By Julia Stepanuk, PhD student, department of Ecology and Evolution, Stony Brook University
Hello GEMM Lab blog readers! I’m a PhD student in Lesley Thorne’s lab at Stony Brook University in New York and I spent this past week with the GEMM Lab learning their protocol for drone flights and gaining experience flying over whales. I saw my first gray whales just off the coast of Newport, Oregon and assisted with the GEMM Lab’s summer field research. We luckily had 4 days of great weather in a row, so I got tons of experience conducting research that integrates drone flights that I can bring home to our lab. It was really exciting to observe and learn from the well-oiled machine that is the GEMM Lab. Information about their gray whale project can be found here and here, but I want to focus on how my experiences here in Newport can translate to my research interests off the coast of Long Island.
Our lab in New York has a range of interesting projects currently underway: we study everything from decadal trends in sea turtle diets to how frequently herring gulls visit urban habitats for food around New York City. My research focuses on the whales around New York, specifically humpback whales. Humpback whales are very well studied in many parts of the world, especially in the Northwest Atlantic. The initial photo-identification studies were conducted in the Gulf of Maine in the 1970s (Katona et al., 1979), and the North Atlantic Humpback Whale Catalogue is still going strong with over 8,000 individual whales catalogued! Recently though, many people have reported humpback whales in a new area: the waters around New York and Long Island. Yet, we don’t understand how these whales fit in with the rest of the humpback population in the North Atlantic. We do know that they feed along the shores of New York City and Long Island, and they are primarily consuming menhaden (also known as bunker or pogy), a forage fish that is vital to both our economic and environmental systems in the Northeast U.S. (see: Six reasons why menhaden is the greatest fish we ever fished).
The habitat use and behavior of humpbacks in this part of the world is important for two reasons: 1) this population of humpback whales has recovered from the detrimental population-level impacts of industrial whaling in the 18th and 19th centuries, and thus was recently delisted from the endangered species list; and 2) humpback whales in the Northwest Atlantic are at-risk from ship strikes and fishing gear entanglement, so much so that NOAA declared an unusual mortality event for 2016-2018. In fact, 4 humpback whales washed up dead on the shore of Long Island in the last 30 days! These facts lead to my motivation for my PhD studies: where are humpback whales in the vicinity of New York City and how do they use the environment around Long Island? I specifically want to investigate the trophic relationship between humpback whales and menhaden.
There are a number of studies where researchers have used photogrammetry from drones to document the body condition of marine mammal species (Burnett et al., in press; Christiansen et al., 2016; Christiansen et al., 2018; Dawson et al., 2017; Perryman and Lynn., 2002), which I plan to extend to the humpback whales around Long Island. I will conduct photogrammetry of the humpback whales off Long Island and will document the individual whales, their behaviors, and their prey sources. Because scientists are now documenting and monitoring body condition of humpback whales in many parts of the world, we can compare the overall health and body condition of humpbacks in New York to those in other habitats. Further, by documenting the schools of menhaden they are consuming, we can better assess the trophic relationship between humpbacks and menhaden in a foraging habitat adjacent to one of the largest cities on the planet.
I am so grateful to the GEMM Lab for sharing information and skills with me over the past week and am excited to bring my new skillset back to our lab at Stony Brook! Aside from drone skills, I learned that gray whales are very flexible, and their mottled skin is absolutely beautiful! I also learned that my peanut butter and jelly sandwich making skills are passable (you have to find a way to keep the jelly from leaking through the bread on a hot day on a boat!) and I learned how to collect fecal samples from whales (put a net in the water, and scoop up the pieces of whale poo). I am also now hooked on the FIFA World Cup matches and will be losing lots of sleep in the next few weeks while I diligently follow my new favorite teams. Thank you again to the GEMM lab for being so supportive and welcoming! For an influx of east coast megafauna research, follow the Thorne Lab blog as our many spatial marine megafauna projects get underway, and follow me on twitter as I pursue a PhD!
Burnett, J.D., Lemos, L., Barlow, D.R., Wing, M.G., Chandler, T.E. & Torres, L.G. (in press) Estimating morphometric attributes of baleen whales with photogrammetry from small UAS: A case study with blue and gray whales. Marine Mammal Science.
Christiansen, F., Dujon, A.M., Sprogis, K.R., Arnould, J.P.Y., Bejder, L., 2016. Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere 7
Christiansen, F., Vivier, F., Charlton, C., Ward, R., Amerson, A., Burnell, S., Bejder, L., 2018. Maternal body size and condition determine calf growth rates in southern right whales. Marine Ecology Progress Series 592, 267–281.
Dawson, S.M., Bowman, M.H., Leunissen, E., Sirguey, P., 2017. Inexpensive Aerial Photogrammetry for Studies of Whales and Large Marine Animals. Front. Mar. Sci. 4.
