Month: July 2019

Is there life after graduate school?

By Amanda Holdman, MS, GEMM Lab Alumni 2016

I graduated in March 2017 from the GEMM lab at Oregon State, with a Master’s of Science in Wildlife Management. Graduate school was finally over! No more constant coffee refills, popcorn dinners and overnight library stays; I had submitted my final thesis and I was done! Graduate school was no walk in the park for me, and finishing a master’s or a doctorate degree for anyone is no easy feat! It takes years of hard work, commitment, long hours, and a dedication to learning. I remember feeling both excited and a bit disoriented to be done with this phase of much stress and growth. After submitting my thesis, I took a much-needed month off to unknot the muscles in my back and get myself reacquainted with sunlight. The breath of fresh air was exactly what I needed to recover, but it took no time at all for a new type of challenge to emerge: the arduous task of finding a job.

I did what most job seekers do, I sat behind my computer applying for opportunities, hit as many roles as I could, and hoped for the best. Days turned into weeks and weeks turned into months. I was getting desperate, I resorted to applying for a whole spectrum of roles – consulting, project management, administration, youth team leader – hoping that something would land. Soon enough, almost 3 months had passed and I was still in the same spot as before. I was ready to throw in the towel.

In theory, landing a job after graduation sounds like it should be technically easy because more education should mean you are more qualified for the job, but anyone who has been out of grad school for more than an hour can tell you that landing a job after graduate school can be a long and frustrating process. I did not enter this field and its job prospects blindly – that is, I had a working idea of what type of research career I wanted when I completed my education and how much education I would need to get there. I was aware that navigating the job market in a competitive field could be tricky and time-consuming, especially as a green-job seeker. I knew it would be an added difficulty to land a position near the ocean but also close enough to family (I’m from the Midwest). Or at least, I thought I knew how hard it would be to secure a job. The process turned out to be much harder. Mental preparation alone was not enough and months and months of rejection and feeling stuck within the hamster wheel of the job search cycle was becoming my normal.

So, when I was stuck in the depths of a seemingly fruitless job search, and trying as hard as I possibly could, it was hard for me to do anything but roll my eyes, sigh, and give up. But I had to find a way to work through an apparently endless string of rejection by figuring out some way to accept, address and navigate my emotions. I needed to take charge of my own personal development. I started reflecting on what areas of my work on my master’s thesis that I found most difficult and wanted to improve, and would be  an important component of the job I wanted. Identifying my own “knowledge gaps” led me to seek out courses, workshops, job-shadowing and online courses that could fill those holes.

The first thing at the top of my list was to be more efficient at coding. Every job description that made me excited to apply had some description of a coding program: R, Python, MATLAB.  I was lucky enough to attend courses and workshops during my time at the GEMM lab that provided me much of the code I would need to create my habitat models with minimal tweaking. On top of that I was surrounded by supervisors and a lab full of coding geniuses that had an almost, if not completely, open door policy. When I was stuck and a deadline was quickly approaching, it was great to have an army of people to help me get through my obstacles. However, I knew if I wanted to be successful, I needed to become like them: experts and not a beginner. I purchased a subscription to DataCamp, and started searching out courses that could help keep my skills fresh and learn new things. I was over the moon to discover the course “Where are the Fishes?”. It checked all my boxes: geospatial analysis, R, marine related, acoustics…. perfect. Within this course, there were plenty of DataCamp prerequisites, like working with data in the tidyverse and working with dates and times in R, so I had plenty to keep me busy.

I also started looking for in-person, hands-on courses I could enroll in. Since the majority of my marine experience took place on the west coast but I was searching for jobs on the east coast, I enrolled in the Marine Mammal and Sea Turtle Observer Certification Course for the US Atlantic and Gulf of Mexico Oceans in order to learn a little more about identifying species I did not commonly see in nearshore, northern Pacific waters. In this course, I learned about regulations surrounding protected species monitoring, proper camera settings for photographing marine life, and gained the certification needed to work as an observer during seismic surveys for Bureau of Ocean Energy Management (BOEM) and Bureau of Safety and Environmental Enforcement (BSEE) in coordination with the National Marine Fisheries Service. Most of these topics were familiar to me, other than identifying new species, but it was nice to have the refresher and the renewed certification. Heads up this course is coming to Newport in October and I highly recommend it! During this observer course in Charleston, I was able to network with others in the field taking the course, the Charleston aquarium, and the South Carolina DNR. By introducing myself and providing a little bit of my background, I was invited by the South Carolina DNR to watch a satellite tag and release of a sea turtle that the aquarium had been rehabilitating. From the sea turtle release I learned of the International Sea Turtle Symposium that would take place in February in Myrtle Beach, North Carolina and was invited to attend and network by one of the conference chairs, which lead me to my current position. See below…

  • Releasing of a satellite-tagged Kemp’s Ridley Sea Turtle off Foley Beach, South Carolina
  • Downtown Charleston

I tried everything I could to keep myself attached to the field. I attended the Biannual Marine Mammal Conference, enrolled in a bioacoustics short course, watched webinars every Friday, read recent journal articles, looked for voluntary work. I even dropped in on offices like NOAA or Universities of towns I was driving through or visiting to see what they were researching, and if they were looking for researchers. Continuous learning and developing took a lot of time, money, and energy but being conscientious about my personal development kept me motivated and engaged. Graduate school prepared me for all of this. My GEMM lab experience taught me to be open to learning, to be flexible and adaptable, to accept, overcome and learn from failures and find solutions. In fact, graduate school provided me a variety of skills that have been transferable to almost everything I have done since graduation.

