Eschrichtius robustus). J. Cetacean Res. Manag.2, 85–99 (2000).
11. Calambokidis, J. et al. Abundance, range and movements of a feeding aggregation of gray whales (Eschrictius robustus) from California to southeastern Alaska in 1998. J. Cetacean Res. Manag.4, 267–276 (2002).
12. Harvey, J. T. & Mate, B. R. Dive Characteristics and Movements of Radio-Tagged Gray Whales in San Ignacio Lagoon, Baja California Sur, Mexico. in The Gray Whale: Eschrichtius Robustus (eds. Jones, M. Lou, Folkens, P. A., Leatherwood, S. & Swartz, S. L.) 561–575 (Academic Press, 1984).
13. Lagerquist, B. A. et al. Feeding home ranges of pacific coast feeding group gray whales. J. Wildl. Manage.83, 925–937 (2019).
14. Barrett-Lennard, L. G., Matkin, C. O., Durban, J. W., Saulitis, E. L. & Ellifrit, D. Predation on gray whales and prolonged feeding on submerged carcasses by transient killer whales at Unimak Island, Alaska. Mar. Ecol. Prog. Ser.421, 229–241 (2011).
15. Luksenburg, J. A. Prevalence of External Injuries in Small Cetaceans in Aruban Waters, Southern Caribbean. PLoS One9, e88988 (2014).
16. Findlay, K. P. et al. Humpback whale “super-groups” – A novel low-latitude feeding behaviour of Southern Hemisphere humpback whales (Megaptera novaeangliae) in the Benguela Upwelling System. PLoS One12, e0172002 (2017).
17. Villegas-Amtmann, S., Schwarz, L. K., Gailey, G., Sychenko, O. & Costa, D. P. East or west: The energetic cost of being a gray whale and the consequence of losing energy to disturbance. Endanger. Species Res.34, 167–183 (2017).
18. Brower, A. A., Ferguson, M. C., Schonberg, S. V., Jewett, S. C. & Clarke, J. T. Gray whale distribution relative to benthic invertebrate biomass and abundance: Northeastern Chukchi Sea 2009–2012. Deep. Res. Part II Top. Stud. Oceanogr.144, 156–174 (2017).
By Alejandro Fernandez Ajo, PhD student at the Department of Biology, Northern Arizona University, Visiting scientist in the GEMM Lab working on the gray whale physiology and ecology project
Whales are among the most amazing and enigmatic animals in the world. Whales are not only fascinating, they are also biologically special. Due to their key ecological role and unique biological traits (i.e., their large body size, long lifespans, and sizable home ranges), whales are extremely important in helping sustain the entire marine ecosystem.
Working towards the conservation of marine megafauna, and large charismatic animals in general, is often seen as a mere benevolent effort that conservationist groups, individuals, and governments do on behalf of the individual species. However, mounting evidence demonstrates that restoring populations of marine megafauna, including large whales, can help buffer marine ecosystems from destabilizing stresses like human driven CO2 emissions and global change due to their ability to sequester carbon in their bodies (Pershing et al. 2010). Furthermore, whales can enhance primary production in the ocean through their high consumption and defecation rates, which ultimately provides nutrients to the ecosystem and improves fishery yields (Roman-McCarthy, 2010; Morissette et al. 2012).
Relationships between humans and whales have a long history, however, these relationships have changed. For centuries, whales were valued in terms of the number of oil barrels they could yield, and the quality of their baleen and meat. In the North Atlantic, whaling started as early as 1000 AD with “shore whaling” of North Atlantic right whales by Basque whalers. This whaling was initially limited to the mother and calve pairs that were easy to target due to their coastal habits and the fact that calves are more vulnerable and slower (Reeves-Smith, 2006). Once the calving populations of near-shore waters off Europe were depleted, offshore whaling began developing. Whalers of multiple nations (including USA, British, French, Norwegian, Portuguese, and Dutch, among others), targeted whales around the world, mainly impacting the gray whale populations, and all three right whale species along with the related bowhead whale. Later, throughout the phase of modern whaling using industrialized methods, the main target species consisted of the blue, fin, humpback, minke, sei and sperm whale (Schneider- Pearce, 2004).
By the early twentieth century, many of the world´s whale populations where reduced to a small fraction of their historical numbers, and although pre-whaling abundance of whale stocks is a subject of debate, recent studies estimate that at least the 66%, and perhaps as high as 90% for some whale species and populations (Branch-Williams 2006; Christensen, 2006), where taken during this period. This systematic and serial depletion of whale papulations reduced the biomass and abundance of great whales around the world, which has likely altered the structure and function of the oceans (Balance et al. 2006; Roman et al. 2014; Croll, et al. 2006).