Katona, S., B. Baxter, 0. Brazier, S. Kraus, J. Perkins AND H. Whitehead. 1979. Identification of humpback whales by fluke photographs. Pages 33-44 in H.E. Winn and B.L. Olla, eds. Behavior of marine animals. Current perspectives in research. Vol. 3: Cetaceans. Plenum Press. New York.
Perryman WL, Lynn MS. 2002. Evaluation of nutritive condition and reproductive status of migrating gray whales (Eschrichtius robustus) based on analysis of photogrammetric data. J. Cetacean Res. Manage. 4(2):155-164.
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.
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.
Dr. Leigh Torres, Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute, Oregon State University
Publication of our science in peer-reviewed journals is an extremely important part of our lives as scientists. It’s how we communicate our work, check each other’s work, and improve, develop and grow our scientific fields. So when our manuscript is finally written with great content, we could use some instructions for how to get it through the publication process. Who gets authorship? How do I respond to reviewers? Who pays for publication costs?
There is some good advice online about manuscript preparation and selecting the right journal. But there is no blueprint for manuscript preparation. That’s because it’s a complicated and variable process to navigate, even when you’ve done it many times. Every paper is different. Every journal has different content and format requirements. And every authorship list is different, with different expectations. As an academic supervisor of many graduate students, and as author on many peer-reviewed papers, I have seen or been a part of more than a few publication blunders, hiccups, road-blocks, and challenges.
Recently I’ve had students puzzle over the nuances of the publication process: “I had no idea that was my role as lead author!”, “How do I tell a reviewer he’s wrong?”, “Who should I recommend as reviewers?” So, I have put together some advice about how to navigate through a few of the more common pitfalls and questions of the scientific publication process. I’m not going to focus on manuscript content, structure, or journal choice – that advice is elsewhere and for authors to evaluate. My intent here is to discuss some of the ‘unwritten’ topics and expectations of the publication process. This guidance and musings are based my 20 years of experience as a scientist trying to navigate the peer-review publication maze myself. I encourage others to add their advice and comments below based on their experiences so that we can engage as a community in an open dialog about these topics, and add transparency to an already difficult and grueling, albeit necessary, process.
Authorship: Deciding who should – and shouldn’t be – be a co-author on a paper is often a challenging, sensitive, and angst-filled experience. Broad collaboration is so common and often necessary today that we often see very long author lists on papers. It’s best to be inclusive and recognize contribution where it is deserved, but we also don’t want to be handing out co-authorship as a token of appreciation or just to pad someone’s CV or boost their H-index. Indeed, journals don’t want that, and we don’t want to promote that trend. Sometimes it is more appropriate to recognize someone’s contribution in the acknowledgements section.
The best advice I can give about how to determine authorship is advice that was given to me by my graduate advisor, Dr. Andy Read at Duke University: To deserve authorship the person must have contributed to at least three of these five areas: concept development, acquisition of funding, data collection, data analysis, manuscript writing. Of course, this rule is not hard and fast, and thoughtful judgement and discussions are needed. Often someone has contributed to only one or two of these areas, but in such a significant manner that authorship is warranted.
I have also seen situations where someone has contributed only a small, but important, piece of data. What happens then? My gut feeling is this should be an acknowledgment, especially if it’s been published previously, but sometimes the person is recognized as a co-author to ensure inclusion of the data. Is this right? That’s up to you and your supervisor(s), and is often case-specific. But I do think we need to limit authorship-inflation. Some scientists in this situation will gracefully turn down co-authorship and ask only for acknowledgement, while others will demand co-authorship when it’s not fully deserved. This is the authorship jungle we all must navigate, which does not get easier with time or experience. So, it’s best to just accept the complexity and make the best decisions we can based on the science, not necessarily the scientists.
Next, there is the decision of author order, which can be another challenging decision. A student with the largest role in data collection/analysis and writing, will often be the lead author, especially if the paper is also forming a chapter of his/her thesis. But, if lead authorship is not clear (maybe the student’s work focuses on a small part of a much larger project) then its best to discuss authorship order with co-authors sooner rather than later. The lead author should be the person with the largest role in making the study happen, but often a senior scientist, like an academic supervisor, will have established the project and gained the funding support independent of a student’s involvement. This ‘senior scientist’ role is frequently recognized by being listed last in the authorship list – a trend that has developed in the last ~15 years. Or the senior scientists will be the corresponding author. The order of authors in between the first and last author is often grey, muddled and confusing. To sort this order out, I often think about who else had a major role in the project, and list them near the front end, after the lead author. And then after that, it is usually just based on alphabetical order; you can often see this trend when you look at long author lists.
