Tag: New Zealand

What we know now about New Zealand blue whales

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

Snacks at the surface: New GEMM Lab publication reveals insights into blue whale surface foraging through drone observations and prey data

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

As the largest animals on the planet, blue whales have massive prey requirements to meet energy demands. Despite their enormity, blue whales feed on a tiny but energy-rich prey source: krill. Furthermore, they are air-breathing mammals searching for aggregations of prey in the expansive and deep ocean, and must therefore budget breath-holding and oxygen consumption, the travel time it takes to reach prey patches at depth, the physiological constraints of diving, and the necessary recuperation time at the surface. Additionally, blue whales employ an energetically demanding foraging strategy known as lunge feeding, which is only efficient if they can locate and target dense prey aggregations that compensate for the energetic costs of diving and lunging. In our recent paper, published today in PeerJ, we examine how blue whales in New Zealand optimize their energy use through preferentially feeding on dense krill aggregations near the water’s surface.

Figure 1. A blue whale lunges on a dense aggregation of krill at the surface. Note the krill jumping away from the mouth of the onrushing whale. UAS piloted by Todd Chandler.
Figure 2. Survey tracklines in 2017 in the South Taranaki Bight (STB) with locations of blue whale sightings, and where surface lunge feeding was observed, denoted. Inset map shows location of the STB within New Zealand. Figure reprinted from Torres et al. 2020.

To understand how predators such as blue whales optimize foraging strategies, knowledge of predator behavior and prey distribution is needed. In 2017, we surveyed for blue whales in New Zealand’s South Taranaki Bight region (STB, Fig. 2) while simultaneously collecting prey distribution data using an echosounder, which allowed us to identify the location, depth, and density of krill aggregations throughout the region. When blue whales were located, we observed their behavior from the research vessel, recorded their dive times, and used an unmanned aerial system (UAS; “drone”) to assess their body condition and behavior.

Much of what is known about blue whale foraging behavior and energetics comes from extensive studies off the coast of California, USA using accelerometer tags to track fine-scale kinematics (i.e., body movements) of the whales. In the California Current, the krill species targeted by blue whales are denser at depth, and therefore blue whales regularly dive to depths of 300 meters to lunge on the most energy-rich prey aggregations. However, given the reduced energetic costs of feeding closer to the surface, optimal foraging theory predicts that blue whales should only forage at depth when the energetic gain outweighs the cost. In New Zealand, we found that blue whales foraged where krill aggregations were relatively shallow and dense compared to the availability of krill across the whole study area (Fig. 3). Their dive times were quite short (~2.5 minutes, compared to ~10 minutes in California), and became even shorter in locations where foraging behavior and surface lunge feeding were observed.

Figure 3. Density contours comparing the depth and density (Sv) of krill aggregations at blue whale foraging sightings (red shading) and in absence of blue whales (gray shading). Density contours: 25% = darkest shade, 755 = medium shade, 95% = light shade. Blue circles indicate krill aggregations detected within 2 km of the sighting of the UAS filmed surface foraging whale analyzed in this study. Figure reprinted from Torres et al. 2020.
Figure 4. Kinematics of a blue whale foraging dive derived from a suction cup tag. Upper panel shows the dive profile (yellow line), with lunges highlighted (green circles), superimposed on a prey field map showing qualitative changes in krill density (white, low; blue, medium; red, high). The lower panels show the detailed kinematics during lunges at depth. Here, the dive profile is shown by a black line. The orange line shows fluking strokes derived from the accelerometer data, the green line represents speed estimated from flow noise, and the grey circles indicate the speed calculated from the vertical velocity of the body divided by the sine of the body pitch angle, which is shown by the red line. Figure and caption reprinted from Goldbogen et al. 2011.

Describing whale foraging behavior and prey in the surface waters has been difficult due to logistical limitations of conventional data collection methods, such as challenges inferring surface behavior from tag data and quantifying echosounder backscatter data in surface waters. To compliment these existing methods and fill the knowledge gap surrounding surface behavior, we highlight the utility of a different technological tool: UAS. By analyzing video footage of a surface lunge feeding sequence, we obtained estimates of the whale’s speed, acceleration, roll angle, and head inclination, producing a figure comparable to what is typically obtained from accelerometer tag data (Fig. 4, Fig. 5). Furthermore, the aerial perspective provided by the UAS provides an unprecedented look at predator-prey interactions between blue whales and krill. As the whale approaches the krill patch, she first observes the patch with her right eye, then turns and lines up her attack angle to engulf almost the entire prey patch through her lunge. Furthermore, we can pinpoint the moment when the krill recognize the impending danger of the oncoming predator—at a distance of 2 meters, and 0.8 seconds before the whale strikes the patch, the krill show a flee response where they leap away from the whale’s mouth (see video, below).

Figure 5. Body kinematics during blue whale surface lunge feeding event derived from Unmanned Aerial Systems (UAS) image analysis. (A) Mean head inclination and roll (with CV in shaded areas), (B) relative speed and acceleration, and (C) distance from the tip of the whale’s rostrum to the nearest edge of krill patch. Blue line on plots indicate when krill first respond to the predation event, and the purple dashed lines indicate strike at time = 0. The orange lines indicate the time at which the whale’s gape is widest, head inclination is maximum, and deceleration is greatest. Figure reprinted from Torres et al. 2020

In this study, we demonstrate that surface waters provide important foraging opportunities and play a key role in the ecology of New Zealand blue whales. The use of UAS technology could be a valuable and complimentary tool to other technological approaches, such as tagging, to gain a comprehensive understanding of foraging behavior in whales.

To see the spectacle of a blue whale surface lunge feeding, we invite you to take a look at the video footage, below:

The publication is led by GEMM Lab Principal Investigator Dr. Leigh Torres. I led the prey data analysis portion of the study, and co-authors include our drone pilot extraordinaire Todd Chandler and UAS analysis guru Dr. Jonathan Burnett. We are grateful to all who assisted with fieldwork and data collection, including Kristin Hodge, Callum Lilley, Mike Ogle, and the crew of the R/V Star Keys (Western Workboats, Ltd.). Funding for this research was provided by The Aotearoa Foundation, The New Zealand Department of Conservation, The Marine Mammal Institute at Oregon State University, Greenpeace New Zealand, OceanCare, Kiwis Against Seabed Mining, The International Fund for Animal Welfare, and The Thorpe Foundation.

Read Oregon State University’s press release about the publication here.

Marine heatwaves and their impact on marine mammals

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

In recent years, anomalously warm ocean temperatures known as “marine heatwaves” have sparked considerable attention and concern around the world. Marine heatwaves (MHW) occur when seawater temperatures rise above a seasonal threshold (greater than the 90th percentile) for five consecutive days or longer (Hobday et al. 2016; Fig. 1). With global ocean temperatures continuing to rise, we are likely to see more frequent and more intense MHW conditions in the future. Indeed, the global prevalence of MHWs is increasing, with a 34% rise in frequency, a 17%  increase in duration, and a 54% increase in annual MHW days globally since 1925 (Oliver et al. 2018). With sustained anomalously warm water temperatures come a range of ecological, sociological, and economic consequences. These impacts include changes in water column structure, primary production, species composition, marine life distribution and health, and fisheries management including closures and quota changes (Oliver et al. 2018).