After centuries of unregulated whale hunting, commercial whaling was banned in the mid-twentieth century. This ban was the result of multiple factors including reduced whale stocks below the point where commercial whaling would be profitable, and a fortunate shift in public perception of whales and the emergence of conservation initiatives (Schneider- Pearce, 2004). Since this moratorium on whaling, several whale populations have recovered around the world, and some populations that were listed as endangered have been delisted (i.e., the Eastern North Pacific gray whale) and some populations are estimated to have re-bounced to their pre-whaling abundance.
Although, the recovery of some populations has motivate some communities or nations to obtain or extend their whaling quotas (see Blog Post by Lisa Hildebrand), it is important to acknowledge that the management of whale populations is arguably one of the most complicated tasks, and is distinguished from management of normal fisheries due to various biological aspects. Whales are long living mammals with slow reproduction rates, and on average a whale can only produce a calf every two or three years. Hence, the gross addition to the stock rarely would exceed 25% of the number of adults (Schneider- Pearce, 2004), which is a much lower recovery rate that any fish stock. Also, whales usually reach their age of sexual maturity at 6-10 years old, and for many species there are several uncertainties about their biology and natural history that make estimations of population abundance and growth rate even harder to estimate.
Moreover, while today´s whales are generally not killed directly by hunting, they are exposed to a variety of other increasing human stressors (e.g., entanglement in fishing gear, vessel strikes, shipping noise, and climate change). Thus, scientists must develop novel tools to overcome the challenges of studying whales and distinguish the relative importance of the different impacts to help guide conservation actions that improve the recovery and restoration of whale stocks (Hunt et al. in press). With the restoration of great whale populations, we can expect positive changes in the structure and function of the world’s oceans (Chami et al. 2019; Roman et al. 2010).
So, why it is worth keeping whales healthy?
Whales facilitate the transfer of nutrients by (1) releasing nutrient-rich fecal plumes near the surface after they have feed at depth and (2) by moving nutrients from highly productive, polar and subpolar latitude feeding areas to the low latitude calving areas (Roman et al. 2010). In this way, whales help increase the productivity of phytoplankton that in turn support zooplankton production, and thus have a bottom up effect on the productivity of many species including fish, birds, and marine mammals, including whales. These fertilization events can also facilitate mitigation of the negative impacts of climate change. The amount of iron contained in the whales’ feces can be 10 million times greater than the level of iron in the marine environment, triggering important phytoplankton blooms, which in turn sequester thousands of tons of carbon from, and release oxygen to, the atmosphere annually (Roman et al. 2016; Smith et al. 2013; Willis, 2007). Furthermore, when whales die, their massive bodies fall to the seafloor, making them the largest and most nutritious source of food waste, which is capable of sustaining a succession of macro-fauna assemblages for several decades, including some invertebrate species that are endemic to whale carcasses (Smith et al. 2015).
Despite the several environmental services that whales provide, and the positive impact on local economies that depend on whale watching tourism, which has been valued in millions of dollars per year (Hoyt E., 2001), the return of whales and other marine mammals has often been implicated in declines in fish populations, resulting in conflicts with human fisheries (Lavigne, D.M. 2003). Yet there is insufficient direct evidence for such competition (Morissette et al. 2010). Indeed, there is evidence of the contrary: In ecosystem models where whale abundances are reduced, fish stocks show significant decreases, and in some cases the presence of whales in these models result in improved fishery yields. Consistent with these findings, several models have shown that alterations in marine ecosystems resulting from the removal of whales and other marine mammals do not lead to increases in human fishery yields (Morissette et al. 2010; 2012). Although the environmental services and benefits provided by great whales, which potentially includes the enhancement of fisheries yields, and enhancement on ocean oxygen production and capturing carbon, are evident and make a strong argument for improved whale conservation, it is overwhelming how little we know about many aspects of their lives, their biology, and particularly their physiology.
This lack of knowledge is because whales are really hard to study. For many years research was limited to the observation of the brief surfacing of the whales, yet most of their lives occurs beneath the surface and were completely unknown. Fortunately, new technologies and the creativity of whale researchers are helping us to better understand many aspects of their lives that were cryptic to us even a decade ago. I am committed to filling some of these knowledge gaps. My research examines how different environmental and anthropogenic impacts affect whale health, and particularly how these impacts may relate to cases of large whale mortalities and declines in whale populations. I am applying novel methods in conservation physiology for measuring hormone levels that promise to improve our understanding of the relationship between different (extrinsic and intrinsic) stressors and the physiological response of whales. Ultimately, this research will help address important conservation questions, such as the causes of unusual whale mortality events and declines in whale populations.
