Hello from the RV Bell M. Shimada! We are currently sampling at an inshore station on the Heceta Head Line, which begins just south of Newport and heads out 45 nautical miles west into the Pacific Ocean. We’ll spend 10 days total at sea, which have so far been full of great weather, long days of observing, and lots of whales.
Dawn and Rachel in matching, many-layered outfits, 125 miles offshore on the flying bridge of the RV Bell M. Shimada.
Run by NOAA, this Northern California Current (NCC) cruise takes place three times per year. It is fabulously interdisciplinary, with teams concurrently conducting research on phytoplankton, zooplankton, seabirds, and more. The GEMM Lab will use the whale survey, krill, and oceanographic data to fuel species distribution models as part of Project OPAL. I’ll be working with this data for my PhD, and it’s great to be getting to know the region, study system, and sampling processes.
I’ve been to sea a number of times and always really enjoyed it, but this is my first time as part of a marine mammal survey. The type and timing of this work is so different from the many other types of oceanographic science that take place on a typical research cruise. While everyone else is scurrying around, deploying instruments and collecting samples at a “station” (a geographic waypoint in the ocean that is sampled repeatedly over time), we – the marine mammal team – are taking a break because we can only survey when the boat is moving. While everyone else is sleeping or relaxing during a long transit between stations, we’re hard at work up on the flying bridge of the ship, scanning the horizon for animals.
Top left: marine mammal survey effort (black lines), and oceanographic sampling stations (red diamonds). Top right: humpback whale sighting locations. Bottom left: fin whale sighting locations. Bottom right: pacific white-sided dolphin sighting locations.
During each “on effort” survey period, Dawn and I cover separate quadrants of ocean, each manning either the port or starboard side. We continuously scan the horizon for signs of whale blows or bodies, alternating between our eyes and binoculars. During long transits, we work in chunks – forty minutes on effort, and twenty minutes off effort. Staring at the sea all day is surprisingly tiring, and so our breaks often involve “going to the eye spa,” which entails pulling a neck gaiter or hat over your eyes and basking in the darkness.
Dawn has been joining these NCC cruises for the last four years, and her wealth of knowledge has been a great resource as I learn how to survey and identify marine mammals. Beyond learning the telltale signs of separate species, one of the biggest challenges has been learning how to read the sea better, to judge the difference between a frothy whitecap and a whale blow, or a distant dark wavelet and a dorsal fin. Other times, when conditions are amazing and it feels like we’re surrounded by whales, the trick is to try to predict the positions and trajectory of each whale so we don’t double-count them.
Over the last week, all our scanning has been amply rewarded. We’ve seen pods of dolphins play in our wake, and spotted Dall’s porpoises bounding alongside the ship. Here on the Heceta Line, we’ve seen a diversity of pinnipeds, including Northern fur seals, Stellar sea lions, and California sea lions. We’ve been surprised by several groups of fin whales, farther offshore than expected, and traveled alongside a pod of about 12 orcas for several minutes, which is exactly as magical as it sounds.
Killer whales traveling alongside the Bell M. Shimada, putting on a show for the NCC science team and ship crew. Photo by Dawn Barlow.
Notably, we’ve also seen dozens of humpbacks, including along what Dawn termed “the humpback highway” during our transit offshore of southern Oregon. One humpback put on a huge show just 200 meters from the ship, demonstrating fluke slapping behavior for several minutes. We wanted to be sure that everyone onboard could see the spectacle, so we radioed the news to the bridge, where the officers control the ship. They responded with my new favorite radio call ever: “Roger that, we are currently enamored.”
A group of humpbacks traveling along the humpback highway. Photo by Dawn Barlow.A humpback whale fluke slapping. Photo by Dawn Barlow.
Even with long days and tired eyes, we are still constantly enamored as well. It has been such a rewarding cruise so far, and it’s hard to think of returning back to “real life” next week. For now, we’re wishing you the same things we’re enjoying – great weather, unlimited coffee, and lots of whales!
Clara Bird, PhD Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab
When I thought about what doing fieldwork would be like, before having done it myself, I imagined that it would be a challenging, but rewarding and fun experience (which it is). However, I underestimated both ends of the spectrum. I simultaneously did not expect just how hard it would be and could not imagine the thrill of working so close to whales in a beautiful place. One part that I really did not consider was the pre-season phase. Before we actually get out on the boats, we spend months preparing for the work. This prep work involves buying gear, revising and developing protocols, hiring new people, equipment maintenance and testing, and training new skills. Regardless of how many successful seasons came before a project, there are always new tasks and challenges in the preparation phase.
