Self-improvement as Revenge – a strategy of persistent hope

By Florence Sullivan, MSc (GEMM Lab alumni, 2017)

Frustrating. Exhausting. Time-consuming. Repetitive. Draining. De-Motivating. A sine wave of cautious excitement followed by the crash of disappointment at another rejection.  The longer my job search continues, the more adjectives I have to describe it.

Last spring, I got rejected from a marine mammal and bird survey technician position because I didn’t have enough experience identifying birds. I found this immensely frustrating. So, fueled by the desire to prove “them” wrong, I embarked on my journey of revenge. First, I registered for a free online bird ID course at the Cornell Lab of Ornithology. Then, I got my bird books out, and started paying more attention to the species I encountered in my neighborhood. Next, I attended a training session for the Puget Sound Seabird Survey with the Seattle Audubon Society, and joined a citizen science monitoring team. We are responsible for documenting seabird habitat use at 3 beaches in the South Puget Sound on the first Saturday of each month. Most of my team members have been birding for decades, and they have been helpfully pointing out ID tricks like flight patterns, wing shapes, and color bands to distinguish one species from another. I feel like my marine bird ID is coming along nicely, but there are SO MANY bird species out there…. I know I learn better, and am more focused, when I am working for a team effort, so two weeks ago I attended a training for the Secretive Wetland Bird Monitoring project with the Puget Sound Bird Observatory. We’ll be doing playback surveys for species like American Bittern, Virginia Rail, and Green Herons during three survey windows from April to June. I’m excited for this project because even if I don’t learn to ID the birds by sight (they are secretive after all), it’s a chance to improve my ‘birding by ear’ skills! With all this, I think the next time a job application asks about my experience with birds, I’ll be able to give some more informed answers.

In Summer 2018, I had a rather tumultuous field research experience with a very disorganized project leader.  I ended up leaving the project after a series of poor safety choices by the leadership culminated in the vessel running aground on a well-marked reef.  Several of my colleagues and I were injured in the accident, and it was the first time in my 10 year maritime career that I grabbed my emergency bag and seriously thought I might have to abandon ship.  In this case, we made it to shore, and there was a clinic nearby where we got treated, but what if there hadn’t been?  The more I reflected on what happened, the more I realized how bad the situation could have been.  My revenge on that feeling of helplessness was to sign up for a NOLS Wilderness First Aid Course.  During the course, we practiced patient assessment, discussed the most common injuries when adventuring in the remote areas, and played out scenarios, as both patients and first responders. We discussed proper scene assessment, basic wound care and splints (those were fun to practice), situations like hypo and hyperthermia, and how to make a radio call for help that transmits the most relevant information. After this two day course, I feel much more confident in my ability to manage emergency situations for myself and any team I work with. Handily enough, many field technician jobs list ‘Wilderness First Aid/Wilderness First Responder” in their desired qualifications sections, so I can check that bullet off now too!

One of the best bits of finishing my grad degree has been getting my evenings and weekends back from the depths of homework and research fueled need-to-be-productive-all-the-time depression.  I like making things.  Shortly after turning in my thesis, I traded labor for a sheep fleece & two alpaca fleeces.

This alpaca’s name was ‘Timid’. Here we are leading him to the shearing area.

An acquaintance needed help shearing his small flock, and I saw the opportunity to try a “Sheep to Shawl” project – where you take the raw fiber, clean it, spin it into thread, and weave it into a shawl. I learned how to weave in high school, but I did not know how to spin my own thread.  I borrowed a spinning wheel from my fiber arts mentor, found a spinning group at my local yarn store, and since January have been spinning my own thread!

The bundle of blue/green fiber front and center is the raw wool “roving” that is fed onto the bobbin in the spinning process. The bobbin on the spinning wheel holds a single thread. Thread from two bobbins is then “plyed” together to create yarn – The final yarn is draped over the wheel.

I started with some practice wool to figure the whole thing out, and have just started to spin the fleeces I helped to harvest. It’s going to take me a while, but I’m more interested in the process than any sort of speed. There’s an unfortunate cultural dichotomy between “art” and “science”, but I find that the sort of thinking needed to plan how the threads will intertwine to make a solid and beautiful cloth, is the same sort of thinking needed to understand the myriad processes that inform how an ecosystem functions. If you think about it sideways, knitting & weaving pattern drafts are the first form of binary computer programs – repetitive patterns that when followed result in a product. The creativity needed to make beautiful art is the same creativity that helps problem solve in the field, and long term project planning, forethought and tenacity are all necessary in both research and in fiber arts. While the art itself may not be relevant to the jobs I apply for, the skills are transferable, and the actions recharge my batteries so I can keep solving problems creatively.

I knit my first hand spun yarn into a fun scarf!

It’s an easy trap to fall into – the idea that learning only happens in the classroom, and that once you’ve finally finished school and thrown off the trappings of academia you’re done and never have to learn again.

But never learning anything new would get boring quickly, wouldn’t it?

I may be frustrated by how long it is taking me to find ‘a career’, but I can’t regret the lily pads that I have landed on in the mean-time, or the skills that I have had the opportunity to pick up.

Exciting. Inspiring. Educational. Opportunistic. Expanding my network. Hopeful. A sine wave of disappointment followed by renewed determination to keep trying.  The longer my job search continues, the more adjectives I have to describe it.

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

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

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

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

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

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

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

Combining multiple data streams for a comprehensive health assessment

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

Figure 2. An example aerial drone image of a blue whale that will be used to asses body condition, i.e. how healthy or malnourished the whale is. (Drone piloted by Todd Chandler).
Figure 3. Images of blue whale skin condition, affected by A) blistering and B) cookie cutter shark bites.

Comparing body shape and morphology between species

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

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

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

References

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

Signs you’re an ecologist – you don’t spend nearly enough time geeking out about your study species…

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

This past week has been very busy for me as I gave three quite important, yet very different, presentations. The first was on Tuesday at the Pacific High School in Port Orford, near my study site. The aim of the game was recruitment – my quest for two eager local high schoolers to be my interns for this 2019 summer field season has begun (read blogs written by our 2017 HS interns Nathan Malamud and Quince Nye)! I was lucky enough to be given an entire class period to talk to the students and so I hope that the picture I painted of kayaks, gray whales and sun will be enough to entice students to apply to the internship.

The second was a short presentation in one of the classes I took this term, GEOG 561: GIScience II Analysis and Applications. The class focuses on developing and conducting geospatial analyses in R and throughout the term each student develops a small independent research project using some of their own data. For my research project, I decided to do a small cluster analysis of the zooplankton community data that we have collected from the kayak net samples.

