“Applied conservation science”

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

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

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

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

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

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

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

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

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

Finding the right fit: a journey into cetacean distribution models

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Source: internet

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

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

Cool presentation by Adam Smith about SDM:

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

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

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

 

Literature cited

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

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

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

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

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

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

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

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

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

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

How to apply my PhD?

By Leila Lemos, PhD candidate, Fisheries and Wildlife Department

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

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

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

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

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

 

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

 

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

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

 

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

 

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

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

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

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

 

References:

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