The Intersection of Science and Politics

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

As much as I try to keep politics out of my science vocabulary, there are some ties between the two that cannot be severed. Often, science in the United States is very linked to the government because funding can be dependent on federal, state, and/or local government decisions. Therefore, it is part of our responsibility as scientists to be, at least, informed on governmental proceedings.

The United States has one agency that is particularly important to those of us conducting marine science: the National Oceanic and Atmospheric Administration (NOAA). NOAA’s mission is science, service, and stewardship with three major components:

  1. To understand and predict changes in climate, weather, oceans and coasts
  2. To share that knowledge and information with others
  3. To conserve and manage coastal and marine ecosystems and resources
noaa org chart
Organizational Chart of NOAA. (Image source: OrgCharting)

Last year, the U.S. Senate confirmed Retired Rear Admiral Timothy Gallaudet, Ph.D., as the Assistant Secretary of Commerce for Oceans and Atmosphere for the Department of Commerce in NOAA. This position is an appointment by the current President of the United States, and is tasked with overseeing the daily functions and the strategic and operational future of NOAA. NOAA oversees the National Marine Fisheries Service (NMFS), which is an agency responsible for the stewardship and management of the nation’s living marine resources. NMFS is a major player when it comes to marine science, particularly through the determination of priorities for research and management of marine species and habitats within the United States’ exclusive economic zone (EEZ).

In dark blue, the United States’ Exclusive Economic zones, surrounding land masses in green. (Figure by K. Laws)

Recently, I had the opportunity to hear Dr. Gallaudet speak to scientists who work for, or in conjunction with, a NMFS office. After the 16% budget cut from the fiscal year 2017 to 2018, many marine scientists are concerned about how budget changes will impact research. Therefore, I knew Dr. Gallaudet’s visit would provide insight about the future of marine science in the United States.

Dr. Gallaudet holds master’s and doctoral degrees in oceanography from Scripps Institution of Oceanography, as well as a bachelor’s degree from the United States Naval Academy. He spent 32 years in the Navy before stepping into his current role as Assistant Secretary. Throughout the meeting, Dr. Gallaudet emphasized his leadership motto: All in, All Good, and All for One.

Dr. Gallaudet also spoke about where he sees NOAA moving towards: the private sector.

A prominent conservation geneticist asked Dr. Gallaudet how NOAA can better foster advanced degree-seeking students. The geneticist commented that a decade ago there were 10-12 PhD students in this one science center alone. Today, there is “maybe one”. Dr. Gallaudet responded that the science centers should start reaching out to private industry. In response to other questions, he continued to redirect scientists toward United States-based corporations that could join forces with government agencies. He believes that if NMFS scientists share data and projects with local biotechnology, medical, and environmental companies, the country can foster positive relationships with industry. Dr. Gallaudet commented that the President wants to create these win-win situations: where the US government pairs with for-profit companies. It is up to us, as the scientists, how we make those connections.

As scientists, we frequently avoid heated political banter in the hopes of maintaining an objective and impartial approach to our research. However, these lines can be blurred. Much of our science depends on political decisions that mold our future, including how funding is allocated and what goals are prioritized. In 2010, Science Magazine published an online article, “Feeding your Research into the Policy Debate” where Elisabeth Pain highlighted the interdisciplinary nature of science and policy. In Pain’s interview with Troy Benn, a PhD student in Urban Ecology at the time, Benn comments that he learned just how much scientists play a role in policy and how research contributes to policy deliberations. Sometimes our research becomes of interest to politicians and sometimes it is the other way around.

From my experiences collaborating with government entities, private corporations, and nonprofit organizations, I realize that science-related policy is imperative. California established a non-profit, the California Ocean Science Trust (OST), for the specific objective supporting management decisions with the best science and bridging science and policy. A critical analysis of the OST by Pietri et al., “Using Science to Inform Controversial Issues: A Case Study from the California Ocean Science Trust”, matches legislation with science. For example, the Senate Bill (SB) 1319, better known as the California Ocean Protection Act (COPA), calls for “decisions informed by good science” and to “advance scientific understanding”. Science is explicitly written into legislation and I think that is a call to action. If an entire state can mobilize resources to create a team of interdisciplinary experts, I can inform myself on the politics that have potential to shape my future and the future of my science.

An image of the NOAA ship Bell M. Shimada transiting between stations. Multiple members of the GEMM Lab conducted surveys from this NOAA vessel in 2018. (Image source: Alexa Kownacki)

Hundreds and hundreds and hundreds of models: An ecologist’s love for programming

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

When people hear that I study blue whales, they often ask me questions about what it’s like to be close to the largest animal on the planet, where we do fieldwork, and what data we are interested in collecting. While I love time at sea, my view on a daily basis is rarely like this:

Our small research vessel at sunset in New Zealand’s South Taranaki Bight at the end of a day of blue whale survey. Photo by D. Barlow.

More often than not, it looks something like this:

In my application letter to Dr. Leigh Torres, I wrote something along the lines of “while I relish remote fieldwork, I also find great satisfaction in the analysis process.” This statement is increasingly true for me as I grow more proficient in statistical modeling and computer programming. When excitedly telling my family about how I am trying to model relationships between oceanography, krill, whales, and satellite imagery, I was asked what I meant by “model”. Put simply, a model is a formula or equation that we can use to describe a pattern. I have been told, “all models are wrong, but some models work.” What does this mean? While we may never know exactly every pattern of whale feeding behavior, we can use the data we have to describe some of the important relationships. If our model performance is very good, then we have likely described most of what drives the patterns we see. If model performance is poor, then there is more to the pattern that we have not yet captured in either our data collection or in our analytical methods. Another common saying about models is, “A model is only ever as good as the data you put into it.” While we worked hard during field seasons to collect a myriad of data about what could be influencing blue whale distribution patterns, we inevitably could not capture everything, nor do we know everything that should be measured.

