….aaaand we’re off! The blue whale team heads to New Zealand

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

Today we are flying to the other side of the world and boarding a 63-foot boat to study the largest animals ever to have inhabited this planet: blue whales (Balaenoptera musculus). Why do we study them, and how will we do it? Before I tell you, first let me say that no fieldwork is ever straightforward, and consequently no fieldwork lacks exciting learning opportunities. I have learned a lot about the logistics of an international field season in the past month, which I will share with you here!

The South Taranaki Bight, which lies between the north and south islands of New Zealand, is the study area for this survey.
Research vessel Star Keys will be our home for the month of February as we look for whales.

Unmanned aerial systems (UAS, a.k.a. “drones”) are becoming more prevalent in our field as a powerful and minimally invasive tool for studying marine mammals. Last year, our team was able to capture what we believe is the first aerial footage of nursing behavior in baleen whales, in addition to feeding and traveling behaviors. And beyond behavior, these aerial images contain morphological and physiological information about the whales such as how big they are, whether they are pregnant or lactating, and if they are in good health. I’ll start making a packing list for you to follow along with. So far it contains two drones and all of their battery supplies and chargers.

Aerial image of a blue whale mother and calf captured by a drone during the 2016 field season.

Perhaps you read my first GEMM Lab blog post, about identifying individual blue whales from photographs? Using these individual IDs, I plan to generate an abundance estimate for this blue whale population, as well as look at residency and movement patterns of individuals. Needless to say, we will be collecting photo-ID images this year as well! Add two large pelican cases with cameras and long lenses to the packing list.

Blue whale photo-ID image, showing the left and right sides of the same whale. I have identified 99 unique individuals so far, and look forward to adding to our catalog this year!

Now wouldn’t it be great to capture video of animal behavior in some way other than with the UAS? Maybe even from underwater? Add two GoPros and all of their associated paraphernalia to the mounting gear pile.

Now, bear with me. There is a wealth of physiological information contained in blue whale fecal matter. And when hormone analysis from fecal samples is paired with photogrammetry from UAS images, we can develop a valuable picture of individual and population-level health, stress, nutrition, and reproductive status. So, say we are able to scoop up lots of blue whale fecal samples – wouldn’t that be fantastic? Yes! Alright, add two nets, a multitude of jars, squirt bottles, and gloves to the gear list. And then we still need to bring them back to our lab here in Newport. How does that happen? Well, we need to filter out the sea water, transfer the samples to smaller tubes, and freeze them… in the field, on a moving vessel. Include beakers, funnels, spatulas, and centrifuge tubes on the list. Yes, we will be flying back with a Styrofoam cooler full of blue whale “poopsicles”. Of course, we need a cooler!

Alright, and now remember the biopsy sampling that took place last season? Collecting tissue samples allows us to assess the genetic structure of this population, their stable isotopic trophic feeding level, and hormone levels. Well, we are prepared to collect tissue samples once again! Remember to bring small tubes and scalpel blades for storing the samples, and to get ethanol when we arrive in Wellington.

An important piece in investigating the habitat of a marine predator is learning about the prey they are consuming. In the case of our blue whales, this prey is krill (Nyctiphanes australis). We study the prey layer with an echo sounder, which sends out high frequency pings that bounce off anything they come in contact with. From the strength of the signal that bounces back it is possible to tell what the composition of the prey layer is, and how dense. The Marine Mammal Institute here at OSU has an echo sounder, and with the help of colleagues and collaborators, positive attitudes, and perseverance, we successfully got the transducer to communicate with the receiver, and the receiver to communicate with the software, and the software to communicate with the GPS.  Add one large pelican case for the receiver. Can we fit the transducer in there as well? Hmmm, this is going to be heavy…

Blue whale team members and colleagues troubleshoot and test the Simrad EK60 echo sounder before packing it to take to New Zealand.

Now the daunting, ever-growing to-do lists have been checked off and re-written and changed and checked off again. The mountain of research gear has been evaluated and packed and unpacked and moved and re-evaluated and packed again. The countdown to our departure date has ended, and this evening Leigh, Todd, and I fly out of Portland and make our way to Wellington, New Zealand. To think that from here all will be smooth and flawless is naïve, but not being able to contain my excitement seems reasonable. Maybe it’s the lack of sleep, but more likely it’s the dreams coming true for a marine ecologist who loves nothing more than to be at sea with the wind in her face, looking for whales and creatively tackling fieldwork challenges.

