Tag: Data Analysis

Eyes from Space: Using Remote Sensing as a Tool to Study the Ecology of Blue Whales

By Christina Garvey, University of Maryland, GEMM Lab REU Intern

It is July 8th and it is my 4th week here in Hatfield as an REU intern for Dr. Leigh Torres. My name is Christina Garvey and this summer I am studying the spatial ecology of blue whales in the South Taranaki Bight, New Zealand. Coming from the east coast, Oregon has given me an experience of a lifetime – the rugged shorelines continue to take my breath away and watching sea lions in Yaquina Bay never gets old. However, working on my first research project has by far been the greatest opportunity and I have learned so much in so little time. When Dr. Torres asked me to contribute to this blog I was unsure of how I would write about my work thus far but I am excited to have the opportunity to share the knowledge I have gained with whoever reads this blog post.

The research project that I will be conducting this summer will use remotely sensed environmental data (information collected from satellites) to predict blue whale distribution in the South Taranaki Bight (STB), New Zealand. Those that have read previous blogs about this research may remember that the STB study area is created by a large indentation or “bight” on the southern end of the Northern Island. Based on multiple lines of evidence, Dr. Leigh Torres hypothesized the presence of an unrecognized blue whale foraging ground in the STB (Torres 2013). Dr. Torres and her team have since proved that blue whales frequent this region year-round; however, the STB is also very industrial making this space-use overlap a conservation concern (Barlow et al. 2018). The increasing presence of marine industrial activity in the STB is expected to put more pressure on blue whales in this region, whom are already vulnerable from the effects of past commercial whaling (Barlow et al. 2018) If you want to read more about blue whales in the STB check out previous blog posts that talk all about it!

Figure 1. A blue whale surfaces in front of a floating production storage and offloading vessel servicing the oil rigs in the South Taranaki Bight. Photo by D. Barlow.
Figure 2. South Taranaki Bight, New Zealand, our study site outlined by the red box. Kahurangi Point (black star) is the site of wind-driven upwelling system.

The possibility of the STB as an important foraging ground for a resident population of blue whales poses management concerns as New Zealand will have to balance industrial growth with the protection and conservation of a critically endangered species. As a result of strong public support, there are political plans to implement a marine protected area (MPA) in the STB for the blue whales. The purpose of our research is to provide scientific knowledge and recommendations that will assist the New Zealand government in the creation of an effective MPA.

In order to create an MPA that would help conserve the blue whale population in the STB, we need to gather a deeper understanding of the relationship between blue whales and this marine environment. One way to gain knowledge of the oceanographic and ecological processes of the ocean is through remote sensing by satellites, which provides accessible and easy to use environmental data. In our study we propose remote sensing as a tool that can be used by managers for the design of MPAs (through spatial and temporal boundaries). Satellite imagery can provide information on sea surface temperature (SST), SST anomaly, as well as net primary productivity (NPP) – which are all measurements that can help describe oceanographic upwelling, a phenomena that is believed to be correlated to the presence of blue whales in the STB region.

Figure 3. The stars of the show: blue whales. A photograph captured from the small boat of one animal fluking up to dive down as another whale surfaces close by. (Photo credit: L. Torres)

Past studies in the STB showed evidence of a large upwelling event that occurs off the coast of Kahurangi Point (Fig. 2), on the northwest tip of the South Island (Shirtcliffe et al. 1990). In order to study the relationship of this upwelling to the distribution of blue whales, I plan to extract remotely sensed data (SST, SST anomaly, & NPP) off the coast of Kahurangi and compare it to data gathered from a centrally located site within the STB, which is close to oil rigs and so is of management interest. I will first study how decreases in sea surface temperature at the site of upwelling (Kahurangi) are related to changes in sea surface temperature at this central site in the STB, while accounting for any time differences between each occurrence. I expect that this relationship will be influenced by the wind patterns, and that there will be changes based on the season. I also predict that drops in temperature will be strongly related to increases in primary productivity, since upwelling brings nutrients important for photosynthesis up to the surface. These dips in SST are also expected to be correlated to blue whale occurrence within the bight, since blue whale prey (krill) eat the phytoplankton produced by the productivity.

