Tag: ArcGIS

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)

 

Over the Ocean and Under the Bridges: STEM Cruise on the R/V Oceanus

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

From September 22nd through 30th, the GEMM Lab participated in a STEM research cruise aboard the R/V Oceanus, Oregon State University’s (OSU) largest research vessel, which served as a fully-functioning, floating, research laboratory and field station. The STEM cruise focused on integrating science, technology, engineering and mathematics (STEM) into hands-on teaching experiences alongside professionals in the marine sciences. The official science crew consisted of high school teachers and students, community college students, and Oregon State University graduate students and professors. As with a usual research cruise, there was ample set-up, data collection, data entry, experimentation, successes, and failures. And because everyone in the science party actively participated in the research process, everyone also experienced these successes, failures, and moments of inspiration.

The science party enjoying the sunset from the aft deck with the Astoria-Megler bridge in the background. (Image source: Alexa Kownacki)

Dr. Leigh Torres, Dr. Rachael Orben, and I were all primarily stationed on flybridge—one deck above the bridge—fully exposed to the elements, at the highest possible location on the ship for best viewing. We scanned the seas in hopes of spotting a blow, a splash, or any sign of a marine mammal or seabird. Beside us, students and teachers donned binoculars and positioned themselves around the mast, with Leigh and I taking a 90-degree swath from the mast—either to starboard or to port. For those who had not been part of marine mammal observations previously, it was a crash course into the peaks and troughs—of both the waves and of the sightings. We emphasized the importance of absence data: knowledge of what is not “there” is equally as important as what is. Fortunately, Leigh chose a course that proved to have surprisingly excellent environmental conditions and amazing sightings. Therefore, we collected a large amount of presence data: data collected when marine mammals or seabirds are present.

High school student, Chris Quashnick Holloway, records a seabird sighting for observer, Dr. Rachael Orben. (Image source: Alexa Kownacki).

When someone sighted a whale that surfaced regularly, we assessed the conditions: the sea state, the animal’s behavior, the wind conditions, etc. If we deemed them as “good to fly”, our licensed drone pilot and Orange Coast Community College student, Jason, prepared his Phantom 4 drone. While he and Leigh set up drone operations, I and the other science team members maintained a visual on the whale and stayed in constant communication with the bridge via radio. When the drone was ready, and the bridge gave the “all clear”, Jason launched his drone from the aft deck. Then, someone tossed an unassuming, meter-long, wood plank overboard—keeping it attached to the ship with a line. This wood board serves as a calibration tool; the drone flies over it at varying heights as determined by its built-in altimeter. Later, we analyze how many pixels one meter occupied at different heights and can thereby determine the body length of the whale from still images by converting pixel length to a metric unit.

High school student, Alishia Keller, uses binoculars to observe a whale, while PhD student, Alexa Kownacki, radios updates on the whale’s location to the bridge and the aft deck. (Image source: Tracy Crews)

Finally, when the drone is calibrated, I radio the most recent location of our animal. For example, “Blow at 9 o’clock, 250 meters away”. Then, the bridge and I constantly adjust the ship’s speed and location. If the whale “flukes” (dives and exposes the ventral side of its tail), and later resurfaced 500 meters away at our 10 o’clock, I might radio to the bridge to, “turn 60 degrees to port and increase speed to 5 knots”. (See the Hidden Math Lesson below). Jason then positions the drone over the whale, adjusting the camera angle as necessary, and recording high-quality video footage for later analysis. The aerial viewpoint provides major advantages. Whales usually expose about 10 percent of their body above the water’s surface. However, with an aerial vantage point, we can see more of the whale and its surroundings. From here, we can observe behaviors that are otherwise obscured (Torres et al. 2018), and record footage that to help quantify body condition (i.e. lengths and girths). Prior to the batteries running low, Jason returns the drone back to the aft deck, the vessel comes to an idle, and Leigh catches the drone. Throughout these operations, those of us on the flybridge photograph flukes for identification and document any behaviors we observe. Later, we match the whale we sighted to the whale that the drone flew over, and then to prior sightings of this same individual—adding information like body condition or the presence of a calf. I like to think of it as whale detective work. Moreover, it is a team effort; everyone has a critical role in the mission. When it’s all said and done, this noninvasive approach provides life history context to the health and behaviors of the animal.