For example, as the GEMM Lab GRANITE project team geared up for its seventh field season, we had a few new components to prepare for. Just to remind you, the GRANITE (Gray whale Response to Ambient Noise Informed by Technology and Ecology) project’s field season typically takes place from June to mid-October of each year. Throughout this time period the field team goes out on a small RHIB (rigid hull inflatable boat), whenever the weather is good enough, to collect photo-ID data, fecal samples, and drone imagery of the Pacific Coast Feeding Group (PCFG) gray whales foraging near Newport, OR, USA. We use the data to assess the health, ecology and population dynamics of these whales, with our ultimate goal being to understand the effect of ambient noise on the population. As previous blogs have described, a typical field day involves long hours on the water looking for whales and collecting data. This year, one of our exciting new updates is that we are going out on two boats for the first part of the field season and starting our season 10 days early (our first day was May 20th). These updates are happening because a National Science Foundation funded seismic survey is being conducted within our study area starting in June. The aim of this survey is to assess geophysical structures but provides us with an opportunity to assess the effect of seismic noise on our study group by collecting data before, during, and after the survey. So, we started our season early in order to capture the “before seismic survey” data and we are using a two-boat approach to maximize our data collection ability.
While this is a cool opportunistic project, implementing the two-boat approach came with a new set of challenges. We had to find a second boat to use, buy a new set of gear for the second boat, figure out the best way to set up our gear on a boat we had not used before, and update our data processing protocols to include data collected from two boats on the same day. Using two boats also means that everyone on the core field team works every day. This core team includes Leigh (lab director/fearless leader), Todd (research assistant), Lisa (PhD student), Ale (new post-doc), and me (Clara, PhD student). Leigh and Todd are our experts in boat driving and working with whales, Todd is our experienced drone pilot, I am our newly certified drone pilot, and Lisa, Ale, and myself are boat drivers. Something I am particularly excited about this season is that Lisa, Ale, and I all have at least one field season under our belts, which means that we get to become more involved in the process. We are learning how to trailer and drive the boats, fly the drones, and handling more of the post-field work data processing. We are becoming more involved in every step of a field day from start to finish, and while it means taking on more responsibility, it feels really exciting. Throughout most of graduate school, we grow as researchers as we develop our analytical and writing skills. But it’s just as valuable to build our skillset for field work. The ocean conditions were not ideal on the first day of the field season, so we spent our first day practicing our field skills.
For our “dry run” of a field day, we went through the process of a typical day, which mostly involved a lot of learning from Leigh and Todd. Lisa practiced her trailering and launching of the boat (figure 1), Ale and Lisa practiced driving the boat, and I practiced flying the drone (figure 2). Even though we never left the bay or saw any whales, I thoroughly enjoyed our dry run. It was useful to run through our routine, without rushing, to get all the kinks out, and it also felt wonderful to be learning in a supportive environment. Practicing new skills is stressful to say the least, especially when there is expensive equipment involved, and no one wants to mess up when they’re being watched. But our group was full of support and appreciation for the challenges of learning. We cheered for successful boat launchings and dockings, and drone landings. I left that day feeling good about practicing and improving my drone piloting skills, full of gratitude for our team and excited for the season ahead.
Figure 1. Lisa (driving the truck) launching the boat.
Figure 2. Clara (seated, wearing a black jacket) landing the drone in Ale’s hands.
All the diligent prep work paid off on Saturday with a great first day (figure 3). We conducted five GoPro drops (figure 4), collected seven fecal samples from four different whales (figure 5), and flew four drone flights over three individuals including our star from last season, Sole. Combined, we collected two trifectas (photo-ID images, fecal samples, and drone footage)! Our goal is to get as many trifectas as possible because we use them to study the relationship between the drone data (body condition and behavior) and the fecal sample data (hormones). We were all exhausted after 10 hours on the water, but we were all very excited to kick-start our field season with a great day.
Figure 3. Lisa on the bow pulpit during our first sighting of the day.
Figure 4. Lisa doing a GoPro drop, she’s lowering the GoPro into the water using the line in her hands.
Figure 5. Clara and Ale collecting a fecal sample.
On Sunday, just one boat went out to collect more data from Sole after a rainy morning and I successfully flew over her from launching to landing! We have a long season ahead, but I am excited to learn and see what data we collect. Stay tuned for more updates from team GRANITE as our season progresses!
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.)
Figure 1. Blue Line indicates route from Charlottesville to Corvallis (Google Maps)
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.)
Table 1. Driving Hours and Miles Per Day
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.
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).
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!
Figure 2. A blue whale surfaces in front of an oil extraction platform in the South Taranaki Bight, demonstrating the overlap between whales and industry in the region. Photo by D. Elvines.
References:
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.
Slide the arrows to compare sea conditions (BSS 1 [left] vs BSS 6 [right]) experienced by Alexa, the GEMM Lab marine mammal observer on the May 2019 Northern California Current cruise onboard NOAA ship Bell M. Shimada. Source: A. Kownacki/GEMM Lab.
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).
One of the roadside ditches where a bottlenose dolphin was trapped in Louisiana following Hurricane Rita. Taken from 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.
There are many tools that can be used to record underwater sounds, including passive acoustic monitoring (PAM; shown in orange), real-time acoustic data collection (green), and active acoustics (blue.) Source: NOAA Fisheries.
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…
References
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.
Figure 1 : What is sketchnoting ? By verbaltovisual.com
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.
Figure 2 : A few drawing tips by sketchnoter Carol Anne McGuire.
Draw to create and appeal
Figure 3 (source: ASIDE 2013)
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)?
Figure 4 : My first attempt at sketchnoting! Illustration of the OPAL project that I am working on (credit : S. Derville).