Figure 1. Illustration of how marine heatwaves are defined. Source: marineheatwaves.org

The notorious “warm blob” was an MHW event that plagued the northeast Pacific Ocean from 2014-2016. Some of the most notable consequences of this MHW were extremely high levels of domoic acid, extreme changes in the biodiversity of pelagic species, and an unprecedented delay in the opening of the Dungeness crab fishery, which is an important and lucrative fishery for the West Coast of the United States (Santora et al. 2020). The “warm blob” directly impacted the California Current ecosystem, which is typically a highly productive coastal area driven by seasonal upwelling. Yet, as a consequence of the 2014-2016 MHW, upwelling habitat was compressed and constricted to the coastal boundary, resulting in a contraction in available habitat for humpback whales and a shift in their prey (Santora et al. 2020; Fig. 2).

Figure 2. A figure from Santora et al. 2020 illustrating the compression in available upwelling habitat, defined by areas with SST<12°C (delineated by the black line), during the 2014-2016 marine heatwave in the California Current ecosystem.

Shifting to an example from another part of the world, the austral summer of 2015-2016 coincided with a strong regional MHW in the Tasman Sea between Australia and New Zealand, which lasted for 251 days and had a maximum intensity of 2.9°C above the climatological average (Oliver et al. 2017). Subsequently, the conditions were linked to a significant shift in zooplankton species composition and abundance in Australia (Evans et al. 2020). Ocean warming, including MHWs, also appears to decrease primary production in the Tasman Sea and large portions of New Zealand’s marine ecosystem (Chiswell & Sutton 2020). In New Zealand’s South Taranaki Bight region, where we study the ecology of blue whales, we observed a shift in blue whale distribution in the MWH conditions of February 2016 relative to more typical ocean conditions in 2014 and 2017 (Fig. 3). The first chapter of my dissertation includes a detailed analysis of the impacts of the 2016 MHW on New Zealand oceanography, krill, and blue whales, documenting how the warm, stratified water column of 2016 led to consequences across multiple trophic levels, from phytoplankton, to zooplankton, to whales.

Figure 3. Maps showing monthly sea surface temperature (SST) in the South Taranaki Bight region of New Zealand during our three years of survey effort to document blue whale distribution (February 2014, 2016, and 2017). Vessel tracklines are shown in black, with blue whale sighting locations shown in dark red. Red circles are scaled by the number of blue whales observed at each sighting. The color ramp of SST values is consistent across the three maps, making the dramatically warmer ocean conditions of 2016 evident.

The response of marine mammals is tightly linked to shifts in their environment and prey (Silber et al. 2017). With MHWs and changing ocean conditions, there will likely be “winners” and “losers” among marine predators including large whales. Blue whales are highly selective krill specialists (Nickels et al. 2019), whereas other species of whales, such as humpback whales, have evolved flexible feeding tactics that allow them to switch target prey species when needed (Cade et al. 2020). In California, humpback whales have been shown to switch their primary prey from krill to fish during warm years (Fossette et al. 2017, Santora et al. 2020). By contrast, blue whales shift their distribution in response to changing krill availability during warm years (Fossette et al. 2017), however this strategy comes with increased risk and energetic cost associated with searching for prey in new areas. Furthermore, in instances when a prey resource such as krill becomes increasingly scarce for a multi-year period (Santora et al. 2020), krill specialist predators such as blue whales are at a considerable disadvantage. It is also important to acknowledge that although the humpbacks in California may at first seem to have a winning strategy for adaptation by switching their food source, this tactic may come with unforeseen consequences. Their distribution overlapped substantially with Dungeness crab fishing gear during MHW conditions in the warm blob years, resulting in record numbers of entanglements that may have population-level repercussions (Santora et al. 2020).

While this is certainly not the most light-hearted blog topic, I believe it is an important one. As warming ocean temperatures contribute to the increase in frequency, intensity, and duration of extreme conditions such as MHW events, it is paramount that we understand their impacts and take informed management actions to mitigate consequences, such as lethal entanglements as a result of compressed whale habitat. But perhaps more importantly, even as we do our best to manage consequences, it is critical that we as individuals realize the role we have to play in reducing the root cause of warming oceans, by being conscious consumers and being mindful of the impact our actions have on the climate. 

References

Cade DE, Carey N, Domenici P, Potvin J, Goldbogen JA (2020) Predator-informed looming stimulus experiments reveal how large filter feeding whales capture highly maneuverable forage fish. Proc Natl Acad Sci USA.

Chiswell SM, Sutton PJH (2020) Relationships between long-term ocean warming, marine heat waves and primary production in the New Zealand region. New Zeal J Mar Freshw Res.

Evans R, Lea MA, Hindell MA, Swadling KM (2020) Significant shifts in coastal zooplankton populations through the 2015/16 Tasman Sea marine heatwave. Estuar Coast Shelf Sci.

Fossette S, Abrahms B, Hazen EL, Bograd SJ, Zilliacus KM, Calambokidis J, Burrows JA, Goldbogen JA, Harvey JT, Marinovic B, Tershy B, Croll DA (2017) Resource partitioning facilitates coexistence in sympatric cetaceans in the California Current. Ecol Evol.

Hobday AJ, Alexander L V., Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Sen Gupta A, Wernberg T (2016) A hierarchical approach to defining marine heatwaves. Prog Oceanogr.

Nickels CF, Sala LM, Ohman MD (2019) The euphausiid prey field for blue whales around a steep bathymetric feature in the southern California current system. Limnol Oceanogr.

Oliver ECJ, Benthuysen JA, Bindoff NL, Hobday AJ, Holbrook NJ, Mundy CN, Perkins-Kirkpatrick SE (2017) The unprecedented 2015/16 Tasman Sea marine heatwave. Nat Commun.

Oliver ECJ, Donat MG, Burrows MT, Moore PJ, Smale DA, Alexander L V., Benthuysen JA, Feng M, Sen Gupta A, Hobday AJ, Holbrook NJ, Perkins-Kirkpatrick SE, Scannell HA, Straub SC, Wernberg T (2018) Longer and more frequent marine heatwaves over the past century. Nat Commun.

Santora JA, Mantua NJ, Schroeder ID, Field JC, Hazen EL, Bograd SJ, Sydeman WJ, Wells BK, Calambokidis J, Saez L, Lawson D, Forney KA (2020) Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat Commun.