Figure 4. A blue whale lunges on an aggregation of krill. UAS piloted by Todd Chandler.

To test the relationships I determine between remotely sensed data at different locations in the STB, I plan to use blue whale observations from marine mammal observers during a seismic survey conducted in 2013, as well as sightings recorded from the 2014, 2016, and 2017 field studies led by Dr. Leigh Torres. By studying the statistical relationships between all of these variables I hope to prove that remote sensing can be used as a tool to study and understand blue whale distribution.

I am very excited about this research, especially because the end goal of creating an MPA really gives me purpose. I feel very lucky to be part of a project that could make a positive impact on the world, if only in just a little corner of New Zealand. In the mean time I’ll be here in Hatfield doing the best I can to help make that happen.

References: 

Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.

Shirtcliffe TGL, Moore MI, Cole AG, Viner AB, Baldwin R, Chapman B (1990) Dynamics of the Cape Farewell upwelling plume, New Zealand. New Zeal J Mar Freshw Res 24:555–568.

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.

Species distribution modeling: Part statistics, part philosophy, and there is no “right answer”

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

Just like that, I have wrapped up year 1 of my PhD in Wildlife Science. For my PhD, I am investigating the ecology and distribution of blue whales in New Zealand across multiple spatial and temporal scales. In a region where blue whales overlap with industrial activity, there is considerable interest from managers to be able to reliably forecast when and where blue whales are most likely to be in the area. In a series of five chapters and utilizing multiple different data sources (dedicated boat surveys, oceanographic data, acoustic recordings, remotely sensed environmental data, opportunistic blue whale sightings information), I will attempt to describe, quantify, and predict where blue whales are found in relation to their environment. Each chapter will evaluate the distribution of blue whales relative to the environment at different scales in space (ranging from 4 km to 25 km resolution) and time (ranging from daily to seasonal resolution). One overarching method I am using throughout my PhD is species distribution modeling. Having just completed my research review with my doctoral committee last week, I’ll share this aspect of my research proposal that I’ve particularly enjoyed reading, writing, and thinking about.

A pair of blue whales surfacing in the South Taranaki Bight region of New Zealand. Drone piloted by Todd Chandler during the 2017 field season.

Species distribution models (SDMs), which are sometimes referred to as habitat models or ecological niche models, are mathematical algorithms that combine observations of a species with environmental conditions at their observed locations, to gain ecological insight and predict spatial distributions of the species (Elith and Leathwick, 2009; Redfern et al., 2006). Any model is just one description of what is occurring in the natural world. Just as there are many ways to describe something with words and many languages to do so, there are many options for modeling frameworks and approaches, with stark and nuanced differences. My labmate and friend Solene Derville has equated the number of choices one has for SDMs to the cracker section in an American grocery store. When navigating all of these choices and considerations, it is important to remember that no model will ever be completely correct—it is our best attempt at describing a complex natural system—and as an analyst we need to do the best that we can with the data available to address the ecological questions at hand. As it turns out, the dividing line between quantitative analysis and philosophy is thin at times. What may seem at first like a purely objective, statistical endeavor requires careful consideration and fundamental decision-making on the part of the analyst.

Ecosystems are multifaceted, complex, and hierarchical. They are comprised of multiple physical and biological components, which operate at multiple scales across space and time. As Dr. Simon Levin stated in at 1989 MacArthur Award lecture on the topic of scale in ecology:

“A good model does not attempt to reproduce every detail of the biological system; the system itself suffices for that purpose as the most detailed model of itself. Rather, the objective of a model should be to ask how much detail can be ignored without producing results that contradict specific sets of observations, on particular scales of interest” (Levin, 1992).