So, how do you go about finding the ‘best’ model? This question is what I’ve been grappling with over the last several weeks. My goal is to describe the patterns in the krill that drive patterns in whale distribution, the patterns in oceanography that drive patterns in the krill, and the patterns in the oceanography that drive patterns in whale distribution. The thing is, we have many metrics to describe oceanographic patterns (surface temperature, mixed layer depth, strength of the thermocline, integral of fluorescence, to name just a few), as well as several metrics to describe the krill (number of aggregations, aggregation density, depth, and thickness). When I multiplied out how many possible combinations of predictor variables and parameters we’re interested in modeling, I realized this meant running nearly 300 models in order to settle on the best ten. This is where programming comes in, I told myself, and caught my breath.

I’ve always loved languages. When I was much younger, I thought I might want to study linguistics. As a graduate student in wildlife science, the language I’ve spent the most time learning, and come to love, is the statistical programming language R. Just like any other language, R has syntax and structure. Like any other language, there are many ways in which to articulate something, to make a particular point or reach a particular end goal. Well-written code is sometimes described as “elegant”, much like a well-articulated piece of writing. While I certainly do not consider myself “fluent” in R, it is a language I love learning. I like to think that the R scripts I write are an attempt to eloquently uncover and describe ecological patterns.

Rather than running 300 models one by one, I wrote an R script to run many models at a time, and then sort the outputs by model performance. I may look at the five best models of 32 options in order to select one. But this is where Leigh reminds me to step back from the programming for a minute and put my ecologist hat back on. Insight on the part of the modeler is needed in order to discern between what are real ecological relationships and what are spurious correlations in the data. It may not be quite as simple as choosing the model with the highest explanatory power when my goal is to make ecological inferences.

So, where does this leave me? Hundreds of models later, I am still not entirely sure which ones are best, although I’ve narrowed it down considerably. My programming proficiency and confidence continue to grow, but that only goes so far in ecology. Knowledge of my study system is equally important. So my workflow lately goes something like this: write code, try to interpret model outputs, consider what I know about the oceanography of my study region, re-write code, re-interpret the revised results, and so on. Hopefully this iterative process is bringing us gradually closer to an understanding of the ecology of blue whales on a foraging ground… stay tuned.

A blue whale lunges on an aggregation of krill in New Zealand’s South Taranaki Bight. Drone piloted by Todd Chandler.

Albatrosses at sunrise, dolphins at sunset: Northern California Current cruise

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

Sun on my face and wind in my hair, scanning the expanse of blue. Forty minutes on, twenty minutes off, from sunrise until sunset, day after day. Hours of seemingly empty blue, punctuated by graceful black-footed albatrosses wheeling and gliding over the swells, by the splashing approach of a curious group of Pacific white-sided dolphins coming to play in the bow of the ship, by whale spouts on the horizon and the occasional breaching humpback. A flurry of data entry—geographic coordinates, bearing and distance from the ship, number of animals, species identification, behavior—and then back to blue.

Scanning for marine mammals from the flying bridge of NOAA ship Bell M. Shimada. Photo: Jess O’Loughlin.

I’ve just returned from the Northern California Current (NCC) ecosystem cruise aboard NOAA ship Bell M. Shimada. My role on board was the marine mammal observer, logging marine mammal sightings during the transits between sampling stations. We surveyed and sampled between Cape Mears, Oregon and Trinidad, California, from right along the coast out to 200 nautical miles offshore. Resources in the marine environment are patchy, and our coastline is highly productive. This diversity in environmental conditions creates niche habitats for many species, which is one reason why surveying and sampling across a broad geographic range can be so informative. We left Newport surrounded by gray whales, feeding in green, chilly waters at temperatures around 12°C. Moving west, the marine mammal and seabird sightings were increasingly sparse, the water increasingly blue, and the surface temperature warmed to a balmy 17°C. We had reached offshore waters, an ocean region sometimes referred to as the “blue desert”. For an entire day I didn’t see a single marine mammal and only just a few seabirds, until a handful of common dolphins—more frequently seen in warm-temperate and tropical waters to the south—joined the ship at sunset. As we transited back inshore over the productive Heceta Bank, the water became cooler and greener. I stayed busy logging sightings of humpback and gray whales, harbor porpoise and Dall’s porpoise, pacific white-sided dolphins and sea lions. These far-ranging marine predators must find a way to make a living in the patchy and dynamic ocean environment, and therefore their distribution is also patchy—aggregated around areas of high productivity and prey availability, and occasionally seen transiting in between.

Here are a few cruise highlights:

Curious groups of common dolphins (Delphinus delphis) came to play in the bow wake of the ship and even checked out the plankton nets when they were deployed. Common dolphins are typically found further south, however we saw several groups of them in the warmer waters far offshore.

Ocean sunfish (Mola mola) will occasionally lay themselves flat at the surface so that seabirds will pick them clean of any parasites. I was delighted to observe this for the first time just off Newport! There were several more sunfish sightings throughout the cruise.

Gull picking parasites off an ocean sunfish (Mola mola). Photo: Dawn Barlow.

A masked booby (Sula dactylatra) hung around the ship for a bit, 16 nautical miles from shore, just south of the Oregon-California border. Considered a tropical species, a sighting this far north is extremely rare. While masked boobies are typically distributed in the Caribbean and tropical Pacific from Mexico to Australia, one found its way to the Columbia River in 2006 (first record in the state of Oregon) and another showed up here to Newport in 2015 – reportedly only the second to be recorded north of Mendocino County, California. Perhaps this sighting is the third?

Masked booby (Sula dactylatra). Photo: Dawn Barlow.

While most of my boat-based fieldwork experiences have been focused on marine mammal research, this was an interdisciplinary cruise aimed at studying multiple aspects of the northern California current ecosystem. There were researchers on board studying oceanography, phytoplankton and harmful algal blooms, zooplankton, and microplastics. When a group of enthusiastic scientists with different areas of expertise come together and spend long days at sea, there is a wonderful opportunity to learn from one another. The hydroacoustic backscatter on the scientific echosounder prompted a group discussion about vertical migration of plankton one evening. Another evening I learned about differences in energetic content between krill species, and together we mused about what that might mean for marine predators. This is how collaborations are born, and I am grateful for the scientific musings with so many insightful people.

Thank you to the Shimada crew and the NCC science team for a wonderful cruise!

The NCC science team after a successful cruise!

