Inference, and the intersection of ecology and statistics

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

Recently, I had the opportunity to attend the International Statistical Ecology Conference (ISEC), a biennial meeting of researchers at the interface of ecology and statistics. I am a marine ecologist, fascinated by the interactions between animals and the dynamic ocean environment they inhabit. If you had asked me five years ago whether I thought I would ever consider myself a statistician or a computer programmer, my answer would certainly have been “no”. Now, I find myself studying the ecology of blue whales in New Zealand using a variety of data streams and methodologies, but a central theme for my dissertation is species distribution modeling. Species distribution models (SDMs) are mathematical algorithms that correlate observations of a species with environmental conditions at their observed locations to gain ecological insight and predict spatial distributions of the species (Fig. 1; Elith and Leathwick 2009). I still can’t say I would identify as a statistician, but I have a growing appreciation for the role of statistics to gain inference in ecology.

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

Before I continue, let’s take a look at just a few definitions from Merriam-Webster’s dictionary:

Statistics: a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data

Ecology: a branch of science concerned with the interrelationship of organisms and their environments

Inference: a conclusion or opinion that is formed because of known facts or evidence

Ecological data are notoriously noisy, messy, and complex. Statistical tests are meant to help us understand whether a pattern in the data is different from what we would expect through random chance. When we study how organisms interact with one another and their environment, it is impossible to completely capture all elements of the ecosystem. Therefore, ecology is a field ripe with challenges for statisticians. How do we quantify a meaningful biological signal amidst all the noise? How can we gain inference from ecological data to enhance knowledge, and how can we use that knowledge to make informed predictions? Marine mammals are notoriously difficult to study. They inhabit an environment that is relatively inaccessible and inhospitable to humans, they occur in low numbers, they are highly mobile, and they are rarely visible. All ecological data are difficult and noisy and riddled with small sample sizes, but counting trees presents fewer logistical challenges than counting moving whales in an ever-changing open-ocean setting. Therefore, new methodologies in areas like species distribution modeling are often developed using large, terrestrial datasets and eventually migrate to applications in the marine environment (Robinson et al. 2011).

Many presentations I attended at the conference were geared toward moving beyond correlative SDMs. SDMs were developed to correlate species occurrence patterns with features of the environment they inhabit (e.g. temperature, precipitation, terrain, etc.). However, those relationships do not actually explain the underlying mechanism of why a species is more likely to occur in one environment compared to another. Therefore, ecological statisticians are now using additional information and modeling approaches within SDMs to incorporate information such as species co-occurrence patterns, population demographic information, and physiological constraints. Building SDMs to include such process-explicit information allows us to make steps toward understanding not just when and where a species occurs, but why.

Machine learning is an area that continues to advance and open doors to new applications in ecology. Machine learning approaches differ fundamentally from classical statistics. In statistics, we formulate a hypothesis, select the appropriate model to test that hypothesis (for example, linear regression), then test how well the data fit the model (“Is the relationship linear?”), and test the strength of that inference (“Is the linear pattern different from what we would expect due to random chance?”). Machine learning, on the other hand, does not use a predetermined notion of relationships between variables. Rather, it tries to create an algorithm that fits the patterns in the data. Statistics asks how well the data fit a model, and machine learning asks how well a model fits the data.

Machine learning approaches allow for very complex relationships to be included in models and can be excellent for making predictions. However, sometimes the relationships fitted by a machine learning algorithm are so complex that it is not possible to infer any ecological meaning from them. As one ISEC presenter put it, in machine learning “the computer learns but the scientist does not”. The most important thing when selecting your methodology is to remember your question and your goal. Do you want to understand the mechanism of why an animal is where it is? Or do you not need to understand the driver, but rather want to make the best predictions of where an animal will be? In my case, the answer to that question differs from one of my PhD chapters to the next. We want to understand the functional relationships between oceanography, krill availability, and blue whale distribution (Barlow et al. 2020), and subsequently we want to develop forecasting models that can reliably predict blue whale distribution to inform conservation efforts (Fig. 2).

Figure 2. An example predictive map of where we expect blue whales to be distributed based on environmental conditions. Warmer colors represent areas with a higher probability of blue whale occurrence, and the blue crosses represent locations where blue whales were observed.

