Tag: Leila Lemos

Burning it down

By Leila S. Lemos, PhD Candidate in Wildlife Sciences, Fisheries and Wildlife Department, OSU

As you might know, the GEMM Lab (Geospatial Ecology of MARINE Megafauna Laboratory) researches the marine environment, but today I am going to leave the marine ecosystem aside and I will discuss the Amazon biome. As a Brazilian, I cannot think of anything else to talk about this week than the terrifying fire that is burning down the Amazon forest in this exact minute.

For some context, the Amazon biome is known as the biome with the highest biodiversity in the world (ICMBio, 2019). It is the largest biome in Brazil, accounting for ~49% of the Brazilian territory. This biome houses the biggest tropical forest and hydrographic basin in the world. The Amazon forest also extends through eight other countries: Bolivia, Colombia, Ecuador, Guiana, French Guiana, Peru, Suriname and Venezuela. To date, at least 40,000 plant species, 427 mammals, 1,300 birds, 378 reptiles, more than 400 amphibians, around 3,000 freshwater fishes, and around 100,000 invertebrate species have been described by scientists in the Amazon, comprising more than 1/3 of all fauna species on the planet (Da Silva et al. 2005, Lewinsohn and Prado 2005). And, these numbers are likely to increase; According to Patterson (2000), one new genus and eight new species of Neotropical mammals are discovered each year in the region.

I feel very connected to the Amazon as I worked as an environmental consultant and field coordinator in 2014 and 2015 (Figs. 1 and 2) along the Madeira river (or “Wood” river) in Rondonia, Brazil (Fig. 3). I monitored Amazon river dolphins (Inia geoffrensis; Fig. 4), a species considered endangered by the IUCN Red List in 2018 (Da Silva et al. 2018). The Madeira river originates in Bolivia and flows into the great Amazon river, comprising one of its main tributaries (Fig. 3).

Figure 1: Me, working along the Madeira river, Rondonia, Brazil, in 2015.
Source: Laura K. Honda, 2015.
Figure 2: Me, helping to rescue a sloth from the Madeira river, Rondonia, Brazil, in 2014.
Source: Roberta Lanziani, 2014.
Figure 3: The Amazon hydrographic basin, with the Madeira river highlighted.
Source: Wikipedia, 2019.
Figure 4: Amazon river dolphins (I. geoffrensis) along the Madeira river, Rondonia, Brazil.
Source: Leila S. Lemos, 2014; 2015.

Here is also a video where you can see some Amazon river dolphins along the Madeira river:

Source: Leila S. Lemos, 2014; 2015.

In addition to the dolphins, I witnessed the presence of many other fauna specimens like birds (including macaws and parrots), monkeys, alligators and sloths (Fig. 5). The biodiversity of the Amazon is unquestionable.

Figure 5: Macaws (Ara chloropterus), parrots (Amazona sp.) and the Guariba monkey or brown howler (Allouatta guariba) along the Madeira river, Rondonia, Brazil.
Source: Leila S. Lemos

Other than its great biodiversity, the Amazon is known as the “lungs of the Earth”, which is an erroneous statement since plants consume as much oxygen as they produce (Malhi et al. 2008, Malhi 2019). But still, the Amazon forest is responsible for 16% of the oxygen produced by photosynthesis on land and 9% of the oxygen on the global scale (Fig. 6). This seems a small percentage, but it is still substantial, especially because the plants use carbon dioxide during photosynthesis, which accounts for a 10% reduction of atmospheric carbon dioxide. Thus, imagine if there was no Amazon rainforest. The rise in carbon dioxide would be enormous and have serious implications on the global climate, surpassing safe temperature boundaries for many regions.

Figure 6: Total photosynthesis of each major land biome. This value is multiplied by 2.67 to convert to total oxygen production. Hence total oxygen production by photosynthesis on land is around 330 Pg of oxygen per year. The Amazon (just under half of the tropical forests) is around 16% of this, around 54 Pg of oxygen per year.
Source: Malhi 2019.

Unfortunately, this scenario is not really far from us. Even though deforestation indices have fallen in the last 15 years, fire incidence associated with droughts and carbon emissions have increased (Aragão et al. 2018; Fig. 7).

Figure 7: Linear trends (2003–2015) of annual (a) deforestation rates, and (b) active fires counts in the Brazilian Amazon. Red circles indicate the analyzed drought years by Aragão et al. (2018).
Source: Aragão et al. 2018.

Since August 2019, the Amazon forest has experienced extreme fire outbreaks (Figs. 8 and 9). Around 80,000 fires occurred only in 2019. Despite 2019 not being an extreme drought year, the period of January-August 2019 is characterized by an ~80% increase in fires compared to the previous year (Wagner and Hayes 2019). The intensification of the fires has been linked to the Brazilian President’s incentive to “open the rainforest to development”. Leaving politics aside, the truth is that the majority of these fires have been set by loggers and ranchers seeking to clear land to expand the agro-cattle area (Yeung 2019).

Figure 8: The Amazon in July 28: just clouds; and in August 22: choked with smoke.
Source: NOAA, in: Wagner and Hayes, 2019.
Figure 9: Images showing some of the destruction caused by the fires in the Amazon region in 2019.
Source: Buzz Feed News 2019, Sea Mashable 2019.

Here you can see some videos showing the extension of the problem:

Video 1 – by NBC News:

Video 2 – a drone footage by The Guardian:

I consider myself lucky for the opportunity to have worked in the Amazon rainforest before these chaotic fires have destroyed so much biodiversity. The Amazon is a crucial home for countless animal and plant species, and to ~900,000 indigenous individuals that live in the region. They are all at risk of losing their homes and lives. We are all at risk of global warming.

References

Aragão LEOC, Anderson LO, Fonseca MG, Rosan TM, Vedovato LB, Wagner FH, Silva CVJ, Silva Junior CHL, Arai E, Aguiar AP, Barlow J, Berenguer E, Deeter MN, Domingues LG, Gatti L, Gloor M, Malhi Y, Marengo JA, Miller JB, Phillips OL, and Saatchi S. 2018. 21stCentury drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nature Communications 9(536):1-12.

Buzz Feed News. 2019. These Heartbreaking Photos Show The Devastation Of The Amazon Fires. Retrieved 1 September 2019 from https://www.buzzfeednews.com/article/gabrielsanchez/photos-trending-devastation-amazon-wildfire

Da Silva JMC, Rylands AB, and Da Fonseca GAB. 2005. The Fate of the Amazonian Areas of Endemism. Conservation Biology 19(3):689-694.

Da Silva V, Trujillo F, Martin A, Zerbini AN, Crespo E, Aliaga-Rossel E, and Reeves R. 2018. Inia geoffrensis. The IUCN Red List of Threatened Species 2018: e.T10831A50358152. http://dx.doi.org/10.2305/IUCN.UK.2018-2.RLTS.T10831A50358152.en. Downloaded on 27 August 2019.

ICMBio. 2019. Amazônia. Retrieved 26 August 2019 from http://www.icmbio.gov.br/portal/unidades deconservacao/biomas-brasileiros/amazonia

Lewinsohn TM, and Prado PI. 2005. How Many Species Are There in Brazil? Conservation Biology 19(3):619.

Malhi Y. 2019. does the amazon provide 20% of our oxygen? Travels in ecosystem science. Retrieved 29 August 2019 from http://www.yadvindermalhi.org/blog/does-the-amazon-provide-20-of-our-oxygen

Malhi Y., Roberts JT, Betts RA, Killeen TJ, Li W, Nobre CA. 2008. Climate Change, Deforestation, and the Fate of the Amazon. Science 319:169-172.

Patterson BD. 2000. Patterns and trends in the discovery of new Neotropical mammals. Diversity and Distributions, 6, 145-151.

Sea Mashable. 2019. The Amazon forest is burning to the ground. Here’s how it happened and what you can do to help. Retrieved 1 September 2019 from https://sea.mashable.com/culture/5813/the-amazon-forest-is-burning-to-the-ground-heres-how-it-happened-and-what-you-can-do-to-help

Wagner M, and Hayes M. 2019. Wildfires rage in the Amazon. CNN. Retrieved 26 August 2019 from https://www.cnn.com/americas/live-news/amazon-wildfire-august-2019/index.html

Wikipedia. 2019. Madeira river. Retrieved 29 August 2019 from https://en.wikipedia.org/wiki/Madeira_River

Yeung J. 2019. Blame humans for starting the Amazon fires, environmentalists say. CNN. Retrieved 26 August 2019 from https://www.cnn.com/2019/08/22/americas/amazon-fires-humans-intl-hnk-trnd/index.html

Current gray whale die-off: a concern or simply the circle of life?