The third and final presentation of the week happened on Thursday and marked one of the big milestones on my Master’s journey: my research review. The research review is a mandatory (and extremely helpful) process in the Department of Fisheries & Wildlife where the student (in this case me), the committee (Dr Leigh Torres, Dr Rachael Orben, Dr Kim Bernard and Dr Susanne Brander) and a department representative (Dr Brian Sidlauskas) all assemble to discuss the student’s research proposal, which lays out the intended work, chapters, analysis and timeline for the students’ thesis. My proposal (which currently bears the title: “Tonight’s specials include mysids, gammarids and more: An examination of the zooplankton prey of Oregon gray whales and its impact on individual foraging patterns”) proposes a two-chapter thesis where the first examines the quality of zooplankton prey, while the second looks at potential individual foraging specialization of gray whales along the Oregon coast. While my entire committee agreed that what I have set forth to do in the next two or so years is ambitious, they provided me with excellent feedback and confidence that I would be able to achieve what I have planned.

Now that it’s the weekend and I’ve had some time to sit back and think about the week, I realized one major commonality between all three presentations I gave. None of the Powerpoints featured more than one image of a gray whale. How could this be?! It is after all my study species and I spend so much of my summer looking at them – how could it be that so little of what I showed and talked about was the thing that I am most passionate about and is so central to my research?

In the course of doing research, it’s easy to get wound up in the nitty gritty and forget about the big picture. While the nitty gritty is also imperative to conducting the research (and ultimately getting results), I sometimes forget about why I do what I do, which is that gray whales are AWESOME. Looking into the past, it seems that some of my lab mates have had the same realizations about their study species before too: see here and here. So for this blog, I want to bring it back to basics and share some of the things that I think are most fascinating about gray whales.

  1. Gray whales are the only baleen whale that feeds benthically. This behavior is facilitated by the shorter and tougher baleen that gray whales possess in comparison to other baleen whale species (Pivorunas 1979). The majority of the Eastern North Pacific (ENP) gray whale population feeds benthically in the Bering Sea where they eat ampeliscid amphipods, which are a type of benthic invertebrates (Nerini 1984). It is estimated that gray whales must regain 11-29% of critical body mass during the feeding season (Villegas-Amtmann et al. 2015) in order to obtain the energy stores they require for the entire year. Besides the personal benefit of sea floor foraging, by using this feeding tactic gray whales create depressions in the soft sediment that benefit other species besides themselves. The highly disruptive nature of this action can increase the biodiversity of the seafloor and initiate scavenging events by lysiannassid amphipods on other infauna (Oliver & Slattery 1985). Furthermore, Grebmeier & Harrison (1992) documented that a variety of seabirds including northern fulmars, black-legged kittiwakes and thick-billed murres feed on benthic amphipods brought to the surface by this unique foraging behavior performed by gray whales.
  1. Gray whales are essentially acrobats. A preference for benthic prey goes hand in hand with a preference for shallow, coastal waters, as for example Pacific Coast Feeding Group gray whales tend to forage within the 5-15 m depth range (Weller et al. 1999). With female adults ranging between 13-15 m in length (females tend to be slightly larger than adult males) and weighing anywhere between 15-33 tons (Jones et al. 1984), I am continuously fascinated by how gracefully and slowly gray whales can navigate extremely shallow waters.

    However, it is more than just simple navigation – the behaviors and moves that some gray whales display while in the shallows is phenomenal too. Last year Torres et al. (2018) documented this agility through unmanned aerial systems (UAS) footage that provided evidence for some novel foraging tactics including headstands, side-swimming, and jaw snapping and flexing.

  1. They sure are resilient. Commercial whaling of gray whales began in 1846 after two commercial whaling vessels first discovered the winter breeding grounds in Baja California, Mexico (Henderson 1984). Following this discovery, the ENP were targeted for roughly a century before receiving full protection under the International Convention for the Regulation of Whaling in 1946 (Reeves 1984). Through genetic analyses, it has been estimated that the pre-whaling abundance of the ENP population was between 76,000 – 118,000 individuals (Alter et al. 2012), which is roughly three to five times larger than current estimates (24,000 – 26,000; Scordino et al. 2018). While the gray whale populations that once existed in the Atlantic Ocean were not as fortunate as those in the Pacific (Atlantic gray whales were declared extinct in the 18thcentury due to extensive whaling; Bryant 1995), the ENP has definitely made a strong comeback. Additionally, gray whale resilience is not only evident on this long temporal scale but it can also be seen annually when gray whale mothers fight relentlessly to keep their calves alive when under attack from killer whales. A study on predation of gray whales by transient killer whales in Alaska reported that attacks were quickly abandoned if calves were aggressively defended by their mothers or if gray whales succeeded in reaching depths of 3 m or less (Barrett-Lennard et al. 2011).
  1. For some unimaginable reason, gray whales appear to feel a strong connection to us. For many, gray whales might be best known for actively seeking out human contact during their breeding season in the Mexican lagoons. I find this actuality particularly interesting because of the bloody history we share with Pacific gray whales.

Those are just some of the things about gray whales that make them so fascinating to me. I look forward to potentially discovering one or two more things that we don’t know about them yet through my research. Even if that doesn’t turn out to be the case, I feel so lucky that I at least get to spend so much time with them during their feeding season here along the Oregon coast.

 

References

Alter, E.S., et al., Pre-whaling genetic diversity and population ecology in Eastern Pacific gray whales: Insights from ancient DNA and stable isotopes.PLoS ONE, 2012. doi.org/10.1371/journal.pone.0035039.

Barrett-Lennard, L.G., et al., Predation on gray whales and prolonged feeding on submerged carcasses by transient killer whales at Unimak Island, Alaska. Marine Ecology Progress Series, 2011. 421: 229-241.

Bryant, P.J., Dating remains of gray whales from the Eastern North Atlantic. Journal of Mammalogy, 1995. 76(3): 857-861.

Grebmeier, J.M., & Harrison, N.M., Seabird feeding on benthic amphipods facilitated by gray whale feeding activity in the northern Bering Sea. Marine Ecology Progress Series, 1992. 80: 125-133.

Henderson, D.A., Nineteenth century gray whaling: Grounds, catches and kills, practices and depletion of the whale population.Pages 159-186 inJones, M.L. et al., eds. The gray whale: Eschrichtius robustus, 1984. Academic Press, Orlando.

Jones, M.L., et al., The gray whale: Eschrichtius robustus. 1984. Academic Press, Orlando.

Nerini, M., A review of the gray whale feeding ecology. Pages 423-448 inJones, M.L. et al., eds. The gray whale: Eschrichtius robustus, 1984. Academic Press, Orlando.

Oliver, J.S., & Slattery, P.N., Destruction and obstruction on the sea floor: effects of gray whale feeding.Ecology, 1985. 66: 1965-1975.

Pivorunas, A., The feeding mechanisms of baleen whales.American Scientist, 1979. 67(4): 432-440.