Silber GK, Lettrich MD, Thomas PO, Baker JD, Baumgartner M, Becker EA, Boveng P, Dick DM, Fiechter J, Forcada J, Forney KA, Griffis RB, Hare JA, Hobday AJ, Howell D, Laidre KL, Mantua N, Quakenbush L, Santora JA, Stafford KM, Spencer P, Stock C, Sydeman W, Van Houtan K, Waples RS (2017) Projecting marine mammal distribution in a changing climate. Front Mar Sci.

It all starts with the wind: The importance of upwelling

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

The focus of my PhD research is on the ecology and distribution of blue whales in New Zealand. However, it has been a long time since I’ve seen a blue whale, and much of my time recently has been spent thinking about wind. What does wind matter to a blue whale? It actually matters a whole lot, because the wind drives an important biological process in many coastal oceans called upwelling. Wind blowing along shore, paired with the rotation of the earth, leads to a net movement of surface waters offshore (Fig. 1). As the surface water is pushed away, it is replaced by cold, nutrient-rich water from much deeper. When those nutrients become exposed to sunlight, they provide sustenance for the little planktonic lifeforms in the ocean, which in turn provide food for much larger predators including marine mammals such as blue whales. This “wind-to-whales” trophic pathway was coined by Croll et al. (2005), who demonstrated that off the West Coast of the United States, aggregations of whales could be expected downstream of upwelling centers, in concert with high productivity and abundant krill prey.

Figure 1. Graphic of the upwelling process, illustrating that when the wind blows along shore, surface waters are replaced by deeper water that is cold and nutrient rich. Source: NOAA
Figure 2. Map of New Zealand, with the South Taranaki Bight region (STB) denoted by the black box.

Much of what is understood today about upwelling comes from decades of research on the California Current ecosystem off the West Coast of the United States. Yet, the focus of my research is on an upwelling system on the other side of the world, in the South Taranaki Bight region (STB) of New Zealand (Fig. 2). In the case of the STB, westerly winds over Kahurangi Shoals lead to decreased sea level nearshore, forcing cold, nutrient rich waters to rise to the surface. The wind, along with the persistence of the Westland Current, then pushes a cold and productive plume of upwelled waters around Cape Farewell and into the STB (Fig. 3; Shirtcliffe et al. 1990).

Figure 3. Satellite image of the cold water plume in the South Taranaki Bight, indicative of upwelling. The origin of the upwelling at Kahurangi Shoals, Cape Farewell, and the typical path of the upwelling plume are denoted.

Through research conducted by the GEMM Lab over the years, we have demonstrated that blue whales utilize the STB region for foraging (Torres 2013, Barlow et al. 2018). Recent research on the oceanography of the STB region has further illuminated the mechanisms of this upwelling system, including the path and persistence of the upwelling plume in the STB across years and seasons (Chiswell et al. 2017, Stevens et al. 2019). However, the wind-to-whales pathway has not yet been described for this part of the world, and that is where the next section of my PhD research comes in. The whole system does not respond instantaneously to wind; the pathway from wind to whales takes time. But how much time is required for each step? How long after a strong wind event can we expect aggregations of feeding blue whales? These are some of the questions I am trying to tackle. For example, we hypothesize that some of the mechanisms and their respective lag times can be sketched out as follows:

Figure 4. The wind-to-whales trophic pathway, and hypothesized lags between steps.

All of these questions involve integrating oceanography, satellite imagery, wind data, and lag times, leading me to delve into many different analytical approaches including time series analysis and predictive modeling. If we are able to understand the lag times along this series of events leading to blue whale feeding opportunities, then we may be able to forecast blue whale occurrence in the STB based on the current wind and upwelling conditions. Forecasting with some amount of lead time could be a very powerful management tool, allowing for protection measures that are dynamic in space and time and therefore more effective in conserving this blue whale population and balancing human impacts.

Figure 5. A blue whale lunges on a patch of krill. The end of the wind-to-whales pathway. Drone piloted by Todd Chandler.

References:

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

Chiswell SM, Zeldis JR, Hadfield MG, Pinkerton MH (2017) Wind-driven upwelling and surface chlorophyll blooms in Greater Cook Strait. New Zeal J Mar Freshw Res.

Croll DA, Marinovic B, Benson S, Chavez FP, Black N, Ternullo R, Tershy BR (2005) From wind to whales: Trophic links in a coastal upwelling system. Mar Ecol Prog Ser 289:117–130.

Shirtcliffe TGL, Moore MI, Cole AG, Viner AB, Baldwin R, Chapman B (1990) Dynamics of the Cape Farewell upwelling plume, New Zealand. New Zeal J Mar Freshw Res 24:555–568.

Stevens CL, O’Callaghan JM, Chiswell SM, Hadfield MG (2019) Physical oceanography of New Zealand/Aotearoa shelf seas–a review. New Zeal J Mar Freshw Res.

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

Detecting blue whales from acoustic data

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

In January of 2016, five underwater recording units were dropped to the seafloor in New Zealand to listen for blue whales (Fig. 1). These hydrophones sat listening for two years, brought to the surface only briefly every six months to swap out batteries and offload the data. Through all seasons and conditions when scientists couldn’t be on the water, they recorded the soundscape, generating a wealth of acoustic data with the potential to greatly expand our knowledge of blue whale ecology. 

Figure 1. Locations of the five Marine Autonomous Recording Units (MARUs) in the South Taranaki Bight region of New Zealand.

We have established that blue whales are present in New Zealand waters year-round 1. However, many questions remain regarding their distribution across daily, seasonal, and yearly scales. Our two-year acoustic dataset from five hydrophones throughout the STB region is a goldmine of information on blue whale occurrence patterns and the soundscape they inhabit. Having year-round occurrence data will allow us to examine what environmental and anthropogenic factors may influence blue whale distribution patterns. The hydrophones were listening for whales around the clock, every day, while we were on the other side of the world awaiting the recovery of the data to answer our questions.

Before any questions of seasonal distribution or anthropogenic impacts and noise can be addressed, however, we need to know something far more basic: when and where did we record blue whale vocalizations? This may seem like a simple, stepping-stone question, but it is actually quite involved, and the reason I spent the last month working with a team of acousticians at Cornell University’s Center for Conservation Bioacoustics. The expert research group here at Cornell, led by Dr. Holger Klinck, have been instrumental in our New Zealand blue whale research, including developing and building the recording units, hydrophone deployment and recovery, data processing, analysis, and advice. I am thrilled to work with all of them, and had an incredibly productive month of learning about acoustics from the best.

Blue whales produce multiple vocalizations that we are interested in documenting. The New Zealand song (Fig. 2A) is highly stereotyped and unique to the Southwest Pacific Ocean 2,3. Low-frequency downsweeps, or “D calls” (Fig. 2B), are far more variable and produced by blue whale populations around the world 4. Furthermore, Antarctic blue whales produce a highly-stereotyped “Z call” (Fig. 2C) and are known to be present in New Zealand waters occasionally 5.