By Leila Lemos, PhD Candidate in Wildlife Sciences, Fisheries and Wildlife Department / OSU

Examination of a dead gray whale found in Pacifica, California, in May 2019.
Source: CNN 2019.

 

The avalanche of news on gray whale deaths this year is everywhere. And because my PhD thesis focuses on gray whale health, I’ve been asked multiple times now why this is happening. So, I thought it was a current and important theme to explore in our blog. The first question that comes to (my) mind is: is this a sad and unusual event for the gray whales that raises concern, or is this die-off event expected and simply part of the circle of life?

At least 64 gray whales have washed-up on the West Coast of the US this year, including the states of California, Oregon and Washington. According to John Calambokidis, biologist and founder of the Cascadia Research Collective, the washed-up whales had one thing in common: all were in poor body condition, potentially due to starvation (Calambokidis in: Paris 2019). Other than looking skinny, some of the whale carcasses also presented injuries, apparently caused by ship strikes (CNN 2019).

Cascadia Research Collective examining a dead gray whale in 9 May 2019, washed up in Washington state. Cause of death was not immediately apparent but appeared consistent with nutritional stress.
Source: Cascadia Research Collective 2019.

To give some context, gray whales migrate long distances while they fast for long periods. They are known for performing the longest migration ever seen for a mammal, as they travel up to 20,000 km roundtrip every year from their breeding grounds in Baja California, Mexico, to their feeding grounds in the Bering and Chukchi seas (Calambokidis et al. 2002, Jones and Swartz 2002, Sumich 2014). Thus, a successful feeding season is critical for energy replenishment to recover from the previous migration and fasting periods, and for energy storage to support their metabolic needsduring the migration and fasting periods that follow. An unsuccessful feeding season could likely result in poor body condition, affecting individual performance in the following seasons, a phenomenon known as the carry-over effect(Harrison et al., 2011).

In addition, environmental change, such as climate variations, might impact shifts in prey availability and thus intensify energetic demands on the whales as they need to search harder and longer for food. These whales already fast for months and spend large energy reserves supporting their migrations. When they arrive at their feeding grounds, they need to start feeding. If they don’t have access to predictable food sources, their fitness is affected and they become more vulnerable to anthropogenic threats, including ship strikes, entanglement in fishery gear, and contamination.

For the past three years, I have been using drone-based photogrammetry to assess gray whale body condition along the Oregon coast, as part of my PhD project. Coincident to this current die-off event, I have observed that these whales presented good body condition in 2016, but in the past two years their condition has worsened. But these Oregon whales are feeding on different prey in different areas than the rest of the ENP that heads up to the Bering Sea to feed. So, are all gray whales suffering from the same broad scale environmental impacts? I am currently looking into environmental remote sensing data such as sea surface temperature, chlorophyll-a and upwelling index to explore associations between body condition and environmental anomalies that could be associated.

Trying to answer the question I previously mentioned “is this event worrisome or natural?”, I would estimate that this die-off is mostly due to natural patterns, mainly as a consequence of ecological patterns. This Eastern North Pacific (ENP) gray whale population is now estimated at 27,000 individuals (Calambokidis in: Paris 2019) and it has been suggested that this population is currently at its carrying capacity(K), which is estimated to be between 19,830 and 28,470 individuals (Wade and Perryman, 2002). Prey availability on their primary foraging grounds in the Bering Sea may simply not be enough to sustain this whole population.

The plot below illustrates a population in exponential growth over the years. The population reaches a point (K) that the system can no longer support. Therefore, the population declines and then fluctuates around this K point. This pattern and cycle can result in die-off events like the one we are currently witnessing with the ENP gray whale population.

Population at a carrying capacity (K)
Source: Conservation of change 2019.

 

According to the American biologist Paul Ehrlich: “the idea that we can just keep growing forever on a finite planet is totally imbecilic”. Resources are finite, and so are populations. We should expect die-off events like this.

Right now, we are early on the 2019 feeding season for these giant migrators. Mortality numbers are likely to increase and might even exceed previous die-off events. The last ENP gray whale die-off event occurred in the 1999-2000 season, when a total of 283 stranded whales in 1999 and 368 in 2000 were found displaying emaciated conditions (Gulland et al. 2005). This last die-off event occurred 20 years ago, and thus in my opinion, it is too soon to raise concerns about the long-term impacts on the ENP gray whale population, unless this event continues over multiple years.

 

References

Calambokidis, J. et al. 2002. Abundance, range and movements of a feeding aggregation of gray whales (Eschrichtius robustus) from California to southeastern Alaska in 1998. Journal of Cetacean research and Management. 4, 267-276.

Cascadia Research Collective (2019, May 10). Cascadia and other Washington stranding network organizations continue to respond to growing number of dead gray whales along our coast and inside waters. Retrieved from http://www.cascadiaresearch.org/washington-state-stranding-response/cascadia-and-other-washington-stranding-networkorganizations?fbclid=Iw AR1g7zc4EOMWr_wp_x39ertvzpjOnc1zZl7DoMbBcjI1Ic_EbUx2bX8_TBw

Conservation of change (2019, May 31). Limits to Growth: the first law of sustainability. Retrieved from http://www.conservationofchange.org/limits

CNN (2019, May 15). Dead gray whales keep washing ashore in the San Francisco Bay area.Retrieved from https://www.cnn.com/2019/05/15/us/gray-whale-deaths-trnd-sci/index.html

Gulland, F. M. D., H. Pérez-Cortés M., J. Urbán R., L. Rojas-Bracho, G. Ylitalo, J. Weir, S. A. Norman, M. M. Muto, D. J. Rugh, C. Kreuder, and T. Rowles. 2005. Eastern North Pacific gray whale (Eschrichtius robustus) unusual mortality event, 1999-2000. U. S. Dep. Commer., NOAA Tech. Memo. NMFS-AFSC-150, 33 p.

Harrison, X. A., et al., 2011. Carry-over effects as drivers of fitness differences in animals. Journal of Animal Ecology. 80, 4-18.

Jones, M. L., Swartz, S. L., Gray Whale, Eschrichtius robustus. Encyclopedia of Marine Mammals. Academic Press, San Diego, 2002, pp. 524-536.

Paris (2019, May 27). Gray Whales Wash Up On West Coast At Near-Record Levels.Retrieved from https://www.wbur.org/hereandnow/2019/05/27/gray-whales-wash-up-record-levels

Sumich, J. L., 2014. E. robustus: The biology and human history of gray whales. Whale Cove Marine Education.

Wade, P. R., Perryman, W., An assessment of the eastern gray whale population in 2002. IWC, Vol. SC/54/BRG7 Shimonoseki, Japan, 2002, pp. 16.

 

Photogrammetry Insights

By Leila Lemos, PhD Candidate, Fisheries and Wildlife Department, Oregon State University

After three years of fieldwork and analyzing a large dataset, it is time to finally start compiling the results, create plots and see what the trends are. The first dataset I am analyzing is the photogrammetry data (more on our photogrammetry method here), which so far has been full of unexpected results.

Our first big expectation was to find a noticeable intra-year variation. Gray whales spend their winter in the warm waters of Baja California, Mexico, period while they are fasting. In the spring, they perform a big migration to higher latitudes. Only when they reach their summer feeding grounds, that extends from Northern California to the Bering and Chukchi seas, Alaska, do they start feeding and gaining enough calories to support their migration back to Mexico and subsequent fasting period.

 

Northeastern gray whale migration route along the NE Pacific Ocean.
Source: https://journeynorth.org/tm/gwhale/annual/map.html

 

Thus, we expected to see whales arriving along the Oregon coast with a skinny body condition that would gradually improve over the months, during the feeding season. Some exceptions are reasonable, such as a lactating mother or a debilitated individual. However, datasets can be more complex than we expect most of the times, and many variables can influence the results. Our photogrammetry dataset is no different!

In addition, I need to decide what are the best plots to display the results and how to make them. For years now I’ve been hearing about the wonders of R, but I’ve been skeptical about learning a whole new programming/coding language “just to make plots”, as I first thought. I have always used statistical programs such as SPSS or Prism to do my plots and they were so easy to work with. However, there is a lot more we can do in R than “just plots”. Also, it is not just because something seems hard that you won’t even try. We need to expose ourselves sometimes. So, I decided to give it a try (and I am proud of myself I did), and here are some of the results:

 

Plot 1: Body Area Index (BAI) vs Day of the Year (DOY)

 

In this plot, we wanted to assess the annual Body Area Index (BAI) trends that describe how skinny (low number) or fat (higher number) a whale is. BAI is a simplified version of the BMI (Body Mass Index) used for humans. If you are interested about this method we have developed at our lab in collaboration with the Aerial Information Systems Laboratory/OSU, you can read more about it in our publication.

