Geospatial Ecology of Marine Megafauna Laboratory

I love it when a plan comes together

By Dr. Leigh Torres

GEMM Lab

After four full-on days at sea covering 873 nautical miles, we are back in port as the winds begin to howl again and I now sip my coffee with a much appreciated still horizon. Our dedicated team worked the available weather windows hard and it paid off with more great absence data and excellent presence data too: blue whales, killer whales, common dolphins, and happily swimming pilot whales not headed to nearby Farewell Spit where a sad, massive stranding has occurred. It has been an exhausting, exhilarating, frustrating, exciting, and fulfilling time. As I reflect on all this work and reward, I can’t help but feel gratified for our persistent and focused planning that made it happen successfully. So, as we clean-up, organize data, process samples, and sit in port for a few days I would like to share some of our highlights over the past four days. I hope you enjoy them as much as we did.

The team in action on the RV Star Keys. Callum Lilley (DOC) on the bow waiting for a biopsy opportunity, Dawn Barlow (OSU) on the radio communicating with the small boat, Kristin Hodge (Cornell) taking photos of whales, Captain James Dalzell (Western Work Boats) on the helm, and Chief Engineer Spock (Western Work Boats) keeping his eyes peeled for a blow. (Photo credit: L. Torres)

In the small boat off looking for whales in a lovely flat, calm sea with an oil rig in the background. (Photo credit: D. Barlow)

Small boat action with Todd Chandler (OSU) at the helm, Leigh Torres (OSU) on the camera getting photo-id images, and Callum Lilley (DOC) taking the biopsy shot, and the dart is visible flying toward the whale in the black circle. (Photo credit: D. Barlow)

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)

Collecting oceanographic data: Spock and Jason (Western Work Boats) deploy the CTD from the Star Keys. The CTD is an instrument that measures temperature, salinity, fluorescence and depth continuously as it descends to the bottom and back up again. (Photo credit: L. Torres)

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

The small boat returns to the Star Keys loaded with data and samples, including a large fecal sample in the net: The pooper scooper Leigh Torres (OSU), the biopsy rifle expert Callum Lilley (DOC), and the boat operator Todd Chandler (OSU). (Photo credit: D. Barlow)

Drone operator and videographer, Todd Chandler (OSU) under the towel (crucial piece of gear) to minimize glare on the screen as he locates and records blue whales. (Photo credit: K. Hodge)

A still shot captured from the drone footage of two adult blue whales surfacing in close proximity. (Photo credit: T. Chandler)

The team in action looking for blue whales in ideal survey conditions with Mt. Taranaki in the background. Todd Chandler (OSU) enters survey data while Dawn Barlow (OSU) spies for whale blows. (Photo credit: L. Torres)

A late evening at-sea after a big day sees Callum Lilley (DOC) processing a blue whale biopsy sample for transport, storage and analysis. (Photo credit: K. Hodge)

And we can’t forget why so many have put time, money and effort into this project: These blue whales are feeding and living within a space exploited by humans for multiple purposes, so we must ensure minimal impacts to these whales and their sustained health. (Photo credit: D. Barlow)

The worst summer ever!

By Dr. Leigh Torres

Geospatial Ecology of Marine Megafauna Lab

“This is the worst summer ever in New Zealand.” During our four days of prep in Wellington before heading off on our vessel, almost all my friends and colleagues I spoke to said this statement (often with added emphasis). It’s been cold and windy here all summer long, and when the weather has cleared it has brought only brief respite. These comments don’t bode well for our blue whale survey dependent on calm survey conditions, but February is typically the prime month for good weather in New Zealand so I’m holding out hope. And this unpredictable weather is the common denominator of all field work. Despite months (years?) of preparation, with minute attention to all sort of details (e.g., poop net handle length, bag size limits, length of deployment lines), one of the most important factors to success is something we have absolutely no control over: the weather.