Drone pilot, Jason Miranda, flying his drone using his handheld ground station on the aft deck. (Photo source: Tracy Crews)

Hidden Math Lesson: The location of 10 o’clock and 60 degrees to port refer to the exact same direction. The bow of the ship is our 12 o’clock with the stern at our 6 o’clock; you always orient yourself in this manner when giving directions. The same goes for a compass measurement in degrees when relating the direction to the boat: the bow is 360/0. An angle measure between two consecutive numbers on a clock is: 360 degrees divided by 12-“hour” markers = 30 degrees. Therefore, 10 o’clock was 0 degrees – (2 “hours”)= 0 degrees- (2*30 degrees)= -60 degrees. A negative degree less than 180 refers to the port side (left).

Killer whale traveling northbound.

Our trip was chalked full of science and graced with cooperative weather conditions. There were more highlights than I could list in a single sitting. We towed zooplankton nets under the night sky while eating ice cream bars; we sang together at sunset and watched the atmospheric phenomena: the green flash; we witnessed a humpback lunge-feeding beside the ship’s bow; and we saw a sperm whale traveling across calm seas.

Sperm whale surfacing before a long dive.

On this cruise, our lab focused on the marine mammal observations—which proved excellent during the cruise. In only four days of surveying, we had 43 marine mammal sightings containing 362 individuals representing 9 species (See figure 1). As you can see from figure 2, we traveled over shallow, coastal and deep waters, in both Washington and Oregon before inland to Portland, OR. Because we ventured to areas with different bathymetric and oceanographic conditions, we increased our likelihood of seeing a higher diversity of species than we would if we stayed in a single depth or area.

Humpback whale lunge feeding off the bow.
Number of sightings Total number of individuals
Humpback whale 22 40
Pacific white-sided dolphin 3 249
Northern right whale dolphin 1 9
Killer whale 1 3
Dall’s porpoise 5 49
Sperm whale 1 1
Gray whale 1 1
Harbor seal 1 1
California sea lion 8 9
Total 43 362

Figure 1. Summary table of all species sightings during cruise while the science team observed from the flybridge.

Pacific white-sided dolphins swimming towards the vessel.

Figure 2. Map with inset displaying study area and sightings observed by species during the cruise, made in ArcMap. (Image source: Alexa Kownacki).

Even after two days of STEM outreach events in Portland, we were excited to incorporate more science. For the transit from Portland, OR to Newport, OR, the entire science team consisted two people: me and Jason. But even with poor weather conditions, we still used science to answer questions and help us along our journey—only with different goals than on our main leg. With the help of the marine technician, we set up a camera on the bow of the ship, facing aft to watch the vessel maneuver through the famous Portland bridges.

Video 1. Time-lapse footage of the R/V Oceanus maneuvering the Portland Bridges from a GoPro. Compiled by Alexa Kownacki, assisted by Jason Miranda and Kristin Beem.

Prior to the crossing the Columbia River bar and re-entering the Pacific Ocean, the R/V Oceanus maneuvered up the picturesque Columbia River. We used our geospatial skills to locate our fellow science team member and high school student, Chris, who was located on land. We tracked each other using GPS technology in our cell phones, until the ship got close enough to use natural landmarks as reference points, and finally we could use our binoculars to see Chris shining a light from shore. As the ship powered forward and passed under the famous Astoria-Megler bridge that connects Oregon to Washington, Chris drove over it; he directed us “100 degrees to port”. And, thanks to clear directions, bright visual aids, and spatiotemporal analysis, we managed to find our team member waving from shore. This is only one of many examples that show how in a few days at sea, students utilized new skills, such as marine mammal observational techniques, and honed them for additional applications.

On the bow, Alexa and Jason use binoculars to find Chris–over 4 miles–on the Washington side of the Columbia River. (Image source: Kristin Beem)

Great science is the result of teamwork, passion, and ingenuity. Working alongside students, teachers, and other, more-experienced scientists, provided everyone with opportunities to learn from each other. We created great science because we asked questions, we passed on our knowledge to the next person, and we did so with enthusiasm.

High school students, Jason and Chris, alongside Dr. Leigh Torres, all try to get a glimpse at the zooplankton under Dr. Kim Bernard’s microscope. (Image source: Tracy Crews).