Reeves, R.R., Modern commercial pelagic whaling for gray whales. Pages 187-200 inJones, M.L. et al., eds. The gray whale: Eschrichtius robustus, 1984. Academic Press, Orlando.

Scordino, J., et al., Report of gray whale implementation review coordination call on 5 December 2018.

Torres, L.G., et al., Drone up! Quantifying whale behavior from a new perspective improves observational capacity.Frontiers in Marine Science, 2018. 5: doi:10.3389/fmars.2018.00319.

Villegas-Amtmann, S., et al., A bioenergetics model to evaluate demographic consequences of disturbance in marine mammals applied to gray whales. Ecosphere, 2015. 6(10): 1-19.

Weller, D.W., et al., Gray whale (Eschrichtius robustus) off Sakhalin Island, Russia: Seasonal and annual patterns of occurrence. Marine Mammal Science, 1999. 15(4): 1208-1227.

Data Wrangling to Assess Data Availability: A Data Detective at Work

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

Data wrangling, in my own loose definition, is the necessary combination of both data selection and data collection. Wrangling your data requires accessing then assessing your data. Data collection is just what it sounds like: gathering all data points necessary for your project. Data selection is the process of cleaning and trimming data for final analyses; it is a whole new bag of worms that requires decision-making and critical thinking. During this process of data wrangling, I discovered there are two major avenues to obtain data: 1) you collect it, which frequently requires an exorbitant amount of time in the field, in the lab, and/or behind a computer, or 2) other people have already collected it, and through collaboration you put it to a good use (often a different use then its initial intent). The latter approach may result in the collection of so much data that you must decide which data should be included to answer your hypotheses. This process of data wrangling is the hurdle I am facing at this moment. I feel like I am a data detective.

Data wrangling illustrated by members of the R-programming community. (Image source: R-bloggers.com)

My project focuses on assessing the health conditions of the two ecotypes of bottlenose dolphins between the waters off of Ensenada, Baja California, Mexico to San Francisco, California, USA between 1981-2015. During the government shutdown, much of my data was inaccessible, seeing as it was in possession of my collaborators at federal agencies. However, now that the shutdown is over, my data is flowing in, and my questions are piling up. I can now begin to look at where these animals have been sighted over the past decades, which ecotypes have higher contaminant levels in their blubber, which animals have higher stress levels and if these are related to geospatial location, where animals are more susceptible to human disturbance, if sex plays a role in stress or contaminant load levels, which environmental variables influence stress levels and contaminant levels, and more!

Alexa, alongside collaborators, photographing transiting bottlenose dolphins along the coastline near Santa Barbara, CA in 2015 as part of the data collection process. (Image source: Nick Kellar).

Over the last two weeks, I was emailed three separate Excel spreadsheets representing three datasets, that contain partially overlapping data. If Microsoft Access is foreign to you, I would compare this dilemma to a very confusing exam question of “matching the word with the definition”, except with the words being in different languages from the definitions. If you have used Microsoft Access databases, you probably know the system of querying and matching data in different databases. Well, imagine trying to do this with Excel spreadsheets because the databases are not linked. Now you can see why I need to take a data management course and start using platforms other than Excel to manage my data.

A visual interpretation of trying to combine datasets being like matching the English definition to the Spanish translation. (Image source: Enchanted Learning)

In the first dataset, there are 6,136 sightings of Common bottlenose dolphins (Tursiops truncatus) documented in my study area. Some years have no sightings, some years have fewer than 100 sightings, and other years have over 500 sightings. In another dataset, there are 398 bottlenose dolphin biopsy samples collected between the years of 1992-2016 in a genetics database that can provide the sex of the animal. The final dataset contains records of 774 bottlenose dolphin biopsy samples collected between 1993-2018 that could be tested for hormone and/or contaminant levels. Some of these samples have identification numbers that can be matched to the other dataset. Within these cross-reference matches there are conflicting data in terms of amount of tissue remaining for analyses. Sorting these conflicts out will involve more digging from my end and additional communication with collaborators: data wrangling at its best. Circling back to what I mentioned in the beginning of this post, this data was collected by other people over decades and the collection methods were not standardized for my project. I benefit from years of data collection by other scientists and I am grateful for all of their hard work. However, now my hard work begins.

The cutest part of data wrangling: finding adorable images of bottlenose dolphins, photographed during a coastal survey. (Image source: Alexa Kownacki).

There is also a large amount of data that I downloaded from federally-maintained websites. For example, dolphin sighting data from research cruises are available for public access from the OBIS (Ocean Biogeographic Information System) Sea Map website. It boasts 5,927,551 records from 1,096 data sets containing information on 711 species with the help of 410 collaborators. This website is incredible as it allows you to search through different data criteria and then download the data in a variety of formats and contains an interactive map of the data. You can explore this at your leisure, but I want to point out the sheer amount of data. In my case, the OBIS Sea Map website is only one major platform that contains many sources of data that has already been collected, not specifically for me or my project, but will be utilized. As a follow-up to using data collected by other scientists, it is critical to give credit where credit is due. One of the benefits of using this website, is there is information about how to properly credit the collaborators when downloading data. See below for an example:

Example citation for a dataset (Dataset ID: 1201):

Lockhart, G.G., DiGiovanni Jr., R.A., DePerte, A.M. 2014. Virginia and Maryland Sea Turtle Research and Conservation Initiative Aerial Survey Sightings, May 2011 through July 2013. Downloaded from OBIS-SEAMAP (http://seamap.env.duke.edu/dataset/1201) on xxxx-xx-xx.

Citation for OBIS-SEAMAP:

Halpin, P.N., A.J. Read, E. Fujioka, B.D. Best, B. Donnelly, L.J. Hazen, C. Kot, K. Urian, E. LaBrecque, A. Dimatteo, J. Cleary, C. Good, L.B. Crowder, and K.D. Hyrenbach. 2009. OBIS-SEAMAP: The world data center for marine mammal, sea bird, and sea turtle distributions. Oceanography 22(2):104-115

Another federally-maintained data source that boasts more data than I can quantify is the well-known ERDDAP website. After a few Google searches, I finally discovered that the acronym stands for Environmental Research Division’s Data Access Program. Essentially, this the holy grail of environmental data for marine scientists. I have downloaded so much data from this website that Excel cannot open the csv files. Here is yet another reason why young scientists, like myself, need to transition out of using Excel and into data management systems that are developed to handle large-scale datasets. Everything from daily sea surface temperatures collected on every, one-degree of latitude and longitude line from 1981-2015 over my entire study site to Ekman transport levels taken every six hours on every longitudinal degree line over my study area. I will add some environmental variables in species distribution models to see which account for the largest amount of variability in my data. The next step in data selection begins with statistics. It is important to find if there are highly correlated environmental factors prior to modeling data. Learn more about fitting cetacean data to models here.