The question of scale is central to ecology. As many biology students learn in their first introductory classes, parsimony is “The principle that the most acceptable explanation of an occurrence, phenomenon, or event is the simplest, involving the fewest entities, assumptions, or changes” (Oxford Dictionary). In other words, the best explanation is the simplest one. One challenge in ecological modeling, including SDMs, is to select spatial and temporal scales as coarse as possible for the most parsimonious—the most straightforward—model, while still being fine enough to capture relevant patterns. Another critical consideration is the scale of the question you are interested in answering. The scale of the analysis must match the scale at which you want to make inferences about the ecology of a species.

Similarly, the issue of complexity is central to distribution modeling. Overly simple models may not be able to adequately describe the relationship between species occurrence and the environment. In contrast, highly complex models may have very high explanatory power, but risk ascribing an ecological pattern to noise in the data (Merow et al., 2014), in other words, finding patterns that aren’t real. Furthermore, highly complex models tend to have poorer predictive capacity than simpler models (Merow et al., 2014). There is a trade-off between descriptive and predictive power in SDMs (Derville et al., 2018). Therefore, a key component in the SDM process is establishing the end goal of the model with respect to the region of interest, scale, explanatory power, predictive capacity, and in many cases management need.

Finally, any model is ultimately limited by the data available and the scale at which it was collected (Elith and Leathwick, 2009; Guillera-Arroita et al., 2015; Redfern et al., 2006). Prior knowledge of what environmental features are important to the species of interest is often limited at the time of the data collection effort, and data collection is constrained by when it is logistically feasible to sample. For example, we collect detailed oceanographic data during the summer months when it is practical to get out on the water, satellite imagery of sea surface temperature might be unavailable during times of cloud cover, and people are more likely to report blue whale sightings in areas where there is more human activity. Therefore, useful SDMs that address both ecological and management needs typically balance the scale of analysis and model complexity with the limitations of the data.

Managers and politicians within the New Zealand government are interested in a tool to predict when and where blue whales are most likely to be, based on sound ecological analysis. This is one of the end-goals of my PhD, but in the meantime, I am grappling with the appropriate scales of analysis, and attempting to balance questions of model complexity, explanatory power, and predictive capacity. There is no single, correct answer, and so my process is in part quantitative analysis, part philosophy, and all with the goal of increased ecological understanding and conservation of a species.

A blue whale breaks the surface. As I grapple with questions of model complexity and scale of analysis, I sometimes need a reminder that behind each data point is a blue whale, and what a privilege it is to study them. Photo by Leigh Torres.

References:

Derville, S., Torres, L. G., Iovan, C., and Garrigue, C. (2018). Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Divers. Distrib. 24, 1657–1673. doi:10.1111/ddi.12782.

Elith, J., and Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697. doi:10.1146/annurev.ecolsys.110308.120159.

Guillera-Arroita, G., Lahoz-Monfort, J. J., Elith, J., Gordon, A., Kujala, H., Lentini, P. E., et al. (2015). Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292. doi:10.1111/geb.12268.

Levin, S. A. (1992). The problem of pattern and scale. Ecology 73, 1943–1967.

Merow, C., Smith, M. J., Edwards, T. C., Guisan, A., Mcmahon, S. M., Normand, S., et al. (2014). What do we gain from simplicity versus complexity in species distribution models? Ecography (Cop.). 37, 1267–1281. doi:10.1111/ecog.00845.

Redfern, J. V., Ferguson, M. C., Becker, E. A., Hyrenbach, K. D., Good, C., Barlow, J., et al. (2006). Techniques for cetacean-habitat modeling. Mar. Ecol. Prog. Ser. 310, 271–295. doi:10.3354/meps310271.

The “demon whale-biter”, and why I am learning about an elusive little shark

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

There is an ancient Samoan legend that upon entry into a certain bay in Samoa, tuna would sacrifice pieces of their flesh to the community chief1. This was the explanation given for fish with circular shaped wounds where a plug of flesh had been removed. Similar round wounds are also observed on swordfish2, sharks3, and marine mammals including whales4,5, dolphins6, porpoises7, and pinnipeds8,9. In 1971, Everet C. Jones posited that the probable cause of these crater wounds was a small shark only 42-56 cm in length, Isistius brasiliensis1. The species was nicknamed “demon whale-biter” by Stewart Springer, who subsequently popularized the common name for the species, cookie cutter shark.

