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How Unmanned Aircraft Systems (UAS, aka “drones”) are being applied in conservation research

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

Unmanned Aircraft Systems (UAS), also known as “drones”, have been increasingly used in many diverse areas. Concerning field research, the use of drones has brought about reduced errors, increased safety and survey efforts, among other benefits, as described in a previous blog post of mine.

Several study groups around the world have been applying this new technology to a great variety of research applications, aiding in the conservation of certain areas and their respective fauna and flora. Examples of these studies include forest monitoring and tree cover analyses, .

Using drones for forest monitoring and tree cover analyses allows for many applications, such as biodiversity and tree height monitoring, forest classification and inventory, and plant disease and detection. The Ugalla Primate Project, for example, performed an interesting study on tree coverage mapping in western Tanzania (Figure 1).

Figure 1: Tree coverage analyses in Tanzania.
Source: Conservation Drones, 2016.

The access to this data (not possible before from the ground) and the acquired knowledge on tree density and structure were important to better understand how wild primates exploit a mosaic landscape. Here is a video about this project:

Forest restoration activities can also be monitored by drones. Rainforests around the world have been depleted through deforestation, partly to open up space for agriculture. To meet conservation goals, large areas are being restored to rainforests today (Elsevier 2015). It is important to monitor the success of the forest regeneration and to ensure that the inspected area is being replenished with the right vegetation. Since inspection events can be costly, labor intensive and time consuming, drones can facilitate these procedures, making the monitoring process more feasible.

Zahawi et al. (2015) conducted an interesting study in Costa Rica, being able to keep up with the success of the forest regeneration. They were also able to spot many fruit-eating birds important for forest regeneration (eg. mountain thrush, black guan and sooty-capped bush tanager). Researchers concluded that the automation of the process lead to equally accurate results.

Drones can also be used to inspect areas for illegal logging and habitat destruction. Conservationists have struggled to identify illegal activities, and the use of drones can accelerate the identification process of these activities and help to monitor their spread and ensure that they do not intersect with protected areas.

The Amazon Basin Conservation Association Los Amigos conservancy concession (LACC) has been monitoring 145,000 hectars of the local conservation area. Illegal gold mining and logging activities were identified (Figure 2) and drones have aided in tracking the spread of these activities and the progress of reforestation efforts.

Figure 2: Identification of illegal activities in the Amazon Basin.
Source: NPR, 2015.

Another remarkable project was held in Mexico, in one of the most important sites for monarch butterflies in the country: the Monarch Butterfly Biosphere Reserve. Around 10 hectars of vital trees were cut down in the reserve during 2013-2015, and a great decrease of the monarch population was perceived. The reserve did not allow researchers to enter in the area for inspection due to safety concerns. Therefore, drones were used and were able to reveal the illegal logging activity (Figure 3).

Figure 3: Identification of illegal logging at the Monarch Butterfly Biosphere Reserve, Mexico.
Source: Take Part, 2016.

Regarding the use of drones for mapping vulnerable areas, this new technology can be used to map potential exposed areas to avoid catastrophes. Concerning responses to fires or other natural disasters, drones can fly immediately, while planes and helicopters require a certain time. The drone material also allows for operating successfully under challenging conditions such as rain, snow and high temperatures, as in the case of fires. Data can be assessed in real time, with no need to have firefighters or other personnel at a dangerous location anymore. Drones can now fulfill this role. Examples of drone applications in this regard are the detection, monitoring and support for catastrophes such as landslides, tsunamis, ship collisions, volcanic eruptions, nuclear accidents, fire scenes, flooding, storms and hurricanes, and rescue of people and wildlife at risk. In addition, the use of a thermal image camera can better assist in rescue operations.

Researchers from the Universidad Politécnica de Madrid (UPM) are developing a system to detect forest fires by using a color index (Cruz et al. 2016). This index is based on vegetation classification techniques that have been adapted to detect different tonalities for flames and smoke (Figure 4). This new technique would result in more cost-effective outcomes than conventional systems (eg. helicopters, satellites) and in reaching inaccessible locations.

