Measuring dolphin response to Navy sonar

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

During the summer of 2017 I was an intern for Cascadia Research Collective (CRC), a non-profit organization based out of Olympia, Washington, that conducts research on marine mammal behavior, ecology, and population status along the western US coast and around Hawaii. My internship was primarily office-based and involved processing photographs of humpback and blue whales along the US west coast to add to CRC’s long-term photo-identification catalogues. However, I was asked to join a research project investigating the behavioral and physiological responses of four dolphin species in southern California (Fig. 1). The research project is a collaborative effort lead by Dr. Brandon Southall and involves researchers from CRC, Kelp Marine Research, NOAA’s Southwest Fisheries Science Center, and SR3. Since my internship with CRC, there have been three pilot efforts and one full field effort of this project, called the SOCAL Tagless Behavioral and Physiological Response Study (BPRS), and I have been a part of all of them.

The marine environment is stressed out, and so are the millions of flora and fauna that inhabit the global ocean. Humans are a big contributor to this stress. During the last few decades, we have produced more and more things that have ended up in the ocean, whether by choice or by chance. Plastic pollution has become a pervasive and persistent problem, especially after the discovery that when large plastic items are exposed to UV light and seawater they break down into smaller pieces, termed micro- and nano-plastics (Jambeck et al. 2015). Increased demand for oil and gas to supply a growing human population has led to much more marine oil and gas exploration and exploitation (World Ocean Review 2013). Since 1985, global container shipping has increased by approximately 10% annually (World Ocean Review 2010) and it is estimated that global freight demand will triple by 2050 (International Transport Forum 2019). The list of impacts is long. Our impact on the earth, of which the ocean makes up 71%, has been so extreme that expert groups suggest that a new geological epoch – the Anthropocene – needs to be declared to define the time that we now find ourselves in and the impact humanity is having on the environment (Lewis and Maslin 2015). While this term has not been officially recognized, it is irrefutable that humans have and continue to alter ecosystems, impacting the organisms within them. 

Noise is an impact often overlooked when thinking about anthropogenic effects in the marine environment, likely because we as humans do not hear much of what happens beneath the ocean surface. However, ocean noise is of particular concern for cetaceans as sound is their primary sense, both over long and short distances. Sound travels extremely efficiently underwater and therefore anthropogenic sounds can be propagated for thousands of kilometers or more (Weilgart 2007a). While it is widely agreed upon that anthropogenic noise is likely a significant stressor to cetaceans (Weilgart 2007b; Wright et al. 2007; Tyack 2008), very few studies have quantified their responses to noise to date. This knowledge gap is likely because behavioral and physiological responses to sound can be subtle, short-lived or slowly proliferate over time, hence making them hard to study. However, growing concern over this issue has resulted in more research into impacts of noise on marine mammals, including the GEMM Lab’s impacts of ocean noise on gray whales project.

The most extreme impact of sound exposure on marine mammals is death. Mass strandings of a few cetacean species have coincided in time and space with Navy sonar operations (Jepson et al. 2003; Fernández et al. 2005; Filadelfo et al. 2009). While fatal mass strandings of cetaceans are extremely troubling, they are a relatively rare occurrence. A cause for perhaps greater concern are sub-lethal changes in important behaviors such as feeding, social interactions, and avoidance of key habitat as a result of exposure to Navy sonar. All of these potential outcomes have raised interest within the U.S. Navy to better understand the responses of cetaceans to sonar. 

The SOCAL Tagless BPRS is just one of several studies that has been funded by the U.S. Office of Naval Research to improve our understanding of Navy sonar impact on cetaceans, in particular the sub-lethal effects described earlier. It builds upon knowledge and expertise gained from previous behavioral response studies led by Dr. Southall on a variety of marine mammal species, including beaked whales, baleen whales, and sperm whales. Those efforts included deploying tags on individual whales to obtain high-resolution movement and passive acoustic data paired with controlled exposure experiments (CEEs) during which simulated Navy mid-frequency active sonar (MFAS) or real Navy sonar were employed. Results from that multi-year effort have shown that for blue whales, responses generally only lasted for as long as the sound was active and highly dependent on exposure context such as behavioral state, prey availability and the horizontal distance between the sound source and the individual whale. Blue whales identified as feeding in shallow depths showed no changes in behavior, however over 50% of deep-feeding whales responded during CEEs (Southall et al. 2019).

The SOCAL Tagless BPRS, as the name implies, does not involve deploying tags on the animals. Tags were omitted from this study design because tags on dolphins have not had high success rates of staying on for a very long time. Furthermore, dolphins are social species that typically occur in groups and individuals within a group are likely to interact or react together when exposed to an external stimuli. Therefore, the project integrates established methods of quantifying dolphin behavior and physiology in a novel way to measure broad and fine-scale group and individual changes of dolphin behavior and physiology to simulated Navy MFAS or real Navy sonars using CEEs. 

During these tagless CEEs, a dolphin group is tracked from multiple platforms using several different tools. Kelp Marine Research is our on-shore team that spots workable groups (workable meaning that a group should be within range of all platforms and not moving too quickly so that they will leave this range during the CEE), tracks the group using a theodolite (just like I do for my Port Orford gray whale project), and does focal follows to record behavior of the group over a period of time. Ziphiid, one of CRC’s RHIBs, is tasked with deploying three passive acoustic sensors to record sounds emitted by the dolphins and to measure the intensity of the sound of the simulated Navy MFAS or the real Navy sonars. Musculus, the second CRC RHIB, has a dual-function during CEEs; it holds the custom vertical line array sound source, which emits the simulated Navy MFAS, and it is also the ‘biopsy boat’ tasked with obtaining biopsy samples of individuals within the dolphin group to measure potential changes in stress hormone levels. And last but not least, the Magician, the third vessel on the water, serves as ‘home-base’ for the project (Fig. 3). Quite literally it is where the research team eats and sleeps, but it is also the spotting vessel from which visual observations occur, and it is the launch pad for the unmanned aerial system (UAS) used to measure potential changes in group composure, spacing, and speed of travel.

The project involves a lot of moving parts and we are careful to conduct the research with explicit monitoring and mitigation requirements to ensure our work is carried out safely and ethically. These factors, as well as the fact that we are working with live, wild animals that we cannot ‘control’, are why three pilot efforts were necessary. Our first ‘official’ phase this past October was a success: in just eight days we conducted 11 CEEs. Six of these involved experimental sonar transmissions (two being from real Navy sonars dipped from hovering helicopters) and five were no-sonar controls that are critical to be able to experimentally associate sonar exposure with potential response. There are more phases to come in 2020 and 2021 and I look forward to continue working on such a collaborative project.

For more information on the project, you can visit Southall Environmental Associates project page, or read the blog posts written by Dr. Brandon Southall (this one or this one).

For anyone attending the World Marine Mammal Conference in Barcelona, Spain, there will be several talks related to this research:

  • Dr. Brandon Southall will be presenting on the Atlantic BRS on beaked whales and short-finned pilot whales on Wednesday, December 11 from 2:15 – 2:30 pm
  • Dr. Caroline Casey will be presenting on the experimental design and results of this SOCAL Tagless BPRS project on Wednesday, December 11 from 2:30 – 2:45 pm

All research is authorized under NMFS permits #16111, 19091, and 19116 as well as numerous Institutional Animal Care and Use Committee and other federal, state, and local authorizations. More information is available upon request from the project chief scientist at Brandon.Southall@sea-inc.net

Literature cited

Fernández, A., J. F. Edwards, F. Rodríguez, A. Espinosa de los Monteros, P. Herráez, P. Castro, J. R. Jaber, V. Martín, and M. Arbelo. 2005. “Gas and fat embolic syndrome” involving a mass stranding of beaked whales (Family Ziphiidae) exposed to anthropogenic sonar signals. Veterinary Pathology 42(4):446-457.

Filadelfo, R., J. Mintz, E. Michlovich, A. D’Amico, P. L. Tyack, and D. R. Ketten. 2009. Correlating military sonar use with beaked whale mass strandings: what do the historical data show? Aquatic Mammals 35(4):435-444.