  • Manatees spotted while working as a Protected Species Observer in the Gulf, 2018

In December of 2017, I began volunteering at the University of Alabama, Birmingham, under the supervision of Dr. Thane Wibbels, and I began to use those skills I learned from graduate school more than ever. Flash forward and I am now part of a team, called the Kemp’s Ridley Working Group, which is made up of researchers from state, federal and international agencies working together on conservation strategies and programs for Kemp’s Ridley Sea Turtles. Specifically, we are hoping to identify the cues Kemp’s Ridley sea turtles are using to control arribadas (synchronized, large-scale nesting behaviors) in Rancho Nuevo, Mexico. We have a long-term dataset on the number of nests and weather conditions during arribadas from 2007 to 2019 collected using a variety of methods that we are trying to standardize and analyze. Historically, the number of nests has been counted by hand, but over the last few years Dr. Wibbels and his lab have worked to create a protocol for using drones to track the number of sea turtle nests, which has been highly successful. In 2018, the drone recorded the largest sea turtle arribada in 30 years, which consisted of about 4,000 Kemp’s Ridley sea turtle nests within 900 meters of beach.

June 2018 Kemp’s Ridley Sea Turtle Arribada, Rancho Nuevo, Mexico

It’s ironic how incredibly similar my current project is to my master’s thesis I am gathering environmental data from weather stations and remote sensing to analyze tides, currents, wind speed, wind direction, water temperature, air temperature, salinity, etc. in relation to these large arribadas. I am arguably much faster at this process than I was before due to my GEMM lab experience.  I am quickly able to recognize when something isn’t right, and am able to debug where I went wrong. I feel comfortable contributing new ideas and approaches of how to standardize data from old and new technology, how close to fly drones to the animals to capture the data we need without animal disturbance, and at what scales to look for temporal and spatial patterns within our data. The GEMM lab allowed me to gain knowledge through my own work and by association of my lab mates projects, trials and tribulations that have directly transferred into what I am doing now. I am still grant-writing, presenting, collaborating, managing time, and mentoring – all of which I learned in graduate school. I am also still coding, and I have joined a local coding group in Birmingham, Bham Quants, and have been asked to give a series of lectures called “Introduction to R”. The GEMM lab and my own drawn-out job-hunting process allowed me to end up in the position that I am in today, and the struggles and cycle of no’s I heard along the way led me to these opportunities that I am so grateful that I took.

Building on the foundation of my GEMM lab experience, adding my personal development and a couple of years of post-graduate work experience, I no longer feel disoriented. I feel like I have an identity and I know how I want to market myself in the future. I have always considered myself a spatial ecologist, as this is the GEMM labs specializes in, but now I know I’m more of a generalist in terms of species, methods, models and analysis and I want to continue learning and growing in this field to become a jack-of-all-trades. I’ve always had a love for the marine environment, but I also know I have the skills and confidence to transition into terrestrial if I need to. I have fallen in love with geospatial ecology and it isn’t a field that would have even been on my radar, if I had not met Leigh almost 5 years ago *gasp*. Working and studying in the GEMM lab opened up doors for me that I will appreciate for the rest of my life. My advice for anyone studying and working in this field is to stay alert with your eye always on the next step, poised for the next opportunity, whatever it is: to present a paper, attend a conference, meet a scholar in your field, forge a connection, gain a professional skill. There are tons of opportunities (and jobs) that are never posted online, which you will only find out about if you talk to people in your personal network or start knocking on doors. You never know where these doors might lead.

Applying novel methods in conservation physiology to understand cases of large whale mortalities

By Alejandro Fernánez Ajó, PhD student at NAU and GEMM Lab research technician

Although commercial whaling is currently banned and several whale populations show evidence of recovery, today´s whales are exposed to a variety of other human stressors (e.g., entanglement in fishing gear, vessel strikes, shipping noise, climate change, etc.; reviewed in Hunt et al., 2017a). The recovery and conservation of large whale populations is particularly important to the oceanic environment due to their key ecological role and unique biological traits, including their large body size, long lifespan and sizable home ranges (Magera et al., 2013; Atkinson et al., 2015; Thomas and Reeves, 2015). Thus, scientists must develop novel tools to overcome the challenges of studying whale physiology in order to distinguish the relative importance of the different impacts and guide conservation actions accordingly (Ayres et al., 2012; Hunt et al., 2013).

To this end, baleen hormone analysis represents a powerful tool for retrospective assessment of patterns in whale physiology (Hunt et al., 2014, 2016, 2017a, 2017b, 2018; Lysiak et. al., 2018; Fernández Ajó et al., 2018; Rolland et al., 2019). Moreover, hormonal panels, which include multiple hormones, are helping to better clarify and distinguish between the physiological effects of different sources of anthropogenic and environmental stressors (Ayres et al., 2012; Wasser et al., 2017; Lysiak et al., 2018; Romero et al., 2015).

What is Baleen? Baleen is a stratified epithelial tissue consisting of long, fringed plates that grow downward from the upper jaw, which collectively form the whale´s filter-feeding apparatus (Figure 1). This tissue accumulates hormones as it grows. Hormones are deposited in a linear fashion with time so that a single plate of baleen allows retrospective assessment and evaluation of a whales’ physiological condition, and in calves baleen provides a record of the entire lifespan including part of their gestation. Baleen samples are also readily accessible and routinely collected during necropsy along with other samples and relevant information.

Figure 1: Top: A baleen plate from a southern right whale calf (Source: Fernández Ajó et al. 2018). Bottom: A southern right whale with mouth open exposing its baleen (photo credit: Stephen Johnson).

