A new addition to the GEMM Lab

By Dr. Leigh Torres, GEMM Lab, OSU, Marine Mammal Institute

Prepping for fieldwork is always a complex mental and physical juggling act, especially for an equipment-rich, multi-disciplinary, collaborative project like our research project on the impacts of ocean noise on gray whale physiology. For me, the past couple months has consisted of remembering to coordinate equipment purchasing/testing/updating (cameras, drones, GoPros), obtaining all needed permits/licenses (NMFS, FAA, vessel), prepping data recording and management protocols (data sheets, dropbox), scheduling personnel (7 people over 5 months), organizing sampling gear (fecal nets, zooplankton traps), gathering all needed lab supplies (jars, filters, tubes), and hoping for good weather.

This list would normally be enough to overwhelm me, but this year we have also had the (fortunate) opportunity to outfit our own research vessel. The OSU Marine Mammal Institute (MMI) obtained a surplus 5.4 m coast guard RHIB (rigid inflatable haul boat) and generously handed it off to the GEMM Lab for our coastal Oregon research. Fantastic! But not perfect, of course. What the coast guard needs as a vessel, is not exactly what we need for whale research. When the vessel arrived it had a straddle seat occupying most of the limited interior space, which would make it very hard for three people to ride comfortably during a long day of survey effort or move around during whale sightings.

The RHIB in its original state, with the straddle seat taking up a majority of the interior space.

So, the boat needed a re-fit. And who better to do this re-fit than someone who has spent more than 15 years conducting whale research in a RHIB, is a certified ABYC marine electrician, and runs his own marine repair business? Who has such a qualified resume? My research technician (and husband), Todd Chandler.

Over the last two months Todd has meticulously rearranged the interior of the vessel to maximize the space, prioritize safety and comfort, balance the boat for stability, and allow for effective data collection. He removed the straddle seat, had a light-weight aluminum center console and leaning post built to just the right size and specs, installed and updated electronics (VHF, GPS chart plotter), re-ran the engine wiring (throttle, tilt, kill-switch), patched up a few (8!) leaks in the pontoons, ran new nav lights, installed new fuel tanks, and serviced the engine. Phew! He did an amazing job and really demonstrated his skills, handiwork, and knowledge of field research.

Todd, rightfully proud, with our newly designed RHIB.

The vessel now looks great, runs smoothly, and gives us the space needed for our work. But, she needed a name! So, on Saturday afternoon we hosted a GEMM Lab boat naming BBQ. Our research team and lab gathered in the sun to admire the vessel, eat good food, watch the kids run and play, and come up with boat names.

The gang gets a laugh at another good proposed name.

I was impressed by the appropriate, thoughtful, clever names put forth, like Adam’s rib, Cetacea, Oppo (re-arrange poop), and Whale Done. I was faced with a tough decision so I made everyone vote; three ticks each.

Sharon puts her votes down.

And the winner is…… Ruby: An appropriate name for a research vessel in the GEMM Lab. Perhaps someday we will have a fleet: Ruby, Emerald, Diamond… Ah, a girl can dream.

The kids tally up the votes.
The final count, with Ruby the winner.

Now it’s time for the many hiccups, challenges, and rewards of a field season. So thanks to Todd, the MMI, the GEMM Lab, and our awesome team for getting us ready to go. Stay tuned for updates on the actual research (and how Ruby performs).

RV Ruby, ready to splash and find some whales.

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.

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.
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.
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.
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!

 

 

 

The five senses of fieldwork

By Leila Lemos, PhD student

 

This summer was full of emotions for me: I finally started my first fieldwork season after almost a year of classes and saw my first gray whale (love at first sight!).

During the fieldwork we use a small research vessel (we call it “Red Rocket”) along the Oregon coast to collect data for my PhD project. We are collecting gray whale fecal samples to analyze hormone variations; acoustic data to assess ambient noise changes at different locations and also variations before, during and after events like the “Halibut opener”; GoPro recordings to evaluate prey availability; photographs in order to identify each individual whale and assess body and skin condition; and video recordings through UAS (aka “drone”) flights, so we can measure the whales and classify them as skinny/fat, calf/juvenile/adult and pregnant/non-pregnant.

However, in order to collect all of these data, we need to first find the whales. This is when we use our first sense: vision. We are always looking at the horizon searching for a blow to come up and once we see it, we safely approach the animal and start watching the individual’s behavior and taking photographs.