Check out other blog posts written by the science team about the trip here.

The Land of Maps and Charts: Geospatial Ecology

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

I love maps. I love charts. As a random bit of trivia, there is a difference between a map and a chart. A map is a visual representation of land that may include details like topology, whereas a chart refers to nautical information such as water depth, shoreline, tides, and obstructions.

Map of San Diego, CA, USA. (Source: San Diego Metropolitan Transit System)
Chart of San Diego, CA, USA. (Source: NOAA)

I have an intense affinity for visually displaying information. As a child, my dad traveled constantly, from Barrow, Alaska to Istanbul, Turkey. Immediately upon his return, I would grab our standing globe from the dining room and our stack of atlases from the coffee table. I would sit at the kitchen table, enthralled at the stories of his travels. Yet, a story was only great when I could picture it for myself. (I should remind you, this was the early 1990s, GoogleMaps wasn’t a thing.) Our kitchen table transformed into a scene from Master and Commander—except, instead of nautical charts and compasses, we had an atlas the size of an overgrown toddler and salt and pepper shakers to pinpoint locations. I now had the world at my fingertips. My dad would show me the paths he took from our home to his various destinations and tell me about the topography, the demographics, the population, the terrain type—all attribute features that could be included in common-day geographic information systems (GIS).

Uncle Brian showing Alexa where they were on a map of Maui, Hawaii, USA. (Photo: Susan K. circa 1995)

As I got older, the kitchen table slowly began to resemble what I imagine the set from Master and Commander actually looked like; nautical charts, tide tables, and wind predictions were piled high and the salt and pepper shakers were replaced with pencil marks indicating potential routes for us to travel via sailboat. The two of us were in our element. Surrounded by visual and graphical representations of geographic and spatial information: maps. To put my map-attraction this in even more context, this is a scientist who grew up playing “Take-Off”, a board game that was “designed to teach geography” and involved flying your fleet of planes across a Mercator projection-style mapboard. Now, it’s no wonder that I’m a graduate student in a lab that focuses on the geospatial aspects of ecology.

A precocious 3-year-old Alexa, sitting with the airplane pilot asking him a long list of travel-related questions (and taking his captain’s hat). Photo: Susan K.

So why and how did geospatial ecology became a field—and a predominant one at that? It wasn’t that one day a lightbulb went off and a statistician decided to draw out the results. It was a progression, built upon for thousands of years. There are maps dating back to 2300 B.C. on Babylonian clay tablets (The British Museum), and yet, some of the maps we make today require highly sophisticated technology. Geospatial analysis is dynamic. It’s evolving. Today I’m using ArcGIS software to interpolate mass amounts of publicly-available sea surface temperature satellite data from 1981-2015, which I will overlay with a layer of bottlenose dolphin sightings during the same time period for comparison. Tomorrow, there might be a new version of software that allows me to animate these data. Heck, it might already exist and I’m not aware of it. This growth is the beauty of this field. Geospatial ecology is made for us cartophiles (map-lovers) who study the interdependency of biological systems where location and distance between things matters.

Alexa’s grandmother showing Alexa (a very young cartographer) how to color in the lines. Source: Susan K. circa 1994

In a broader context, geospatial ecology communicates our science to all of you. If I posted a bunch of statistical outputs in text or even table form, your eyes might glaze over…and so might mine. But, if I displayed that same underlying data and results on a beautiful map with color-coded symbology, a legend, a compass rose, and a scale bar, you might have this great “ah-ha!” moment. That is my goal. That is what geospatial ecology is to me. It’s a way to SHOW my science, rather than TELL it.

Would you like to see this over and over again…?

A VERY small glimpse into the enormous amount of data that went into this map. This screenshot gave me one point of temperature data for a single location for a single day…Source: Alexa K.

Or see this once…?

Map made in ArcGIS of Coastal common bottlenose dolphin sightings between 1981-1989 with a layer of average sea surface temperatures interpolated across those same years. A picture really is worth a thousand words…or at least a thousand data points…Source: Alexa K.

