Classifying cetacean behavior

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

The GEMM lab recently completed its fourth field season studying gray whales along the Oregon coast. The 2019 field season was an especially exciting one, we collected rare footage of several interesting gray whale behaviors including GoPro footage of a gray whale feeding on the seafloor, drone footage of a gray whale breaching, and drone footage of surface feeding (check out our recently released highlight video here). For my master’s thesis, I’ll use the drone footage to analyze gray whale behavior and how it varies across space, time, and individual. But before I ask how behavior is related to other variables, I need to understand how to best classify the behaviors.

How do we collect data on behavior?

One of the most important tools in behavioral ecology is an ‘ethogram’. An ethogram is a list of defined behaviors that the researcher expects to see based on prior knowledge. It is important because it provides a standardized list of behaviors so the data can be properly analyzed. For example, without an ethogram, someone observing human behavior could say that their subject was walking on one occasion, but then say strolling on a different occasion when they actually meant walking. It is important to pre-determine how behaviors will be recorded so that data classification is consistent throughout the study. Table 1 provides a sample from the ethogram I use to analyze gray whale behavior. The specificity of the behaviors depends on how the data is collected.

Table 1. Sample from gray whale ethogram. Based on ethogram from Torres et al. (2018).

In marine mammal ecology, it is challenging to define specific behaviors because from the traditional viewpoint of a boat, we can only see what the individuals are doing at the surface. The most common method of collecting behavioral data is called a ‘focal follow’. In focal follows an individual, or group, is followed for a set period of time and its behavioral state is recorded at set intervals.  For example, a researcher might decide to follow an animal for an hour and record its behavioral state at each minute (Mann 1999). In some studies, they also recorded the location of the whale at each time point. When we use drones our methods are a little different; we collect behavioral data in the form of continuous 15-minute videos of the whale. While we collect data for a shorter amount of time than a typical focal follow, we can analyze the whole video and record what the whale was doing at each second with the added benefit of being able to review the video to ensure accuracy. Additionally, from the drone’s perspective, we can see what the whales are doing below the surface, which can dramatically improve our ability to identify and describe behaviors (Torres et al. 2018).

Categorizing Behaviors

In our ethogram, the behaviors are already categorized into primary states. Primary states are the broadest behavioral states, and in my study, they are foraging, traveling, socializing, and resting. We categorize the specific behaviors we observe in the drone videos into these categories because they are associated with the function of a behavior. While our categorization is based on prior knowledge and critical evaluation, this process can still be somewhat subjective.  Quantitative methods provide an objective interpretation of the behaviors that can confirm our broad categorization and provide insight into relationships between categories.  These methods include path characterization, cluster analysis, and sequence analysis.

Path characterization classifies behaviors using characteristics of their track line, this method is similar to the RST method that fellow GEMM lab graduate student Lisa Hildebrand described in a recent blog. Mayo and Marx (1990) analyzed the paths of surface foraging North Atlantic Right Whales and were able to classify the paths into primary states; they found that the path of a traveling whale was more linear and then paths of foraging or socializing whales that were more convoluted (Fig 1). I plan to analyze the drone GPS track line as a proxy for the whale’s track line to help distinguish between traveling and foraging in the cases where the 15-minute snapshot does not provide enough context.

Figure 1. Figure from Mayo and Marx (1990) showing different track lines symbolized by behavior category.

Cluster analysis looks for natural groupings in behavior. For example, Hastie et al. (2004) used cluster analysis to find that there were four natural groupings of bottlenose dolphin surface behaviors (Fig. 2). I am considering using this method to see if there are natural groupings of behaviors within the foraging primary state that might relate to different prey types or habitat. This process is analogous to breaking human foraging down into sub-categories like fishing or farming by looking for different foraging behaviors that typically occur together.

Figure 2. Figure from Hastie et al. (2004) showing the results of a hierarchical cluster analysis.