If the animal is surfacing regularly to allow a successful drone overflight, we stay with the whale and launch the UAS in order to collect photogrammetry and behavior data.

Each team member performs different functions on the boat, as seen in the figure below.

Figure 1: UAS image showing each team members’ functions in the boat at the moment just after the UAS launch.
Figure 1: UAS image showing each team members’ functions in the boat at the moment just after the UAS launch.

 

While one member pilots the boat, another operates the UAS. Another team member is responsible for taking photos of the whales so we can match individuals with the UAS videos. And the last team member puts the calibration board of known length in the water, so that we can later calculate the exact size of each pixel at various UAS altitudes, which allows us to accurately measure whale lengths. Team members also alternate between these and other functions.

Sometimes we put the UAS in the air and no whales are at the surface, or we can’t find any. These animals only stay at the surface for a short period of time, so working with whales can be really challenging. UAS batteries only last for 15-20 minutes and we need to make the most of that time as we can. All of the members need to help the UAS pilot in finding whales, and that is when, besides vision, we need to use hearing too. The sound of the whale’s respiration (blow) can be very loud, especially when whales are closer. Once we find the whale, we give the location to the UAS pilot: “whale at 2 o’clock at 30 meters from the boat!” and the pilot finds the whale for an overflight.

The opposite – too many whales around – can also happen. While we are observing one individual or searching for it in one direction, we may hear a blow from another whale right behind us, and that’s the signal for us to look for other individuals too.

But now you might be asking yourself: “ok, I agree with vision and hearing, but what about the other three senses? Smell? Taste? Touch?” Believe it or not, this happens. Sometimes whales surface pretty close to the boat and blow. If the wind is in our direction – ARGHHHH – we smell it and even taste it (after the first time you learn to close your mouth!). Not a smell I recommend.

Fecal samples are responsible for the 5th sense: touch!

Once we identify that the whale pooped, we approach the fecal plume in order to collect as much fecal matter as possible (Fig.2).

Figure 2: A: the poop is identified; B: the boat approaches the feces that are floating at the surface (~30 seconds); C: one of the team members remains at the bow of the boat to indicate where the feces are; D: another team member collects it with a fine-mesh net. Filmed under NOAA/NMFS permit #16111 to John Calambokidis).
Figure 2: A: the poop is identified; B: the boat approaches the feces that are floating at the surface (~30 seconds); C: one of the team members remains at the bow of the boat to indicate where the feces are; D: another team member collects it with a fine-mesh net. Filmed under NOAA/NMFS permit #16111 to John Calambokidis).

 

After collecting the poop we transfer all of it from the net to a small jar that we then keep cool in an ice chest until we arrive back at the lab and put it in the freezer. So, how do we transfer the poop to the jar? By touching it! We put the jar inside the net and transfer each poop spot to the jar with the help of water pressure from a squeeze bottle full of ambient salt water.

Figure 3: Two gray whale individuals swimming around kelp forests. Filmed under NOAA/NMFS permit #16111 to John Calambokidis).
Figure 3: Two gray whale individuals swimming around kelp forests. Filmed under NOAA/NMFS permit #16111 to John Calambokidis).

 

That’s how we use our senses to study the whales, and we also use an underwater sensory system (a GoPro) to see what the whales were feeding on.

GoPro video of mysid swarms that we recorded near feeding gray whales in Port Orford in August 2016:

Our fieldwork is wrapping up this week, and I can already say that it has been a success. The challenging Oregon weather allowed us to work on 25 days: 6 days in Port Orford and 19 days in the Newport and Depoe Bay region, totaling 141 hours and 50 minutes of effort. We saw 195 whales during 97 different sightings and collected 49 fecal samples. We also performed 67 UAS flights, 34 drifter deployments (to collect acoustic data), and 34 GoPro deployments.

It is incredible to see how much data we obtained! Now starts the second part of the challenge: how to put all of this data together and find the results. My next steps are:

– photo-identification analysis;

– body and skin condition scoring of individuals;

– photogrammetry analysis;

– analysis of the GoPro videos to characterize prey;

– hormone analysis laboratory training in November at the Seattle Aquarium

 

For now, enjoy some pictures and a video we collected during the fieldwork this summer. It was hard to choose my favorite pictures from 11,061 photos and a video from 13 hours and 29 minutes of recording, but I finally did! Enjoy!