The plots above are three versions of the same data displayed in different ways. The first plot on the left shows all the data points by year, with polynomial best fit lines, and the confidence intervals (in gray). There are many overlapping observation points, so for the middle plot I tried to “clean up the plot” by reducing the size of the points and taking out the gray confidence interval range around the lines. In the last plot on the right, I used a linear regression best fit line, instead of polynomial.

We can see a general trend that the BAI was considerably higher in 2016 (red line), when compared to the following years, which makes us question the accuracy of the dataset for that year. In 2016, we also didn’t sample in the month of July, which is causing the 2016 polynomial line to show a sharp decrease in this month (DOY: ~200-230). But it is also interesting to note that the increasing slope of the linear regression line in all three years is very similar, indicating that the whales gained weight at about the same rate in all years.

 

Plot 2: Body Area Index (BAI) vs Body Condition Score (BCS)

 

In addition to the photogrammetry method of assessing whale body condition, we have also performed a body condition scoring method for all the photos we have taken in the field (based on the method described by Bradford et al. 2012). Thus, with this second set of plots, we wanted to compare both methods of assessing whale body condition in order to evaluate when the methods agree or not, and which method would be best and in which situation. Our hypothesis was that whales with a ‘fair’ body condition would have a lower BAI than whales with a ‘good’ body condition.

The plots above illustrate two versions of the same data, with data in the left plot grouped by year, and the data in the right plot grouped by month. In general, we see that no whales were observed with a poor body condition in the last analysis months (August to October), with both methods agreeing to this fact. Additionally, there were many whales that still had a fair body condition in August and September, but less whales in the month of October, indicating that most whales gained weight over the foraging seasons and were ready to start their Southbound migration and another fasting period. This result is important information regarding monitoring and conservation issues.

However, the 2016 dataset is still a concern, since the whales appear to have considerable higher body condition (BAI) when compared to other years.

 

Plot 3:Temporal Body Area Index (BAI) for individual whales

 

In this last group of plots, we wanted to visualize BAI trends over the season (using day of year – DOY) on the x-axis) for individuals we measured more than once. Here we can see the temporal patterns for the whales “Bit”, “Clouds”, “Pearl”, “Scarback, “Pointy”, and “White Hole”.

We expected to see an overall gradual increase in body condition (BAI) over the seasons, such as what we can observe for Pointy in 2018. However, some whales decreased their condition, such as Bit in 2018. Could this trend be accurate? Furthermore, what about BAI measurements that are different from the trend, such as Scarback in 2017, where the last observation point shows a lower BAI than past observation points? In addition, we still observe a high BAI in 2016 at this individual level, when compared to the other years.

My next step will be to check the whole dataset again and search for inconsistencies. There is something causing these 2016 values to possibly be wrong and I need to find out what it is. The overall quality of the measured photogrammetry images was good and in focus, but other variables could be influencing the quality and accuracy of the measurements.

For instance, when measuring images, I often struggled with glare, water splash, water turbidity, ocean swell, and shadows, as you can see in the photos below. All of these variables caused the borders of the whale body to not be clearly visible/identifiable, which may have caused measurements to be wrong.

 

Examples of bad conditions for performing photogrammetry: (1) glare and water splash, (2) water turbidity, (3) ocean swell, and (4) a shadow created in one of the sides of the whale body.
Source: GEMM Lab. Taken under NMFS permit 16111 issued to John Calambokidis.

 

Thus, I will need to check all of these variables to identify the causes for bad measurements and “clean the dataset”. Only after this process will I be able to make these plots again to look at the trends (which will be easy since I already have my R code written!). Then I’ll move on to my next hypothesis that the BAI of individual whales varied by demographics including sex, age and reproductive state.

To carry out robust science that produces results we can trust, we can’t simply collect data, perform a basic analysis, create plots and believe everything we see. Data is often messy, especially when developing new methods like we have done here with drone based photogrammetry and the BAI. So, I need to spend some important time checking my data for accuracy and examining confounding variables that might affect the dataset. Science can be challenging, both when interpreting data or learning a new command language, but it is all worth it in the end when we produce results we know we can trust.

 

 

 

GEMM Lab 2018: A Year in the Life

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

As 2018 draws to a close, it is gratifying to step back and appreciate the accomplishments of the past year. For all members of the GEMM Lab, 2018 has certainly been one for the books! Here are some of our highlights for your holiday enjoyment.

We conducted fieldwork to collect new data in multiple seasons, multiple hemispheres, and across oceans. For the first time, GEMM Lab members joined the Northern California Current Ecosystem cruises aboard NOAA ship Bell M. Shimada as marine mammal observers—Florence in February, Alexa in May, and me in September.

An image of the NOAA ship Bell M. Shimada transiting between stations. Multiple members of the GEMM Lab conducted surveys from this NOAA vessel in 2018. (Image source: Alexa Kownacki)
Common dolphins (Delphinus delphis). Photo: Dawn Barlow.
Dawn Barlow scans for marine mammals from the flying bridge of NOAA ship Bell M. Shimada. Photo: Jess O’Loughlin.
Alexa on-effort using binoculars to estimate the distance and bearing of a marine mammal sighted off the starboard bow. Image source: Alexa K.

Summertime in the Pacific Northwest brings the gray whales to the Oregon Coast. The drone-flying, poop-scooping, plankton-trapping team of Leigh, Todd, Leila, Joe, and Sharon took to the water for the third year to investigate the health of this gray whale population. It was a successful field season, ending with 72 fecal samples collected! Visiting students joined our experienced members to shadow the gray whale fieldwork—Julia Stepanuk and Alejandro Fernandez Ajo came from across the country to hop on board with us for a bit. Friendship and collaboration were built quickly in a little boat chasing after whale poop, bonding over peanut butter and jelly sandwiches.

Launching the drone. Photo by Alejandro Fernandez Ajo.
The gray whale team aboard R/V Ruby. Photo by Alejandro Fernandez Ajo.
Gray whale photo-identification. Photo by Dawn Barlow.

Another GEMM Lab team tracked the gray whales from the cliff in Port Orford. Lisa Hildebrand joined us as the GEMM Lab’s newest graduate student, and immediately led a team of interns on Oregon’s southern coast to track gray whale movements and sample their prey from a trusty research kayak.

The summer 2018 gray whale foraging ecology team, affectionately known as “team whale storm”, at the Port Orford Field Station.

Rachael observed seabirds from Yaquina Head in May and June, where the colony of common murres had the highest reproductive success in 10 years! Then she left the summertime in July to travel to the other end of the world, braving winter in the remote South Atlantic to study South American fur seals in the Falkland Islands.

Dr. Rachael Orben and Dr. Alistair Bayliss looking out towards the fur seals. Photo: Kayleigh Jones

In New Caledonia, Solene and a research team ventured to Antigonia Seamount and Orne Bank to study the use of these offshore areas by breeding humpback whales. They collected numerous biopsy samples and successfully deployed satellite tags. Solene was also selected to receive the Louis Herman research scholarship to continue studying humpback whale movement and diving behavior around seamounts.

Sorting biopsy samples during a successful expedition to study humpback whales around remote seamounts in the South Pacific.

Beyond fieldwork, our members have been busily disseminating our findings. In July, Leigh and I traveled to Wellington to present our latest findings on New Zealand blue whales to scientists, managers, politicians, industry representatives, and advocacy groups. Because of our documentation of a unique New Zealand blue whale population, which was published earlier this year, the New Zealand government has proposed to create a Marine Mammal Sanctuary for the protection of blue whales. This is quite a feat, considering blue whales were classified as only “migrant” in New Zealand waters prior to our work. Fueled by flat whites in wintery Wellington, we navigated government buildings, discussing blue whale distribution patterns, overlap with the oil and gas industry, what we now know based on our latest analyses, and what we consider to be the most pressing gaps in our knowledge.

Dr. Leigh Torres and Dawn Barlow in front of Parliament in Wellington, New Zealand following the presentation of their recent findings.

Alexa spent the summer and fall in San Diego, where she collaborated with researchers at NOAA Southwest Fisheries Science Center on her study of about the health of bottlenose dolphins off the California coast. Her time down south has been productive and we look forward to having her back in Oregon with us to round out the second year of her PhD program.

In the fall, Dom and Leigh participated in the first ever Oregon Sea Otter Status of Knowledge Symposium. With growing interest in a potential sea otter reintroduction, the symposium brought together a range of experts – including scientists, managers, and tribes – to discuss what we currently know about sea otters in other regions and how this knowledge could be applied to an Oregon reintroduction effort. Dom was one of many speakers at this event, and gave a well-received talk on Oregon’s previous sea otter reintroduction attempt and brief discussion on his thesis research. Over the next year, Dom not only plans to finish his thesis, but also to join an interdisciplinary research team to further investigate other social, genetic, and ecological implications of a potential sea otter reintroduction.