In the midst of the flurry of preparations, it can be easy to lose sight of why we are doing this—why we are worrying ourselves over poopsicle transport and customs forms and endless pelican cases of valuable equipment for the purpose of spending several weeks on a vessel we haven’t yet set foot on when we can’t even guarantee that we’ll find whales at all. This area where we will work (Figure 1) is New Zealand’s most industrially active region, where endangered whales share the space with oil rigs, shipping vessels, and seismic survey vessels that have been active since October in search of more oil and gas reserves. It is a place where we have the opportunity to study how these majestic giants fit into this ecosystem, to learn what about this habitat is driving the presence of the whales and how they’re using the space relative to industry. It is an opportunity for me as a scientist to pursue questions in ecology—the field of study that I love. It is also an opportunity for me as a conservation advocate to find my voice on issues of industry presence, resource extraction, and conflicts over ocean spaces that extend far beyond one endangered species and one region of the world.

Fieldwork preparations have made clear to me once again the strength and importance of collaboration in science. Kim Bernard from OSU’s College of Earth, Ocean, and Atmospheric Sciences and Craig Hayslip from the Marine Mammal Institute’s Whale Telemetry Group spent half a day troubleshooting the echosounder with us. Western Work Boats has manufactured a pole mount for the echosounder transducer, and Kristin Hodge is joining us from Cornell University’s Bioacoustics Research Program to assist with data collection. Callum Lilley and Mike Ogle from the New Zealand Department of Conservation will join us in Wellington to collect the biopsy samples, and Rochelle Constantine and Scott Baker will facilitate the archiving and transport of the tissue samples back to Newport for analysis. Scientific colleagues at NIWA will collaborate on oceanographic aspects and conduct stable isotope analysis of tissue samples. We are also grateful to the indispensable logistical support from Kathy Minta and Minda Stiles in the OSU Marine Mammal Institute. And, of course we could not do any of this work without the generous funding support from The Aotearoa Foundation, The New Zealand Department of Conservation, Greenpeace Aotearoa New Zealand, OceanCare, The International Fund for Animal Welfare Oceanea Office, Kiwis Against Seabed Mining, the OSU Marine Mammal Institute, and the Thorpe Foundation. Our science is stronger when we pool our energy and expertise, and I am thrilled to be working with this great group of people.

Stay tuned, the next several blogs will be posted from the field by the New Zealand blue whale team!

Challenges of fecal hormone analyses (Round 2): finally in Seattle!

By Leila Lemos, Ph.D. Student, Department of Fisheries and Wildlife, OSU

In a previous blog of mine, you could read about the challenges I have been facing while I am learning to analyze the hormone content in fecal samples of gray whales (Eschrichtius robustus). New challenges appeared along the way over the last month, while I was doing my training at the Seattle Aquarium (Fig. 1).

Figure 1: View of the Seattle Aquarium.


My training lasted a week and I am truly grateful to the energy and time our collaborators Shawn Larson (research coordinator), Amy Green and Angela Smith (laboratory technicians) contributed. They accompanied me throughout my training to ensure I would be able to conduct hormonal analysis in the future, and to handle possible problems along the way.

The first step was weighing all of the fecal samples (Fig. 2A). Subsequently, the samples were transferred to appropriate glass tubes (Figs. 2B & 2C) for the next laboratorial step.

Figure 2: Analytical processes: (A) Sample weighing; (B) Transference of the sample to a glass tube; (C) Result from the performed steps.


The second conducted step was the hormone extraction. The extraction began with the addition of an organic solvent, called methanol (CH3OH), to the sample tubes (Fig. 3A & 3B). Hormones leach out from the samples and dissolve in the methanol, due to their affinity for this polar solvent.

Tubes were then placed on a plate shaker (Fig. 3C) for 30 minutes, which is used to mix the substances, in order extract the hormones from the fecal samples. The next step was to place the tubes in a centrifuge (Fig. 3D) for 20 minutes. The centrifuge uses the sedimentation principle, causing denser substances or particles to settle to the bottom of the tube, while the less dense substances rise to the top.

Figure 3: Analytical processes: (A) Methanol addition; (B) Sample + methanol; (C) Plate shaker; (D) Centrifuge.


After this process, the two different densities were separated: the high-density particles of the feces were in the bottom of the tube, while the methanol containing the extracted hormones was at the top. The top phase (methanol + hormones) was then pipetted into a different tube (Fig. 4A). The solvent was then evaporated, by using an air dryer apparatus (Fig. 4B), with only the hormones remaining in the tube.