Ballance LT, Pitman RL, Hewitt R, et al. 2006. The removal of large whales from the Southern Ocean: evidence for long-term ecosystem effects. In: Estes JA, DeMaster DP, Doak DF, et al. (Eds). Whales, whaling and ocean ecosystems. Berkeley, CA: University of California Press.
Branch TA and Williams TM. 2006. Legacy of industrial whaling. In: Estes JA, DeMaster DP, Doak DF, et al. (Eds). Whales, whaling and ocean ecosystems. Berkeley, CA: University of California Press.
Chami, R. Cosimano, T. Fullenkamp, C. & Oztosun, S. (2019). Nature’s solution to climate change. Finance & Development, 56(4).
Christensen LB. 2006. Marine mammal populations: reconstructing historical abundances at the global scale. Vancouver, Canada: University of British Columbia.
Croll DA, Kudela R, Tershy BR (2006) Ecosystem impact of the decline of large whales in the North Pacific. In: Estes JA, DeMaster DP, Doak DF, Williams TM, BrownellJr RL, editors. Whales, Whaling, and Ocean Ecosystems. Berkeley: University of California Press. pp. 202–214.
Hoyt, E. 2001. Whale Watching 2001: Worldwide Tourism Numbers, Expenditures and Expanding Socioeconomic Benefits
Hunt, K.E., Fernández Ajó, A. Lowe, C. Burgess, E.A. Buck, C.L. In press. A tale of two whales: putting physiological tools to work for North Atlantic and southern right whales. In: “Conservation Physiology: Integrating Physiology Into Animal Conservation And Management”, ch. 12. Eds. Madliger CL, Franklin CE, Love OP, Cooke SJ. Oxford University press: Oxford, UK.
Lavigne, D.M. 2003. Marine mammals and fisheries: the role of science in the culling debate. In: Gales N, Hindell M, and Kirkwood R (Eds). Marine mammals: fisheries, tourism, and management issues. Melbourne, Australia: CSIRO.
Morissette L, Christensen V, and Pauly D. 2012. Marine mammal impacts in exploited ecosystems: would large scale culling benefit fisheries? PLoS ONE 7: e43966.
Morissette L, Kaschner K, and Gerber LR. 2010. “Whales eat fish”? Demystifying the myth in the Caribbean marine ecosystem. Fish Fish 11: 388–404.
Pershing AJ, Christensen LB, Record NR, Sherwood GD, Stetson PB (2010) The impact of whaling on the ocean carbon cycle: Why bigger was better. PLoS ONE 5(8): e12444.
Reeves, R. and Smith, T. (2006). A taxonomy of world whaling. In DeMaster, D. P., Doak, D. F., Williams, T. M., and Brownell Jr., R. L., eds. Whales, Whaling, and Ocean Ecosystems. University of California Press, Berkeley, CA.
Roman, J. Altman I, Dunphy-Daly MM, et al. 2013. The Marine Mammal Protection Act at 40: status, recovery, and future of US marine mammals. Ann NY Acad Sci; doi:10.1111/nyas.12040.
Roman, J. and McCarthy, J.J. 2010. The whale pump: marine mammals enhance primary productivity in a coastal basin. PLoS ONE. 5(10): e13255.
Roman, J. Estes, J.A. Morissette, L. Smith, C. Costa, D. McCarthy, J. Nation, J.B. Nicol, S. Pershing, A.and Smetacek, V. 2014. Whales as marine ecosystem engineers. Frontiers in Ecology and the Environment. 12(7). 377-385.
Roman, J. Nevins, J. Altabet, M. Koopman, H. and McCarthy, J. 2016. Endangered right whales enhance primary productivity in the Bay of Fundy. PLoS ONE. 11(6): e0156553.
Schneider, V. Pearce, D. What saved the whales? An economic analysis of 20th century whaling. Biodiversity and Conservation 13, 543–562 (2004). https://doi org.libproxy.nau.edu/10.1023/B:BIOC.0000009489.08502.1
Smith LV, McMinn A, Martin A, et al. 2013. Preliminary investigation into the stimulation of phyto- plankton photophysiology and growth by whale faeces. J Exp Mar Biol Ecol 446: 1–9.