Figure 4: Fire detection with Forest Fire Detection Index (FFDI) in different scenes.
Source: UPM, 2016.

Marine debris detection by drones is another great functionality. The right localization and the extent of the problem can be detected through drone footage, and action plans for clean-ups can be developed.

A research conducted by the Duke University Marine Lab has been detecting marine debris on beaches around the world. They indicate that marine debris impacts water quality, and harms wildlife (eg. whales, sea birds, seals and sea turtles) that might confuse floating plastic with food. You can read a bit more about their research and its importance for conservation ends here.

Drones are also being extensively used for wildlife monitoring. Through drone footage, researchers around the world have been able to detect and map wildlife and habitat use, estimate densities and evaluate population status, detect rare behaviors, combat poaching, among others. One of the main benefits of using a drone instead of using helicopters or airplanes, or having researchers in the area, is the lower disturbance it may cause on wildlife.

A research team from Monash University is using drones for seabird monitoring in remote islands in northwestern Australia (Figure 5). After some tests, researchers were able to detect which altitude (~75 meters) the drone would not cause any disturbances to the birds. Results achieved by projects like this should be used in the future for approaching the species safely.

Figure 5: Photograph taken by a drone of a crested tern colony on a remote island in Australia.
Source: Conservation Drones, 2014.

Drones are also being used to combat elephant and rhino poaching in Africa. They are being implemented to predict, trace, track and catch suspects of poaching. The aim is to reduce the number of animals being killed for the detusking and dehorning practices and the illegal trade. You can read more about this theme here. The drone application on combating one of these illegal practices is also shown here in this video.

As if the innovation of this device alone was not enough, drones are also being used to load other tools. A good example is the collection of whale breath samples by attaching Petri dishes or sterile sponges in the basal part of the drones.

The collection of lung samples allows many health-monitoring applications, such as the analysis of virus and bacteria loads, DNA, hormones, and the detection of environmental toxins in their organisms. This non-invasive physiological tool, known as “Snotbot”, allows sampling collection without approaching closely the individuals and with minimal or no disturbance of the animals. The following video better describes about this amazing project:

It is inspiring to look at all of these wonderful applications of drones in conservation research. Our GEMM Lab team is already applying this great tool in the field and is hoping to support the conservation of wildlife.

References

Conservation Drones. 2014. Conservation Drones for Seabird Monitoring. Available at: https://conservationdrones.org/2014/05/05/conservation-drones-for-seabird-monitoring/

Conservation Drones. 2016. Tree cover analyses in Tanzania in collaboration with Envirodrone. Available at: https://conservationdrones.org/2016/09/17/tree-cover-analyses-in-tanzania-in-collaboration-with-envirodrone/

Cruz H, Eckert M, Meneses J and Martínez JF. 2016. Efficient Forest Fire Detection Index for Application in Unmanned Aerial Systems (UASs). Sensors 16(893):1-16.

Elsevier. 2015. Drones Could Make Forest Conservation Monitoring Significantly Cheaper: new study published in the Biological Conservation wins Elsevier’s Atlas award for September 2015. Available at: https://www.elsevier.com/about/press-releases/research-and-journals/drones-could-make-forest-conservation-monitoring significantly-cheaper

NPR. 2015. Eyes In The Sky: Foam Drones Keep Watch On Rain Forest Trees. Available at: http://www.npr.org/sections/goatsandsoda/2015/05/19/398765759/eyes-in-the-sky-styrofoam-drones-keep-watch-on-rainforest-trees

Take Part. 2016. Drones Uncover Illegal Logging in Critical Monarch Butterfly Reserve. Available at: http://www.takepart.com/article/2016/06/22/drones-uncover-illegal-logging-monarch-butterfly-habitat

UPM. 2016. New automatic forest fire detection system by using surveillance drones. Available at: http://www.upm.es/internacional/UPM/UPM_Channel/News/dc52fff26abf7510VgnVCM10000009c7648aRCRD

Zahawi RA, Dandois JP, Holl KD, Nadwodny D, Reid JL and Ellis EC. 2015. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biological Conservation 186:287–295.

