There are moments in our individual lifetimes that we can define as noteworthy and right now, as I prepare to start my graduate career within the Marine Mammal Institute (MMI) at OSU, I would say this is it for me. As I sit down to write this blog and document how surreal my future adventure is, I simultaneously feel this path is felicitous. After a year of being cooped up due to COVID, time presently seems to be going by at rocket speed. I am moving constantly in through my day to continue running my current life, while simultaneously arranging all that will encompass my new life. And while I answer questions to my 10-year-old daughter who is doing geometry homework in the living room, while hollering “That is not yours!” to the kitchen where the recently adopted feral dog is sticking his entire head under the trash can lid, while arranging our books in a cardboard box at the packing station I set up on the dining room table, I cannot deny a sense of serenity. This moment in my life, becoming a part of the GEMM Lab and MMI, and relocating to Corvallis is great.
This moment’s noteworthiness is emphasized by embarking on probably the most variable-heavy road trip I have planned to date. Since the age of 19, when I left my small mountain town on the Appalachian trail in Pennsylvania, I have transferred locations ~20 times. Due to extensive travel while serving in the Army (various Army trainings and overseas mission deployments), I have bounced around the US and to other countries often. Over time, one becomes acclimated to the hectic nature of this sort of lifestyle, and yet this new adventure holds significance.
So here are the details of the adventure trip that lies ahead: I will drive my 2002 Jeep Grand Cherokee across the country; from Charlottesville, Virginia to Corvallis, Oregon. My projected route will extend 2,822 miles and take ~43 driving hours total. The route will fall within the boundaries of 11 states (see Figure 1.)
Attached to the hitch of the Jeep will be a 6×12 rented cargo trailer containing our treasured books, furniture and things. Inside the Jeep will be three living variables: Mia (the 10-year-old), Angus (hyperactive border collie/ pit bull mix) and Mr. Gibbs (feral pirate dog); all three will need to be closely monitored for potential hiccups in the plan.
If we are going to make it to our destination hotel/Airbnb each night of the trip, I must be organized and calculate road time each day while factoring in breaks to the loo and fueling up. These calculations need to be precise, with little margin for error. I cannot play it too safely either, or it will take us too long to get across the country (I must start my graduate work after all). On the other hand, I cannot realistically expect too many road hours in a day. I think at this point I have got it worked out (Table 1.)
When I look back on my career, I had no idea that my not-so-smooth road would lead me to my dream goal of studying marine mammals. I took the Army placement tests at the age of 19, which led me to the field of “information operations” where I earned a great knowledge base in data analysis and encountered fantastic leaders whom I might not have known otherwise. I learned immensely on this path and it set me up very well for moving forward into research and collaboration in the sciences. I am so grateful that my life took this journey because working in the military provided me with the utmost respect for my opportunities and greater empathy for others. This route had many extreme obstacles and was intensely intimidating at times, but I am all the better for it. And I was never able to shake the dream of where I wanted to be (see Figures 2 & 3.) Timing is everything.
Figure 2 & 3. Two of the images of the Pacific coast I have hung up in my house. Keeping my eye on the prize, so to speak.
It will feel great to cross over the Oregon state line. I cannot wait to meet GEMM Lab in-person and all the other wonderful researchers and staff at MMI and Hatfield Marine Science Center. I am eager to step onto the RV Pacific Storm and begin my thesis research on the magnificent cetaceans off the Oregon coast, and hopefully do some good in the end. As I evaluate the logistics of my trip from Charlottesville to Corvallis, I feel relieved rather than overwhelmed. We could attribute this relief to my not-so-smooth road to get to where I am. Looking ahead, of course, I see a road that will require focus, attention, passion, care, and lots of fuel. Even if this road is not completely smooth, I will have my hands on 10 and 2, and feel so grateful and ready to be on it.
“What is the weather going to be like tomorrow?” “How long will it take to drive there, with traffic?” We all rely on forecasts to make decisions, such as whether to bring a rain jacket, when to get in the car to arrive at a certain destination on time, or any number of situations where we want a prediction of what will happen in the near future. Statistical models underpin many of these examples, using past data to inform future predictions.
Early on in graduate school, I was told that “all models are wrong, but some models work.” Any model is essentially a best approximation, using mathematical relationships, of how we understand a pattern. Models are powerful tools in ecology, enabling us to distill complex, dynamic, and interacting systems into terms and parameters that can be quantified. This ability can help us better understand our study systems and use that understanding to make predictions. We will never be able to describe every nuance of an ecosystem. Instead, the challenge is to collect enough information to build an informed model that can enhance our understanding, without over-simplifying or unnecessarily complicating the system we aim to describe. As Dr. Simon Levin stated in his 1989 seminal paper:
“A good model does not attempt to reproduce every detail of the biological system; the system itself suffices for that purpose as the most detailed model of itself. Rather, the objective of a model should be to ask how much detail can be ignored without producing results that contradict specific sets of observations, on particular scales of interest.”1
Species distribution models (SDMs) are the particular branch of models that underpin much of my PhD research on blue whale ecology and distribution in New Zealand. 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)2. The model is a best attempt to quantify and describe the relationships between predictors, e.g., temperature and the observed species distribution pattern. For example, blue whale occurrence is higher in areas of lower temperatures and greater krill availability, and these relationships can be described with models3. So, a model essentially takes all the data available, and synthesizes that information in terms of the relationships between the predictors (environment) and response (species occurrence). Then, we can look at the fitted relationships to ask what we would expect from the species distribution pattern when temperature, or krill availability, or any other predictor, is at a particular value.
So, if a model is simply a mathematical description of how terms interact to produce a particular outcome, how do predictions work? To make a spatial prediction, e.g., a map of the probability of a species being present, you need two things: a model describing the functional relationships between species presence and your environmental predictors, and the values of your predictor variables on the day you are interested in predicting to. For example, you may need to obtain a map of sea surface temperature, productivity, temperature anomaly, and surface currents on a day you want to know where whales are expected to be. Your model is the applied across that stack of spatial environmental layers and, based on the functional relationships derived by the model, you get an estimate of the probability of species occurrence based on the temperature, productivity, anomaly, and surface current values at each location. By applying the model over a range of values, you can obtain a continuous surface with the probability of presence, in the form of a map. These maps are typically for the past or present because that is when we can typically acquire spatial environmental layers. However, to make predictions for a future time of interest, we need to have spatial environmental layers for the future.
Forecasts are predictions for the future. Recent advances in technology and computing have led to an emergence of environmental and ecological forecasting tools that are being developed around the world to produce marine forecasts. These tools include predictions of the physical environment such as ocean temperatures or currents, and biological patterns such as where species will be distributed in space and timing of events like salmon spawning or lobster landings4. The ability to generate forecast of marine ecosystems is of particular interest to resource users and managers because it can allow them to be proactive rather than reactive. Forecasts enable us to anticipate events or patterns and prepare, rather than having to respond in real-time or after the fact.