Smith, C.R. Glover, A.G. Treude, T. Higgs, N.D. and Amon, D.J. 2015. Whale-fall ecosystems: Recent insights into ecology, paleoecology, and evolution. Annu. Rev. Marine. Sci. 7:571-596.
Willis, J. 2007. Could whales have maintained a high abundance of krill? Evol Ecol Res 9: 651–662.
Leigh Torres, Assistant Professor,PI of the GEMM Lab, Marine Mammal Institute,Department of Fisheries and Wildlife, Oregon Sea Grant, Oregon State University
Writing a blog post this week that focuses on marine mammals seems inappropriate amidst the larger social justice issues that our country – and our global community – are facing. However, I have been leaning on my scientific background recently to help me understand these events, how we got here, and where we can go. But first I want to acknowledge and thank the people on the front lines around the world who are giving a voice to this fight for equality. Equality that is deserved, inherent, and just.
There is a concept in ecology, and in particular in fisheries management, termed shifting baselines, which was developed by the brilliant scientist Dr. Daniel Pauly in 1995 (who, by the way, is a person of color but that’s not the point here). Shifting baselines has to do with how humans judge change based on their own experiences and perceptions, and not necessarily on objective data collected over a longer period than a lifetime. Over one generation, knowledge is lost about ‘how the state of the natural world used to be’, so people don’t perceive the change that is actually taking place over time.
This article has a nice description of the shifting baseline theory: …due to short life-spans and faulty memories, humans have a poor conception of how much of the natural world has been degraded by our actions, because our ‘baseline’ shifts with every generation, and sometimes even in an individual. In essence, what we see as pristine nature would be seen by our ancestors as hopelessly degraded, and what we see as degraded our children will view as ‘natural’.
The concept of shifting baselines explains so much about why convincing policy makers to protect natural resources is challenging. People with short-term goals (political election cycles) and short-term memories don’t see the long-term trends of environmental degradation.
This week I have been thinking about how the concept of shifting baselines can also be applied to the social injustice we are grappling with today and for centuries. Yet, rather than shifting baselines, its more akin to uncommon baselines.
In school, we hopefully learn about the realities of slavery, the Civil War, Abraham Lincoln and the Emancipation Proclamation, Fredrick Douglas, Jim Crow laws, the Civil Rights Movement and Martin Luther King, the Civil Rights Act of 1964, the Voting Rights Act of 1965, and more. Often, this information comes to us in an incomplete, white-washed, biased fashion. So, if we are white and privileged in this country, we may pat ourselves on the back for what we’ve been taught is progress; for example, we might be proud of seeing integration in schools, and feel good about regularly using words like diversity and inclusion. But my baseline is very different from a black American’s baseline. Where I see progress relative to an old standard, black Americans continue to suffer from a legacy of slavery, poverty, and discrimination. My baseline cannot just be progress while people of color are still experiencing the same race inequality, police bias, economic injustice and an imbalanced power structure as their grandparents and great grandparents.
Our uncommon baselines are shaped by our previous experiences, which are culturally based, and create different perceptions of where we are in the trajectory of social and economic justice. When scientists want to adjust for the influence of shifting baselines in ecology, we first need to recognize the influence of shifted baselines and then probe for ‘historical data’ (e.g., whaling records of the actual numbers of whales killed) or speak with those who know what it was like “before” (e.g., traditional ecological knowledge) to help us account for a broader scale of change. Thus, we can use a better baseline. Perhaps in this social justice context, to achieve more common baselines of race equality across cultures, we need more conversations with people of color to share past and present experiences and perceptions.
While these recent events have been heart wrenching to witness, I do feel this period is a critical reality check, forcing those of us who are privileged and powerful to acknowledge our uncommon baselines. I hope to learn by reading and talking honestly with others so we can all work toward a common baseline of equality and justice for all.
Knowing what and how much prey a predator feeds on are key components to better understanding and conserving that predator. Prey abundance and availability are frequently predictors for marine predator reproductive success and population dynamics. It is the reason why the GEMM Lab makes a concerted effort to not only track our main taxa of interest (marine mammals) but to simultaneously measure their prey. However, over the last decade or two, there has been increased recognition that prey quality is also highly important in understanding a predator’s ecology (Spitz et al. 2012). Optimal foraging theory is a widely accepted framework that posits that predators should attempt to maximize energy gained and minimize energy spent during a foraging event (Charnov 1976, Krebs 1978, Pyke 1984). Thus, knowledge of how valuable a prey item is in terms of its energetic content is an important part of the equation when applying optimal foraging theory to a predator of interest.