After just one day on the water, I can see that the oceanographic conditions this year are nothing like the hot-water El Niño conditions we experienced last summer. Surface water temperatures today ranged between 12.8 and 13.6 ⁰C. These temps are 10 degrees (Celsius) cooler than the 22 ⁰C water we often surveyed last summer. 10 degrees! Additionally, the current windy conditions have stirred up the upper portion of the ocean water column causing the productive mixed layer to be much deeper (therefore larger) than last year. While Kiwis may complain about the ‘terrible’ weather this summer, the resulting cold and productive oceanographic conditions are likely preferable for the whales. But where are the whales and can we find them with all this wind?

Today we had a pocket of calm conditions so our dedicated research team and crew hit it with enthusiasm, and collected a whole lot of great absence data. “Absence data?” you may ask. Absence data is all the information about where the whales are not, and is just as important as presence data (information about where the whales are) because it’s the comparison between the two sets of data (Presence vs Absence) that allows us to describe an animal’s “habitat use patterns”. Today we surveyed a small portion of the South Taranaki Bight for blue whales for about 6 hours, but the only blue animals we saw were little blue penguins and a blue shark (plus fur seals, dolphins, albatrosses, shearwaters, gannets, prions, kahawai, and saury). But during this survey effort we collected a lot of synoptic environmental data to describe these habitats, including continuous depth and temperature data along our track, nine CTD water column profiles of temperature, salinity and florescence (productivity) from the surface to the seafloor, and continuous prey (zooplankton) availability data with our transducer (echosounder).

So, now that we have absence data, we need presence data. But, the winds are howling again and are predicted to continue for the next few days. As we hunker down in a beautiful protected cove I know the blue whales continue to search this region for dense food patches, unencumbered by human-perceived obstacles of high wind and swell. So, while my Kiwi friends are right – this summer is not like previous years – I also know that it is the effects of these dynamic weather patterns that we have come so far, and worked so hard, to study. Even as my patience wears thin and my frustrations mount, I will continue to wait to pounce on the right weather window to collect our needed presence data (and more absence data too, I’m sure).

Our research team collecting absence data aboard the RV Star Keys:

….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).

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, let’s 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).

p1080393 — 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). I keep an updated version of the R code, a short guide, an example dataset on a GitHub repository: https://github.com/raorben/RST.

Here is the spaghetti from above (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!

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.

References:

Baldwin, M., & Lammers, J. (2016). Past-focused environmental comparisons promote proenvironmental outcomes for conservatives. Proceedings of the National Academy of Sciences, 113(52), 14953-14957.

Feinberg, M., & Willer, R. (2013). The moral roots of environmental attitudes. Psychological Science, 24(1), 56-62.

Nisbet, M. C., & Mooney, C. (2009). Framing science. Science, 316.

GEMM Lab 2016: A Year in the Life

By Dawn Barlow, MSc Student, Department of Fisheries and Wildlife, Oregon State University

The year is rapidly coming to a close, and what a busy year it has been in the Geospatial Ecology of Marine Megafauna Lab! In 2016, our members have traveled to six continents for work (all seven if we can carry Rachael’s South African conference over from the end of 2015…), led field seasons in polar, temperate, and tropical waters, presented at international conferences, processed and analyzed data, and published results. Now winter finds us holed up in our offices in Newport, and various projects are ramping up and winding down. With all of the recent turmoil 2016 has brought, it is a nice to reflect on the good work that was accomplished over the last 12 months. In writing this, I am reminded of how grateful I am to work with this talented group of people!

The year started with a flurry of field activity from our southern hemisphere projects! Erin spent her second season on the Antarctic peninsula, where she contributed to the Palmer Station Long Term Ecological Research Project.

Erin collecting a crabeater seal scat sample. — Erin in action collecting a crabeater seal scat sample along the West Antarctic Peninsula.

Aerial image of the research vessel and a pair of blue whales during the 2016 New Zealand survey.

The New Zealand blue whale project launched a comprehensive field effort in January and February, and it was a fruitful season to say the least. The team deployed hydrophones, collected tissue biopsy and fecal samples, and observed whales feeding, racing and nursing. The data collected by the blue whale team is currently being analyzed to aid in conservation efforts of these endangered animals living in the constant presence of the oil and gas industry.