Sea otter mom and pup. Source: Hakai Magazine.
2018-19 OSU NRT Cohort. Source: Oregon State University.

Several GEMM Lab members reached academic milestones this year. Rachael was promoted to Assistant Professor in the spring! She now leads the Seabird Oceanography Lab, and remains involved in multiple projects studying seabirds and pinnipeds all over the world. Leila passed her PhD qualifying exams and advanced to candidacy in the spring, a major accomplishment toward completing her doctoral degree. I successfully defended my MS degree in June, and my photo was added to our wall gallery of GEMM Lab graduates. I won’t be leaving the GEMM Lab anytime soon, however, as I will be continuing my research on New Zealand blue whales as a PhD student. The GEMM Lab welcomed a new MS student in the summer—Lisa Hildebrand will be studying gray whale foraging ecology on the Oregon Coast. Welcome, Lisa! In early December, Solene successfully defended her PhD, officially becoming Dr. Derville. Congratulations to all on these milestones, and congratulations to Leigh for continuing to grow such a successful lab and guiding us all toward these accomplishments.

Dawn Barlow answers questions during her M.Sc. defense seminar.
Dr. Solene Derville and co-supervisors Dr. Claire Garrigue and Dr. Leigh Torres after a successful PhD Defense!

Perhaps you’re looking to do some reading over the holidays? The GEMM Lab has been publishing up a storm this year! The bulletin board outside our lab is overflowing with new papers. Summarizing our work and sharing our findings with the scientific community is a critical piece of what we do. The 21 new publications this year in 14 scientific journals include contributions from Leigh (13), Rachael (3), Solene (3), Leila (6), Florence (1), Amanda (1), Erin (1), Courtney (1), Theresa (1), and myself (3). Scroll down to the end of this post to see the complete list!

If you are reading this, thank you for your support of our lab, our members, and our work. Our successes come not only from our individual determination, but more importantly from our support of one another and the support of our communities. We look forward to what’s ahead in 2019. Happy holidays from the GEMM Lab!

The whole GEMM Lab (lab dogs included) gathered for an evening playing “Evolution” at Leigh’s house.

Barlow, D. R., Torres, L. G., Hodge, K. B., Steel, D., Baker, C. S., Chandler, T. E., Bott, N., Constantine, R., Double, M. C., Gill, P., Glasgow, D., Hamner, R. M., Lilley, C., Ogle, M., Olson, P. A., Peters, C., Stockin, K. A., Tessaglia-Hymes, C. T., & Klinck, H. (2018). Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endangered Species Research, 36, 27-40.

Barlow, D. R., Fournet, M., & Sharpe, F. (2018). Incorporating tides into the acoustic ecology of humpback whales. Marine Mammal Science.

Baylis, A. M., Tierney, M., Orben, R. A., Staniland, I. J., & Brickle, P. (2018). Geographic variation in the foraging behaviour of South American fur seals. Marine Ecology Progress Series, 596, 233-245.

Bishop, A., Brown, C., Rehberg, M., Torres, L., & Horning, M. (2018). Juvenile Steller sea lion (Eumetopias jubatus) utilization distributions in the Gulf of Alaska. Movement ecology, 6(1), 6.

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2018). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science.

Cardoso, M. D., Lemos, L. S., Roges, E. M., de Moura, J. F., Tavares, D. C., Matias, C. A. R., … & Siciliano, S. (2018). A comprehensive survey of Aeromonas sp. and Vibrio sp. in seabirds from southeastern Brazil: outcomes for public health. Journal of applied microbiology, 124(5), 1283-1293.

Derville, S., Torres, L. G., Iovan, C., & Garrigue, C. (2018). Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Diversity and Distributions, 24(11), 1657-1673.

Derville, S., Torres, L. G., & Garrigue, C. (2018). Social segregation of humpback whales in contrasted coastal and oceanic breeding habitats. Journal of Mammalogy, 99(1), 41-54.

Hann, C. H., Stelle, L. L., Szabo, A., & Torres, L. G. (2018). Obstacles and Opportunities of Using a Mobile App for Marine Mammal Research. ISPRS International Journal of Geo-Information, 7(5), 169.

Holdman, A. K., Haxel, J. H., Klinck, H., & Torres, L. G. (2018). Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, USA. Marine Mammal Science.

Kirchner, T., Wiley, D. N., Hazen, E. L., Parks, S. E., Torres, L. G., & Friedlaender, A. S. (2018). Hierarchical foraging movement of humpback whales relative to the structure of their prey. Marine Ecology Progress Series, 607, 237-250.

Moura, J. F., Tavares, D. C., Lemos, L. S., Acevedo-Trejos, E., Saint’Pierre, T. D., Siciliano, S., & Merico, A. (2018). Interspecific variation of essential and non-essential trace elements in sympatric seabirds. Environmental pollution, 242, 470-479.

Moura, J. F., Tavares, D. C., Lemos, L. S., Silveira, V. V. B., Siciliano, S., & Hauser-Davis, R. A. (2018). Variation in mercury concentration in juvenile Magellanic penguins during their migration path along the Southwest Atlantic Ocean. Environmental Pollution, 238, 397-403.

Orben, R. A., Kokubun, N., Fleishman, A. B., Will, A. P., Yamamoto, T., Shaffer, S. A., Takahashi, A., & Kitaysky, A. S. (2018). Persistent annual migration patterns of a specialist seabird. Marine Ecology Progress Series, 593, 231-245.

Orben, R. A., Connor, A. J., Suryan, R. M., Ozaki, K., Sato, F., & Deguchi, T. (2018). Ontogenetic changes in at-sea distributions of immature short-tailed albatrosses Phoebastria albatrus. Endangered Species Research, 35, 23-37.

Pickett, E. P., Fraser, W. R., Patterson‐Fraser, D. L., Cimino, M. A., Torres, L. G., & Friedlaender, A. S. (2018). Spatial niche partitioning may promote coexistence of Pygoscelis penguins as climate‐induced sympatry occurs. Ecology and Evolution, 8(19), 9764-9778.

Siciliano, S., Moura, J. F., Tavares, D. C., Kehrig, H. A., Hauser-Davis, R. A., Moreira, I., Lavandier, R., Lemos, L. S., & Quinete, N. S. (2018). Legacy Contamination in Estuarine Dolphin Species From the South American Coast. In Marine Mammal Ecotoxicology (pp. 95-116). Academic Press.

Sullivan, F. A., & Torres, L. G. (2018). Assessment of vessel disturbance to gray whales to inform sustainable ecotourism. The Journal of Wildlife Management, 82(5), 896-905.

Sztukowski, L. A., Cotton, P. A., Weimerskirch, H., Thompson, D. R., Torres, L. G., Sagar, P. M., Knights, A. M., Fayet, A. L., & Votier, S. C. (2018). Sex differences in individual foraging site fidelity of Campbell albatross. Marine Ecology Progress Series, 601, 227-238.

Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science, 5.

Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., … & Sequeira, A. M. M. (2018). Outstanding challenges in the transferability of ecological models. Trends in ecology & evolution.

 

Scientific publishing: Impact factor, open access and citations

By Leila Lemos1 and Rachel Ann Hauser-Davis2

1PhD candidate, Fisheries and Wildlife Department, OSU

2PhD, CESTEH/ENSP/Fiocruz, Rio de Janeiro, Brazil

Scientific publishing not only communicates new knowledge, but also is a measure of each scientist’s success: the impact each scientist has on his/her field is often measured by his/her number of publications and the reputation of the journals he/she published in. Therefore, publishing in reputable journals, with a high impact factor, is often essential to get a job, promotion and tenure. So, what is an impact factor?

The impact factor (IF) was first created in the 1960’s and is a measure of a journal’s impact on science, as reflected by the yearly average number of citations to recent articles published in that journal. The IF is used to compare the impact of journals within disciplines. Journals with higher impact factors are deemed as more prestigious and of better quality than those with lower ones.

The IF of a journal for any given year is calculated as the number of citations, received in that year, of articles published in that journal during the two preceding years, divided by the total number of articles published in that journal during the two preceding years, as follows:

In recent years, open access (OA) journals have emerged, changing how we perceive publications. However, the role and significance of IF is still present, valuable and used worldwide.

Conventional (non-open access) journals cover publishing costs through access fees, such as subscriptions, site licenses or download charges, which can be paid by universities, research institutions and, sometimes, by individuals. Papers published in OA journals, on the other hand, are distributed online and free of cost. However, there are still publication costs, which are usually paid by the authors. And, open access article processing charges are not cheap, ranging from a few hundred to several thousand dollars, depending on the field (more thoughts on this theme here).