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.

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!

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.

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 5^th 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).

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).

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 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):

Olympians in Rio: keep your mouths closed! But what are the resident marine animals to do?

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

August 5th was the Olympic games opening date in Rio de Janeiro, Brazil, the city where I am from. The opening ceremony was a big success and everybody seems to be enjoying the sporting events and all of the news that the city is offering. However, behind all the colors, magic and joy of this big event, Brazilians are very unsatisfied about hosting an event like this while the whole country is simultaneously dealing with a big educational, health, political and economic crisis at the moment.

Unfortunately, the crisis also affects the environment and is consequently affecting athletes that are competing in our “carioca” waters. Guanabara Bay, more specifically, where the sailing competitions are taking place, receive waters from more than 50 rivers and streams, as displayed below.

Figure 1: Hydrographic map of the Guanabara Bay region, Rio de Janeiro, Brazil, showing rivers and streams (in blue) that feed into the Bay.

Much of the water is not treated and brings sewage and garbage from upstream (Fig.2). Although the government reports that the pollution index in the Bay conforms to national and international standards, and that the areas where competitions are taking place are clean and present no risk to athlete health, public health experts advise athletes to keep their mouth closed whenever they are in contact with the water, as reported by the Independent newspaper (http://www.independent.co.uk/sport/olympics/2016-rio-olympics-water-feces-athletes -mouth-shut-brazil-a7163021.html). The goal was to clean up 80% of the Bay in time for the Olympic games, however this goal was far from achieved and the “solution” was to install barriers to try to avoid waste and untreated sewage reaching the event area.

Figure 2: Pollution contrasting with the beauty of the Sugar Loaf, one of the main tourist attractions in the city. The photo shows the area where competitions are taking place. Source: http://www.insidethegames.biz/articles/1027142/brazilian-politician-accused-of-undermining-effort-to-clean-guanabara-bay-by-publicity-seeking-jump-into-water — Figure 2: Pollution contrasting with the beauty of the Sugar Loaf, one of the main tourist attractions in the city. The photo shows the area where competitions are taking place.
Source: http://www.insidethegames.biz/articles/1027142/brazilian-politician-accused-of-undermining-effort-to-clean-guanabara-bay-by-publicity-seeking-jump-into-water.

Bacteria, fecal coliforms and metals occur in the Bay. Professionals from Oswaldo Cruz Foundation (Fiocruz), one of the world’s main public health research institutions, found a drug-resistant bacterium in the Bay waters, which is resistant to antibiotics and may cause multiple infections (https://www.rt.com/news/214807-brazil-olympic-venue-superbug/). Metals like mercury, one of the most toxic metals, can also be found in the Bay and shows long-term effects on marine life of the ecosystem.

Guanabara Bay used to be part of the migratory route of Southern right whales (Eubalaena australis), but unfortunately we do not see the whales in the area anymore. We also do not see turtles any longer and populations of prawns are extremely reduced. On the other hand, mussels, biological indicators of ambient pollution due to their sessile and filter-feeding habits, are continuously proliferating in the Bay. These individuals can accumulate high pollutant levels and are not safe to eat when present in polluted areas. However, local fishermen persist in eating mussels and fish from the Bay.