Big Data: Big possibilities with bigger challenges

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

Did you know that Excel has a maximum number of rows? I do. During Winter Term for my GIS project, I was using Excel to merge oceanographic data, from a publicly-available data source website, and Excel continuously quit. Naturally, I assumed I had caused some sort of computer error. [As an aside, I’ve concluded that most problems related to technology are human error-based.] Therefore, I tried reformatting the data, restarting my computer, the program, etc. Nothing. Then, thanks to the magic of Google, I discovered that Excel allows no more than 1,048,576 rows by 16,384 columns. ONLY 1.05 million rows?! The oceanography data was more than 3 million rows—and that’s with me eliminating data points. This is what happens when we’re dealing with big data.

According to Merriam-Webster dictionary, big data is an accumulation of data that is too large and complex for processing by traditional database management tools (www.merriam-webster.com). However, there are journal articles, like this one from Forbes, that discuss the ongoing debate of how to define “big data”. According to the article, there are 12 major definitions; so, I’ll let you decide what you qualify as “big data”. Either way, I think that when Excel reaches its maximum row capacity, I’m working with big data.

Collecting oceanography data aboard the R/V Shimada. Photo source: Alexa K.

Here’s the thing: the oceanography data that I referred to was just a snippet of my data. Technically, it’s not even MY data; it’s data I accessed from NOAA’s ERDDAP website that had been consistently observed for the time frame of my dolphin data points. You may recall my blog about maps and geospatial analysis that highlights some of the reasons these variables, such as temperature and salinity, are important. However, what I didn’t previously mention was that I spent weeks working on editing this NOAA data. My project on common bottlenose dolphins overlays environmental variables to better understand dolphin population health off of California. These variables should have similar spatiotemporal attributes as the dolphin data I’m working with, which has a time series beginning in the 1980s. Without taking out a calculator, I still know that equates to a lot of data. Great data: data that will let me answer interesting, pertinent questions. But, big data nonetheless.

This is a screenshot of what the oceanography data looked like when I downloaded it to Excel. This format repeats for nearly 3 million rows.

Excel Screen Shot. Image source: Alexa K.

I showed this Excel spreadsheet to my GIS professor, and his response was something akin to “holy smokes”, with a few more expletives and a look of horror. It was not the sheer number of rows that shocked him; it was the data format. Nowadays, nearly everyone works with big data. It’s par for the course. However, the way data are formatted is the major split between what I’ll call “easy” data and “hard” data. The oceanography data could have been “easy” data. It could have had many variables listed in columns. Instead, this data  alternated between rows with variable headings and columns with variable headings, for millions of cells. And, as described earlier, this is only one example of big data and its challenges.

Data does not always come in a form with text and numbers; sometimes it appears as media such as photographs, videos, and audio files. Big data just got a whole lot bigger. While working as a scientist at NOAA’s Southwest Fisheries Science Center, one project brought in over 80 terabytes of raw data per year. The project centered on the eastern north pacific gray whale population, and, more specifically, its migration. Scientists have observed the gray whale migration annually since 1994 from Piedras Blancas Light Station for the Northbound migration, and 2 out of every 5 years from Granite Canyon Field Station (GCFS) for the Southbound migration. One of my roles was to ground-truth software that would help transition from humans as observers to computer as observers. One avenue we assessed was to compare how well a computer “counted” whales compared to people. For this question, three infrared cameras at the GCFS recorded during the same time span that human observers were counting the migratory whales. Next, scientists, such as myself, would transfer those video files, upwards of 80 TB, from the hard drives to Synology boxes and to a different facility–miles away. Synology boxes store arrays of hard drives and that can be accessed remotely. To review, three locations with 80 TB of the same raw data. Once the data is saved in triplet, then I could run a computer program, to detect whale. In summary, three months of recorded infrared video files requires upwards of 240 TB before processing. This is big data.

Scientists on an observation shift at Granite Canyon Field Station in Northern California. Photo source: Alexa K.
Alexa and another NOAA scientist watching for gray whales at Piedras Blancas Light Station. Photo source: Alexa K.

In the GEMM Laboratory, we have so many sources of data that I did not bother trying to count. I’m entering my second year of the Ph.D. program and I already have a hard drive of data that I’ve backed up three different locations. It’s no longer a matter of “if” you work with big data, it’s “how”. How will you format the data? How will you store the data? How will you maintain back-ups of the data? How will you share this data with collaborators/funders/the public?

The wonderful aspect to big data is in the name: big and data. The scientific community can answer more, in-depth, challenging questions because of access to data and more of it. Data is often the limiting factor in what researchers can do because increased sample size allows more questions to be asked and greater confidence in results. That, and funding of course. It’s the reason why when you see GEMM Lab members in the field, we’re not only using drones to capture aerial images of whales, we’re taking fecal, biopsy, and phytoplankton samples. We’re recording the location, temperature, water conditions, wind conditions, cloud cover, date/time, water depth, and so much more. Because all of this data will help us and help other scientists answer critical questions. Thus, to my fellow scientists, I feel your pain and I applaud you, because I too know that the challenges that come with big data are worth it. And, to the non-scientists out there, hopefully this gives you some insight as to why we scientists ask for external hard drives as gifts.

Leila launching the drone to collect aerial images of gray whales to measure body condition. Photo source: Alexa K.
Using the theodolite to collect tracking data on the Pacific Coast Feeding Group in Port Orford, OR. Photo source: Alexa K.

References:

https://support.office.com/en-us/article/excel-specifications-and-limits-1672b34d-7043-467e-8e27-269d656771c3

https://www.merriam-webster.com/dictionary/big%20data

Cloudy with a chance of blue whales

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

As a PhD student studying the ecology of blue whales in New Zealand, my time is occupied by questions such as: When and where are the blue whales? Can we predict where they will be based on environmental conditions? How does their distribution overlap with human activity such as oil and gas exploration?

Leigh and I have just returned from New Zealand, where I gave an oral presentation at the Society for Conservation Biology Oceania Congress entitled “Cloudy with a chance of whales: Forecasting blue whale presence to mitigate industrial impacts based on tiered, bottom-up models”. While the findings I presented are preliminary, an exciting ecological story is emerging, and one with clear management implications.