Midway atoll is home to one of the largest albatross colony in the world, and Rachael visited during the winter breeding season. In addition to deploying tracking devices to study flight heights and potential conflict with wind energy development, she became acutely aware of the hazards facing these birds, including egg predation by mice and the consumption of plastic debris.

Laysan albatross equipped with a GPS data logger.

Fledgling from last year with a stomach full of plastic.

Early summertime brought red-legged kittiwakes to the remote Pribilof Islands in Alaska to nest, and Rachael met them there to study their physiology and behavior.

Rachael with a noosepole on St. George Island, Alaska

Solene with Dr. Claire Garrigue during fieldwork at the Chesterfield Reefs, New Caledonia.

As the weather warmed for us in the northern hemisphere, Solene spent the austral winter with the humpback whales on their breeding grounds in New Caledonia. Her team traveled to the Chesterfield Reefs, where they collected tissue biopsy samples and photo-IDs, and recorded the whale’s songs. But Solene studies far more than just these whales! She is thoroughly examining every piece of environmental, physical, and oceanographic data she can get her hands on in an effort to build a thorough model of humpback whale distribution and habitat use.

A humpback whale in New Caledonia's South Lagoon. — A humpback whale in New Caledonia’s South Lagoon.

Summertime came to Oregon, and the gray whales returned to these coastal waters. Leigh, Leila, and Todd launched into fieldwork on the gray whale stress physiology project. The poop-scooping, drone-flying team has gotten a fair bit of press recently, follow this link to listen to more!

The overhead drone captures a pair of gray whales surfacing between kelp beds off Cape Blanco, Oregon, with the research vessel nearby. Take under NOAA/NMFS permit #16111 given to John Calambokidis.

And while Leigh, Leila, and Todd followed the grays from the water, Florence and her team watched them from shore in Port Orford, tracking their movement and behavior. In an effort to gain a better understanding of the foraging ecology of these whales, Florence and crew also sampled their mysid prey from a trusty research kayak.

Florence and the summer 2016 gray whale field team.

DSCF0758 — Kelli Iddings sampling mysid near Port Orford.

With the influx of gray whales came an influx of new and visiting GEMM Lab members, as Florence’s team of interns joined for the summer season. I was lucky enough to join this group as the lab’s newest graduate student!

All summer 2016 GEMM Lab members. — All of the summer 2016 GEMM Lab members.

Our members have presented their work to audiences far and wide. This summer Leigh, Amanda, and Florence attended the International Marine Conservation Congress, and Amanda was awarded runner-up for the best student presentation award! Erin traveled to Malaysia for the Scientific Convention on Antarctic Research, and Rachael and Leigh presented at the International Albatross and Petrel Conference in Barcelona. With assistance from Florence and Amanda, Leigh led an offshore expedition on OSU’s research vessel R/V Oceanus to teach high school students and teachers about the marine environment.

Science Party musters in the dry lab for safety debrief aboard R/V Oceanus. — Science Party musters in the dry lab for safety debrief aboard *R/V Oceanus*.

Courtney fledged from the GEMM Lab nest before 2016 began, but the work she did while here was published in Marine Mammal Science this year. Congrats Courtney! And speaking of publications, additional congratulations to Solene for her publication in Marine Ecology Progress Series, Rachael for her four publications this year in PLOS ONE, Marine Ecology Progress Series, Marine Ornithology, and the Journal of Experimental Biology, and Leigh for her five publications this year in Polar Biology, Diversity and Distributions, Marine Ecology Progress Series, and Marine Mammal Science!

Wintertime in Newport has us tucked away indoors with our computers, cranking through analyses and writing, and dreaming about boats, islands, seabirds, and whales… Solene visited from the South Pacific this fall, and graced us with her presence and her coding expertise. It is a wonderful thing to have labmates to share ideas, frustrations, and accomplishments with.