The ERDAPP website combined all of the average Sea Surface Temperatures collected daily from 1981-2018 over my study site into a graphical display of monthly composites. (Image Source: ERDDAP)

As you can imagine, this amount of data from many sources and collaborators is equal parts daunting and exhilarating. Before I even begin the process of determining the spatial and temporal spread of dolphin sightings data, I have to identify which data points have sex identified from either hormone levels or genetics, which data points have contaminants levels already quantified, which samples still have tissue available for additional testing, and so on. Once I have cleaned up the datasets, I will import the data into the R programming package. Then I can visualize my data in plots, charts, and graphs; this will help me identify outliers and potential challenges with my data, and, hopefully, start to see answers to my focal questions. Only then, can I dive into the deep and exciting waters of species distribution modeling and more advanced statistical analyses. This is data wrangling and I am the data detective.

What people may think a ‘data detective’ looks like, when, in reality, it is a person sitting at a computer. (Image source: Elder Research)

Like the well-known phrase, “With great power comes great responsibility”, I believe that with great data, comes great responsibility, because data is power. It is up to me as the scientist to decide which data is most powerful at answering my questions.

Data is information. Information is knowledge. Knowledge is power. (Image source: thedatachick.com)

 

Looking Back: Three Years After Grad School

By Courtney Hann (NOAA Fisheries, West Coast Sustainable Fisheries Division)

Thinking back, as Leigh’s first M.Sc. student for the GEMM Lab, I wonder what poignant insight could have prepared me for my future endeavors. And having faced years of perseverance and dedication in the face of professional unknowns, perhaps the answer is none at all; fore maybe it was the many unknown challenges met that led me to where I am today.

I graduated in December of 2015, with my Masters in Marine Resource Management, and stamped completion of my research with the GEMM Lab. While my research focused on marine mammals, my broader love for the Earth’s oceans and lands guided my determination to help keep our planet’s precious ecosystem resources wild and free. So when I landed a position in terrestrial ecology after graduating, I chose to embrace the challenging decision of jumping away from theoretical research and moving back towards applied research. Consequently, I fell in love with botany, moth identification, birding, and explored the unknowns of a whole new world of conservation biology in Scotland with the Royal Society for the Protection of Birds. Not only was this work incredibly fun, interesting, and spontaneous, it offered me an opportunity to take my knowledge of developing research projects and apply it to nature reserve management. Every survey I completed and dataset I analyzed provided information required to determine the next land management steps for maximizing the conservation of rare and diverse species. From the GEMM Lab, I brought skills on: how to work through what, at times, seemed like an impassible barrier, complete tasks efficiently under a tight deadline, juggle multiple activities and obligations, and still make time to ponder the importance of seeing the bigger picture, while having fun learning new things.

Above: Botanizing and birding in Scotland with the best botanist I have ever known and my boss, Jeff Waddell, with the Royal Society for the Protection of Birds.

For me, the long game of seeing the bigger picture has always been key. And at the end of the day, I remained steadfast in answering the questioned I posed myself: Why do all of this work if not to make a truly positive impact? With that in mind, and with an expiring visa, I moved back to the West Coast of the U.S. and landed a contracting position with NOAA Fisheries. Where I met my second female mentor, Heidi Taylor, who inspired me beyond words and introduced me to the amazing world of fisheries management. All the while, I kept working my second part-time job with the West Coast Regional Planning Body (now called the West Coast Ocean Alliance, WCOA). Working two jobs allowed me to not only accelerate my learning capacity through more opportunities, but also allowed me to extend the reach of growing a positive impact.  For example, I learned about coordinating region-wide ocean management, facilitation of diverse groups, and working with tribes, states, and federal agencies while working for the WCOA. While there were moments that I struggled with overworking and fatigue, my training in graduate school to persevere really kicked in. Driven by the desire to attain a permanent position that complimented my talents and determination to provide sustained help for our Earth’s ecosystems, I worked for what sometimes felt endlessly to reach my goal. Getting there was tough, but well worth it!

One of the most challenging aspects for me was finishing my last publication for the GEMM Lab. I was no longer motivated by the research, since my career path had taken a different turn, and I was already burnt out form working overtime every week. Therefore, if it was not for Leigh’s encouraging words, the promise I made to her to complete the publication, and my other co-author’s invitation to submit a paper for a particular journal, then I likely would have thrown in the towel. I had to re-do the analysis several times, had the paper rejected once, and then ended up re-writing and re-structuring the entire paper for the final publication. In total, it took me two and half years and 100s of hours to complete this paper after graduating. Of course, there was no funding, so I felt a bit like an ongoing graduate student until the paper was finally accepted and the work complete. But the final acceptance of the paper was so sweet, and after years of uncertain challenges, a heavy weight had finally been lifted. So perhaps, if there is one piece of advice I would say to young graduate students, it is to get your work published before you graduate! I had one paper and one book chapter published before I graduated, and that made my life much easier. While I am proud for finishing the final third publication, I would have much preferred to have just taken one extra semester and finished that publication while in school. But regardless, it was completed. And in a catharsis moment, maybe the challenge of completing it taught me the determination I needed to persevere through difficult situations.

Above: Elephant seal expressing my joy of finishing that last publication! Wooohoooooo!

With that publication out of the way, I was able to focus more time on my career. While I no longer use R on a daily basis and do not miss the hours of searching for that one pesky bug, I do analyze, critique, and use scientific literature everyday. Moreover, the critical thinking, creative, and collaborative skills I honed in the GEMM Lab, have been and will be useful for the rest of my life. Those hours of working through complicated statistical analyses and results in Leigh’s office pay off everyday. Reading outside of work, volunteering and working second jobs, all of this I learned from graduate school. Carrying this motivation, hard work, determination, and perseverance on past graduate school was undeniably what led me to where I am today. I have landed my dream job, working for NOAA Fisheries Sustainable Fisheries Division on salmon management and policy, in my dream location, the Pacific Northwest.  My work now ties directly into ongoing management and policy that shapes our oceans, conservation efforts, and fisheries management. I am grateful for all the people who have supported me along the way, with this blog post focusing on the GEMM Lab and Leigh Torres as my advisor. I hope to be a mentor and guide for others along their path, as so many have helped me along mine. Good luck to any grad student reading this now! But more than luck, carry passion and determination forward because that is what will propel you onward on your own path. Thank you GEMM Lab, it is now time for me to enjoy my new job.

Above: Enjoying in my new home in the Pacific Northwest.

 

 

 

Midway Atoll: the next two weeks at the largest albatross colony in the world (two years later)

By Rachael Orben, Assistant Professor (Senior Research), Seabird Oceanography Lab

This February I had the opportunity to spend two weeks at Midway Atoll National Wildlife Refuge in the Papahānaumokuākea Marine National Monument. I was there to GPS track black-footed and Laysan albatross during their short chick-brooding foraging trips. Two weeks is just enough time since the albatross are taking short trips (3-5 days) to feed their rapidly growing chicks.