The South Taranaki Bight (STB) region of New Zealand is an important area for a population of blue whales which are unique to New Zealand. A wind-driven upwelling system brings cold, productive waters into the bight [1], which sustains high densities of krill [2], blue whale prey. The region is also frequented by busy shipping traffic, oil and gas drilling and extraction platforms as well as seismic survey effort for subsurface oil and gas reserves, and is the site of a recently-permitted seabed mine for iron sands (Fig. 1). However, a lack of knowledge on blue whale distribution and habitat use patterns has impeded effective management of these potential anthropogenic threats.

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

Three surveys were conducted in the STB region in the summer months of 2014, 2016, and 2017. During that time, we not only looked for blue whales, we also collected oceanographic data and hydroacoustic backscatter data to map and measure aspects of the krill in the region. These data streams will help us understand the functional, ecological relationships between the environment (oceanography), prey (krill), and predators (blue whales) in the ecosystem (Fig. 2). But in practice these data are costly and time-consuming to collect, while other data sources such as satellite imagery are readily accessible to managers at a variety of spatial and temporal scales. Therefore, another one of my aims is to link the data we collected in the field to satellite imagery, so that managers can have a practical tool to predict when and where the blue whales are most likely to be found in the region.

Figure 2. Data streams collected during surveys of the South Taranaki Bight Region in 2014, 2016, and 2017. 

So what did I find? Here are the highlights from my preliminary analyses:

  • The majority of the patterns in blue whale distribution can be explained by the density, depth, and thickness of the krill patches.
  • Patterns in the krill are driven by oceanography.
  • Those same oceanographic parameters that drive the krill can be used to explain blue whale distribution.
  • There are tight relationships between the important oceanographic variables and satellite images of sea surface temperature.
  • Blue whale distribution can, to some degree, be explained using just satellite imagery.

We were able to identify a sea surface temperature range in the satellite imagery of approximately 18°C where the likelihood of finding a blue whale is the highest. Is this because blue whales really like 18° water? Well, more likely this relationship exists because the satellite imagery is reflective of the oceanography, and the oceanography drives patterns in the krill distribution, and the krill drives the distribution of blue whales (Fig. 3). We were able to make each of these functional linkages through our series of models, which is quite exciting.

Figure 3. The tiered modeling approach we took to investigate the ecological relationships between blue whales, krill, oceanography, and satellite imagery. Because of the ecological linkages we made, we are able to say that any relationship between whale distribution and satellite imagery most likely reflects a relationship between the blue whales and their prey. 

That’s all well and good, but we were interested in testing these relationships to see if our identified habitat associations hold up even when we do not have field data (oceanographic, krill, and whale data). This past austral summer, we did not have a field season to collect data, but there was a large seismic airgun survey of the STB region. Seismic survey vessels are required to have trained marine mammal observers on board, and we were given access to the blue whale sightings data they recorded during the survey. In December, when the water was right around the preferred temperature identified by our models (18°C), the observers made 52 blue whale sightings (Fig. 4). In January and February, the waters warmed and only two sightings were made in each month. This is not only reassuring because it supports our model results, it also implies that there is the potential to balance industrial use of the area with protection of blue whale habitat, based on our understanding of the ecology. In January and February, very few blue whales were likely disturbed by the industrial activity in the STB, as conditions were not favorable for foraging at the location of the seismic survey. In contrast, the blue whales that were in the STB region in December may have experienced physiological consequences of sustained exposure to airgun noise since the conditions were favorable for foraging in the STB. In other words, the whales may have tolerated the noise exposure to gain access to good food, but this could have significant biological repercussions such as increased stress [3].

Figure 4. Monthly sea surface temperature (MODIS Aqua) overlaid with blue whale sightings from marine mammal observers aboard seismic survey vessel R/V Amazon Warrior. Black rectangles represent areas of seismic survey effort. Blue whale sighting location data were provided by RPS Energy Pty Ltd & Schlumberger, and Todd Energy.

In the first two weeks of July, we presented these latest findings to managers at the New Zealand Department of Conservation, the Minister of Conservation, the CEO and Policy Advisor of a major oil and gas conglomerate, NGOs, advocacy groups, and scientific colleagues. It was valuable to gather feedback from many different stakeholders, and satisfying to see such a clear interest in, and management application of, our work.

Dr. Leigh Torres and Dawn Barlow in front of Parliament in Wellington, New Zealand, following the presentation of their recent findings.

What’s next? We’re back in Oregon, and diving back into analysis. We intend to take the modeling work a step further to make the models predictive—for example, can we forecast where the blue whales will be based on the temperature, productivity, and winds two weeks prior? I am excited to see where these next steps lead!

References:

  1. 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)
  2. 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)
  3. Rolland RM, Parks SE, Hunt KE, Castellote M, Corkeron PJ, Nowacek DP, Wasser SK, Kraus SD. 2012 Evidence that ship noise increases stress in right whales. Proc. Biol. Sci. 279, 2363–8. (doi:10.1098/rspb.2011.2429)

Forecasting blue whale presence: Small steps toward big goals

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

In 2013, Leigh first published a hypothesis that the South Taranaki Bight region between New Zealand’s North and South Islands is important habitat for blue whales  (Torres 2013). Since then, we have collected three years of data and conducted dedicated analyses, so we now understand that a unique population of blue whales is found in New Zealand, and that they are present in the South Taranaki Bight year-round (Barlow et al. in press).

A blue whale surfaces in the South Taranaki Bight. Photo by Leigh Torres.

This research has garnered quite a bit of political and media attention. A major platform item for the New Zealand Green Party around the last election was the establishment of a marine mammal sanctuary in the South Taranaki Bight. When the world’s largest seismic survey vessel began surveying the South Taranaki Bight this summer for more oil and gas reserves using tremendously loud airguns, there were rallies on the lawn in front of Parliament featuring a large inflatable blue whale that the protesters affectionately refer to as “Janet”. Needless to say, blue whales have made their way into the spotlight in New Zealand.

Janet the inflatable blue whale accompanies protesters on the lawn in front of Parliament in Wellington, New Zealand. Image credit: Greenpeace.

