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.
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.
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.
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.
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.
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.
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
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.
A few good websites/presentations that have helped me through my SDM journey:
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
Dr. Leigh Torres, Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute, Oregon State University
The GEMM Lab is always active – running field projects, leading outreach events, giving seminars, hosting conferences, analyzing data, mentoring young scientists, oh the list goes on! (Yes, I am a proud lab PI). And, recently we have had a flurry of scientific papers either published or accepted for publication that I want to highlight. These are all great pieces of work that demonstrate our quality work, poignant and applied science, and strong collaborations. For each paper listed below I provide a short explanation of the study and implications. (Those names underlined are GEMM Lab members, and I provided a weblink where available.)
Sullivan, F.A. & Torres, L.G.Assessment of vessel disturbance to gray whales to inform sustainable ecotourism. The Journal of Wildlife Management, doi:10.1002/jwmg.21462.
This project integrated research and outreach regarding gray whale behavioral response to vessels. We simultaneously tracked whales and vessels, and data analysis showed significant differences in gray whale activity budgets when vessels were nearby. Working with stakeholders, we translated these results into community-developed vessel operation guidelines and an informational brochure to help mitigate impacts on whales.
Hann, C., Stelle, L., Szabo, A. & Torres, L. (2018) Obstacles and Opportunities of Using a Mobile App for Marine Mammal Research. ISPRS International Journal of Geo-Information, 7, 169. http://www.mdpi.com/2220-9964/7/5/169
This study demonstrates the strengths (fast and cheap data collection) and weaknesses (spatially biased data) of marine mammal data collected using the mobile app Whale mAPP. We emphasize the need for increased citizen science participation to overcome obstacles, which will enable this data collection method to achieve its great potential.
Barlow, D.R., Torres, L.G., Hodge, K., Steel, D., Baker, C.S., Chandler, T.E., Bott, N., Constantine, R., Double, M.C., Gill, P.C., Glasgow, D., Hamner, R.M., Lilley, C., Ogle, M., Olson, P.A., Peters, C., Stockin, K.A., Tessaglia-Hymes, C.T. & Klinck, H. (in press) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endangered Species Research. https://doi.org/10.3354/esr00891.
This study used genetics, acoustics, and photo-id to document a new population of blue whales around New Zealand that is genetically isolated, has high year-round residence, and shows limited connectivity to other blue whale populations. This discovery has important implication for population management, especially in the South Taranaki Bight region of New Zealand where the whales forage among industrial activity.
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.
Here we developed methods to measure whale body morphometrics using images captured via Unmanned Aerial Systems (UAS; ‘drones’). The paper presents three freely available analysis programs and a protocol to help the community standardize methods, assess and minimize error, and compare data between studies.
Holdman, A.K., Haxel, J.H., Klinck, H. & Torres, L.G. (in press) Acoustic monitoring reveals the times and tides of harbor porpoise distribution off central Oregon, USA. Marine Mammal Science.
Right off the Newport, Oregon harbor entrance we listened for harbor porpoises at two locations using hydrophones. We found that porpoise presence at the shallow rocky reef site corresponds with the ebb tidal phase, while harbor porpoise presence at the deeper site with sandy bottom was associated with night-time foraging. It appears that harbor porpoise change their spatial and temporal patterns of habitat use to increase their foraging efficiency.
Derville, S., Torres, L.G., Iovan, C. & Garrigue, C. (in press) Finding the right fit: Comparative cetacean distribution models using multiple data sources. Diversity and Distributions.
Species distribution models (SDM) are used widely to understand the drivers of cetacean distribution patterns, and to predict their space-use patterns too. Using humpback whale sighting datasets in New Caledonia, this study explores the performance of different SDM algorithms (GAM, BRT, MAXENT, GLM, SVM) and methods of modeling presence-only data. We highlight the importance of controlling for model overfitting and thorough model validation.
Bishop, A.M., Brown, C., Rehberg, M., Torres, L.G. & Horning, M. (in press) Juvenile Steller sea lion (Eumetopias jubatus) utilization distributions in the Gulf of Alaska. Movement Ecology.
This study examines the distribution patterns of juvenile Steller sea lions in the Gulf of Alaska to gain a better understanding of the habitat needs of this vulnerable demographic group within a threatened population. Utilization distributions were derived for 84 tagged sea lions, which showed sex, seasonal and spatial differences. This information will support the development of a species recovery plan.