For many, maps are visually easy to interpret, allowing quick message communication. Yet, there are many different learning styles. From my personal story, I think it’s relatively obvious that I’m, at least partially, a visual learner. When I was in primary school, I would read the directions thoroughly, but only truly absorb the material once the teacher showed me an example. Set up an experiment? Sure, I’ll read the lab report, but I’m going to refer to the diagrams of the set-up constantly. To this day, I always ask for an example. Teach me a new game? Let’s play the first round and then I’ll pick it up. It’s how I learned to sail. My dad described every part of the sailboat in detail and all I heard was words. Then, my dad showed me how to sail, and it came naturally. It’s only as an adult that I know what “that blue line thingy” is called. Geospatial ecology is how I SEE my research. It makes sense to me. And, hopefully, it makes sense to some of you!

Alexa’s dad teaching her how to sail. (Source: Susan K. circa 2000)
Alexa’s first solo sailboat race in Coronado, San Diego, CA. Notice: Alexa’s dad pushing the bow off the dock and the look on Alexa’s face. (Source: Susan K. circa 2000)
Alexa mapping data using ArcGIS in the Oregon State University Library. (Source: Alexa K circa a few minutes prior to posting).

I strongly believe a meaningful career allows you to highlight your passions and personal strengths. For me, that means photography, all things nautical, the great outdoors, wildlife conservation, and maps/charts.  If I converted that into an equation, I think this is a likely result:

Photography + Nautical + Outdoors + Wildlife Conservation + Maps/Charts = Geospatial Ecology of Marine Megafauna

Or, better yet:

? + ⚓ + ? + ? + ? =  GEMM Lab

This lab was my solution all along. As part of my research on common bottlenose dolphins, I work on a small inflatable boat off the coast of California (nautical ✅, outdoors ✅), photograph their dorsal fin (photography ✅), and communicate my data using informative maps that will hopefully bring positive change to the marine environment (maps/charts ✅, wildlife conservation✅). Geospatial ecology allows me to participate in research that I deeply enjoy and hopefully, will make the world a little bit of a better place. Oh, and make maps.

Alexa in the field, putting all those years of sailing and chart-reading to use! (Source: Leila L.)

 

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.

Making a Splash

By: Cathryn Wood, Lawrence University ’17, summer REU in the GEMM Lab

Greetings from Port Orford! My name is Cathryn, and I am the fourth member of the GEMM Lab’s gray whale foraging ecology research team, which includes Florence, Kelli, and the other Catherine (don’t worry, I go by Cat). Nearly 5 weeks into field season, I am still completely amazed with my first West Coast experience and doing what I’ve always dreamt of: studying marine mammals. Coming from Michigan’s Upper Peninsula, this may seem slightly out of place, but my mom can attest; she read “Baby Beluga” to me every night when I was a toddler. Now a rising senior majoring in biology at Lawrence University, I’ve been focusing my coursework on aquatic and marine ecology to prepare for graduate school where I plan to specialize in marine science. Being part of this research is a very significant step for me into the field.

So how did I end up here, as part of this amazing project and dream, women-in-science team? I am interning through OSU’s Ocean Sciences REU program at the Hatfield Marine Science Center, where the GEMM Lab is located. REU stands for “Research Experience for Undergraduates ”, and is an NSF-funded research internship program found in numerous universities around the country. These internships allow undergrads to conduct independent research projects under the guidance of a faculty mentor at the program’s institution. I applied to several REUs this past winter, and was one of 12 undergrads accepted for the program at HMSC. Each of us is paired with different faculty members to work on various projects that cover a diverse range of topics in the marine sciences; everything from estuarine ecology, to bioacoustics. I was ecstatic to learn that I had been paired with Dr. Torres as my faculty mentor to work on Florence’s gray whale project, which had been my first choice during the application process.

My particular research this summer is going to complement Florence’s master’s thesis work by asking new questions regarding the foraging data. While her project focuses on the behavioral states of foraging whales, I will be looking at the whale tracks to see if there are patterns in their foraging behavior found at the individual level. Traditionally, ecological studies have accepted classical niche theory, treating all individuals within a population as ecological equivalents with the same niche width. Any variances present among individuals are often disregarded as having an insignificant consequence on the population dynamics as a whole, but this simplification can overlook the true complexity of that population . The presence of niche variation among conspecifics is known to occur in at least 93 species across a diverse array of taxa, so the concept of individual specialization, and how it can affect ecological processes is gaining recognition progressively in the field (Bolnick et al., 2003). My goal is to determine whether or not the gray whales in this study, and presumably others in the Pacific Coast Feeding Group (PCFG), exhibit individual specialization in their foraging strategies . There are many ways in which individuals can specialize in foraging, but I will be specifically determining if fine scale spatial patterns in the location of foraging bouts exists, regardless of time.