Why are the Southern Right Whales calves (SRW) dying in Patagonia?

I am a Fulbright Ph.D. student in the Buck Laboratory  at Northern Arizona University since Fall 2017, a researcher with the Whale Conservation Institute of Argentina (Instituto de Conservación de Ballenas) and Field Technician for the GEMM Lab over the summer. I focus my research on the application and development of novel methods in conservation physiology to improve our understanding of how physiological parameters are affected by human pressures that impact large whales and marine mammals. I am especially interested in understanding the underlaying causes of large whale mortalities with the aim of preventing their occurrence when possible. In particular, for my Ph.D. dissertation, I am studying a die-off case of Southern Right Whale (SRW) calves, Eubalaena australis, off Peninsula Valdés (PV) in Patagonia-Argentina (Figure 2).

Prior to 2000, annual calf mortality at PV was considered normal and tracked the population growth rate (Rowntree et al., 2013). However, between 2007 and 2013, 558 whales died, including 513 newborn calves (Sironi et al., 2018). Average total whale deaths per year increased tenfold, from 8.2 in 1993-2002 to 80 in 2007-2013. These mortality levels have never before been observed for the species or any other population of whales (Thomas et al., 2013, Sironi et al., 2018).

Figure 2: Study area, the red dots along the shoreline indicate the location where the whales were found stranded at Península Valdés in 2018 (Source: The Right Whale Program Research Report 2018, Sironi and Rowntree, 2018)

Among several hypotheses proposed to explain these elevated calf mortalities, harassment by Kelp Gulls, Larus dominicanus, on young calves stands out as a plausible cause and is a unique problem only seen at the PV calving ground. Kelp gull parasitism on SRWs near PV was first observed in the 1970’s (Thomas, 1988). Gulls primarily harass mother-calf pairs, and this parasitic behavior includes pecking on the backs of the whales and creating open wounds to feed on their skin and blubber. The current intensity of gull harassment has been identified as a significant environmental stressor to whales and potential contributor to calf deaths (Marón et al., 2015b; Fernández Ajó et al., 2018).

Figure 3: The significant preference for calves as a target of gull attacks highlights the impact of this parasitic behavior on this age class. The situation continues to be worrisome and serious for the health and well-being of newborn calves at Península Valdés. Left: A Kelp Gull landing on whale´s back to feed on her skin and blubber (Photo credit: Lisandro Crespo). Right: A calf with multiple lesions on its back produced by repeated gull attacks (Photo credit: ICB).

Quantifying gull inflicted wounds

Photographs of gull wounds on whales taken during necropsies and were quantified and assigned to one of seven objectively defined size categories (Fig. 4): extra-small (XS), small (S), medium (M), large (L), extra-large (XL), double XL (XXL) and triple XL (XXXL). The size and number of lesions on each whale were compared to baleen hormones to determine the effect of the of the attacks on the whales health.

Figure 4. Kelp gull lesion scoring. Source: Maron et al. 2015).

How baleen hormones are applied

Impact factors such as injuries, predation avoidance, storms, and starvation promote an increase in the secretion of the glucocorticoids (GCs) cortisol and corticosterone (stress hormones), which then induce a variety of physiological and behavioral responses that help animals cope with the stressor. Prolonged exposure to chronic stress, however, may exceed the animal’s ability to cope with such stimuli and, therefore, adversely affects its body condition, its health, and even its survival. Triiodothyronine (T3), is the most biologically active form of the thyroid hormones and helps regulate metabolism. Sustained food deprivation causes a decrease in T3 concentrations, slowing metabolism to conserve energy stores. Combining GCs and T3 hormone measures allowed us to investigate and distinguish the relative impacts of nutritional and other sources of stressors.

Combining these novel methods produced unique results about whale physiology. With my research, we are finding that the GCs concentrations measured in calves´ baleen positively correlate with the intensity of gull wounding (Figure 4, 1 and 2), while calf’s baleen thyroid hormone concentrations are relative stable across time and do not correlate with intensity of gull wounding (Figure 4 – 3). Taken together these findings indicate that SRW calves exposed to Kelp gull parasitism and harassment experience high levels of physiological stress that compromise their health and survival. Ultimately these results will inform government officials and managers to direct conservation actions aimed to reduce the negative interaction between Kelp gulls and Southern Right Whales in Patagonia.

Figure 4: Physiological stress correlates with number of gull lesions (1 and 2). According to the best-fit linear model, immunoreactive baleen corticosterone (B) and cortisol (F) concentrations increased with wound severity (i.e. number of gull lesions). However, nutritional status indexed by baleen immunoreactive triiodothyronine (T3) concentrations does not correlate with the number of gull lesions (3). (Fernández Ajó et al. 2019, manuscript under revision)

Baleen hormones as a conservation tool

Baleen hormones represent a powerful tool for retrospective assessments of longitudinal trends in whale physiology by helping discriminate the underlying mechanisms by which different stressors may affect a whale’s health and physiology. Moreover, while most sample types used for studying whale physiology provide single time-point measures of current circulating hormone levels (e.g., skin or respiratory vapor), or information from previous few hours or days (e.g., urine and feces), baleen tissue provides a unique opportunity for longitudinal analyses of hormone patterns. These retrospective analyses can be conducted for both stranded or archived specimens, and can be conducted jointly with other biological markers (e.g., stable isotopes and biotoxins) to describe migration patterns and exposure to pollutants. Further research efforts on baleen hormones should focus on completing biological validations to better understand the hormone measurements in baleen and its correlation with measurements from alternative sample matrices (i.e., feces, skin, blubber, and respiratory vapors).

