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

Cloudy with a chance of blue whales

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

As a PhD student studying the ecology of blue whales in New Zealand, my time is occupied by questions such as: When and where are the blue whales? Can we predict where they will be based on environmental conditions? How does their distribution overlap with human activity such as oil and gas exploration?

Leigh and I have just returned from New Zealand, where I gave an oral presentation at the Society for Conservation Biology Oceania Congress entitled “Cloudy with a chance of whales: Forecasting blue whale presence to mitigate industrial impacts based on tiered, bottom-up models”. While the findings I presented are preliminary, an exciting ecological story is emerging, and one with clear management implications.

The South Taranaki Bight (STB) region of New Zealand is an important area for a population of blue whales which are unique to New Zealand. A wind-driven upwelling system brings cold, productive waters into the bight [1], which sustains high densities of krill [2], blue whale prey. The region is also frequented by busy shipping traffic, oil and gas drilling and extraction platforms as well as seismic survey effort for subsurface oil and gas reserves, and is the site of a recently-permitted seabed mine for iron sands (Fig. 1). However, a lack of knowledge on blue whale distribution and habitat use patterns has impeded effective management of these potential anthropogenic threats.

Figure 1. A blue whale surfaces in front of a floating production storage and offloading vessel servicing the oil rigs in the South Taranaki Bight. Photo by D. Barlow.

Three surveys were conducted in the STB region in the summer months of 2014, 2016, and 2017. During that time, we not only looked for blue whales, we also collected oceanographic data and hydroacoustic backscatter data to map and measure aspects of the krill in the region. These data streams will help us understand the functional, ecological relationships between the environment (oceanography), prey (krill), and predators (blue whales) in the ecosystem (Fig. 2). But in practice these data are costly and time-consuming to collect, while other data sources such as satellite imagery are readily accessible to managers at a variety of spatial and temporal scales. Therefore, another one of my aims is to link the data we collected in the field to satellite imagery, so that managers can have a practical tool to predict when and where the blue whales are most likely to be found in the region.

Figure 2. Data streams collected during surveys of the South Taranaki Bight Region in 2014, 2016, and 2017. 

So what did I find? Here are the highlights from my preliminary analyses:

  • The majority of the patterns in blue whale distribution can be explained by the density, depth, and thickness of the krill patches.
  • Patterns in the krill are driven by oceanography.
  • Those same oceanographic parameters that drive the krill can be used to explain blue whale distribution.
  • There are tight relationships between the important oceanographic variables and satellite images of sea surface temperature.
  • Blue whale distribution can, to some degree, be explained using just satellite imagery.

We were able to identify a sea surface temperature range in the satellite imagery of approximately 18°C where the likelihood of finding a blue whale is the highest. Is this because blue whales really like 18° water? Well, more likely this relationship exists because the satellite imagery is reflective of the oceanography, and the oceanography drives patterns in the krill distribution, and the krill drives the distribution of blue whales (Fig. 3). We were able to make each of these functional linkages through our series of models, which is quite exciting.

Figure 3. The tiered modeling approach we took to investigate the ecological relationships between blue whales, krill, oceanography, and satellite imagery. Because of the ecological linkages we made, we are able to say that any relationship between whale distribution and satellite imagery most likely reflects a relationship between the blue whales and their prey. 

That’s all well and good, but we were interested in testing these relationships to see if our identified habitat associations hold up even when we do not have field data (oceanographic, krill, and whale data). This past austral summer, we did not have a field season to collect data, but there was a large seismic airgun survey of the STB region. Seismic survey vessels are required to have trained marine mammal observers on board, and we were given access to the blue whale sightings data they recorded during the survey. In December, when the water was right around the preferred temperature identified by our models (18°C), the observers made 52 blue whale sightings (Fig. 4). In January and February, the waters warmed and only two sightings were made in each month. This is not only reassuring because it supports our model results, it also implies that there is the potential to balance industrial use of the area with protection of blue whale habitat, based on our understanding of the ecology. In January and February, very few blue whales were likely disturbed by the industrial activity in the STB, as conditions were not favorable for foraging at the location of the seismic survey. In contrast, the blue whales that were in the STB region in December may have experienced physiological consequences of sustained exposure to airgun noise since the conditions were favorable for foraging in the STB. In other words, the whales may have tolerated the noise exposure to gain access to good food, but this could have significant biological repercussions such as increased stress [3].

Figure 4. Monthly sea surface temperature (MODIS Aqua) overlaid with blue whale sightings from marine mammal observers aboard seismic survey vessel R/V Amazon Warrior. Black rectangles represent areas of seismic survey effort. Blue whale sighting location data were provided by RPS Energy Pty Ltd & Schlumberger, and Todd Energy.

In the first two weeks of July, we presented these latest findings to managers at the New Zealand Department of Conservation, the Minister of Conservation, the CEO and Policy Advisor of a major oil and gas conglomerate, NGOs, advocacy groups, and scientific colleagues. It was valuable to gather feedback from many different stakeholders, and satisfying to see such a clear interest in, and management application of, our work.

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

What’s next? We’re back in Oregon, and diving back into analysis. We intend to take the modeling work a step further to make the models predictive—for example, can we forecast where the blue whales will be based on the temperature, productivity, and winds two weeks prior? I am excited to see where these next steps lead!