International Transport Forum. 2019. Transport demand set to triple, but sector faces potential disruptions. Retrieved from: https://www.itf-oecd.org/transport-demand-set-triple-sector-faces-potential-disruptions

Jambeck, J. R., R. Geyer, C. Wilcox, T. R. Siegler, M. Perryman, A. Andrady, R. Narayan, and K. L. Law. 2015. Plastic waste inputs from land into the ocean. Science 347(6223):768-771.

Jepson, P. D., M. Arbelo, R. Deaville, I A. P. Patterson, P. Castro, J. R. Baker, E. Degollada, H. M. Ross, P. Herráez, A. M. Pocknell, F. Rodríguez, F. E. Howie II, A. Espinosa, R. J. Reid, J. R. Jaber, V. Martin, A. A. Cunningham, and A. Fernández. 2003. Gas-bubble lesions in stranded cetaceans. Nature 425:575.

Lewis, S. L., and M. A. Maslin. 2015. Defining the Anthropocene. Nature 519:171-180.

Southall, B. L., S. L. DeRuiter, A. Friedlaender, A. K. Stimpert, J. A. Goldbogen, E. Hazen, C. Casey, S. Fregosi, D. E. Cade, A. N. Allen, C. M. Harris, G. Schorr, D. Moretti, S. Guan, and J. Calambokidis. 2019. Behavioral responses of individual blue whales (Balaenoptera musculus) to mid-frequency military sonar. Journal of Experimental Biology 222: doi. 10.1242/jeb.190637.

Tyack, P. L. 2008. Implications for marine mammals of large-scale changes in the marine acoustic environment. Journal of Mammalogy 89(3):549-558.

Weilgart, L. S. 2007a. The impacts of anthropogenic ocean noise on cetaceans and implications for management. Canadian Journal of Zoology 85(11):1091-1116.

Weilgart, L. S. 2007b. A brief review of known effects of noise on marine mammals. International Journal of Comparative Psychology 20(2):159-168.

World Ocean Review. 2014. WOR 3: Marine resources – opportunities and risks. Report No 3. Retrieved from: https://worldoceanreview.com/en/wor-3/oil-and-gas/.

World Ocean Review. 2010. WOR 1: Marine resources – Living with the oceans. A report on the state of the world’s oceans. Report No 1. Retrieved from: https://worldoceanreview.com/en/wor-1/transport/global-shipping/3/

Wright, A. J., N. A. Soto, A. L. Baldwin, M. Bateson, C. M. Beale, C. Clark, T. Deak, E. F. Edwards, A. Fernández, A. Godinho, L. T. Hatch, A. Kakuschke, D. Lusseau, D. Martineau, M. L. Romero, L. S. Weilgart, B. A. Wintle, G. Notarbartolo-di-Sciara, and V. Martin. Do marine mammals experience stress related to anthropogenic noise? International Journal of Comparative Psychology 20(2):274-316.

Can sea otters help kelp under a changing climate?

By Dominique Kone1 and Sara Hamilton2

1Masters Student in Marine Resource Management, 2Doctoral Student in Integrative Biology

Five years ago, the North Pacific Ocean experienced a sudden increase in sea surface temperature (SST), known as the warm blob, which altered marine ecosystem function and structure (Leising et al. 2015). Much research illustrated how the warm blob impacted pelagic ecosystems, with relatively less focused on the nearshore environment. Yet, a new study demonstrated how rising ocean temperatures have partially led to bull kelp loss in northern California. Unfortunately, we are once again observing similar warming trends, representing the second largest marine heatwave over recent decades, and signaling the potential rise of a second warm blob. Taken together, all these findings could forecast future warming-related ecosystem shifts in Oregon, highlighting the need for scientists and managers to consider strategies to prevent future kelp loss, such as reintroducing sea otters.

In northern California, researchers observed a dramatic ecosystem shift from productive bull kelp forests to purple sea urchin barrens. The study, led by Dr. Laura Rogers-Bennett from the University of California, Davis and California Department of Fish and Wildlife, determined that this shift was caused by multiple climatic and biological stressors. Beginning in 2013, sea star populations were decimated by sea star wasting disease (SSWD). Sea stars are a main predator of urchins, causing their absence to release purple urchins from predation pressure. Then, starting in 2014, ocean temperatures spiked with the warm blob. These two events created nutrient-poor conditions, which limited kelp growth and productivity, and allowed purple urchin populations to grow unchecked by predators and increase grazing on bull kelp. The combined effect led to approximately 90% reductions in bull kelp, with a reciprocal 60-fold increase in purple urchins (Figure 1).

Figure 1. Kelp loss and ecosystem shifts in northern California (Rogers-Bennett & Catton 2019).

These changes have wrought economic challenges as well as ecological collapse in Northern California. Bull kelp is important habitat and food source for several species of economic importance including red abalone and red sea urchins (Tegner & Levin 1982). Without bull kelp, red abalone and red sea urchin populations have starved, resulting in the subsequent loss of the recreational red abalone ($44 million) and commercial red sea urchin fisheries in Northern California. With such large kelp reductions, purple urchins are also now in a starved state, evidenced by noticeably smaller gonads (Rogers-Bennett & Catton 2019).

Biogeographically, southern Oregon is very similar to northern California, as both are composed of complex rocky substrates and shorelines, bull kelp canopies, and benthic macroinvertebrates (i.e. sea urchins, abalone, etc.). Because Oregon was also impacted by the 2014-2015 warm blob and SSWD, we might expect to see a similar coastwide kelp forest loss along our southern coastline. The story is more complicated than that, however. For instance, ODFW has found purple urchin barrens where almost no kelp remains in some localized places. The GEMM Lab has video footage of purple urchins climbing up kelp stalks to graze within one of these barrens near Port Orford, OR (Figure 2, left). In her study, Dr. Rogers-Bennett explains that this aggressive sea urchin feeding strategy is potentially a sign of food limitation, where high-density urchin populations create intense resource competition. Conversely, at sites like Lighthouse Reef (~45 km from Port Orford) outside Charleston, OR, OSU and University of Oregon divers are currently seeing flourishing bull kelp forests. Urchins at this reef have fat, rich gonads, which is an indicator of high-quality nutrition (Figure 2, right).

Satellites can detect kelp on the surface of the water, giving scientists a way to track kelp extent over time. Preliminary results from Sara Hamilton’s Ph.D. thesis research finds that while some kelp forests have shrunk in past years, others are currently bigger than ever in the last 35 years. It is not clear what is driving this spatial variability in urchin and kelp populations, nor why southern Oregon has not yet faced the same kind of coastwide kelp forest collapse as northern California. Regardless, it is likely that kelp loss in both northern California and southern Oregon may be triggered and/or exacerbated by rising temperatures.

Figure 2. Left: Purple urchin aggressive grazing near Port Orford, OR (GEMM Lab 2019). Right: Flourishing bull kelp near Charleston, OR (Sara Hamilton 2019).

The reintroduction of sea otters has been proposed as a solution to combat rising urchin populations and bull kelp loss in Oregon. From an ecological perspective, there is some validity to this idea. Sea otters are a voracious urchin predator that routinely reduce urchin populations and alleviate herbivory on kelp (Estes & Palmisano 1974). Such restoration and protection of bull kelp could help prevent red abalone and red sea urchin starvation. Additionally, restoring apex predators and increasing species richness is often linked to increased ecosystem resilience, which is particularly important in the face of global anthropogenic change (Estes et al. 2011)

While sea otters could alleviate grazing pressure on Oregon’s bull kelp, this idea only looks at the issue from a top-down, not bottom-up, perspective. Sea otters require a lot of food (Costa 1978, Reidman & Estes 1990), and what they eat will always be a function of prey availability and quality (Ostfeld 1982). Just because urchins are available, doesn’t mean otters will eat them. In fact, sea otters prefer large and heavy (i.e. high gonad content) urchins (Ostfeld 1982). In the field, researchers have observed sea otters avoiding urchins at the center of urchin barrens (personal communication), presumably because those urchins have less access to kelp beds than on the barren periphery, and therefore, are constantly in a starved state (Konar & Estes 2003) (Figure 3). These findings suggest prey quality is more important to sea otter survival than just prey abundance.