Lastly, sequence analysis also looks for groupings of behaviors but, unlike cluster analysis, it also uses the order in which behaviors occur. Slooten (1994) used this method to classify Hector’s dolphin surface behaviors and found that there were five classes of behaviors and certain behaviors connected the different categories (Fig. 3). This method is interesting because if there are certain behaviors that are consistently in the same order then that indicates that the order of events is important. What function does a specific sequence of behaviors provide that the behaviors out of that order do not?

Figure 3. Figure from Slooten (1994) showing the results of sequence analysis.

Think about harvesting fruits and vegetables from a garden: the order of how things are done matters and you might use different methods to harvest different kinds of produce. Without knowing what food was being harvested, these methods could detect that there were different harvesting methods for different fruits or veggies. By then studying when and where the different methods were used and by whom, we could gain insight into the different functions and patterns associated with the different behaviors. We might be able to detect that some methods were always used in certain habitat types or that different methods were consistently used at different times of the year.

Behavior classification methods such as these described provide a more refined and detailed analysis of categories that can then be used to identify patterns of gray whale behaviors. While our ultimate goal is to understand how gray whales will be affected by a changing environment, a comprehensive understanding of their current behavior serves as a baseline for that future study.

References

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2019). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science, 35(1), 108–139. https://doi.org/10.1111/mms.12527

Darling, J. D., Keogh, K. E., & Steeves, T. E. (1998). Gray whale (Eschrichtius robustus) habitat utilization and prey species off Vancouver Island, B.C. Marine Mammal Science, 14(4), 692–720. https://doi.org/10.1111/j.1748-7692.1998.tb00757.x

Hastie, G. D., Wilson, B., Wilson, L. J., Parsons, K. M., & Thompson, P. M. (2004). Functional mechanisms underlying cetacean distribution patterns: Hotspots for bottlenose dolphins are linked to foraging. Marine Biology, 144(2), 397–403. https://doi.org/10.1007/s00227-003-1195-4

Mann, J. (1999). Behavioral sampling methods for cetaceans: A review and critique. Marine Mammal Science, 15(1), 102–122. https://doi.org/10.1111/j.1748-7692.1999.tb00784.x

Slooten, E. (1994). Behavior of Hector’s Dolphin: Classifying Behavior by Sequence Analysis. Journal of Mammalogy, 75(4), 956–964. https://doi.org/10.2307/1382477

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science, 5(SEP). https://doi.org/10.3389/fmars.2018.00319

Mayo, C. A., & Marx, M. K. (1990). Surface foraging behaviour of the North Atlantic right whale, Eubalaena glacialis, and associated zooplankton characteristics. Canadian Journal of Zoology, 68(10), 2214–2220. https://doi.org/10.1139/z90-308

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).


Vaquita: a porpoise caught between people and money

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

When I first learned of the critically endangered vaquita in early 2015, there were an estimated 97 individuals remaining as reported by CIRVA* (Morell 2014). I was a recent graduate with a bachelor’s degree in Wildlife, Fish, and Conservation Biology, and I, of all people, had never heard of the vaquita. Today, there are an estimated 19 vaquita left (Roth 2019).

Digital painting of a vaquita mother with her calf (Image Source: Aquarium of the Pacific).

The vaquita (Phocoena sinus) is a small porpoise endemic to the Sea of Cortez in the northern region of the Gulf of California, Mexico. It is the most endangered marine mammal and has been for many years, and yet, I had not heard of the vaquita. It wasn’t until I listened to a lunchtime seminar hosted by NOAA Fisheries, that I heard about the porpoise. As a young scientist, “in the field”, I was shocked to realize that I was just learning about an animal, let alone a cetacean, actively going extinct in my lifetime. I believe it’s our job to inform those around us of news in our expertise, and I had failed. I wasn’t informed. As much as I tried in the past four years to describe the decline of the smallest cetacean to anyone who’d listen, I was only reaching a few people at a time. But, today, the vaquita is finally capturing the public’s eye thanks to celebrity support and a feature-length film.