Figure 4: Gray whale breaching in Port Orford on August 27th. (Photo by Leila Lemos; Taken under NOAA/NMFS permit #16111 to John Calambokidis).
Figure 4: Gray whale breaching in Port Orford on August 27th. (Photo by Leila Lemos; Taken under NOAA/NMFS permit #16111 to John Calambokidis).

 

Figure 5: Rainbow formation through sunlight refraction on the water droplets of a gray whale individual's blow in Newport on September 15th. (Photo by Leila Lemos; Taken under NOAA/NMFS permit #16111 to John Calambokidis).
Figure 5: Rainbow formation through sunlight refraction on the water droplets of a gray whale individual’s blow in Newport on September 15th. (Photo by Leila Lemos; Taken under NOAA/NMFS permit #16111 to John Calambokidis).

 

Likely gray whale nursing behavior (Taken under NOAA/NMFS permit #16111 to John Calambokidis):

Making a Splash

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

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

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

 

 

 

 

 

 

 

Unmanned Aircraft Systems: keep your distance from wildlife!

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

Unmanned aircraft systems (UAS) or “drones” are becoming commonly used to observe natural landscapes and wildlife. These systems can provide important information regarding habitat conditions, distribution and abundance of populations, and health, fitness and behavior of the individuals (Goebel et al. 2015, Durban et al. 2016).

The benefits for the use of UAS by researchers and wildlife managers are varied and include reduced errors of population estimations, reduced observer fatigue, increased observer safety, increased survey effort, and access to remote settings and harsh environments (Koski et al. 2010, Vermeulen et al. 2013, Goebel et al. 2015, Smith et al. 2016). Importantly, data gathered from UAS can provide needed information for the conservation and management of several species. Although it is often assumed that wildlife incur minimal disturbance from UAS due to the reduced noise compared to traditional aircraft used for wildlife monitoring (Acevedo-Whitehouse et al. 2010), the impacts of UAS on most wildlife populations is currently unexplored.

Several studies have tried to comprehend the effects of UAS flights over animals and so far there is no evidence of behavioral disturbance. For instance Vermeulen et al. (2013) conducted a study where authors observed a group of elephants’ reaction or warning behavior while a UAS passed ten times over the individuals at altitudes of 100 and 300 meters, and no disturbance was recorded. Furthermore, a study conducted by Acevedo-Whitehouse et al. (2010) reported that six different species of large cetaceans (Bryde’s whale, fin whale, sperm whale, humpback whale, blue whale and gray whale) did not display avoidance behavior when approached by the UAS for blow sampling, suggesting that the system caused minimal distress (negative stress) to the individuals.

However, the fact that we cannot visually see an effect in the animal does not mean that a stress response is not occurring. A study analyzed the effects of UAS flights on movements and heart rate responses of American black bears in northwestern Minnesota (Ditmer et al. 2015). It was observed that all bears, including an individual that was hibernating, responded to UAS flights with increased heart rates (123 beats per minute above the pre-flight baseline). In contrast, no behavioral response by the bears was recorded (Figure 1).

By Leila Lemos, Ph.D. Student, Department of Fisheries and Wildlife, OSU Unmanned aircraft systems (UAS) or “drones” are becoming commonly used to observe natural landscapes and wildlife. These systems can provide important information regarding habitat conditions, distribution and abundance of populations, and health, fitness and behavior of the individuals (Goebel et al. 2015, Durban et al. 2016). The benefits for the use of UAS by researchers and wildlife managers are varied and include reduced errors of population estimations, reduced observer fatigue, increased observer safety, increased survey effort, and access to remote settings and harsh environments (Koski et al. 2010, Vermeulen et al. 2013, Goebel et al. 2015, Smith et al. 2016). Importantly, data gathered from UAS can provide needed information for the conservation and management of several species. Although it is often assumed that wildlife incur minimal disturbance from UAS due to the reduced noise compared to traditional aircraft used for wildlife monitoring (Acevedo-Whitehouse et al. 2010), the impacts of UAS on most wildlife populations is currently unexplored. Several studies have tried to comprehend the effects of UAS flights over animals and so far there is no evidence of behavioral disturbance. For instance Vermeulen et al. (2013) conducted a study where authors observed a group of elephants’ reaction or warning behavior while a UAS passed ten times over the individuals at altitudes of 100 and 300 meters, and no disturbance was recorded. Furthermore, a study conducted by Acevedo-Whitehouse et al. (2010) reported that six different species of large cetaceans (Bryde’s whale, fin whale, sperm whale, humpback whale, blue whale and gray whale) did not display avoidance behavior when approached by the UAS for blow sampling, suggesting that the system caused minimal distress (negative stress) to the individuals. However, the fact that we cannot visually see an effect in the animal does not mean that a stress response is not occurring. A study analyzed the effects of UAS flights on movements and heart rate responses of American black bears in northwestern Minnesota (Ditmer et al. 2015). It was observed that all bears, including an individual that was hibernating, responded to UAS flights with increased heart rates (123 beats per minute above the pre-flight baseline). In contrast, no behavioral response by the bears was recorded (Figure 1).
Figure 1: (A) Movement rates (meters per hour) of an adult female black bear with cubs prior to, during, and after a UAS flight (gray bar); (B) The corresponding heart rate (beats per minute) of the adult female black bear. Source: Modified from Figure 1 from Ditmer et al. 2015.