References:

  1. Shirtcliffe TGL, Moore MI, Cole AG, Viner AB, Baldwin R, Chapman B. 1990 Dynamics of the Cape Farewell upwelling plume, New Zealand. New Zeal. J. Mar. Freshw. Res. 24, 555–568. (doi:10.1080/00288330.1990.9516446)
  2. Bradford-Grieve JM, Murdoch RC, Chapman BE. 1993 Composition of macrozooplankton assemblages associated with the formation and decay of pulses within an upwelling plume in greater cook strait, New Zealand. New Zeal. J. Mar. Freshw. Res. 27, 1–22. (doi:10.1080/00288330.1993.9516541)
  3. Rolland RM, Parks SE, Hunt KE, Castellote M, Corkeron PJ, Nowacek DP, Wasser SK, Kraus SD. 2012 Evidence that ship noise increases stress in right whales. Proc. Biol. Sci. 279, 2363–8. (doi:10.1098/rspb.2011.2429)

Searching for seabirds on the Garden Island

By Erin Pickett, M.Sc. (GEMM Lab member 2014-2016)

Field Assistant, Kaua’i Endangered Seabird Recovery Project

I heaved my body up with both arms, swung one leg up and attempted to muster any remaining energy I had into standing on the ridgeline of the valley that I had just crawled out of. Soaked from the rain, face covered with bits of dirt and with ferns sticking out of my hair I probably resembled a creature crawling out of a swamp. I smiled at this thought knowing that my dramatic emergence from the swamp might have been captured on a nearby motion-sensing trail camera.

I surveyed my surroundings to gain my bearings. I was searching for seabird burrows in a densely vegetated valley called Upper Limahuli Preserve in the mountains of Kaua’i, Hawaii. I was looking for the nests of the endangered Hawaiian Petrel (or ‘Ua’u in Hawaiian) and the threated Newell’s Shearwater (A’o), Hawaii’s only two endemic (found nowhere else in the world) Procellarid species. I registered the trail, the nearby fence line and the two valleys on either side of the ridge I was standing on. If a drone had photographed me from above, the scene of lush green mountains, waterfalls and rugged cliffs would not only look like the views from the helicopter arrival scene in the movie Jurassic Park, but indeed was the same Nā Pali coastline.

Northeastern facing view from the trail at Upper Limahuli Preserve looking toward the author’s hometown of Kīlauea and the site of the Nihokū predator-fence at Kīlauea National Wildlife Refuge

When I finished my graduate program at Oregon State University in 2017, I began working for a project called the Kaua’i Endangered Seabird Recovery Project (KESRP). Our work at KESRP focuses on monitoring Kauai’s populations of breeding a’o and ‘ua’u, mitigating on-land threats through recovery activities and conducting research (e.g. habitat modeling & at-sea tracking) to learn more about the two species.

An estimated 90% of the Newell’s Shearwater population breeds on the island of Kaua’i, as does a large portion of the Hawaiian Petrel population. Both populations have declined rapidly on Kaua’i over the past two decades, where radar surveys found a 78% decrease of Hawaiian Petrels and a 94% decrease in overall numbers of Newell’s Shearwaters (Raine et al., 2017). Light pollution, collision with electrical power lines, and invasive vertebrate predators represent primary threats to both the a’o and ‘ua’u while on land during the breeding season. As with all seabirds that nest on islands, the a’o and ‘ua’u are easy prey for invasive species such as feral cats and black rats, thus, there is a large effort within our study area to alleviate the threat of these predators.

A ‘ua’u adult incubating an egg at Upper Limahuli Preserve, 2018

The purpose of my burrow search effort on this day was to find suitable candidate burrows for a translocation project that KESRP has undertaken since 2015. This fall, we will attempt to relocate via helicopter up to 20 a’o and ‘ua’u chicks from the mountains of Kaua’i, where they are vulnerable to invasive predators, to a predator-proof fenced area located within nearby Kīlauea National Wildlife Refuge. The ultimate aim of our translocation project, a critical component of the Nihokū Ecosystem Restoration Project, is to establish successful breeding colonies of a’o and ‘ua’u within the protected boundaries of a fence that is impermeable to rats, cats, and pigs.

On Kaua’i, the imperiled a’o and ‘ua’u nest on verdant cliffs amid native Hawaiian uluhe ferns and ‘ohi‘a lehua trees. Both species raise their chicks in burrows that can only be located by humans after an extensive search effort that involves scanning the densely vegetated forest floor for tiny feathers and guano trails, and following the musty scent of seabirds until an underground tunnel is found, sometimes with a bird nestled inside.

The author with an a’o chick that was relocated to the Nihokū Ecosystem Restoration Site in 2017

My afternoon of burrow searching had been strenuous, and being day three it had already been a long week in the field so I sighed and started heading in the direction that would lead me back to our field camp. Though, after a few steps I caught the musty smell of seabird in the air and immediately stopped walking. Like an animal, I followed my nose and turned my head over my right shoulder and sniffed the air. I climbed over the fence that separated the trail I was hiking on from the 3,000 foot drop into the valley below, carefully positioned my feet on the fragile cliff side and lifted a large tuft of grass to find a freshly dug hole that smelled unmistakably like a seabird.