To address my question, I am using the whale tracking data from both 2015 and 2016, and learning to use some very important software in the spatial ecology world along the way through a method that Dr. Torres introduced to me. Starting in ArcGIS, I generate a kernel density layer of a raw track (Fig. 1 ), which describes the relative distribution of where the tracked whale spent time (Fig. 2 ). Next, using the isopleth function in the software Geospatial Modelling Environment, I generate a 50% density contour line that distinguishes where the whale spent at least 50% of its time during the track (Fig. 3 ). Under the assumption that foraging took place in these high density areas, we use these 50% contour lines to describe foraging bout locations. I now go back to ArcGIS to make centroids within each 50% line, which mark the exact foraging bout locations (Fig. 4 ).

Fig.1 Raw individual whale track.
Fig. 1 Raw individual whale track.
Fig. 2 Kernel Density map of whale track.
Fig. 2 Kernel Density map of whale track.
Fig. 3 50% isopleth contours of locations with highest foraging densities
Fig. 3 50% isopleth contours of locations with highest foraging densities
Fig. 4 Final centroids to signify foraging bouts
Fig. 4 Final centroids to signify foraging bouts

These centroids will be determined for every track by an individual whale, and then compared relative to foraging locations of all tracked whales to determine if the individual is foraging in different locations than the population. Then, the tracks of individuals who repeatedly visit the site at least three times will be compared with one another to determine if the repeat whales show spatial and/or temporal patterns in their foraging bout locations, and if specialization at a fine scale is occurring in this population. If you did not quite follow all those methods, no worries, it was a lot for me to take in at first too. I’ve finally gotten the hang of it though, and am grateful to now have these skills going into grad school.

Because I am interested in behavioral ecology and the concept of individuality in animal populations, I am extremely excited to see how this research plays out. Results could be very eye-opening into the fine scale foraging specialization of the PCFG sub-population because they already demonstrate diet specialization on mysid (as opposed to their counterparts in the Bering Sea who feed on benthic organisms) and large scale individual residency patterns along the Pacific Northwest (Newell, 2009; Calambokidis et al., 2012). Most significantly, understanding how individuals vary in their feeding strategies could have very important implications for future conservation measures for the whales, especially during this crucial foraging season where they replenish their energy reserves.  Management efforts geared for an “average population” of gray whales could ultimately be ineffective if in fact individuals vary from one another in their foraging strategies. Taking into account the ways in which variation occurs amongst individuals is therefore crucial knowledge for successful conservation approaches.

My project is unique from those of the other REUs because I am simultaneously in the midst of assisting in field season number two of Florence’s project. While most of the other interns are back at Hatfield spending their days in the lab and doing data analyses like a 9-5 job, I am with the team down in Port Orford for field season. This means we’re out doing research every dawn as weather allows. Though I may never have an early bird bone in my body, the sleepy mornings are totally worth it because ecology field work is my favorite part of research. To read more about our methods in the field, check out Florence’s post.

Since Catherine’s last update, we’ve had an eventful week. To our dismay, Downrigger Debacle 2.0 occurred. (To read about the first one, see Kelli’s post). This time it was not the line – our new line has been great. It was a little wire that connected the downrigger line to the pipe that the GoPro and TDR are connected to. It somehow snapped due to what I presume was stress from the currents.   Again, it was Catherine and I in the kayak, with a very successful morning on the water coming to a close when it happened. Again, I was in the bow, and she was in the stern deploying the equipment – very déjà vu. When she reeled in an equipment-less line, we at first didn’t know how to break it to Florence and Kelli who were up on the cliff that day. Eventually, Catherine radioed “Brace yourselves…” and we told them the bad news. Once again, they both were very level-headed, methodical, and un-blaming in the moments to follow. We put together the same rescue dive team as last time, and less than a week later, they set off on the mission using the GPS coordinates I had marked while in the kayak. Apparently, between the dredging taking place in the harbor and the phytoplankton bloom, visibility was only about 2 feet during the dive, but they still recovered the equipment, with nothing but baked goods and profuse thanks as payment. We are very grateful for another successful recovery, and are confident that our new attachment mechanism for the downrigger will not require a third rescue mission (Fig. 6-8). Losing the equipment twice now has taught us some very important things about field work. For one, no matter how sound you assume your equipment to be, it is necessary to inspect it for weak points frequently – especially when salt water and currents are in the picture. Perhaps even more importantly, we’ve gotten to practice our problem solving skills and see firsthand how necessary it is to act efficiently and calmly when something goes wrong. In ecological field research you have to be prepared for  anything.