A rare photo of a vaquita (Image Source: Tom Jefferson via the Marine Mammal Center)

From executive producer, Leonardo DiCaprio, comes the Sundance Film Festival Audience Award winner, “Sea of Shadows”. The story of the vaquita truly is an “eco-thriller” and one worth watching. This is not your typical plot line of an endangered species tragically going extinct without action. The vaquita’s story boasts big-name players, such as the Mexican Navy, internationally recognized scientists, Mexican cartels, Chinese mafia, celebrities, the National Marine Mammal Foundation, and Sea Shepherd. At the center of this documentary is the elusive vaquita. The vaquita is not hunted, in fact, this species is not desirable for fisherman. The animal is not aggressive and, in contrast, is notoriously shy, only surfacing to breathe. Furthermore, its name roughly translates into “little cow” because of the rings around its eyes and its docile nature. So, why is this cute creature on the road to extinction? The answer: the wrong place at the wrong time.

“Sea of Shadows” official trailer by National Geographic

The vaquita occupy a small part of the Sea of Cortez where totoaba (Totoaba macdonaldi), a large fish in the drum family, is also endemic. If you’re wondering what a small porpoise and a large fish have in common, then you’d be close to recognizing that is the key to understanding this tragedy. Both species are roughly the same size, one to two meters in length with similar girths. The totoaba, although said to have tender meat, is caught for only one organ: the swim bladder. Now referred to as the “cocaine of the sea”, the dried swim bladders of the totoaba are sold to Mexican cartels who then export the product to China. Once in China, illegal markets sell the swim bladders for up to $100,000USD. Unfortunately, the nets used to illegally catch totoaba, also catch the vaquita. The porpoise has no economic value to the fishermen and therefore are tossed as bycatch. The vaquita is the innocent bystander in a war for money and power.

A man displays the catch from an illegal gillnet, including the totoaba in his arms, and a vaquita, below, that was bycatch (Image Source: Omar Vidal via Aquarium of the Pacific/NOAA Fisheries).

Watching a charismatic species severely decline because of human greed is horrific. The film, however, focuses on the effort of a few incredible organizations that band together in the fight to save the vaquita. Moreover, the multimillion-dollar project, Vaquita CPR, is still ongoing. On a more positive note, in October of 2019, scientists spotted six vaquita during continued conservation and monitoring efforts (Blust & Desk 2019). The path to saving a critically endangered species, especially one that is thought not to do well in captivity, is challenging. The vaquita’s recovery path has many complicated connections which for what appears to be an uphill battle. But, we, the people, are responsible for this. We must support research and conservation by using our voice to share what is happening, for a porpoise and for the world.

*Comité Internacional para la Recuperación de la Vaquita (International Committee for the Recovery of the Vaquita)

Citations:

Blust, Kendal, and Fronteras Desk. “Photo Sparks Increased Concern over Fishing in Vaquita Refuge.” Arizona Public Media, 25 Oct. 2019, https://news.azpm.org/p/news-topical-nature/2019/10/25/160806-photo-sparks-increased-concern-over-fishing-in-vaquita-refuge/.

Morell, Virginia. “Vaquita Porpoise Faces Imminent Extinction-Can It Be Saved?” National Geographic, 15 Aug. 2014, https://www.nationalgeographic.com/news/2014/8/140813-vaquita-gulf-california-mexico-totoaba-gillnetting-china-baiji/.

Roth, Annie. “The ‘Little Cow’ of the Sea Nears Extinction.” National Geographic, 17 Sept. 2019, https://www.nationalgeographic.com/animals/2019/09/vaquita-the-porpoise-familys-smallest-member-nears-extinction/#close.

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.

Whispers of fear

By Leila S. Lemos, Ph.D. candidate in Wildlife Sciences, Fisheries and Wildlife Department

 

What did you do when playing hide-and-seek? You would try your best not to move or make any noise that would cause the seeker to hear you and find you, right? So, I always associated the prey-predator relationship to a hide-and-seek game, where prey hide, and predators seek. Thus, if you are the prey in this food chain game you should try to hide and not make any noise.

I read an article last week that made me think of this relationship again. The article, “Right whale moms ‘whisper’ to their babies so sharks won’t hear”, announced the study findings from Susan E. Parks and collaborators (2019), which really called my attention.