A triumphant selfie by the author after finding a particularly difficult to locate a’o burrow

Either a prospecting Hawaiian Petrel or Newell’s Shearwater had broken ground on this new burrow the night before. The birds had been busy digging into the cliff side while I had been conducting an auditory survey a few hundred meters away. The auditory survey had begun at sunset and over the course of the next two hours I listened for and recorded the locations of seabirds transiting overhead, heading from the sea to the mountains and calling from their burrows nearby. Ideally, this auditory survey would help me pinpoint locations of ‘ground callers’ who’s raucous would lead me to their burrows the next day.

Finding a burrow is not often as easy as pinpointing the location of a ground caller, catching a whiff of seabird near that location and immediately locating a hole in the ground. Yet, finding a burrow that is ‘reachable’ and that is reasonably close to a helicopter landing zone, is even more difficult. And this task is one of our objectives throughout the field season this year.

If you’re interested in keeping up with our progress you can follow KESRP on Facebook: https://www.facebook.com/kauaiseabirdproject/

Reference(s):

Raine, A. F., Holmes, N. D., Travers, M., Cooper, B. A., & Day, R. H. (2017). Declining population trends of Hawaiian Petrel and Newell’s Shearwater on the island of Kaua‘i, Hawaii, USA. The Condor, 119(3), 405-415.

Collaboration – it’s where it’s at.

By Dominique Kone, Masters Student in Marine Resource Management

As I finish my first year of graduate school, I’ve been reflecting on what has helped me develop as a young scientist over the past year. Some of these lessons are somewhat expected: making time for myself outside of academia, reading the literature, and effectively managing my time. Yet, I’ve also learned that working with my peers, other scientists, and experts outside my scientific field can be extremely rewarding.

For my thesis, I will be looking at the potential to reintroduce sea otters to the Oregon coast by identifying suitable habitat and investigating their potential ecological impacts. During this first year, I’ve spent much time getting to know various stakeholder groups, their experiences with this issue, and any advice they may have to inform my work. Through these interactions, I’ve benefitted in ways that would not have been possible if I tried tackling this project on my own.

Source: Seapoint Center for Collaborative Leadership.

When I first started my graduate studies, I was eager to jump head first into my research. However, as someone who had never lived in Oregon before, I didn’t yet have a full grasp of the complexities and context behind my project and was completely unfamiliar with the history of sea otters in Oregon. By engaging with managers, scientists, and advocates, I quickly realized that there was a wealth of knowledge that wasn’t covered in the literature. Information from people who were involved in the initial reintroduction; theories behind the cause of the first failed reintroduction; and most importantly, the various political, social, and culture implications of a potential reintroduction. This information was crucial in developing and honing my research questions, which I would have missed if I had solely relied on the literature.

As my first year in graduate school progressed, I also quickly realized that most people familiar with this issue also had strong opinions and views about how I should conduct my study, whether and how managers should bring sea otters back, and if such an effort will succeed. This input was incredibly helpful in getting to know the issue, and also fostered my development as a scientist as I had to quickly improve my listening and critically-thinking skills to consider my research from different perspectives. One of the benefits of collaboration – particularly with experts outside the marine ecology or sea otter community – is that everyone looks at an issue in a different way. Through my graduate program, I’ve worked with students and faculty in the earth, oceanic, and atmospheric sciences, whom have challenged me to consider other sources of data, other analyses, or different ways of placing my research within various contexts.

Most graduate students when they first start graduate school. Source: Know Your Meme.

One of the major advantages of being a graduate student is that most researchers – including professors, faculty, managers, and fellow graduate students – are more than happy to analyze and discuss my research approach. I’ve obtained advice on statistical analyses, availability and access to data, as well as contacts to other experts. As a graduate student, it’s important for me to consult with more-experienced researchers who can not only explain complex theories or concepts, but who can also validate the appropriateness of my research design and methods. Collaborating with senior researchers is a great way to become established and recognized within the scientific community. Because of this project, I’ve started to become adopted into the marine mammal and sea otter research communities, which is obviously beneficial for my thesis work, but also allows me to start building strong relationships for a career in marine conservation.

Source: Oregon State University.

Looking ahead to my second year of graduate school, I’m eager to make a big push toward completing my thesis, writing manuscripts for journal submission, and communicating my research to various audiences. Throughout this process, it’s still important for me to continue to reach out and collaborate with others within and outside my field as they may help me reach my personal goals. In my opinion, this is exactly what graduate students should be doing. While graduate students may have the ability and some experience to work independently, we are still students, and we are here to learn from and make lasting connections with other researchers and fellow graduate students through these collaborations.

If there’s any advice I would give to an incoming graduate student, it’s this: Collaborate, and collaborate often. Don’t be afraid to work with others because you never know whether you’ll come away with a new perspective, learn something new, come across new research or professional opportunities, or even help others with their research.

Methods in UAS marine mammal research from coast to coast

By Julia Stepanuk, PhD student, department of Ecology and Evolution, Stony Brook University

Hello GEMM Lab blog readers! I’m a PhD student in Lesley Thorne’s lab at Stony Brook University in New York and I spent this past week with the GEMM Lab learning their protocol for drone flights and gaining experience flying over whales. I saw my first gray whales just off the coast of Newport, Oregon and assisted with the GEMM Lab’s summer field research. We luckily had 4 days of great weather in a row, so I got tons of experience conducting research that integrates drone flights that I can bring home to our lab. It was really exciting to observe and learn from the well-oiled machine that is the GEMM Lab. Information about their gray whale project can be found here and here, but I want to focus on how my experiences here in Newport can translate to my research interests off the coast of Long Island.