The South Taranaki Bight region in New Zealand is an area where blue whale foraging habitat frequently coincides with industry pressures, including petroleum and mineral extraction, exploration for petroleum reserves using seismic airgun surveys, vessel traffic between ports, and even an ongoing proposal for seabed mining5. Static spatial restrictions to mitigate impacts from these activities on blue whales may be met with resistance from industry user groups, but dynamic spatial management6–8 of blue whale habitat could be more attractive and acceptable. The key for successful dynamic management is knowing where and when to put those boundaries; and this is where ecological forecast models can show their strength. If we can predict suitable blue whale habitat for the future, proactive regulations can be applied to enhance conservation management in the region. Can we develop reliable and useful ecological forecasts for the South Taranaki Bight? Well, given that we have already developed robust models of the relationships between blue whales and their habitat3 and have documented the spatial and temporal lags between wind, upwelling, and blue whales9, we feel confident that we can develop forecast models to predict where blue whales will be in the STB region. As we continue working hard toward this goal, we invite you to check back for our findings in the future. So, consider this blog post a forecast of sorts, and stay tuned!
1. Levin, S. A. The problem of pattern and scale. Ecology73, 1943–1967 (1992).
2. Elith, J. & Leathwick, J. R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst.40, 677–697 (2009).
3. Barlow, D. R., Bernard, K. S., Escobar-Flores, P., Palacios, D. M. & Torres, L. G. Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar. Ecol. Prog. Ser.642, 207–225 (2020).
4. Payne, M. R. et al. Lessons from the first generation of marine ecological forecast products. Front. Mar. Sci.4, 1–15 (2017).
5. Torres, L. G. Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal. J. Mar. Freshw. Res.47, 235–248 (2013).
6. Hyrenbach, K. D., Forney, K. A. & Dayton, P. K. Marine protected areas and ocean basin management. Aquat. Conserv. Mar. Freshw. Ecosyst.10, 437–458 (2000).
7. Maxwell, S. M. et al. Dynamic ocean management: Defining and conceptualizing real-time management of the ocean. Mar. Policy58, 42–50 (2015).
8. Oestreich, W. K., Chapman, M. S. & Crowder, L. B. A comparative analysis of dynamic management in marine and terrestrial systems. Front. Ecol. Environ.18, 496–504 (2020).
9. Barlow, D. R., Klinck, H., Ponirakis, D., Garvey, C. & Torres, L. G. Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci. Rep.11, (2021).
¹PhD Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab
²Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Cooperative Institute for Marine Resources Studies, Hatfield Marine Science Center
Many aspects of studying cetacean ecology, behavior, population dynamics, health, and sociality depend on being able to see and/or sample cetaceans when they come to the surface. While this research is not necessarily easy given that cetaceans spend the majority of their time underwater out of human sight, it is definitely feasible, as evidenced by decades of cetacean research. However, in order for researchers to observe cetaceans at the surface they need to get out to sea, and this boat-based effort can realistically only be done in good ocean conditions. Any sea-going individual likely uses the Beaufort sea state (BSS) scale as a measure of ocean conditions. For a full breakdown and excellent explanation of what the BSS is, check out our beloved Alexa’s blog; but for the purposes of this blog all you really need to know is that the smaller the BSS (which starts at 0), the calmer the ocean, and the higher the BSS, the rougher & stormier the ocean. There are two main reasons for conducting cetacean research in low BSS: 1) above a certain threshold (usually BSS 4) it becomes difficult to reliably spot and recognize cetaceans at the surface, thus compromising good data collection, and/or 2) to ensure safety and comfort of the research team.
So, when the BSS gets too high, us humans usually do not go out to sea to study cetaceans, which means that the cetaceans, for the most part, go unobserved. So, many questions arise about what cetaceans are doing during these rough ocean conditions. What does an increased BSS mean for them? Are they unfazed by big waves and strong winds, or are they affected by the weather and take longer dives or seek out fairer seas? A conversation among friends sparked our curiosity of what cetaceans do in stormy conditions and inspired us to collaborate on this blog. Here, we report on what is and is not known about cetaceans in storms, and discuss some ideas about how best to quantify the effects of rough sea conditions on cetaceans.
A literature search of cetaceans during storms did not generate many results, which was not surprising to us given the above reasons about researchers not being able to survey in rough sea conditions. However, we did find a couple of interesting studies about cetacean behavior and distribution after storms.
Changes in foraging behavior
Autumnal storms in Maryland, USA resulted in less frequent and shorter encounters of bottlenose dolphins in the US Mid-Atlantic Bight. However, dolphins spent a significantly higher percentage of their encounters feeding after storms than they did before or during them (Fandel et al. 2020). Similarly, bottlenose dolphins in Mississippi Sound displayed an approximately 15% increase in foraging activity for up to 2 years following Hurricane Katrina (Smith et al. 2013). These changes in foraging behavior are attributed to shifts in distributions and behavior of dolphin prey species as a result of altered environmental conditions (primarily sea surface temperature and salinity) following the hurricanes.
Out-of-habitat events and strandings
An out-of-habitat event occurs when an animal is displaced out of its typical habitat. Seven of these events were reported following Hurricane Rita, which hit the southwest Louisiana coast in 2005, with bottlenose dolphins found in flooded roadside ditches, canals, shallow flooded fields, and a natural creek area (Rosel & Watts 2008). These locations ranged from 2.5 to 11 km inland from the coast of the Gulf of Mexico, where these dolphins were displaced from. It is believed that the animals were carried inland on the storm surge that accompanied Hurricane Rita and were left stranded in areas that held water the longest once it started receding (Rosel & Watts 2008).
There have been two mass strandings of pygmy killer whales that are believed to have been a result of hurricanes. In 1995, five pygmy killer whales stranded (three of which died, while two were successfully refloated) in the British Virgin Islands a day after Hurricane Marilyn (Mignucci-Giannoni et al. 1999). In 2006, six pygmy killer whales (five of which died) stranded in New Caledonia during and after Hurricane Jim (Clua et al. 2014). Both studies hypothesize that increased energetic costs, as a result of attempting to evade the hurricanes, coupled with animals becoming disoriented and ending up in shallow waters, is what caused them to strand.
While these studies reveal post-storm effects on cetaceans, we still do not know exactly how these individuals behaved during the storms. Did they attempt longer dives to stay away from the rough conditions at the surface, thus becoming disoriented? Or were they behaving normally (i.e. foraging, travelling) and were simply “pushed” into waters that they did not intend to go into? Given that very stormy sea conditions do not allow for visual, boat-based surveys, we need to employ different technologies to study cetacean behavior and distribution during storms.