Responsibility as lead author: The role of a lead author is to ‘herd the cats’. Unless otherwise specified by co-authors/supervisor, this process includes formatting the manuscript as per journal specifications, correspondence with journal editors (letters to editors and response to reviewer comments), correspondence with co-authors, consideration and integration of all co-author comments and edits into the manuscript, manuscript revisions, staying on time with re-submissions to the journal, finding funding for publication costs, and review of final proofs before publication. Phew! Lots to do. To help you through this process, here are some tips:
How to get edits back from co-authors: When you send out the manuscript for edits/comments, give your co-authors a deadline. This deadline should be at least 2 weeks out, but best to give more time if you can. Schedules are so packed these days. And, say in the email something like, ‘If I don’t hear back from you by such and such a date I’ll assume you are happy with the manuscript as is.” This statement often spurs authors to respond.
How to respond to reviewer comments: Always be polite and grateful, even when you completely disagree with the comment or feel the reviewer has not understood your work. Phrases like “we appreciate the feedback”, “we have considered the comment”, and “the reviewers provided thoughtful criticism” are good ways to show appreciation for reviewer comments, even when it’s followed by a ‘but’ statement. When revising a manuscript, you do not need to incorporate all reviewer comments, but you do need to go through each comment one-by-one and say “yes, thanks for this point. We have now done that,” or thoughtfully explain why you have not accepted the reviewer advice.
While receiving negative criticism about your work is hard, I have found that the advice is often right and helpful in the long run. When I first receive reviewer comments back on a manuscript, especially if it is a rejection – yes, this happens, and it sucks – I usually read through it all. Fume a bit. And then put it aside for a week or so. This gives me time to process and think about the feedback. By the time I come back to it, my emotional response has subsided and I can appreciate the critical comments with objectivity.
Journal formatting can be a nightmare: Some editor may read this post and hate me, but my advice is don’t worry too much about formatting a manuscript perfectly to journal specs. During the initial manuscript submission, reviewers will be assessing content, not how well you match the journal’s formatting. So don’t kill yourself at this stage to get everything perfect, although you should be close. Once your paper gets through the first round of reviews, then you should worry about formatting perfectly in the revision.
Who should I recommend as a reviewer? Editors like it when you make their lives easier by recommending appropriate reviewers for your manuscript. Obviously you should not recommend close friends or colleagues. Giving useful, appropriate reviewer suggestions can be challenging. My best advice for this step is to look at the authors you have referenced in the manuscript. Those authors referenced multiple times may have interest in your work, and be related to the subject matter.
Who pays or how to pay for publication? Discuss this issue with your co-authors/supervisor and plan ahead. Most journals have publication fees that often range between $1000 and $2000. Sometimes color figures cost more. And, if you want your paper to be open access, plan on paying > $3000. So, when deciding on a journal, keep these costs in mind if you are on a limited budget. These days I add at least $2000 to almost every project budget to pay for publication costs. Publication is expensive, which is ridiculous considering we as scientists provide the content, review the content for free, and then often have to pay for the papers once published. But that’s the frustrating, unbalanced racket of scientific publication today – a topic for another time, but this article is definitely worth a read, if interested.
So that’s it from me. Please add your advice, feedback, and thoughts below in the comments section.
Joe Haxel, Acoustician, Assistant Professor, CIMRS/OSU
Greetings GEMM Lab blog readers. My name is Joe Haxel and I’m a close collaborator with Leigh and other GEMM lab members on the gray whale ecology, physiology and noise project off the Oregon coast. Leigh invited me for a guest blog appearance to share some of the acoustics work we’ve been up to and as you’ve probably guessed by now, my specialty is in ocean acoustics. I’m a PI in NOAA’s Pacific Marine Environmental Laboratory’s Acoustics Program and OSU’s Cooperative Institute for Marine Resources Studies where I use underwater sound to study a variety of earth and ocean processes.
As a component of the gray whale noise project, during the field seasons of 2016 and 2017 we recorded some of the first measurements of ambient sound in the shallow coastal waters off Oregon between 7 and 20 meters depth. In the passive ocean acoustics world this is really shallow, and with that comes all kinds of instrument and logistical challenges, which is probably one of the main reasons there is little or no acoustic baseline information in this environment.
For instance, one of the significant challenges is rooted in the hydrodynamics surrounding mobile recording systems like the drifting hydrophone we used during the summer field season in 2016 (Fig 1). Decoupling motion of the surface buoy (e.g., caused by swell and waves) from the submerged hydrophone sensor is critical, and here’s why. Hydrophones convert pressure fluctuations at the sensor/ water interface to a calibrated voltage recorded by a logging system. Turbulence resulting from moving the sensor up and down in the water column with surface waves introduces non-acoustic pressure changes that severely contaminate the data for noise level measurements. Vertical and horizontal wave motions are constantly acting on the float, so we needed to engineer compliance between the surface float and the suspended hydrophone sensor to decouple these accelerations. To overcome this, we employed a couple of concepts in our drifting hydrophone design. 1) A 10 cm diameter by 3 m long spar buoy provided floatation for the system. Spar buoys are less affected by wave motion accelerations compared to most other types of surface floatation with larger horizontal profiles and drag. 2) A dynamic shock cord that could stretch up to double its resting length to accommodate vertical motion of the spar buoy; 3) a heave plate that significantly reduced any vertical motion of the hydrophone suspended below it. This was a very effective design, and although somewhat cumbersome in transport with the RHIB between deployment sites, the acoustic data we collected over 40 different drifts around Newport and Port Orford in 2016 was clean, high quality and devoid of system induced contamination.