Figure 3. Left: Sea urchin barren (Annie Crawley). Right: Urchin gonads (Sea to Table).

Purple urchin quality has not been widely assessed in Oregon, but early results show that gonad size varies widely depending on urchin density and habitat type. In places where urchin barrens have formed, like Port Orford, purple urchins are likely starving and thus may be a poor source of nutrition for sea otters. Before we decide whether sea otters are a viable tool to combat kelp loss, prey surveys may need to be conducted to assess if a sea otter population could be sustained based on their caloric requirements. Furthermore, predictions of how these prey populations may change due to rising temperatures could help determine the potential for sea otters to become reestablished in Oregon under rapid environmental change.

Recent events in California could signal climate-driven processes that are already impacting some parts of Oregon and could become more widespread. Dr. Rogers-Bennett’s study is valuable as she has quantified and described ecosystem changes that might occur along Oregon’s southern coastline. The resurgence of a potential second warm blob and the frequency between these warming events begs the question if such temperature spikes are still anomalous or becoming the norm. If the latter, we could see more pronounced kelp loss and major shifts in nearshore ecosystem baselines, where function and structure is permanently altered. Whether reintroducing sea otters can prevent these changes will ultimately depend on prey and habitat availability and quality, and should be carefully considered.

References:

Costa, D. P. 1978. The ecological energetics, water, and electrolyte balance of the California sea otter (Enhydra lutris). Ph.D. dissertation, University of California, Santa Cruz.

Estes, J. A. and J.F. Palmisano. 1974. Sea otters: their role in structuring nearshore communities. Science. 185(4156): 1058-1060.

Estes et al. 2011. Trophic downgrading of planet Earth. Science. 333(6040): 301-306.

Harvell et al. 2019. Disease epidemic and a marine heat wave are associated with the continental-scale collapse of a pivotal predator (Pycnopodia helianthoides). Science Advances. 5(1).

Konar, B., and J. A. Estes. 2003. The stability of boundary regions between kelp beds and deforested areas. Ecology. 84(1): 174-185.

Leising et al. 2015. State of California Current 2014-2015: impacts of the warm-water “blob”. CalCOFI Reports. (56): 31-68.

Ostfeld, R. S. 1982. Foraging strategies and prey switching in the California sea otter. Oecologia. 53(2): 170-178.

Reidman, M. L. and J. A. Estes. 1990. The sea otter (Enhydra lutris): behavior, ecology, and natural history. United States Department of the Interior, Fish and Wildlife Service, Biological Report. 90: 1-126.

Rogers-Bennett, L., and C. A. Catton. 2019. Marine heat wave and multiple stressors tip bull kelp forest to sea urchin barrens. Scientific Reports. 9:15050.

Tegner, M. J., and L. A. Levin. 1982. Do sea urchins and abalones compete in California? International Echinoderms Conference, Tampa Bay. J. M Lawrence, ed.

What is that whale doing? Only residence in space and time will tell…

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

For my research in Port Orford, my field team and I track individual gray whales continuously from a shore-based location: once we spot a whale we will track it for the entire time that it remains in our study site. The time spent tracking a whale can vary widely. In the 2018 field season, our shortest trackline was three minutes, and our longest track was over three hours in duration.

This variability in foraging time is partly what sparked my curiosity to investigate potential foraging differences between individuals of the Pacific Coast Feeding Group (PCFG) gray whales. I want to know why some individuals, like “Humpy” who was our longest tracked individual in 2018, stayed in an area for so long, while others, like “Smokey”, only stayed for three minutes (Figure 1). It is hard to pinpoint just one variable that drives these decisions (e.g., prey, habitat) made by individuals about where they forage and how long because the marine environment is so dynamic. Foraging decisions are likely dictated by several factors acting in concert with one another. As a result, I have many research questions, including (but certainly not limited to):

  1. Does prey density drive length of individual foraging bouts?
  2. Do individual whales have preferences for a particular prey species?
  3. Are prey patches containing gravid zooplankton targeted more by whales?
  4. Do whales prefer to feed closer to kelp patches?
  5. How does water depth factor into all of the above decisions and/or preferences? 

I hope to get to the bottom of these questions through the data analyses I will be undertaking for my second chapter of my Master’s thesis. However, before I can answer those questions, I need to do a little bit of tidying up of my whale tracklines. Now that the 2019 field season is over and I have all of the years of data that I will be analyzing for my thesis (2015-2019), I have spent the past 1-2 weeks diving into the trackline clean-up and analysis preparation.

The first step in this process is to run a speed filter over each trackline. The aim of the speed filter is to remove any erroneous points or outliers that must be wrong based on the known travel speeds of gray whales. Barb Lagerquist, a Marine Mammal Institute (MMI) colleague who has tracked gray whales for several field seasons, found that the fastest individual she ever encountered traveled at a speed of 17.3 km/h (personal communication). Therefore, based on this information,  my tracklines are run through a speed filter set to remove any points that suggest that the whale traveled at 17.3 km/h or faster (Figure 2). 

Fig 3. Trackline of “Humpy” after interpolation. The red points are interpolated.

Next, the speed-filtered tracklines are interpolated (Figure 3). Interpolation fills spatial and/or temporal gaps in a data set by evenly spacing points (by distance or time interval) between adjacent points. These gaps sometimes occur in my tracklines when the tracking teams misses one or several surfacings of a whale or because the whale is obscured by a large rock. 

After speed filtration and interpolation has occurred, the tracklines are ready to be analyzed using Residence in Space and Time (RST; Torres et al. 2017) to assign behavior state to each location. The questions I am hoping to answer for my thesis are based upon knowing the behavioral state of a whale at a given location and time. In order for me to draw conclusions over whether or not a whale prefers to forage by a reef with kelp rather than a reef without kelp, or whether it prefers Holmesimysis sculpta over Neomysis rayii, I need to know when a whale is actually foraging and when it is not. When we track whales from our cliff site, we assign a behavior to each marked location of an individual. It may sound simple to pick the behavior a whale is currently exhibiting, however it is much harder than it seems. Sometimes the behavioral state of a whale only becomes apparent after tracking it for several minutes. Yet, it’s difficult to change behaviors retroactively while tracking a whale and the qualitative assignment of behavior states is not an objective method. Here is where RST comes in.

Those of you who have been following the blog for a few years may recall a post written in early 2017 by Rachael Orben, a former post-doc in the GEMM Lab who currently leads the Seabird Oceanography Lab. The post discussed the paper “Classification of Animal Movement Behavior through Residence in Space Time” written by Leigh and Rachael with two other collaborators, which had just been published a few days prior. If you want to know the nitty gritty of what RST is and how it works, I suggest reading Rachael’s blog, the GEMM lab’s brief description of the project and/or the actual paper since it is an open-access publication. However, in a nut shell, RST allows a user to identify three primary behavioral states in a tracking dataset based on the time and distance the individual spent within a given radius. The three behavioral categories are as follows:

Fig 4. Visualization of the three RST behavioral categories. Taken from Torres et al. (2017).
  • Transit – characterized by short time and distance spent within an area (radius of given size), meaning the individual is traveling.
  • Time-intensive – characterized by a long time spent within an area, meaning the individual is spending relatively more time but not moving much distance (such as resting in one spot). 
  • Time & distance-intensive – characterized by relatively high time and distances spent within an area, meaning the individual is staying within and moving around a lot in an area, such as searching or foraging. 

What behavior these three categories represent depends on the resolution of the data analyzed. Is one point every day for two years? Then the data are unlikely to represent resting. Or is the data 1 point every second for 1 hour? In which case travel segments may cover short distances. On average, my gray whale tracklines are composed of a point every 4-5 minutes for 1-2 hours.  Bases on this scale of tracking data, I will interpret the categories as follows: Transit is still travel, time & distance-intensive points represent locations where the whale was searching because it was moving around one area for a while, and time-intensive points represent foraging behavior because the whale has ‘found what it is looking for’ and is spending lots of time there but not moving around much anymore. The great thing about RST is that it removes the bias that is introduced by my field team when assigning behavioral states to individual whales (Figure 5). RST looks at the tracklines in a very objective way and determines the behavioral categories quantitatively, which helps to remove the human subjectivity.