Figure 2. Spectrograms of (A) the New Zealand blue whale song, (B), D calls, and (C) Antarctic Z calls.

One way to determine when blue whales were vocalizing is for an analyst to manually review the entirety of the two years of sound recordings for each of the five hydrophones by hand to scan for and select individual vocalizations. An alternative approach is to develop a detector algorithm to locate calls in the data based on their stereotypical characteristics. Over the past month I built, tested, and ran detectors for each blue whale call type using what is called a data template detector. This technique uses example signals from the data that the analyst selects as templates. The templates should be clear signals, and representative of the variation in calls contained in the dataset. Then, by comparing pixel characteristics between the template spectrograms and the spectrogram of the recording of interest using certain matching criteria (e.g. threshold for spectrogram correlation, detection frequency range), the algorithm searches for other signals like the templates in the full dataset. For example, in Fig. 3 you can see units of blue whale song I selected as templates for my detector.

Figure 3. Spectrogram of selected sound clips of New Zealand blue whale song, with units used as templates for a detector shown inside the teal boxes.

Testing the performance of a detector algorithm is critical. Therefore, a dataset is needed where calls were identified by an analyst and then used as the “ground truth”, to which the detector results are compared. For my ground truth dataset, I took a subset of 52 days and hand-browsed the spectrograms to identify and log New Zealand blue whale song, D calls, and Antarctic Z calls. In evaluating detector performance, there are three important metrics that need to be weighed: precision (the proportion of detections that are true), recall (the proportion of true calls identified by the detector), and false alarm rate (the number of false positive detections per hour). Ideally, the detector should be optimized to maximize precision and recall and minimize the false positives.

The STB region is highly industrial, and our two-year acoustic dataset contains periods of pervasive seismic airgun noise from oil and gas exploration. Ideally, a detector would be able to identify blue whale vocalizations even in the presence of airgun operations that dominate the soundscape for months. For blue whale song, the detector did quite well! With a precision of 0.91 and recall of 0.93, the detector could pick out song units over airgun noise (Fig. 4). A false alarm rate of 8 false positives per hour is a sacrifice worth making to identify song during seismic operations (and the false positives will be removed in a subsequent step). For D calls, seismic survey activity presented a different challenge. While the detector did well at identifying D calls during airgun operation, the first several detector attempts also logged every single airgun blast as a blue whale vocalization—clearly problematic. Through an iterative process of selecting template signals, and adjusting the number of templates used and the correlation threshold, I was able to come up with a detector which selected D calls and missed most airgun blasts. This success felt like a victory.

Figure 4. An example of spectrograms of simultaneous recordings from the five hydrophones illustrating seismic airgun noise (strong broadband signals that appear as repetitive black, vertical lines) overlapping New Zealand blue whale song. The red boxes are detection events selected by the detector, demonstrating its ability to capture song even during airgun operation.

After this detector development and validation process, I ran each detector on the full two-year acoustic dataset for all five recording units. This step was a good exercise in patience as I eagerly awaited the outputs for the many hours they took to run. The next step in the process will be for me to go through and validate each detector event to eliminate any false positives. However, running the detectors on the full dataset has allowed for exciting preliminary examinations of seasonal blue whale acoustic patterns, which need to be refined and expanded upon as the analysis continues. For example, sometimes the New Zealand song dominates the recordings on all hydrophones (Fig. 5), whereas other times of year song is less common. Similarly, there appear to be seasonal patterns in D calls and Antarctic Z calls, with peaks and dips in detections during different times of year.

Figure 5. An example spectrogram of simultaneous recordings from all five hydrophones during a time when New Zealand blue whale song dominated the recordings, with numerous, overlapping calls.

As with many things, the more questions you ask, the more questions you come up with. From preliminary explorations of the acoustic data my head is buzzing with ideas for further analysis and with new questions I hadn’t thought to ask of the data before. My curiosity has been fueled by scrolling through spectrograms, looking, and listening, and I am as excited as ever to continue researching blue whale ecology. I would like to thank the team at the Center for Conservation Bioacoustics for their support and guidance over the past month, and I look forward to digging deeper into the stories being told in the acoustic data!

Figure 6. A pair of blue whales observed in February 2017 in the South Taranaki Bight. Photo: L. Torres.

References

1.          Barlow, D. R. et al. Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res. 36, 27–40 (2018).

2.          McDonald, M. A., Mesnick, S. L. & Hildebrand, J. A. Biogeographic characterisation of blue whale song worldwide: using song to identify populations. J. Cetacean Res. Manag. 8, 55–65 (2006).

3.          Balcazar, N. E. et al. Calls reveal population structure of blue whales across the Southeast Indian Ocean and the Southwest Pacific Ocean. J. Mammal. 96, 1184–1193 (2015).

4.          Oleson, E. M. et al. Behavioral context of call production by eastern North Pacific blue whales. Mar. Ecol. Prog. Ser. 330, 269–284 (2007).

5.          McDonald, M. A. An acoustic survey of baleen whales off Great Barrier Island, New Zealand. New Zeal. J. Mar. Freshw. Res. 40, 519–529 (2006).

Eyes from Space: Using Remote Sensing as a Tool to Study the Ecology of Blue Whales

By Christina Garvey, University of Maryland, GEMM Lab REU Intern

It is July 8th and it is my 4th week here in Hatfield as an REU intern for Dr. Leigh Torres. My name is Christina Garvey and this summer I am studying the spatial ecology of blue whales in the South Taranaki Bight, New Zealand. Coming from the east coast, Oregon has given me an experience of a lifetime – the rugged shorelines continue to take my breath away and watching sea lions in Yaquina Bay never gets old. However, working on my first research project has by far been the greatest opportunity and I have learned so much in so little time. When Dr. Torres asked me to contribute to this blog I was unsure of how I would write about my work thus far but I am excited to have the opportunity to share the knowledge I have gained with whoever reads this blog post.

The research project that I will be conducting this summer will use remotely sensed environmental data (information collected from satellites) to predict blue whale distribution in the South Taranaki Bight (STB), New Zealand. Those that have read previous blogs about this research may remember that the STB study area is created by a large indentation or “bight” on the southern end of the Northern Island. Based on multiple lines of evidence, Dr. Leigh Torres hypothesized the presence of an unrecognized blue whale foraging ground in the STB (Torres 2013). Dr. Torres and her team have since proved that blue whales frequent this region year-round; however, the STB is also very industrial making this space-use overlap a conservation concern (Barlow et al. 2018). The increasing presence of marine industrial activity in the STB is expected to put more pressure on blue whales in this region, whom are already vulnerable from the effects of past commercial whaling (Barlow et al. 2018) If you want to read more about blue whales in the STB check out previous blog posts that talk all about it!

Figure 1. A blue whale surfaces in front of a floating production storage and offloading vessel servicing the oil rigs in the South Taranaki Bight. Photo by D. Barlow.