No heat in the lab can't stop us from solving a coding problem together on a wintery evening! — Solving a coding problem together on a wintery evening.

As the year comes to a close, we have two newly-minted Masters of Science! Congratulations to Amanda and Erin on successfully defending their theses, and stay tuned for their upcoming publications!

Amanda's post-defense celebration! — Amanda’s post-defense celebration!

Erin's post-defense celebration! — Erin’s post-defense celebration!

We are looking forward to what 2017 brings for this team of marine megafauna enthusiasts. Happy holidays from the GEMM Lab!

Happy GEMM Lab members. — Happy GEMM Lab members, enjoying one another’s company and playing Evolution.

Feed from the scientific network: the digital library of a millennial student

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

If you are a follower of our blog, you may have noticed that bioinformatics and statistics hold a very important role in the everyday life of the GEMM Lab. As good-old field observations remain essential to the study of animal behaviour and ecosystems, the ecology field has greatly benefited from advances in information technologies. In fact, data analysis is now a discipline in itself, as innovative solutions must continuously be developed to cope with the challenges of ever increasing dataset size and complexity.

So how does a poor biology student find her/his way in this digital and mathematical world? Most ecology departments will provide classes to learn the basics of statistical modelling and data analysis, but there is only so much you can learn through formal education. In practice, we ultimately always run into a problem, an exception that we have never heard of, and we have to figure it out on our own. As my initial training was in fundamental biology, self-teaching of other disciplines (statistics and bioinformatics) has taken a lot of my time as a Master’s student and now as a PhD student. This has made me feel lonely and a bit lost at times when I run into challenges that always seemed too big for me. But in the end, there is nothing more rewarding then solving problems by yourself after long hours of mind-scrambling.

Oh, sorry, did I say by myself? Nothing could be more wrong and more true at the same time! Because the place where I find all the answers to my questions, is in fact born from the contribution of thousands of scientists, which, despite not actually knowing each other, all work together to develop innovative solutions to modern world scientific challenges. The internet scientific network has been my best colleague over these past years and here I would like to share my enthusiasm for some of its best features that have helped me in my research.

If you look at my Firefox toolbar you will find two types of websites: let’s call them the “practical” and the “reflectional”.

The practical websites:

These are the websites I consult if I have a specific and practical question. Many forums exist where people exchange their experiences solving a great variety of problems. But sometimes conversations get lost in never-ending exchanges of opinions, some of which are not always scientifically well-founded. On the contrary, the StackExchange platform launched in 2009 has a strict policy on how questions should be asked (as precise and focused as possible) and should be answered (in an objective, opinion-free way). This makes it a very powerful tool to find quick and practical solutions to your everyday problems. This platform includes 136 different websites, each dedicated to a different topic. In my field, I mostly use: CrossValidated for statistical issues (e.g., Why does including latitude and longitude in a GAM account for spatial autocorrelation?) and StackOverflow for programming (e.g., plotting pie graphs on map in ggplot).

The latter will usually provide you with codes in the programming language of your choice (R, python, java, sql, etc.). Interestingly, even with more queries regarding Python to StackOverflow in 2015, R was the fastest-growing language between 2013 and 2015 on this same platform. If you haven’t decided on the language you want to “speak” yet, check out this fun infographic. But always remember that these tools keep evolving…

Academia can also be useful for questions regarding publications. For instance: How to reference multiple authors of a chapter from a book [APA]? Why might a journal editor reject a submission, but suggest submission to a sister journal? Or, h ow to best kill a manuscript as a peer reviewer?