It seems imbalanced that researchers have to pay for their work to be published. They have carried out a study and have obtained results that should be shared with the community. These results should not be treated as a commercial item to be sold. Also, it ends strengthening those who have resources and weakening those who do not have, increasing the division between Northern and Southern hemispheres, and narrowing the knowledge-production system (Burgman 2018).

Thus, a free-of-charge research paper would be interesting for everyone. PeerJ is a good example of a recent OA, free of publishing costs, peer-reviewed, and scholarly journal, that was released in 2013. It’s a totally new model and pushes the boundaries. In addition, there are hybrid journals (i.e., Conservation Biology) that offer both conventional and OA modes, leaving it to the authors to decide what they prefer (Burgman 2018). In many cases, disadvantaged authors might also be able to appeal for waivers. Thus, authors who cannot pay publishing fees might still see their work getting published.

However, this is not how the publishing system typically works. Therefore, researchers need to determine where to publish based on the journal IF and focus/audience, on the different price structures and fees, and whether it is OA or not.

Researchers in general want their articles to be openly accessible for everyone, not just those who can afford to pay the journal for access, so they can increase visibility of their work. Open access can increase the impact/reach of a research paper by facilitating paper downloads, access, and use in other scientific research, which may, in turn, lead to higher citation rate (Eyesenbach 2006).

Higher citation rates would also improve researchers’ H-index: an author-level metric that measures both productivity and citation impact of a scientist or scholar, based on the scientist’s most cited papers and the number of citations that they have received in publications.

The graph below exemplifies the h-index that is based on the maximum value of h such that the given author/journal has published h papers that have each been cited at least h times. In other words, the index is designed to improve with number of publications or citations. The index can only be compared between researchers from same field, as citation conventions might differ widely among different fields.

H-index from a plot of decreasing citations for numbered papers
Source: Wikipedia

 

However, publishing in an OA journal might easily increase researchers’ H-index and journals’ IF. Many researchers have also considered OA as an “artificial citation enhancer”.

As with any new system, some are opposed to the establishment of the OA system, including researchers, academics, librarians, university administrators, funding agencies, government officials, publishers and editorial staff, among many others (Markin 2017). This opposition claims that OA publishing leads to financial damages to the large publishers worldwide, and, mainly, that this system may damage the peer review system in place today, leading to reduced scientific quality (such as “you pay, you publish” predatory journals that take advantage of the paid system by publishing as fast as possible, without any scientific rigor whatsoever).

However, many reputable journals, such as Elsevier, Springer, Wiley and Blackwell, now offer OA as an option for their established journals. This approach is simply another option for authors, where they may pay if they want for their paper to be available for everyone. Even if this option is available, manuscripts still go through a rigorous peer-review that occurs with both conventional and OA journals. Thus, publishing in OA should be just as rigorous.

Open access papers would be the most “scientifically ethical”, as science is aimed at society, for society, and this type of publishing furthers research reach. However, paying thousands of dollars is sometimes very complicated, as this means less money for fieldwork costs, gears, laboratory analyses, among others.

All in all, OA is a recent development that is changing scientist approach to publication. The future of scientific publication seems uncertain and likely to hold new developments in the near future.

 

References:

Burgman M. 2018. Open access and academic imperialism. Conservation Biology 0 (0): 1–2. DOI: 10.1111/cobi.13248.

Eysenbach G. 2006. Citation Advantage of Open Access Articles. PLoS Biology 4 (5): e157. doi:10.1371/journal.pbio.0040157. PMC 1459247. PMID 16683865.

Markin P. 2017. The Sustainability of Open Access Publishing Models Past a Tipping Point. Open Science. Retrieved 26 April 2017.

Remote Sensing Applications

By Leila Lemos, PhD candidate

Fisheries and Wildlife Department, OSU

 

I am finally starting my 3rd and last year of my PhD. Just a year left and yet so many things to do. As per department requirements, I still need to take some class credits, but what classes could I take? In this short amount of time it is important to focus on my research project and on what could help me better understand the many branches of the project and what could improve my analyses. Thinking of that, both my advisor (Dr. Leigh G. Torres) and I agreed that it would be useful for me to take a class on remote sensing. So, I could learn more about this field, as well as try to include some remote sensing analyses in my project, such as sea surface temperature (SST) and chlorophyll (i.e., as a productivity indicator) conditions over the years we have collected data on gray whales off the Oregon coast.

 

Our photogrammetry data indicates that whales gradually increased their body condition over the feeding seasons of 2016 and 2018, while 2017 is different. Whales were still looking skinny in the middle of the season, and we were not collecting many fecal samples up to that point (indicating not much feeding). These findings made us wonder if this was related to delayed seasonal upwelling events and consequently low prey availability. These questions are what motivated me the most to join this class so that we might be able to link environmental correlates with our observations of gray whale body condition.

Figure 01: Skinny body condition state of the gray whale “Pancake” in August 2017.
Source: Leila S. Lemos

 

If we stop to think about what remote sensing is, we have already been implementing this method in our project since the beginning, as my favorite definition for remote sensing is “the art of collecting information of objects or phenomenon without touching it”. So, yes, the drone is a type of sensor that remotely collects information of objects (in this case, whales).

Figure 02: Drone remotely collecting information of a whale in September 2018. Drone in detail. Collected under NOAA/NMFS permit #16111.
Source: Leila Lemos

 

However, satellites, all the way up in the space, are also remotely sensing the Earth and its objects and phenomena. Even from thousands of km above Earth, these sensors are capable of generating a great amount of detailed data that is easily and freely accessible (i.e., NASA, NOAA), and can be used for multiple applications in different fields of study. Satellites are also able to collect data from remote areas like the Antarctica and the Arctic, as well as other areas that are not easily reached by humans. One important application of the use of satellite imagery is wildlife monitoring.

For example, satellite data was used to detect variation in the abundance of Weddell seals (Leptonychotes weddellii) in Erebus Bay, Antarctica (LaRue et al., 2011). Because this is a well-studied seal population, the object of this study was to test if satellite imagery could produce reliable abundance estimates. The authors used high-resolution (0.6 m) satellite imagery (from satellites Quick-Bird-2 and WorldView-1) to compare counts from the ground with counts from satellite images in the same locations at the same time. This study demonstrated a reliable methodology for further studies to replicate.

Figure 03: WorldView-1 image (0.6 m resolution) of Weddell seals hauled out east of Inaccessible Island, Erebus Bay, Antarctica.
Source: LaRue et al. (2011).

 

Satellite imagery was also applied to estimate colony sizes of Adélie penguins in Antarctica (LaRue et al., 2014). High-resolution (0.6 m) satellite imagery combined with spectral analysiswas used to estimate the sizes of the penguin breeding colonies. Ground counts were also used in order to check the reliability of the applied method. The authors then created a model to predict the abundance of breeding pairs as a function of the habitat, which was identified terrain slope as an important component of nesting density.

The identification of whales using satellite imagery is also possible. Fretwell et al. (2014)pioneered this method by successfully identifing Southern Right Whales (Eubalaena australis) in the Golfo Nuevo, Península Valdés, in Argentina in satellite images. By using very high-resolution satellite imagery (50 cm resolution) and a water penetrating coastal band that was able to see deeper into the water column, the researchers were able to successfully identify and count the whales (Fig. 04). The importance of this study was very significant, since this species was extensively hunted from the 17ththrough to the 20thcentury. Since then, the species has shown a strong recovery, but population estimates are still at <15% of historical estimates. Thus, being able to use new tools to identify, count and monitor individuals in this recovering population is a great development, especially in remote and hard to reach areas.

Figure 04: Identification of Southern Right Whales by using imagery from the WorldView2 satellite in the Golfo Nuevo Bay, Península Valdés, Argentina.
Source: Fretwell et al. (2014).

 

Polar bears (Ursus maritimus) have also been studied in the Foxe Basin, in Nunavut and Quebec, Canada (LaRue et al., 2015). Researchers used high-resolution satellite imagery in an attempt to identify and count the bears, but spectral signature differences between bears and other objects were insufficient to yield useful results. Therefore, researchers developed an automated image differencing, also known as change detection, that identifies differences between remotely sensed images collected at different times and “subtract of one image from another”. This method correctly identified nearly 90% of the bears. The technique also generated false positives, but this problem can be corrected by a manual review.

Figure 05 shows the difference in resolution of two types of satellite imagery, the panchromatic (0.6 m resolution) and the multispectral (2.4 m resolution). LaRue et al. (2015)decided not to use the multispectral imagery due to resolution constraints.

Figure 05: Polar Bears on panchromatic (0.6 m resolution) and multispectral (2.4 m resolution) imagery.
Source: LaRue et al. (2015).