The third performed step was dilution. A specific amount of water, measured in correlation with sample weight and to the amount of the methanol mixed with each sample, was added to each tube (Fig. 4C). Since the hormones were concentrated in the methanol, the readings would exceed the measurement limits of the equipment (plate reader). Thus, in order to prepare the extracts for the immunoassays, different dilutions were made.

Figure 4: Analytical processes: (A) Methanol transference; (B) Methanol drying; (C) Water addition.


The fourth and final step was to finally conduct the assays. Each assay kit is specific to the hormone to be analyzed with specified instructions for each kit. Since we were analyzing four different hormones (cortisol, testosterone, progesterone, and triiodothyronine – T3) we followed four different processes accordingly.

First, a table was filled with the identification numbers of the samples to be analyzed in that specific kit (Fig. 5A). The kit (Fig. 5B) includes the plate reader and several solutions that are used in the process to prepare standard curves, to initiate or stop chemical reactions, among other functions.

A standard curve, also known as calibration curve, is a common procedure in laboratory analysis for determining the concentration of an element in an unknown sample. The concentration of the element is determined by comparison with a set of standard samples of known concentration.

The plate contains several wells (Fig. 5C & 5D), which are filled with the samples and/or these other solutions. When the plate is ready, (Fig.5D) it is carried to the microplate reader that measures the intensity of the color of each of the wells. The intensity of the color is inversely proportional to the concentration of the hormone in both the standards and the samples.

Figure 5: (A) Filling the assay table with the samples to be analyzed; (B) Assay kit to be used; (C) Preparation of the plate; (D) Plate ready to be read.


Since this is the first fecal hormone analysis being performed in gray whales, a validation process of the method is required. Two different tests (parallelism and accuracy) were performed with a pool of three different samples. Parallelism tests that the assay is measuring the antigen (hormone) of interest and also identifies the most appropriate dilution factor to be used for the samples. Accuracy tests that the assay measurement of hormone concentration corresponds to the true concentration of the sample (Brown et al. 2005).

This validation process only needs to be done once. Once good parallelism and accuracy results are obtained, and we have identified the correct dilution factor and approximate concentration of the samples, the samples are ready to be analyzed. Below you can see examples of a good parallelism test (parallel displacement; Fig. 6) and bad parallelism tests (Fig. 7) that indicate no displacement, low concentration or non-parallel displacement; and a good accuracy test (Fig. 8).

Figure 6: Example of a good parallelism test. The dark blue line indicates the standard curve; the pink line indicates a good parallelism test, showing a parallel displacement; and the ratios in black indicate the dilution factors.
Source: Brown et al. (2005)


Figure 7: Examples of bad parallelism tests. The dark blue line indicates the standard curve; the light blue line is an example of no displacement; the pink line is an example of low concentration of the sample; and the green line is an example of non-parallel displacement.
Source: Brown et al. (2005)


Figure 8: Example of a good accuracy test while analyzing hormone levels of pregnanediol glucuronide (Pdg) in elephant urine. The graph shows good linearity (R2 of 0.9986) and would allow for accurate concentration calculations.
Source: Brown et al. (2005)


After the validation tests returned reliable results, the samples were also analyzed. However, many complications were encountered during the assay preparations and important lessons were learned that I know will allow this work to proceed more smoothly and quickly in the future. For instance, I now know to try to buy assay kits of the same brand, and to be extremely careful while reading the manual of the process to be performed with the assay kit. With practice over the coming years, my goal is to master these assay preparations.

Now, the next step will be to analyze all of the results obtained in these analyses and start linking the multiple variables we have from each individual, such as age, sex and body condition. The results of this analysis will lead to a better understanding of how reproductive and stress hormones vary in gray whales, and also link these hormone variations to nutritional status and noise events, one of my PhD research goals.


Cited Literature:

Brown J, Walker S and Steinman K. 2005. Endocrine manual for reproductive assessment of domestic and non-domestic species. Smithsonian’s National Zoological Park, Conservation and Research Center, Virginia 1-69.

Simple behavior classification of tracking data with residence in space and time

By Rachael Orben PhD., Postdoctoral Scholar in the Seabird Oceanography Lab and the Geospatial Ecology and Marine Megafauna Lab 

At 2pm, Jan 3, our paper entitled “Classification of Animal Movement Behavior through Residence in Space and Time” was published. At 14:03 I clicked on the link and there it was, type-set and crisp as a newly minted Open Access scientific contribution.