By Dawn Barlow, MSc student, Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
In 2013, Leigh first published a hypothesis that the South Taranaki Bight region between New Zealand’s North and South Islands is important habitat for blue whales (Torres 2013). Since then, we have collected three years of data and conducted dedicated analyses, so we now understand that a unique population of blue whales is found in New Zealand, and that they are present in the South Taranaki Bight year-round (Barlow et al. in press).
This research has garnered quite a bit of political and media attention. A major platform item for the New Zealand Green Party around the last election was the establishment of a marine mammal sanctuary in the South Taranaki Bight. When the world’s largest seismic survey vessel began surveying the South Taranaki Bight this summer for more oil and gas reserves using tremendously loud airguns, there were rallies on the lawn in front of Parliament featuring a large inflatable blue whale that the protesters affectionately refer to as “Janet”. Needless to say, blue whales have made their way into the spotlight in New Zealand.
Now that we know there is a unique population of blue whales in New Zealand, what is next? What’s next for me is an exciting combination of both ecology and conservation. If an effective sanctuary is to be implemented, it needs to be more than a simple box drawn on a map to check off a political agenda item—the sanctuary should be informed by our best ecological knowledge of the blue whales and their habitat.
In July, Leigh and I will attend the Society for Conservation Biology meeting in Wellington, New Zealand, and I’ll be giving a presentation titled “Cloudy with a chance of whales: Forecasting blue whale presence based on tiered, bottom-up models”. I’ll be the first to admit, I am not yet forecasting blue whale presence. But I am working my way there, step-by-step, through this tiered, bottom-up approach. In cetacean habitat modeling, we often assume that whale distribution on a foraging ground is determined by their prey’s distribution, and that satellite images of temperature and chlorophyll-a provide an accurate picture of what is going on below the surface. Is this true? With our three years of data including in situ oceanography, krill hydroacoustics, and blue whale distribution and behavior, we are in a unique position to test some of those assumptions, as well as provide managers with an informed management tool to predict blue whale distribution.
What questions will we ask using our data? Firstly, can in situ oceanography (i.e., thermocline depth and temperature, mixed layer depth) predict the distribution and density of blue whale prey (krill)? Then, can those prey patterns be accurately predicted in the absence of oceanographic measurements, using just satellite images? Next, we’ll bring the blue whales back into the picture to ask: can we predict blue whale distribution based on our in situ measurements of oceanography and prey? And finally, in the absence of in situ measurements (which is most often the case), can we forecast where the whales will be based just on remotely-sensed images of the region?
So, cloudy with a chance of whales? Well, you’ll have to stay tuned for that story in the coming months. In the meantime, I can tell you that as daunting as it is to aggregate so many data streams, each step of the way has a piece of the story to tell. I can’t wait to see how it falls together, both from an ecological modeling perspective and a conservation management objective.
Torres, L. G. (2013). Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zealand Journal of Marine and Freshwater Research, 47(2), 235-248.
Barlow, D. R., Torres, L. G., Hodge, K. B., Steel, D. Baker, C. S., Chandler, T. E., Bott, N., Constantine, R., Double, M. C., Gill, P., Glasgow, D., Hamner, R. M., Lilley, C., Ogle, M., Olson, P. A., Peters, C., Stockin, K. A., Tessaglia-Hymes, C. T., Klinck, H. (in press). Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endangered Species Research.
By Dominique Kone, Masters Student in Marine Resource Management
When considering a species reintroduction into an area, it is important to assess the suitability of the area’s habitat before such efforts begin. By doing this assessment at the outset, managers and conservationists can gain a better understanding of the capacity of the area to support a viable population overtime, and ultimately the success of the reintroduction. However, to do a habitat assessment, researchers must first have a base understanding of the species’ ecological characteristics, behavior, and the physical habitat features necessary for the species’ survival. For my thesis, I plan to conduct a similar assessment to identify suitable sea otter habitat to inform a potential sea otter reintroduction to the Oregon coast.