Passive acoustic monitoring (PAM) is a great tool that can monitor ocean environments for us when the seas are too stormy. Using fixed or mobile platforms, underwater PAM listening devices (hydrophone and data storage) can record sounds in the ocean for us to listen and analyze from shore. With PAM we are able to track the vocalizations of marine mammals as well as other sounds in the environment, such as waves crashing and rain. Anecdotally, we have spent many days at sea in conditions that were too rough for visual observations, but we could safely use our PAM tools to detect cetaceans. So, just because the seas may be too rough to see cetaceans, this fact does not mean that we cannot observe them – we just need to listen instead of look.
A number of studies have investigated whether whales change their vocalization behavior differently in response to changing ambient sound conditions (for example: Dunlop et al. 2010; Fournet et al. 2018). While research on ocean sound levels is often focused on the impact of human-generated or anthropogenic noise, there are also natural, abiotic sound sources (e.g. wind, rain, ice) that can elevate ambient sound levels. One potential animal response to elevated ambient sound levels is to vocalize at a higher intensity, called the Lombard (or cocktail party) effect. This phenomenon is common for us humans – have you ever been at a party and at some point you realize that you are shouting to someone in order to be heard above the noise of the room? That’s the Lombard effect! Humpback whales in Glacier Bay National Park, Alaska, exhibited the Lombard effect in response to both natural and man-made sounds, but the probability of calling was lower when vessels were present compared to times with only natural sounds (Fournet et al. 2018). It is also possible that whales may vocalize at different frequencies, times, or for shorter durations when the ocean becomes louder, which we can easily track with PAM. Unfortunately, PAM is limited to what we are able to hear, so if we do not hear whales we cannot determine if this result is because their vocalizations are masked by higher intensity sounds, if they stopped vocalizing, or if they left the listening area.
Animal-borne tags are another kind of autonomous observation tool that could help us understand cetacean behavior and distribution in storms. Admittedly, the logistics of applying tags before an imminent storm are probably complex. However, the development of medium-duration archival tags may provide a good trade-off between deploying tags long enough before a storm begins, thus providing safe working conditions for the research team, while minimizing potential physical impacts to the animals (Szesciorka et al. 2016). There are currently no published tag studies that document cetacean behavior during storms, but a study of a gray-headed albatross, fitted with a satellite transmitter, that successfully foraged during an Antarctic storm (Catry et al. 2004) shows the promise of using animal-borne tags to answer these questions.
As with many questions about animal behavior, our best option is to combine all of our research tools to piece together evidence about what might be going on in the deep, dark, stormy ocean. Simultaneously collecting acoustic and movement & behavior data through PAM and animal-borne tags, respectively, could allow us to determine how cetaceans behave during storms. While we are probably not poised to tackle these questions right now, perhaps another curious graduate student can take it on for their own PhD research…
Catry, P., Phillips, R.A., and J.P. Croxall. Sustained fast travel by a gray-headed albatross (Thalassarchie chrysostoma) riding an Antarctic storm. The Auk 121(4):1208-1213.
Clua, E.E., Manire, C.A., and C. Garrigue. 2014. Biological data of pygmy killer whales (Feresa attenuata) from a mass stranding in New Caledonia (South Pacific) associated with Hurricane Jim in 2006. Aquatic Mammals 40(2):162-172.
Dunlop, R.A., Cato, D.H., and M.J. Noad. 2010. Your attention please: increasing ambient noise levels elicits a change in communication behaviour in humpback whales (Megaptera novaeangliae). Proceedings of the Royal Society B 277(1693):doi.org/10.1098/rspb.2009.2319.
Fandel, A.D., Garrod, A., Hoover, A.L., Wingfield, J.E., Lyubchich, V., Secor, D.H., Hodge, K.B., Rice, A.N., and H. Bailey. 2020. Effects of intense storm events on dolphin occurrence and foraging behavior. Scientific Reports 10:19247.
Fournet, M.E.H., Matthews, L.P., Gabriele, C.M., Haver, S., Mellinger, D.K., and H. Klinck. 2018. Humpback whales Megaptera novaeangliae alter calling behavior in response to natural sounds and vessel noise. Marine Ecology Progress Series 607:251-268.
Mignucci-Giannoni, A.A., Toyos-González, G. M., Pérez-Padilla, J., Rodríguez-López, M. A., and J. Overing. 1999. Mass stranding of pygmy killer whales (Feresa attenuata) in the British Virgin Islands. Journal of the Marine Biological Association of the United Kingdom 80:759-760.
Rosel, P.E., and H. Watts. 2008. Hurricane impacts on bottlenose dolphins in the northern Gulf of Mexico. Gulf of Mexico Science 25:7.
Smith, C.E., Hurley, B.J., Toms, C.N., Mackey, A.D., Solangi, M., and S.A. Kuczaj II. 2013. Hurricane impacts on the foraging patterns of bottlenose dolphins Tursiops truncatus in Mississippi Sound. Marine Ecology Progress Series 487:231-244.
Szesciorka, A.R., Calambokidis, J., and J.T. Harvey. 2016. Testing tag attachments to increase the attachment duration of archival tags on baleen whales. Animal Biotelemetry 4:18.
Sketchnoting, also known as « visual notetaking » is a technique combining words with drawings, diagrams and typography to record ideas (Figure 1). This concept was invented by designer Michael Rohde in 2006 to combine tedious notetaking with doodling. He quickly discovered that adding drawings to his notes helped him concentrate and remember better. He would also be more likely to come back to his notes later on (something we must all admit is not so common). Similarly, after I followed a short online class by Magalie Le Gall (Sorbonne Université) I became convinced that sketchnoting shows promise and can have a positive impact on my scientific work.
Draw to remember more
The impact of sketchnoting on memory is not without scientific backing. Back in 1971, Allan Paivio, an American professor of psychology, developed the dual-coding theory. It posits that visual and verbal information are mentally processed in two distinctive systems and have additive effects on cognitive operations such as memory. Numerous experiments have empirically confirmed that dual coding (images + words) improve learning and memory. In addition, converting what you hear or see into visually interconnected drawings and words helps you synthesize content. Personalizing ideas into your own symbols and images also lays a strong basis for remembering. The implications of sketchnoting for educational purposes are therefore huge!
Draw to stay focused
I have only started sketchnoting recently but the impact this method had on my concentration immediately struck me. In the constant stream of information that we experience nowadays, I found that synthesizing ideas on paper using symbols and diagrams helped me stay focused on what I am presently reading or hearing, instead of letting my thoughts drift in a thousand different directions. Again, this outcome can have big implications in the classroom or at your desk. Using very basic lettering, bullets, frames and connectors (Figure 2), sketchnoting appears to be a good didactic tool.