Spatial information from the project’s first year acoustic recordings using the drifting hydrophone system helped us choose sites for the fixed hydrophone stations in 2017. Now that we had some basic information on the spatial variability of noise within the study areas we could focus on the temporal objectives of characterizing the range of acoustic conditions experienced by gray whales over the course of the entire foraging season at these sites in Oregon. In 2017 we deployed “lander” style instrument frames, each equipped with a single, omni-directional hydrophone custom built by Haru Matsumoto at our NOAA/OSU Acoustics lab (Fig. 2). The four hydrophone stations were positioned near each of the ports (Yaquina Bay and Port Orford) and in partnership with the Oregon Department of Fish and Wildlife Marine Reserves program in the Otter Rock Marine Reserve and the Redfish Rocks Marine Reserve. The hydrophones were programmed on a 20% duty cycle, recording 12 minutes of every hour at 32 kHz sample rate, providing spectral information in the frequency band from 10 Hz up to a 13 kHz.
Here’s where the story gets interesting. In my experience so far putting out gear off the Oregon coast, anything that has a surface expression and is left out for more than a couple of weeks is going to have issues. Due to funding constraints, I had to challenge that theory this year and deploy 2 of the units with a surface buoy. This is not typically what we do with our equipment since it usually stays out for up to 2 years at a time, is sensitive, and expensive. The 2 frames with a surface float were going to be deployed in Marine Reserves far enough from the traffic lanes of the ports and in areas with significantly less traffic and presumably no fishing pressure. The surface buoy consisted of an 18 inch diameter hard plastic float connected to an anchor that was offset from the instrument frame by a 150 foot weighted groundline. The gear was deployed off Newport in June and Port Orford in July. What could go wrong?
After monthly buoy checks by the project team, including GPS positions, and buoy cleanings my hopes were pretty high that the surface buoy systems might actually make it through the season with recoveries scheduled in mid-October. Had I gambled and won? Nope. The call came in September from Leigh that one of the whale watching outfits in Depoe Bay recovered a free floating buoy matching ours. Bummer. Alternative recovery plans initiated and this is where things began to get hairy. Fortunately, we had an ace in our back pocket. We have collaborators at the Oregon Coast Aquarium (OCA) who have a top-notch research diving team led by Jim Burke. In the last week of October, they performed a successful search dive on the missing unit near Gull Rock and attached a new set of floats directly to the instrument frame. The divers were in the water for a short 20 minutes thanks to the good series of marks recorded during the buoy checks throughout the summer (Fig. 3).
We had surface marker floats on the frame, but there was a new problem. Video taken by Jenna and Doug from the OCA dive team revealed the landers were pretty sanded in from a couple of recent October storms (Fig. 4). Ugghhh!
Alternative recovery plan adjustment: we’re gonna need a diver assisted recovery with 2 boats. One to bring a dive team to air jet the sand out away from the legs of the frame and another larger vessel with pulling power to recover the freed lander. Enter the R/V Pacific Surveyor and Capt. Al Pazar. Al, Jim and I came up with a new recovery plan and only needed a decent weather window of a few hours to get the job done. Piece of cake in November off the Oregon coast, right?
The weather finally cooperated in early December in-line with the OCA dive team and R/V Pacific Surveyor’s availability. The 2 vessels and crew headed up to Gull Rock for the first recovery operation of the day. At first we couldn’t locate the surface floats. Oh no. It seemed the rough fall/ winter weather and high seas since late October were too much for the crab floats? As it turns out, we eventually found the floats eastward about 200 m but couldn’t initially see them in the glare and whitecapping conditions that morning. The lander frame had broken loose from its weakened anchor legs in the heavy weather (as it was designed to do through an Aluminum/ Stainless Steel galvanic reaction over time) and rolled or hopped eastward by about 200 m (Fig. 5). Oh dear!
Thankfully, the hydrophone was well protected, and no air jetting was required. With OCA divers out of the water and clear, the Pacific Surveyor headed over to the floats and easily pulled the lander frame and hydrophone on board (Fig. 6). Yipee!