ISEC was an excellent opportunity for me to break out of my usual marine mammal-centered bubble and get a taste of what is happening on the leading edge of statistical ecology. I learned about the latest approaches and innovations in species distribution modeling, and in the process I also learned about trees, koalas, birds, and many other organisms from around the world. A fun bonus of attending a methods-focused conference is learning about completely new study species and systems. There are many ways of approaching an ecological question, gaining inference, and making predictions. I look forward to incorporating the knowledge I gained through ISEC into my own research, both in my doctoral work and in applications of new methods to future research projects.

Figure 3. The virtual conference photo of all who attended the biennial International Statistical Ecology Conference. Thank you to the organizers, who made it a truly excellent and engaging conference experience!

References

Barlow, D.R., Bernard, K.S., Escobar-Flores, P., Palacios, D.M., and 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. Mar. Ecol. Prog. Ser. doi:https://doi.org/10.3354/meps13339.

Elith, J., and Leathwick, J.R. 2009. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40(1): 677–697. doi:10.1146/annurev.ecolsys.110308.120159.

Robinson, L.M., Elith, J., Hobday, A.J., Pearson, R.G., Kendall, B.E., Possingham, H.P., and Richardson, A.J. 2011. Pushing the limits in marine species distribution modelling: Lessons from the land present challenges and opportunities. doi:10.1111/j.1466-8238.2010.00636.x.

Dual cameras provide bigger picture

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?

Figure 1. The top row shows varying degrees of mysid density (low to high, left to right). Middle row illustrates different types of substrate you might encounter (reef, sandy, boulders; left to right). Bottom row shows the different levels of kelp health (poor, medium, good).

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).


Figure 3. A compilation of video clips captured by the GEMM Lab during their research on gray whale ecology and physiology off Newport, Oregon using Unoccupied Aerial Systems (UAS, or “drones”) and GoPro cameras. UAS are used to observe gray whale behavior and conduct photogrammetry assessment of body condition. GoPro camera drops assess the benthic habitat and prey density across the study region, with a couple chance encounters of whales. Research is conducted under NOAA/NMFS permit # 21678.

Milling around in definitions

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:

References

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. Zool 67, (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 Kingdom 87, 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 BOEM 117, 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 One 9, 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 One 12, 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).

Whales are amazing, and also provide amazing benefits to our oceans and human society

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.

Fig 1: Human relationship with whales has changed through history. Once valued for their meat and oil, now they are a natural attraction that amaze and attract crowds to whale watching destinations all over the globe. Photo: Stephen Johnson, Península Valdés-Argentina.

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).

Figure 2. The figure shows a conceptual model of the “whale pump”. From Roman-McCarthy, 2010.

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.

Figure 3: Whales are the most fascinating animals in the world, but they are not only amazing animals. They are also extremely important in sustaining the entire marine ecosystem. Photo: Alejandro Fernández Ajó -Instituto de Conservación de ballenas.

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.

References:

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.

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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

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Willis, J. 2007. Could whales have maintained a high abundance of krill? Evol Ecol Res 9: 651–662.

Uncommon baselines in social justice

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.

One last thought:

Vote.

Vote for the change you want to see.

Voting is powerful.

Vote.

References:

Pauly, Daniel. “Anecdotes and the shifting baseline syndrome of fisheries.” Trends in ecology & evolution 10, no. 10 (1995): 430. https://doi.org/10.1016/S0169-5347(00)89171-5

Feasts of junk food or morsels of fine dining: is prey quality or quantity more important to marine predators?

By Lisa Hildebrand, MSc student, OSU Department of Fisheries & Wildlife, Marine Mammal Institute, Geospatial Ecology of Marine Megafauna Lab

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. 

Figure 1. Increasing caloric content with increasing length (a) and wet weight (b). Figures and caption reproduced from Benoit-Bird 2004.

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. 

Figure 2. A question of what or how much. Left image: example of the screenshots we take to estimate relative prey density in Port Orford. Right images: two examples of the main prey species we find (top: mysid shrimp Neomysis rayii with a full brood pouch; bottom: amphipod Polycheria osborni).

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. 

Figure 3. A spectrum of prey quantity and quality. Giant cormorants forage on low prey quality & quantity (Grémillet et al. 2004). African penguin populations are declining despite high abundances of low quality prey, suggesting that high prey quality is important for their survival (Ludynia et al. 2010). Body condition of Barents Sea minke whales decreases when high quality prey is less abundant, however their populations have not declined, suggesting they instead exploit high abundances of low quality prey (Haug et al. 2002). What will the gray whales do?