Draw to create and appeal
Mike Rohde’s motto is « ideas, not art » because a lot of people have an immediate reaction of fear of failure when they are asked to draw something. He emphasizes that sketchnoting is not necessarily meant to be pretty, as it mostly serves a personal purpose. However, if you have an artistic fiber (even slightly!), sketchnoting becomes a great communication tool and can help you convey ideas in posters, slides, blogs, etc. Even very simple drawings are appealing and fun. You can create your own visual libraries from a few basic shapes (Figure 3). Anything can be drawn with a few simple lines! You can also use drawing libraries such as quickdraw.withgoogle.com to find examples and eventually gain confidence… as you realize that the average people’s drawing skills are pretty low (the dolphin drawings on this website are worth a look)!
Now, the key to developing this new skill is clearly to practice! From now on, I have decided to record every one of our monthly GEMM lab meetings in a sketchnote to make sure I keep track of our great discussions. I will also definitely try to apply this approach when reading scientific literature, attending conferences, preparing drafts, teaching and so much more! And for a start, what could be better then to sketchnote the research project I currently working on (Figure 4)?
What are the most unexpected things you’ve done on Zoom in the last year? Since the pandemic dramatically changed all our lives in 2020, I think we’ve all been surprised by the diversity of things we’ve done remotely. I’ve baked bagels with a friend in Finland, done oceanography labs from my kitchen, had dance parties with people across the country, and conducted an award ceremony for my family’s Thanksgiving scavenger hunt – all on Zoom. Over the last several months, I’ve also mentored an Undergraduate Research, Scholarship, & the Arts (URSA) Engage student, named Amanda. Although we haven’t met in person yet, we’ve been connecting over Zoom since October.
Amanda is an Ocean Sciences student working with me and Dr. Kim Bernard (CEOAS) to conduct a literature review about the two species of krill found off the coast of Oregon. Thysanoessa spinifera and Euphausia pacifica are an important food source for many of the animals that live off our coast — including blue, humpback, and fin whales. I am trying to learn how krill distributions shape those of humpback and blue whales as part of project OPAL, as well as which oceanographic factors drive krill abundances and distributions.
We’re also interested in T. spinifera and E. pacifica for the crucial roles they serve in ecosystems, beyond providing dinner for whales. Krill do many things that are beneficial to ecosystems and people, termed “ecosystem services.” These include facilitating carbon drawdown from the surface ocean to the deep, supporting lucrative fisheries species like salmon, flatfish, and rockfish, and feeding seabirds like auklets and shearwaters. We want to understand more fully the niche that T. spinifera and E. pacifica each fill off the coast of Oregon, which will help us anticipate how these important animals can be impacted by forces such as global climate change and marine management efforts.
Trying to understand the ecosystem services fulfilled by krill is inherently interdisciplinary, which means we have to learn a lot of new things, making this project a lot of fun. The questions Amanda and I have pursued together have ranged from intensely specific, to surprisingly broad. How many calories do blue whales need to eat in a day? How many krill do salmon need to eat? How big are krill fecal pellets, and how fast do they sink?
Trying to answer these questions has basically amounted to a heroic scouring of the internet’s krillscape by Amanda. She has hunted down papers dating back to the 1960s, pulled together findings from every corner of the world, and pursued what she refers to as “treasure troves” of data. In the process, she has also revealed the holes that exist in the literature, and given us new questions. This is the basis of the scientific process: understanding the current state of knowledge, identifying gaps in that knowledge, and developing the questions and methods needed to fill those gaps.
Filling in knowledge gaps about T. spinifera and E. pacifica can help us better understand these animals, the ecosystems where they live, and the whales and other animals that depend on them for prey. It’s exciting to know that we will have the opportunity to help fill some of these gaps, as both Amanda and I continue this research over the course of our degrees.
Being able to engage in remote research and mentorship has been really rewarding, and it has shown me how far we’ve all come over the last year. Learning how to work together remotely has been crucial as we have adjusted to the funny new normal of the pandemic. As much as I miss working with people in person, I’ve learned that there’s a lot of great connection to be found even in remote collaboration – I’ve loved meeting Amanda’s pets on Zoom, learning about her career goals, and seeing her incredibly artistic representations of the carbon cycle held up to the camera.
Even though most of our conversations take place on Zoom from our homes, this research still feels plugged into a bigger community. Amanda and I also join Kim’s bigger Zooplankton Ecology Lab meetings, which include two other graduate students and eight undergraduate students, all of whom are working on zooplankton ecology questions that span from the Arctic to the Antarctic. Even though we’ve never met in person, a supportive and curious community has developed among all of us, which I know will persist when we can move back to in-person research and mentorship.
Clara Bird, PhD Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
In order to understand a species’ distribution, spatial ecologists assess which habitat characteristics are most often associated with a species’ presence. Incorporating behavior data can improve this analysis by revealing the functional use of each habitat type, which can help scientists and managers assign relative value to different habitat types. For example, habitat used for foraging is often more important than habitat that a species just travels through. Further complexity is added when we consider that some species, such as gray whales, employ a variety of foraging tactics on a variety of prey types that are associated with different habitats. If individual foraging tactic specialization is present, different foraging habitats could be valuable to specific subgroups that use each tactic. Consequently, for a population that uses a variety of foraging tactics, it’s important to study the associations between tactics and habitat characteristics.
Lukoschek and McCormick’s (2001) study investigating the spatial distribution of a benthic fish species’ foraging behavior is a great example of combining data on behavior, habitat, and morphology. They collected data on the diet composition of individual fish categorized into different size classes (small, medium, and large) and what foraging tactics were used in which reef zones and habitat types. The foraging tactics ranged from feeding in the water column to digging (at a range of depths) in the benthic substrate. The results showed that an interesting combination of fish behavior and morphology explained the observed diet composition and spatial distribution patterns. Small fish foraged in shallower water, on smaller prey, and primarily employed the water column and shallow digging tactics. In contrast, large fish foraged in deep water, on larger prey, and primarily fed by digging deeper into the seafloor (Figure 1). This pattern is explained by both morphology and behavior. Morphologically, the size of the feeding apparatus (mouth gape size) affects the size of the prey that a fish can feed on. The gape of the small fish is not large enough to eat the larger prey that large fish are able to consume. Behaviorally, predation risk also affects habitat selection and tactic use. Small fish are at higher risk of being predated on, so they remain in shallow areas where they are more protected from predators and they don’t dig as deep to forage because they need to be able to keep an eye out for predators. Interestingly, while they found a relationship between the morphology of the fish and habitat use, they did not find an association between specific feeding tactics and habitat types.
Conversely, Torres and Read (2009) did find associations between theforaging tactics of bottlenose dolphins in Florida Bay, FL and habitat type. Dolphins in this bay employ three foraging tactics: herd and chase, mud ring feeding, and deep diving. Observations of the foraging tactics were linked to habitat characteristics and individual dolphins. The study found that these tactics are spatially structured by depth (Figure 2), with deep diving occurring in deep water whereas mud ring feeding occurrs in shallower water. They also found evidence of individual specialization! Individuals that were observed deep diving were not observed mud ring feeding and vice-versa. Furthermore, they found that individuals were found in the habitat type associated with their preferred tactic regardless of whether they were foraging or not. This result indicates that individual dolphins in this bay have a foraging tactic they prefer and tend to stay in the corresponding habitat type. These findings are really intriguing and raise interesting questions regarding how these tactics and specializations are developed or learned. These are questions that I am also interested in asking as part of my thesis.