While it took quite a bit of troubleshooting in R and overcoming error messages to make the codes run on my data, I am proud to have results that are interesting and meaningful with which I can now start to answer some of my many research questions. My next steps are to create interpolated prey density and distance to kelp layers in ArcGIS. I will then be able to overlay my cleaned up tracklines to start teasing out potential patterns and relationships between individual whale foraging movements and their environment. 

Literature cited

Torres, L. G., R. A. Orben, I. Tolkova, and D. R. Thompson. 2017. Classification of animal movement behavior through residence in space and time. PLoS ONE: doi. org/10.1371/journal.pone.0168513.

Demystifying the algorithm

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

Hi everyone! My name is Clara Bird and I am the newest graduate student in the GEMM lab. For my master’s thesis I will be using drone footage of gray whales to study their foraging ecology. I promise to talk about how cool gray whales in a following blog post, but for my first effort I am choosing to write about something that I have wanted to explain for a while: algorithms. As part of previous research projects, I developed a few semi-automated image analysis algorithms and I have always struggled with that jargon-filled phrase. I remember being intimidated by the term algorithm and thinking that I would never be able to develop one. So, for my first blog I thought that I would break down what goes into image analysis algorithms and demystify a term that is often thrown around but not well explained.

What is an algorithm?

The dictionary broadly defines an algorithm as “a step-by-step procedure for solving a problem or accomplishing some end” (Merriam-Webster). Imagine an algorithm as a flow chart (Fig. 1), where each step is some process that is applied to the input(s) to get the desired output. In image analysis the output is usually isolated sections of the image that represent a specific feature; for example, isolating and counting the number of penguins in an image. Algorithm development involves figuring out which processes to use in order to consistently get desired results. I have conducted image analysis previously and these processes typically involve figuring out how to find a certain cutoff value. But, before I go too far down that road, let’s break down an image and the characteristics that are important for image analysis.

Figure 1. An example of a basic algorithm flow chart. There are two inputs: variables A and B. The process is the calculation of the mean of the two variables.

What is an image?

Think of an image as a spread sheet, where each cell is a pixel and each pixel is assigned a value (Fig. 2). Each value is associated with a color and when the sheet is zoomed out and viewed as a whole, the image comes together.  In color imagery, which is also referred to as RGB, each pixel is associated with the values of the three color bands (red, green, and blue) that make up that color. In a thermal image, each pixel’s value is a temperature value. Thinking about an image as a grid of values is helpful to understand the challenge of translating the larger patterns we see into something the computer can interpret. In image analysis this process can involve using the values of the pixels themselves or the relationships between the values of neighboring pixels.

Figure 2. A diagram illustrating how pixels make up an image. Each pixel is a grid cell associated with certain values. Image Source: https://web.stanford.edu/class/cs101/image-1-introduction.html

Our brains take in the whole picture at once and we are good at identifying the objects and patterns in an image. Take Figure 3 for example: an astute human eye and brain can isolate and identify all the different markings and scars on the fluke. Yet, this process would be very time consuming. The trick to building an algorithm to conduct this work is figuring out what processes or tools are needed to get a computer to recognize what is marking and what is not. This iterative process is the algorithm development.

Figure 3. Photo ID image of a gray whale fluke.

Development

An image analysis algorithm will typically involve some sort of thresholding. Thresholds are used to classify an image into groups of pixels that represent different characteristics. A threshold could be applied to the image in Figure 3 to separate the white color of the markings on the fluke from the darker colors in the rest of the image. However, this is an oversimplification, because while it would be pretty simple to examine the pixel values of this image and pick a threshold by hand, this threshold would not be applicable to other images. If a whale in another image is a lighter color or the image is brighter, the pixel values would be different enough from those in the previous image for the threshold to inaccurately classify the image. This problem is why a lot of image analysis algorithm development involves creating parameterized processes that can calculate the appropriate threshold for each image.

One successful method used to determine thresholds in images is to first calculate the frequency of color in each image, and then apply the appropriate threshold. Fletcher et al. (2009) developed a semiautomated algorithm to detect scars in seagrass beds from aerial imagery by applying an equation to a histogram of the values in each image to calculate the threshold. A histogram is a plot of the frequency of values binned into groups (Fig. 4). Essentially, it shows how many times each value appears in an image. This information can be used to define breaks between groups of values. If the image of the fluke were transformed to a gray scale, then the values of the marking pixels would be grouped around the value for white and the other pixels would group closer to black, similar to what is shown in Figure 4. An equation can be written that takes this frequency information and calculates where the break is between the groups. Since this method calculates an individualized threshold for each image, it’s a more reliable method for image analysis. Other characteristics could also be used to further filter the image, such as shape or area.

However, that approach is not the only way to make an algorithm applicable to different images; semi-automation can also be helpful. Semi-automation involves some kind of user input. After uploading the image for analysis, the user could also provide the threshold, or the user could crop the image so that only the important components were maintained. Keeping with the fluke example, the user could crop the image so that it was only of the fluke. This would help reduce the variety of colors in the image and make it easier to distinguish between dark whale and light marking.

Figure 4. Example histogram of pixel values. Source: Moallem et al. 2012

Why algorithms are important

Algorithms are helpful because they make our lives easier. While it would be possible for an analyst to identify and digitize each individual marking from a picture of a gray whale, it would be extremely time consuming and tedious. Image analysis algorithms significantly reduce the time it takes to process imagery. A semi-automated algorithm that I developed to count penguins from still drone imagery can count all the penguins on a one km2 island in about 30 minutes, while it took me 24 long hours to count them by hand (Bird et al. in prep). Furthermore, the process can be repeated with different imagery and analysts as part of a time series without bias because the algorithm eliminates human error introduced by different analysts.

Whether it’s a simple combination of a few processes or a complex series of equations, creating an algorithm requires breaking down a task to its most basic components. Development involves translating those components step by step into an automated process, which after many trials and errors, achieves the desired result. My first algorithm project took two years of revising, improving, and countless trials and errors.  So, whether creating an algorithm or working to understand one, don’t let the jargon nor the endless trials and errors stop you. Like most things in life, the key is to have patience and take it one step at a time.

References

Bird, C. N., Johnston, D.W., Dale, J. (in prep). Automated counting of Adelie penguins (Pygoscelis adeliae) on Avian and Torgersen Island off the Western Antarctic Peninsula using Thermal and Multispectral Imagery. Manuscript in preparation

Fletcher, R. S., Pulich, W. ‡, & Hardegree, B. (2009). A Semiautomated Approach for Monitoring Landscape Changes in Texas Seagrass Beds from Aerial Photography. https://doi.org/10.2112/07-0882.1

Moallem, Payman & Razmjooy, Navid. (2012). Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization. Journal of Applied Research and Technology. 703.

Surveying for marine mammals in the Northern California Current

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

There is something wonderful about time at sea, where your primary obligation is to observe the ocean from sunrise to sunset, day after day, scanning for signs of life. After hours of seemingly empty blue with only an occasional albatross gliding over the swells on broad wings, it is easy to question whether there is life in the expansive, blue, offshore desert. Splashes on the horizon catch your eye, and a group of dolphins rapidly approaches the ship in a flurry of activity. They play in the ship’s bow and wake, leaping out of the swells. Then, just as quickly as they came, they move on. Back to blue, for hours on end… until the next stirring on the horizon. A puff of exhaled air from a whale that first might seem like a whitecap or a smudge of sunscreen or salt spray on your sunglasses. It catches your eye again, and this time you see the dark body and distinctive dorsal fin of a humpback whale.