Literature cited

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.

What we know now about New Zealand blue whales

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

For my PhD, I am using a variety of data sources and analytical tools to study the ecology and distribution of blue whales in New Zealand. I live on the Oregon Coast, across the world and in another season from the whales I study. I love where I live and I am passionate about my work, but I do sometimes feel removed from the whales and the ecosystem that are the focus of my research.

A pair of blue whales surface in the South Taranaki Bight region of New Zealand. Drone piloted by Todd Chandler during the 2017 field season.

Recently, I have turned my attention to processing acoustic data recorded in our study region in New Zealand between 2016 and 2018. In the fall, I developed detector algorithms to identify possible blue whale vocalizations in the recording period, and now I am going through each of the detections to validate whether it is indeed a blue whale call or not. Looking closely at spectrograms for hours and hours is a change of pace from the analysis and writing I have been doing recently. Namely, I am looking at biological signals – not lines of code and numbers on a screen, but depictions of sounds that blue whales produced. I have to say, it is the “closest” I have felt to these whales in a long time. Scrolling through thousands of spectrograms of blue whale calls leaves room for my mind to wander, and I recently had the realization that those whales have absolutely no idea that on the other side of the Pacific Ocean, there are a few scientists dedicating years of their lives to understand and protect them. Which led me to another realization: we know so much more about blue whales in New Zealand now than we did 10 years ago. In fact, we know so much more than we did even a year ago.

Screenshot of the process of reviewing blue whale D call detections in the acoustic analysis program Raven.

Nine years ago, Dr. Leigh Torres had a cup of coffee with a colleague who recounted observer reports of several blue whales during a seismic survey of the South Taranaki Bight region (STB) of New Zealand. This conversation sparked her curiosity, and led to the formulation of a hypothesis that the STB was in fact an unrecognized feeding ground for blue whales in the southern hemisphere (Torres 2013).

A blue whale surfaces in front of an oil rig in the South Taranaki Bight. Compiling opportunistic sightings like this one was an important step in realizing the importance of the region for blue whales. Photo by Deanna Elvines.

After three field seasons and several years of dedicated work, the hypothesis that the STB region is important for blue whales was validated. By drawing together multiple data streams and lines of evidence, we now know that New Zealand is home to a unique population of blue whales, which are genetically distinct from all other documented populations in the Southern Hemisphere. Furthermore, they use the STB for multiple critical life history functions such as feeding, nursing and calf raising, and they are present there year-round (Barlow et al. 2018).

Once we documented the New Zealand population, we were left with perhaps even more questions than we started with. Where do they feed, and why? Are they feeding and breeding there? Does their distribution change seasonally? What is the health of the population? Are they being impacted by industrial activity and human impacts such as noise in the region? We certainly do not have all the answers, but we have been piecing together an increasingly comprehensive story about these whales and their ecology.

For example, we now know that blue whales in New Zealand average around 19 meters in length, which we calculated by measuring images taken via drones and using an analysis program developed in the GEMM Lab (Burnett et al. 2018). The use of drones has opened up a whole new world for studying health and behavior in whales, and we recently used video footage to better understand the movement and kinematics of how blue whales engulf their krill prey. Furthermore, we know that blue whales may preferentially feed on dense krill aggregations at the surface, and that this surface feeding strategy may be an energetically favorable strategy in this part of the world (Torres et al. 2020).

We have also assessed one aspect of the health of blue whales by describing their skin condition. By analyzing thousands of photographs, we now know that nearly all blue whales in New Zealand bear the scars of cookie cutter shark bites, which they seem more likely to acquire at more northerly latitudes, and that 80% are affected by blister lesions (Barlow et al. 2019). Next, we are beginning to draw together multiple data streams such as body condition and hormone analysis, paired with skin condition, to form a detailed understanding about the health of this population.

Most recently we have produced a study describing how oceanography, prey and blue whales are connected within this region of New Zealand. The STB region is home to a wind-driven upwelling system that drives productivity and leads to aggregations of krill, which in turn provide sustenance for blue whales to feed on. By compiling data on oceanography and water column structure, krill availability, and blue whale distribution, we now have a solid understanding of this trophic pathway: how oceanography structures prey, and how blue whales respond to both prey and oceanography (Barlow et al. 2020). Furthermore, we are beginning to understand how those relationships may look under changing ocean conditions, with global sea temperatures rising and the increasing frequency and intensity of marine heatwaves.