To start my assessment, I conducted a literature review of studies that observed and recorded the various types of habitats where sea otters currently exist. In my research, I learned that sea otters use in a range of environments, each with a unique set of habitat characteristics. With so many features to sort through, I have focused on specific habitat features that are consistent across most of the current range of sea otters – from Alaska to California – and are important for at least some aspects of sea otters’ everyday life or behavior, specifically foraging. Focusing my analysis on foraging habitat makes sense as sea otters require around 30% of their body weight in food every day (Costa 1978, Reidman & Estes 1990). Meaning sea otters spend most of their day searching for food.
Here, I present four habitat features I will incorporate into my analysis and explain why these features are important for sea otter foraging behavior and survival.
Kelp: Sea otters are famously known for the benefits they provide to kelp forests. In the classic three-trophic-level model, sea otters allow for the growth of kelp by keeping sea urchins – consumers of kelp – in check (Estes & Palmisano 1974). Additionally, sea otters and kelp have a mutually-beneficial relationship. Sea otters will often wrap themselves amongst the top of kelp stocks while feeding, resting, or grooming to prevent being carried away by surface currents. Meanwhile, it’s thought that kelp provide a refuge for sea otters seeking to avoid predators, such as sharks, as well as their prey.
Distance from Kelp: The use of kelp, by sea otters, is relatively straight-forward. Yet, kelp can still have an influence on sea otter behavior even when not used directly. A 2014 study found that sea otters along the southern California coast were almost 10 times more likely to be located within kelp habitat than outside, while outside kelp beds sea otter numbers declined with distance from the edge of kelp canopies. Sea otters will often forage outside or next to kelp canopies when prey’s available, and even sometimes to socialize in age- or sex-specific rafts (Lafferty & Tinker 2014). These findings indicate that sea otters can and do regularly disperse away from kelp habitat, but because they’re so dependent on kelp, they don’t stray very far.
Seafloor Substrate: Sea otters forage over a variety of sediment substrates, including rocks, gravel, seagrass, and even sometimes sand. For example, sea otters hunt sea urchins over rocky substrates, while in other areas they may hunt for crabs in seagrass beds (Estes & Palmisano 1974, Hughes et al. 2014). The type of substrate sea otters forage in typically depends on the substrate needs of their target prey species. Despite some variability across their range, sea otters predominantly forage in rocky substrate environments. Rocky substrate is also necessary for kelp, whose holdfasts need to attach to hard, stable surfaces (Carney et al. 2005).
Depth: Seafloor depth plays a pivotal role in sea otter foraging behavior and therefore acts as a natural boundary that determines how far away from shore sea otters distribute. Many of the prey species sea otters eat – including sea urchins, crabs, and snails – live on the seafloor of the inner continental shelf, requiring sea otters to dive when foraging. Interestingly, sea otters exhibit a non-linear relationship with depth, where most individuals forage at intermediate depths as opposed to extremely shallow or deep waters. One study found the average foraging depth to be around 15 meters (Lafferty & Tinker 2014). This behavior results in a hump-shaped distribution of diving patterns as illustrated in Figure 1 below.
Of course, local conditions and available habitat are always a factor. For example, a study found that sea otters along the coast of Washington foraged further from shore and in slightly shallower environments than sea otters in California (Laidre et al. 2009), indicating that local topography is important in determining distribution. Additionally, diving requires energy and limits how deep sea otters are able to forage for prey. Therefore, diving patterns are not only a function of local topography, but also availability of prey and foraging efficiency in exploiting that prey. Regardless, most sea otter populations follow this hump-shaped diving pattern.
These features are not a complete list of all habitat characteristics that support viable sea otter populations, but seem to be the most consistent throughout their entire range, as well as present in Oregon’s nearshore environment – making them ideal features to include in my analysis. Furthermore, other studies that have predicted suitable sea otter habitat (Tinker et al. 2017), estimated carrying capacity as a product of suitable habitat identification (Laidre et al. 2002), or simply observed sea otter foraging behavior (Estes & Palmisano 1974), have echoed the importance of these four habitat features to sea otter survival.
As with most reintroduction efforts, the process of identifying suitable habitat for the species of interest can be complicated. No two ecosystems or habitats are exactly alike and each comprise their own unique set of physical features and are impacted by environmental processes to varying degrees. The Oregon coast consists of a unique combination of oceanographic conditions and drivers that likely impact the degree and amount of available habitat to sea otters. Despite this, by focusing on the habitat features that are consistently preferred by sea otters across most of their range, I will be able to identify habitat most suitable for sea otter survival in Oregon. The questions of where this habitat is and how much is available are what I’ll determine soon, so stay tuned.