Fig. 5 Original setup of GoPro and TDR.
Fig. 5 Original setup of GoPro and TDR.
Fig. 6 Photo taken after the wire that connected the pole to the downrigger line snapped.
Fig. 6 Photo taken after the wire that connected the pole to the downrigger line snapped.
Fig. 7 New mechanism for attaching the pole to the downrigger line.
Fig. 7 New mechanism for attaching the pole to the downrigger line.
Fig. 8 Equipment rescue team: Aaron Galloway and Taylor Eaton diving, Greg Ryder operating the boat, and Florence on board to direct the GPS location of where the equipment was lost.
Fig. 8 Equipment rescue team: Aaron Galloway and Taylor Eaton diving, Greg Ryder operating the boat, and Florence on board to direct the GPS location of where the equipment was lost.

In other news, unlike our slow-whale days during the first two weeks of the project, we have recently had whales to track nearly every day from the cliff! In fact, the same, small, most likely juvenile, whale pictured in Catherine’s last post has returned several times, and we’ve nicknamed her “Buttons” due to two distinguishing white spots on her tail peduncle near the fluke. Though we tend to refer to Buttons as “her”, we cannot actually tell what the sex is definitively…until now. Remember in Catherine’s post when she described how Buttons defecated a lot, and how our team if, given the opportunity, is supposed to collect the feces when we’re out in the kayak for Leila’s project?  Everything from hormone levels to reproductive status to, yes, sex, is held in that poop! Well, Miss (or Mr.) Buttons was in Tichenor Cove today, and to our delight, she performed well in the defecation department once again. Florence and I were on cliff duty tracking her and Kelli and Catherine were in Tichenor on the kayak when we first noticed the defecation.  I then radioed down to the kayak team to stop what they were doing and paddle quickly to go collect it before it sank (Fig. 9).  Even in these situations, it is important to stay beyond 100 yards of the animal, as required by the MMPA. Florence and I cheered them on and our ladies did indeed get the poop sample, without disturbing the whale (Fig. 10). It was a sight to behold.

Fig. 9 Kelli and Catherine on a mission.
Fig. 9 Kelli and Catherine on a mission.
Fig. 10 Kelli and Catherine collecting the feces.
Fig. 10 Kelli and Catherine collecting the feces.

We were able to track Buttons for the remainder of our time on the cliff, and were extremely content with the day’s work as we packed all the gear up later in the afternoon. Right before we were about to leave, however, Buttons had one more big treat for us. As we looked to the harbor before starting the trek back to the truck, we paused briefly after noticing a large, white splash in the middle of the harbor, not far from the dock. We paused for a second and thought “No, it can’t be, was that —?” and then we see it again and unanimously yelled “BREACH!” Buttons breached about five times on her way back to Tichenor Cove from where she had been foraging in Mill Rocks. It is rare to see a gray whale breach, so this was really special. Florence managed to capture one of the breaches on video:

At first I thought a big ole humpback had arrived, but nope, it was our Buttons! I am in awe of this little whale, and am forever-grateful to be in the presence of these kinds of moments. She’s definitely made her splash here in Port Orford. I think our team has started to as well.

 

Bolnick, D. I., Svanback, R., Fordyce, J. A., Yang, L. H., Davis, J. M., Hulsey, C. D., & Forrister, M. L. (2003). Ecology of Individuals: Incidence and Implications of Individual Specialization. The American Naturalist, 161(1), 28.

Calambokidis, J., Laake, J. L., & Klimek, A. (2012). Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1998-2010 (Vol. 2010).

Newell, C. (2009). Ecological Interrelationships Between Summer Resident Gray Whales (Eschrichtius robustus) and Their Prey, Mysid Shrimp (Holmesimysis sculpta and Neomysis rayi) along the Central Oregon Coast.