On to the next hydrophone station. This station, deployed ~ 800 m west of the south reef off of South Beach near the Yaquina Bay port entrance. It was deployed entirely subsurface and was outfitted with an acoustic release transponder that I could communicate with from the surface and command to release a pop-up messenger float and line for eventual recovery of the instrument frame. Once on station, communication with the release was established easily (a good start) and we began ranging and moving the OCA vessel Gracie Lynn in to a position within about 2 water depths of the unit (~40 m). I gave the command to the transponder and the submerged release confirmed it was free of its anchor and heading for the surface, but it never made it. Uh oh. Turns out this lander had also broke free of its anchored legs and rolled/ hopped 800 m eastward until it was pinned up against the boulder structure of the south reef. Amazingly, OCA divers Jenna and Doug located the messenger float ~ 5 m below the surface and the messenger line had been fouled by the rolling frame so it could not reach the surface. They dove down the messenger line and attached a new recovery line to the lander frame and the Pacific Surveyor hauled up the frame and hydrophone in-tact (Fig. 6). Double recovery success!
The hydrophone data from both systems looks outstanding and analysis is underway. This recovery effort took a huge amount of patience and the coordination of 3 busy groups (NOAA/OSU, OCA, Capt. Al). Thanks to these incredible collaborations and some heroic diving from Jim Burke and his OCA dive team, we now have a unique and unprecedented shallow water passive acoustic data set from the energetic waters off the Oregon coast.
So that’s some of the story from the 2016 and 2017 field season acoustic point of view. I’ll save the less exciting, but equally successful instrument recoveries from Port Orford for another time.
Dr. Leigh Torres, Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute, Oregon State University
Dr. Holger Klinck, Bioacoustics Research Program, Cornell Lab of Ornithology, Cornell University
For too long the oil and gas industry has polluted the ocean with seismic airgun noise with little consequence. The industry uses seismic airguns in order to find their next lucrative reserve under the seafloor, and because their operations are out of sight and the noise is underwater many have not noticed this deafening (literally1) noise. As terrestrial and vision-dependent animals, we humans have a hard time appreciating the importance of sound in the marine environment. Most of the ocean is a dark place, where vision does not work well, so many animals are dependent on sound to survive. Especially marine mammals like whales and dolphins.
But, hearing is believing, so let’s have a listen to a recording of seismic airguns firing in the South Taranaki Bight (STB) of New Zealand, a known blue whale feeding area. This is a short audio clip of a seismic airgun firing every ~8 seconds (a typical pattern). Before you hit play, close your eyes and imagine you are a blue whale living in this environment.
Now, put that clip on loop and play it for three months straight. Yes, three months. This consistent, repetitive boom is what whales living in a region of oil and gas exploration hear, as seismic surveys often last 1-4 months.
So, how loud is that, really? Your computer or phone speaker is probably not good enough to convey the power of that sound (unless you have a good bass or sub-woofer hooked up). Industrial seismic airgun arrays are among the loudest man-made sources2 and the noise emitted by these arrays can travel thousands of kilometers3. Noise from a single seismic airgun survey can blanket an area of over 300,000 km2, raising local background noise levels 100-fold4.
Now, oil and gas representatives frequently defend their seismic airgun activities with two arguments, both of which are false. You can hear both these arguments made recently in this interview by a representative of the oil and gas industry in New Zealand defending a proposal to conduct a 3 month-long seismic survey in the STB while blue whales will be feeding there.
First, the oil and gas industry claim that whales and dolphins can just leave the area if they choose. But this is their home, where they live, where they feed and breed. These habitats are not just anywhere. Blue whales come to the STB to feed, to sustain their bodies and reproductive capacity. This habitat is special and is not available anywhere else nearby, so if a whale leaves the STB because of noise disturbance it may starve. Similarly, oil and gas representatives have falsely claimed that because whales stay in the area during seismic airgun activity this indicates they are not being disturbed. If you had the choice of starving or listening to seismic booming you might also choose the latter, but this does not mean you are not disturbed (or annoyed and stressed). Let’s think about this another way: imagine someone operating a nail gun for three months in your kitchen and you have nowhere else to eat. You would stay to feed yourself, but your stress level would elevate, health deteriorate, and potentially have hearing damage. During your next home renovation project you should be happy you have restaurants as alternative eateries. Whales don’t.
Second, the oil and gas industry have claimed that the frequency of seismic airguns is out of the hearing range of most whales and dolphins. This statement is just wrong. Let’s look at the spectrogram of the above played seismic airgun audio clip recorded in the STB. A spectrogram is a visual representation of sound (to help us vision-dependent animals interpret sound). Time is on the horizontal axis, frequency (pitch) is on the vertical axis, and the different colors on the image indicate the intensity of sound (loudness) with bright colors illustrating areas of higher noise. Easily seen is that as the seismic airgun blasts every ~8 seconds, there is elevated noise intensity across all frequencies (bright yellow, orange and green bands). This noise intensity is especially high in the 10 – 80 Hz frequency range, which is exactly where many large baleen whales – like the blue whale – hear and communicate.