 

A more recent study is being conducted by my fellow OSU Fisheries and Wildlife graduate student, Jane Dolliveron breeding colonies of three species of North Pacific albatrosses (Phoebastria immutabilis, Phoebastria nigripes, and Phoebastria albatrus)(Dolliver et al., 2017). Jane is using high-resolution multispectral satellite imagery (DigitalGlobe WorldView-2 and -3) and image processing techniques to enumerate the albatrosses. They are also using albatross species at multiple reference colonies in Hawaii and Japan (Fig. 06) to determine species identification accuracy and required correction factor(s). This will allow scientists to accurately count unknown populations on the Senkakus, which are uninhabited islands controlled by Japan in the East China Sea.

Figure 06: Satellite image of a colony of short-tailed albatrosses (Phoebastria albatrus) in Torishima, Japan, 2016.
Source: Satellite image provided by Jane Dolliver.

 

Using satellite imagery to count seals, penguins, whales, bears and albatrosses is just the start of this rapidly advancing technology. Techniques and resolutions are continuously improving. Methods can also be applied to many other endangered species, especially in remote areas, providing data on presence, abundance, annual productivity, population estimates and trends, changes in distribution, and breeding ground usage.

Other than directly monitoring wildlife, satellite images can also provide information on the environmental variables that can be related to wildlife presence, abundance, productivity and distribution.

Gentemann et al. (2017), for example, used satellite data from NASA to analyze SST variations along the west coast of the United States from 2002 to 2016. The NASA Jet Propulsion Laboratory produces global, daily, 1 km, multiscale ultra-high resolution, motion-compensated analysis of SST, and incorporates SSTs from eight different satellites. Researchers were able to identify warmer than usual SSTs (also called anomalies) along the Washington, Oregon, and California coasts from January 2014 to August 2016 (Fig.07) relative to previous years. This marine heat wave started in the Gulf of Alaska and ended in Southern California, where SST reached a maximum temperature anomaly of 6.2°C, causing major disturbances and substantial economic impacts.

Figure 07: Monthly SST anomalies in the West Coast of United States, from January 2014 to August 2016.
Source: Gentemann et al. (2017).

 

Changes in SST and winds may alter events such as the coastal upwelling that supplies nutrients to sustain a whole food chain. A marine heat-wave event as described by Gentemann et al. (2017)could have significant impacts on the health of the marine ecosystem in the subsequent season (Gentemann et al., 2017).

These findings may even relate to our questions regarding the poor gray whale body condition we noticed in 2017: this marine heat wave that lasted until August 2016 along the US west coast could have impacted the ecosystem in the subsequent season. However, I must conduct a more detailed study to determine if this heat wave was related or if another oceanographic process was involved.

So, whether remotely sensed data is generated by satellites, drones, thermal imagery, robots (as I previously wrote about), or another type of technology, it can have important  and informative applications to monitor wildlife or environmental variables associated with their ecology and biology. We can take advantage of remotely sensed technology to aid wildlife conservation efforts.

 

References

Dolliver, J., et al., Multispectral processing of high resolution satellite imagery to determine the abundance of nesting albatross. Ecological Society of America, Portland, OR, United States., 2017.

Fretwell, P. T., et al., 2014. Whales from Space: Counting Southern Right Whales by Satellite. Plos One. 9,e88655.

Gentemann, C. L., et al., 2017. Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophysical Research Letters. 44,312-319.

LaRue, M. A., et al., 2014. A method for estimating colony sizes of Adélie penguins using remote sensing imagery. Polar Biology. 37,507-517.

LaRue, M. A., et al., 2011. Satellite imagery can be used to detect variation in abundance of Weddell seals (Leptonychotes weddellii) in Erebus Bay, Antarctica. Polar Biology. 34,1727–1737.

LaRue, M. A., et al., 2015. Testing Methods for Using High-Resolution Satellite Imagery to Monitor Polar Bear Abundance and Distribution. Wildlife Society Bulletin. 39,772-779.

 

 

 

 

 

Are bacteria important? What do we get by analyzing microbiomes?

By Leila Lemos, PhD candidate, Fisheries and Wildlife Department, OSU

As previously mentioned in one of Florence’s blog posts, the GEMM Lab holds monthly lab meetings, where we share updates about our research and discuss articles and advances in our field, among other activities.

In a past lab meeting we were asked to bring an article to discuss that had inspired us in the past to conduct research in the marine field or in our current position. I brought to the meeting a literature review regarding methodologies to overcome the challenges of studying conservation physiology in large whales [1]. This article discusses different non-invasive or minimally invasive matrices (e.g., feces, blow, skin/blubber) that can be gathered from whales, and what types of analyses could be carried out, as well as their pros and cons.

One of the possible analyses that can be performed with fecal samples that was discussed in the article is the gut microflora (i.e., bacterial gut community) via genetic analysis. Since my PhD project analyzes fecal samples to determine/quantify stress responses in gray whales, we have since discussed the possibility of integrating this extra parameter to our analysis.

But… what is the importance of analyzing the gut microflora of a whale? What is the relationship between microflora and stress responses? Should we really use our limited sample size, time and money to work on this extra analysis? In order to be able to answer all of these questions, I began reading some articles of the field to better understand its importance and what kind of research questions this analysis can answer.

The gut of a mammal comprises a natural habitat for a large and dynamic community of bacteria [2] that is first developed in early life. Colonization of facultative bacteria (i.e., aerobic bacteria) begins at birth [3], and later, anaerobic bacteria also colonizes the gut. In humans, at the age of 1 year old, the microbiome should have a stable adult-like signature (Fig. 1).

Figure 01: Development of the microbiome in early life.
Source: [3]

The gut bacterial community is important for the physiology and pathology of its host and plays an important role in mammal digestion and health [2], responsible for many metabolic activities, including:

  • fermentation of non-digestible dietary residue and endogenous mucus [2];
  • recovery of energy [2];
  • recovery of absorbable nutrients [2];
  • cellulose digestion [4];
  • vitamin K synthesis [4];
  • important trophic effects on intestinal epithelia (cell proliferation and differentiation) [2];
  • angiogenesis promotion [4];
  • enteric nerve function [4];
  • immune structure [2];
  • immune function [2];
  • protection of the colonized host against invasion by alien microbes (barrier effect) [2];

Despite all the benefits, the bacterial community might also be potentially harmful when changes in the community composition (i.e., dysbiosis) occur due to the use of antibiotics, illness, stress, aging, lifestyle, bad dietary habits [4], and prolonged food and water deprivation [5]. Thus, potential pathological disorders might emerge when the microbiome community changes, such as allergy, obesity, diabetes, autism, multisystem organ failure, gastrointestinal and prostate cancers, inflammatory bowel diseases (IBD), and cardiovascular diseases [2, 4].

Changes in gut bacterial composition may also alter the brain-gut axis and the central nervous system (CNS) signaling [3]. More specifically, the core pathway affected is the hypothalamic-pituitary-adrenal (HPA) axis, which is activated by physical/psychological stressors. According to a previous study [6], the microbial community in the gut is critical for the development of an appropriate stress response. In addition, the microbial colonization in early life should occur within a certain time window, otherwise an abnormal development of the HPA axis might happen.

However, the gut microbiome can not only affect the HPA axis, but the opposite can also occur [3]. Signaling molecules released by the axis can alter the gastrointestinal (GIT) environment (i.e., motility, secretion, and permeability) [7]. Stress responses, as well as diseases, may also alter the gut permeability, causing the bacteria to cross the epithelial barrier (reducing the overall numbers of bacteria in the gut), activating immune responses that also alter the composition of the bacterial community in the gut [8, 9].

Figure 02: Communication between the brain, gut and microbiome in a healthily and in a stressed or diseased (mucosal inflammation) mammal.
Source: [3]

Thus, when thinking about whales, monitoring of the gut microflora might allow us to detect changes caused by factors such as aging, illness, prolonged food deprivation, and stressful events [2, 5]. However, since these are two-way factors, it is important to find an association between bacterial composition alterations and stressful events, such as the presence of predators (e.g., killer whales), illness (e.g., bad body condition), prolonged food deprivation (e.g., low prey availability and high competition), noise (e.g., noisy vessel traffic, fisheries opening and seismic surveys), and stressful reproductive status (e.g., pregnancy and lactating period). Examination of possible shifts in the gut microflora may be able to detect and be linked to many of these events, and also forecast possible chronic events within the population. In addition, the bacterial community monitoring study could aid in validating the hormone data (i.e., cortisol) we have been working with.

Therefore, the main research questions that arise in this context that can aid in elucidating the stress physiology in gray whales are:

  1. What is the microflora community content in guts of gray whales along the Oregon coast?
  2. Is it possible to detect shifts in the gut microflora from our gray fecal samples over time?
  3. How do gut microflora and cortisol levels correlate?
  4. Am I able to correlate shifts in gut microflora with any of the stressful events listed above?