To give some context, North Atlantic Right Whales (NARWs; Eubalaena glacialis; Fig. 1) occur primarily in northern Atlantic coastal waters or close to the continental shelf (Fig. 2), yet their presence in deep waters are also known (NOAA 2019).

Figure 1: A mother-calf pair of North Atlantic right whales.
Source: Dana Cusano, Syracuse University (NMFS Permit #775-1875); retrieved from Kooser 2019.

Figure 2: North Atlantic right whale distribution.
Source: NOAA 2019.

The species is critically endangered and estimated at less than 500 individuals (IUCN 2007, Pace et al. 2017). Unlike several other whale populations, NARWs have not rebounded from intense whaling, and its population has begun to decrease since 2010 (Thomas et al. 2016, Pace et al. 2017). NARWs’ biggest threats are associated with anthropogenic activities, including entanglement in fishing lines and collisions with vessels (Fig. 3).

Figure 3: North Atlantic right whales’ biggest threats: (A) entanglement in fishing gear, and (B) vessel collision.
Source (A): Peter Duley (NOAA), retrieved from Guy 2017; (B) Williams 2019.

Other than anthropogenic impacts, NARWs also face natural threats like predation. There are reports on newborn and young right whale calf’s predation by killer whales and large sharks (Taylor et al. 2013, Parks et al. 2019; Fig. 4).

Figure 4: Mother carries her calf carcass presenting two semicircular shark bite marks on its flank.
Source: Taylor et al. 2013.

Whales communicate by acoustic signals that can efficiently propagate underwater and be detected by listening predators (Parks et al. 2019). It is possible that mother-calf pairs may use cryptic behaviors to avoid the attention of predators by shifting their communication patterns, leading to a hypothesis that they produce low-amplitude calls and lower call rates (Tyack 2000; Fig. 5). These two behavioral modifications have been previously observed in mother-calf pairs of humpback whales (Megaptera novaeangliae; Videsen et al. 2017) and southern right whales (Eubalaena australis; Nielsen et al. 2019).

Figure 5: Spectrogram and waveform of a single pulse (low amplitude) and an upcall (high amplitude) produced by a right whale. A louder and longer signal (high-amplitude call) is potentially easier to detect by predators.
Source: Parks et al. 2019.

In order to determine if NARWs exhibited the same behavior, Parks and collaborators (2019) tagged lactating and non-lactating females, and a pregnant female that later was tagged again with her calf, to collect acoustic, movement and orientation data. Their results indicate that lactating females use a significantly higher low-amplitude call rate (mean ± standard deviation: 7.13 ± 2.0 calls) when compared to high-amplitude calls (0.88 ± 0.70 calls). In contrast, non-lactating females exhibited higher rates of high-amplitude calls (3.21 ± 2.29 calls) and lower rates of calls of low-amplitude (0.80 ± 1.15 calls).

Even though their sample size was small (n = 16), the authors had more lactating females sampled than the other demographic groups (n = 11), and their results provide evidence that right whale mother-calf pairs exhibit a shift in their repertoire: Mother-calf pairs reduce high-amplitude calls as compared with other demographic groups in the same habitat (Fig. 6).

Figure 6: Proportion of high and low-amplitude calls by both lactating and non-lactating female right whales on the calving grounds located in the southeastern United States.
Source: Parks et al. 2019.

According to Dr. Parks, these low-amplitude sounds are analogous with human whispers (Kooser, 2019). This ‘whispering’ is a behavioral adaptation that allows communication between mother and calf without drawing the attention of undesirable predators.

Such an adaptation may seem obvious to us when we think back of our hide-and-seek game, but documentation of little details of the cryptic lives of whales is unique and fascinating.  We still don’t know so much about the lives of whales, so determining adaptations, behavioral and physiological changes, and other simple features like “whispering” are crucial for us to better understand the ‘whale world’ and be able to enhance conservation efforts.

 

References

Guy 2017. North Atlantic right whales are going extinct. A new invention could save them. Retrieved from https://oceana.org/blog/north-atlantic-right-whales-are-going-extinct-new-invention-could-save-them. Accessed on 17 Oct 2019.