References:

Atkinson, S., Crocker, D., Houser, D., Mashburn, K., 2015. Stress physiology in marine mammals: how well do they fit the terrestrial model? J. Comp. Physiol. B. 185, 463–486. https://doi.org/10.1007/s00360-015-0901-0.

Ayres, K.L., Booth, R.K., Hempelmann, J.A., Koski, K.L., Emmons, C.K., Baird, R.W., Balcomb-Bartok, K., Hanson, M.B., Ford, M.J., Wasser, S.K., 2012. Distinguishing the impacts of inadequate prey and vessel traffic on an endangered killer whale (Orcinus orca) population. PLoS ONE. 7, e36842. https://doi.org/10.1371/journal.pone.0036842.

Fernández Ajó, A.A., Hunt, K., Uhart, M., Rowntree, V., Sironi, M., Marón, C.F., Di Martino, M., Buck, L., 2018. Lifetime glucocorticoid profiles in baleen of right whale calves: potential relationships to chronic stress of repeated wounding by Kelp Gull. Conserv. Physiol. 6, coy045. https://doi.org/10.1093/conphys/coy045.

Hunt, K., Lysiak, N., Moore, M., Rolland, R.M., 2017a. Multi-year longitudinal profiles of cortisol and corticosterone recovered from baleen of North Atlantic right whales (Eubalaena glacialis). Gen. Comp. Endocrinol. 254: 50–59. https://doi.org/10.1016/j.ygcen.2017.09.009.

Hunt, K.E., Hunt, K.E., Lysiak, N.S., Matthews, C.J.D., Lowe, C., Fernández-Ajo, A., Dillon, D., Willing, C., Heide-Jørgensen, M.P., Ferguson, S.H., Moore, M.J., Buck, C.L., 2018. Multi-year patterns in testosterone, cortisol and corticosterone in baleen from adult males of three whale species. Conserv. Physiol. 6, coy049. https://doi.org/10.1093/conphys/coy049.

Hunt, K.E., Hunt, K.E., Lysiak, N.S., Moore, M.J., Rolland R.M., 2016. Longitudinal progesterone profiles in baleen from female North Atlantic right whales (Eubalaena glacialis) match known calving history. Conserv. Physiol. 4, cow014. https://doi.org/10.1093/conphys/cow014.

Hunt, K.E., Lysiak, N.S., Moore, M.J., Seton, R.E., Torres, L., Buck, C.L., 2017b. Multiple steroid and thyroid hormones detected in baleen from eight whale species. Conserv. Physiol. 5, cox061. https://doi.org/10.1093/conphys/cox061.

Hunt, K.E., Moore, M.J., Rolland, R.M., Kellar, N.M., Hall, A.J., Kershaw, J., Raverty, S.A., Davis, C.E., Yeates, L.C., Fauquier, D.A., Rowles, T.K., Kraus, S.D., 2013. Overcoming the challenges of studying conservation physiology in large whales: a review of available methods. Conserv. Physiol. 1: cot006. https://doi.org/10.1093/conphys/cot006.

Hunt, K.E., Stimmelmayr, R., George, C., Hanns, C., Suydam, R., Brower, H., Rolland, R.M., 2014. Baleen hormones: a novel tool for retrospective assessment of stress and reproduction in bowhead whales (Balaena mysticetus). Conserv. Physiol. 2, cou030. doi: https://doi.org/10.1093/conphys/cou030.

Lysiak, N., Trumble, S., Knowlton, A., Moore, M., 2018. Characterizing the duration and severity of fishing gear entanglement on a North Atlantic right whale (Eubalaena glacialis) using stable isotopes, steroid and thyroid hormones in baleen. Front. Mar. Sci. 5: 168. https://doi.org/10.3389/fmars.2018.00168.

Magera, A.M., Flemming, J.E.M., Kaschner, K., Christensen, L.B., Lotze, H.K., 2013. Recovery trends in marine mammal populations. PLoS ONE. 8, e77908. https://doi.org/10.1371/journal.pone.0077908.

Marón, C.F., Beltramino, L., Di Martino, M., Chirife, A., Seger, J., Uhart, M., Sironi, M., Rowntree, V.J., 2015b Increased wounding of southern right whale (Eubalaena australis) calves by Kelp Gulls (Larus dominicanus) at Península Valdés, Argentina., PLoS ONE. 10, p. e0139291. https://doi.org/10.1371/journal.pone.0139291.

Marón, C.F., Rowntree, V.J., Sironi, M., Uhart, M., Payne, R.S., Adler, F.R., Seger, J., 2015a. Estimating population consequences of increased calf mortality in the southern right whales off Argentina. SC/66a/BRG/1 presented to the IWC Scientific Committee, San Diego, USA. Available at: https://iwc.int/home

Rolland, R.M., Graham, K.M., Stimmelmayr, R., Suydam, R. S., George, J.C., 2019. Chronic stress from fishing gear entanglement is recorded in baleen from a bowhead whale (Balaena mysticetus). Mar. Mam. Sci. https://doi.org/10.1111/mms.12596.

Romero, L.M., Platts, S.H., Schoech, S.J., Wada, H., Crespi, E., Martin, L.B., Buck, C.L., 2015. Understanding Stress in the Healthy Animal – Potential Paths for Progress. Stress. 18(5), 491-497.