And finally, if you’ve always wondered, “W hy don’t we remove door handles and let doors open both ways (inwards, outwards)?”, you’ll be pleased to know that other out-of-the-box-thinking people are sharing their opinion on the web…

Coming back to serious matters, it is important to recognize that you need the right key-word to access this gold-mine of website knowledge and sharing. The accuracy of your search answer will only be proportional to the quality of your question. In R for instance, if you keep googling “table” instead of “dataframe”, “list” instead of “vector”, or “size” instead of “dimensions”, you will likely get quickly drowned in the google-limbo. One way to be more efficient at your search strategy is to make sure you know your basics. Most of the programming languages used in ecology (e.g., R, Python, Matlab) share a similar vocabulary and structure, but before you start to run all sorts of crazy statistical analysis it is important to know what types of objects you are working with and how you want to format them. In R, I have found Hadley Wickham’s book, Advanced R, particularly useful to understand what happens back-stage.

Another good reference in the spatial ecology field is ZevRoss “Technical Tidbits From Spatial Analysis & Data Science”. This website is a particularly up-to-date blog for data processing and visualization in R.

More generally, I regularly check R-bloggers or simply the Comprehensive R Archive Network. A note on the latter: I know it doesn’t look pretty and the reference manuals for R packages are rather intimidating but it is still the number one reference to check when encountering a problem with a given function. Some authors make a special effort to write more user-friendly tutorials to their packages. Check for those by looking at the CRAN page of a given package, in the “downloads” section, “vignettes” subsection (e.g., for the adehabitatLT package vignette).

The reflectional websites:

The web is also an amazing media to reflect on our scientific practices, learn about current ecological theories, and acquire general knowledge across disciplines. In the scientific network, many blogs and forums exist where scientists can converse and debate ideas without the pressure of publication requirements. As a student trying to find my way in the great world of statistical modelling, I find these discussions and blogposts most useful to put my methodological choices in perspective and progressively build myself an opinion (still rather vague I’ll admit). Some of my most recent findings are: Dynamic Ecology Multa novit vulpes and From the bottom of the heap, the musings of a geographer. I am sure each of you has your own “rock star of the web”, so please share your favorite sites with us in the comments below.

Science not longer needs to wait for publication to be shared between peers and with the general public. The web offers us a new space to communicate, not only on that small part of our work that led to positive results, but also our negative results, frustrations and failures, which can at times be as informative and useful to the scientific community than our successes. So, wherever you stand, tell us about your ideas, and tell us about the challenges you have encountered, where you failed and where you succeeded. Because, this is what ecology is all about. Sharing knowledge across borders and cultures to understand the planet we live on and together take better care of it.

Reflecting on the graduate school experience

By: Amanda Holdman, MS student, Geospatial Ecology and Marine Megafauna Lab & Oregon State Research Collective for Applied Acoustics, MMI

This Thanksgiving I had a little something extra to be thankful for; two and a half weeks ago I successfully defended my master’s thesis, “Spatio-temporal patterns and ecological drivers of harbor porpoise off the central Oregon Coast”. It’s a good thing too because I think it was starting to turn me into a harbor porpoise. The last month, I was solitary, constantly eating, and I think I was starting to sleep with one hemisphere of my brain, while the other kept working.

In the weeks leading up to the submission of my thesis, I daydreamed about my life on the ‘other side’. As a means of pushing myself over the final hurdle I envisioned what it would be like to be free of a thesis, to reclaim my weekends, and how relieved I would feel to hand over the culmination of two and a half years of work – and at last here I am: on the other side, well almost.

The first week after my defense was just about as busy as the weeks leading up to my defense. I spent my time filing paperwork and moving things, packing up my office and house to head back to my home state of Indiana for the holidays, and tying up loose ends in Newport. For the past couple of weeks, I have been finishing up revisions on my thesis and formatting my work for publication, all while starting to look for a new job. After defending my masters I found time to “actually breathe” – I’m still as busy as always but now with a more consistent sleep schedule. The shift from all-research-and-writing-all-the-time has given me time to reflect a bit on what I’ve gained from the graduate school experience and what I know I still want or need to learn from it. Graduate school has supplied me with a tool box of skills that I didn’t realize I was acquiring day to day. Now, however, looking back over the years I realize how much I’ve grown as both a scholar and a person and in more ways than just learning how to craft scientific tweets in less than 140 characters (this really does take some skill).