 

Therefore, behavioral analysis alone may not be able to describe the complete effects of UAS on wildlife, and it is important to consider other possible stress responses of wildlife.

Regarding marine mammals, only a few studies have systematically documented the effects of UAS on these animals. A review of these studies was produced by Smith et al. (2016) and the main factors influencing behavioral disturbance were identified as (1) noise and visual stimulus (from the UAS or its shadow), and (2) flight altitude of the UAS. Thus, studies that approach marine mammals closely with UAS (e.g., blow sampling in cetaceans) should be closely monitored for behavioral reactions because the noise level and visual stimulus will likely be increased.

Fortunately, when UAS work is applied to cetaceans and sirenians (manatees and dugongs) the air-water interface acts as a barrier to sound so these animals are unlikely to be acoustically disturbed by UAS. However, acoustic detection and response are still possible when an animal’s ears are exposed in the air during a surfacing event.

The best way to minimize stress responses in wildlife is to use caution while operating UAS at any altitude. According to National Oceanic and Atmospheric Administration (NOAA), “UAS can also be disruptive to both people and animals if not used safely, appropriately, or responsibly”. Therefore, since 2012, the Federal Aviation Administration (FAA) has required UAS operators in the United States to have a certified and registered aircraft, a licensed pilot, and operational approval, known as Section 333 Exemption (Note: in late August 2016, the 333 will be replaced by a revision to part 107). These authorizations require an air worthiness statement or certificate and registered aircraft. Public entities, like Oregon State University, operate under a certificate of authorization (COA.) As a public entity OSU certifies its own aircraft and sets standards for UAS operators. These permit requirements discourage illegal operations and improves safety.

Regarding marine mammals, all UAS operators should also be aware of The Marine Mammal Protection Act (MMPA) of 1972. This law makes it illegal to harass marine mammals in the wild, which may cause disruption to behavioral patterns, including, but not limited to, migration, breathing, nursing, breeding, feeding, or sheltering. A close UAS approach has the potential to cause harassments to marine mammals, thus federal guidelines recommend keeping a safe distance from these animals in the wild. The required vertical distance is 1000 ft for most marine mammals, but increases for endangered animals such as the North Atlantic right whales with a required buffer of 1500 ft (http://www.nmfs.noaa.gov/pr/uas.html). Therefore, NOAA evaluates all scientific research that use UAS within 1000 ft of marine mammals in order to ensure that the benefits outweigh possible hazards. NOAA distributes research permits accordingly.

Of course, with new technology the rules are always changing. In fact, last week the Department of Transportation (DOT) and the FAA finalized the first operational rules for routine commercial use of small UAS. These new guidelines aim to support new innovations in order to spur job growth, advance critical scientific research and save lives, and are designed to minimize risks to other aircraft and people and property on the ground. These new regulations include several requirements (e.g., height and speed restrictions) and hopefully allow for a streamlined system that enables beneficial and exciting wildlife research.

For my PhD project we are using UAS to collect aerial images from gray whales in order to describe behavioral patterns and apply a photogrammetry methodology. Through these methods we will determine the overall body condition and health of the individuals for comparison to variable ambient ocean noise levels. This project is conducted in collaboration with the NOAA Pacific Marine Environmental Lab.