My first visit to Midway (2016 blog post) occurred right as the black-footed albatross chicks were hatching (quickly followed by the Laysan albatross chicks). This time, we arrived almost exactly when I had left off. The oldest chicks were just about two weeks old. This shift in phenology meant that, though subtle, each day offered new insights for me as I watched chicks transform into large aware and semi-mobile birds. By the time we left, unattended chicks were rapidly multiplying as the adults shifted to the chick-rearing stage. During chick rearing, both parents leave the chick unattended and take longer foraging trips.

Our research goal was to collect tracking data from both species that can be used to address a couple of research questions. First of all, winds can aid, or hinder albatross foraging and flight efficiency (particularly during the short brooding trips). In the North Pacific, the strength and direction of the winds are influenced by the ENSO (El Niño Southern Oscillation) cycles. The day after we left Midway, NOAA issued an El Niño advisory indicating weak El Nino conditions. We know from previous work at Tern Island (farther east and farther south at 23.87 N, -166.28 W) that El Niño improves foraging for Laysan albatrosses during chick brooding, while during La Niña reproductive success is lower (Thorne et al., 2016). However, since Midway is farther north, and farther west the scenario might be different there. Multiple years of GPS tracking data are needed to address this question and we hope to return to collect more data next year (especially if  La Niña follows the El Niño as is often the case).

We will also overlap the tracking data with fishing boat locations from the Global Fishing Watch database to assess the potential for birds from Midway to interact with high seas fisheries during this time of year (project description, associated blog post). Finally, many of the tags we deployed incorporated a barometric pressure sensor and the data can be used to estimate flight heights relative to environmental conditions such as wind strength. This type of data is key to assessing the impact of offshore wind energy (Kelsey et al., 2018).

How to track an albatross

To track an albatross we use small GPS tags that we tape to the back feathers. After the bird returns from a foraging trip, we remove the tape from the feathers and take the datalogger off. Then we recharge the battery and download the data!

Catching a tagged albatross that just returned from its foraging trip. Photo V. Ternisien
Recording data. Photo V. Ternisien
Getting ready to remove the GPS datalogger. Photo V. Ternisien
The GPS datalogger was taped to the back feathers of the bird. To remove the tag we simply peel off the tape. Photo V. Ternisien

This research is a collaboration between Lesley Thorne (Stony Brook University), Scott Shaffer (San Jose State University), myself (Oregon State University), and Melinda Conners (Washington State University). The field effort was generously supported by the Laurie Landeau Foundation via the Minghua Zhang Early Career Faculty Innovation Fund at Stoney Brook University to Lesley Thorne.

My previous visit to Midway occurred just after house mice were discovered attacking incubating adult albatrosses. Since then, a lot of thought and effort had gone into developing a plan to eradicate mice from Midway. You can find out more via Island Conservation’s Midway blogs and the USFWS.
Dancing.
There is always something happening in a seabird colony.
Red-tailed tropicbird
From left to right: A Laysan albatross, a hybrid, and a black-footed albatross.
The colony was never quiet. Even at night.
A GPS tagged Laysan albatross.
Habitat restoration.
A short-tailed albatross sitting next to his chick (the large black chick the his right).
References

Kelsey, E. C., Felis, J. J., Czapanskiy, M., Pereksta, D. M., & Adams, J. (2018). Collision and displacement vulnerability to offshore wind energy infrastructure among marine birds of the Pacific Outer Continental Shelf. Journal of Environmental Management, 227, 229–247. http://doi.org/10.1016/j.jenvman.2018.08.051

Thorne, L. H., Conners, M. G., Hazen, E. L., Bograd, S. J., Antolos, M., Costa, D. P., & Shaffer, S. A. (2016). Effects of El Niño-driven changes in wind patterns on North Pacific albatrosses. Journal of the Royal Society Interface, 13(119), 20160196. http://doi.org/10.1098/rsif.2016.0196

Photogrammetry Insights

By Leila Lemos, PhD Candidate, Fisheries and Wildlife Department, Oregon State University

After three years of fieldwork and analyzing a large dataset, it is time to finally start compiling the results, create plots and see what the trends are. The first dataset I am analyzing is the photogrammetry data (more on our photogrammetry method here), which so far has been full of unexpected results.

Our first big expectation was to find a noticeable intra-year variation. Gray whales spend their winter in the warm waters of Baja California, Mexico, period while they are fasting. In the spring, they perform a big migration to higher latitudes. Only when they reach their summer feeding grounds, that extends from Northern California to the Bering and Chukchi seas, Alaska, do they start feeding and gaining enough calories to support their migration back to Mexico and subsequent fasting period.

 

Northeastern gray whale migration route along the NE Pacific Ocean.
Source: https://journeynorth.org/tm/gwhale/annual/map.html

 

Thus, we expected to see whales arriving along the Oregon coast with a skinny body condition that would gradually improve over the months, during the feeding season. Some exceptions are reasonable, such as a lactating mother or a debilitated individual. However, datasets can be more complex than we expect most of the times, and many variables can influence the results. Our photogrammetry dataset is no different!

In addition, I need to decide what are the best plots to display the results and how to make them. For years now I’ve been hearing about the wonders of R, but I’ve been skeptical about learning a whole new programming/coding language “just to make plots”, as I first thought. I have always used statistical programs such as SPSS or Prism to do my plots and they were so easy to work with. However, there is a lot more we can do in R than “just plots”. Also, it is not just because something seems hard that you won’t even try. We need to expose ourselves sometimes. So, I decided to give it a try (and I am proud of myself I did), and here are some of the results:

 

Plot 1: Body Area Index (BAI) vs Day of the Year (DOY)

 

In this plot, we wanted to assess the annual Body Area Index (BAI) trends that describe how skinny (low number) or fat (higher number) a whale is. BAI is a simplified version of the BMI (Body Mass Index) used for humans. If you are interested about this method we have developed at our lab in collaboration with the Aerial Information Systems Laboratory/OSU, you can read more about it in our publication.

The plots above are three versions of the same data displayed in different ways. The first plot on the left shows all the data points by year, with polynomial best fit lines, and the confidence intervals (in gray). There are many overlapping observation points, so for the middle plot I tried to “clean up the plot” by reducing the size of the points and taking out the gray confidence interval range around the lines. In the last plot on the right, I used a linear regression best fit line, instead of polynomial.

We can see a general trend that the BAI was considerably higher in 2016 (red line), when compared to the following years, which makes us question the accuracy of the dataset for that year. In 2016, we also didn’t sample in the month of July, which is causing the 2016 polynomial line to show a sharp decrease in this month (DOY: ~200-230). But it is also interesting to note that the increasing slope of the linear regression line in all three years is very similar, indicating that the whales gained weight at about the same rate in all years.