Gray whale off the Newport, Oregon coast. Photo by Julia Stepanuk, under NMFS/NOAA permit # 16111

Our lab in New York has a range of interesting projects currently underway: we study everything from decadal trends in sea turtle diets to how frequently herring gulls visit urban habitats for food around New York City. My research focuses on the whales around New York, specifically humpback whales. Humpback whales are very well studied in many parts of the world, especially in the Northwest Atlantic. The initial photo-identification studies were conducted in the Gulf of Maine in the 1970s (Katona et al., 1979), and the North Atlantic Humpback Whale Catalogue is still going strong with over 8,000 individual whales catalogued! Recently though, many people have reported humpback whales in a new area: the waters around New York and Long Island. Yet, we don’t understand how these whales fit in with the rest of the humpback population in the North Atlantic. We do know that they feed along the shores of New York City and Long Island, and they are primarily consuming menhaden (also known as bunker or pogy), a forage fish that is vital to both our economic and environmental systems in the Northeast U.S. (see: Six reasons why menhaden is the greatest fish we ever fished).

Opportunistic humpback whale sightings, NYS GIS Data
Menhaden, https://maineguides.com/maine-saltwater-fish-species/atlantic-menhaden/

 

The habitat use and behavior of humpbacks in this part of the world is important for two reasons: 1) this population of humpback whales has recovered from the detrimental population-level impacts of industrial whaling in the 18th and 19th centuries, and thus was recently delisted from the endangered species list; and 2) humpback whales in the Northwest Atlantic are at-risk from ship strikes and fishing gear entanglement, so much so that NOAA declared an unusual mortality event for 2016-2018. In fact, 4 humpback whales washed up dead on the shore of Long Island in the last 30 days! These facts lead to my motivation for my PhD studies: where are humpback whales in the vicinity of New York City and how do they use the environment around Long Island? I specifically want to investigate the trophic relationship between humpback whales and menhaden.

Humpback whale feeding off the Rockaways, Long Island; Artie Raslich

There are a number of studies where researchers have used photogrammetry from drones to document the body condition of marine mammal species (Burnett et al., in press; Christiansen et al., 2016; Christiansen et al., 2018; Dawson et al., 2017; Perryman and Lynn., 2002), which I plan to extend to the humpback whales around Long Island. I will conduct photogrammetry of the humpback whales off Long Island and will document the individual whales, their behaviors, and their prey sources. Because scientists are now documenting and monitoring body condition of humpback whales in many parts of the world, we can compare the overall health and body condition of humpbacks in New York to those in other habitats. Further, by documenting the schools of menhaden they are consuming, we can better assess the trophic relationship between humpbacks and menhaden in a foraging habitat adjacent to one of the largest cities on the planet.

Drone imagery off Long Island from a recreational drone pilot in 2017. Top: two humpback whales next to a dense school of menhaden. Middle: two humpback whales with pectoral fins clearly visible. Bottom: humpback whales lunge feeding from above; http://fireislandandbeyond.com/video-pair-of-humpback-whales-between-old-inlet-and-davis-park-fire-island-ny/2/

 

I am so grateful to the GEMM Lab for sharing information and skills with me over the past week and am excited to bring my new skillset back to our lab at Stony Brook! Aside from drone skills, I learned that gray whales are very flexible, and their mottled skin is absolutely beautiful! I also learned that my peanut butter and jelly sandwich making skills are passable (you have to find a way to keep the jelly from leaking through the bread on a hot day on a boat!) and I learned how to collect fecal samples from whales (put a net in the water, and scoop up the pieces of whale poo). I am also now hooked on the FIFA World Cup matches and will be losing lots of sleep in the next few weeks while I diligently follow my new favorite teams. Thank you again to the GEMM lab for being so supportive and welcoming! For an influx of east coast megafauna research, follow the Thorne Lab blog as our many spatial marine megafauna projects get underway, and follow me on twitter as I pursue a PhD!

 

References

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

Christiansen, F., Dujon, A.M., Sprogis, K.R., Arnould, J.P.Y., Bejder, L., 2016. Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7

Christiansen, F., Vivier, F., Charlton, C., Ward, R., Amerson, A., Burnell, S., Bejder, L., 2018. Maternal body size and condition determine calf growth rates in southern right whales. Marine Ecology Progress Series 592, 267–281.

Dawson, S.M., Bowman, M.H., Leunissen, E., Sirguey, P., 2017. Inexpensive Aerial Photogrammetry for Studies of Whales and Large Marine Animals. Front. Mar. Sci. 4.

Katona, S., B. Baxter, 0. Brazier, S. Kraus, J. Perkins AND H. Whitehead. 1979. Identification of humpback whales by fluke photographs. Pages 33-44 in H.E. Winn and B.L. Olla, eds. Behavior of marine animals. Current perspectives in research. Vol. 3: Cetaceans. Plenum Press. New York.

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.