In the big, dark ocean, whales use sound to communicate, find food, and navigate. So, let’s try to imagine what it’s like for a whale trying to communicate in an environment with seismic airgun activity. First, let’s listen to a New Zealand blue whale call (vocalization) recorded in the STB. [This audio clip is played at 10X the original speed so that it is more audible to the human hearing frequency range. You can see the real time scale in the top plot.]
Now, let’s look at a spectrogram of seismic airgun pulses and a blue whale call happening at the same time. The seismic airgun blasts are still evident every ~8 seconds, and the blue whale call is also evident at about the 25 Hz frequency (within the pink box). Because blue whales call at such a low frequency humans cannot hear their call when played at normal speed, so you will only hear the airgun pulses if you hit play. But you can see in the spectrogram that five airgun blasts overlapped with the blue whale call.
No doubt this blue whale heard the repetitive seismic airgun blasts, and vocalized in the same frequency range at the same time. Yet, the blue whale’s call was partially drowned out by the intense seismic airgun blasts. Did any other whale hear it? Could this whale hear other whales? Did it get the message across? Maybe, but probably not very well.
Some oil and gas representatives point toward their adherence to seismic survey guidelines and use of marine mammal observers to reduce their impacts on marine life. In New Zealand these guidelines only stop airgun blasting when animals are within 1000 m of the vessel (1.5 km if a calf is present), yet seismic airgun blasts are so intense that the noise travels much farther. So, while these guidelines may be a start, they only prevent hearing damage to whales and dolphins by stopping airguns from blasting right on top of animals.
So, what does this mean for whales and other marine animals living in habitat where seismic airguns are operating? It means their lives are disturbed and dramatically altered. Multiple scientific studies have shown that whales change behavior5, distribution6, and vocalization patterns7 when seismic airguns are active. Other marine life like squid8, spiny lobster9, scallops10, and plankton11 also suffer when exposed to airgun noise. The evidence has mounted. There is no longer a scientific debate: seismic airguns are harmful to marine animals and ecosystems.
What we are just starting to study and understand is the long-term and population level effects of seismic airguns on whales and other marine life. How do short term behavioral changes, movement to different areas, and different calling patterns impact an individual’s ability to survive or a population’s ability to persist? These are the important questions that need to be addressed now.
Seismic airgun surveys to find new oil and gas reserves are so pervasive in our global oceans, that airgun blasts are now heard year round in the equatorial Atlantic3, 12. As reserves shrink on land, the industry expands their search in our oceans, causing severe and persistent consequences to whales, dolphins and other marine life. The oil and gas industry must take ownership of the impacts of their seismic airgun activities. It’s imperative that political, management, scientific, and public pressure force a more complete assessment of each proposed seismic airgun survey, with an honest evaluation of the tradeoff between economic benefits and costs to marine life.
Here are a few ways we can reduce the impact of seismic airguns on marine life and ecosystems:
Restrict seismic airgun operation in and near sensitive environmental areas, such as marine mammal feeding and breeding areas.
Prohibit redundant seismic surveys in the same area. If one group has already surveyed an area, that data should be shared with other groups, perhaps after an embargo period.
Cap the number and duration of seismic surveys allowed each year by region.
Promote the use of renewable energy sources.
Develop new and quieter survey methods.
Even though we cannot hear the relentless booming, this does not mean it’s not happening and harming animals. Please listen one more time to 1 minute of what whales hear for months during seismic airgun operations.
More information on seismic airgun surveys and their impact on marine life:
Gordon, J., et al., A review of the effects of seismic surveys on marine mammals. Marine Technology Society Journal, 2003. 37(4): p. 16-34.
National Research Council (NRC), Ocean Noise and Marine Mammals. 2003, National Academy Press: Washington. p. 204.
Nieukirk, S.L., et al., Sounds from airguns and fin whales recorded in the mid-Atlantic Ocean, 1999–2009. The Journal of the Acoustical Society of America, 2012. 131(2): p. 1102-1112.
Weilgart, L., A review of the impacts of seismic airgun surveys on marine life. 2013, Submitted to the CBD Expert Workshop on Underwater Noise and its Impacts on Marine and Coastal Biodiversity 25-27 February 2014: London, UK. .
Miller, P.J., et al., Using at-sea experiments to study the effects of airguns on the foraging behavior of sperm whales in the Gulf of Mexico. Deep Sea Research Part I: Oceanographic Research Papers, 2009. 56(7): p. 1168-1181.
Castellote, M., C.W. Clark, and M.O. Lammers, Acoustic and behavioural changes by fin whales (Balaenoptera physalus) in response to shipping and airgun noise. Biological Conservation, 2012. 147(1): p. 115-122.