 

Plot 2: Body Area Index (BAI) vs Body Condition Score (BCS)

 

In addition to the photogrammetry method of assessing whale body condition, we have also performed a body condition scoring method for all the photos we have taken in the field (based on the method described by Bradford et al. 2012). Thus, with this second set of plots, we wanted to compare both methods of assessing whale body condition in order to evaluate when the methods agree or not, and which method would be best and in which situation. Our hypothesis was that whales with a ‘fair’ body condition would have a lower BAI than whales with a ‘good’ body condition.

The plots above illustrate two versions of the same data, with data in the left plot grouped by year, and the data in the right plot grouped by month. In general, we see that no whales were observed with a poor body condition in the last analysis months (August to October), with both methods agreeing to this fact. Additionally, there were many whales that still had a fair body condition in August and September, but less whales in the month of October, indicating that most whales gained weight over the foraging seasons and were ready to start their Southbound migration and another fasting period. This result is important information regarding monitoring and conservation issues.

However, the 2016 dataset is still a concern, since the whales appear to have considerable higher body condition (BAI) when compared to other years.

 

Plot 3:Temporal Body Area Index (BAI) for individual whales

 

In this last group of plots, we wanted to visualize BAI trends over the season (using day of year – DOY) on the x-axis) for individuals we measured more than once. Here we can see the temporal patterns for the whales “Bit”, “Clouds”, “Pearl”, “Scarback, “Pointy”, and “White Hole”.

We expected to see an overall gradual increase in body condition (BAI) over the seasons, such as what we can observe for Pointy in 2018. However, some whales decreased their condition, such as Bit in 2018. Could this trend be accurate? Furthermore, what about BAI measurements that are different from the trend, such as Scarback in 2017, where the last observation point shows a lower BAI than past observation points? In addition, we still observe a high BAI in 2016 at this individual level, when compared to the other years.

My next step will be to check the whole dataset again and search for inconsistencies. There is something causing these 2016 values to possibly be wrong and I need to find out what it is. The overall quality of the measured photogrammetry images was good and in focus, but other variables could be influencing the quality and accuracy of the measurements.

For instance, when measuring images, I often struggled with glare, water splash, water turbidity, ocean swell, and shadows, as you can see in the photos below. All of these variables caused the borders of the whale body to not be clearly visible/identifiable, which may have caused measurements to be wrong.

 

Examples of bad conditions for performing photogrammetry: (1) glare and water splash, (2) water turbidity, (3) ocean swell, and (4) a shadow created in one of the sides of the whale body.
Source: GEMM Lab. Taken under NMFS permit 16111 issued to John Calambokidis.

 

Thus, I will need to check all of these variables to identify the causes for bad measurements and “clean the dataset”. Only after this process will I be able to make these plots again to look at the trends (which will be easy since I already have my R code written!). Then I’ll move on to my next hypothesis that the BAI of individual whales varied by demographics including sex, age and reproductive state.

To carry out robust science that produces results we can trust, we can’t simply collect data, perform a basic analysis, create plots and believe everything we see. Data is often messy, especially when developing new methods like we have done here with drone based photogrammetry and the BAI. So, I need to spend some important time checking my data for accuracy and examining confounding variables that might affect the dataset. Science can be challenging, both when interpreting data or learning a new command language, but it is all worth it in the end when we produce results we know we can trust.

 

 

 

Tricky fin

By Paul Lask

Paul Lask teaches writing at Oregon Coast Community College, and is a faculty fellow with Portland State University’s Institute for Sustainable Solutions. His writing can be found at prlask.com. 

I pulled my kayak down to the beach, where a woman stood pointing toward the ocean. A fin rose from the water about a hundred yards offshore.

“It’s an orca,” she said.

“Naw,” the man beside her said. “That’s a gray.”

I recalled a documentary scene of a group of orcas spy-hopping near a seal marooned on an ice chunk. After their pogoing taunts, they left it alone. Another clip showed the orcas band together and charge forward, pushing a big wave over the ice and knocking the seal in.

I brought myself back to the beach. I wanted it to be a gray. It was one of my first solo ocean paddles, and I stood in my dry suit, PFD and helmet, having checked my weather and swell apps, having spent many hours in pools and bays learning rolls and rescues, and many dollars on courses, gear and guidebooks, now arguing a dubious fin into goodness.

It had to be a gray.

I dragged my boat to the water. Small dumping waves sucked back dark gravelly sand. The fin flopped over.

Aspiring rough water sea kayakers are trained in safety and rescue. We learn about dealing with battering surf, longshore currents, T-rescues and re-entry rolls. We don’t learn about sea life. I grew up in northern Illinois, where the nearest sea animal was a river dragon fashioned out of a downed tree that got painted annually, and TV specials on Loch Ness.

Paddling around rock near Cape Meares, Oregon.

I stuffed myself into my boat, suddenly remembering the shark story an instructor told me: They were out near Pacific City when the bad fin emerged. My instructor had a Go Pro on his helmet. His buddy dared him to roll to get a shot of their follower. My instructor declined.

Sealing my spray skirt over the cockpit, I focused on launch prep. I checked my radio. Made sure my extra paddle was secure. Confirmed I hadn’t sealed the skirt over my skeg rope. Here at North Fogarty Creek beach there was a gap between where the fin had been and a rock the size of a two story house. I waited for a set of waves to pass, then pushed off.

I saw the gray whale’s back split the water, heard the great sigh. A misty rainbow evaporated. I darted past the whale into the open sea. Other puffs dotted the horizon.

In time I would learn the kelp forest I had just paddled through hosted galaxies of tiny shrimp-like zooplankton. The gray was “sharking,” a foraging behavior in shallow water wherein it lays on its side with half its tail sticking out. Of the 20,000 gray whales that annually migrate from Mexico to Alaska, about 200 mysteriously break away and feed nearshore in Oregon. Scientists don’t know[i] for sure why this occurs, but the abundance of those shrimp-like animals is one theory.

Gray whale landing after a breach off Newport, Oregon. Taken under NMFS permit 16111 by Leigh Torres.

The mavericks are good for the tourism industry. From late spring through summer Depoe Bay is a frenzy of camera clicks and selfie sticks. A gauntlet of vehicles cram both sides of Hwy 101. Whale watching boats enter and exit the “world’s smallest harbor” through a bottleneck I’ve heard can be sketchy for kayakers.

As I paddled I toyed with wishful thinking—because I was a non-motorized vessel, the whales might better appreciate my presence. I was not there to photograph them. I just liked being in the sway of the water. “No cradle is so comfortable,” Rudyard Kipling wrote, “as the long, rocking swell of the Pacific.”[ii] Especially on an uncharacteristically calm day like this.