Figure 1. A yellowfin tuna with a circular bite, characteristic of a cookie cutter shark (Isistius brasiliensis). Photo: John Soward.

I am currently preparing a manuscript on blue whale skin condition. While this is only tangentially related to my doctoral research, it is an exciting side project that has encouraged me to stretch my comfort zone as an ecologist. This analysis of skin condition is part of a broader health assessment of blue whales in New Zealand, where we will be linking skin lesion severity with stress and reproductive hormone levels as well as body condition. Before I continue, I owe a major shout-out to Acacia Pepper, a senior undergraduate student at Oregon State University who has been working with me for nearly the past year through the Fisheries and Wildlife mentorship program. Acacia’s rigor in researching methodologies led us to develop a comprehensive protocol that can be applied widely to any cetacean photo-identification catalog. This method allows us to quantify prevalence and severity of different marking types in a standardized manner. Her passion for marine mammal science and interest in the subject matter is enough to excite this ecologist into fascination with wound morphology and blister concavity. Next thing you know, we are preparing a paper for publication together with P.I. Dr. Leigh Torres on a comprehensive skin condition assessment of blue whales that includes multiple markings and lesion types, but for the purpose of this blog post, I will share just a “bite-sized” piece of the story.

Figure 2. Jaws of a cookie cutter shark. Photo: George Burgess.

Back to the demon whale-biter. What do we know about cookie cutter sharks? Not a whole lot, it turns out. They are elusive, and are thought to live in deep (>1,000 m), offshore waters. They are considered to be both an ectoparasite and an ambush predator. Their distribution is tropical and sub-tropical. Much of what we know and assume about their distribution comes from the bite wounds they leave on their prey2.

In New Zealand where we study a unique population of blue whales10, the southernmost record of cookie cutter sharks is ~ 39⁰S11. We found that in our dataset of 148 photo-identified blue whales, 96% were affected by cookie cutter shark bites. Furthermore, 38% were categorized as having “severe” cookie cutter bite wounds or scars. The latitude of our blue whale sightings ranges from 29-48⁰S and blue whales are highly mobile, so any of the whales in our dataset could theoretically swim in and out of the known range of cookie cutter sharks. In our skin condition assessment, we also categorized cookie cutter bite “freshness” and phase of healing as follows:

We wanted to know if the freshness of cookie cutter shark bites was related in to the latitude at which the whales were photographed. Of the whales photographed north of 39⁰S (n=46), 76% had phase 1 or 2 cookie cutter shark bites present. In contrast, 57.1% of whales photographed south of 39⁰S (n=133) had phase 1 or 2 cookie cutter shark bites. It therefore appears that in New Zealand, the freshness of cookie cutter shark bites on blue whales is related to the latitude at which the whales were sighted, with fresher bites being more common at more northerly latitudes.

Figure 3. A whale with fresh cookie cutter shark bites, photographed in the Bay of Islands, latitude 35.164⁰S. Photo courtesy of Dr. Catherine Peters.
Figure 4. A whale with mostly healed cookie cutter shark bites, photographed off of Kaikoura, latitude 42.464⁰S. Photo courtesy of Jody Weir.

In the midst of a PhD on distribution modeling and habitat use of blue whales, I find myself reading about Samoan legends of tuna with missing flesh and descriptions of strange circular lesions from whaling records, and writing a paper about blue whale skin condition. Exciting “side projects” like this one emerge from rich datasets and good collaboration.