 

“Applied conservation science”

By Dawn Barlow, M.S.
Ph.D. student, Department of Fisheries and Wildlife, Oregon State University

For years, I have said I want to do “applied conservation science”. As an undergraduate student at Pitzer College I was a double major in Biology and Environmental Policy. While I have known that I wanted to study the oceans on some level my whole life, and I have known for about a decade that I wanted to be a scientist, I realized in college that I wanted to learn how science could be a tool for effective conservation of the marine ecosystems that fascinate me.

Answering questions during my public defense seminar. Photo by Leila Lemos.

Just over a week ago, I successfully defended my MS thesis. When Leigh introduced me at the public seminar, she read a line from my initial letter to her expressing my interest in being her graduate student: “My passion for cetacean research lies not only in fascination of the animals but also how to translate our knowledge of their biology and ecological roles into effective conservation and management measures.” I believe I’ve grown and learned a lot in the two and a half years since I crafted that email and nervously hit send, but the statement is still true.

My graduate research in many ways epitomizes what I am passionate about. I am part of a team studying the ecology of blue whales in a highly industrial area of New Zealand. Not only is it a system in which we can address fascinating questions in ecology, it is also a region that experiences extensive pressure from human use and so all of our findings have direct management implications.

We recently published a paper documenting and describing this New Zealand blue whale population, and the findings reached audiences and news outlets far and wide. Leigh and I are headed to New Zealand for the first two weeks in July. During this time we will not only present our latest findings at the Society for Conservation Biology Oceania Conference, we will also meet with managers at the New Zealand Department of Conservation, speak with the Minister of Energy and Resources as well as the Minster of Conservation, meet with the CEO and Policy Advisor of PEPANZ (a representative group of oil and gas companies in New Zealand), and participate in a symposium of scientists and stakeholders aiming to establish goals for the protection of whales in New Zealand. Now, “applied conservation science” extends well beyond a section in the discussion of a paper outlining the implications of the findings for management.

A blue whale surfaces in front of a floating production storage and offloading (FPSO) vessel servicing the oil rigs in the South Taranaki Bight. Photo by Dawn Barlow. 

During our 2017 field season in New Zealand, Leigh and I found ourselves musing on the flying bridge of the research vessel about all the research questions still to be asked of this study system and these blue whales. How do they forage? What are their energetic demands? How does disturbance from oil and gas exploration impact their foraging and their energetic demands? Leigh smiled and told me, “You better watch out, or this will turn into your PhD.” I said that maybe it should. Now I am thrilled to immerse myself into the next phase of this research project and the next chapter of my academic journey as a PhD student. This work is applied conservation science, and I am a conservation biologist. Here’s to retaining my passion for ecology and fascination with my study system, while not losing sight of the implications and applications of my work for conservation. I am excited for what is to come!

Dawn Barlow and Dr. Leigh Torres aboard the R/V Star Keys during the 2017 blue whale field season in New Zealand. Photo by Todd Chandler.

Finding the right fit: a journey into cetacean distribution models

Solène Derville, Entropie Lab, French National Institute for Sustainable Development (IRD – UMR Entropie), Nouméa, New Caledonia

 Ph.D. student under the co-supervision of Dr. Leigh Torres

Species Distribution Models (SDM), also referred to as ecological niche models, may be defined as “a model that relates species distribution data (occurrence or abundance at known locations) with information on the environmental and/or spatial characteristics of those locations” (Elith & Leathwick, 2009)⁠. In the last couple decades, SDMs have become an indispensable part of the ecologists’ and conservationists’ toolbox. What scientist has not dreamed of being able to summarize a species’ environmental requirements and predict where and when it will occur, all in one tiny statistical model? It sounds like magic… but the short acronym “SDM” is the pretty front window of an intricate and gigantic research field that may extend way beyond the skills of a typical ecologist (even so for a graduate student like myself).

As part of my PhD thesis about the spatial ecology of humpback whales in New Caledonia, South Pacific, I was planning on producing a model to predict their distribution in the region and help spatial planning within the Natural Park of the Coral Sea. An innocent and seemingly perfectly feasible plan for a second year PhD student. To conduct this task, I had at my disposal more than 1,000 sightings recorded during dedicated surveys at sea conducted over 14 years. These numbers seem quite sufficient, considering the rarity of cetaceans and the technical challenges of studying them at sea. And there was more! The NGO Opération Cétacés  also recorded over 600 sightings reported by the general public in the same time period and deployed more than 40 satellite tracking tags to follow individual whale movements. In a field where it is so hard to acquire data, it felt like I had to use it all, though I was not sure how to combine all these types of data, with their respective biases, scales and assumptions.

One important thing about SDM to remember: it is like a cracker section in a US grocery shop, there is sooooo much choice! As I reviewed the possibilities and tested various modeling approaches on my data I realized that this study might be a good opportunity to contribute to the SDM field, by conducting a comparison of various algorithms using cetacean occurrence data from multiple sources. The results of this work was just published  in Diversity and Distributions:

Derville S, Torres LG, Iovan C, Garrigue C. (2018) Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Divers Distrib. 2018;00:1–17. https://doi. org/10.1111/ddi.12782

There are simply too many! Anonymous grocery shops, Corvallis, OR
Credit: Dawn Barlow

If you are a new-comer to the SDM world, and specifically its application to the marine environment, I hope you find this interesting. If you are a seasoned SDM user, I would be very grateful to read your thoughts in the comment section! Feel free to disagree!