I have met paddlers who are indifferent to our resident grays. One referred to them as squirrels. Another claimed he got too near a spout, and was covered in the slime geyser, which he’d found disgusting. Others want to get close. A friend is interested in bringing snorkeling gear out next season, and a non-paddling acquaintance wants to get a kayak so he can sneak up and swim with one.

Dr. Roger Payne, the biologist famous for discovering that humpbacks sing, discusses Baja’s “‘friendly gray whale phenomenon’, wherein gray whales come so close to whale-watching boats that the tourists can reach out and pat them.”[iii] Grays weren’t always treated like housecats. When whaling was in full swing, Dr. Payne continues, they were referred to as “devil fish” by whalers in Scammon’s Lagoon in Baja. The whales were being routinely harpooned, so they fought back, earning a fierce reputation. Their numbers plummeted. Federal protections helped them recover, and in 1994 eastern Pacific gray whales were removed from the U.S. Endangered Species List.

Paddling under arch at Three Arch Rocks.

U.S. federal law requires people keep a hundred yards away from whales. Natural law supports this precaution. Once paddling through my shark and orca anxiety, I developed an ambivalence about my proximity to the grays. It was not fear of aggression, but indifference. I was sneaking around the living room of 35-ton animals. Despite their boxcar bulk, they moved with quick snaky grace; regardless of my attempts at putting a football field between us, what was to keep one from accidentally rolling over me or smashing me with its tail?

With shipwrecks in mind, Herman Melville pondered the power of a whale fluke: “But as if this vast local power in the tendinous tail were not enough, the whole bulk of the leviathan is knit over with a warp and woof of muscular fibers and filaments, which passing on either side of the loins and running down into the flukes, insensibly blend with them, and largely contribute to their might; so that in the tail the confluent measureless force of the whole whale seems concentrated to a point. Could annihilation occur to matter, this were the thing to do it.”[iv]

Whale-caused shipwrecks didn’t end in the nineteenth century. Contemplating how his sloop went down, Steven Callahan, a sailor lost at sea for 76 days, recalls how his nineteen-ton, forty-three-foot schooner and a heavy cruiser were both sunk by whales in the 1970s.[v] Dr. Payne also has boat breaching stories. “There’s a woman who works in my laboratory who had a whale breach directly on top of her boat. Not a glancing blow, but a direct hit across the bow. The boat was totaled…”

In 2015, a 33-ton humpback breached onto a tandem kayak in Monterey Bay, California. Reanalyzing video footage, Tom Mustill, one of the struck kayakers, believes he can see the whale “sticking its eyes out and taking a look at us while he’s in the air.” He speculates that the whale may have calculated its landing so as to avoid full body impact. Mustill is currently making a BBC2 documentary about the incident titled “Humpback Whales: A Detective Story.”

How whales behave around vessels is still an open scientific question. OSU whale mammologist Dr. Leigh Torres asks: “Are there behavior differences based on boat traffic and composition? Whales might react to some boats, but perhaps not others based on speed, approach, motor type, etc.”[vi] The ocean is also getting noisier. One study shows that over the last sixty years ambient noise in the ocean has increased about three to five decibels per decade.[vii] To what extent is this noise stressing out whales, and what kind of reactions will we begin to see?

***

            Dr. Torres told me whales were like a gateway drug for getting people hooked on marine ecology. Since that tricky fin at Fogarty Creek I’ve given them a good amount of thought. It’s partially their size that inspires awe and reflection. Writer Julia Whitty gets at their enormity by thinking about their deaths, comparing whales to old growth trees. She describes whalefall beautifully:

“…the downward journey takes place in the slow motion of the underwater world, as the processes of decomposition produce buoyant gases that duel with the force of gravity in such a way that the carcass rides a gentle elevator up and down on its way down” (178). Once the body hits the ocean floor it provides a “nutritional bonanza of a magnitude that might otherwise take thousands of years to accumulate from the background flow of small detritus from the surface.” A gray takes a year and a half to be “stripped to the bone by the scalpels and stomachs of the deep.” A blue whale can take as long as eleven years. [viii]

But I don’t think it’s just their size that hooks us. They’re mammals, nurse their young, sing to one another. “Flowing like breathing planets,” Gary Snyder writes,[ix] we can only wonder what a whale might know.

As I continue exploring our coast by kayak, I occasionally talk to whales. It no longer seems strange to want to hug one. I attempt to maintain the lawful distance, though now and then one rises close enough to see the individual barnacles studded among old scratches and scribbles. This wordless poetry is like a map into deep time. I realize I want to keep being humbled and a little afraid. I realize I’m hooked.

Author paddling near Three Arch Rocks. Photo by Bruce Moreira.

 

References

[i] Oregon State University. (2015, August 4). Researchers studying Oregon’s “resident population” of gray whales. Retrieved from                 https://today.oregonstate.edu/archives/2015/aug/researchers-studying-oregon’s-“resident-population”-gray-whales

[ii] Kipling, R. (1914). The Jungle Book (p. 145). New York, NY: Double Day. Retrieved from          https://play.google.com/store/books/details?id=LO88AQAAIAAJ&rdid=book-LO88AQAAIAAJ&rdot=1

[iii] White, J. (2016). Talking on the Water (pp. 25-26). San Antonio, TX: Trinity University Press.

[iv] Friends of the Earth. (1970). Wake of the Whale (p. 71). San Francisco, CA: Friends of the Earth, Inc.

[v] Steven, C. (2002). Adrift (p. 37). New York, NY: First Mariner Books.

[vi]Oregon State University. (2015, August 4). Researchers studying Oregon’s “resident population” of gray whales. Retrieved from

https://today.oregonstate.edu/archives/2015/aug/researchers-studying-oregon’s-“resident-population”-gray-whales

[vii] Lemos, L. (2016, April 6). Does ocean noise stress-out whales?. In Geospatial Ecology of Marine Megafauna Laboratory.       Retrieved from http://blogs.oregonstate.edu/gemmlab/2016/04/06/does-ocean-noise-stress-out-whales/

[viii] Whitty, J. (2010). Deep Blue Home (pp. 178-181). New York, NY: Houghton Mifflin Harcourt.

[ix] Snyder, G. (1974). Turtle Island. New York, NY: New Directions Publishing Group. Retrieved from                 https://www.poets.org/poetsorg/poem/mother-earth-her-whales-0

 

More than just whales: The importance of studying an ecosystem

 

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

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

Figure 1. A map of New Zealand, with the South Taranaki Bight (STB) region delineated by the black box. Cape Farewell is denoted by a star, and Kahurangi point is denoted by an X.
Figure 4. A blue whale lunges on an aggregation of krill. UAS piloted by Todd Chandler.
Krill (Nyctiphanes australis), blue whale prey in New Zealand’s South Taranaki Bight region. Image: Te Papa.