IUCN 2007. North Atlantic Right Whale. Retrieved from https://www.iucnredlist.org/species/41712/10541234. Accessed on 16 Oct 2019.

Kooser A. 2019. Right whale moms ‘whisper’ to their babies so sharks won’t hear. CNET. Retrieved from https://www.cnet.com/news/right-whale-moms-whisper-to-their-babies-for-an-important-reason/?fbclid=IwAR0JcKgYPII4a-BTjm7VPtOfjyVIb63F-SLAjyZZ2KXA6GvYJozfazcfHjA. Accessed on 16 Oct 2019.

Nielsen ML, Bejder L, Videsen SK, Christiansen F, Madsen PT. 2019. Acoustic crypsis in southern right whale mother-calf pairs: infrequent, low-output calls to avoid predation? J. Exp. Biol. 222:jeb190728.

NOAA 2019. North Atlantic Right Whale. NOAA Fisheries. Retrieved from https://www.fisheries.noaa.gov/species/north-atlantic-right-whale. Accessed on 16 Oct 2019.

Pace III RM, Corkeron PJ, Kraus SD. 2017. State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales. Ecology and Evolution 7:8730–8741.

Parks SE, Cusano DA, Van Parijs SM, Nowacek DP. 2019. Acoustic crypsis in communication by North Atlantic right whale mother-calf pairs on the calving grounds. Biology Letters 15:20190485.

Taylor JKD, Mandelman JW, McLellan WA, Moore MJ, Skomal GB, Rotstein DS, Kraus SD. 2013. Shark predation on North Atlantic right whales (Eubalaena glacialis) in the southeastern United States calving ground. Marine Mammal Science 29(1): 204–212.

Thomas PO, Reeves RR, Brownell RL. 2016. Status of the world baleen whales. Marine Mammal Science 32:682–734.

Tyack PL. 2000. Functional aspects of cetacean communication. In Cetacean societies: field studies of dolphins and whales (eds J Mann, RC Connor, PL Tyack, H Whitehead), pp. 270–307. Chicago, IL:University of Chicago Press.

Videsen SKA, Bejder L, Johnson M, Madsen PT. 2017. High suckling rates and acoustic crypsis of humpback whale neonates maximise potential for mother-calf energy transfer. Funct. Ecol. 31:1561–1573.

Williams 2019. Right whale grandmother known as Punctuation killed by ship strike. Retrieved from https://www.cbc.ca/news/canada/nova-scotia/north-atlantic-right-whale-punctuation-died-after-ship-strike-1.5191987. Accessed on 17 Oct 2019.

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.

What does it mean to be an effective science communicator?

By Dominique Kone, Masters Student in Marine Resource Management

To succeed as a scientist, you not only need to be well-trained in the scientific method, but also be familiar with the standards and practices in your discipline. While many scientists are skilled in the production of scientific information, fewer are as well-prepared to disseminate and communicate that information to diverse audiences. As a graduate student, learning effective science communication is one of my top priorities because I believe scientific information can and should be accessible to everyone. As I’ve been building and expanding upon my own communication toolbox, I constantly ask myself, what is effective science communication?

Simply put, communication can be thought of as the two-way transfer of information and knowledge. On one side, information is broadcasted and amplified out into the world, and on the other side, that information is received and understood, ideally. If communicating were this easy, people would never have to worry about being misinterpreted. Yet, this ideal is far from reality, and information is oftentimes misconstrued and/or ignored. This scenario is quite common when scientists communicate technical concepts or findings to non-scientists, either due to differences in communication styles or terminology use. In connecting with these types of audiences, I think effective science communication is a function of three key qualities: intentionality, creativity, and knowledge.

Source: ISTOCKPHOTO/THINKSTOCK

Intentionality

When scientists communicate information, being intentional with what they say and when they say it, can greatly influence how messages resonate with their audience. There’s often a big disconnect between the very specific scientific terms scientists use and the terms their non-technical audiences use. One way scientists can bridge this disconnect and be more intentional (thoughtful), is with word-choice. When scientists change their words, this doesn’t mean they “dumb down” their presentations; rather, they substitute words to better explain concepts in terms the audience easily understands. For example, if I tell the public “I’m predicting sea otter populations at carrying capacity in Oregon using a Bayesian habitat model”, this sentence has three jargon words (carrying capacity, Bayesian, model) that likely mean nothing to this audience. Instead, what I say is, “I’m predicting how many sea otters could live in Oregon based on available habitat”. Now I’m speaking in terms that resonate with my audience, and I have effectively made the same point. An intentional science communicator knows how to deliver information to meet their audience’s ability to take in and process that information.