Figure 2. South Taranaki Bight, New Zealand, our study site outlined by the red box. Kahurangi Point (black star) is the site of wind-driven upwelling system.

The possibility of the STB as an important foraging ground for a resident population of blue whales poses management concerns as New Zealand will have to balance industrial growth with the protection and conservation of a critically endangered species. As a result of strong public support, there are political plans to implement a marine protected area (MPA) in the STB for the blue whales. The purpose of our research is to provide scientific knowledge and recommendations that will assist the New Zealand government in the creation of an effective MPA.

In order to create an MPA that would help conserve the blue whale population in the STB, we need to gather a deeper understanding of the relationship between blue whales and this marine environment. One way to gain knowledge of the oceanographic and ecological processes of the ocean is through remote sensing by satellites, which provides accessible and easy to use environmental data. In our study we propose remote sensing as a tool that can be used by managers for the design of MPAs (through spatial and temporal boundaries). Satellite imagery can provide information on sea surface temperature (SST), SST anomaly, as well as net primary productivity (NPP) – which are all measurements that can help describe oceanographic upwelling, a phenomena that is believed to be correlated to the presence of blue whales in the STB region.

Figure 3. The stars of the show: blue whales. A photograph captured from the small boat of one animal fluking up to dive down as another whale surfaces close by. (Photo credit: L. Torres)

Past studies in the STB showed evidence of a large upwelling event that occurs off the coast of Kahurangi Point (Fig. 2), on the northwest tip of the South Island (Shirtcliffe et al. 1990). In order to study the relationship of this upwelling to the distribution of blue whales, I plan to extract remotely sensed data (SST, SST anomaly, & NPP) off the coast of Kahurangi and compare it to data gathered from a centrally located site within the STB, which is close to oil rigs and so is of management interest. I will first study how decreases in sea surface temperature at the site of upwelling (Kahurangi) are related to changes in sea surface temperature at this central site in the STB, while accounting for any time differences between each occurrence. I expect that this relationship will be influenced by the wind patterns, and that there will be changes based on the season. I also predict that drops in temperature will be strongly related to increases in primary productivity, since upwelling brings nutrients important for photosynthesis up to the surface. These dips in SST are also expected to be correlated to blue whale occurrence within the bight, since blue whale prey (krill) eat the phytoplankton produced by the productivity.

Figure 4. A blue whale lunges on an aggregation of krill. UAS piloted by Todd Chandler.

To test the relationships I determine between remotely sensed data at different locations in the STB, I plan to use blue whale observations from marine mammal observers during a seismic survey conducted in 2013, as well as sightings recorded from the 2014, 2016, and 2017 field studies led by Dr. Leigh Torres. By studying the statistical relationships between all of these variables I hope to prove that remote sensing can be used as a tool to study and understand blue whale distribution.

I am very excited about this research, especially because the end goal of creating an MPA really gives me purpose. I feel very lucky to be part of a project that could make a positive impact on the world, if only in just a little corner of New Zealand. In the mean time I’ll be here in Hatfield doing the best I can to help make that happen.

References: 

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

Shirtcliffe TGL, Moore MI, Cole AG, Viner AB, Baldwin R, Chapman B (1990) Dynamics of the Cape Farewell upwelling plume, New Zealand. New Zeal J Mar Freshw Res 24:555–568.

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

The “demon whale-biter”, and why I am learning about an elusive little shark

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

There is an ancient Samoan legend that upon entry into a certain bay in Samoa, tuna would sacrifice pieces of their flesh to the community chief1. This was the explanation given for fish with circular shaped wounds where a plug of flesh had been removed. Similar round wounds are also observed on swordfish2, sharks3, and marine mammals including whales4,5, dolphins6, porpoises7, and pinnipeds8,9. In 1971, Everet C. Jones posited that the probable cause of these crater wounds was a small shark only 42-56 cm in length, Isistius brasiliensis1. The species was nicknamed “demon whale-biter” by Stewart Springer, who subsequently popularized the common name for the species, cookie cutter shark.

Figure 1. A yellowfin tuna with a circular bite, characteristic of a cookie cutter shark (Isistius brasiliensis). Photo: John Soward.

I am currently preparing a manuscript on blue whale skin condition. While this is only tangentially related to my doctoral research, it is an exciting side project that has encouraged me to stretch my comfort zone as an ecologist. This analysis of skin condition is part of a broader health assessment of blue whales in New Zealand, where we will be linking skin lesion severity with stress and reproductive hormone levels as well as body condition. Before I continue, I owe a major shout-out to Acacia Pepper, a senior undergraduate student at Oregon State University who has been working with me for nearly the past year through the Fisheries and Wildlife mentorship program. Acacia’s rigor in researching methodologies led us to develop a comprehensive protocol that can be applied widely to any cetacean photo-identification catalog. This method allows us to quantify prevalence and severity of different marking types in a standardized manner. Her passion for marine mammal science and interest in the subject matter is enough to excite this ecologist into fascination with wound morphology and blister concavity. Next thing you know, we are preparing a paper for publication together with P.I. Dr. Leigh Torres on a comprehensive skin condition assessment of blue whales that includes multiple markings and lesion types, but for the purpose of this blog post, I will share just a “bite-sized” piece of the story.

Figure 2. Jaws of a cookie cutter shark. Photo: George Burgess.

Back to the demon whale-biter. What do we know about cookie cutter sharks? Not a whole lot, it turns out. They are elusive, and are thought to live in deep (>1,000 m), offshore waters. They are considered to be both an ectoparasite and an ambush predator. Their distribution is tropical and sub-tropical. Much of what we know and assume about their distribution comes from the bite wounds they leave on their prey2.

In New Zealand where we study a unique population of blue whales10, the southernmost record of cookie cutter sharks is ~ 39⁰S11. We found that in our dataset of 148 photo-identified blue whales, 96% were affected by cookie cutter shark bites. Furthermore, 38% were categorized as having “severe” cookie cutter bite wounds or scars. The latitude of our blue whale sightings ranges from 29-48⁰S and blue whales are highly mobile, so any of the whales in our dataset could theoretically swim in and out of the known range of cookie cutter sharks. In our skin condition assessment, we also categorized cookie cutter bite “freshness” and phase of healing as follows:

We wanted to know if the freshness of cookie cutter shark bites was related in to the latitude at which the whales were photographed. Of the whales photographed north of 39⁰S (n=46), 76% had phase 1 or 2 cookie cutter shark bites present. In contrast, 57.1% of whales photographed south of 39⁰S (n=133) had phase 1 or 2 cookie cutter shark bites. It therefore appears that in New Zealand, the freshness of cookie cutter shark bites on blue whales is related to the latitude at which the whales were sighted, with fresher bites being more common at more northerly latitudes.

Figure 3. A whale with fresh cookie cutter shark bites, photographed in the Bay of Islands, latitude 35.164⁰S. Photo courtesy of Dr. Catherine Peters.