So what is the take-home message from this work?

  • There is no such thing as a “best model”; it all depends on what you want your model to be good at (the descriptive vs predictive dichotomy), and what criteria you use to define the quality of your models.

The predictive vs descriptive goal of the model: This is a tricky choice to make, yet it should be clearly identified upfront. Most times, I feel like we want our models to be decently good at both tasks… It is a risky approach to blindly follow the predictions of a complex model without questioning the meaning of the ecological relationships it fitted. On the other hand, conservation applications of models often require the production of predicted maps of species’ probability of presence or habitat suitability.

The criteria for model selection: How could we imagine that the complexity of animal behavior could be summarized in a single metric, such as the famous Akaike Information criterion (AIC) or the Area under the ROC Curve (AUC)? My study, and that of others (e.g. Elith & Graham  H., 2009),⁠ emphasize the importance of looking at multiple aspects of model outputs: raw performance through various evaluation metrics (e.g. see AUCdiff; (Warren & Seifert, 2010)⁠, contribution of the variables to the model, shape of the fitted relationships through Partial Dependence Plots (PDP, Friedman, 2001),⁠ and maps of predicted habitat suitability and associated error. Spread all these lines of evidence in front of you, summarize all the metrics, add a touch of critical ecological thinking to decide on the best approach for your modeling question, and Abracadabra! You end up a bit lost in a pile of folders… But at least you assessed the quality of your work from every angle!

  • Cetacean SDMs often serve a conservation goal. Hence, their capacity to predict to areas / times that were not recorded in the data (which is often scarce) is paramount. This extrapolation performance may be restricted when the model relationships are overfitted, which is when you made your model fit the data so closely that you are unknowingly modeling noise rather than a real trend. Using cross-validation is a good method to prevent overfitting from happening (for a thorough review: Roberts et al., 2017)⁠. Also, my study underlines that certain algorithms inherently have a tendency to overfit. We found that Generalized Additive Models and MAXENT provided a valuable complexity trade-off to promote the best predictive performance, while minimizing overfitting. In the case of GAMs, I would like to point out the excellent documentation that exist on their use (Wood, 2017)⁠, and specifically their application to cetacean spatial ecology (Mannocci, Roberts, Miller, & Halpin, 2017; Miller, Burt, Rexstad, & Thomas, 2013; Redfern et al., 2017).⁠
  • Citizen science is a promising tool to describe cetacean habitat. Indeed, we found that models of habitat suitability based on citizen science largely converged with those based on our research surveys. The main issue encountered when modeling this type of data is the absence of “effort”. Basically, we know where people observed whales, but we do not know where they haven’t… or at least not with the accuracy obtained from research survey data. However, with some information about our citizen scientists and a little deduction, there is actually a lot you can infer about opportunistic data. For instance, in New Caledonia most of the sightings were reported by professional whale-watching operators or by the general public during fishing/diving/boating day trips. Hence, citizen scientists rarely stray far from harbors and spend most of their time in the sheltered waters of the New Caledonian lagoon. This reasoning provides the sort of information that we integrated in our modeling approach to account for spatial sampling bias of citizen science data and improve the model’s predictive performance.

Many more technical aspects of SDM are brushed over in this paper (for detailed and annotated R codes of the modeling approaches, see supplementary information of our paper). There are a few that are not central to the paper, but that I think are worth sharing:

  • Collinearity of predictors: Have you ever found that the significance of your predictors completely changed every time you removed a variable? I have progressively come to discover how unstable a model can be because of predictor collinearity (and the uneasy feeling that comes with it …). My new motto is to ALWAYS check cross-correlation between my predictors, and do it THOROUGHLY. A few aspects that may make a big difference in the estimation of collinearity patterns are to: (1) calculate Pearson vs Spearman coefficients, (2) check correlations between the values recorded at the presence points vs over the whole study area, and (3) assess the correlations between raw environmental variables vs between transformed variables (log-transformed, etc). Though selecting variables with Pearson coefficients < 0.7 is usually a good rule (Dormann et al., 2013), I would worry of anything above 0.5, or at least keep it in mind during model interpretation.
  • Cross-validation: If removing 10% of my dataset greatly impacts the model results, I feel like cross-validation is critical. The concept is based on a simple assumption, if I had sampled a given population/phenomenon/system slightly differently, would I have come to the same conclusion? Cross-validation comes in many different methods, but the basic concept is to run the same model several times (number of times may depend on the size of your data set, hierarchical structure of your data, computation power of your computer, etc.) over different chunks of your data. Model performance metrics (e.g., AUC) and outputs (e.g., partial dependence plots) are than summarized on the many runs, using mean/median and standard deviation/quantiles. It is up to you how to pick these chunks, but before doing this at random I highly recommend reading Roberts et al. (2017).