I have just returned from 10 days aboard the NOAA ship Bell M. Shimada, where I was the marine mammal observer on the Northern California Current (NCC) Cruise. These research cruises have sampled the NCC in the winter, spring, and fall for decades. As a result, a wealth of knowledge on the oceanography and plankton community in this dynamic ocean ecosystem has been assimilated by a dedicated team of scientists (find out more via the Newportal Blog). Members of the GEMM Lab have joined this research effort in the past two years, conducting marine mammal surveys during the transits between sampling stations (Fig. 2).

Figure 2. Northern California Current cruise sampling locations, where oceanography and plankton data are collected. Marine mammal surveys were conducted on the transits between stations.

The fall 2019 NCC cruise was a resounding success. We were able to survey a large swath of the ecosystem between Crescent City, CA and La Push, WA, from inshore to 200 miles offshore. During that time, I observed nine different species of marine mammals (Table 1). As often as I use some version of the phrase “the marine environment is patchy and dynamic”, it never fails to sink in a little bit more every time I go to sea. On the map in Fig. 3, note how clustered the marine mammal sightings are. After nearly a full day of observing nothing but blue water, I would find myself scrambling to keep up with recording all the whales and dolphins we were suddenly in the midst of. What drives these clusters of sightings? What is it about the oceanography and prey community that makes any particular area a hotspot for marine mammals? We hope to get at these questions by utilizing the oceanographic data collected throughout the surveys to better understand environmental drivers of these distribution patterns.

 Table 1. Summary of marine mammal sightings from the September 2019 NCC Cruise.

Species # sightings Total # individuals
Northern Elephant Seal 1 1
Northern Fur Seal 2 2
Common Dolphin 2 8
Pacific White-sided Dolphin 8 143
Dall’s Porpoise 4 19
Harbor Porpoise 1 3
Sperm Whale 1 1
Fin Whale 1 1
Humpback Whale 22 36
Unidentified Baleen Whale 14 16
Figure 3. Map of marine mammal sighting locations from the September NCC cruise.

It was an auspicious time to survey the Northern California Current. Perhaps you have read recent news reports warning about the formation of another impending marine heatwave, much like the “warm blob” that plagued the North Pacific in 2015. We experienced it first-hand during the NCC cruise, with very warm surface waters off Newport extending out to 200 miles offshore (Fig. 4). A lot of energy input from strong winds would be required to mix that thick, warm layer and allow cool, nutrient-rich water to upwell along the coast. But it is already late September, and as the season shifts from summer to fall we are at the end of our typical upwelling season, and the north winds that would typically drive that mixing are less likely. Time will tell what is in store for the NCC ecosystem as we face the onset of another marine heatwave.

Figure 4. Temperature contours over the upper 150 m from 1-200 miles off Newport, Oregon from Fall 2014-2019. During Fall 2014, the Warm Blob inundated the Oregon shelf. Surface temperatures during that survey were 17°- 18°C along the entire transect. During 2015 and 2016 the warm water (16°C) layer had deepened and occupied the upper 50 m. During 2018, the temperature was 16°C in the upper 20 m and cooler on the shelf, indicative of residual upwelling. During this survey in 2019, we again saw very warm (18°C) temperatures in the upper water column over the entire transect. Image and caption credit: Jennifer Fisher.

It was a joy to spend 10 days at sea with this team of scientists. Insight, collaboration, and innovation are born from interdisciplinary efforts like the NCC cruises. Beyond science, what a privilege it is to be on the ocean with a group of people you can work with and laugh with, from the dock to 200 miles offshore, south to north and back again.

Dawn Barlow on the flying bridge of NOAA Ship Bell M. Shimada, heading out to sea with the Newport bridge in the background. Photo: Anna Bolm.

The significance of blubber hormone sampling in conservation and monitoring of marine mammals

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

Marine mammals are challenging to study for many reasons, and specifically because they inhabit the areas of the Earth that are uninhabited by people: the oceans. Monitoring marine mammal populations to gather baselines on their health condition and reproductive status is not as simple as trap and release, which is a method often conducted for terrestrial animals. Marine mammals are constantly moving in vast areas below the surface. Moreover, cetaceans, which do not spend time on land, are arguably the most challenging to sample.

One component of my project, based in California, USA, is a health assessment analyzing hormones of the bottlenose dolphins that frequent both the coastal and the offshore waters. Therefore, I am all too familiar with the hurdles of collecting health data from living marine mammals, especially cetaceans. However, the past few decades have seen major advancements in technology both in the laboratory and with equipment, including one tool that continues to be critical in understanding cetacean health: blubber biopsies.

Biopsy dart hitting a bottlenose dolphin below the dorsal fin. Image Source: NMFS

Blubber biopsies are typically obtained via low-powered crossbow with a bumper affixed to the arrow to de-power it once it hits the skin. The arrow tip has a small, pronged metal attachment to collect an eraser-tipped size amount of tissue with surface blubber and skin. I compare this to a skin punch biopsies in humans; it’s small, minimally-invasive, and requires no follow-up care. With a small team of scientists, we use small, rigid-inflatable vessels to survey the known locations of where the bottlenose dolphins tend to gather. Then, we assess the conditions of the seas and of the animals, first making sure we are collecting from animals without potentially lowered immune systems (no large, visible wounds) or calves (less than one years old). Once we have photographed the individual’s dorsal fin to identify the individual, one person assembles the biopsy dart and crossbow apparatus following sterile procedures when attaching the biopsy tips to avoid infection. Another person prepares to photograph the animal to match the biopsy information to the individual dolphin. One scientist aims the crossbow for the body of the dolphin, directly below the dorsal fin, while the another photographs the biopsy dart hitting the animal and watches where it bounces off. Then, the boat maneuvers to the floating biopsy dart to recover the dart and the sample. Finally, the tip with blubber and skin tissue is collected, again using sterile procedures, and the sample is archived for further processing. A similar process, using an air gun instead of a crossbow can be viewed below:

GEMM Lab members using an air gun loaded with a biopsy dart to procure marine mammal blubber from a blue whale in New Zealand. Video Source: GEMM Laboratory.

Part of the biopsy process is holding ourselves to the highest standards in our minimally-invasive technique, which requires constant practice, even on land.

Alexa practicing proper crossbow technique on land under supervision. Image Source: Alexa Kownacki

Blubber is the lipid-rich, vascularized tissue under the epidermis that is used in thermoregulation and fat storage for marine mammals. Blubber is an ideal matrix for storing lipophilic (fat-loving) steroid hormones because of its high fat content. Steroid hormones, such as cortisol, progesterone, and testosterone, are naturally circulating in the blood stream and are released in high concentrations during specific events. Unlike blood, blubber is less dynamic and therefore tells a much longer history of the animal’s nutritional state, environmental exposure, stress level, and life history status. Blubber is the cribs-notes version of a marine mammal’s biography over its previous few months of life. Blood, on the other hand, is the news story from the last 24 hours. Both matrices serve a specific purpose in telling the story, but blubber is much more feasible to obtain from a cetacean and provides a longer time frame in terms of information on the past.

A simplified depiction of marine mammal blubber starting from the top (most exterior surface) being the skin surface down to the muscle (most interior). Image Source: schoolnet.org.za

I use blubber biopsies for assessing cortisol, testosterone, and progesterone in the bottlenose dolphins. Cortisol is a glucocorticoid that is frequently associated with stress, including in humans. Marine mammals utilize the same hypothalamic-pituitary-adrenal (HPA) axis that is responsible for the fight-or-flight response, as well as other metabolic regulations. During prolonged stressful events, cortisol levels will remain elevated, which has long-term repercussions for an animal’s health, such as lowered immune systems and decreased ability to respond to predators. Testosterone and progesterone are sex hormones, which can be used to indicate sex of the individual and determine reproductive status. This reproductive information allows us to assess the population’s composition and structure of males and females, as well as potential growth or decline in population (West et al. 2014).