Figure 4. A whale with mostly healed cookie cutter shark bites, photographed off of Kaikoura, latitude 42.464⁰S. Photo courtesy of Jody Weir.

In the midst of a PhD on distribution modeling and habitat use of blue whales, I find myself reading about Samoan legends of tuna with missing flesh and descriptions of strange circular lesions from whaling records, and writing a paper about blue whale skin condition. Exciting “side projects” like this one emerge from rich datasets and good collaboration.

References

  1. Jones, E. C. Isistius brasiliensis, a squaloid shark, the probable cause of crater wounds on fishes and cetaceans. Fish. Bull. 69, 791–798 (1971).
  2. Papastamatiou, Y. P., Wetherbee, B. M., O’Sullivan, J., Goodmanlowe, G. D. & Lowe, C. G. Foraging ecology of Cookiecutter Sharks (Isistius brasiliensis) on pelagic fishes in Hawaii, inferred from prey bite wounds. Environ. Biol. Fishes 88, 361–368 (2010).
  3. Hoyos-Padilla, M., Papastamatiou, Y. P., O’Sullivan, J. & Lowe, C. G. Observation of an Attack by a Cookiecutter Shark ( Isistius brasiliensis ) on a White Shark ( Carcharodon carcharias ) . Pacific Sci. 67, 129–134 (2013).
  4. Mackintosh, N. A. & Wheeler, J. F. G. Southern blue and fin whales. Discov. Reports 1, 257–540 (1929).
  5. Best, P. B. & Photopoulou, T. Identifying the ‘demon whale-biter’: Patterns of scarring on large whales attributed to a cookie-cutter shark Isistius sp. PLoS One 11, (2016).
  6. Heithaus, M. R. Predator-prey and competitive interactions between sharks (order Selachii) and dolphins (suborder Odontoceti): A review. J. Zool. 253, 53–68 (2001).
  7. Van Utrecht, W. L. Wounds And Scars In The Skin Of The Common Porpoise, Phocaena Phocaena (L.). Mammalia 23, 100–122 (1959).
  8. Gallo‐Reynoso, J. ‐P & Figueroa‐Carranza, A. ‐L. A COOKIECUTTER SHARK WOUND ON A GUADALUPE FUR SEAL MALE. Mar. Mammal Sci. 8, 428–430 (1992).
  9. Le Boeuf, B. J., McCosker, J. E. & Hewitt, J. Crater wounds on northern elephant seals: the cookiecutter shark strikes again. Fish. Bull. 85, 387–392 (1987).
  10. Barlow, D. R. et al. Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res. 36, 27–40 (2018).
  11. Dwyer, S. L. & Visser, I. N. Cookie cutter shark (Isistius sp.) bites on cetaceans, with particular reference to killer whales (Orca) (Orcinus orca). Aquat. Mamm. 37, 111–138 (2011).

More data, more questions, more projects: There’s always more to learn

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

As you may have read in previous blog posts, my research focuses on the ecology of blue whales in New Zealand. Through my MS research and years of work by a dedicated team, we were able to document and describe a population of around 700 blue whales that are unique to New Zealand, present year-round, and genetically distinct from all other known populations [1]. While this is a very exciting discovery, documenting this population has also unlocked a myriad of further questions about these whales. Can we predict when and where the whales are most likely to be? How does their distribution change seasonally? How often do they overlap with anthropogenic activity? My PhD research will aim to answer these questions through models of blue whale distribution patterns relative to their environment at multiple spatial and temporal scales.

Because time at sea for vessel-based surveys is cost-limited and difficult to come by, it is in any scientist’s best interest to collect as many concurrent streams of data as possible while in the field. When Dr. Leigh Torres designed our blue whale surveys that were conducted in 2014, 2016, and 2017, she really did a miraculous job of maximizing time on the water. With more data, more questions can be asked. These complimentary datasets have led to the pursuit of many “side projects”. I am lucky enough to work on these questions in parallel with what will form the bulk of my PhD, and collaborate with a number of people in the process. In this blog post, I’ll give you some short teasers of these “side projects”!

Surface lunge feeding as a foraging strategy for New Zealand blue whales

Most of what we know about blue whale foraging behavior comes from studies conducted off the coast of Southern California[2,3] using suction cup accelerometer tags. While these studies in the California Current ecosystem have led to insights and breakthroughs in our understanding of these elusive marine predators and their prey, they have also led us to adopt the paradigm that krill patches are denser at depth, and blue whales are most likely to target these deep prey patches when they feed. We have combined our prey data with blue whale behavioral data observed via a drone to investigate blue whale foraging in New Zealand, with a particular emphasis on surface feeding as a strategy. In our recent analyses, we are finding that in New Zealand, lunge feeding at the surface may be more than just “snacking”. Rather, it may be an energetically efficient strategy that blue whales have evolved in the region with unique implications for conservation.

Figure 1. A blue whale lunges on an aggregation of krill. UAS piloted by Todd Chandler.

Combining multiple data streams for a comprehensive health assessment

In the field, we collected photographs, blubber biopsy samples, fecal samples, and conducted unmanned aerial system (UAS, a.k.a. “drone”) flights over blue whales. The blubber and fecal samples can be analyzed for stress and reproductive hormone levels; UAS imagery allows us to quantify a whale’s body condition[4]; and photographs can be used to evaluate skin condition for abnormalities. By pulling together these multiple data streams, this project aims to establish a baseline understanding of the variability in stress and reproductive hormone levels, body condition, and skin condition for the population. Because our study period spans multiple years, we also have the ability to look at temporal patterns and individual changes over time. From our preliminary results, we have evidence for multiple pregnant females from elevated pregnancy and stress hormones, as well as apparent pregnancy from the body condition analysis. Additionally, a large proportion of the population appear to be affected by blistering and cookie cutter shark bites.

Figure 2. An example aerial drone image of a blue whale that will be used to asses body condition, i.e. how healthy or malnourished the whale is. (Drone piloted by Todd Chandler).

Figure 3. Images of blue whale skin condition, affected by A) blistering and B) cookie cutter shark bites.

Comparing body shape and morphology between species

The GEMM Lab uses UAS to quantitatively study behavior[5] and health of large whales. From various projects in different parts of the world we have now assimilated UAS data on blue, gray, and humpback whales. We will measure these images to investigate differences in body shape and morphology among these species. We plan to explore how form follows function across baleen whales, based on their different  life histories, foraging strategies, and ecological roles.

Figure 4 . Aerial images of A) a blue whale in New Zealand’s South Taranaki Bight, B) a gray whale off the coast of Oregon, and C) a humpback whale off the coast of Washington. Drone piloted by Todd Chandler (A and B) and Jason Miranda (C). 