References

  1. Jones, E. C. Isistius brasiliensis, a squaloid shark, the probable cause of crater wounds on fishes and cetaceans. Fish. Bull. 69, 791–798 (1971).
  2. Papastamatiou, Y. P., Wetherbee, B. M., O’Sullivan, J., Goodmanlowe, G. D. & Lowe, C. G. Foraging ecology of Cookiecutter Sharks (Isistius brasiliensis) on pelagic fishes in Hawaii, inferred from prey bite wounds. Environ. Biol. Fishes 88, 361–368 (2010).
  3. Hoyos-Padilla, M., Papastamatiou, Y. P., O’Sullivan, J. & Lowe, C. G. Observation of an Attack by a Cookiecutter Shark ( Isistius brasiliensis ) on a White Shark ( Carcharodon carcharias ) . Pacific Sci. 67, 129–134 (2013).
  4. Mackintosh, N. A. & Wheeler, J. F. G. Southern blue and fin whales. Discov. Reports 1, 257–540 (1929).
  5. Best, P. B. & Photopoulou, T. Identifying the ‘demon whale-biter’: Patterns of scarring on large whales attributed to a cookie-cutter shark Isistius sp. PLoS One 11, (2016).
  6. Heithaus, M. R. Predator-prey and competitive interactions between sharks (order Selachii) and dolphins (suborder Odontoceti): A review. J. Zool. 253, 53–68 (2001).
  7. Van Utrecht, W. L. Wounds And Scars In The Skin Of The Common Porpoise, Phocaena Phocaena (L.). Mammalia 23, 100–122 (1959).
  8. Gallo‐Reynoso, J. ‐P & Figueroa‐Carranza, A. ‐L. A COOKIECUTTER SHARK WOUND ON A GUADALUPE FUR SEAL MALE. Mar. Mammal Sci. 8, 428–430 (1992).
  9. Le Boeuf, B. J., McCosker, J. E. & Hewitt, J. Crater wounds on northern elephant seals: the cookiecutter shark strikes again. Fish. Bull. 85, 387–392 (1987).
  10. Barlow, D. R. et al. Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res. 36, 27–40 (2018).
  11. Dwyer, S. L. & Visser, I. N. Cookie cutter shark (Isistius sp.) bites on cetaceans, with particular reference to killer whales (Orca) (Orcinus orca). Aquat. Mamm. 37, 111–138 (2011).

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

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

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

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

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

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

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

Combining multiple data streams for a comprehensive health assessment

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

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

Comparing body shape and morphology between species

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

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

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

References

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

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)

 

More than just whales: The importance of studying an ecosystem

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Understanding sea otter effects through complexity

By Dominique Kone, Masters Student in Marine Resource Management

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

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

Source: Belleza.

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

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

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

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

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

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

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

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

Source: Giancarlo Thomae.

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

References:

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

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

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

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

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

Science (or the lack thereof) in the Midst of a Government Shutdown

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

In what is the longest government shutdown in the history of the United States, many people are impacted. Speaking from a scientist’s point of view, I acknowledge the scientific community is one of many groups that is being majorly obstructed. Here at the GEMM Laboratory, all of us are feeling the frustrations of the federal government grinding to a halt in different ways. Although our research spans great distances—from Dawn’s work on New Zealand blue whales that utilizes environmental data managed by our federal government, to new projects that cannot get federal permit approvals to state data collection, to many of Leigh’s projects on the Oregon coast of the USA that are funded and collaborate with federal agencies—we all recognize that our science is affected by the shutdown. My research on common bottlenose dolphins is no exception; my academic funding is through the US Department of Defense, my collaborators are NOAA employees who contribute NOAA data; I use publicly-available data for additional variables that are government-maintained; and I am part of a federally-funded public university. Ironically, my previous blog post about the intersection of science and politics seems to have become even more relevant in the past few weeks.

Many graduate students like me are feeling the crunch as federal agencies close their doors and operations. Most people have seen the headlines that allude to such funding-related issues. However, it’s important to understand what the funding in question is actually doing. Whether we see it or not, the daily operations of the United States Federal government helps science progress on a multitude of levels.