Rowntree, V.J., Uhart, M.M., Sironi, M., Chirife, A., Di Martino, M., La Sala, L., Musmeci, L., Mohamed, N., Andrejuk, J., McAloose, D., Sala, J., Carribero, A., Rally, H., Franco, M., Adler, F., Brownell, R. Jr, Seger, J., Rowles, T., 2013. Unexplained recurring high mortality of southern right whale Eubalaena australis calves at Península Valdés, Argentina. Mar. Ecol. Prog. Ser. 493:275–289. https://doi.org/10.3354/meps10506.

Sironi, M. Rowntree, V., Di Martino, M., Alzugaray, L.,Rago, V., Marón, C.F., Uhart M., 2018. Southern right whale mortalities at Península Valdes, Argentina: updated information for 2016-2017. SC/67B/CMP/06 presented to the IWC Scientific Committee, Slovenia. Available at: https://iwc.int/home.

Sironi, M. Rowntree, V., Snowdon, C., Valenzuela, L., Marón C., 2009. Kelp Gulls (Larus dominicanus) feeding on southern right whales (Eubalaena australis) at Península Valdes, Argentina: updated estimates and conservation implications. SC/61/BRG19. presented to the IWC Scientific Committee, Portugal. Available at: https://iwc.int/home.

Thomas, P., Reeves, R., 2015. Status of the world’s baleen whales. Mar. Mam. Sci. 32:682–734. https://doi.org/10.1111/mms.12281.

Thomas, P., Uhart, M., McAloose, D., Sironi, M., Rowntree, V.J., Brownell, Jr. R., Gulland, F.M.D., Moore, M., Marón, C., Wilson, C., 2013. Workshop on the southern right whale die-off at Península Valdés, Argentina. SC/60/BRG15 presented to the IWC Scientific Committee, South Korea. Available at: https://iwc.int/home

Thomas, P.O. 1988. Kelp Gulls, Larus dominicanus, are parasites on flesh of the right whale, Eubalaena australis. Ethology. 79:89-103. https://doi.org/10.1111/j.1439-0310.1988.tb00703.x.

Wasser, S.K., Lundin, J.I., Ayres, K., Seely, E., Giles, D., Balcomb, K., Hempelmann, J., Parsons, K., Booth, R., 2017. Population growth is limited by nutritional impacts on pregnancy success in endangered Southern Resident killer whales (Orcinus orca). PLoS ONE. 12, e0179824. https://doi.org/10.1371/journal.pone.0179824.

Lingering questions on the potential to bring sea otters back to Oregon

By Dominique Kone, Masters Student in Marine Resource Management

By now, I’m sure you’re aware of recent interests to reintroduce sea otters to Oregon. To inform this effort, my research focuses on predicting suitable sea otter habitat and investigating the potential ecological effects if sea otters are reintroduced in the future. This information will help managers gain a better understanding of the potential for sea otters to reestablish in Oregon, as well as how Oregon’s ecosystems may change via top-down processes. These analyses will address some sources of uncertainties of this effort, but there are still many more questions researchers could address to further guide this process. Here, I note some lingering questions I’ve come across in the course of conducting my research. This is not a complete list of all questions that could or should be investigated, but they represent some of the most interesting questions I have and others have in Oregon.

Credit: Todd Mcleish

The questions, and our associated knowledge on each of these topics:

Is there enough available prey to support a robust sea otter population in Oregon?

Sea otters require approximately 30% of their own body weight in food every day (Costa 1978, Reidman & Estes 1990). With a large appetite, they not only need to spend most of their time foraging, but require a steady supply of prey to survive. For predators, we assume the presence of suitable habitat is a reliable proxy for prey availability (Redfern et al. 2006). Whereby, quality habitat should supply enough prey to sustain predators at higher trophic levels.

In making these habitat predictions for sea otters, we must also recognize the potential limitations of this “habitat equals prey” paradigm, in that there may be parcels of habitat where prey is unavailable or inaccessible. In Oregon, there could be unknown processes unique to our nearshore ecosystems that would support less prey for sea otters. This possibility highlights the importance of not only understanding how much suitable habitat is available for foraging sea otters, but also how much prey is available in these habitats to sustain a viable otter population in the future. Supplementing these habitat predictions with fishery-independent prey surveys is one way to address this question.

Credit: Suzi Eszterhas via Smithsonian Magazine

How will Oregon’s oceanographic seasonality alter or impact habitat suitability?

Sea otters along the California coast exist in an environment with persistent Giant kelp beds, moderate to low wave intensity, and year-round upwelling regimes. These environmental variables and habitat factors create productive ecosystems that provide quality sea otter habitat and a steady supply of prey; thus, supporting high densities of sea otters. This environment contrasts with the Oregon coast, which is characterized by seasonal changes in bull kelp and wave intensity. Summer months have dense kelp beds, calm surf, and strong upwellings. While winter months have little to no kelp, weak upwellings, and intense wave climates. These seasonal variations raise the question as to how these temporal fluctuations in available habitat could impact the number of sea otters able to survive in Oregon.

In Washington – an environment like Oregon – sea otters exhibit seasonal distribution patterns in response to intensifying wave climates. During calm summer months, sea otters primarily forage along the outer coast, but move into more protected areas, such as the Strait of Juan de Fuca, during winter months (Laidre et al. 2009). If sea otters were reintroduced to Oregon, we may very well observe similar seasonal movement patterns (e.g. dispersal into estuaries), but the degree to which this seasonal redistribution and reduction in foraging habitat could impact sea otter reestablishment and recovery is currently unknown.

Credit: Oregon Coast Aquarium

In the event of a reintroduction, do northern or southern sea otters have a greater capacity to adapt to Oregon environments?