Di lorio, L. and C.W. Clark, Exposure to seismic survey alters blue whale acoustic communication. Biology Letters, 2010. 6(1): p. 51-54.
Fewtrell, J. and R. McCauley, Impact of air gun noise on the behaviour of marine fish and squid. Marine pollution bulletin, 2012. 64(5): p. 984-993.
Fitzgibbon, Q.P., et al., The impact of seismic air gun exposure on the haemolymph physiology and nutritional condition of spiny lobster, Jasus edwardsii. Marine Pollution Bulletin, 2017.
Day, R.D., et al., Exposure to seismic air gun signals causes physiological harm and alters behavior in the scallop Pecten fumatus. Proceedings of the National Academy of Sciences, 2017. 114(40): p. E8537-E8546.
McCauley, R.D., et al., Widely used marine seismic survey air gun operations negatively impact zooplankton. Nature Ecology & Evolution, 2017. 1(7): p. s41559-017-0195.
Haver, S.M., et al., The not-so-silent world: Measuring Arctic, Equatorial, and Antarctic soundscapes in the Atlantic Ocean. Deep Sea Research Part I: Oceanographic Research Papers, 2017. 122: p. 95-104.
By Dr. Leigh Torres, GEMM Lab, OSU, Marine Mammal Institute
Prepping for fieldwork is always a complex mental and physical juggling act, especially for an equipment-rich, multi-disciplinary, collaborative project like our research project on the impacts of ocean noise on gray whale physiology. For me, the past couple months has consisted of remembering to coordinate equipment purchasing/testing/updating (cameras, drones, GoPros), obtaining all needed permits/licenses (NMFS, FAA, vessel), prepping data recording and management protocols (data sheets, dropbox), scheduling personnel (7 people over 5 months), organizing sampling gear (fecal nets, zooplankton traps), gathering all needed lab supplies (jars, filters, tubes), and hoping for good weather.
This list would normally be enough to overwhelm me, but this year we have also had the (fortunate) opportunity to outfit our own research vessel. The OSU Marine Mammal Institute (MMI) obtained a surplus 5.4 m coast guard RHIB (rigid inflatable haul boat) and generously handed it off to the GEMM Lab for our coastal Oregon research. Fantastic! But not perfect, of course. What the coast guard needs as a vessel, is not exactly what we need for whale research. When the vessel arrived it had a straddle seat occupying most of the limited interior space, which would make it very hard for three people to ride comfortably during a long day of survey effort or move around during whale sightings.
So, the boat needed a re-fit. And who better to do this re-fit than someone who has spent more than 15 years conducting whale research in a RHIB, is a certified ABYC marine electrician, and runs his own marine repair business? Who has such a qualified resume? My research technician (and husband), Todd Chandler.
Over the last two months Todd has meticulously rearranged the interior of the vessel to maximize the space, prioritize safety and comfort, balance the boat for stability, and allow for effective data collection. He removed the straddle seat, had a light-weight aluminum center console and leaning post built to just the right size and specs, installed and updated electronics (VHF, GPS chart plotter), re-ran the engine wiring (throttle, tilt, kill-switch), patched up a few (8!) leaks in the pontoons, ran new nav lights, installed new fuel tanks, and serviced the engine. Phew! He did an amazing job and really demonstrated his skills, handiwork, and knowledge of field research.
The vessel now looks great, runs smoothly, and gives us the space needed for our work. But, she needed a name! So, on Saturday afternoon we hosted a GEMM Lab boat naming BBQ. Our research team and lab gathered in the sun to admire the vessel, eat good food, watch the kids run and play, and come up with boat names.
I was impressed by the appropriate, thoughtful, clever names put forth, like Adam’s rib, Cetacea, Oppo (re-arrange poop), and Whale Done. I was faced with a tough decision so I made everyone vote; three ticks each.
And the winner is…… Ruby: An appropriate name for a research vessel in the GEMM Lab. Perhaps someday we will have a fleet: Ruby, Emerald, Diamond… Ah, a girl can dream.
Now it’s time for the many hiccups, challenges, and rewards of a field season. So thanks to Todd, the MMI, the GEMM Lab, and our awesome team for getting us ready to go. Stay tuned for updates on the actual research (and how Ruby performs).
Dr. Leigh Torres GEMM Lab, OSU, Marine Mammal Institute
It’s often difficult to directly see the application of our research to environmental management decisions. This was not the case for me as I stepped off our research vessel Tuesday morning in Wellington and almost directly (after pausing for a flat white) walked into an environmental court hearing regarding a permit application for iron sands mining in the South Taranaki Bight (STB) of New Zealand (Fig. 1). The previous Thursday, while we surveyed the STB for blue whales, I received a summons from the NZ Environmental Protection Authority (EPA) to appear as an expert witness regarding blue whales in NZ and the potential impacts of the proposed mining activity by Trans-Tasman Resources Ltd. (TTR) on the whales. As I sat down in front of the four members of the EPA Decision Making Committee, with lawyers for and against the mining activity sitting behind me, I was not as prepared as I would have liked – no business clothes, no powerpoint presentation, no practiced summary of evidence. But, I did have new information, fresh perspective, and the best available knowledge of blue whales in NZ. I was there to fill knowledge gaps, and I could do that.