Carney, L. T., Robert Waaland, J., Kilinger, T., and K. Ewing. 2005. Restoration of the bull kelp Nereocystis luetkeana in nearshore rocky habitats. Marine Ecology Progress Series. 302: 49-61.
Costas, D. P. 1978. The ecological energetics, waters, and electrolyte balance of the California sea otter (Enhydra lutris). Ph.D. dissertation, University of California, Santa Cruz.
Estes, J. A. and J. F. Palmisano. 1974. Sea otters: their role in structuring nearshore communities. Science. 185(4156): 1058-1060.
Hughes et al. 2014. Recovery of a top predator mediate negative eutrophic effects on seagrass. Proceedings of the National Academy of Sciences. 110(38): 15313-15318.
Lafferty, K. D. and M. T. Tinker. 2014. Sea otters are recolonizing southern California in fits and starts. Ecosphere. 5(5): 1-11.
Laidre et al. 2002. Estimates of carrying capacity for sea otters in Washington state. Wildlife Society Bulletin. 30(4): 1172-1181.
Laidre et al. 2009. Spatial habitat use patterns of sea otters in coastal Washington. Journal of Mammalogy. 90(4): 906-917.
Tinker et al. 2017. Southern sea otter range expansion and habitat use in the Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2017-1001 (OCS Study BOEM 2017-022), 76 p., http://doi.org/10.3133/ofr20171001.
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.
By Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
When we hear “marine policy” we broadly lump it together with environmental policy. However, marine ecosystems differ greatly from their terrestrial counterparts. We wouldn’t manage a forest like an ocean, nor would we manage an ocean like a forest. Why not? The answer to this question is complex and involves everything from ecology to politics.
Oceans do not have borders; they are fluid and dynamic. Interestingly, by defining marine ecosystems we are applying some kind of borders. But water (and all its natural and unnatural content) flows between these ‘ecosystems’. Marine ecosystems are home to a variety of anthropogenic activities such as transportation and recreation, in addition to an abundance of species that represent the three major domains of biology: Archaea, Bacteria, and Eukarya. Humans are the only creatures who “recognize” the borders that policymakers and policy actors have instilled. A migrating gray whale does not have a passport stamped as it travels from its breeding grounds in Mexican waters to its feeding grounds in the Gulf of Alaska. In contrast, a large cargo ship—or even a small sailing vessel—that crosses those boundaries is subjected to a series of immigration checkpoints. Combining these human and the non-human facets makes marine policy complex and variable.
Environmental policy of any kind can be challenging. Marine environmental policy adds many more convoluted layers in terms of unknowns; marine ecosystems are understudied relative to terrestrial ecosystems and therefore have less research conducted on how to best manage them. Additionally, there are more hands in the cookie jar, so to speak; more governments and more stakeholders with more opinions (Leslie and McLeod 2007). So, with fewer examples of successful ecosystem-based management in coastal and marine environments and more institutions with varied goals, marine ecosystems become challenging to manage and monitor.
With this in mind, it is understandable that there is no official manual on policy development. There is, however, a broadly standardized process of how to develop, implement, and evaluate environmental policies: 1) recognize a problem 2) propose a solution 3) choose a solution 4) put the solution into effect and 4) monitor the results (Zacharias pp. 16-21). For a policy to be deemed successful, specific criteria must be met, which means that a common policy is necessary for implementation and enforcement. Within the United States, there are a multiple governing bodies that protect the ocean, including the National Oceanic and Atmospheric Administration (NOAA), Environmental Protection Agency (EPA), Fish and Wildlife Service (USFWS), and the Department of Defense (DoD)—all of which have different mission statements, budgets, and proposals. To create effective environmental policies, collaboration between various groups is imperative. Nevertheless, bringing these groups together, even those within the same nation, requires time, money, and flexibility.
This is not to say that environmental policy for terrestrial systems, but there are fewer moving parts to manage. For example, a forest in the United States would likely not be an international jurisdiction case because the borders are permanent lines and national management does not overlap. However, at a state level, jurisdiction may overlap with potentially conflicting agendas. A critical difference in management strategies is preservation versus conservation. Preservation focuses on protecting nature from use and discourages altering the environment. Conservation, centers on wise-use practices that allow for proper human use of environments such as resource use for economic groups. One environmental group may believe in preservation, while one government agency may believe in conservation, creating friction amongst how the land should be used: timber harvest, public use, private purchasing, etc.