In the early 1970’s, Oregon’s first sea otter translocation effort failed (Jameson et al. 1982). Since then, hypotheses on the potential ecological differences between northern and southern sea otters have been proposed as potential factors of the failed effort, potentially due to different abilities to exploit specific prey species. Studies have demonstrated that northern and southern sea otters have slight morphological differences – northern otters having larger skulls and teeth than southern otters (Wilson et al. 1991). This finding has created the hypothesis that the northern otter’s larger skull and teeth allow it to consume prey with denser exoskeletons, and thereby can exploit a greater diversity of prey species. However, there appears to be a lack of evidence to suggest larger skulls and teeth translate to greater bite force. Based on morphology alone, either sub-species could be just as successful in exploiting different prey species.

A different direction to address questions around adaptability is to look at similarities in habitat and oceanographic characteristics. Sea otters exist along a gradient of habitat types (e.g. kelp forests, estuaries, soft-sediment environments) and oceanographic conditions (e.g. warm-temperature to cooler sub-Arctic waters) (Laidre et al. 2009, Lafferty et al. 2014). Yet, we currently don’t know how well or quickly otters can adapt when they expand into new habitats that differ from ones they are familiar with. Sea otters must be efficient foragers and need to acquire skills that allow them to effectively hunt specific prey species (Estes et al. 2003). Hypothetically, if we take sea otters from rocky environments where they’ve developed foraging skills to hunt sea urchins and abalones, and place them in a soft-sediment environment, how quickly would they develop new foraging skills to exploit soft-sediment prey species? Would they adapt quickly enough to meet their daily prey requirements?

Credit: Eric Risberg/Associated Press via The Columbian

In Oregon, specifically, how might climate change impact sea otters, and how might sea otters mediate climate impacts?

Climate change has been shown to directly impact many species via changes in temperature (Chen et al. 2011). Some species have specific thermal tolerances, in which they can only survive within a specified temperature range (i.e. maximum and minimum). Once the temperature moves out of that range, the species can either move with those shifting water masses, behaviorally adapt or perish (Sunday et al. 2012). It’s unclear if and how changing temperatures will impact sea otters, directly. However, sea otters could still be indirectly affected via impacts to their prey. If prey species in sea otter habitat decline due to changing temperatures, this would reduce available food for otters. Ocean acidification (OA) is another climate-induced process that could indirectly impact sea otters. By creating chemical conditions that make it difficult for species to form shells, OA could decrease the availability of some prey species, as well (Gaylord et al. 2011).

Interestingly, these pathways between sea otters and climate change become more complex when we consider the potentially mediating effects from sea otters. Aquatic plants – such as kelp and seagrass – can reduce the impacts of climate change by absorbing and taking carbon out of the water column (Krause-Jensen & Duarte 2016). This carbon sequestration can then decrease acidic conditions from OA and mediate the negative impacts to shell-forming species. When sea otters catalyze a tropic cascade, in which herbivores are reduced and aquatic plants are restored, they could increase rates of carbon sequestration. While sea otters could be an effective tool against climate impacts, it’s not clear how this predator and catalyst will balance each other out. We first need to investigate the potential magnitude – both temporal and spatial – of these two processes to make any predictions about how sea otters and climate change might interact here in Oregon.

Credit: National Wildlife Federation

In Summary

There are several questions I’ve noted here that warrant further investigation and could be a focus for future research as this potential sea otter reintroduction effort progresses. These are by no means every question that should be addressed, but they do represent topics or themes I have come across several times in my own research or in conversations with other researchers and managers. I think it’s also important to recognize that these questions predominantly relate to the natural sciences and reflect my interest as an ecologist. The number of relevant questions that would inform this effort could grow infinitely large if we expand our disciplines to the social sciences, economics, genetics, so on and so forth. Lastly, these questions highlight the important point that there is still a lot we currently don’t know about (1) the ecology and natural behavior of sea otters, and (2) what a future with sea otters in Oregon might look like. As with any new idea, there will always be more questions than concrete answers, but we – here in the GEMM Lab – are working hard to address the most crucial ones first and provide reliable answers and information wherever we can.

References:

Chen, I., Hill, J. K., Ohlemuller, R., Roy, D. B., and C. D. Thomas. 2011. Rapid range shifts of species associated with high levels of climate warming. Science. 333: 1024-1026.

Costa, D. P. 1978. The ecological energetics, water, and electrolyte balance of the California sea otter (Enhydra lutris). Ph.D. dissertation, University of California, Santa Cruz.

Estes, J. A., Riedman, M. L., Staedler, M. M., Tinker, M. T., and B. E. Lyon. 2003. Individual variation in prey selection by sea otters: patterns, causes and implications. Journal of Animal Ecology. 72: 144-155.

Gaylord et al. 2011. Functional impacts of ocean acidification in an ecologically critical foundation species. Journal of Experimental Biology. 214: 2586-2594.

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(2): 100-107.

Krause-Jensen, D., and C. M. Duarte. 2016. Substantial role of macroalgae in marine carbon sequestration. Nature Geoscience. 9: 737-742.

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 Marine Mammalogy. 90(4): 906-917.

Redfern et al. 2006. Techniques for cetacean-habitat modeling. Marine Ecology Progress Series. 310: 271-295.

Reidman, M. L. and J. A. Estes. 1990. The sea otter (Enhydra lutris): behavior, ecology, and natural history. United States Department of the Interior, Fish and Wildlife Service, Biological Report. 90: 1-126.

Sunday, J. M., Bates, A. E., and N. K. Dulvy. 2012. Thermal tolerance and the global redistribution of animals. Nature: Climate Change. 2: 686-690.