Ideally, the prey species with the highest energetic value would also be the easiest, most ubiquitous and least energetically expensive prey item to capture and consume, such that a predator truly could expend very little energy to get very high energetic rewards. However, it rarely is this straightforward. The caloric content of several marine prey species has been shown to increase with increasing size (e.g. Benoit-Bird 2004; Fig. 1), both length and weight. Yet, increasing size often also means increased mobility and, as a result, ability to evade and escape predation. Furthermore, increasing size also inherently means decreasing abundances – there will always be billions more krill in the ocean than whales based solely on cost of reproduction. Therefore, just based on sheer numbers, there are fewer big prey items, which increases the time between, and decreases the likelihood of, a predator encountering big prey items. So, there are clear trade-offs here. It may take longer to locate and capture a high value prey item, which costs more energy to capture, but the payout could potentially be much bigger. However, if a predator gambles too much, then their net energy expenditure to obtain high value prey may be higher than the net energy gained. Instead, it may be worth pursuing smaller prey items with lower energetic values, where discovery and capture success are higher and more frequent. However, in this case, many, many more pursuits are likely needed, thus costing more energy to meet daily energetic demands.
Is your head spinning as much as mine? Let me try and simplify this complex web of interactions with a tangible example. Bowen et al. (2002) investigated foraging of harbor seals in Nova Scotia to assess prey profitability of different species. By attaching camera systems to the backs of 39 adult male harbor seals, the authors identified sand lance and flounder to be the most targeted prey species. However, there were significant differences in pursuit/handling cost per prey type (kJ/min) with sand lance only requiring 14.8 ± 2.7, whereas flounder required significantly more at 30.3 ± 7.9. Therefore, based solely on energy required to capture prey, the sand lance would seem to be the better option. In fact, to a certain degree, this hypothesis is actually true when we compare the energetic content of the two prey types. Sand lance have a higher energetic value at lengths of 10 and 15 cm (53.6 and 95.8 kJ, respectively) compared to flounder (22.6 and 88.6 kJ, respectively). So, the net gain of a harbor seal foraging on a 15 cm sand lance (assuming that it only takes 1 minute to catch the fish – this is more for explanatory purposes as it likely takes much longer for a harbor seal to capture a fish) would be 81 kJ. This gain is larger than that of a 15 cm flounder (58.3 kJ). However, once we compare these fish at 20 and 25 cm lengths, the flounder actually becomes the more beneficial prey item at 232.6 and 492.3 kJ, respectively, over the sand lance (158.1 and 233.8 kJ). Now, assuming once again that it only takes 1 minute to catch the fish, the harbor seal enjoys a net energetic gain of a whopping 462 kJ when capturing a 25 cm flounder compared to 219 kJ for a sand lance of the same size – that makes the flounder more than twice as profitable!
The Bowen et al. study is an excellent demonstration of the importance of considering the quality of prey items when studying the ecology of marine predators. However, the authors did not assess the relative availability of sand lance and flounder. Ideally, foraging ecology studies aimed at understanding prey choice would try to address both important prey metrics – quality and quantity. This goal is the exact aim of my second Master’s thesis chapter where I am investigating whether prey quality (determined through community composition and caloric content) or prey quantity (measured as relative density) is more important in driving fine-scale gray whale foraging behavior in Port Orford, Oregon (Fig. 2). This question can be simplified by asking does it matter more what prey is in an area, or how much prey there is in an area? Or we can relate it back to the title of this post by asking whether individual gray whales would rather attend a cheap all-you-can-eat buffet or an expensive fine-dining restaurant. I am unfortunately not quite done with my analyses yet (but I’m getting closer!) and therefore am not ready to answer these questions. However, I have done extensive research on this topic and therefore am in a position to briefly mention a few other studies that have investigated these questions for other marine predators.
Ludynia et al. (2010) explored reasons why African penguin (Spehniscus demersus) numbers have declined in Namibia. They found that after the collapse of pelagic fish stocks in the 1970s (including the principal penguin prey item, sardine), African penguins switched to feeding on bearded goby, which are considered a low-energy prey species. Bearded goby are relatively abundant along Namibia’s southern coast and as such, limited prey availability is not the reason for declining African penguin numbers. Therefore, the authors concluded that the low quality of bearded goby (compared to sardine) appears to be the reason for declining population trends of the penguins. This study demonstrates that African penguins do better when eating at a fine-dining restaurant, rather than loading up a whole plate of junk food.