Now that we know there is a unique population of blue whales in New Zealand, what is next? What’s next for me is an exciting combination of both ecology and conservation. If an effective sanctuary is to be implemented, it needs to be more than a simple box drawn on a map to check off a political agenda item—the sanctuary should be informed by our best ecological knowledge of the blue whales and their habitat.

In July, Leigh and I will attend the Society for Conservation Biology meeting in Wellington, New Zealand, and I’ll be giving a presentation titled “Cloudy with a chance of whales: Forecasting blue whale presence based on tiered, bottom-up models”. I’ll be the first to admit, I am not yet forecasting blue whale presence. But I am working my way there, step-by-step, through this tiered, bottom-up approach. In cetacean habitat modeling, we often assume that whale distribution on a foraging ground is determined by their prey’s distribution, and that satellite images of temperature and chlorophyll-a provide an accurate picture of what is going on below the surface. Is this true? With our three years of data including in situ oceanography, krill hydroacoustics, and blue whale distribution and behavior, we are in a unique position to test some of those assumptions, as well as provide managers with an informed management tool to predict blue whale distribution.

What questions will we ask using our data? Firstly, can in situ oceanography (i.e., thermocline depth and temperature, mixed layer depth) predict the distribution and density of blue whale prey (krill)? Then, can those prey patterns be accurately predicted in the absence of oceanographic measurements, using just satellite images? Next, we’ll bring the blue whales back into the picture to ask: can we predict blue whale distribution based on our in situ measurements of oceanography and prey? And finally, in the absence of in situ measurements (which is most often the case), can we forecast where the whales will be based just on remotely-sensed images of the region?

The transducer pole in the water off the RV Star Keys (left) deployed with the echosounder to collect prey availability data, including this image (right) of krill swarms near feeding blue whales. Photo by Leigh Torres.

So, cloudy with a chance of whales? Well, you’ll have to stay tuned for that story in the coming months. In the meantime, I can tell you that as daunting as it is to aggregate so many data streams, each step of the way has a piece of the story to tell. I can’t wait to see how it falls together, both from an ecological modeling perspective and a conservation management objective.

A blue whale surfaces in front of a floating production storage and offloading (FPSO) vessel which services the oil rigs in the South Taranaki Bight. Photo by Dawn Barlow.

 

References:

Torres, L. G. (2013). Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zealand Journal of Marine and Freshwater Research47(2), 235-248.

Barlow, D. R., Torres, L. G., Hodge, K. B., Steel, D. Baker, C. S., Chandler, T. E., Bott, N., Constantine, R., Double, M. C., Gill, P., Glasgow, D., Hamner, R. M., Lilley, C., Ogle, M., Olson, P. A., Peters, C., Stockin, K. A., Tessaglia-Hymes, C. T., Klinck, H. (in press). Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endangered Species Research. 

Living the Dream – life as a marine mammal observer

By Florence Sullivan, MSc.

Living the dream as a marine mammal observer onboard the R/V Bell Shimada Photo credit: Dave Jacobsen

I first learned that “Marine Mammal Observer” was a legitimate career field during the summer after my junior year at the University of Washington.  I had the good fortune to volunteer for the BASIS fisheries-oceanography survey onboard the R/V Oscar Dyson where I met two wonderful bird observers who taught me how to identify various pelagic bird species and clued me in to just how diverse the marine science job market can be. After the cruise, younger Florence went off with an expanded world view and a small dream that maybe someday she could go out to sea and survey for marine mammals on a regular basis (and get paid for it?!).  Eight years later, I am happy to report that I have just spent the last week as the marine mammal observer on the North California Current Survey on the Dyson’s sister ship, the R/V Bell M. Shimada.  While we may not have seen as many marine mammals as I would have liked, the experience has still been everything younger Florence hoped it would be.

Finally leaving port a few days behind schedule due to stormy weather! photo credit: Florence Sullivan

If you’ve ever wondered why the scientists in your life may refer to summer as “field work season”, it’s because attempting to do research outside in the winter is an exercise in frustration, troubleshooting, and flexibility. Case in point; this cruise was supposed to sail away from port on the 24th of February, but did not end up leaving until the 27th due to bad weather.  This weather delay meant that we had to cut some oceanographic stations we would like to have sampled, and even when we made it out of the harbor, the rough weather made it impossible to sample some of the stations we still had left on our map.  That being said, we still got a lot of good work done!

The original station map. The warm colors are the west coast of the US, the cold colors are the ocean, and the black dots are planned survey stations

The oceanographers were able to conduct CTD casts at most planned stations, as well as sample the water column with a vertical zooplankton net, a HAB net (for looking for the organisms that cause Harmful Algal Blooms),  and a Bongo Net (a net that specializes in getting horizontal samples of the water column).  When it wasn’t too windy, they were also able to sample with the Manta net (a net specialized for surface sampling – it looks like a manta ray’s mouth) and at certain near-shore stations they did manage to get some bottom beam trawls in to look at the benthic community of fishes and invertebrates.  All this was done while dodging multitudes of crab pots and storm fronts.  The NOAA corps officers who drive the boat, and the deck crew who handle all the equipment deployments and retrievals really did their utmost to make sure we were able to work.

Stormy seas make for difficult sampling conditions! photo credit: Florence Sullivan

For my part, I spent the hours between stations searching the wind-tossed waves for any sign of marine mammals. Over the course of the week, I saw a few Northern fur seals, half a dozen gray whales, and a couple of unidentified large cetaceans.  When you think about the productivity of the North Pacific Ecosystem this may not seem like very much.  But remember, it is late winter, and I do not have x-ray vision to see through the waves.  It is likely that I missed a number of animals simply because the swell was too large, and when we calculate our “detection probability” these weather factors will be taken into account. In addition, many of our local marine mammals are migrators who might be in warmer climates, or are off chasing different food sources at the moment.  In ecology, when you want to know how a population of animals is distributed across a land- or sea-scape, it is just as important to understand where the animals are NOT as where they ARE. So all of this “empty” water was very important to survey simply because it helps us refine our understanding of where animals don’t want to be.  When we know where animals AREN’T we can ask better questions about why they occur where they ARE.