By Rachel Kaplan, PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
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.
Euphausia pacifica (source: University of Irvine California, Peter J. Bryant).
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.
Figure 1. Figure from Lukoschek and McCormick (2001) showing that small fish (black bar) were found in shallow habitat while large fish (white bar) were found in deep habitat.
Conversely, Torres and Read (2009) did find associations between theforaging tactics of bottlenose dolphins in Florida Bay, FL and habitat type. Dolphins in this bay employ three foraging tactics: herd and chase, mud ring feeding, and deep diving. Observations of the foraging tactics were linked to habitat characteristics and individual dolphins. The study found that these tactics are spatially structured by depth (Figure 2), with deep diving occurring in deep water whereas mud ring feeding occurrs in shallower water. They also found evidence of individual specialization! Individuals that were observed deep diving were not observed mud ring feeding and vice-versa. Furthermore, they found that individuals were found in the habitat type associated with their preferred tactic regardless of whether they were foraging or not. This result indicates that individual dolphins in this bay have a foraging tactic they prefer and tend to stay in the corresponding habitat type. These findings are really intriguing and raise interesting questions regarding how these tactics and specializations are developed or learned. These are questions that I am also interested in asking as part of my thesis.
Figure 2. Figure from Torres and Read (2009) showing that deep diving is associated with deeper habitat while mud ring feeding is associated with shallow habitat.
Both of these studies are cool examples that, combined, exemplify questions I am interested in examining using our study population of Pacific Coast Feeding Group (PCFG) gray whales. Like both studies, I am interested in assessing how specific foraging tactics are associated with habitat types. Our hypothesis is that different prey types live in different habitat types, so each tactic corresponds to the best way to feed on that prey type in that habitat. While predation risk doesn’t have as much of an effect on foraging gray whales as it does on small benthic fish, I do wonder how disturbance from boats could similarly affect tactic preference and spatial distribution. I am also curious to see if depth has an effect on tactic choice by using the morphology data from our drone-based photogrammetry. Given that these whales forage in water that is sometimes as deep as they are long, it stands to reason that maneuverability would affect tactic use. As described in a previous blog, I’m also looking for evidence of individual specialization. It will be fascinating to see how foraging preference relates to space use, habitat preference, and morphology.
These studies demonstrate the complexity involved in studying a population’s relationship to its habitat. Such research involves considering the morphology and physiology of the animals, their social, individual, foraging, and predator-prey behaviors, and the relationship between their prey and the habitat. It’s a bit daunting but mostly really exciting because better understanding each puzzle piece improves our ability to estimate how these animals will react to changing environmental conditions.
While I don’t have any answers to these questions yet, I will be working with a National Science Foundation Research Experience for Undergraduates intern this summer to develop a habitat map of our study area that will be used in this analysis and potentially answer some preliminary questions about PCFG gray whale habitat use patterns. So, stay tuned to hear more about our work this summer!
References
Lukoschek, V., & McCormick, M. (2001). Ontogeny of diet changes in a tropical benthic carnivorous fish, Parupeneus barberinus (Mullidae): Relationship between foraging behaviour, habitat use, jaw size, and prey selection. Marine Biology, 138(6), 1099–1113. https://doi.org/10.1007/s002270000530
Torres, L. G., & Read, A. J. (2009). Where to catch a fish? The influence of foraging tactics on the ecology of bottlenose dolphins ( Tursiops truncatus ) in Florida Bay, Florida. Marine Mammal Science, 25(4), 797–815. https://doi.org/10.1111/j.1748-7692.2009.00297.x
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?
Southern right whales stranded at the coast of Peninsula Valdés, Patagonia-Argentina. Photo: Matias DiMartino / Southern Right Whale Health Monitoring Program.
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.
Left: Single southern right whale calf stranded at the coast of Peninsula Valdés, Patagonia-Argentina. Ph.: Mariano Sironi / ICB. Right: Mass stranding of common dolphins in Patagonia-Argentina. Photo: www.elpais.com
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.
Southern right whale necropsy and external measurements. Source: Southern Right Whale Health Monitoring Program / ICB.
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.
Figure 1. A blue whale comes up for air in New Zealand’s South Taranaki Bight. Photo: L. Torres.
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.
Figure 2. Top panel: Map of the study region within the South Taranaki Bight (STB) of New Zealand, with location denoted by the white rectangle on inset map in the upper right panel. All spatial sampling locations for sea surface temperature implemented in our timeseries analyses are denoted by the boxes, with the four focal boxes shown in white that represent the typical path of the upwelling plume originating off Kahurangi shoals and moving north and east into the STB. The purple triangle represents the Farewell Spit weather station where wind measurements were acquired. The location of the focal hydrophone (MARU2) where blue whale D calls were recorded is shown by the green star. (Reproduced from Barlow et al. 2021). Bottom panel: Results of the timeseries cross-correlation analyses, illustrating the lag between some of the metrics and locations examined.
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.
Figure 3. Dawn and Leigh on the flying bridge of R/V Star Keys on a windy day in New Zealand during the 2017 field season. Photo: T. Chandler.
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