The Guiana dolphin (Sotalia guianensis) is the only mammal that still frequents the Bay waters and, while about 400 Guiana dolphins inhabited the region in the 80s, currently there are only 34 individuals (http://www.abc.net.au/news/2016-06-27/rio27s-dolphins-need-olympic-effort-to-survive-toxic-waters/7543544). The project MAQUA, responsible for monitoring the dolphins in the Guanabara Bay, correlated the decline of the population with worsening water quality, fishing and noise, as published in an article in “O Globo”, the main Brazilian newspaper (http://oglobo.globo.com/rio/populacao-de-golfinhos-da-baia-de-guanabara-sofre-reducao-de-90-em-tres-decadas-1-16110633).
In this article they presented pictures of dolphins from the Guiana dolphin population in the Bay, including the unfortunate consequences on human interactions (Fig.3).

Figure 3: Guiana dolphins in Guanabara Bay, Rio de Janeiro. A: some of the remaining individuals of Guiana dolphin population from the Guanabara Bay; B: a dolphin plays with a plastic bag; C: a dolphin that suffered an accident with a nylon yarn when young presents a scar across its whole circumference; D: a dolphin exhibit the absence of the pectoral fin. Source: O Globo, 2015 (http://oglobo.globo.com/rio/populacao-de-golfinhos-da-baia-de-guanabara-sofre-reducao-de-90-em-tres-decadas-1-16110633). — Figure 3: Guiana dolphins in Guanabara Bay, Rio de Janeiro. A: some of the remaining individuals of Guiana dolphin population from the Guanabara Bay; B: a dolphin plays with a plastic bag; C: a dolphin that suffered an accident with a nylon yarn when young presents a scar across its whole circumference; D: a dolphin exhibit the absence of the pectoral fin.
Source: O Globo, 2015 (http://oglobo.globo.com/rio/populacao-de-golfinhos-da-baia-de-guanabara-sofre-reducao-de-90-em-tres-decadas-1-16110633).

This dolphin population is living in heavily polluted waters caused solely by human behavior. Although dolphins may distinguish between trash and food, they feed on contaminated fish – a consequence of bioaccumulation.

During my master’s degree at the Oswaldo Cruz Foundation in Rio de Janeiro, I undertook a toxicological analysis of different species of dolphins (Lemos et al. 2013; http://www.sciencedirect.com/science/article/pii/S0147651313003370). We found high levels of different metals, such as mercury and cadmium, in animals along the north coast of Rio de Janeiro. Just like the mussels, dolphins bioaccumulate high pollutant levels in their tissues and organs, primarily via feeding, but also through dermal contact. Metals and other pollutants present in polluted waters, like the Guanabara Bay, enter the food chain and affect multiple trophic levels, compromising health.

Dolphins from the Guanabara Bay are feeding on the same prey as the local fisherman, and act as sentinels of the environment, warning of public health concerns for humans. Just like humans, these dolphins are long-lived and large mammals, but they live every day in these waters and must open their mouths to survive. If we are concerned about human athletes spending a few hours in the water, we should be outraged at the conditions we force marine animals to live in daily in the Rio de Janeiro region. The dolphins have the intrinsic right to live in a non-polluted environment and be healthy.

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.

Does ocean noise stress-out whales?

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

We’ve all been stressed. You might be stressed right now. Deadlines, demands, criticism, bills, relationships. It all adds up and can boil over: Chronic stress is linked with poor health, and also increased risk of illness (like cancer; Glaser et al. 2005, Godbout and Glaser 2006).

Biologically, stress manifests itself in vertebrate animals as variable levels of hormones, particularly the hormone cortisol. This academic term I am taking a class at OSU called “vertebrate endocrinology” where I am learning the different types of secretions and organs involved and the mechanisms of hormone action: how secretion, transport and signaling happen. These issues are important for my research because I will be examining stress levels in gray whales.

It may seem strange to study how stressed out a whale is, but there are reasons to believe that the long-term health of animals, including whales, is significantly related to their stress-hormone levels (Jepson et al. 2003, Cox et al. 2006, Wright et al. 2007, Rolland et al. 2012).