Wilson, D. E., Bogan, M. A., Brownell, R. L., Burdin, A. M., and M. K. Maminov. 1991. Geographic variation in sea otters, Ehydra lutris. Journal of Mammalogy. 72(1): 22-36.

Eyes from Space: Using Remote Sensing as a Tool to Study the Ecology of Blue Whales

By Christina Garvey, University of Maryland, GEMM Lab REU Intern

It is July 8th and it is my 4th week here in Hatfield as an REU intern for Dr. Leigh Torres. My name is Christina Garvey and this summer I am studying the spatial ecology of blue whales in the South Taranaki Bight, New Zealand. Coming from the east coast, Oregon has given me an experience of a lifetime – the rugged shorelines continue to take my breath away and watching sea lions in Yaquina Bay never gets old. However, working on my first research project has by far been the greatest opportunity and I have learned so much in so little time. When Dr. Torres asked me to contribute to this blog I was unsure of how I would write about my work thus far but I am excited to have the opportunity to share the knowledge I have gained with whoever reads this blog post.

The research project that I will be conducting this summer will use remotely sensed environmental data (information collected from satellites) to predict blue whale distribution in the South Taranaki Bight (STB), New Zealand. Those that have read previous blogs about this research may remember that the STB study area is created by a large indentation or “bight” on the southern end of the Northern Island. Based on multiple lines of evidence, Dr. Leigh Torres hypothesized the presence of an unrecognized blue whale foraging ground in the STB (Torres 2013). Dr. Torres and her team have since proved that blue whales frequent this region year-round; however, the STB is also very industrial making this space-use overlap a conservation concern (Barlow et al. 2018). The increasing presence of marine industrial activity in the STB is expected to put more pressure on blue whales in this region, whom are already vulnerable from the effects of past commercial whaling (Barlow et al. 2018) If you want to read more about blue whales in the STB check out previous blog posts that talk all about it!

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.

Figure 2. South Taranaki Bight, New Zealand, our study site outlined by the red box. Kahurangi Point (black star) is the site of wind-driven upwelling system.

The possibility of the STB as an important foraging ground for a resident population of blue whales poses management concerns as New Zealand will have to balance industrial growth with the protection and conservation of a critically endangered species. As a result of strong public support, there are political plans to implement a marine protected area (MPA) in the STB for the blue whales. The purpose of our research is to provide scientific knowledge and recommendations that will assist the New Zealand government in the creation of an effective MPA.

In order to create an MPA that would help conserve the blue whale population in the STB, we need to gather a deeper understanding of the relationship between blue whales and this marine environment. One way to gain knowledge of the oceanographic and ecological processes of the ocean is through remote sensing by satellites, which provides accessible and easy to use environmental data. In our study we propose remote sensing as a tool that can be used by managers for the design of MPAs (through spatial and temporal boundaries). Satellite imagery can provide information on sea surface temperature (SST), SST anomaly, as well as net primary productivity (NPP) – which are all measurements that can help describe oceanographic upwelling, a phenomena that is believed to be correlated to the presence of blue whales in the STB region.

Figure 3. The stars of the show: blue whales. A photograph captured from the small boat of one animal fluking up to dive down as another whale surfaces close by. (Photo credit: L. Torres)

Past studies in the STB showed evidence of a large upwelling event that occurs off the coast of Kahurangi Point (Fig. 2), on the northwest tip of the South Island (Shirtcliffe et al. 1990). In order to study the relationship of this upwelling to the distribution of blue whales, I plan to extract remotely sensed data (SST, SST anomaly, & NPP) off the coast of Kahurangi and compare it to data gathered from a centrally located site within the STB, which is close to oil rigs and so is of management interest. I will first study how decreases in sea surface temperature at the site of upwelling (Kahurangi) are related to changes in sea surface temperature at this central site in the STB, while accounting for any time differences between each occurrence. I expect that this relationship will be influenced by the wind patterns, and that there will be changes based on the season. I also predict that drops in temperature will be strongly related to increases in primary productivity, since upwelling brings nutrients important for photosynthesis up to the surface. These dips in SST are also expected to be correlated to blue whale occurrence within the bight, since blue whale prey (krill) eat the phytoplankton produced by the productivity.

Figure 4. A blue whale lunges on an aggregation of krill. UAS piloted by Todd Chandler.

To test the relationships I determine between remotely sensed data at different locations in the STB, I plan to use blue whale observations from marine mammal observers during a seismic survey conducted in 2013, as well as sightings recorded from the 2014, 2016, and 2017 field studies led by Dr. Leigh Torres. By studying the statistical relationships between all of these variables I hope to prove that remote sensing can be used as a tool to study and understand blue whale distribution.

I am very excited about this research, especially because the end goal of creating an MPA really gives me purpose. I feel very lucky to be part of a project that could make a positive impact on the world, if only in just a little corner of New Zealand. In the mean time I’ll be here in Hatfield doing the best I can to help make that happen.

References: 

Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.

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.

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.

Species distribution modeling: Part statistics, part philosophy, and there is no “right answer”

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

Just like that, I have wrapped up year 1 of my PhD in Wildlife Science. For my PhD, I am investigating the ecology and distribution of blue whales in New Zealand across multiple spatial and temporal scales. In a region where blue whales overlap with industrial activity, there is considerable interest from managers to be able to reliably forecast when and where blue whales are most likely to be in the area. In a series of five chapters and utilizing multiple different data sources (dedicated boat surveys, oceanographic data, acoustic recordings, remotely sensed environmental data, opportunistic blue whale sightings information), I will attempt to describe, quantify, and predict where blue whales are found in relation to their environment. Each chapter will evaluate the distribution of blue whales relative to the environment at different scales in space (ranging from 4 km to 25 km resolution) and time (ranging from daily to seasonal resolution). One overarching method I am using throughout my PhD is species distribution modeling. Having just completed my research review with my doctoral committee last week, I’ll share this aspect of my research proposal that I’ve particularly enjoyed reading, writing, and thinking about.