The evil of the R2: I am probably not the first student to feel like what I have learned in my statistical classes at school is in practice, at best, not very useful, and at worst, dangerously misleading. Of course, I do understand that we must start somewhere, and that learning the basics of inferential statistics is a necessary step to, one day, be able to answer your one research questions. Yet, I feel like I have been carrying the “weight of the R2” for far too long before actually realizing that this metric of model performance (R2 among others) is simply not  enough to trust my results. You might think that your model is robust because among the 1000 alternative models you tested, it is the one with the “best” performance (deviance explained, AIC, you name it), but the model with the best R2 will not always be the most ecologically meaningful one, or the most practical for spatial management perspectives. Overfitting is like a sword of Damocles hanging over you every time you create a statistical model All together, I sometimes trust my supervisor’s expertise and my own judgment more than an R2.

Source: internet

A few good websites/presentations that have helped me through my SDM journey:

General website about spatial analysis (including SDM): http://rspatial.org/index.html

Cool presentation by Adam Smith about SDM:

http://www.earthskysea.org/!ecology/sdmShortCourseKState2012/sdmShortCourse_kState.pdf

Handling spatial data in R: http://www.maths.lancs.ac.uk/~rowlings/Teaching/UseR2012/introductionTalk.html

“The magical world of mgcv”, a great presentation by Noam Ross: https://www.youtube.com/watch?v=q4_t8jXcQgc

 

Literature cited

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., … Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 027–046. https://doi.org/10.1111/j.1600-0587.2012.07348.x

Elith, J., & Graham  H., C. (2009). Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models . Ecography, 32(Table 1), 66–77. https://doi.org/10.1111/j.1600-0587.2008.05505.x

Elith, J., & Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annual Review of Ecology, Evolution, and Systematics, 40(1), 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159

Friedman, J. H. (2001). Greedy Function Approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. Retrieved from http://www.jstor.org/stable/2699986

Mannocci, L., Roberts, J. J., Miller, D. L., & Halpin, P. N. (2017). Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas. Conservation Biology, 31(3), 601–614. https://doi.org/10.1111/cobi.12856.This

Miller, D. L., Burt, M. L., Rexstad, E. A., & Thomas, L. (2013). Spatial models for distance sampling data: Recent developments and future directions. Methods in Ecology and Evolution, 4(11), 1001–1010. https://doi.org/10.1111/2041-210X.12105

Redfern, J. V., Moore, T. J., Fiedler, P. C., de Vos, A., Brownell, R. L., Forney, K. A., … Ballance, L. T. (2017). Predicting cetacean distributions in data-poor marine ecosystems. Diversity and Distributions, 23(4), 394–408. https://doi.org/10.1111/ddi.12537

Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., … Dormann, C. F. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical or phylogenetic structure. Ecography, 0, 1–17. https://doi.org/10.1111/ecog.02881

Warren, D. L., & Seifert, S. N. (2010). Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 21(2), 335–342. https://doi.org/10.1890/10-1171.1

Wood, S. N. (2017). Generalized additive models: an introduction with R (second edi). CRC press.

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.

 

Oregon sea otter reintroduction: opinions, perspectives, and theories

By Dominique Kone, Masters Student in Marine Resource Management

Species reintroductions can be hotly contested issues because they can negatively impact other species, ecosystems, and society, as well as failing, altogether. The uncertainty of their outcomes forces stakeholder groups to form their own opinions on whether it’s a good idea to proceed with a reintroduction. When you have several groups with conflicting values and views, managers need to focus on the information most important for them to make a well-informed decision on whether to pursue a reintroduction.

As researchers, we can play an important role by carefully considering and addressing these views through our research, if the appropriate data is available. Despite being in the early days of our study on the potential sea otter reintroduction to Oregon, we have already heard several perspectives regarding its potential success, the type of research we should do, and if sea otters should be brought back to Oregon. Here, I present some of the most interesting and relevant opinions, perspectives, and theories I’ve heard regarding this reintroduction idea.

Source: Suzi Eszterhas

The first reintroduction failed because of X, Y, and Z.

From 1970-1971, managers translocated 93 sea otters to Oregon in a reintroduction effort (Jameson et al. 1982). However, in a matter of 5-6 years, all sea otters disappeared, and the effort was considered a failure. Researchers have theorized that sea otters left Oregon due to a lack of suitable habitat and prey, or to return home to sites from which they were captured. Others have reasoned that managers should have introduced southern sea otters instead of northern sea otters, suggesting one subspecies’ genetic pre-disposition may improve their chance for survival.

Knowing the reasons for this failure may help managers avoid these causes in a future reintroduction attempt and increase its chance of success. We, as scientists, can also gain insight from knowing these causes because this may help us better tailor our research to potentially investigate whether those causes still pose a threat to sea otters during a second attempt. Unfortunately, we lack concrete evidence on what exactly caused this failure, but we can still work to test some these theories.

Source: Mike Baird.

An otter is an otter, no matter where you put it.

There is evidence that northern and southern sea otters are genetically distinct, to a certain degree (Valentine et al. 2008, Larson et al. 2012), and hypotheses have been put forward that the two subspecies may be behaviorally- and ecologically-distinct, too. Studies have shown that northern and southern sea otters have different sized and shaped skulls and teeth, which researchers hypothesize may be a specialized foraging adaptation for consuming different prey species (Campbell & Santana 2017, Timm-Davis et al. 2015). This view suggests that each subspecies has developed unique traits to adapt to the environmental conditions specific to their current ranges. Therefore, when considering which subspecies to bring to Oregon, managers should reintroduce the subspecies with traits better-suited to cope with the types of habitat, prey assemblages, and oceanographic conditions specific to Oregon.