So what could be stressing out whales? Probably lots of things including food availability, predators and mating. However, we are mostly interested in describing how much added stress human activities in the oceans are causing, particularly from increased ocean noise. Ambient ocean noise levels have increased considerably over the last decades: according to IUCN, the increase was about 3 dB per decade over the past 60 years and now it seems to be increasing from 3 to 5 dB per decade (Simard and Spadone 2012).

Baleen whales communicate through low-frequency signals, reaching between 20 and 200 Hz and large ships generate noise in the same frequency band, which can mask whale vocalizations and potentially add stress (Rolland et al. 2012), especially in areas with high anthropogenic activities such as big ports, where an intense traffic occurs.

To get a sense of how noisy oceans can disrupt the acoustic lives of whales, the Monterey Institute has created an excellent interactive website where it is possible to listen to the whales’ vocalization and add other sources of sounds, like ship traffic, to compare the difference in noise levels.

We still do not understand the population level consequences of this increased ocean noise on whales, however it has been demonstrated that whales respond behaviorally to increased noise, including changes in vocalization rates and habitat displacement (Morton and Symonds 2002, Nowacek et al. 2007, Weilgart 2007, Rolland et al. 2012). In order to understand how acoustic disturbance may influence marine mammal populations, the population consequences of acoustic disturbance model (PCAD) was developed by the US National Academies of Sciences (National Resource Council 2005; http://dels.nas.edu/Report/Marine-Mammal-Populations-Ocean-Noise/11147). We believe that examining stress levels in whales can provide a useful link between ocean noise and population-level impacts.

One previous study convincingly demonstrated the impacts of ocean noise on whale stress hormones due to the chance experiment caused by the shut-down of all air and vessel traffic during the days following September 11, 2001. Rolland et al. (2012) collected acoustic samples from the Bay of Fundy, Canada, and fecal samples from north Atlantic right whales in the area before and after September 11^th over five years. The results found a 6 dB decrease in ocean noise (Figure 1) in the area after this date and an associated decrease in glucocorticoids (GC) metabolite (stress) levels in the whales (Figure 2 – highlighted in red).

Spectrum of the noise in different days along the Bay of Fundy, Canada. Source: Rolland et al. (2012) — Figure 1: Spectrum of the noise in different days along the Bay of Fundy, Canada.
Source: Rolland et al. (2012)

Figure 2: (a) Levels of fecal glucocorticoids metabolites (ng g -1) in North Atlantic right whales before (gray) and after (white) 11 September; (b) Yearly difference in median fecal GC levels. Source: Rolland et al. (2012)

For my PhD research we are attempting to assess how multiple, confounding factors contribute to stress levels in individual whales: prey availability, body condition, location, ocean noise, sex and sexual maturity. My advisor, Dr. Leigh Torres, developed a conceptual pathway diagram that illustrates potential scenarios caused by dichotomous levels of three major ecological components and their hypothesized influence on whale stress levels (Figure 3).

Figure 3: Conceptual pathway diagram of the hypothesized stress response of whales based on high or low levels of the three contributing ecological factors on stress that will be measured (developed by L. Torres).

From this diagram we can generate different hypotheses for our research to test. Distinct levels (high or low) of noise, prey availability, and health condition can lead to varied responses in the amplitude and duration of stress. We will measure prey availability through GoPro camera drops, hormone levels through fecal sample collection, and body condition through photogrammetry measurements of aerial images captured through an Unmanned Aerial System (aka drone).

Watch a video clip filmed via a UAS of a gray whale defecation event, and the field team collecting the sample for analysis.

Our study species is the gray whale, a non endangered species that regularly visits the Oregon coast during summer and fall months to feed, allowing accessibility to whales and repeatability of sighting individual animals. This ability to resight individual whales within and between years is important so that we can evaluate natural stress variability, thus allowing us to identify ‘stressful events’ and potential causes.