The knowledge we have accumulated better enables managers to make informed decisions for the conservation of these blue whales and the ecosystem they inhabit. To me, there are several take-away messages from the story that continues to unfold about these blue whales. One is the importance of following a hunch, and then gathering the necessary tools and team to explore and tackle challenging questions. An idea that started over a cup of coffee and many years of hard work and dedication have led to a whole new body of knowledge. Another message is that the more questions you ask and the more questions you try to answer, the more questions you are often left with. That is a beautiful truth about scientific inquiry – the questions we ask drive the knowledge we uncover, but that process is never complete because new questions continue to emerge. Finally, it is easy to get swept up in details, outputs, timelines, and minutia, and every now and then it is important to take a step back. I have appreciated taking a step back and musing on the state of our knowledge about these whales, about how much we have learned in less than 10 years, and mostly about how many answers and new questions are still waiting to be uncovered.

A victorious field team celebrates a successful end to the 2017 field season with an at-sea sunset dance party. A good reminder of sunny, salty days on the water and where the data come from!

References

Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG (2020) Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar Ecol Prog Ser.

Barlow DR, Pepper AL, Torres LG (2019) Skin Deep: An Assessment of New Zealand Blue Whale Skin Condition. Front Mar Sci.

Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.

Burnett JD, Lemos L, Barlow DR, Wing MG, Chandler TE, Torres LG (2018) Estimating morphometric attributes on baleen whales using small UAS photogrammetry: A case study with blue and gray whales. Mar Mammal Sci.

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

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

How we plan to follow whales

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

The GEMM Lab gray whale team is in the midst of preparing for our fifth field season studying the Pacific Coast Foraging Group (PCFG): whales that forage off the coast of Newport, OR, USA each summer. On any given good weather day from June to October, our team is out on the water in a small zodiac looking for gray whales (Figure 1). When we find a gray whale, we try to collect photo ID data, fecal samples, drone data, and behavioral data. We use the drone data to study both the whale’s body condition and their behavior. In a previous blog, I described ethograms and how I would like to use the behavior data from drone videos to classify behaviors, with the ultimate goal of understanding how gray whale behavior varies across space, time, and by individual. However, this explanation of studying whale behavior is actually a bit incomplete. Before we start fieldwork, we first need to decide how to collect that data.

Figure 1. Image of GEMM lab team collecting gray whale UAS data. Image taken under NOAA/NMFS permit #16111

As observers, we are far from omnipresent and there is no way to know what the animals are doing all of the time. In any environment, scientists have to decide when and where to observe their animals and what behaviors they are interested in recording. In many studies, behavior is recorded live by an observer. In those studies, other limitations need to be taken into account, such as human error and observer fatigue. Collecting behavioral data is particularly challenging in the marine environment. Cetaceans spend most of their lives out of sight from humans, their time at the surface is brief, and when they appear together in large groups it can be very difficult to keep track of who is doing what when. Imagine being in a boat trying to keep track of what three different whales are doing without a pre-determined method – the task could quickly become overwhelming and biased. This is why we need a methodology for collecting and classifying behavior. We cannot study behavior without acknowledging these limitations and the potential biases that come with the methods we choose. Different data collection methods are better suited to address different questions.

The use of drones gives us the ability to record cetacean behavior non-invasively, from a perspective that allows greater observation (Figure 2, Torres et al. 2018), and for later review, which is a significant improvement. However, as we prepare to collect more behavior data, we need to study the methods and understand the benefits and disadvantages of each approach so that we capture the information we need without bias. Altmann (1974) provides a thorough overview of behavioral sampling methods.

Figure 2. Diagram illustrating “whale surface time” relative to “whale visible time” data as collected from an unmanned aerial systems (UAS) aircraft flying over a gray whale as it moves sequentially (from right to left) from “headstand” foraging to surfacing. Figure from Torres et al. (2018).