Perseverance and Diligence

One big thing I learned from graduate school was how to transition from “panic” to “problem solving”. There were endless days of back breaking work that I had nothing to show for and days when I succumbed to imposter syndrome. I learned to pick myself up and solve the problems at hand though and find a way to move forward, sometimes even scratching my original idea to move towards something that worked in the end. That’s life. Things go wrong, plans don’t work out and yet our ability to pick ourselves up and carry on is one of the best skills we have. In graduate school you learn to never give up.

Time Management

I now assume that anyone who has been to graduate school is essentially an expert at multi-tasking. Between running a field season, taking and teaching courses, submitting research proposals, and trying to balance a social life, I didn’t realize how much of a pro I became at juggling many things at once. In other words, gaining a fundamental skill for being a working scientist

Resourcefulness

Graduate school taught me how to find the information I need. Every day I had a moment where I didn’t know an answer. In the beginning, I thought all you had to do was ask, but sometimes the first person you ask doesn’t know the answer either. Over time, I learned to dig through the literature, ask an expert in the field, or my favorite “try several different things and see how they differ”. Once I learned the hard lesson that there isn’t always an easy way out, I had subconsciously created an order of operations to figure it out.

Collaboration (and giving back the help to others who struggle where you once did)

Not many jobs teach us to work with a bunch of different minded people, but grad school does! I learned to work as a team, with scientists within and outside my lab who had personalities different from mine. Graduate school taught me to collaborate as much as possible and, more importantly, help someone with less experience to figure out their coding problems, or help them get their research proposals or publications out. Offering advice or expertise for a certain skill or method when I was busy helped me develop my team-building skills and proved to myself that I had skills to share.

There are good moments within bad moments

When a bad moment presented itself, I learned to focus on the good and recognize the moments that things worked out because I didn’t give up. These included following a bad presentation with a strong one, sparking the interest of collaborators, receiving an award for a conference presentation, or just the simple self-satisfaction of getting an R code to work properly. There are a lot of good moments in grad school, and it became important for me to celebrate them when they happened, but also not to take them for granted because they don’t come as often as the bad ones.

Importance of a strong support system

Unlike a harbor porpoise – I am very social person. Some can get through graduate school without any social interaction or encouragement from others, but there was no way that would have worked for me. Everyone copes with bad moments in graduate school in different ways – so my friends and family were my life-raft, especially those living in Newport. Mental and physical health are important to maintain in graduate school and it was beneficial for me to form a community early to help me through the tough moments. Although friends and family cannot completely relate to your situation (unless maybe they are also a graduate student) they will hear you out, care, listen and pull you out of a slump. Accepting their support and help drastically improved my mental health.

Work smarter, not necessarily harder, and forge your own path

When I look back at my past 2.5 years of graduate school now, I realize how hard I did truly work. I worked nights, weekends and evenings on weekdays. But in my last few months, I became more competent as my productivity peaked. I learned how to multi-task and plan better – not just in school, but also in my daily life. Graduate programs force you to do unique research, as you can’t write a thesis by reproducing someone else’s work. You have to learn from what others have done and then get creative. Creating something original demands trust in yourself, and avoiding trying to compare yourself to others. Forging your own path can be uncomfortable, but necessary.

I am confident to say that graduate school overall made me a better scientist, and a better person. I value the training and education that I was fortunate enough to receive. It seems everyone starts graduate school with stars in their eyes, and then sometime in the middle we get weighed down by the failures and frustrations of graduate life, and we can fail to remember what brought us here in the first place: Intense curiosity, a desire to learn, and a chance to improve the world. These factors made me opt for this experience and in spite of all the hardships along the way, grad school gave me a set of life skills. So if you are contemplating graduate school, currently working toward a graduate degree, or in a transitional phase of job-seeking or career-changing, I suggest taking a minute to reflect on what you have already, or could gain, from graduate school.

My days as a current GEMM lab member are dwindling down as finish my edits, preparing publications, search for jobs, and rekindle my network – but I look forward to being a long distance cheerleader for the current and future members of the GEMM lab.