Overall, the main aim of my PhD research is to better understand how gray whale hormone levels vary across individual, time, body condition, location, and ambient noise environments. We may then be able to scale-up our results to better understand the population level impacts of elevated ocean noise on reproduction, distribution and abundance of whales.

We plan to study the correlation between stress levels in whales and ocean noise over many years to compile a robust database that allows us to identify how animals may be impacted physiologically at short- and long- term scales. These results will inform environmental management decisions regarding thresholds of ambient ocean noise levels in order to limit harm posed to baleen whales.

Bibliographic References:

Cox T, Ragen T, Read A, Vos E, Baird R, Balcomb K, Barlow J, Caldwell J, Cranford T, Crum L, D’Amico A, D’Spain G, Fernandez A, Finneran J, Gentry R, Gerth W, Gulland F, Hildebrand J, Houser D, Hullar T, Jepson P, Ketten D, MacLeod C, Miller P, Moore S, Mountain D, Palka D, Rommel S, Rowles T, Taylor B, Tyack P, Wartzok D, Gisiner R, Mead J, Benner L. 2006. Understanding the impacts of anthropogenic sound on beaked whales. Journal of Cetacean Research and Management 7:177-187.

Glaser R, Padgett DA, Litsky ML, Baiocchi RA, Yang EV, Chen M, Yeh PE, Klimas NG, Marshall GD, Whiteside T, Herberman R, Kiecolt-Glaser J, Williams MV (2005) Stress-associated changes in the steady-state expression of latent Epstein-Barr virus: implications for chronic fatigue syndrome and cancer. Brain Behav. Immun. 19(2):91-103.

Godbout JP, Glaser R. 2006. Stress-Induced Immune Dysregulation: Implications for Wound Healing, Infectious Disease and Cancer. J. Neuroimmune Pharm. 1:421-427.

Simard F, Spadone A (eds). 2012. An Ecosystem Approach to Management of Seamounts in the Southern Indian Ocean. Volume 2 – Anthropogenic Threats to Seamount Ecosystems and Biodiversity. Gland, Switzerland: IUCN. 64pp.

Jepson PD, Arbelot M, Deaville R, Patterson IAP, Castro P, Baker JR, Degollada E, Ross HM, Herraez P, Pocknell AM, Rodriguez F, Howie II FE, Espinosa A, Reid RJ, Jaber JR, Martin V, Cunningham AA, Fernandez A. 2003. Gas-bubble lesions in stranded cetaceans: Was sonar responsible for a spate of whale deaths after an Atlantic military exercise? Nature 425:575-576.

Morton AB, Symonds HK. 2002. Displacement of Orcinus orca (L.) by high amplitude sound in British Columbia, Canada. ICES Journal of Marine Science 59:71-80.

National Resource Council. 2005. Marine Mammal Populations and Ocean Noise: Determining When Noise Causes Biologically Significant Effects. National Academies Press, Washington D.C.

Nowacek DP, Thorne LH, Johnston DW, Tyack PL. 2007. Responses of cetaceans to anthropogenic noise. Mammal Rev. 37, 81–115.

Rolland RM, Parks SE, Hunt KE, Castellote M, Corkeron PJ, Nowacek DP, Wasser SK, Kraus SD. 2012. Evidence that ship noise increases stress in righ whales. Proc. R. Soc. B 279:2363-2368.

Weilgart LS. 2007 The impacts of anthropogenic ocean noise on cetaceans and implications for management. Can. J. Zool. 85, 1091–1116.

Wright AJ, Soto NA, Baldwin AL, Bateson M, Beale CM, Clark C, Deak T, Edwards EF, Fernández A, Godinho A. 2007. Do Marine Mammals Experience Stress Related to Anthropogenic Noise? International Journal of Comparative Psychology 20.

Entering in the world of Photogrammetry

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

Hello everybody with the first post of the year from the GEMM Lab!!

The year of 2016 has just begun and with that comes new projects and great expectations about my PhD project.