Federal research in the United States is critical. Most governmental branches support research with the most well-known agencies for doing so being the National Science Foundation (NSF), the US Department of Agriculture (USDA), the National Oceanic and Atmospheric Administration (NOAA), and the National Aeronautics and Space Administration. There are 137 executive agencies in the USA (cei.org). On a finer scale, NSF alone receives approximately 40,000 scientific proposals each year (nsf.gov).

If I play a word association game and I am given the word “science”, my response would be “data”. Data—even absence data—informs science. The largest aggregate of metadata with open resources lives in the centralized website, data.gov, which is maintained by the federal government and is no longer accessible and directs you to this message:Here are a few more examples of science that has stopped in its track from lesser-known research entities operated by the federal government:

Currently, the National Weather Service (NWS) is unable to maintain or improve its advanced weather models. Therefore, in addition to those of us who include weather or climate aspects into our research, forecasters are having less and less information on which to base their weather predictions. Prior to the shutdown, scientists were changing the data format of the Global Forecast System (GFS)—the most advanced mathematical, computer-based weather modeling prediction system in the USA. Unfortunately, the GFS currently does not recognize much of the input data it is receiving. A model is only as good as its input data (as I am sure Dawn can tell you), and currently that means the GFS is very limited. Many NWS models are upgraded January-June to prepare for storm season later in the year. Therefore, there are long-term ramifications for the lack of weather research advancement in terms of global health and safety. (https://www.washingtonpost.com/weather/2019/01/07/national-weather-service-is-open-your-forecast-is-worse-because-shutdown/?noredirect=on&utm_term=.5d4c4c3c1f59)

An example of one output from the GFS model. (Source: weather.gov)

The Food and Drug Administration (FDA)—a federal agency of the Department of Health and Human Services—that is responsible for food safety, has reduced inspections. Because domestic meat and poultry are at the highest risk of contamination, their inspections continue, but by staff who are going without pay, according to the agency’s commissioner, Dr. Scott Gottlieb. Produce, dry foods, and other lower-risk consumables are being minimally-inspected, if at all.  Active research projects investigating food-borne illness that receive federal funding are at a standstill.  Is your stomach doing flips yet? (https://www.nytimes.com/2019/01/09/health/shutdown-fda-food-inspections.html?rref=collection%2Ftimestopic%2FFood%20and%20Drug%20Administration&action=click&contentCollection=timestopics&region=stream&module=stream_unit&version=latest&contentPlacement=2&pgtype=collection)

An FDA field inspector examines imported gingko nuts–a process that is likely not happening during the shutdown. (Source: FDA.gov)

The National Parks Service (NPS) recently made headlines with the post-shutdown acts of vandalism in the iconic Joshua Tree National Park. What you might not know is that the shutdown has also stopped a 40-year study that monitors how streams are recovering from acid rain. Scientists are barred from entering the park and conducting sampling efforts in remote streams of Shenandoah National Park, Virginia. (http://www.sciencemag.org/news/2019/01/us-government-shutdown-starts-take-bite-out-science)

A map of the sampling sites that have been monitored since the 1980s for the Shenandoah Watershed Study and Virginia Trout Stream Sensitivity Study that cannot be accessed because of the shutdown. (Source: swas.evsc.virginia.edu)

NASA’s Stratospheric Observatory for Infrared Astronomy (SOFIA), better known as the “flying telescope” has halted operations, which will require over a week to bring back online upon funding restoration. SOFIA usually soars into the stratosphere as a tool to study the solar system and collect data that ground-based telescopes cannot. (http://theconversation.com/science-gets-shut-down-right-along-with-the-federal-government-109690)

NASA’s Stratospheric Observatory for Infrared Astronomy (SOFIA) flies over the snowy Sierra Nevada mountains while the telescope gathers information. (Source: NASA/ Jim Ross).