Grémillet et al. (2004) studied the foraging effort and number of successful prey captures per foraging trip (yield) of great cormorants (Phalacrocorax carbo) in Greenland in relation to prey abundance and quality within their foraging areas. The authors radio-tracked 11 great cormorants during a total of 163 foraging trips to estimate foraging effort and yield. The study found that contrary to the authors’ hypothesis, great cormorants foraged in areas of low prey abundance where the average caloric value was also relatively low. Therefore, in this example, it would seem that the predator of interest prioritizes neither high quality nor quantity when foraging.
Haug et al. (2002) investigated the variations in minke whale (Balaenoptera acutorostrata) diet and body condition in response to ecosystem changes in the Barents Sea. The main prey item of minke whales in the Barents Sea is immature herring. However, when recruitment failure and subsequent weak cohorts leads to reduced availability of immature herring, minke whales switched their diet to other prey items such as krill, capelin, and sometimes other gadoid fish species. The authors found a correlation between body condition of minke whales and immature herring abundances, such that minke whales displayed a poor body condition during low immature herring abundances. However, in the years of low immature herring abundance, abundances of krill and capelin were not low. Therefore, similar to the Ludynia et al. (2010) study, it seems that minke whales in the Barents Sea also do better in years when the prey type of highest caloric value is the most abundant. However, decreases in high quality prey has not led to population declines in minke whales in the Barents Sea, indicating that they likely take advantage of high quantities of low quality prey, unlike the African penguins.
Clearly, the answer as to whether marine predators prefer quality over quantity is not simple and constant. Rather, prey preference varies based on predator needs and ecology, falling anywhere on a broad spectrum from low to high prey quality and low to high prey quantity (Fig. 3). To a certain extent, it probably also is not solely predator choice that determines what they eat but many other factors, such as climate, disturbance, and health. As a result, these preferences and choices will likely be fluid, rather than fixed. While I anticipate that individual gray whales will be flexible foragers, I do hypothesize that when there is a prey patch of a higher energetic value in the area, whales will preferentially consume these patches over areas where there is less energetically rich prey, even if it is more abundant.
Benoit-Bird, K. J. 2004. Prey caloric value and predator energy needs: foraging predictions for wild spinner dolphins. Marine Biology 145:435-444.
Bowen, W. D., D. Tuley, D. J. Boness, B. M. Bulheier, and G. J. Marshall. 2002. Prey-dependent foraging tactics and prey profitability in a marine mammal. Marine Ecology Progress Series 244:235-245.
Charnov, E. L. 1976. Optimal foraging, the marginal value theorem. Theoretical Population Biology 9(2):129-136.
Grémillet D., G. Kuntz, F. Delbart, M. Mellet, A. Kato, J-P. Robin, P-E. Chaillon, J-P. Gendner, S-H. Lorentsen, and Y. Le Maho. 2004. Linking the foraging performance of a marine predator to local prey abundance. Functional Ecology 18(6):793-801.
Haug, T., U. Lindstrøm, and K. T. Nilssen. 2002. Variations in minke whale (Balaenoptera acutorostrata) diet and body condition in response to ecosystem changes in the Barents Sea. Sarsia 87(6):409-422.
Krebs, J. R. 1978. Optimal foraging: decision rules for predators. Behvaioral Ecology: An Evolutionary Approach, eds. Krebs, J. R., and N. B. Davies. Oxford: Blackwell.
Ludynia, J., J-P. Roux, R. Jones, J. Kemper, and L. G. Underhill. 2010. Surviving off junk: low-energy prey dominates the diet of African penguins Spheniscus demersus at Mercury Island, Namibia, between 1996 and 2009. African Journal of Marine Science 32(3):563-572.
Pyke, G. H. 1984. Optimal foraging theory: a critical review. Annual Reviews of Ecology and Systematics 15:523-575.
Spitz, J., A. W. Trites, V. Becquet, A. Brind’Amour, Y. Cherel, R. Galois, and V. Ridoux. 2012. Cost of living dictates what whales, dolphins and porpoises eat: the importance of prey quality on predator foraging strategies. PLoS ONE 7(11):e50096.
Young, J. K., B. A. Black, J. T. Clarke, S. V. Schonberg, and K. H. Dunton. 2017. Abundance, biomass and caloric content of Chukchi Sea bivalves and association with Pacific walrus (Odobenus rosmarus divergens) relative density and distribution in the northeastern Chukchi Sea. Deep-Sea Research Part II 144:125-141.