During this week I am going to learn how to measure gray whales (Eschrichtius robustus) using aerial images that were captured during last summer’s pilot field season along the Oregon Coast led by my advisor Dr. Leigh Torres.

Dr. Torres aimed to test the methodology for our project that will combine these whales’ measurements data with hormonal analysis to assess the overall health of gray whales.

The aerial videos and images were taken through an unmanned aerial system (UAS) that is composed of a flying unit and an on-board camera. An example of this system can be seen below, in Figure 1.

Figure 1: Dr. Leigh Torres re-captures the UAS (DJI Phantom 3) while at sea after an over flight of a gray whale.

Source: Leigh Torres, 2015.

The measurement of the whales through aerial images is known as “photogrammetry” and this method can give us important information about the whales through this unique overhead perspective, such as individual identification using natural markings, sex and reproductive condition based on size estimation, and individual-based changes in growth, health and body condition (nutritive condition) over time through replicate samples.

Perryman and Lynn (2002) used images captured from planes and adopted four different measurements for each photographed whale: the total length (L_t), the width of the whale at its widest point (W_m), the distance from the tip of the rostrum to the widest point (RW_m), and the width of the flukes (F_w), as shown in the Figure 2. Using these methods, this study was able to identify pregnant females and found that southbound migrating gray whales were significantly wider than northbound whales.

Figure 2: Features measured on vertical photographs in gray whales

Source: Perryman and Lynn, 2002.

We plan to build upon this established method by measuring width at multiple points along the whale’s body, in addition to the total length.

Images taken of the same individuals during different temporal periods can reveal variations in their body condition.

We aim to collect images of the same individuals at the beginning and end of a foraging season and hypothesize that due to weight gain and increased blubber mass the width of animals will increase. Additionally, when images of indiviudals are compared between years we hypothesize that body condition changes due to major events such as pregnancy, entanglements, skin lesions, and predation events, will be linked to changes in body condition.

We will relate these photogrammetry data to hormonal data on stress and reproductive status in order to describe individual stress variation as it relates to size, health, location, year, reproductive status and ocean noise levels.

During the pilot field season, six gray whale fecal samples were collected and hormonal levels in these samples were analyzed showing positive results. Based on the success of the pilot field season, I believe my PhD project will produce exciting and informative data about gray whale ecology by linking physiology and morphometrics.

I am excited to begin my thesis research and, until my field season starts next summer, you can find me measuring gray whales!

To illustrate, below are a few aerial images taken of gray whales off Newport, Oregon, using a UAS, which we will use to conduct photogrammetry (all photos taken under NMFS permit 16111 issued to John Calambokidis).

And, just for fun, here is a UAS clip of a foraging gray whale in a kelp bed off the coast of Oregon to give a sense of the unique perspective we can get on animal behavior.

* Taken under NMFS permit 16111 issued to John Calambokidis.

This research is facilitated through the collaboration with OSU’s Aerial Imaging Systems Lab (http://ais.forestry.oregonstate.edu/), and Cascadia Research Collective (http://www.cascadiaresearch.org/).

Until next time and thanks for reading!

Bibliographic Reference:

Perryman WL, Lynn MS. 2002. Evaluation of nutritive condition and reproductive status of migrating gray whales (Eschrichtius robustus) based on analysis of photogrammetric data. J. Cetacean Res. Manage. 4(2):155-164.

Fishing with dolphins

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

Hello everybody! I am Leila Lemos, a new member of the GEMM Lab. I am from Rio de Janeiro, Brazil, and moved to Corvallis just 2 months ago where I am now taking classes at OSU. Although I have not yet travelled around Oregon to see my surroundings I am loving the fall colors! We don’t have all of this yellow/orange/red in our Brazilian trees; it’s amazing! The green of the pines also enchanted me. What a beautiful place! However, I confess that I do miss being close to the ocean, so I am looking forward to being based in Newport next year. So, since I cannot see the ocean for now, let’s talk a bit about it and the dynamic cetaceans that live there.