Furthermore, a terrestrial forest has distinct edges with measurable and observable qualities; it possesses intrinsic and extrinsic values that are broadly recognized because humans have been utilizing them for centuries. Intrinsic values are things that people can monetize, such as commercial fisheries or timber harvests whereas extrinsic values are things that are challenging to put an actual price on in terms of biological diversity, such as the enjoyment of nature or the role of species in pest management; extrinsic values generally have a high level of human subjectivity because the context of that “resource” in question varies upon circumstances (White 2013). Humans are more likely to align positively with conservation policies if there are extrinsic benefits to them; therefore, anthropocentric values associated with the resources are protected (Rode et al. 2015). Hence, when creating marine policy, monetary values are often placed on the resources, but marine environments are less well-studied due to lack of accessibility and funding, making any valuation very challenging.
Assigning a cost or benefit to environmental services is subjective (Dearborn and Kark 2010). What is the benefit to a child seeing an endangered killer whale for the first time? One could argue priceless. In order for conservation measures to be implemented, values—intrinsic and extrinsic—are assigned to the goods and services that the marine environment provides—such as seafood and how the ocean functions as a carbon sink. Based off of the four main criteria used to evaluate policy, the true issue becomes assessing the merit and worth. There is an often-overlooked flaw with policy models: it assumes rational behavior (Zacharias 126). Policy involves relationships and opinions, not only the scientific facts that inform them; this is true in terrestrial and marine environments. People have their own agendas that influence, not only the policies themselves, but the speed at which they are proposed and implemented.
One example of how marine policy evolves is through groups, such as the International Whaling Commission, that gather to discuss such policies while representing many different stakeholders. Some cultures value the whale for food, others for its contributions to the surrounding ecosystems—such as supporting healthy seafood populations. Valuing one over the other goes beyond a monetary value and delves deeper into the cultures, politics, economics, and ethics. Subjectivity is the name of the game in environmental policy, and, in marine environmental policy, there are many factors unaccounted for, that decision-making is incredibly challenging.
Efficacy in terms of the public policy for marine systems presents a challenge because policy happens slowly, as does research. There is no equation that fits all problems because the variables are different and dynamic; they change based on the situation and can be unpredictable. When comparing institutional versus impact effectiveness, they both are hard to measure without concrete goals (Leslie and McLeod 2007). Marine ecosystems are open environments which add an additional hurdle: setting measurable and achievable goals. Terrestrial environments contain resources that more people utilize, more frequently, and therefore have more set goals. Without a problem and potential solution there is no policy. Terrestrial systems have problems that humans recognize. Marine systems have problems that are not as visible to people on a daily basis. Therefore, terrestrial systems have more solutions presented to mitigate problems and more policies enacted.
As marine scientists, we don’t always immediately consider how marine policy impacts our research. In the case of my project, marine policy is something I constantly have to consider. Common bottlenose dolphins are protected under the Marine Mammal Protection Act (MMPA) and inhabit coastal of both the United States and Mexico, including within some Marine Protected Areas (MPA). In addition, some funding for the project comes from NOAA and the DoD. Even on the surface-level it is clear that policy is something we must consider as marine scientists—whether we want to or not. We may do our best to inform policymakers with results and education based on our research, but marine policy requires value-based judgements based on politics, economics, and human objectivity—all of which are challenging to harmonize into a succinct problem with a clear solution.
Dearborn, D. C. and Kark, S. 2010. Motivations for Conserving Urban Biodiversity. Conservation Biology, 24: 432-440. doi:10.1111/j.1523-1739.2009.01328.x
Leslie, H. M. and McLeod, K. L. (2007), Confronting the challenges of implementing marine ecosystem‐based management. Frontiers in Ecology and the Environment, 5: 540-548. doi:10.1890/060093
Munguia, P., and A. F. Ojanguren. 2015. Bridging the gap in marine and terrestrial studies. Ecosphere 6(2):25. http://dx.doi.org/10.1890/ES14-00231.1
Rode, J., Gomez-Baggethun, E., Krause, M., 2015. Motivation crowding by economic payments in conservation policy: a review of the empirical evidence. Ecol. Econ. 117, 270–282 (in this issue).