Black Footed Albatross soars near the boat. Photo credit: Florence Sullivan

Notable species of the week aside from the marine mammals include Laysan and Black Footed Albatrosses, a host of Vellella vellella (sailor by the wind hydroid colonies) and the perennial favorite of oceanographers; the shrinking Styrofoam cup.  (See pictures)

We sent these styrofoam cups down to 1800 meters depth. The pressure at those depths causes all the air to escape from the styrofoam, and it shrinks! This is a favorite activity of oceanographers to demonstrate the effects on increased pressure!

These sorts of interdisciplinary cruises are quite fun and informative to participate in because we can build a better picture of the ecosystem as a whole when we use a multitude of methods to explore it.  This strength of cooperation makes me proud to add my little piece to the puzzle. As I move forward in life, whether I get to be the marine mammal observer, the oceanographer, or perhaps an educator, I will always be glad to contribute to collaborative research.

 

Exploring the Coral Sea in Search of Humpbacks

By: Solène Derville, Entropie Lab, Institute of Research for Development, Nouméa, New Caledonia (Ph.D. student under the co-supervision of Dr. Leigh Torres)

Once again the austral winter is ending, and with it ends the field season for the scientific team studying humpback whales in New Caledonia. Through my PhD, I have become as migratory as my study species so this is also the time for me to fly back to Oregon for an intense 3 months of data analysis at the GEMM Lab. But before packing, it is time for a sum-up!

In 2014, the government of New Caledonia has declared all waters of the Economic Exclusive Zone to be part of a giant marine protected area: the Natural Park of the Coral Sea. These waters are seasonally visited by a small and endangered population of humpback whales whose habitat use patterns are poorly known. Indeed, the park spans more than 1.3 million km2 and its most remote and pristine areas therefore remained pretty much unexplored in terms of cetacean presence… until recently.

In 2016, the project WHERE “Humpback Whale Habitat Exploration to improve spatial management in the natural park of the CoRal Sea” was launch by my PhD supervisor, Dr. Garrigue, and I, to conduct surveys in remote reefs, seamounts and shallow banks surrounding New Caledonia mainland. The aim of the project is to increase our understanding of habitat use and movements of humpback whales in breeding grounds over a large spatial scale and predict priority conservation areas for the park.

Fig. 1. A humpback whale with our research vessel, the oceanographic vessel Alis, in the background.

This season, three specific areas were targeted for survey during the MARACAS expeditions (Marine Mammals of the Coral Sea):

– Chesterfield and Bellona reefs that surround two huge 30- to 60m-deep plateaus and are located halfway between New Caledonia and Australia (Fig. 4). Considered as part of the most pristine reefs in the Coral Sea, these areas were actually identified as one of the main hotspots targeted by the 19th century commercial whaling of humpback whales in the South Pacific (Oremus and Garrigue 2014). Last year’s surveys revealed that humpback whales still visit the area, but the abundance of the population and its connection to the neighboring breeding grounds of New Caledonia and Australia is yet to establish.

Fig. 2. The tiny islands along the Chesterfield and Bellona reefs also happen to host nesting sites for several species of boobies and terns. Here, a red-footed booby (Sula sula).

– Walpole Island and Orne bank are part of the shallow areas East of the mainland of New Caledonia (Fig. 4), where several previously tagged whales were found to spend a significant amount of time. This area was explored by our survey team for the first time last year, revealing an unexpected density of humpback whales displaying signs of breeding (male songs, competitive groups) and nursing activity (females with their newborn calf).

Fig. 3. The beautiful cliffs of Walpole Island rising from the Pacific Ocean.

Antigonia seamount, an offshore breeding site located South of the mainland (Fig. 4) and known for its amazingly dense congregations of humpback whales.  The seamount rises from the abyssal seabed to a depth of 60 m, with no surfacing island or reef to shelter either the whales or the scientists from rough seas.

Fig. 4. Map of the New Caledonia Economic Exclusive Zone (EEZ) and the project WHERE study areas (MARACAS expeditions).

During our three cruises, we spent 37 days at-sea while a second team continued monitoring the South Lagoon breeding ground. Working with two teams at the same time, one covering the offshore breeding areas and the other monitoring the coastal long-term study site of the South Lagoon, allowed us to assess large scale movements of humpback whales within the breeding season using photo-ID matches. This piece of information is particularly important to managers, in order to efficiently protect whales both within their breeding spots, and the potential corridors between them.

So how would you study whales over such a large scale?

Well first, find a ship. A LARGE ship. It takes more than 48 hours to reach the Chesterfield reefs. The vessel needs to carry enough gas necessary to survey such an extensive region, plus the space for a dinghy big enough to conduct satellite tagging of whales. All of this could not have been possible without the Amborella, the New Caledonian governement’s vessel, and the Alis, a French oceanographic research vessel.

Second, a team needs to be multidisciplinary. Surveying remote waters is logistically challenging and financially costly, so we had to make it worth our time. This season, we combined 1) photo-identification and biopsy samplings to estimate population connectivity, 2) acoustic monitoring using moored hydrophone (one of which recorded in Antigonia for more than two months, Fig. 5), 3) transect lines to record encounter rates of humpback whales, 4) in situ oceanographic measurements, and finally 5) satellite tracking of whales using the recent SPLASH10 tags (Wildlife Computers) capable of recording dive depths in addition to geographic positions (Fig. 6).

Fig. 5. Claire, Romain and Christophe standing next to our moored hydrophone, ready for immersion.

Satellite tracks and photo-identification have already revealed some interesting results in terms of connectivity within the park and with neighboring wintering grounds.

Preliminary matching of the caudal fluke pictures captured this season and in 2016 with existing catalogues showed that the same individuals may be resighted in different regions of the Park. For instance, some of the individuals photographed in Chesterfield – Bellona, had been observed around New Caledonia mainland in previous years! This match strengthens our hypothesis of a connection between Chesterfield reef complex and New Caledonia.