However, other scientists hold the opposite view, and argue that “an otter is an otter” no matter where you put it. This perspective suggests that both subspecies have an equal chance at surviving in any type of suitable habitat because all otters behave in similar ways. Therefore, ecologically, it may not matter which subspecies managers bring to Oregon.

Source: Trover

Oregon doesn’t have enough sea otter habitat.

Kelp is considered important sea otter habitat. In areas with high sea otter densities, such as central and southern California, kelp forests are persistent throughout the year. However, in Oregon, our kelp primarily consists of bull kelp – a slightly more fragile species compared to the durable giant kelp in California. In winter, this bull kelp gets dislodged during intense storms, resulting in seasonal changes in kelp availability. Managers worry that this seasonality could reduce the amount of suitable habitat, to the point where Oregon may not be able to support sea otters.

Yet, we know sea otters used to exist here; therefore, we can assume there must have been some suitable habitat that may persist today. Furthermore, sea otters use a range of habitats, including estuaries, bays, and reefs (Laidre et al. 2009, Lafferty & Tinker 2014, Kvitek et al. 1988). Therefore, even during times when kelp is less abundant, sea otters could use these other forms of habitat along the Oregon coast. Luckily, we have the spatial tools and data to assess how much, where, and when we have suitable habitat, and I will specifically address this in my thesis.

They’ll eat everything!

Sea otters are famous for their voracious appetites for benthic invertebrates, some of which are of commercial and recreational importance to nearshore fisheries. In some cases, sea otters have significantly reduced prey densities, such as sea urchins and Dungeness crab (Garshelis & Garshelis 1984, Estes & Palmisano 1974). However, without a formal analysis, it’s difficult to know if sea otters will have similar impacts on Oregon’s nearshore species, as well as at spatial scale these impacts will occur and whether our fisheries will be affected. We can predict where sea otters are likely to occur based on the presence of suitable habitat, but foraging impacts could be more localized or widespread across sea otter’s entire potential range. To better anticipate these impacts, managers will need an understanding of how much sea otters eat, where foraging could occur based on the availability of prey, and where sea otters and fisheries are likely to interact. I will also address this concern in my thesis.

Source: Suzi Eszterhas

To reintroduce or not to reintroduce? That is the question.

I have found that many scientists and managers have strong opinions on whether it’s appropriate to bring sea otters back to Oregon. Those who argue against a reintroduction often highlight many of the theories already mentioned here – lack of habitat, potential impacts to fisheries, and genetics. While other opponents provided more logistical and practical justifications, such as confounding politics, as well as difficulties in getting public support and regulatory permission to move a federally-listed species.

In contrast, proponents of this idea argue that a reintroduction could augment the recovery of the species by providing additional habitat for the species to rebound to pre-exploitation levels, as well as allowing for increased gene flow between southern and northern sea otter populations. Other proponents have brought up potential benefits to humans, such restoring ecosystem services, providing an economic boost through tourism, or preserving tribal and cultural connections. Such benefits may be worth attempting another reintroduction effort.

As you can see, there are several opinions and perspectives related to a potential sea otter reintroduction to Oregon. While it’s important to consider all opinions, managers still need facts to make key decisions. Scientists can play an important role in providing this information, so managers can make a well-informed decision. Oregon managers have not yet decided whether to proceed with a sea otter reintroduction, but our lab is working to provide them with reliable and accurate science, so they may form their own opinions and arrive at their own decision.

References:

Estes, J. A. and J. F. Palmisano. 1974. Sea otters: the role in structuring nearshore communities. Science. 185: 1058-1060.

Garshelis, D. L. and J. A. Garshelis. 1984. Movements and management of sea otters in Alaska. The Journal of Wildlife Management. 48: 665-678.

Jameson, R. J, Kenyon, K. W., Johnson, A. M., and H. M. Wight. 1982. History and status of translocated sea otter populations in North America. Wildlife Society Bulletin. 10: 100-107.

Lafferty, K. D., and M. T. Tinker. 2014. Sea otters are recolonizing southern California in fits and starts. Ecosphere. 5(5).

Laidre, K. L., Jameson, R. J., Gurarie, E., Jeffries, S. J., and H. Allen. 2009. Spatial habitat use patterns of sea otters in coastal Washington. Journal of Mammalogy. 90(4): 906-917.

Kvitek, R. G. ,Fukayama, A. K., Anderson, B. S., and B. K. Grimm. 1988. Sea otter foraging on deep-burrowing bivalves in a California coastal lagoon. Marine Biology. 98: 157-167.

Larson, S., Jameson, R., Etnier, M., Jones, T., and R. Hall. 2012. Genetic diversity and population parameters of sea otters, Enhydra lutris, before fur trade extirpation from 1741-1911. PLoS ONE. 7(3).

Timm-Davis, L. L, DeWitt, T. J., and C. D. Marshall. 2015. Divergent skull morphology supports two trophic specializations in otters (Lutrinae). PLoS ONE. 10(12).

Valentine et al. 2008. Ancient DNA reveals genotypic relationships among Oregon populations of the sea otter (Enhydra lutris). Conservation Genetics. 9:933-938.