Alexa using a crossbow from a small boat off of San Diego, CA. Image Source: Alexa Kownacki

The coastal and offshore bottlenose dolphin ecotypes of interest in my research occupy different locations and are therefore exposed to different health threats. This is a primary reason for conducting health assessments, specifically analyzing blubber hormone levels. The offshore ecotype is found many kilometers offshore and is most often encountered around the southern Channel Islands. In contrast, the coastal ecotype is found within 2 kilometers of shore (Lowther-Thieleking et al. 2015) where they are subjected to more human exposure, both directly and indirectly, because of their close proximity to the mainland of the United States. Coastal dolphins have a higher likelihood of fishery-related mortality, the negative effects of urbanization including coastal runoff and habitat degradation, and recreational activities (Hwang et al. 2014). The blubber hormone data from my project will inform which demographics are most at-risk. From this information, I can provide data supporting why specific resources should be allocated differently and therefore help vulnerable populations. Further proving that the small amount of tissue from a blubber biopsy can help secure a better future for population by adjusting and informing conservation strategies.

Literature Cited:

Hwang, Alice, Richard H Defran, Maddalena Bearzi, Daniela. Maldini, Charles A Saylan, Aime ́e R Lang, Kimberly J Dudzik, Oscar R Guzo n-Zatarain, Dennis L Kelly, and David W Weller. 2014. “Coastal Range and Movements of Common Bottlenose Dolphins (Tursiops Truncatus) off California and Baja California, Mexico.” Bulletin of the Southern California Academy of Sciences 113 (1): 1–13. https://doi.org/10.3390/toxins6010211.

Lowther-Thieleking, Janet L., Frederick I. Archer, Aimee R. Lang, and David W. Weller. 2015. “Genetic Differentiation among Coastal and Offshore Common Bottlenose Dolphins, Tursiops Truncatus, in the Eastern North Pacific Ocean.” Marine Mammal Science 31 (1): 1–20. https://doi.org/10.1111/mms.12135.

West, Kristi L., Jan Ramer, Janine L. Brown, Jay Sweeney, Erin M. Hanahoe, Tom Reidarson, Jeffry Proudfoot, and Don R. Bergfelt. 2014. “Thyroid Hormone Concentrations in Relation to Age, Sex, Pregnancy, and Perinatal Loss in Bottlenose Dolphins (Tursiops Truncatus).” General and Comparative Endocrinology 197: 73–81. https://doi.org/10.1016/j.ygcen.2013.11.021.

The Seascape of Fear: What are the ecological implications of being afraid in the marine environment?

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

In the GEMM Lab, our research focuses largely on the ecology of marine top predators. Inherent in our work are often assumptions that our study species—wide-ranging predators including whales, dolphins, otters, or seabirds—will distribute themselves relative to their prey. In order to make a living in the highly patchy and dynamic marine environment, predators must find ways to predictably locate and exploit prey resources.

But what about the prey? How do the prey structure themselves relative to their predators? This question is explored in depth in a paper titled “The Landscape of Fear: Ecological Implications of Being Afraid” (Laundre et al. 2010), which we discussed in our most recent lab meeting. When wolves were re-introduced in Yellowstone, the elk increased their vigilance and altered their grazing patterns. As a result, the plant community was altered to reflect this “landscape of fear” that the elk move through, where their distribution not only reflected opportunities for the elk to eat but also the risk of being eaten.

Translating the landscape of fear concept to the marine environment is tricky, but a fascinating exercise in ecological theory. We grappled with drawing parallels between the example system of wolves, elk, and vegetation and baleen whales, zooplankton, and phytoplankton. Relative to grazing mammals like elk, the cognitive abilities of zooplankton like krill, copepods, and mysid might pale in comparison. How could we possibly measure “fear” or “vigilance” in zooplankton? The swarming behavior of mysid and krill into dense patches is a defense mechanism—the strategy they have evolved to lessen the likelihood that any one of them will be eaten by a predator. I would posit that the diel vertical migration (DVM) of zooplankton is a manifestation of fear, at least on some level. DVM occurs over the course of each day, with plankton in pelagic ecosystems migrating vertically in the water column to avoid predators by hiding at depth during the daylight hours, and then swimming upward to feed on phytoplankton under the cover of darkness. I won’t speculate any further on the intelligence of zooplankton, but the need to survive predation has driven them to evolve this effective evolutionary strategy of hiding in the ocean’s twilight zone, swimming upward to feed only after dark so that they’re less likely to linger in spaces occupied by predators.

Laundre et al. (2010) present a visual representation of the landscape of fear (Fig. 1, reproduced below), where as an animal moves through space (represented as distance in meters or kilometers, for example), they also move through varying levels of predation risk. Environmental gradients (temperature, for example) tend to be much more stable across space in terrestrial ecosystems such as in the Yellowstone example from the paper. I wonder whether the same concept and visual depiction of a landscape of fear could be translated as risk across various environmental gradients, rather than geographic distances? In this proposed illustration, a landscape of fear would vary based on gradients of environmental conditions rather than geographic space. Such a shift in spatial reference —from geographic to environmental space—might make the model more applicable in the dynamic ocean ecosystems that we study.

What about cases when the predators we study become prey? One example we discussed was gray whales migrating from breeding lagoons in Mexico to feeding grounds in the Bering Sea. Mother-calf pairs hug the coastline tightly, by no means taking the most direct route between locations and adding considerable travel distance to their migration. The leading hypothesis is that mother gray whales take the coastal route to minimize the risk that their calves will fall prey to killer whale attacks. Are there other cases where the predators we study operate in a seascape of fear that we do not yet understand? Likely so, and the predators’ own seascape of fear may account for cases when we cannot explain predator distribution simply by their prey and their environment. To take this a step further, it might be beneficial not only to think of predation risk as only the potential to be eaten, but expand our definition to include human disturbance. While humans may not directly prey on marine predators, the disturbance from human activity in the ocean likely creates a layer of fear which animals must navigate, even in the absence of actual predation.

Our lively lab meeting discussion prompted me to look into how the landscape of fear model has been applied to the highly dynamic and intricate marine environment. In a study examining predator-prey dynamics of three species of marine mammals—bottlenose dolphins, harbor seals, and dugongs—Wirsing et al. (2007) found that in all three cases, the study species spent less time in more desirable prey patches or decreased riskier behavior in the presence of predators. Most studies in marine ecology are observational, as we rarely have the opportunity to manipulate our study system for experimental design and hypothesis testing. However, a study of coral reefs in the Florida Keys conducted by Catano et al. (2015) used fabricated predators—decoys of black grouper, a predatory fish—to investigate the influence of fear of predation on the reef system. What they found was that herbivorous fish consumed significantly less and fed at a much faster rate in the presence of this decoy predator. The grouper, even in decoy form, created a “reefscape of fear”, altering patterns in herbivory with potential ramifications for the entire ecosystem.

My takeaway from our discussion and my musings in this week’s blog post is that predator and prey distribution and behavior is highly interconnected. While predators distribute themselves to maximize their ability to find a meal, their prey respond accordingly by balancing finding a meal of their own with minimizing the risk that they will be eaten. Ecology is the study of an ecosystem, which means the questions we ask are complicated and hierarchical, and must be considered from multiple angles, accounting for biological, environmental, and behavioral elements to name a few. These challenges of studying ecosystems are simultaneously what make ecology fascinating, and exciting.

References:

Laundré, J. W., Hernández, L., & Ripple, W. J. (2010). The landscape of fear: ecological implications of being afraid. Open Ecology Journal3, 1-7.

Catano, L. B., Rojas, M. C., Malossi, R. J., Peters, J. R., Heithaus, M. R., Fourqurean, J. W., & Burkepile, D. E. (2016). Reefscapes of fear: predation risk and reef hetero‐geneity interact to shape herbivore foraging behaviour. Journal of Animal Ecology85(1), 146-156.

Wirsing, A. J., Heithaus, M. R., Frid, A., & Dill, L. M. (2008). Seascapes of fear: evaluating sublethal predator effects experienced and generated by marine mammals. Marine Mammal Science24(1), 1-15.