Both of these studies are cool examples that, combined, exemplify questions I am interested in examining using our study population of Pacific Coast Feeding Group (PCFG) gray whales. Like both studies, I am interested in assessing how specific foraging tactics are associated with habitat types. Our hypothesis is that different prey types live in different habitat types, so each tactic corresponds to the best way to feed on that prey type in that habitat. While predation risk doesn’t have as much of an effect on foraging gray whales as it does on small benthic fish, I do wonder how disturbance from boats could similarly affect tactic preference and spatial distribution. I am also curious to see if depth has an effect on tactic choice by using the morphology data from our drone-based photogrammetry. Given that these whales forage in water that is sometimes as deep as they are long, it stands to reason that maneuverability would affect tactic use. As described in a previous blog, I’m also looking for evidence of individual specialization. It will be fascinating to see how foraging preference relates to space use, habitat preference, and morphology.
These studies demonstrate the complexity involved in studying a population’s relationship to its habitat. Such research involves considering the morphology and physiology of the animals, their social, individual, foraging, and predator-prey behaviors, and the relationship between their prey and the habitat. It’s a bit daunting but mostly really exciting because better understanding each puzzle piece improves our ability to estimate how these animals will react to changing environmental conditions.
While I don’t have any answers to these questions yet, I will be working with a National Science Foundation Research Experience for Undergraduates intern this summer to develop a habitat map of our study area that will be used in this analysis and potentially answer some preliminary questions about PCFG gray whale habitat use patterns. So, stay tuned to hear more about our work this summer!
Lukoschek, V., & McCormick, M. (2001). Ontogeny of diet changes in a tropical benthic carnivorous fish, Parupeneus barberinus (Mullidae): Relationship between foraging behaviour, habitat use, jaw size, and prey selection. Marine Biology, 138(6), 1099–1113. https://doi.org/10.1007/s002270000530
Torres, L. G., & Read, A. J. (2009). Where to catch a fish? The influence of foraging tactics on the ecology of bottlenose dolphins ( Tursiops truncatus ) in Florida Bay, Florida. Marine Mammal Science, 25(4), 797–815. https://doi.org/10.1111/j.1748-7692.2009.00297.x
By Alejandro Fernandez Ajo, PhD student in the Department of Biology, Northern Arizona University, visiting scientist in the GEMM Lab working on the gray whale physiology and ecology project
When a cetacean (whales and dolphins) is ashore or trapped in nearshore waters and cannot return to the open waters, it is considered stranded. Frequently, the stranded animal is in distress, dying, or dead. Although rare, the stranded cetacean can be a healthy animal trapped due to changes in tide or disorientation. Every year many cetacean strandings are reported from along the coasts around the world, and likely many more stranding events go unnoticed when they occur in remote areas. In all cases, the question is: why do cetaceans beach?
There may be different causes for whales and dolphins to strand on beaches, either dead or alive. Understanding and investigating the causes of cetaceans strandings is critical because they can be indicators of ocean health, can help identify anthropogenic sources of disturbance, and can give insights into larger environmental issues that may also have implications for human health (NOAA). In this context, when scientists are analyzing a stranding event, they consider both possibilities that the event was natural or human-caused and classify strandings according to specific characteristics to study the causes of these events.
Types of cetacean strandings:
Live or Dead Stranding:
A stranding can involve live animals or dead animals if the death occurs in the sea and the body is thrown ashore by wind or currents. In live strandings, when they occur near urbanized areas, usually significant efforts are made to rescue and return the animals to the water; with small odontocetes, sometimes there is success, and animals can be rescued. However, when large whales are beached alive, their own weight out of the water can compress their organs and can cause irreversible internal damage. Although not externally visible, such damage can sometimes cause the death of the animal even after returning to the sea.
According to the number of individuals:
Single strandings occur when only a single specimen is affected at the time. The cetaceans that most frequently strand individually are the baleen (or mysticete) whales, such as right and humpback whales, due to their often solitary habits.
Mass strandings comprise two or more specimens, and in some cases, it can involve tens or even a few hundred animals. The mass strandings are more frequently observed for the odontocetes, such as pilot whales, false killer whales, and sperm whales with more complex social structures and gregarious habits.
Unusual Mortality Events
The Marine Mammal Protection Act defines an unusual mortality event (UME) as a stranding event that is unexpected, involves a significant die-off of any marine mammal population, and demands immediate response. Seven criteria make a mortality event “unusual.” Source: https://www.fisheries.noaa.gov.
A marked increase in the magnitude or a marked change in morbidity, mortality, or strandings when compared with prior records.
A temporal change in morbidity, mortality, or strandings is occurring.
A spatial change in morbidity, mortality, or strandings is occurring.
The species, age, or sex composition of the affected animals is different than that of animals that are normally affected.
Affected animals exhibit similar or unusual pathologic findings, behavior patterns, clinical signs, or general physical condition (e.g., blubber thickness).
Potentially significant morbidity, mortality, or stranding is observed in species, stocks, or populations that are particularly vulnerable (e.g., listed as depleted, threatened, or endangered, or declining). For example, stranding of three or four right whales may be cause for great concern, whereas stranding of a similar number of fin whales may not.
Morbidity is observed concurrent with or as part of an unexplained continual decline of a marine mammal population, stock, or species.
The purpose of the classification of a mortality event as a UME is to activate an emergency response that aims to minimize deaths, determine the event cause, or causes, determine the effect of the event on the population, and identify the role of environmental parameters in the event. Such classification authorizes a federal investigation that is led by the expertise of the Working Group on Marine Mammal Unusual Mortality Events to investigate the event. This working group is comprised of experts from scientific and academic institutions, conservation organizations, and state and federal agencies, all of whom work closely with stranding networks and have a wide variety of experience in biology, toxicology, pathology, ecology, and epidemiology.
What can be learned from strandings and UMEs?
Examining stranded marine mammals can provide valuable insight into marine mammal health and identify environmental factors leading to strandings. Through forensic examinations, the aim is to identify possible risks to whales’ health and evaluate their susceptibility to diseases, pollutants, and other stressors. This information can contribute to cetacean conservation through informed management strategies. However, the quality of the data derived from a necropsy (the postmortem examination of carcasses) is highly contingent upon how early the stranding event is reported. As soon as the animal is deceased, decomposition starts, hindering the possibilities of detailed investigations of the cause of death.