Source: Andrew Grossman

Creativity

Scientists typically follow structured and defensible protocols when conducting analyses. Far fewer standards apply to how they communicate that research, which can free them up to be more creative in their delivery. One way scientists can be both intentional and creative is by using analogies, examples, or metaphors. When I give talks, I always talk about the high metabolism of sea otters (30% of their own body weight in food, daily) (Costa 1978, Riedman & Estes 1990). Most researchers seem intrigued by this fact, but anyone younger than the age of 10, honestly, could care less. To catch their attention, I always follow up this fact by estimating how many pizza slices I would need to eat to reach that daily food requirement, based on my own weight (230 pizza slices, if you’re curious). By using this analogy, my young audience not only understands my point, but they’re now way more interested because they can’t fathom a human eating that much pizza. It’s a simple comparison, but effective.

Creativity can also be applied to the different ways scientific information is delivered. Scientists regularly publish their work in peer-review scientific journals to reach other scientists. But they also produce short reports and fact sheets to briefly summarize studies for managers or policy-makers. They hold events or workshops to engage stakeholders. They use blogs, webpages, and YouTube to reach the broader public. They even use Twitter to share papers! Scientists do so much more than just publishing their work, and they have several options for delivering and communicating their research. All these different options create more opportunities for scientists to experiment and find new and exciting ways to deliver their science.

A stoic scientist communicating to the masses. Source: Dave Allen via NIWA.

Knowledge

It’s important for scientists to be knowledgeable about their subjects when communicating, but they can’t know everything. Rather, I think a more reasonable goal is for scientists to be comfortable and prepared to say what they know and what they don’t know. Scientists have a thirst for knowledge, but some communicate false information because they have a drive to answer every question they’re asked. They can sometimes get into trouble when they’re asked to talk about something they’re less familiar with. When asked a difficult question, I’ve witnessed a lot of scientists say, “I don’t know”, or, “I don’t know, but I could speculate [insert answer] based on other information”.  This response allows them to answer the question, while also being truthful. The alternative could have real negative implications (e.g. a certain President spreading false information about a dangerous hurricane).

Aside from factual knowledge, contextual knowledge is underappreciated in science communication, but can be vitally important. Some management issues are politically contentious, and effective science communicators can play vital roles in those management processes or actions. One study found that by scientists engaging with stakeholders in the planning process for renewable energy development along the coast of Maine, community members felt the development planning process was being conducted in the most effective manner (Johnson et al. 2015). In this example, a seemingly contentious situation was defused because scientists understood the political and social landscape, and were able to carefully communicate with stakeholders before any management actions took place. Scientists are not required to engage with stakeholders to this degree, but being sensitive to the broader (political, social, cultural, economic) environment in which those stakeholders live and operate can help them better target your messages and relieve potential tension.

GEMM Lab booth at Hatfield Marine Science Day! Source: Leila Lemos.

These three qualities (intentionality, creativity, and knowledge) are not meant to serve as hard, fast science communication rules. Instead, these are simply some qualities I’ve observed in other scientists skilled in effective communication. Scientists don’t automatically enter this space as expert communicators. For those that are great at it, it probably took some time and practice to hone their skills and find their own voice. It might come more naturally to some scientists, but I would argue most – like myself – have to work really hard to develop those skills. As I progress through my career, I’m excited to develop my own skills in effective science communication, and perhaps discover new and exciting approaches along the way.

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.

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.

Johnson, T. R., Jansujwiez, J. S., and G. Zydlewski. 2015. Tidal power development n Maine: stakeholder identification and perceptions of engagement. Estuaries and Coasts 38: S266-S278.