We can answer so many other questions by analyzing the microbiome of baleen whales. Microbiomes are mainly correlated with host diet [10], so the composition of a microbiome can be associated with specific diets and functional gut capacity, and consequently, be linked to other animal populations, which helps to decode evolutionary questions. Results of a previous study on baleen whale microbiomes [10] point out that whales harbor unique gut microbiomes that are actually similar to those of terrestrial herbivores. Baleen whales and terrestrial herbivores have a shared physical structure of the GIT tract itself (i.e., multichambered foregut) and a shared hole for fermentative metabolisms. The multichambered foregut of baleen whales fosters the maintenance of the gut microbiome that is capable of extracting relatively unavailable nutrients from zooplankton (i.e., chitin, “sea cellulose”).

Figure 03: The similarities between whale and other terrestrial herbivore gut microbiomes: sea and land ruminants.
Source: [11]

Thus, the importance of studying the gut microbiome of a baleen whale is clear. Monitoring of the bacterial community and possible shifts can help us elucidate many questions regarding diet, overall health, stress physiology and evolution. Thinking about my PhD project, it may also help in validating our cortisol level results. I am confident that a microbiome analysis would significantly enhance my studies on the health and ecology of gray whales.

 

References

  1. Hunt, K.E., et al., Overcoming the challenges of studying conservation physiology in large whales: a review of available methods.Conservation Physiology, 2013. 1: p. 1-24.
  2. Guarner, F. and J.-R. Malagelada, Gut flora in health and disease.The Lancet, 2003. 360: p. 512–519.
  3. Grenham, S., et al., Brain–gut–microbe communication in health and disease.Frontiers in Physiology, 2011. 2: p. 1-15.
  4. Zhang, Y., et al., Impacts of Gut Bacteria on Human Health and Diseases.International Journal of Molecular Sciences, 2015. 16: p. 7493-7519.
  5. Bailey, M.T., et al., Stressor exposure disrupts commensal microbial populations in the intestines and leads to increased colonization by Citrobacter rodentium.Infection and Immunity, 2010. 78: p. 1509–1519.
  6. Sudo, N., et al., Postnatal microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response in mice.The Journal of Physiology, 2004. 558: p. 263–275.
  7. Rhee, S.H., C. Pothoulakis, and E.A. Mayer, Principles and clinical implications of the brain–gut–enteric microbiota axis Nature Reviews Gastroenterology & Hepatology, 2009. 6: p. 306–314.
  8. Kiliaan, A.J., et al., Stress stimulates transepithelial macromolecular uptake in rat jejunum.American Journal of Physiology, 1998. 275: p. G1037–G1044.
  9. Dinan, T.G. and J.F. Cryan, Regulation of the stress response by the gut microbiota: Implications for psychoneuroendocrinology.Psychoneuroendocrinology 2012. 37: p. 1369—1378.
  10. Sanders, J.G., et al., Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores.Nature Communications, 2015. 6(8285): p. 1-8.
  11. El Gamal, A. Of whales and cows: the baleen whale microbiome revealed. Oceanbites 2016[cited 2018 07/31/2018]; Available from: https://oceanbites.org/of-whales-and-cows-the-baleen-whale-microbiome-revealed/.

 

Big Data: Big possibilities with bigger challenges

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

Did you know that Excel has a maximum number of rows? I do. During Winter Term for my GIS project, I was using Excel to merge oceanographic data, from a publicly-available data source website, and Excel continuously quit. Naturally, I assumed I had caused some sort of computer error. [As an aside, I’ve concluded that most problems related to technology are human error-based.] Therefore, I tried reformatting the data, restarting my computer, the program, etc. Nothing. Then, thanks to the magic of Google, I discovered that Excel allows no more than 1,048,576 rows by 16,384 columns. ONLY 1.05 million rows?! The oceanography data was more than 3 million rows—and that’s with me eliminating data points. This is what happens when we’re dealing with big data.

According to Merriam-Webster dictionary, big data is an accumulation of data that is too large and complex for processing by traditional database management tools (www.merriam-webster.com). However, there are journal articles, like this one from Forbes, that discuss the ongoing debate of how to define “big data”. According to the article, there are 12 major definitions; so, I’ll let you decide what you qualify as “big data”. Either way, I think that when Excel reaches its maximum row capacity, I’m working with big data.

Collecting oceanography data aboard the R/V Shimada. Photo source: Alexa K.

Here’s the thing: the oceanography data that I referred to was just a snippet of my data. Technically, it’s not even MY data; it’s data I accessed from NOAA’s ERDDAP website that had been consistently observed for the time frame of my dolphin data points. You may recall my blog about maps and geospatial analysis that highlights some of the reasons these variables, such as temperature and salinity, are important. However, what I didn’t previously mention was that I spent weeks working on editing this NOAA data. My project on common bottlenose dolphins overlays environmental variables to better understand dolphin population health off of California. These variables should have similar spatiotemporal attributes as the dolphin data I’m working with, which has a time series beginning in the 1980s. Without taking out a calculator, I still know that equates to a lot of data. Great data: data that will let me answer interesting, pertinent questions. But, big data nonetheless.

This is a screenshot of what the oceanography data looked like when I downloaded it to Excel. This format repeats for nearly 3 million rows.

Excel Screen Shot. Image source: Alexa K.

I showed this Excel spreadsheet to my GIS professor, and his response was something akin to “holy smokes”, with a few more expletives and a look of horror. It was not the sheer number of rows that shocked him; it was the data format. Nowadays, nearly everyone works with big data. It’s par for the course. However, the way data are formatted is the major split between what I’ll call “easy” data and “hard” data. The oceanography data could have been “easy” data. It could have had many variables listed in columns. Instead, this data  alternated between rows with variable headings and columns with variable headings, for millions of cells. And, as described earlier, this is only one example of big data and its challenges.

Data does not always come in a form with text and numbers; sometimes it appears as media such as photographs, videos, and audio files. Big data just got a whole lot bigger. While working as a scientist at NOAA’s Southwest Fisheries Science Center, one project brought in over 80 terabytes of raw data per year. The project centered on the eastern north pacific gray whale population, and, more specifically, its migration. Scientists have observed the gray whale migration annually since 1994 from Piedras Blancas Light Station for the Northbound migration, and 2 out of every 5 years from Granite Canyon Field Station (GCFS) for the Southbound migration. One of my roles was to ground-truth software that would help transition from humans as observers to computer as observers. One avenue we assessed was to compare how well a computer “counted” whales compared to people. For this question, three infrared cameras at the GCFS recorded during the same time span that human observers were counting the migratory whales. Next, scientists, such as myself, would transfer those video files, upwards of 80 TB, from the hard drives to Synology boxes and to a different facility–miles away. Synology boxes store arrays of hard drives and that can be accessed remotely. To review, three locations with 80 TB of the same raw data. Once the data is saved in triplet, then I could run a computer program, to detect whale. In summary, three months of recorded infrared video files requires upwards of 240 TB before processing. This is big data.

Scientists on an observation shift at Granite Canyon Field Station in Northern California. Photo source: Alexa K.
Alexa and another NOAA scientist watching for gray whales at Piedras Blancas Light Station. Photo source: Alexa K.

In the GEMM Laboratory, we have so many sources of data that I did not bother trying to count. I’m entering my second year of the Ph.D. program and I already have a hard drive of data that I’ve backed up three different locations. It’s no longer a matter of “if” you work with big data, it’s “how”. How will you format the data? How will you store the data? How will you maintain back-ups of the data? How will you share this data with collaborators/funders/the public?

The wonderful aspect to big data is in the name: big and data. The scientific community can answer more, in-depth, challenging questions because of access to data and more of it. Data is often the limiting factor in what researchers can do because increased sample size allows more questions to be asked and greater confidence in results. That, and funding of course. It’s the reason why when you see GEMM Lab members in the field, we’re not only using drones to capture aerial images of whales, we’re taking fecal, biopsy, and phytoplankton samples. We’re recording the location, temperature, water conditions, wind conditions, cloud cover, date/time, water depth, and so much more. Because all of this data will help us and help other scientists answer critical questions. Thus, to my fellow scientists, I feel your pain and I applaud you, because I too know that the challenges that come with big data are worth it. And, to the non-scientists out there, hopefully this gives you some insight as to why we scientists ask for external hard drives as gifts.

Leila launching the drone to collect aerial images of gray whales to measure body condition. Photo source: Alexa K.
Using the theodolite to collect tracking data on the Pacific Coast Feeding Group in Port Orford, OR. Photo source: Alexa K.

References:

https://support.office.com/en-us/article/excel-specifications-and-limits-1672b34d-7043-467e-8e27-269d656771c3

https://www.merriam-webster.com/dictionary/big%20data

How to apply my PhD?