It is important to remember that science happens outside of laboratories and field sites; it happens at meetings and conferences where collaborations with other great minds brainstorm and discover the best solutions to challenging questions. The shutdown has stopped most federal travel. The annual American Meteorological Society Meeting and American Astronomical Society meeting were two of the scientific conferences in the USA that attract federal employees and took place during the shutdown. Conferences like these are crucial opportunities with lasting impacts on science. Think of all the impressive science that could have sparked at those meetings. Instead, many sessions were cancelled, and most major agencies had zero representation (https://spacenews.com/ams-2019-overview/). Topics like lidar data applications—which are used in geospatial research, such as what the GEMM Laboratory uses in some its projects, could not be discussed. The cascade effects of the shutdown prove that science is interconnected and without advancement, everyone’s research suffers.

It should be noted, that early-career scientists are thought to be the most negatively impacted by this shutdown because of financial instability and job security—as well as casting a dark cloud on their futures in science: largely unknown if they can support themselves, their families, and their research. (https://eos.org/articles/federal-government-shutdown-stings-scientists-and-science). Graduate students, young professors, and new professionals are all in feeling the pressure. Our lives are based on our research. When the funds that cover our basic research requirements and human needs do not come through as promised, we naturally become stressed.

An adult and a juvenile common bottlenose dolphin, forage along the San Diego coastline in November 2018. (Source: Alexa Kownacki)

So, yes, funding—or the lack thereof—is hurting many of us. Federally-funded individuals are selling possessions to pay for rent, research projects are at a standstill, and people are at greater health and safety risks. But, also, science, with the hope for bettering the world and answering questions and using higher thinking, is going backwards. Every day without progress puts us two days behind. At first glance, you may not think that my research on bottlenose dolphins is imperative to you or that the implications of the shutdown on this project are important. But, consider this: my study aims to quantify contaminants in common bottlenose dolphins that either live in nearshore or offshore waters. Furthermore, I study the short-term and long-term impacts of contaminants and other health markers on dolphin hormone levels. The nearshore common bottlenose dolphin stocks inhabit the highly-populated coastlines that many of us utilize for fishing and recreation. Dolphins are mammals, that respond to stress and environmental hazards, in similar ways to humans. So, those blubber hormone levels and contamination results, might be more connected to your health and livelihood than at first glance. The fact that I cannot download data from ERDDAP, reach my collaborators, or even access my data (that starts in the early 1980s), does impact you. Nearly everyone’s research is connected to each other’s at some level, and that, in turn has lasting impacts on all people—scientists or not. As the shutdown persists, I continue to question how to work through these research hurdles. If anything, it has been a learning experience that I hope will end soon for many reasons—one being: for science.

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.

Why Feeling Stupid is Great: How stupidity fuels scientific progress and discovery

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

It all started with a paper. On Halloween, I sat at my desk, searching for papers that could answer my questions about bottlenose dolphin metabolism and realized I had forgotten to check my email earlier. In my inbox, there was a new message with an attachment from Dr. Leigh Torres to the GEMM Lab members, saying this was a “must-read” article. The suggested paper was Martin A. Schwartz’s 2008 essay, “The importance of stupidity in scientific research”, published in the Journal of Cell Science, highlighted universal themes across science. In a single, powerful page, Schwartz captured my feelings—and those of many scientists: the feeling of being stupid.

For the next few minutes, I stood at the printer and absorbed the article, while commenting out loud, “YES!”, “So true!”, and “This person can see into my soul”. Meanwhile, colleagues entered my office to see me, dressed in my Halloween costume—as “Amazon’s Alexa”, talking aloud to myself. Coincidently, I was feeling pretty stupid at that moment after just returning from a weekly meeting, where everyone asked me questions that I clearly did not have the answers to (all because of my costume). This paper seemed too relevant; the timing was uncanny. In the past few weeks, I have been writing my PhD research proposal —a requirement for our department— and my goodness, have I felt stupid. The proposal outlines my dissertation objectives, puts my work into context, and provides background research on common bottlenose dolphin health. There is so much to know that I don’t know!

Alexa dressed as “Amazon Alexa” on Halloween at her office in San Diego, CA.