Since October 2015, we have conducted 31 over-flights of gray whales using our UAS (DJI Phantom 3) and no behavioral disturbance has been observed. When over the whale(s) we generally fly between 25 and 40 m above the animals. We have a FAA certified UAS operator and fly under our NOAA/NMFS permit 16111. Prior to each flight we ensure that the weather conditions are safe, the whales are behaving normally, and that no on-lookers from shore or other boats will be disturbed.

Here is a video showing the launch and retrieval of the UAS system, our research vessel, the surrounding Oregon coastline beauty and gray whale individuals. The video includes some interesting footage of a gray whale foraging over a shallow reef, indicating that this UAS flight did not disturb the animal’s natural behavior patterns.

We all have the responsibility to help keep wildlife safe. Here in the GEMM Lab, we commit to using UAS safely and responsibly, and aim to use this new and exciting technology to continue our efforts to better protect and understand marine mammals.

 

References

Acevedo‐Whitehouse K, Rocha‐Gosselin A and Gendron D. 2010. A novel non‐invasive tool for disease surveillance of free‐ranging whales and its relevance to conservation programs. Anim. Conserv. 13(2):217–225.

Ditmer MA, Vincent JB, Werden LK, Tanner JC, Laske TG, Iaizzo PA, Garshelis DL and Fieberg JR. 2015. Bears Show a Physiological but Limited Behavioral Response to Unmanned Aerial Vehicles. Current Biology 25:2278–2283.

Durban JW, Moore MJ, Chiang G, Hickmott LS, Bocconcelli A, Howes G, Bahamonde PA, Perryman WL and Leroi DJ. 2016. Photogrammetry of blue whales with an unmanned hexacopter. Marine Mammal Science. DOI: 10.1111/mms.12328.

Goebel ME, Perryman WL, Hinke JT, Krause DJ, Hann NA, Gardner S and LeRoi DJ. 2015. A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biol. 38(5):619-630.

Koski WR, Abgrall P and Yazvenko SB. 2010. An inventory and evaluation of unmanned aerial systems for offshore surveys of marine mammals. J. Cetacean Res. Manag. 11(3):239–247.

NOAA. Unmanned Aircraft Systems: Responsible Use to Help Protect Marine Mammals. In: http://www.nmfs.noaa.gov/pr/uas.html. Accessed in: 06/12/2016.

Smith CE, Sykora-Bodie ST, Bloodworth B, Pack SM, Spradlin TR and LeBoeuf NR. 2016. Assessment of known impacts of unmanned aerial systems (UAS) on marine mammals: data gaps and recommendations for researchers in the United States1 J. Unmanned Veh. Syst. 4:1–14.

Vermeulen C, Lejeune P, Lisein J, Sawadogo P and Bouché P. 2013. Unmanned aerial survey of elephants. PLoS One. 8(2):e54700.

 

How can we reconstruct life-history pathways of whales?

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

 

Have you ever heard of statistical modeling? What about Hierarchical Bayes Models?

Hard words, I know…

Modeling is when known data (previously collected) is analyzed using sophisticated computer algorithms to look for patterns in these data. Models can be very useful for filling in data gaps where and when no sampling occurred. Hierarchical Bayes model is a type of statistical model that hierarchically integrates the observed data to estimate parameters. This type of model can analyze long-term data from individual animals to predict into data gaps and inform us about population dynamics.

When studying wild animals we often only collect data from brief and random encounters. Therefore, many researchers struggle with the reconstruction of possible pathways that could connect different sightings of wild animals to determine where, when and how the animal was doing in between sightings.

For instance, consider an animal that was observed in healthy condition at one sighting but in a subsequent sighting it was in poor health. How can we estimate what happened to this animal between sightings? Can we estimate where, when and how health deteriorated?

This is where the modeling comes in! It is a powerful tool used by many researchers to fill in gaps in our scientific knowledge using data that we do have. We use these ‘known data’ to estimate patterns and determine probabilities. The hierarchical Bayes model is a type of modeling that can be used to estimate the probability of pathways between known events. Schick et al. (2013) used hierarchical Bayes models to estimate the many factors that impact whale health and survivorship including distribution and movement patterns, true health condition of the individual and survival rates.

Modeling is very advantageous when studying aquatic animals like dolphins and whales that are very hard to spot since they spend a higher proportion of their lives submerged than above water. Also, sea conditions can hamper visual detection.