Therefore, a solid network that can report and respond quickly to a stranding event is fundamental; this includes trained personnel, infrastructure, funding, and expertise to respond in a manner that provides for animal welfare (in the case of live strandings) and obtains data on marine mammal health and causes of death. Moreover, a coordinated international organization that integrates national marine mammal stranding networks has also been identifying as a critical aspect to enable adequate response to such mortality events. In many locations and countries around the world, funding, logistical support, and training remain challenging to stranding response.
In response to these concerns and needs, at the last World Marine Mammal Conference, which took place in Barcelona in December of 2019, The Global Stranding Network was founded to “enhance and strengthen international collaboration to (1) ensure consistent, high-quality response to stranded marine mammals globally, and (2) support conservation efforts for species under threat of extinction.” Monitoring marine mammal health worldwide can guide conservation and help identify priority areas for management (Gulland and Stockin, 2020).
What to do in case of finding a whale or dolphin on the beach?
When strandings occur, it is essential to know how to act. Unfortunately, untrained people, often with good intentions, can worsen the situation of stress and injury to the animal or can put themselves at risk of injury or exposure to pathogens. If you find a cetacean alive or dead on the beach, the most important things to do are:
Record information about the location and the animal´s characteristics (the species, if known; the animal’s approximate size; and status (alive or dead)).
Keep at a safe distance: the animal may appear dead to the naked eye and not be. It is important to remember that cetaceans are wild animals and that in stressful situations such as strandings, they can try to defend themselves.
Do not touch the animal: one of the causes of strandings is diseases; therefore, it is advisable not to contact the individuals to avoid exposure to potential pathogens.
If the animal is alive, keep a distance from the animal, especially from its head and tail. Prevent children or dogs from approaching the animal.
Keep calm and do not make noise that could disturb the stranded animal.
Do not take the animal out of the water if it is on the shore or return it to the sea if it is on the beach: Such movement could cause serious injuries, or even death.
Do not feed the animal or give it water: keep the blowhole clear because it is where they breathe.
Proceedings of the workshop “Harmonizing Global Stranding Response.” (2020) World marine mammal Conference Barcelona, Catalonia, Spain. Editors: Gulland F and Stockin K; Ecs Special Publication Series No. 62.
Mazzariol S., Siebert U., Scheinin A., Deaville R., Brownlow A., Uhart M.., Marcondes M., Hernandez G., Stimmelmayr R., Rowles T., Moore K., Gulland F., Meyer M., Grover D., Lindsay P., Chansue N., Stockin K. (2020). Summary of Unusual Cetaceans Strandings Events worldwide (2018-2020). SC-68B/E/09 Rev1.
To understand the complex dynamics of an ecosystem, we need to examine how physical forcing drives biological response, and how organisms interact with their environment and one another. The largest animal on the planet relies on the wind. Throughout the world, blue whales feed areas where winds bring cold water to the surface and spur productivity—a process known as upwelling. In New Zealand’s South Taranaki Bight region (STB), westerly winds instigate a plume of cold, nutrient-rich waters that support aggregations of krill, and ultimately lead to foraging opportunities for blue whales. This pathway, beginning with wind input and culminating in blue whale occurrence, does not take place instantaneously, however. Along each link in this chain of events, there is some lag time.
Our recent paper published in Scientific Reports examines the lags between wind, upwelling, and blue whale occurrence patterns. While marine ecologists have long acknowledged that lag plays a role in what drives species distribution patterns, lags are rarely measured, tested, and incorporated into studies of marine predators such as whales. Understanding lags has the potential to greatly improve our ability to predict when and where animals will be under variable environmental conditions. In our study, we used timeseries analysis to quantify lag between different metrics (wind speed, sea surface temperature, blue whale vocalizations) at different locations. While our methods are developed and implemented for the STB ecosystem, they are transferable to other upwelling systems to inform, assess, and improve predictions of marine predator distributions by incorporating lag into our understanding of dynamic marine ecosystems.
So, what did we find? It all starts with the wind. Wind instigates upwelling over an area off the northwest coast of the South Island of New Zealand called Kahurangi Shoals. This wind forcing spurs upwelling, leading to the formation of a cold water plume that propagates into the STB region, between the North and South Islands, with a lag of 1-2 weeks. Finally, we measured the density of blue whale vocalizations—sounds known as D calls, which are produced in a social context, and associated with foraging behavior—recorded at a hydrophone downstream along the upwelling plume’s path. D call density increased 3 weeks after increased wind speeds near the upwelling source. Furthermore, we looked at the lag time between wind events and aggregations in blue whale sightings. Blue whale aggregations followed wind events with a mean lag of 2.09 ± 0.43 weeks, which fits within our findings from the timeseries analysis. However, lag time between wind and whales is variable. Sometimes it takes many weeks following a wind event for an aggregation to form, other times mere days. The variability in lag can be explained by the amount of prior wind input in the system. If it has recently been windy, the water column is more likely to already be well-mixed and productive, and so whale aggregations will follow wind events with a shorter lag time than if there has been a long period without wind and the water column is stratified.
This publication forms the second chapter of my PhD dissertation. However, in reality it is the culmination of a team effort. Just as whale aggregations lag wind events, publications lag years of hard work. The GEMM Lab has been studying New Zealand blue whales since Leigh first hypothesized that the STB was an undocumented foraging ground in 2013. I was fortunate enough to join the research effort in 2016, first as a Masters student and now as a PhD Candidate. I remember standing on the flying bridge of R/V Star Keys in New Zealand in 2017, when early in our field season we saw very few blue whales. Leigh and I were discussing this, with some frustration. Exclamations of “This is cold, upwelled water! Where are the whales?!” were followed by musings of “There must be a lag… It has to take some time for the whales to respond.” In summer 2019, Christina Garvey came to the GEMM Lab as an intern through the NSF Research Experience for Undergraduates program. She did an outstanding job of wrangling remote sensing and blue whale sighting data, and together we took on learning and understanding timeseries analysis to quantify lag. In a meeting with my PhD committee last spring where I presented preliminary results, Holger Klinck chimed in with “These results are interesting, but why haven’t you incorporated the acoustic data? That is a whale timeseries right there and would really add to your analysis”. Dimitri Ponirakis expertly computed the detection area of our hydrophone so we could adequately estimate the density of blue whale calls. Piecing everything together, and with advice and feedback from my PhD committee and many others, we now have a compelling and quantitative understanding of the upwelling dynamics in the STB ecosystem, and have thoroughly described the pathway from wind to whales in the region.
Our findings are exciting, and perhaps even more exciting are the implications. Understanding the typical patterns that follow a wind event and how the upwelling plume propagates enables us to anticipate what will happen one, two, or up to three weeks in the future based on current conditions. These spatial and temporal lags between wind, upwelling, productivity, and blue whale foraging opportunities can be harnessed to generate informed forecasts of blue whale distribution in the region. I am thrilled to see this work in print, and equally thrilled to build on these findings to predict blue whale occurrence patterns.