A pair of blue whales surfacing in the South Taranaki Bight region of New Zealand. Drone piloted by Todd Chandler during the 2017 field season.

Species distribution models (SDMs), which are sometimes referred to as habitat models or ecological niche models, are mathematical algorithms that combine observations of a species with environmental conditions at their observed locations, to gain ecological insight and predict spatial distributions of the species (Elith and Leathwick, 2009; Redfern et al., 2006). Any model is just one description of what is occurring in the natural world. Just as there are many ways to describe something with words and many languages to do so, there are many options for modeling frameworks and approaches, with stark and nuanced differences. My labmate and friend Solene Derville has equated the number of choices one has for SDMs to the cracker section in an American grocery store. When navigating all of these choices and considerations, it is important to remember that no model will ever be completely correct—it is our best attempt at describing a complex natural system—and as an analyst we need to do the best that we can with the data available to address the ecological questions at hand. As it turns out, the dividing line between quantitative analysis and philosophy is thin at times. What may seem at first like a purely objective, statistical endeavor requires careful consideration and fundamental decision-making on the part of the analyst.

Ecosystems are multifaceted, complex, and hierarchical. They are comprised of multiple physical and biological components, which operate at multiple scales across space and time. As Dr. Simon Levin stated in at 1989 MacArthur Award lecture on the topic of scale in ecology:

“A good model does not attempt to reproduce every detail of the biological system; the system itself suffices for that purpose as the most detailed model of itself. Rather, the objective of a model should be to ask how much detail can be ignored without producing results that contradict specific sets of observations, on particular scales of interest” (Levin, 1992).

The question of scale is central to ecology. As many biology students learn in their first introductory classes, parsimony is “The principle that the most acceptable explanation of an occurrence, phenomenon, or event is the simplest, involving the fewest entities, assumptions, or changes” (Oxford Dictionary). In other words, the best explanation is the simplest one. One challenge in ecological modeling, including SDMs, is to select spatial and temporal scales as coarse as possible for the most parsimonious—the most straightforward—model, while still being fine enough to capture relevant patterns. Another critical consideration is the scale of the question you are interested in answering. The scale of the analysis must match the scale at which you want to make inferences about the ecology of a species.

Similarly, the issue of complexity is central to distribution modeling. Overly simple models may not be able to adequately describe the relationship between species occurrence and the environment. In contrast, highly complex models may have very high explanatory power, but risk ascribing an ecological pattern to noise in the data (Merow et al., 2014), in other words, finding patterns that aren’t real. Furthermore, highly complex models tend to have poorer predictive capacity than simpler models (Merow et al., 2014). There is a trade-off between descriptive and predictive power in SDMs (Derville et al., 2018). Therefore, a key component in the SDM process is establishing the end goal of the model with respect to the region of interest, scale, explanatory power, predictive capacity, and in many cases management need.

Finally, any model is ultimately limited by the data available and the scale at which it was collected (Elith and Leathwick, 2009; Guillera-Arroita et al., 2015; Redfern et al., 2006). Prior knowledge of what environmental features are important to the species of interest is often limited at the time of the data collection effort, and data collection is constrained by when it is logistically feasible to sample. For example, we collect detailed oceanographic data during the summer months when it is practical to get out on the water, satellite imagery of sea surface temperature might be unavailable during times of cloud cover, and people are more likely to report blue whale sightings in areas where there is more human activity. Therefore, useful SDMs that address both ecological and management needs typically balance the scale of analysis and model complexity with the limitations of the data.

Managers and politicians within the New Zealand government are interested in a tool to predict when and where blue whales are most likely to be, based on sound ecological analysis. This is one of the end-goals of my PhD, but in the meantime, I am grappling with the appropriate scales of analysis, and attempting to balance questions of model complexity, explanatory power, and predictive capacity. There is no single, correct answer, and so my process is in part quantitative analysis, part philosophy, and all with the goal of increased ecological understanding and conservation of a species.

A blue whale breaks the surface. As I grapple with questions of model complexity and scale of analysis, I sometimes need a reminder that behind each data point is a blue whale, and what a privilege it is to study them. Photo by Leigh Torres.

References:

Derville, S., Torres, L. G., Iovan, C., and Garrigue, C. (2018). Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Divers. Distrib. 24, 1657–1673. doi:10.1111/ddi.12782.

Elith, J., and Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697. doi:10.1146/annurev.ecolsys.110308.120159.

Guillera-Arroita, G., Lahoz-Monfort, J. J., Elith, J., Gordon, A., Kujala, H., Lentini, P. E., et al. (2015). Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292. doi:10.1111/geb.12268.

Levin, S. A. (1992). The problem of pattern and scale. Ecology 73, 1943–1967.

Merow, C., Smith, M. J., Edwards, T. C., Guisan, A., Mcmahon, S. M., Normand, S., et al. (2014). What do we gain from simplicity versus complexity in species distribution models? Ecography (Cop.). 37, 1267–1281. doi:10.1111/ecog.00845.

Redfern, J. V., Ferguson, M. C., Becker, E. A., Hyrenbach, K. D., Good, C., Barlow, J., et al. (2006). Techniques for cetacean-habitat modeling. Mar. Ecol. Prog. Ser. 310, 271–295. doi:10.3354/meps310271.