Yet, because the study of whale behavior is never straightforward, one tagged whale also indicated a potential connection between Chesterfield-Bellona and Australia East coast (Fig. 6). This is the first time a humpback whale is tracked moving between New Caledonia and East Australia within a breeding season. Previous matches of fluke catalogues had shown a few exchanges between these two areas but these comparisons did not include Chesterfield. Is it possible that the Chesterfield-Bellona coral reef complex form a connecting platform between Australia and New Caledonia? The matching of our photos with those captured by our Australian colleagues who collected data at the Great Barrier Reef  in 2016 and 2017 should help answer this question…

Fig. 6. “Splash” was tagged in Chesterfield in August and after spending some time in Bellona it initiated a migration south. Seamounts seem to play an important role for humpback whales in the region, as “Splash” stopped on Kelso and Capel seamount during its trip. It reached the Australian coast a couple of days ago and we are looking forward to discover the rest of its route!

While humpback whales often appear like one of the most well documented cetacean species, it seems that there is yet a lot to discover about them!

Acknowledgements:

These expeditions would not have been possible without the financial and technical support of the French Institute of Research for Development, the New Caledonian government, the French  Ministère de la Transition Ecologique et Solidaire, and the World Wide Fund for Nature. And of course, many thanks to the Alis and Amborella crews, and to our great fieldwork teammates: Jennifer Allen, Claire Bonneville, Hugo Bourgogne, Guillaume Chero, Rémi Dodémont, Claire Garrigue, Nicolas Job, Romain Le Gendre, Marc Oremus, Véronique Pérard, Leena Riekkola, and Mike Williamson.

Fig. 7A. The teams of the three 2017 MARACAS expeditions (Marine Mammals of the Coral Sea).
Fig. 7B. The teams of the three 2017 MARACAS expeditions (Marine Mammals of the Coral Sea).
Fig. 7C. The teams of the three 2017 MARACAS expeditions (Marine Mammals of the Coral Sea).

Finding the edge: Preliminary insights into blue whale habitat selection in New Zealand

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

I was fortunate enough to spend the Austral summer in the field, and so while the winter rain poured down on Oregon I found myself on the water with the sun and wind on my face, looking for blue whales in New Zealand. This spring I switched gears and spent time taking courses to build my analytical toolbox. In a course on technical writing and communication, I was challenged to present my research using only pictures and words with no written text, and to succinctly summarize the importance of my research in an introduction to a technical paper. I attended weekly seminars to learn about the diverse array of marine science being conducted at Oregon State University and beyond. I also took a course entitled “Advanced Spatial Statistics and Geographic Information Science”. In this skill-building course, we were given the opportunity to work with our own data. Even though my primary objective was to expand the tools in my toolbox, I was excited to explore preliminary results and possible insight into blue whale habitat selection in my study area, the South Taranaki Bight region (STB) of New Zealand (Figure 1).

Figure 1. A map of New Zealand, with the South Taranaki Bight (STB) region delineated by the black box. Farewell Spit is denoted by a star, and Kahurangi point is denoted by an X.

Despite the recent documentation of a foraging ground in the STB, blue whale distribution remains poorly understood in New Zealand. The STB is New Zealand’s most industrially active marine region, and the site of active oil and gas extraction and exploration, busy shipping traffic, and proposed seabed mining. This potential space-use conflict between endangered whales and industry warrants further investigation into the spatial and temporal extent of blue whale habitat in the region. One of my research objectives is to investigate the relationship between blue whales and their environment, and ultimately to build a model that can predict blue whale presence based on physical and biological oceanographic features. For this spring term, the question I asked was:

Is the number of blue whales present in an area correlated with remotely-sensed sea surface temperature and chlorophyll-a concentration?

For the purposes of this exploration, I used data from our 2017 survey of the STB. This meant importing our ship’s track and our blue whale sighting locations into ArcGIS, so that the data went from looking like this:

… to this:

The next step was to get remote-sensed images for sea surface temperature (SST) and chlorophyll-a (chl-a) concentration. I downloaded monthly averages from the NASA Moderate Resolution Imaging Spectrometer (MODIS aqua) website for the month of February 2017 at 4 km2 resolution, when our survey took place. Now, my images looked something more like this:

But, I can’t say anything reliable about the relationships between blue whales and their environment in the places we did not survey.  So next I extracted just the portions of my remote-sensed images where we conducted survey effort. Now my maps looked more like this one:

The above map shows SST along our ship’s track, and the locations where we found whales. Just looking at this plot, it seems like the blue whales were observed in both warmer and colder waters, not exclusively in one or the other. There is a productive plume of cold, upwelled water in the STB that is generated off of Kahurangi point and curves around Farewell Spit and into the bight (Figure 1). Most of the whales we saw appear to be near that plume. But how can I find the edges of this upwelled plume? Well, I can look at the amount of change in SST and chl-a across a spatial area. The places where warm and cold water meet can be found by assessing the amount of variability—the standard deviation—in the temperature of the water. In ArcGIS, I calculated the deviation in SST and chl-a concentration across the surrounding 20 km2 for each 4 km2 cell.

Now, how do I tie all of these qualitative visual assessments together to produce a quantitative result? With a statistical model! This next step gives me the opportunity to flex some other analytical muscles, and practice using another computational tool: R. I used a generalized additive model (GAM) to investigate the relationships between the number of blue whales observed in each 4 km2 cell our ship surveyed and the remote-sensed variables. The model can be written like this:

Number of blue whales ~ SST + chl-a + sd(SST) + sd(chl-a)

In other words, are SST, chl-a concentration, deviation in SST, and deviation in chl-a concentration correlated with the number of blue whales observed within each 4 km2 cell on my map?

This model found that the most important predictor was the deviation in SST. In other words, these New Zealand blue whales may be seeking the edges of the upwelling plume, honing in on places where warm and cold water meet. Thinking back on the time I spent in the field, we often saw feeding blue whales diving along lines of mixing water masses where the water column was filled with aggregations of krill, blue whale prey. Studies of marine mammals in other parts of the world have also found that eddies and oceanic fronts—edges between warm and cold water masses—are important habitat features where productivity is increased due to mixing of water masses. The same may be true for these New Zealand blue whales.