Reference: Barlow, D.R., Klinck, H., Ponirakis, D., Garvey, C., Torres, L.G. Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep 11, 6915 (2021). https://doi.org/10.1038/s41598-021-86403-y
In September of 2016, Leigh Torres, associate professor at Oregon State University, and I attended the 6th International Albatross and Petrel Conference. Somehow, amid all of the science that filled the week, Leigh first saw the Global Fishing Watch fishing map. She shouted with joy. She immediately envisioned a study to assess interactions between seabirds and fishing boats, and started considering a spatial overlap analysis between telemetry tracks of albatross with the Global Fishing Watch database. Such a study could help reduce bycatch, or the incidental catch of non-target species, like seabirds, in fisheries. Five years later, we executed that study in partnership with Global Fishing Watch, one of the first to look at fine-scale overlap between fishing vessels and marine life on the high seas (Orben et al. 2021).
Transparent data means opportunity for analysis
Despite knowing that bycatch from fisheries is a real, significant problem for many albatross populations, we have long struggled to know where birds go, where boats fish, and where the two interact in the vast ocean, especially in largely unregulated international waters. Albatross are long-lived seabirds and 15 out of the 22 species are threatened with extinction. Scientists have been tracking albatross for three decades, but assessing individual seabird encounters with vessels has traditionally been limited by a lack of transparency in fishing activity data. Some seabirds are attracted to fishing vessels because of the bait and offal, but we don’t know the whole story of why some birds approach vessels while others don’t.
When we first put our relatively large datasets together – 9,992 days of albatross tracking data from 150 birds and Global Fishing Watch fishing effort data from 2012-2016 – we weren’t sure what we would find. The ocean is a big place, and so finding where one bird and one vessel overlap is kind of like trying to find a needle in a haystack. Would we have enough encounters between birds and boats for an analysis? Would birds encounter fishing vessels as often as we think?
Measuring encounters between albatross and vessels
After overlaying the tracking data with a gridded daily layer of fishing effort, we identified potential encounters between birds and fishing boats. We identified when an albatross could detect a vessel, at a radius or 30 kilometers, and when an albatross had a close encounter with a vessel, within a radius of 3 kilometers (following methods developed in Collet Patrick & Weimerskirch, 2015). Then, we investigated factors that influenced the occurrence and duration of close encounters, considering the bird’s behavior, environmental conditions and habitat, fishing vessel and fisheries characteristics, and temporal variables, such as time of day and month.
Species variation of encounters
We conducted our analysis for three species of albatross that forage in the north Pacific ocean, Laysan albatross, black-footed albatross, and short-tailed albatross.
Adult black-footed albatrosses approached vessels for a close encounter 61.9 percent of the time they detected a fishing vessel.
Adult Laysan albatross had close encounters with a fishing boat 35.7 percent of the time they detected a vessel.
Juvenile short-tailed albatross had a lower frequency of close encounters (28.6 percent),
Understanding close encounters and their duration
Due to a low sample size of encounters, we were unable to investigate the reason for close encounters or their duration for black-footed albatrosses. More tracking data is critical to understand factors influencing the impact of vessels on this vulnerable species.
Laysan albatross were more likely to approach fishing vessels when fishing effort was high, but fishing boat density was low. Laysan albatross also had close encounters with vessels more frequently while they were foraging. Due to sample size, we could not further investigate the reason for the duration of encounters for this species.
Short-tailed albatrosses were also more likely to approach fishing vessels when they were searching for prey, fishing effort was high, and fishing boat density was low. They were more likely to have close encounters with vessels during the day and in habitats with water depths from 75-1500 meters.
Vessel attendance by short-tailed albatrosses was longer when sea surface temperatures were warmer and less productive, and during periods with lower wind speeds.
A useful approach
The information available to fisheries managers in order to reduce bycatch is most often limited to data collected from the perspective of the fishing vessels. Our analysis provides an alternative view – an albatross’ view of when and where boats are encountered in the seascape. While our analysis didn’t specifically look at bycatch, our estimates of proximity between birds and boats can be considered a proxy for increased bycatch risk.
For the endangered short-tailed albatross, bycatch events are few, but they come with high consequences for the bird population and fishing industry. Extending our study in a dynamic ocean management framework to provide an early warning system to predict when short-tailed albatross might make close and longer encounters with fishing vessels could be the next step. Furthermore, our analysis methods to assess when, where and why marine animals interact with fishing vessels can be applied to many other marine species in order to understand and reduce conflicts with fisheries.
This blog was for the Global Fishing Watch blog at globalfishingwatch.org
Collet, J., Patrick, S. C., & Weimerskirch, H. (2015). Albatrosses redirect flight towards vessels at the limit of their visual range. Marine Ecology Progress Series, 526, 199–205. http://doi.org/10.3354/meps11233
Orben, RA J Adams, M Hester, SA Shaffer, R Suryan, T Deguchi, K Ozaki, F Sato, LC Young, C Clatterbuck, MG Conners, DA Kroodsma, LG Torres. 2021. Across borders: External factors and prior behavior influence North Pacific albatross associations with fishing vessels. Journal of Applied Ecology. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.13849
Last week marked the one year anniversary of the pandemic reality we have all been living. It has been an extremely challenging year, with everyone experiencing different kinds of difficulties and hurdles. One challenge that likely unites the majority of us is having to forego seeing our loved ones. For me personally, this is the longest time I have not seen my family (445 days and counting) and I know I am not alone in this situation. My homesickness started a train of thought about cetacean parental care and inspired me to write a blog about this topic. As you can see from the title, this post focuses on maternal care, rather than parental care. This bias isn’t due to my lack of research on this topic or active exclusion, but rather because there are currently no known cetacean species where paternal participation in offspring production and development extends beyond copulation (Rendell et al. 2019). Thus, this blog is all about the role of mothers in the lives of cetacean calves.
Like humans, cetacean mothers invest a lot of energy into their offspring. Most species have a gestation period of 10 or more months (Oftedal 1997). For baleen whale females in particular, pregnancy is not an easy feat given that they only feed during summer feeding seasons. They must therefore acquire all of the energy they will need for two migrations, birth, and (almost) complete lactation, before they will have access to food on feeding grounds again. For pregnant gray whales, a mere 4% loss in average energy intake on the foraging grounds will prevent her from successfully producing and/or weaning a calf (Villegas-Amtmann et al. 2015), demonstrating how crucial the foraging season is for a pregnant baleen whale. Once a calf is born, lactation ensues, ranging in length between approximately 6-8 months for most baleen whale species to upwards of one or two years in odontocetes (Oftedal 1997). The very short lactation period in baleen whales is offset by the large volume (for blue whales, up to 220 kg per day) and high fat percentage (30-50%) of milk that mothers provide for their calves (Oftedal 1997). In contrast, odontocetes (or toothed whales) have a more prolonged period of lactation with less fatty milk (10-30%). This discrepancy in lactation period lengths is in part because odontocete species do not undertake long migrations, which allows females to feed year-round and therefore allocate energy to nursing young for a longer time.