So, what is this paper about? It presents a simple – yes simple – method of identifying simple behaviors states in two-dimensional animal tracking data (think latitude and longitude). Since the paper is open access you can go find the methods there. Categorizing these “dots on a map” into behaviors allows us to ask questions about how often, why, when and where simple behaviors happen. These behaviors really are simple (hopefully the somewhat grating repetitiveness of the word ‘simple’ has driven that point home by now!). We are identifying three basic, but fundamental, states:

1) transit, characterized by fast somewhat straight line movement from a to b,

2) a sedentary state characterized by relatively more time spent in an area with little distance traveled (such as resting behavior) and

3) an active state characterized by lots of time spent in an area where an animal is also moving around a lot and covering a lot of ground.

This new method, that we termed Residence in Space and Time (RST), can assist the fast-growing, sophisticated, big-data generating, conservation-orientated field of animal movement ecology. One of the first hurdles is data exploration and visualization. Modern ecologists deploy tracking devices that collect location data remotely to understand animal distribution and behavior. But at first glance tracks (like the figure below) can look like spaghetti dinner. Identifying movement behaviors can help to us see patterns in the tangles.

24 GPS tracks of grey-headed albatross incubation foraging trips; tracked from Campbell Island, New Zealand.

So how might this method work? First lets start with a track. Below is a very short foraging trip from a thick-billed murre tracked with a GPS logger during chick rearing from St. Paul Island in Alaska (see Parades et al 2015).

A thick-billed murre (Uria lomvia), St. Paul Island, Alaska.

The track below has points every second and we can imagine the murre flying from the colony, landing on the water, and then diving (indicated by the lack of GPS position data when the bird dives below the water to forage). Then the bird flies back to the colony to feed its chick. This trip is roughly 14 minutes long.


So I can take this track and run RST to identify three behavior states. As color-coded below, the black points indicate transit, red indicates relatively stationary behavior, and blue indicates points where the bird was flying in a less direct manner than pure transit potentially circling around before landing and moving between dives. The high resolution of the GPS data really helps us to understand how this bird was moving. Such behavior information is easily conserved in a high-resolution track like this. Though in this case the bird did a lot of transiting and only exhibited different movement behaviors in the vicinity of the two dives.


Logging locations at 1 second intervals is a stretch for the battery life of these miniaturized GPS loggers (~15g), and more often than not we would like the loggers to last much longer than 14 mins. So instead of 1 second we typically have tracks with less frequent locations. To me this is akin to taking a 1 second track and then taking off my glasses and trying to see the same behaviors. Deciphering behavior states becomes a bit (or a lot) fuzzier. In the case of this murre track, when we down-sample the locations to every 10 seconds much of the resolution of this track is lost (see plot below). What happens when we run RST?


As you can see some of the behavior is maintained and some of it is a bit fuzzier.

A good rule of thumb is that if a behavior happens faster than the sampling interval the logger is recording at, then the behavior is not recorded. Seems simple, but it is an important consideration when programming loggers and designing animal movement studies. For murres these quick trips to forage for their chicks are easily lost even at a 5 minute sampling interval, which is often used in seabird tracking studies where the birds are at-sea for days. Often we work with such lower resolution location data and, instead of one trip from one bird, we have many trips from many individuals. RST allows a fast way to quickly and accurately identify simple behaviors in order to help with initial data exploration efforts and for answering more complex questions such as behavior specific habitat models.

So, if you have some tracking data – of birds, marine mammals, or your dog! – you can learn how RST works (basically by summing up time and distance covered within a circle). The R code, a short guide, and example dataset are available as links at the end of the paper.

Here is the spaghetti from above (really tracks of Grey-headed albatrosses) with the behavioral states labeled using RST:  93,481 points and this behavior classification took only 14 seconds to run!  albatrosstracks_rst

How we craft our messages

By: Erin Pickett, MSc

Communicating science has become more important than ever as major social and political issues, such as climate change, require increasing input from scientists. In a recent article published by the Proceedings of the National Academy of Science, a research group from the University of Cologne in Cologne, Germany, explores how social cognition influences our ability to market science.

This article, titled “Past-focused environmental comparisons promote pro-environmental outcomes for conservatives” focuses specifically on understanding why there is a political divide in the United States regarding the issue of climate change (Baldwin & Lammers 2016). While our research in the GEMM lab focuses on spatial ecology (rather than social science) I thought this article was worth sharing because of its insights about “framing science”. The conservation science that we conduct in the GEMM lab will not be effective if we cannot properly communicate our objectives and our findings to funders and stakeholders.