So it goes—my dissertation will contain a series of chapters that build on one another to explore blue whale distribution patterns at increasing scales, as well as a growing number of appendices for these “side projects”. Explorations and collaborations like I’ve described here allow me to broaden my perspectives and diversify my analytical skills, as well as work with many excellent teams of scientists. The more data we collect, the more questions we are able to ask. The more questions we ask, the more we seem to uncover that is yet to be understood. So stay tuned for some exciting forthcoming results from all of these analyses, as well as plenty of new questions, waiting to be posed.

References

  1. Barlow DR et al. 2018 Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res. 36, 27–40. (doi:https://doi.org/10.3354/esr00891)
  2. Hazen EL, Friedlaender AS, Goldbogen JA. 2015 Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Sci. Adv. 1, e1500469–e1500469. (doi:10.1126/sciadv.1500469)
  3. Goldbogen JA, Calambokidis J, Oleson E, Potvin J, Pyenson ND, Schorr G, Shadwick RE. 2011 Mechanics, hydrodynamics and energetics of blue whale lunge feeding: efficiency dependence on krill density. J. Exp. Biol. 214, 131–146. (doi:10.1242/jeb.048157)
  4. Burnett JD, Lemos L, Barlow DR, Wing MG, Chandler TE, Torres LG. 2018 Estimating morphometric attributes on baleen whales using small UAS photogrammetry: A case study with blue and gray whales. Mar. Mammal Sci. (doi:10.1111/mms.12527)
  5. Torres LG, Nieukirk SL, Lemos L, Chandler TE. 2018 Drone Up! Quantifying Whale Behavior From a New Perspective Improves Observational Capacity. Front. Mar. Sci. 5. (doi:10.3389/fmars.2018.00319)

More than just whales: The importance of studying an ecosystem

 

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

I have the privilege of studying the largest animals on the planet: blue whales (Balaenoptera musculus). However, in order to understand the ecology, distribution, and habitat use patterns of these ocean giants, I have dedicated the past several months to studying something much smaller: krill (Nyctiphanes australis). New Zealand’s South Taranaki Bight region (“STB”, Figure 1) is an important foraging ground for a unique population of blue whales [1,2]. A wind-driven upwelling system off of Kahurangi Point (the “X” in Figure 1) generates productivity in the region [3], leading to an abundance of krill [4], the desired blue whale prey [5].

Our blue whale research team collected a multitude of datastreams in three different years, including hydroacoustic data to map krill distribution throughout our study region. The summers of 2014 and 2017 were characterized by what could be considered “typical” conditions: A plume of cold, upwelled water curving its way around Cape Farewell (marked with the star in Figure 1) and entering the South Taranaki Bight, spurring a cascade of productivity in the region. The 2016 season, however, was different. The surface water temperatures were hot, and the whales were not where we expected to find them.

Figure 2. Sea surface temperature maps of the South Taranaki Bight region in each of our three study years. The white circles indicate where most blue whale sightings were made in each year. Note the very warm temperatures in 2016, and more westerly location of blue whale sightings.

What happened to the blue whales’ food source under these different conditions in 2016? Before I share some preliminary findings from my recent analyses, it is important to note that there are many possible ways to measure krill availability. For example, the number of krill aggregations, as well as how deep, thick, and dense those aggregations are in an area will all factor into how “desirable” krill patches are to a blue whale. While there may not be “more” or “less” krill from one year to the next, it may be more or less accessible to a blue whale due to energetic costs of capturing it. Here is a taste of what I’ve found so far:

In 2016, when surface waters were warm, the krill aggregations were significantly deeper than in the “typical” years (ANOVA, F=7.94, p <0.001):

Figute 3. Boxplots comparing the median krill aggregation depth in each of our three survey years.

The number of aggregations was not significantly different between years, but as you can see in the plot below (Figure 4) the krill were distributed differently in space:

Figure 4. Map of the South Taranaki Bight region with the number of aggregations per 4 km^2, standardized by vessel survey effort. The darker colors represent areas with a higher density of krill aggregations. 

While the bulk of the krill aggregations were located north of Cape Farewell under typical conditions (2014 and 2017), in the warm year (2016) the krill were not in this area. Rather, the area with the most aggregations was offshore, in the western portion of our study region. Now, take a look at the same figure, overlaid with our blue whale sighting locations:

Figure 5. Map of standardized number of krill aggregations, overlaid with blue whale sighting locations in red stars.

Where did we find the whales? In each year, most whale encounters were in the locations where the most krill aggregations were found! Not only that, but in 2016 the whales responded to the difference in krill distribution by shifting their distribution patterns so that they were virtually absent north of Cape Farewell, where most sightings were made in the typical years.

The above figures demonstrate the importance of studying an ecosystem. We could puzzle and speculate over why the blue whales were further west in the warm year, but the story that is emerging in the krill data may be a key link in our understanding of how the ecosystem responds to warm conditions. While the focus of my dissertation research is blue whales, they do not live in isolation. It is through understanding the ecosystem-scale story that we can better understand blue whale ecology in the STB. As I continue modeling the relationships between oceanography, krill, and blue whales in warm and typical years, we are beginning to scratch the surface of how blue whales may be responding to their environment.

  1. Torres LG. 2013 Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal. J. Mar. Freshw. Res. 47, 235–248. (doi:10.1080/00288330.2013.773919)
  2. Barlow DR et al. 2018 Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res. 36, 27–40. (doi:https://doi.org/10.3354/esr00891)
  3. Shirtcliffe TGL, Moore MI, Cole AG, Viner AB, Baldwin R, Chapman B. 1990 Dynamics of the Cape Farewell upwelling plume, New Zealand. New Zeal. J. Mar. Freshw. Res. 24, 555–568. (doi:10.1080/00288330.1990.9516446)
  4. Bradford-Grieve JM, Murdoch RC, Chapman BE. 1993 Composition of macrozooplankton assemblages associated with the formation and decay of pulses within an upwelling plume in greater cook strait, New Zealand. New Zeal. J. Mar. Freshw. Res. 27, 1–22. (doi:10.1080/00288330.1993.9516541)
  5. Gill P. 2002 A blue whale (Balaenoptera musculus) feeding ground in a southern Australian coastal upwelling zone. J. Cetacean Res. Manag. 4, 179–184.

Science (or the lack thereof) in the Midst of a Government Shutdown

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

In what is the longest government shutdown in the history of the United States, many people are impacted. Speaking from a scientist’s point of view, I acknowledge the scientific community is one of many groups that is being majorly obstructed. Here at the GEMM Laboratory, all of us are feeling the frustrations of the federal government grinding to a halt in different ways. Although our research spans great distances—from Dawn’s work on New Zealand blue whales that utilizes environmental data managed by our federal government, to new projects that cannot get federal permit approvals to state data collection, to many of Leigh’s projects on the Oregon coast of the USA that are funded and collaborate with federal agencies—we all recognize that our science is affected by the shutdown. My research on common bottlenose dolphins is no exception; my academic funding is through the US Department of Defense, my collaborators are NOAA employees who contribute NOAA data; I use publicly-available data for additional variables that are government-maintained; and I am part of a federally-funded public university. Ironically, my previous blog post about the intersection of science and politics seems to have become even more relevant in the past few weeks.