Aside from the energetically costly task of lactation, cetacean mothers must also assist their calves as they learn to swim. Echelon swimming is a common position of mother-calf pairs whereby the calf is in very close proximity to its mother’s mid-lateral flank and provides calves with hydrodynamic benefits. Studies in bottlenose dolphins have shown that swimming in echelon results in a 24% reduction in mean maximum swim speeds and a 13% decrease in distance per stroke (Noren 2008) for mothers, while concurrently increasing average swim speeds and distance per stroke of calves by 28% and 19%, respectively (Noren et al. 2007). While these studies have only been conducted in odontocete species, echelon swimming is also observed in baleen whales (Smultea et al. 2017), indicating that baleen whale females may experience the same reductions in swimming efficiency. Furthermore, mothers will forgo sleep in the first days after birth (killer whales & bottlenose dolphins; Lyamin et al. 2005) and/or shorten their dive foraging times to accommodate calf diving ability (bottlenose dolphins [Miketa et al. 2018] & belugas [Heide-Jørgensen et al. 2001]). Females must endure these losses in foraging opportunities and decreased swimming efficiency when they are at their most nutritionally stressed to ensure the well-being and success of their offspring.
It is at the time of weaning (when a calf becomes independent), that we start to see differences in the maternal role between baleen and toothed whale mothers. Odontocetes have much stronger sociality than baleen whales causing offspring to stay with their mothers for much longer periods. Among the largest toothed whales, such as killer and sperm whales, offspring stay with their mothers in stable matrilineal units for often a lifetime. Among the smaller toothed whales, such as bottlenose dolphins, maternal kin maintain strong bonds in dynamic fission-fusion societies. In contrast, post-weaning maternal care in baleen whales is limited, with the mother-calf pair typically separating soon after the calf is weaned (Rendell et al. 2019).
The long-term impact of social bonds in odontocetes is evident through examples of vertically transmitted behaviors (from mother to calf) in a number of species. For example, the use of three unique foraging tactics (sponge carrying, rooster-tail foraging, and mill foraging) by bottlenose dolphin calves in Shark Bay, Australia, was only significantly explained by maternal use of these tactics (Sargeant & Mann 2009). In Brazil, individuals of four bottlenose dolphin populations along the coast cooperatively forage with artisanal fishermen, which involves specialized and coordinated behaviors from both species. This cooperative foraging tactic among dolphins is primarily maintained across generations via social learning from mothers to calves (Simões-Lopeset al. 2016). The risky tactic of intentional stranding by killer whales on beaches to capture elephant seal pups requires a high degree of skill and high parental investment to reduce the associated risk of stranding (Guinet & Bouvier 1995).
Evidence for vertical transmission of specialized foraging tactics in baleen whales currently does not exist. Bubble-net feeding is a specialized tactic employed by humpback whales in three oceanic regions where multiple individuals work together to herd and trap prey (Wiley et al. 2011). However, it remains unknown whether this behavior is vertically transmitted. Simultaneous video tags from a mother-calf humpback whale pair in the Western Antarctic Peninsula documented synchrony in dives, with the calf’s track lagging behind the mother’s by 4.5 seconds, suggesting that the calf was following its mother (Tyson et al. 2012). Synchronous diving likely allows calves to observe their mothers and practice their diving, and could offer a pathway for them to mimic foraging behaviors and tactics displayed by mothers.
While there currently may not be evidence for vertical transmission of specialized foraging tactics among the baleen whales, there is documentation of matrilineal fidelity to both foraging (Weinrich 1998, Barendse et al. 2013, Burnham & Duffus 2020) and breeding grounds (Carroll et al. 2015). Matrilineal site fidelity to foraging grounds is not exclusive to baleen whales and has also been documented in a number of odontocete species (Palsbøll et al. 1997, Turgeon et al. 2012).
In the GEMM Lab, we are interested in exploring the potential long-term bonds, role and impact of Pacific Coast Feeding Group (PCFG) gray whale mothers on their calves. GEMM Lab PhD student Clara Bird is digging into whether specialized foraging tactics, such as bubble blasts and headstands, are passed down from mothers to calves. I hope to assess whether using the PCFG range as a foraging ground (rather than the Arctic region) is a vertically transmitted behavior or whether environmental factors may play a larger role in the recruitment and dynamics of the PCFG. It will take us a while to get to the bottom of these questions, so in the meantime hug your loved ones if it’s safe to do so or, if you’re in my boat, continue to talk to them virtually until it is safe to be reunited.
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Burnham, R. E., and D. A. Duffus. 2020. Maternal behaviors of gray whales (Eschrichtius robustus) on a summer foraging site. Marine Mammal Science 36:1212-1230.
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Guinet, C., and J. Bouvier. 1995. Development of intentional stranding hunting techniques in killer whale (Orcinus orca) calves at Crozet Archipelago. Canadian Journal of Zoology 73:27-33.
Heide-Jørgensen, M. P., Hammeken, N., Dietz, R., Orr, J., and P. R. Richard. 2001. Surfacing times and dive rates for narwhals and belugas. Arctic 54:207-355.
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Noren, S. R., Biedenbach, F., Redfern, J. V., and E. F. Edwards. 2007. Hitching a ride: the formation locomotion strategy of dolphin calves. Functional Ecology 22:278-283.
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Sargeant, B. L., and J. Mann. 2009. Developmental evidence for foraging traditions in wild bottlenose dolphins. Animal Behavior 78:715-721.
Simões-Lopes, P. C., Daura-Jorge, F. G., and M. Cantor. 2016. Clues of cultural transmission in cooperative foraging between artisanal fishermen and bottlenose dolphins, Tursiops truncatus (Cetacea: Delphinidae). Zoologia (Curitiba) 33:e20160107.
Smultea, M. A., Fertl, D., Bacon, C. E., Moore, M. R., James, V. R., and B. Würsig. 2017. Cetacean mother-calf behavior observed from a small aircraft off Southern California. Animal Behavior and Cognition 4:1-23.
Turgeon, J., Duchesne, P., Colbeck, G. J., Postma, L. D., and M. O. Hammill. 2011. Spatiotemporal segregation among summer stocks of beluga (Delphinapterus leucas) despite nuclear gene flow: implication for the endangered belugas in eastern Hudson Bay (Canada). Conservation Genetics 13:419-433.
Tyson, R. B., Friedlaender, A. S., Ware, C., Stimpert, A. K., and D. P. Nowacek. 2012. Synchronous mother and calf foraging behaviour in humpback whales Megaptera novaeangliae: insights from multi-sensor suction cup tags. Marine Ecology Progress Series 457:209-220.
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Weinrich, M. 1998. Early experience in habitat choice by humpback whales (Megaptera novaeaengliae). Journal of Mammalogy 79:163-170.
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