Ad libitum behavioral sampling has no structure and occurs when we find a group of whales and just write down everything they are doing. This method is a good first step, however it comes with bias.  Without structure, we cannot be sure that there was an equal probability of detecting each kind of behavior; this problem is called detectability bias. This type of bias is an issue if we are trying to answer questions about how often a behavior occurs, or what percent of time is spent in each behavior state. This is a bias to be especially concerned about when it comes to cetaceans because there are many examples of behaviors with different levels of detectability. An extreme example would be the detectability of breaching versus a behavior that takes place under the surface. A breaching whale is easier to spot and more exciting, which could lead to results suggesting that whales breach more often than they do relative to underwater behaviors. While it’s impossible to eliminate detectability bias, other sampling methods employ decision rules to try and reduce its effect. Many decision rules revolve around time, such as setting a minimum or maximum observation time interval. Other time rules involve recording the behavior state at set intervals of time (e.g., every 5 minutes). Setting observation boundaries helps standardize the methods and the data being collected.

In a structured sampling plan, the first big decision that needs to be addressed is the need to know the duration of behaviors. Point events do not include duration data but can be used to study the frequencies of behaviors. For example, if my research question was “Do whales perform “headstands” in a specific habitat type?”, then I would need point events of headstanding behavior. But, if I wanted to ask, “Do whales spend more time spent headstanding in a specific habitat type than in other habitat types?”, I would need headstanding to be a state event. State events are events with associated duration information and can be used for activity budgets. Activity budgets show how much time an animal spends in each behavior state. Some sampling methods focus on collecting only point events. However, to get the most complete understanding of behavior I think it’s important to collect both. Focal animal follows are another method of collecting more detailed data and is commonly used in cetacean studies.

The explanation of a focal follow method is in the name.  We focus on one individual, follow it, and record all of its behaviors. When employing this method, decisions are made about how an individual is chosen and how long it is followed. In some cases, the behavior of this animal is used as a proxy for the behavior of an entire group. I essentially use the focal follow method in my research. While I review drone footage to record behavioral data instead of recording behaviors live in the field, I focus on one individual a time as I go through the videos. To do this I use a software called BORIS (Friard and Gamba 2016) to mark the time of each behavior per individual (Figure 3). If there are three individuals in a video, I’ll review the footage three times to record behaviors once per individual, focusing on each in turn.

Figure 3. Screenshot of BORIS layout.

While the drone footage brings the advantages of time to review and a better view of the whale, we are constrained by the duration of a flight. Focal follows would ideally last longer than the ~15 minutes of battery life per drone flight. Our previously collected footage gives us snapshots of behavior, and this makes it challenging to compare and analyze durations of behaviors. Therefore, I am excited that we are going to try conducting drone focal follows this summer by swapping out drones when power runs low to achieve longer periods of video coverage of whale behavior. I’ll be able to use these data to move from snapshots to analyzing longer clips and better understanding the behavioral ecology of gray whales. As exciting as this opportunity is, it also presents the challenge of method development. So, I now need to develop decision rules and data collection methods to answer the questions that I have been eagerly asking.

References

Altmann, Jeanne. 1974. “Observational Study of Behavior: Sampling Methods.” Behaviour 49 (3–4): 227–66. https://doi.org/10.1163/156853974X00534.

Friard, Olivier, and Marco Gamba. 2016. “BORIS: A Free, Versatile Open-Source Event-Logging Software for Video/Audio Coding and Live Observations.” Methods in Ecology and Evolution 7 (11): 1325–30. https://doi.org/10.1111/2041-210X.12584.

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

“Do Dolphins Get Hives?”: The Skinny on Allergies in Cetaceans

By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab 

While sitting on my porch and watching the bees pollinate the blooming spring flowers, I intermittently pause to scratch the hives along my shoulders and chest. In the middle of my many Zoom calls, I mute myself and stop my video because a wave of pollen hits my face and I immediately have to sneeze. With this, I’m reminded: Welcome to prime allergy season in the Northern Hemisphere. As I was scratching my chronic idiopathic urticaria (hives caused by an overactive immune system), I asked myself “Do dolphins get hives?” I had no idea. I know most terrestrial mammals can and do—just yesterday, one of the horses in the nearby pasture was suffering from a flare of hives. But, what about aquatic and marine mammals? 

Springtime flowers blooming on the Central California Coast 2017. (Image Source: A. Kownacki)

As with most research on marine mammal health, knowledge is scare and is frequently limited to studies conducted on captive and stranded animals. Additionally, most of the current theories on allergic reactions in marine mammals are based on studies from terrestrial wildlife and humans. Because nearly all research on histamine pathways centers on terrestrial animals, I wanted to see what information exists the presence of skin allergies in marine mammals.  