Many graduate students like me are feeling the crunch as federal agencies close their doors and operations. Most people have seen the headlines that allude to such funding-related issues. However, it’s important to understand what the funding in question is actually doing. Whether we see it or not, the daily operations of the United States Federal government helps science progress on a multitude of levels.

Federal research in the United States is critical. Most governmental branches support research with the most well-known agencies for doing so being the National Science Foundation (NSF), the US Department of Agriculture (USDA), the National Oceanic and Atmospheric Administration (NOAA), and the National Aeronautics and Space Administration. There are 137 executive agencies in the USA (cei.org). On a finer scale, NSF alone receives approximately 40,000 scientific proposals each year (nsf.gov).

If I play a word association game and I am given the word “science”, my response would be “data”. Data—even absence data—informs science. The largest aggregate of metadata with open resources lives in the centralized website, data.gov, which is maintained by the federal government and is no longer accessible and directs you to this message:Here are a few more examples of science that has stopped in its track from lesser-known research entities operated by the federal government:

Currently, the National Weather Service (NWS) is unable to maintain or improve its advanced weather models. Therefore, in addition to those of us who include weather or climate aspects into our research, forecasters are having less and less information on which to base their weather predictions. Prior to the shutdown, scientists were changing the data format of the Global Forecast System (GFS)—the most advanced mathematical, computer-based weather modeling prediction system in the USA. Unfortunately, the GFS currently does not recognize much of the input data it is receiving. A model is only as good as its input data (as I am sure Dawn can tell you), and currently that means the GFS is very limited. Many NWS models are upgraded January-June to prepare for storm season later in the year. Therefore, there are long-term ramifications for the lack of weather research advancement in terms of global health and safety. (https://www.washingtonpost.com/weather/2019/01/07/national-weather-service-is-open-your-forecast-is-worse-because-shutdown/?noredirect=on&utm_term=.5d4c4c3c1f59)

An example of one output from the GFS model. (Source: weather.gov)

The Food and Drug Administration (FDA)—a federal agency of the Department of Health and Human Services—that is responsible for food safety, has reduced inspections. Because domestic meat and poultry are at the highest risk of contamination, their inspections continue, but by staff who are going without pay, according to the agency’s commissioner, Dr. Scott Gottlieb. Produce, dry foods, and other lower-risk consumables are being minimally-inspected, if at all.  Active research projects investigating food-borne illness that receive federal funding are at a standstill.  Is your stomach doing flips yet? (https://www.nytimes.com/2019/01/09/health/shutdown-fda-food-inspections.html?rref=collection%2Ftimestopic%2FFood%20and%20Drug%20Administration&action=click&contentCollection=timestopics&region=stream&module=stream_unit&version=latest&contentPlacement=2&pgtype=collection)

An FDA field inspector examines imported gingko nuts–a process that is likely not happening during the shutdown. (Source: FDA.gov)

The National Parks Service (NPS) recently made headlines with the post-shutdown acts of vandalism in the iconic Joshua Tree National Park. What you might not know is that the shutdown has also stopped a 40-year study that monitors how streams are recovering from acid rain. Scientists are barred from entering the park and conducting sampling efforts in remote streams of Shenandoah National Park, Virginia. (http://www.sciencemag.org/news/2019/01/us-government-shutdown-starts-take-bite-out-science)

A map of the sampling sites that have been monitored since the 1980s for the Shenandoah Watershed Study and Virginia Trout Stream Sensitivity Study that cannot be accessed because of the shutdown. (Source: swas.evsc.virginia.edu)

NASA’s Stratospheric Observatory for Infrared Astronomy (SOFIA), better known as the “flying telescope” has halted operations, which will require over a week to bring back online upon funding restoration. SOFIA usually soars into the stratosphere as a tool to study the solar system and collect data that ground-based telescopes cannot. (http://theconversation.com/science-gets-shut-down-right-along-with-the-federal-government-109690)

NASA’s Stratospheric Observatory for Infrared Astronomy (SOFIA) flies over the snowy Sierra Nevada mountains while the telescope gathers information. (Source: NASA/ Jim Ross).

It is important to remember that science happens outside of laboratories and field sites; it happens at meetings and conferences where collaborations with other great minds brainstorm and discover the best solutions to challenging questions. The shutdown has stopped most federal travel. The annual American Meteorological Society Meeting and American Astronomical Society meeting were two of the scientific conferences in the USA that attract federal employees and took place during the shutdown. Conferences like these are crucial opportunities with lasting impacts on science. Think of all the impressive science that could have sparked at those meetings. Instead, many sessions were cancelled, and most major agencies had zero representation (https://spacenews.com/ams-2019-overview/). Topics like lidar data applications—which are used in geospatial research, such as what the GEMM Laboratory uses in some its projects, could not be discussed. The cascade effects of the shutdown prove that science is interconnected and without advancement, everyone’s research suffers.

It should be noted, that early-career scientists are thought to be the most negatively impacted by this shutdown because of financial instability and job security—as well as casting a dark cloud on their futures in science: largely unknown if they can support themselves, their families, and their research. (https://eos.org/articles/federal-government-shutdown-stings-scientists-and-science). Graduate students, young professors, and new professionals are all in feeling the pressure. Our lives are based on our research. When the funds that cover our basic research requirements and human needs do not come through as promised, we naturally become stressed.

An adult and a juvenile common bottlenose dolphin, forage along the San Diego coastline in November 2018. (Source: Alexa Kownacki)

So, yes, funding—or the lack thereof—is hurting many of us. Federally-funded individuals are selling possessions to pay for rent, research projects are at a standstill, and people are at greater health and safety risks. But, also, science, with the hope for bettering the world and answering questions and using higher thinking, is going backwards. Every day without progress puts us two days behind. At first glance, you may not think that my research on bottlenose dolphins is imperative to you or that the implications of the shutdown on this project are important. But, consider this: my study aims to quantify contaminants in common bottlenose dolphins that either live in nearshore or offshore waters. Furthermore, I study the short-term and long-term impacts of contaminants and other health markers on dolphin hormone levels. The nearshore common bottlenose dolphin stocks inhabit the highly-populated coastlines that many of us utilize for fishing and recreation. Dolphins are mammals, that respond to stress and environmental hazards, in similar ways to humans. So, those blubber hormone levels and contamination results, might be more connected to your health and livelihood than at first glance. The fact that I cannot download data from ERDDAP, reach my collaborators, or even access my data (that starts in the early 1980s), does impact you. Nearly everyone’s research is connected to each other’s at some level, and that, in turn has lasting impacts on all people—scientists or not. As the shutdown persists, I continue to question how to work through these research hurdles. If anything, it has been a learning experience that I hope will end soon for many reasons—one being: for science.