Introducing Crew Cinco – the Port Orford Gray Whale Foraging Ecology Field Team of 2019

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

It seems unfathomable to me that one year and two months ago I had never used a theodolite before, never been in an ocean kayak before, never identified zooplankton before, never seen a Time-Depth-Recorder (TDR) before. Now, one year later, it seems like all of those tools, techniques and things are just a couple of old friends with which I am being reunited with again. My second field season as the project team lead of the gray whale foraging ecology project in Port Orford (PO) is slowly getting underway and so many of the lessons I learned from my first field season last year have already helped me tremendously this year. I know how to interpret weather forecasts and determine whether it will be a kayak-appropriate day. I know how to figure out the quirks of Pythagoras, the program we use to interface with our theodolite which helps us track whales from our cliff site. I know how to keep track of a budget and feed a team of hungry researchers after a long day of work. Knowing all of these things ahead of this year’s field season have made me feel a little more prepared and at ease with the training of my team and the work to be done. Nevertheless, there are always new curveballs to be thrown my way and while they can often be frustrating, I enjoy the challenges that being a team leader has to offer as it allows me to continue to grow as a field research scientist. 

Figure 1. Crew Cinco tracks a whale in Tichenor Cove. Source: L Hildebrand.

2019 marks the fifth year that this project has been taking place in PO. Back in the summer of 2015, former GEMM Lab Master’s student Florence Sullivan started this project together with Leigh. That year the research focused more on investigating vessel disturbance to gray whales by comparing sites of heavy (Boiler Bay) to low boat traffic (Port Orford). The effort found that there were significant differences in gray whale activity budgets between the heavy and low boat traffic conditions (Sullivan & Torres 2018). The following year, the focus of the research switched to being more on the foraging ecology side of things and the project was based solely out of Port Orford, as it continues to be to this day. Being in our fifth year means that we are starting to build a humbly-sized database of sightings across multiple years allowing me to investigate potential individual specialization of the whales that we document. Similarly, multiple years of prey sampling is starting to reveal temporal and spatial trends of prey community assemblages.

Figure 2. Buttons (pictured above) is one of the stars of the Port Orford gray whale foraging ecology project as he has been seen every year since 2016. Crew Cinco has already seen him three times since the start of August. Source: L Hildebrand.

It has become a tradition to come up with a name for the field team that spends 6 weeks at the Oregon State University (OSU) Port Orford Field Station to collect the data for the project. It started with Team Ro“buff”stus in 2015, which I believe carried through until 2017. This is understandable since it’s such a clever name. It’s a play on the species name for gray whales, robustus, but the word “Buff” has been substituted in the center. Buffs are pieces of cloth sewn into a cylindrical shape, often with fun patterns or colors, that can be used as face masks, headbands, and scarves, which come in very handy when your face is exposed to the elements. Doing this project, we can be confronted by wind, sun, fog and sea water all in one day, so Buffs have definitely served the team members very well over the years. Last year, as the project’s torch was passed from Florence to myself, I felt a new team name was apt, and so last year’s team decided our name would be Team Whale Storm. I believe it was because we said we would take the whale world by storm with our insanely good theodolite tracking and kayak sampling skills. With a new year and new team upon us, a new team name was in order. As the title of this blog post indicates, this year the team is called Crew Cinco. The reason behind this name is that we are the fifth team to carry out this field work. Since the gray whales breed in the lagoons of Baja California, Mexico, I like to think that their native language is Spanish. Hence, we have decided that instead of being Crew Five, we are Crew Cinco, as cinco is the Spanish word for five (besides, alliteration makes for a much better team name).

Now that you are up to speed on the history of the PO gray whale project, let me tell you a little about who is part of Crew Cinco and what we have been up to already.

This year’s Marine Studies Initiative OSU undergraduate intern is Mia Arvizu. Mia has just finished her sophomore year at OSU and majors in Environmental Science. Besides being my co-captain this year in the field, Mia is also undertaking an independent research project which focuses on the relationship between sea urchin abundance, kelp health and gray whale foraging. She will tell you all about this project in a few weeks when she takes over the GEMM lab blog. Aside from her interest in ecology and the way science can be used to help local communities in a changing environment, Mia is a dancer, having performed in several dances in OSU’s annual luau this year, and she is currently teaching herself Spanish and Hawaiian.

Both of our high school interns this year are from Astoria. Anthony Howe has just graduated from Astoria High School and will be starting at Clatsop Community College in the fall. His plan is to transfer to OSU and to pursue his interest in marine biology. Anthony, like myself, was born in Germany and lived there until he was six, which means that he is able to speak fluent German. He also introduced the team to the wonders of the Instant Pot, which has made cooking for a team of four hungry scientists much simpler.

Donovan Burns is our other high school intern. He will be going into his junior year in the fall. Donovan never ceases to amaze us with the seemingly endless amounts of general knowledge he has, often sharing facts about Astoria’s history to Asimov’s Laws of Robotics to pickling vegetables, specifically carrots, with us during dinner or while scanning for whales on the cliff site. He also named the first whale we saw here this season – Speckles. 

Figure 3. Crew Cinco, from left to right: Anthony Howe, Donovan Burns, Lisa Hildebrand and Mia Arvizu. Source: L Torres.

Crew Cinco has already been in PO for two weeks now. After having a full team meeting with Leigh in Newport and a GEMM lab summer pizza party, we headed south to begin our 6-week field season. It’s hard to believe that the two training weeks are already over. The team worked hard to figure out the subtleties of the theodolite, observe different gray whales and start to understand their dive and foraging patterns, undertake a kayak paddle & safety course, as well as CPR and First Aid training, learn about data processing and management, and how to use a variety of gizmos to aid us in data collection. But it hasn’t all been work. We enjoyed a day in the Californian Redwoods on one of our day’s off and picked blueberries at the Twin Creek Ranch, stocking our freezer with several bags of juicy berries. We have played ‘Sorry!’ perhaps one too many times already (we are in desperate need of some more boardgames if anyone wants to send some our way to the field station!), and enjoyed many walks and runs on beautiful Battle Rock Beach. 

The next four weeks will not be easy – very early mornings, lots of paddling and squinting into the sun, followed by several hours in the lab processing samples and backing up data. But the next four weeks will also be extremely rewarding – learning lots of new skills that will be valuable beyond this 6-week period, revealing ecological trends and relationships, and ultimately (the true reason for why Mia, Anthony, Donovan and myself are more than happy to put in 6 weeks-worth of hard work), the chance to see whales every day up close and personal. Follow Crew Cinco’s journey over the next few weeks as my interns will be posting to the blog for the next three weeks!

References

Sullivan, F.A., & Torres L.G. Assessment of vessel disturbance to gray whales to inform sustainable ecotourism. Journal of Wildlife Management, 2018. 82: 896-905. 

Lingering questions on the potential to bring sea otters back to Oregon

By Dominique Kone, Masters Student in Marine Resource Management

By now, I’m sure you’re aware of recent interests to reintroduce sea otters to Oregon. To inform this effort, my research focuses on predicting suitable sea otter habitat and investigating the potential ecological effects if sea otters are reintroduced in the future. This information will help managers gain a better understanding of the potential for sea otters to reestablish in Oregon, as well as how Oregon’s ecosystems may change via top-down processes. These analyses will address some sources of uncertainties of this effort, but there are still many more questions researchers could address to further guide this process. Here, I note some lingering questions I’ve come across in the course of conducting my research. This is not a complete list of all questions that could or should be investigated, but they represent some of the most interesting questions I have and others have in Oregon.

Credit: Todd Mcleish

The questions, and our associated knowledge on each of these topics:

Is there enough available prey to support a robust sea otter population in Oregon?

Sea otters require approximately 30% of their own body weight in food every day (Costa 1978, Reidman & Estes 1990). With a large appetite, they not only need to spend most of their time foraging, but require a steady supply of prey to survive. For predators, we assume the presence of suitable habitat is a reliable proxy for prey availability (Redfern et al. 2006). Whereby, quality habitat should supply enough prey to sustain predators at higher trophic levels.