Allergic reactions trigger a cascade within the body, beginning with the introduction of a foreign body, which for many people is pollen. The allergen binds to antibodies that are produced to fight potentially harmful substances. Once this allergen binds to different types of cells, including mast cells, chemicals like histamines are released. Histamines cause the production of mucus and constriction of blood vessels, and thus are the reason your eyes water, your nose runs, or you start coughing. 

Basic cartoon of an allergic reaction from exposure to the allergen to the reaction from the animal. (Image Source: Scientific Malaysian)

As you probably can tell just by looking at a marine mammal, they have thicker skin and fewer mucus membranes that humans, due to the fact that they live in the water. However, mast cells or mast cell-like cells have been described in most vertebrate lineages including mammals, birds, reptiles, amphibians, and bony fishes (Hellman et al. 2017, Reite and Evenson 2006). Mast cell-like cells have also been described in an early ancestor of the vertebrates, the tunicate, or sea squirt (Wong et al. 2014). Therefore, allergic-reaction cascades that may present as hives, red and itchy eyes or nose in humans, also exist in marine mammals, but perhaps cause different or less visible symptoms.  

Skin conditions in cetaceans are gathering interest within the marine mammal health community. Even our very own Dawn BarlowDr. Leigh Torres, and Acacia Pepper assessed the skin conditions in New Zealand blue whales in their recent publication. Most visible skin lesions or markings on cetaceans are caused by parasites, shark bits, fungal infections, and fishery or boat interactions (Leone et al. 2019, Sweeney and Ridgway 1985). However, there is very little scientific literature about allergic reactions in marine mammals, let alone cetaceans. That being said, I managed to find a few critical pieces of information supporting the theory that marine mammals do in fact have allergies that can produce dermal reactions similar to hives in humans.  

In one study, three captive bottlenose dolphins developed reddened skin, sloughing, macules, and wheals on their ventral surfaces (Monreal-Pawlowsky et al. 2017). The medical staff first noticed this atopic dermatitis in 2005 and observed the process escalate over the next decade. Small biopsy samples from the affected areas on the three dolphins coincided with the appearance of four pollens in the air within the geographic region: Betula, Pistacia, Celtis, and Fagus (Monreal-Pawlowsky et al. 2017). Topical prednisone treatments were applied to the affected areas at various dosages that slowly resolved the skin irritations. Researchers manufactured an allergy vaccine using a combination of the four pollens in hopes that it would prevent further seasonal outbreaks, but it was unsuccessful. In the coming years, the facility intends to adjust the dosages to create a successful vaccine.  

In the three top images, visible skin irritation including redness, macules, wheals, and sloughing are present. In the image below, the above animal was treated with methylprednisolone and the skin irritation subsides. (Monreal-Pawlowsky et al. 2017)

In addition to the above study, there is an unpublished case of suspected allergic reaction to another pollen that produces a pruritic reaction on the ventral areas of dolphins on a seasonal basis (Vicente Arribes, personal communication). Although there are only a few documented cases of environmentally-triggered allergic reactions that are visible on the dermal layer of cetaceans, I believe this evidence makes the case that some cetaceans suffer from allergies much like us. So, next time you’re enjoying the beautiful blooms and annoyingly scratch your eyes, know that you are not alone. 

Image Source: FurEver Family

Citations: 

Barlow DR, Pepper AL and Torres LG (2019) Skin Deep: An Assessment of New Zealand Blue Whale Skin Condition. Front. Mar. Sci. 6:757.doi: 10.3389/fmars.2019.00757 

Hellman LT, Akula S, Thorpe M and Fu Z (2017) Tracing the Origins of IgE, Mast Cells, and Allergies by Studies of Wild Animals. Front. Immunol. 8:1749. doi: 10.3389/fimmu.2017.01749 

Leone AB, Bonanno Ferraro G, Boitani L, Blasi MF. Skin marks in bottlenose dolphins (Tursiops truncatus) interacting with artisanal fishery in the central Mediterranean Sea. PLoS One. 2019;14(2):e0211767. Published 2019 Feb 5. doi:10.1371/journal.pone.0211767 

Monreal-Pawlowsky T, Fernández-Bellon H, Puigdemont A (2017) Suspected Allergic Reaction in Bottlenose Dolphins (Tursiops truncatus). J Vet Sci Ani Husb 5(1): 108. doi: 10.15744/2348-9790.5.108 

Reite OB, Evensen O. Inflammatory cells of teleostean fish: a review focusing on mast cells/eosinophilic granule cells and rodlet cells. Fish Shellfish Immunol (2006) 20:192–208. doi:10.1016/j.fsi.2005.01.012 

Sweeney, J. C., & Ridgway, S. H. (1975). Common diseases of small cetaceans. J. Am. Vet. Med. Assoc167(7), 533-540. 