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

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

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

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

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

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

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

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

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

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

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

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

Understanding sea otter effects through complexity

By Dominique Kone, Masters Student in Marine Resource Management

Species reintroductions are a management strategy to augment the reestablishment or recovery of a locally-extinct or extirpated species into once native habitat. The potential for reestablishment success often depends on the species’ ecological characteristics, habitat requirements, and relationship and effects to other species in the environment[1]. While the science behind species reintroductions is continuously evolving and improving, reintroductions are still inherently risky and uncertain in nature. Therefore, every effort should be made to fully assess ecological factors before a reintroduction takes place. As Oregon considers a potential sea otter reintroduction, understanding these ecological factors is an important piece of my own graduate research.

Sea otters are oftentimes referred to as keystone species because they can have wide-reaching effects on the community structure and function of nearshore marine environments. Furthermore, relative to other marine mammals or top predators, several papers have documented these effects – partially due to the ease in observing their foraging and social behaviors, which typically take place close to shore. In many of these studies, a classic paradigm repeatedly appears: when sea otters are present, prey densities (e.g., sea urchins) are significantly reduced, while macroalgae (e.g., kelp, seagrass) densities are high.

Source: Belleza.

While this paradigm is widely-accepted amongst researchers, a few key studies have also demonstrated that the effects of sea otters may be more variable than we once thought. The paradigm does not necessarily hold true everywhere sea otters exist, or at least not to the same degree. For example, after observing benthic communities along islands with varying sea otter densities in the Aleutian archipelago, Alaska, researchers found that islands with abundant otter populations consistently supported low sea urchin densities and high, yet variable, kelp densities. In contrast, islands without otters consistently had low kelp densities and high, yet variable, urchin densities[2]. This study demonstrates that while the classic paradigm generally held true, the degree to which the ecosystem belonged to one of two dominant states (sea otters, low urchins, and high kelp or no sea otters, high urchins, and low kelp) was less obvious.

This example demonstrates the danger in applying this one-size-fits-all paradigm to sea otter effects. Hence, we want to achieve a better understanding of potential sea otter effects so that managers may anticipate how Oregon’s nearshore environments may be affected if sea otters were to be reintroduced. Yet, how can we accurately anticipate these effects given these potential variations and deviations from the paradigm? Interestingly, if we look to other fields outside ecology, we find a possible solution and tool for tackling these uncertainties: a systematic review of available literature.

Two ecosystem states as predicted by the classic paradigm (left: kelp-dominated; right: urchin-dominated). Source: SeaOtters.com.

For decades, medical researchers have been conducting systematic reviews to assess the efficacy of treatments and drugs by combining several studies to find common findings[3]. These findings can then be used to determine any potential variation between studies (i.e. instances where the results may conflict or differ from one another) and even test the influence and importance of key factors that may be driving that variation[4]. While systematic reviews are quite popular within the medical research field, they have not been applied regularly in ecology, but recognition of their application to ecological questions is growing[5]. In our case of achieving a better understanding of the drivers of ecological impacts of sea otter, a systematic literature review is an ideal tool to assess variable effects. This review will be the focus of my second thesis chapter.

In conducting my review, there will be three distinct phases: (1) review design and study collection, (2) meta-analysis, and (3) factor testing. In the first phase (review design and study collection), I will search the existing literature to collect studies that explicitly compare the availability of key ecosystem components (i.e. prey species, non-prey species, and macroalgae species) when sea otters are absent and present in the environment. By only including studies that make this comparison, I will define effects as the proportional change in each species’ or organism group’s availability (e.g. abundance, biomass, density, etc.) with and without sea otters. In determining these effects, it’s important to recognize that sea otters alter ecosystems via both direct and indirect pathways. Direct effects can be thought of as any change to prey availability via sea otter predation directly, while indirect effects can be thought of an any alteration to the broader ecosystem (i.e. non-prey species, macroalgae, habitat features) as an indirect result from sea otter predation on prey species. I will record both types of effects.

General schematic of a meta-analysis in a systematic review. A meta-analysis is the process of taking multiple datasets (i.e. Data 1, Data 2 etc.) from literature sources, calculating summary statistics or effects (i.e. Summary 1, Summary 2, etc.) for each dataset, running statistical procedures (e.g. SMA = sequential meta-analysis) to relate summary effects and investigate between study variation, and identifying important features driving variation. Source: MediCeption.

In phase two, I will use meta-analytical procedures (i.e. statistical analyses specific to systematic reviews) to calculate one standardized metric to represent sea otter effects. These effects will be calculated and averaged across all collected studies. As previously discussed, there may be key factors – such as sea otter density – that influence these effects. Therefore, in phase three (factor testing), effects will also be calculated separately for each a priori factor to test their influence on the effects. Such factors may include habitat type (i.e. hard or soft sediment), prey species (i.e. sea urchins, crabs, clams, etc.), otter density, depth, or time after otter recolonization.

In statistical terms, the goal of testing factors is to see if the variation between studies is impacted by calculating sea otter effects separately for each factor versus across all studies. In other words, if we find high variation in effects between studies, there may be important factors driving that variation. Therefore, in systematic reviews, we recalculate effects separately for each factor to try to explain that variation. If, however, after testing these factors, variation remains high, there may be other factors that we didn’t test that could be driving that remaining variation. Yet, without a priori knowledge on what those factors could be, such variation should be reported as a major source of uncertainty.

Source: Giancarlo Thomae.

Predicting or anticipating the effects of reintroduced species is no easy feat. In instances where the ecological role of a species is well known – and there is adequate data – researchers can develop and use ecosystem models to predict with some certainty what these effects may be. Yet, in other cases where the species’ role is less studied, has less data, or is more variable, researchers must look to other tools – such as systematic reviews – to gain a better understanding of these potential effects. In this case, a systematic review on sea otter effects may prove particularly useful in helping managers understand what types of ecological effects of sea otters in Oregon are most likely, what the important factors are, and, after such review, what we still don’t know about these effects.

References:

[1] Seddon, P. J., Armstrong, D. P., and R. F. Maloney. 2007. Developing the science of reintroduction biology. Conservation Biology. 21(2): 303-312.

[2] Estes, J. A., Tinker, M. T., and J. L. Bodkin. 2009. Using ecological function to develop recovery criteria for depleted species: sea otters and kelp forests in the Aleutian Archipelago. Conservation Biology. 24(3): 852-860.

[3] Sutton, A. J., and J. P. T. Higgins. 2008. Recent developments in meta-analysis. Statistics in Medicine. 27: 625-650.

[4] Arnqvist, G., and D. Wooster. 1995. Meta-analysis: synthesizing research findings in ecology and evolution. TREE. 10(6): 236-240.

[5] Vetter, D., Rucker, G., and I. Storch. 2013. Meta-analysis: a need for well-defined usage in ecology and conservation biology. Ecosphere. 4(6): 1-13.