By Leila Lemos, PhD candidate, Fisheries and Wildlife Department

Time has flown. It seems that it was like a month ago that I received the news that I was approved in a public notice from the Brazilian government to study abroad, and began the process of moving to Oregon. But actually almost three years have now passed, and I am starting to wrap up my PhD, since I need to defend it in a little bit more than a year.

Our team is now starting the third and last fieldwork season for my PhD project. I am also working on my study plan to determine the last classes I need to take, and our first manuscripts are ‘in press’ or ‘in prep’ for submission to journals. So, it’s time for me to think about what comes next.

I am from Rio de Janeiro, Brazil, and I am studying in the US through a Brazilian government program called Science Without Borders. This program aims to send students abroad to learn new techniques and to develop innovative projects. The projects needed to be original to be approved by the public notice. The main idea is to bring these students back to Brazil, after their PhD completion, to disseminate the acquired knowledge by applying the learned techniques.

My project includes a few novel aspects that allowed for funding by this program. The main focus of my thesis is to develop an endocrinology study of a cetacean species. This was (and still is) a critical field in Brazil, as reported by the “National Action Plan for the conservation of aquatic mammals: Small cetaceans” (2010). According to this Action Plan, cetacean hormonal analyses are rare and of high priority, but there are limited labs with the capacity to study cetacean endocrinology in Brazil. Other limiting factors are the associated analysis costs and a lack of human knowledge and skills. In addition to the hormonal analyses (Figure 1), I am also using other ‘new technologies’ in the project: drones (Figure 2; Video 1) and GoPros (Video 2).

Figure 1: Learning how to perform hormonal analysis at the Seattle Aquarium, WA.
Source: Angela Smith

 

Figure 2: Learning how to fly a drone in Newport, OR.
Source: Florence Sullivan

 

Video 1: Drone flights performed in Newport, OR, during fieldwork in 2016.

* Taken under NOAA/NMFS permit #16111 to John Calambokidis.

 

Video 2: Video of mysid swarms during a GoPro deployment conducted in Port Orford, OR, during fieldwork in 2016.

 

The importance of studying cetacean hormones includes a better understanding of their reproductive cycles (i.e., sex hormones such as progesterone, testosterone and estradiol) and their physiological stress response (i.e., cortisol) to possible threats (e.g., acoustic pollution, contaminants, lack of prey). In addition, through photographs and videos recorded by drones we can conduct photogrammetry analysis to monitoring cetacean body condition, and through GoPro recordings of the water column we can assess prey availability. Changes in both body condition and prey can help us explaining how and why hormone levels vary.

Through my PhD I have obtained skills in hormone analysis, photogrammetry and video prey assessment by studying the logistically accessible and non-threatened gray whale (Eschrichtius robustus). During method development, these features are important to increase sample size and demonstrate feasibility. But now that the methodologies have proven successful, we can start applying them to other species and regions, and under different circumstances, to improve conservation efforts of threatened populations.

Many cetacean species along the Brazilian coast are threatened, particularly from fishing gear and vessel interactions, chemical and noise pollution. By applying the methods we have developed in the GEMM Lab during my PhD to cetacean conservation issues in Brazil, we could enable a great expansion in knowledge across many fields (i.e., endocrinology, behavior, photogrammetry, diet). Additionally, these skills can promote safer work environments (for the scientist and for the object of study) and cheaper work processes. However, many countries, such as Brazil, do not have the infrastructure and access to technologies to conduct these same analyses, as in developed countries like the USA. These technologies, when sold in Brazil, have many taxes on the top of the product that they can become an extra hurdle, due to budget constraints. Thus, there is a need for researchers to adapt these skills and technologies, in the best manner possible, to the reality of the country.

Now that I am starting to think about ‘life after PhD’, I can see myself returning to my country to spread the knowledge, technologies and skills I have gained through these years at OSU to new research projects so that I am able to assist with conservation efforts for the ocean and marine fauna in Brazil.

 

References:

PAN, 2010. Plano de ação nacional para a conservação dos mamíferos aquáticos: pequenos cetáceos / André Silva Barreto … [et al.]; organizadores Claudia Cavalcante Rocha-Campos, Ibsen de Gusmão Câmara, Dan Jacobs Pretto. – Brasília: Instituto Chico Mendes de Conservação da Biodiversidade, Icmbio, 132 p. Em: <http://www.icmbio.gov.br/portal/images/ stories/docs-plano-de-acao/pan-peqs-cetaceos/pan_pequenoscetaceos_web.pdf> Acessado em: 27 de Maio de 2015.

 

Robots are taking over the oceans

By Leila Lemos, PhD Student

In the past few weeks I read an article on the use of aquatic robots in the ocean for research. Since my PhD project uses technology, such as drones and GoPros, to monitor body condition of gray whales and availability of prey along the Oregon coast, I became really interested by the new perspective these robots could provide. Drones produce aerial images while GoPros generate an underwater-scape snapshot. The possible new perspective provided by a robot under the water could be amazing and potentially be used in many different applications.

The article was published on March 21st by The New York Times, and described a new finned robot named “SoFi” or “Sophie”, short for Soft Robotic Fish (Figure 1; The New York Times 2018). The aquatic robot was designed by scientists at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Lab, with the purpose of studying marine life in their natural habitats.

Figure 1: “SoFi”, a robotic fish designed by MIT scientists.
Source: The New York Times 2018.

 

SoFi’s  first swim trial occurred in a coral reef in Fiji, and the footage recorded can be seen in the following video:

 

SoFi can swim at depths up to 18 meters and at speeds up to half-its-body-length a second (average of 23.5 cm/s in a straight path; Katzschmann et al. 2018). Sofi can swim for up to ~40 minutes, as limited by battery time. The robot is also well-equipped (Figure 2). It has a compact buoyancy control mechanism and includes a wide-view video camera, a hydrophone, a battery, environmental sensors, and operating and communication systems. The operating and communication systems allow a diver to issue commands by using a controller that operates through sound waves.

Figure 2: “SoFi” system subcomponents overview.
Source: Katzschmann et al. 2018.

 

The robot designers highlight that while SoFi was swimming, fish didn’t seem to be bothered or get scared by SoFi’s presence. Some fish were seen swimming nearby the robot, suggesting that SoFi has the potential to integrate into the natural underwater environment and therefore record undisturbed behaviors. However, a limitation of this invention is that SoFi needs a diver on scene to control the robot. Therefore, SoFi’s study of marine life without human interference may be compromised until technology develops further.

Another potential impact of SoFi we might be concerned about is noise. Does this device produce noise levels that marine fauna can sense or maybe be stress by? Unfortunately, the answer is yes. Even if fish don’t seem to be bothered by SoFi’s presence, it might bother other animals with hearing sensitivity in the same frequency range of SoFi. Katzschmann and colleagues (2018) explained that they chose a frequency to operate SoFi that would minimally impact marine fauna. They studied the frequencies used by the aquatic animals and, since the hearing ranges of most aquatic species decays significantly above 10 KHz, they selected a frequency above this range (i.e., 36 KHz). However, this high frequency range can be sensed by some species of cetaceans and pinnipeds, but negative affects on these animals will be dependent on the sound amplitude that is produced.

Although not perfect (but what tool is?), SoFi can be seen as a great first step toward a future of underwater robots to assist research efforts.  Battery life, human disturbance, and noise disturbance are limitations, but through thoughtful application and continued innovation this fishy tool can be the start of something great.

The use of aquatic robots, such as SoFi, can help us advance our knowledge in underwater ecosystems. These robots could promote a better understanding of marine life in their natural habitat by studying behaviors, interactions and responses to threats. These robots may offer important new tools in the protection of animals against the effects caused by anthropogenic activities. Additionally, the use of aquatic robots in scientific research may substitute remote operated vehicles and submersibles in some circumstances, such as how drones are substituting for airplanes sometimes, thus providing a less expensive and better-tolerated way of monitoring wildlife.

Through continued multidisciplinary collaboration by robot designers, biologists, meteorologists, and more, innovation will continue allowing data collection with minimal to non-disturbance to the wildlife, providing lower costs and higher safety for the researchers.

It is impressive to see how technology efforts are expanding into the oceans. As drones are conquering our skies today and bringing so much valuable information on wildlife monitoring, I believe that the same will occur in our oceans in a near future, assisting in marine life conservation.

 

 

References:

Katzschmann RK, DelPreto J, MacCurdy R, Rus D. 2018. Exploration of Underwater Life with an Acoustically Controlled Soft Robotic Fish. Sci. Robot. 3, eaar3449. DOI: 10.1126/scirobotics.aar3449.

The New York Times. 2018. Robotic Fish to Keep a Fishy Eye on the Health of the Oceans. Available at: https://www.nytimes.com/2018/03/21/science/robot-fish.html.