Wong GW, Zhuo L, Kimata K, Lam BK, Satoh N, Stevens RL. Ancient originof mast cells. Biochem Biophys Res Commun (2014) 451:314–8. doi:10.1016/j.bbrc.2014.07.124 

You can’t build a pyramid without the base: diving into the foundations of behavioral ecology to understand cetacean foraging

By Lisa Hildebrand, MSc student, OSU Department of Fisheries & Wildlife, Marine Mammal Institute, Geospatial Ecology of Marine Megafauna Lab

The last two months have been challenging for everyone across the world. While I have also experienced lows and disappointments during this time, I always try to see the positives and to appreciate the good things every day, even if they are small. One thing that I have been extremely grateful and excited about every week is when the clock strikes 9:58 am every Thursday. At that time, I click a Zoom link and after a few seconds of waiting, I am greeted by the smiling faces of the GEMM Lab. This spring term, our Principal Investigator Dr. Leigh Torres is teaching a reading and conference class entitled ‘Cetacean Behavioral Ecology’. Every week there are 2-3 readings (a mix of book chapters and scientific papers) focused on a particular aspect of behavioral ecology in cetaceans. During the first week we took a deep dive into the foundations of behavioral ecology (much of which is terrestrial-based) and we have now transitioned into applying the theories to more cetacean-centric literature, with a different branch of behavior and ecology addressed each week.

Leigh dedicated four weeks of the class to discussing foraging behavior, which is particularly relevant (and exciting) to me since my Master’s thesis focuses on the fine-scale foraging ecology of gray whales. Trying to understand the foraging behavior of cetaceans is not an easy feat since there are so many variables that influence the decisions made by an individual on where and when to forage, and what to forage on. While we can attempt to measure these variables (e.g., prey, environment, disturbance, competition, an individual’s health), it is almost impossible to quantify all of them at the same time while also tracking the behavior of the individual of interest. Time, money, and unworkable weather conditions are the typical culprits of making such work difficult. However, on top of these barriers is the added complication of scale. We still know so little about the scales at which cetaceans operate on, or, more importantly, the scales at which the aforementioned variables have an effect on and drive the behavior of cetaceans. For instance, does it matter if a predator is 10 km away, or just when it is 1 km away? Is a whale able to sense a patch of prey 100 m away, or just 10 m away? The same questions can be asked in terms of temporal scale too.

What is that gray whale doing in the kelp? Source: F. Sullivan.

As such, cetacean field work will always involve some compromise in data collection between these factors. A project might address cetacean movements across large swaths of the ocean (e.g., the entire U.S. west coast) to locate foraging hotspots, but it would be logistically complicated to simultaneously collect data on prey distribution and abundance, disturbance and competitors across this same scale at the same time. Alternatively, a project could focus on a small, fixed area, making simultaneous measurements of multiple variables more feasible, but this means that only individuals using the study area are studied. My field work in Port Orford falls into the latter category. The project is unique in that we have high-resolution data on prey (zooplankton) and predators (gray whales), and that these datasets have high spatial and temporal overlap (collected at nearly the same time and place). However, once a whale leaves the study area, I do not know where it goes and what it does once it leaves. As I said, it is a game of compromises and trade-offs.

Ironically, the species and systems that we study also live a life of compromises and trade-offs. In one of this week’s readings, Mridula Srinivasan very eloquently starts her chapter entitled ‘Predator/Prey Decisions and the Ecology of Fear’ in Bernd Würsig’s ‘Ethology and Behavioral Ecology of Odontocetes’ with the following two sentences: “Animal behaviors are governed by the intrinsic need to survive and reproduce. Even when sophisticated predators and prey are involved, these tenets of behavioral ecology hold.”. Every day, animals must walk the tightrope of finding and consuming enough food to survive and ensure a level of fitness required to reproduce, while concurrently making sure that they do not fall prey to a predator themselves. Krebs & Davies (2012) very ingeniously use the idea of economic analysis of costs and benefits to understand foraging behavior (but also behavior in general). While foraging, individuals not only have to assess potential risk (Fig. 1) but also decide whether a certain prey patch or item is profitable enough to invest energy into obtaining it (Fig. 2).