“Framing” is a term in psychology that describes how you craft a message based on your intended audience. It is important to note that use of the term framing (or marketing) science doesn’t imply misrepresentation of facts (Nisbet & Mooney 2007). Rather,“Frames organize central ideas, defining a controversy to resonate with core values and assumptions” (Nisbet & Mooney 2007). Baldwin & Lammers (2016) demonstrated that subtle differences in framing significantly affect how environmental messages are perceived. These authors investigated the effect of framing with regards to temporal comparisons, environmental attitudes and behavior.

The specific problem these authors address is the failure of climate change advocates to bridge the political divide between liberals and conservatives in the United States. The authors hypothesize that the temporal comparisons used in arguments for action on climate change explain the dichotomy between liberal and conservative views on this issue (which garners less support from conservatives).

The primary hypothesis guiding this study is that conservatives are more likely to favor a “past-focused” message rather than a “future-focused” message about climate change. The authors surmise that this framing bias is rooted in a conservative ideology that favors past traditions over a progressive future, which is more favored by liberals. Many pro-environmental arguments and appeals to address climate change are future focused, e.g. Balwin & Lemmers (2016) quote UN Secretary-General Ban-Ki Moon, speaking about climate change:

“…We need to find a new, sustainable path to the future we want”.

If temporal comparisons do elicit framing bias, then our framing of the issue of climate change, and possibly other environmental issues, could be more effective if presented to conservatives as past-focused messages.

The authors tested these hypotheses on participants in a series of online studies. You can find more details on methods in the papers supporting information found here. In the first three studies, the authors investigated the effect of temporal comparisons on pro-environmental beliefs. Study participants were asked to read messages, or view images, that addressed the issue of climate change by comparing the present to the past, or the present to the future. Following these comparisons, participants ranked their pro-environmental attitudes. Examples of these comparisons were statements such as, “Looking forward to our Nation’s future… there is increasing traffic on the road” (future-focused), and, “Looking back to our Nation’s past…there was less traffic on the road” (past-focused).

You can see examples of past and future-focused images below.

Images of past, present and future conditions (Baldwin & Leemers 2016-Supporting information Fig. S1)

The authors found significant evidence to support their hypothesis that presenting conservatives with past-focused messages is more effective in terms of promoting pro-environmental messages than presenting future-focused messages. Temporal comparisons did not affect the pro-environmental attitudes of liberals.

Liberals pro-environmental attitudes remain similar between conditions, while conservatives pro-environmental attitude is higher given a past-focused condition (Baldwin & Lammers 2016, Fig. 2)

The authors also investigated the temporal focus of environmental organizations and found that overall, environmental charities promote future-focused messages. Study participants were allotted small amounts of cash to donate to these charities, and conservatives gave more to past-focused charities than to future-focused charities. You can see examples of charities with differing temporal focuses below.

Examples of future and past-focused environmental charities (Balwin & Lammers 2016-supporting information Fig. S3)

In a final meta-analysis, these authors found that employing past-focused comparisons nearly made up for the difference between liberals and conservatives in terms of their pro-environmental attitudes. The implication of these findings is that we can improve the way we communicate about controversial issues such as climate change by subtly altering our arguments. For example, in one study that was cited by Baldwin & Lammers (2016), conservatives favored the words ‘purity’ and ‘sanctity’ over ‘harm’ and ‘care’ (Fienberg & Willer 2012). Based on these studies, an example of an effective message for a conservative audience would be, “It is important that we restore the Earth because it has become contaminated”.

These findings could be true for other environmental issues as well, and so it is worth thinking critically about how to craft messages about our scientific findings for our intended audiences. We need to carefully frame our messages whether we are writing grant proposals, peer-reviewed manuscripts, press releases, or posts intended for social media.

I originally discovered this paper after listening to a short radio interview that was conducted by CBC Radio, and if you are interested in this research I encourage you to check it out! You can following this link: how to convince a climate change skeptic.


Baldwin, M., & Lammers, J. (2016). Past-focused environmental comparisons promote proenvironmental outcomes for conservatives. Proceedings of the National Academy of Sciences113(52), 14953-14957.

Feinberg, M., & Willer, R. (2013). The moral roots of environmental attitudes. Psychological Science24(1), 56-62.

Nisbet, M. C., & Mooney, C. (2009). Framing science. Science316.