When I entered graduate school – one of my committee members told me I would go through the five stages of grief – and she was right – but the end reward was more than worth it.

“Being a graduate student is like becoming all of the Seven Dwarves. In the beginning, you’re Dopey and Bashful. In the middle, you are usually sick (Sneezy), tired (Sleepy), and irritable (Grumpy). But at the end, they call you Doc, and then you’re Happy.”

Challenges of fecal analyses (Round 1)

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

Fieldwork is done for the year and lab analyses just started with some challenges. This is not unexpected since no previous hormonal analysis has been conducted with any gray whale tissue, and whale fecal sample analysis is a relatively new technique. So, I have been thinking, learning, consulting, and creating a methodology as I go along. I am grateful to the expert advice and help from many great collaborators:

Kathleen Hunt (Northern Arizona University, AZ, United States)
Shawn Larson (Seattle Aquarium, WA, United States)
Amy Green (Seattle Aquarium, WA, United States)
Rachel Ann Hauser-Davis (Fiocruz, RJ, Brazil)
Maziet Cheseby (Oregon State University, OR, United States)
Scott Klasek (Oregon State University, OR, United States)

I have learned that an important step before undertaking fecal a hormonal analysis is the desalting process of the samples since salts can interfere in hormonal determinations, leading to false results. In order to remove salt content, each sample was first filtered (Fig. 1A), to remove a majority of the salt water content (Fig. 1B) that is inevitably collected along with the fecal sample. Each sample was then re-suspended in ultra-pure water, to dilute the remaining salt content in a higher water volume (Fig. 1C).

Figure 1: Analytical processes: (A) Filtration of the samples; (B) Result from filtration; (C) Addition of pure water to the samples.

After these steps were completed for each sample, the samples were centrifuged (Fig. 2A) to precipitate the fecal matter and leave the lighter salt ions in the supernatant (the liquid lying above a solid residue; Fig. 2B). After finishing these two phases, the water was removed with aid of a plastic pippete (Fig. 2C), and I was left with only desalted fecal at the bottom of the tubes (Fig. 2D).

Figure 2: Analytical processes: (A) Samples centrifugation; (B) Result from the centrifugation; (C, D) Results from separating water and sample.

The fecal samples were then frozen at -80°C (Fig. 3A & 3B) and then freeze-dried on a lyophilizer for 2 days to remove all remaining water content (Fig. 3C). Finally, I have what I need: desalted, dry fecal samples, ready for hormone analysis (Fig. 3D).

Figure 3: Analytical processes: (A) Freezing process of the samples; (B) Frozen samples ready to go to the lyophilizer; (C) Samples in the lyophilizer; (D) Final result of the lyophilizing process.

Writing this now, this process seems simple, but it was laborious, and took time to find the equipment needed at the right times. The end product is crucial to get a good final result, so my time investment (and my own increased stress level!) was worth it. This type of analysis is very new for marine mammals and our research lab is still in the learning the best methods. Along the way we were unsure of some decisions, some mistakes were made, and we were afraid of losing precious fecal material. But, this is the fun and challenge of working with a new species and new type of sample and, importantly, we have developed a working protocol that should make the process more efficient and reduce our stress levels next time around.

At the end of this sample preparation process, our 53 samples look great and are ready to be analyzed during my training at the Seattle Aquarium. We are also planning to analyze the water that was removed from the samples (Fig. 2D) to see if any hormone leached out from the poop into the water.

Results from this process will aid in future whale fecal hormone studies. Perhaps only the centrifugation step is needed and we can discard the water without losing hormone content. Or, perhaps we need to analyze both portions of the sample and sum the hormones found in each. We shall know the answer when we get our hormone metabolite results. Just another protocol to be worked out as I move ahead with the hormone analysis of these fecal samples. And through all these challenges I keep the end goal of this work in my mind: to learn about the reproductive and stress hormonal variation in gray whales and to link these variations to nutritional status and noise events. Onward!

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