Leigh’s class has been great, not only to learn about foundational theories but to then also apply them to each of our study species and systems. It has been exciting to construct hypotheses based on the readings and then dissect them as a group. As an example, Sih’s 1984 paper on the behavioral response race of predators and prey prompted a discussion on responses of predators and prey to one another and how this affects their spatial distributions. Sih posits that since predators target areas with high prey densities, and prey will therefore avoid areas that predators frequent, their responses are in conflict with one another. Resultantly, there will be different outcomes depending on whichever response dominates. If the predator’s response dominates (i.e. predators are able to seek out areas of high prey density before prey can respond), then predators and prey will have positively correlated spatial distributions. However, if the prey responses dominate, then the spatial distributions of the two should be negatively correlated, as predators will essentially always be ‘one step behind’ the prey. Movement is most often the determinant factor to describe the strength of these relationships.

Video 1. Zooplankton closest to the camera will jump or dart away from it. Source: GEMM Lab.

So, let us think about this for gray whales and their zooplankton prey. The latter are relatively immobile. Even though they dart around in the water column (I have seen them ‘jump’ away from the GoPro when we lower it from the kayak on several occasions; Video 1), they do not have the ability to maneuver away fast or far enough to evade a gray whale predator moving much faster. As such, the predator response will most likely always be the strongest since gray whales operate at a scale that is several orders of magnitude greater than the zooplankton. However, the zooplankton may not be as helpless as I have made them seem. Based on our field observations, it seems that zooplankton often aggregate beneath or around kelp. This behavior could potentially be an attempt to evade predators as the kelp and reef crevices may serve as a refuge. So, in areas with a lot of refuges, the prey response may in fact dominate the relationship between gray whales and zooplankton. This example demonstrates the importance of habitat in shaping predator-prey interactions and behavior. However, we have often observed gray whales perform “bubble blasts” in or near kelp (Video 2). We hypothesize that this behavior could be a foraging tactic to tip the see-saw of predator-prey response strength back into their favor. If this is the case, then I would imagine that gray whales must decide whether the energetic benefit of eating zooplankton hidden in kelp refuges outweighs the energy required to pursue them (Fig. 2). On top of all these choices, are the potential risks and threats of boat traffic, fishing gear, noise, and potential killer whale predation (Fig. 1). Bringing us back to the analogy of economic analysis of costs and benefits to predator-prey relationships. I never realized it so clearly before, but gray whales sure do have a lot of decisions to make in a day!

Video 2. Drone footage of a gray whale foraging in kelp and performing a “bubble blast” at 00:40. Footage captured under NMFS permit #21678. Source: GEMM Lab.

Trying to tease apart these nuanced dynamics is not easy when I am unable to simply ask my study subjects (gray whales) why they decided to abandon a patch of zooplankton (Were the zooplankton too hard to obtain because they sought refuge in kelp, or was the patch unprofitable because there were too few or the wrong kind of zooplankton?). Or, why do gray whales in Oregon risk foraging in such nearshore coastal reefs where there is high boat traffic (Does their need for food near the reefs outweigh this risk, or do they not perceive the boats as a risk?). So, instead, we must set up specific hypotheses and use these to construct a thought-out and informed study design to best answer our questions (Mann 2000). For the past few weeks, I have spent a lot of time familiarizing myself with spatial packages and functions in R to start investigating the relationships between zooplankton and kelp hidden in the data we have collected over 4 years, to ultimately relate these patterns to gray whale foraging. I still have a long and steep journey before I reach the peak but once I do, I hope to have answers to some of the questions that the Cetacean Behavioral Ecology class has inspired.

Literature cited

Krebs, J. R., and N. B. Davies. 2012. Economic decisions and the individual in Davies, N. B. et al., eds. An introduction to behavioral ecology. John Wiley & Sons, Oxford.

Mann, J. 2000. Unraveling the dynamics of social life: long-term studies and observational methods in Mann, J., ed. Cetacean societies: field studies of dolphins and whales. University of Chicago Press, Chicago.

Sih, A. 1984. The behavioral response race between predator and prey. The American Naturalist 123:143-150.

Srinivasan, M. 2019. Predator/prey decisions and the ecology of fear in Würsig, B., ed. Ethology and ecology of odontocetes. Springer Nature, Switzerland.