These preliminary findings emphasize the benefit of having both presence and absence data. The analysis I have presented here is certainly strengthened by having environmental measurements for locations where we did not see whales. This is comforting, considering the feelings of impatience generated by days on the water spent like this with no whales to be seen:

Moving forward, I will include the blue whale sighting data from our 2014 and 2016 surveys as well. As I think about what would make this model more robust, it would be interesting to see if the patterns become clearer when I incorporate behavior into the model—if I look at whales that are foraging and traveling separately, are the results different? I hope to explore the importance of the upwelling plume in more detail—does the distance from the edge of the upwelling plume matter? And finally, I want to adjust the spatial and temporal scales of my analysis—do patterns shift or become clearer if I don’t use monthly averages, or if I change the grid cell sizes on my maps?

I feel more confident in my growing toolbox, and look forward to improving this model in the coming months! Stay tuned.

Celebrating Hydrothermal Vents!

By Florence Sullivan, MSc Student OSU

40 years ago, in 1977 OSU researchers led an NSF funded expedition to the Galapagos on a hunt for suspected hydrothermal vents. From the 1960s to the mid-1970s, mounting evidence such as (1) temperature anomalies found deep in the water column, (2) conduction heat flow probes at mid ocean ridges recording temperatures much lower than expected, (3) unusual mounds found on benthic mapping surveys, and (4) frequent, small, localized earthquakes at mid oceanic ridges, had the oceanographic community suspecting the existence of deep sea hydrothermal vents. However, until the 1977 cruise, no one had conclusive evidence that they existed.  During the discovery cruise at the Galapagos rift, the PI (principle investigator), Dr. Jack Corliss from OSU, used tow-yos (a technique where you drag a CTD up and down through the water in a zig zag pattern – see gif) to pinpoint the location of the hydrothermal vent plume. The team then sent the Deep Submergence Vehicle (DSV) Alvin to investigate and returned with the first photographs and samples from a hydrothermal vent. While discovery of the vent systems helped answer many questions about chemical and heat fluxes in the deep sea, it generated so many new questions that novel fields of study were created in biology, microbiology, marine chemistry, marine geology, planetary science, astrobiology and the study of the origin of life.

 “Literally every organism that came up was something that was unknown to science up until that time. It made it terribly exciting. Anything that came [up] on that basket was a new discovery,” – Dr. Richard Lutz (Rutgers University)

In celebration of this great discovery, OSU’s College of Earth, Ocean and Atmospheric Sciences sponsored a seminar looking at the past, present, and future of hydrothermal vent sciences. Dr. Robert Collier began with a timeline of how the search for hydrothermal vents began, and a commemoration of all the excellent researchers and collaborations between institutions and agencies that made the discovery possible. He acknowledged that such collaborations are often somewhat tense in terms of who gets credit for which discovery, and that while Oregon State University was the lead of the project, it takes a team to get the work done.  Dr. Jack Corliss proudly followed up with a wonderful rambling explanation of how vent systems work, and a brief dip into his ground breaking paper, “An Hypothesis concerning the relationship between submarine hot springs and the origin of life on Earth.” Published in 1981, with co-authors Dr. John Baross and Dr. Sarah Hoffman, they postulate that the temperature and chemical gradients seen at hydrothermal vents provide pathways for the synthesis of chemical compounds, formation and evolution of ‘precells’ and eventually, the evolution of free living organisms.

Dr. Corliss, Dr. Baross, and Dr. Hoffman were the first to suggest the now popular theory of the origin of life at hydrothermal vents. (click on image to read full paper)

Because of time constraints, the podium was swiftly handed over to Dr. Bill Chadwick (NOAA PMEL/ HMSC CIMRS) who brought us forward to the present day with an exciting overview of current vent research.  He began by saying “at the beginning, we thought, ‘No one has seen one of these systems before, they must be very rare…’ Now, we have found them [hydrothermal vents] in every ocean basin – including the arctic and southern oceans. We just needed to know how to look!”  Dr. Chadwick also reminded us that even 40 years later, new discoveries are still being made. For example, on his most recent cruise aboard the R/V Falkor in December 2016, they found a sulfur chimney that was alternately releasing bubbles of gas (sulfur, CO2 or other, hard to know without sampling) or bubbles of liquid sulfur! Check out the video below:

Some of the goals for this recent cruise included mapping new areas of the Mariana back-arc, and investigating differences in the biological communities between vents in the Mariana trench region (a subduction zone) and vents in the back arc (a spreading zone) to see if geology plays a role in biological community composition.  For some very cool video footage of the expedition and the various dives performed by the brand new ROV SUBastian (because all scientists love puns), check out the Schmidt Ocean Institute youtube channel.

Dr. Chadwick showed this video to highlight results from his last cruise.

Finally, Dr. Andrew Thurber wrapped up the session with some thoughts about hydrothermal vents from the perspective of an ecosystem services model. Even after 40 years of research, there are still many unknowns about these ecosystems.  Individual vent systems are inherently unique due to their deep sea isolation. However, most explored sites have revealed metals and mineral deposits that have generated a lot of interest from commercial sea floor mining companies. Exploitation of these deposits would be an example of ecosystem “provisioning services” (products that are obtained from the ecosystem). Other examples include the biology of the vents as a source of new genetic material, and the thermal and chemical gradients as natural laboratories that could lead to breakthroughs in pharmaceutical research. Cultural services are those non-material benefits that people obtain from an ecosystem. At hydrothermal vents these include new scientific discoveries, educational uses (British children’s television show “The Octonauts,” has several episodes featuring hydrothermal vent creatures), and creative inspiration for artists and others. Dr. Thurber cautions that there are ethical questions to be answered before considering exploitation of these resources, but there is a lot of potential for commercial and non-commercial use of vent ecosystems.

Vent inspired art by Lily Simonson

As an undergraduate at the University of Washington, I spent time as a research assistant in Dr. John Baross’ astrobiology lab. We studied evolutionary pathways of hydrothermal vent viruses and bacteria to inform the search for life on exoplanets such as Jupiter’s moon Europa.  It was very fun and exciting for me to attend this seminar, hear stories from pioneers in the field, and remember the systems I worked on in undergrad.  I may have moved up the food chain a little now, but as we all work on our pieces of the puzzle, it is important for scientists to remember the interdisciplinary nature of our work, and how there is always something more to learn.