When I read Schwartz’s 2008 paper, there were a few takeaway messages that stood out:

  1. People take different paths. One path is not necessarily right nor wrong. Simply, different. I compared that to how I split my time between OSU and San Diego, CA. Spending half of the year away from my lab and my department is incredibly challenging; I constantly feel behind and I miss the support that physically being with other students provides. However, I recognize the opportunities I have in San Diego where I work directly with collaborators who teach and challenge me in new ways that bring new skills and perspective.

    Image result for different ways
    (Image source: St. Albert’s Place)
  2. Feeling stupid is not bad. It can be a good feeling—or at least we should treat it as being a positive thing. It shows we have more to learn. It means that we have not reached our maximum potential for learning (who ever does?). While writing my proposal I realized just how little I know about ecotoxicology, chemistry, and statistics. I re-read papers that are critical to understanding my own research, like “Nontargeted biomonitoring of halogenated organic compounds in two ecotypes of bottlenose dolphins (Tursiops truncatus) from the Southern California bight” (2014) by Shaul et al. and “Bottlenose dolphins as indicators of persistent organic pollutants in the western north Atlantic ocean and northern gulf of Mexico” (2011) by Kucklick et al. These articles took me down what I thought were wormholes that ended up being important rivers of information. Because I recognized my knowledge gap, I can now articulate the purpose and methods of analysis for specific compounds that I will conduct using blubber samples of common bottlenose dolphins

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    Image source: memegenerator.net
  3. Drawing upon experts—albeit intimidating—is beneficial for scientific consulting as well as for our mental health; no one person knows everything. That statement can bring us together because when people work together, everyone benefits. I am also reminded that we are our own harshest critics; sometimes our colleagues are the best champions of our own successes. It is also why historical articles are foundational. In the hunt for the newest technology and the latest and greatest in research, it is important to acknowledge the basis for discoveries. My data begins in 1981, when the first of many researchers began surveying the California coastline for common bottlenose dolphins. Geographic information systems (GIS) were different back then. The data requires conversions and investigative work. I had to learn how the data were collected and how to interpret that information. Therefore, it should be no surprise that I cite literature from the 1970s, such as “Results of attempts to tag Atlantic Bottlenose dolphins, (Tursiops truncatus)” by Irvine and Wells. Although published in 1972, the questions the authors tried to answer are very similar to what I am looking at now: how are site fidelity and home ranges impacted by natural and anthropogenic processes. While Irvine and Wells used large bolt tags to identify individuals, my project utilizes much less invasive techniques (photo-identification and blubber biopsies) to track animals, their health, and their exposures to contaminants.

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  4. Struggling is part of the solution. Science is about discovery and without the feeling of stupidity, discovery would not be possible. Feeling stupid is the first step in the discovery process: the spark that fuels wanting to explore the unknown. Feeling stupid can lead to the feeling of accomplishment when we find answers to those very questions that made us feel stupid. Part of being a student and a scientist is identifying those weaknesses and not letting them stop me. Pausing, reflecting, course correcting, and researching are all productive in the end, but stopping is not. Coursework is the easy part of a PhD. The hard part is constantly diving deeper into the great unknown that is research. The great unknown is simultaneously alluring and frightening. Still, it must be faced head on. Schwartz describes “productive stupidity [as] being ignorant by choice.” I picture this as essentially blindly walking into the future with confidence. Although a bit of an oxymoron, it resonates the importance of perseverance and conviction in the midst of uncertainty.

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Now I think back to my childhood when stupid was one of the forbidden “s-words” and I question whether society had it all wrong. Maybe we should teach children to acknowledge ignorance and pursue the unknown. Stupid is a feeling, not a character flaw. Stupidity is important in science and in life. Fascination and emotional desires to discover new things are healthy. Next time you feel stupid, try running with it, because more often than not, you will learn something.

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Alexa teaching about marine mammals to students ages 2-6 and learning from educators about new ways to engage young students. San Diego, CA in 2016. (Photo source: Lori Lowder)