White, P. S. (2013), Derivation of the Extrinsic Values of Biological Diversity from Its Intrinsic Value and of Both from the First Principles of Evolution. Conservation Biology, 27: 1279-1285. doi:10.1111/cobi.12125
Zacharias, M. 2014. Marine Policy. London: Routledge.
In the past few weeks I read an article on the use of aquatic robots in the ocean for research. Since my PhD project uses technology, such as drones and GoPros, to monitor body condition of gray whales and availability of prey along the Oregon coast, I became really interested by the new perspective these robots could provide. Drones produce aerial images while GoPros generate an underwater-scape snapshot. The possible new perspective provided by a robot under the water could be amazing and potentially be used in many different applications.
The article was published on March 21st by The New York Times, and described a new finned robot named “SoFi” or “Sophie”, short for Soft Robotic Fish (Figure 1; The New York Times 2018). The aquatic robot was designed by scientists at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Lab, with the purpose of studying marine life in their natural habitats.
SoFi’s first swim trial occurred in a coral reef in Fiji, and the footage recorded can be seen in the following video:
SoFi can swim at depths up to 18 meters and at speeds up to half-its-body-length a second (average of 23.5 cm/s in a straight path; Katzschmann et al. 2018). Sofi can swim for up to ~40 minutes, as limited by battery time. The robot is also well-equipped (Figure 2). It has a compact buoyancy control mechanism and includes a wide-view video camera, a hydrophone, a battery, environmental sensors, and operating and communication systems. The operating and communication systems allow a diver to issue commands by using a controller that operates through sound waves.
The robot designers highlight that while SoFi was swimming, fish didn’t seem to be bothered or get scared by SoFi’s presence. Some fish were seen swimming nearby the robot, suggesting that SoFi has the potential to integrate into the natural underwater environment and therefore record undisturbed behaviors. However, a limitation of this invention is that SoFi needs a diver on scene to control the robot. Therefore, SoFi’s study of marine life without human interference may be compromised until technology develops further.
Another potential impact of SoFi we might be concerned about is noise. Does this device produce noise levels that marine fauna can sense or maybe be stress by? Unfortunately, the answer is yes. Even if fish don’t seem to be bothered by SoFi’s presence, it might bother other animals with hearing sensitivity in the same frequency range of SoFi. Katzschmann and colleagues (2018) explained that they chose a frequency to operate SoFi that would minimally impact marine fauna. They studied the frequencies used by the aquatic animals and, since the hearing ranges of most aquatic species decays significantly above 10 KHz, they selected a frequency above this range (i.e., 36 KHz). However, this high frequency range can be sensed by some species of cetaceans and pinnipeds, but negative affects on these animals will be dependent on the sound amplitude that is produced.
Although not perfect (but what tool is?), SoFi can be seen as a great first step toward a future of underwater robots to assist research efforts. Battery life, human disturbance, and noise disturbance are limitations, but through thoughtful application and continued innovation this fishy tool can be the start of something great.
The use of aquatic robots, such as SoFi, can help us advance our knowledge in underwater ecosystems. These robots could promote a better understanding of marine life in their natural habitat by studying behaviors, interactions and responses to threats. These robots may offer important new tools in the protection of animals against the effects caused by anthropogenic activities. Additionally, the use of aquatic robots in scientific research may substitute remote operated vehicles and submersibles in some circumstances, such as how drones are substituting for airplanes sometimes, thus providing a less expensive and better-tolerated way of monitoring wildlife.
Through continued multidisciplinary collaboration by robot designers, biologists, meteorologists, and more, innovation will continue allowing data collection with minimal to non-disturbance to the wildlife, providing lower costs and higher safety for the researchers.
It is impressive to see how technology efforts are expanding into the oceans. As drones are conquering our skies today and bringing so much valuable information on wildlife monitoring, I believe that the same will occur in our oceans in a near future, assisting in marine life conservation.
Katzschmann RK, DelPreto J, MacCurdy R, Rus D. 2018. Exploration of Underwater Life with an Acoustically Controlled Soft Robotic Fish. Sci. Robot. 3, eaar3449. DOI: 10.1126/scirobotics.aar3449.