My thesis will explore the impact of ocean noise on the physiology of gray whales, but I have not started my fieldwork yet. So for my first blog post I will discuss a unique interaction between bottlenose dolphins (Tursiops truncatus) and fisherman that occurs in the cities of Laguna, in the state of Santa Catarina, and Tramandaí and Imbé, in the state of Rio Grande so Sul, in southern Brazil. Unlike most relationships between fishermen and marine mammals, this interaction is mutually beneficial and both species appear to seek each other out. There are only three other places in the world where a similar interaction occurs: Mauritania, in the west coast of Africa; Myanmar, in the south coast of Asia; and in the east coast of Australia.

In the southern Brazil, dolphins and artisanal mullet fishermen have adapted their hunting strategies to perform a cooperative foraging strategy. Cast net fisherman wait for the dolphins to arrive and then observe their behavior. Only when a specific aggressive behavior pattern is observed do the fishermen enter the water with their nets. The dolphins move closer to the fishermen and begin rolling movements that trap fish close to the margin. The fishermen wait to throw their cast nets into the water until the dolphins perform specific and vigorous behaviors described by Simões-Lopes et al. (1998):

the dolphin shows an arched back;
the dolphin exposes its head and hits the surface with the throat;
the dolphin moves rapidly, showing just the dorsal fin, producing a whirl;
the dolphin slaps its tail against the surface.

Fishermen waiting for a signal to throw the cast net in Laguna, Santa Catarina, Brazil. Source: Diário Catarinense, 2013.

Fishermen waiting for a signal to throw the cast net in Laguna, Santa Catarina, Brazil. Source: Notícias UFSC, 2009. — Another shots of fishermen waiting to throw the cast net in Laguna, Santa Catarina, Brazil. Source: Notícias UFSC, 2009.

This partnership is mutually beneficial. Dolphins use the disturbance caused by the net to separate the mullet school and trap individual prey. This method allows the dolphins to reduce escapees, capture more prey, and ultimately increase their net energy gain.

For fishermen, this cooperative association leads to greatly increased captures of mullet. The water in the southern coast of Brazil is too murky for the fishermen to see the schools and therefore know where to throw their net. By watching the behavior of the dolphins, the fisherman is able to throw his net at the exact time and location of the passing mullet shoal.

While this symbiotic relationship is remarkable, it is also hereditary in both humans and dolphins. The calves follow their mothers during the foraging events and learn the movements used in this cooperative behavior. Likewise, the fishermen learn their techniques from their relatives through observation. This cross-species interaction has created cultural ties of great socioeconomic value for both humans and dolphins. Furthermore, this unique relationship demonstrates how clever and adaptive both taxa are when it comes to capturing prey. Wouldn’t it be great if more teamwork like this were possible?

Here is a video that captures this amazing relationship:

Until next time and thanks for reading!

Bibliographic References:

Diário Catarinense, 2013. Interação entre golfinhos e pescadores em Laguna chama a atenção de produtores da BBC. Retrieved from http://diariocatarinense.clicrbs.com.br/sc/geral/noticia/2013/05/interacao-entre-golfinhos-e-pescadores-em-laguna-chama-a-atencao-de-produtores-da-bbc-4151948.html

Notícias UFSC, 2009. Especial pesquisa: UFSC estuda pesca cooperativa entre golfinhos e pescadores em Laguna. Retrieved from http://noticias.ufsc.br/2009/08/especial-pesquisa-ufsc-estuda-pesca-cooperativa-entre-golfinhos-e-pescadores-em-laguna/

Simões-Lopes, P.C., Fabián, M.E., Menegheti, J.O., 1998. Dolphin Interactions with the mullet artisanal fishing on southern Brazil: a qualitative and quantitative approach. Revta bras. Zool. 15(3), 709-726.

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