In making these habitat predictions for sea otters, we must also recognize the potential limitations of this “habitat equals prey” paradigm, in that there may be parcels of habitat where prey is unavailable or inaccessible. In Oregon, there could be unknown processes unique to our nearshore ecosystems that would support less prey for sea otters. This possibility highlights the importance of not only understanding how much suitable habitat is available for foraging sea otters, but also how much prey is available in these habitats to sustain a viable otter population in the future. Supplementing these habitat predictions with fishery-independent prey surveys is one way to address this question.

Credit: Suzi Eszterhas via Smithsonian Magazine

How will Oregon’s oceanographic seasonality alter or impact habitat suitability?

Sea otters along the California coast exist in an environment with persistent Giant kelp beds, moderate to low wave intensity, and year-round upwelling regimes. These environmental variables and habitat factors create productive ecosystems that provide quality sea otter habitat and a steady supply of prey; thus, supporting high densities of sea otters. This environment contrasts with the Oregon coast, which is characterized by seasonal changes in bull kelp and wave intensity. Summer months have dense kelp beds, calm surf, and strong upwellings. While winter months have little to no kelp, weak upwellings, and intense wave climates. These seasonal variations raise the question as to how these temporal fluctuations in available habitat could impact the number of sea otters able to survive in Oregon.

In Washington – an environment like Oregon – sea otters exhibit seasonal distribution patterns in response to intensifying wave climates. During calm summer months, sea otters primarily forage along the outer coast, but move into more protected areas, such as the Strait of Juan de Fuca, during winter months (Laidre et al. 2009). If sea otters were reintroduced to Oregon, we may very well observe similar seasonal movement patterns (e.g. dispersal into estuaries), but the degree to which this seasonal redistribution and reduction in foraging habitat could impact sea otter reestablishment and recovery is currently unknown.

Credit: Oregon Coast Aquarium

In the event of a reintroduction, do northern or southern sea otters have a greater capacity to adapt to Oregon environments?

In the early 1970’s, Oregon’s first sea otter translocation effort failed (Jameson et al. 1982). Since then, hypotheses on the potential ecological differences between northern and southern sea otters have been proposed as potential factors of the failed effort, potentially due to different abilities to exploit specific prey species. Studies have demonstrated that northern and southern sea otters have slight morphological differences – northern otters having larger skulls and teeth than southern otters (Wilson et al. 1991). This finding has created the hypothesis that the northern otter’s larger skull and teeth allow it to consume prey with denser exoskeletons, and thereby can exploit a greater diversity of prey species. However, there appears to be a lack of evidence to suggest larger skulls and teeth translate to greater bite force. Based on morphology alone, either sub-species could be just as successful in exploiting different prey species.

A different direction to address questions around adaptability is to look at similarities in habitat and oceanographic characteristics. Sea otters exist along a gradient of habitat types (e.g. kelp forests, estuaries, soft-sediment environments) and oceanographic conditions (e.g. warm-temperature to cooler sub-Arctic waters) (Laidre et al. 2009, Lafferty et al. 2014). Yet, we currently don’t know how well or quickly otters can adapt when they expand into new habitats that differ from ones they are familiar with. Sea otters must be efficient foragers and need to acquire skills that allow them to effectively hunt specific prey species (Estes et al. 2003). Hypothetically, if we take sea otters from rocky environments where they’ve developed foraging skills to hunt sea urchins and abalones, and place them in a soft-sediment environment, how quickly would they develop new foraging skills to exploit soft-sediment prey species? Would they adapt quickly enough to meet their daily prey requirements?

Credit: Eric Risberg/Associated Press via The Columbian

In Oregon, specifically, how might climate change impact sea otters, and how might sea otters mediate climate impacts?

Climate change has been shown to directly impact many species via changes in temperature (Chen et al. 2011). Some species have specific thermal tolerances, in which they can only survive within a specified temperature range (i.e. maximum and minimum). Once the temperature moves out of that range, the species can either move with those shifting water masses, behaviorally adapt or perish (Sunday et al. 2012). It’s unclear if and how changing temperatures will impact sea otters, directly. However, sea otters could still be indirectly affected via impacts to their prey. If prey species in sea otter habitat decline due to changing temperatures, this would reduce available food for otters. Ocean acidification (OA) is another climate-induced process that could indirectly impact sea otters. By creating chemical conditions that make it difficult for species to form shells, OA could decrease the availability of some prey species, as well (Gaylord et al. 2011).

Interestingly, these pathways between sea otters and climate change become more complex when we consider the potentially mediating effects from sea otters. Aquatic plants – such as kelp and seagrass – can reduce the impacts of climate change by absorbing and taking carbon out of the water column (Krause-Jensen & Duarte 2016). This carbon sequestration can then decrease acidic conditions from OA and mediate the negative impacts to shell-forming species. When sea otters catalyze a tropic cascade, in which herbivores are reduced and aquatic plants are restored, they could increase rates of carbon sequestration. While sea otters could be an effective tool against climate impacts, it’s not clear how this predator and catalyst will balance each other out. We first need to investigate the potential magnitude – both temporal and spatial – of these two processes to make any predictions about how sea otters and climate change might interact here in Oregon.

Credit: National Wildlife Federation

In Summary

There are several questions I’ve noted here that warrant further investigation and could be a focus for future research as this potential sea otter reintroduction effort progresses. These are by no means every question that should be addressed, but they do represent topics or themes I have come across several times in my own research or in conversations with other researchers and managers. I think it’s also important to recognize that these questions predominantly relate to the natural sciences and reflect my interest as an ecologist. The number of relevant questions that would inform this effort could grow infinitely large if we expand our disciplines to the social sciences, economics, genetics, so on and so forth. Lastly, these questions highlight the important point that there is still a lot we currently don’t know about (1) the ecology and natural behavior of sea otters, and (2) what a future with sea otters in Oregon might look like. As with any new idea, there will always be more questions than concrete answers, but we – here in the GEMM Lab – are working hard to address the most crucial ones first and provide reliable answers and information wherever we can.

References:

Chen, I., Hill, J. K., Ohlemuller, R., Roy, D. B., and C. D. Thomas. 2011. Rapid range shifts of species associated with high levels of climate warming. Science. 333: 1024-1026.

Costa, D. P. 1978. The ecological energetics, water, and electrolyte balance of the California sea otter (Enhydra lutris). Ph.D. dissertation, University of California, Santa Cruz.

Estes, J. A., Riedman, M. L., Staedler, M. M., Tinker, M. T., and B. E. Lyon. 2003. Individual variation in prey selection by sea otters: patterns, causes and implications. Journal of Animal Ecology. 72: 144-155.

Gaylord et al. 2011. Functional impacts of ocean acidification in an ecologically critical foundation species. Journal of Experimental Biology. 214: 2586-2594.

Jameson, R. J., Kenyon, K. W., Johnson, A. M., and H. M. Wight. 1982. History and status of translocated sea otter populations in North America. Wildlife Society Bulletin. 10(2): 100-107.

Krause-Jensen, D., and C. M. Duarte. 2016. Substantial role of macroalgae in marine carbon sequestration. Nature Geoscience. 9: 737-742.

Lafferty, K. D., and M. T. Tinker. 2014. Sea otters are recolonizing southern California in fits and starts. Ecosphere.5(5).

Laidre, K. L., Jameson, R. J., Gurarie, E., Jeffries, S. J., and H. Allen. 2009. Spatial habitat use patterns of sea otters in coastal Washington. Journal of Marine Mammalogy. 90(4): 906-917.

Redfern et al. 2006. Techniques for cetacean-habitat modeling. Marine Ecology Progress Series. 310: 271-295.

Reidman, M. L. and J. A. Estes. 1990. The sea otter (Enhydra lutris): behavior, ecology, and natural history. United States Department of the Interior, Fish and Wildlife Service, Biological Report. 90: 1-126.

Sunday, J. M., Bates, A. E., and N. K. Dulvy. 2012. Thermal tolerance and the global redistribution of animals. Nature: Climate Change. 2: 686-690.

Wilson, D. E., Bogan, M. A., Brownell, R. L., Burdin, A. M., and M. K. Maminov. 1991. Geographic variation in sea otters, Ehydra lutris. Journal of Mammalogy. 72(1): 22-36.

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.