Schick et al. (2013) analyzed decades of data from photo-identifications of North Atlantic right whale resightings along the east coast of North America. They assessed different information from these pictures including body condition, infestation of cyamids, presence of fishing gear entanglements, rake marks and skin condition. The authors also used information of age and calving of the individuals. A model using these data was constructed and a more complete scenario of health and movement patterns of individuals and the populations were estimated. Survival rates of each individual were also estimated using this model. This is an example of a well-informed model and is important to notice that a model is only as good as the data you put into the model.

Using this model, Schick et al. documented variations in annual spatial distribution patterns between sexes (Fig. 1). For example, females arrive earlier to the BOF region than males, and have greater estimated transitions to SEUS region at the end of the year. It is also possible to see that there is a lack of information for the region MIDA, characterizing another advantage of modeling since it can highlight areas where effort should be increased.

Figure 1: Movement transition estimates from North to South regions in the western Atlantic Ocean for male and female right whales over the course of a year. Size of the circles in each region at each month corresponds to the actual number of right whales observed. Lines connecting regions indicate probability of transition. Magnitude of probability is depicted by line thickness. (NRTH: North region; BOF: Bay of Fundy; JL: Jeffreys Ledge; GOM: Gulf of Maine; RB: Roseway Basin; NE: Northeast; GSC: Great South Channel; MIDA: Mid-Atlantic; and SEUS: Southeastern US). Source: Figures 5 and 6 from Schick et al. 2013.
Figure 1: Movement transition estimates from North to South regions in the western Atlantic Ocean for male and female right whales over the course of a year. Size of the circles in each region at each month corresponds to the actual number of right whales observed. Lines connecting regions indicate probability of transition. Magnitude of probability is depicted by line thickness.
(NRTH: North region; BOF: Bay of Fundy; JL: Jeffreys Ledge; GOM: Gulf of Maine; RB: Roseway Basin; NE: Northeast; GSC: Great South Channel; MIDA: Mid-Atlantic; and SEUS: Southeastern US).
Source: Figures 5 and 6 from Schick et al. 2013.

 

When the model is applied to individual whales, the authors were able to estimate survival and health rates across the whale’s life-span (Fig. 2). Whale #1077 was a rarely seen adult male, with a sparse sighting history over 25 years. The last sighting of this whale was in 2004 when its health status was poor due to a poor body condition. According with his condition in the last sighting, the model predicted a high decrease in his health over time and since the whale was not seen for more than six years, so was presumed dead, following the standards set by the North Atlantic Right Whale Consortium.

Figure 2: Health time series for whale #1077. Time series of health observations for body condition, cyamids, entanglements, rake marks and skin condition (circles), estimates with uncertainty of health (thick line and dashed lines) and estimates of survivals (height rectangle at bottom). Photographic observations are color and size coded by class (three categories for body condition: green is good, orange is fair and purple is poor; and two categories for skin condition: green is good and orange is poor). Source: Figure 11 from Schick et al. 2013.
Figure 2: Health time series for whale #1077. Time series of health observations for body condition, cyamids, entanglements, rake marks and skin condition (circles), estimates with uncertainty of health (thick line and dashed lines) and estimates of survivals (height rectangle at bottom). Photographic observations are color and size coded by class (three categories for body condition: green is good, orange is fair and purple is poor; and two categories for skin condition: green is good and orange is poor).
Source: Figure 11 from Schick et al. 2013.

 

As I begin data collection for my thesis project to examine gray whale health along the Oregon coast in relation to ocean noise and inter-annual variability, I am considering how to apply a similar modeling approach to enhance our understanding of what influences individual gray whale health and also connect pathways between our resightings.

The marine environment is constantly changing, across space and over time. Therefore, distinguishing what contributes most significantly to whale stress levels can be very challenging. However, through a model we may be able to decipher the contributions of several factors to individual stress among the many parameters we are monitoring: ocean noise, prey availability, environmental patterns, season, sex, age, geographic area, reproductive status and body condition.

Marine ecology is a complex world, and sometimes complex models are needed to help us to find patterns in our data! Once estimates of these ecological processes are created and different hypotheses are explored, information can then be provided to conservation and environmental management to aid decision making, such as defining thresholds of ambient ocean noise levels in the vicinity of baleen whales.

 

Bibliographic Reference:

Schick RS, Kraus SD, Rolland RM, Knowlton AR, Hamilton PK, Pettis HM, Kenney RD and Clark JS. 2013. Using Hierarchical Bayes to Understand Movement, Health, and Survival in the Endangered North Atlantic Right Whale. PLOS ONE 8(6):e64166.