For over an hour I was questioned on many topics. Here are a few snippets:
Why should the noise impacts from the proposed iron sands mining operation on blue whales be considered when seismic survey activity produces noise 1,000 to 100,000 times louder?
My answer: Seismic survey noise is very loud, but it’s important to note that seismic and mining noises are two different types of sound sources. Seismic surveys noise is an impulsive noise (a loud bang every ~8 seconds), while the mining operation will produce non-impulsive (continuous) sound. Also, the mining operation will likely be continuous for 32 years. Therefore, these two sound sources are hard to compare. It’s like comparing the impacts of listening to pile driving for a month, and listening to a vacuum cleaner for 32 years. What’s important here is to considering the cumulative effects of both these noise sources occurring at the same time: pile driving on top of vacuum cleaner.
How many blue whales have been sighted within 50 km of the proposed mining site?
My answer: Survey effort in the STB has been very skewed because most marine mammal sighting records have come from marine mammal observers aboard seismic survey vessels that primarily work in the western regions of the STB, while the proposed mining site is in the eastern region. So at first glance at a distribution map of blue whale sightings (Fig. 1) we may think that most of the blue whales are found in the western region of the STB, but this is incorrect because we have not accounted for survey effort.
During our past three surveys in the STB we have surveyed closer to the proposed mining site. In 2014 our closest point of survey approach to the mining site was 26 km, and our closest sighting was 63 km away. In 2016, we found no whales north of 40’ 30” in the STB and the closest sighting was 107 km away from the proposed mining site, but this was a different oceanographic year due to El Niño conditions. During this recent survey in 2017, our closest point of survey approach to the proposed mining site was 22 km, and our closest sighting was 29 km, with a total of 9 sightings of 16 blue whales within 50 km of the proposed mining site. With all reported sighting records of blue whales tabulated, there have been 16 sightings of 33 blue whales within 50 km of the proposed mining site. Considering the minimal survey effort in this region, this is actually a relatively high number of blue whale sighting records near the proposed mining site.
Additionally, we have a hydrophone located 18.8 km from the proposed mining site. We have only analyzed the data from January through June 2016 so far, but during this period we have an 89% daily detection rate of blue whale calls.
Why are blue whales in the STB and where else are they found in NZ?
My answer: A wind-driven upwelling system occurs off Kahurangi Point (Fig. 1) along the NW coast of the South Island. This upwelling brings nutrient rich deep water to the surface where it meets the sunlight causing primary productivity to begin. Currents push these productive plumes of water into the STB and zooplankton, such as krill that is the main prey item of blue whales, aggregate in these productive areas to feed on the phytoplankton. Blue whales spend time in the STB because they depend on the predictability of these large krill aggregations in the STB to feed efficiently.
Sightings of blue whales have been reported in other areas around New Zealand, but nowhere with regular frequency or abundance. There may be other areas where blue whales feed occasionally or regularly in New Zealand waters, but these areas have not been documented yet. We don’t know very much about these newly documented New Zealand blue whales, yet what we do know is that the STB is an important foraging area for these animals.
Questions like these went on and on, and I was probed with many insightful questions. Yet, the question that sticks with me now was asked by the Chair of the Decision Making Committee regarding the last sentence in my submitted evidence where I remarked on the importance of recognizing the innate right of animals to live in their habitat without disturbance. “This sounds like an absolute statement,” claimed the Chair, “like no level of disturbance is tolerable”. I was surprised by the Chair’s focus on this statement over others. I reiterated my opinion that we, as a society, need to recognize the right of all animals to live in undisturbed habitats whenever we consider any new human activity. “That’s why we are all here today”, I explained to the committee, “to recognize and evaluate the potential impacts of TTR’s proposed mining operation on blue whales, and other animals, in the STB”. Undisturbed habitat may not always be achievable, but when we make value-based decisions regarding permitting industrial projects we need to recognize biodiversity’s right to live in uncompromised environments.
I do not envy this Decision Making Committee, as over three weeks they are hearing evidence from all sides on a multitude of topics from environmental, to economic, to cultural impacts of the proposed mining operation. They will be left with the very hard task of balancing all this information and deciding to approve or decline the mining permit, which would be a first in NZ and may open the floodgates of seabed mining in the country. My only hope is that our research on blue whales in NZ over the last five years has filled knowledge gaps, allowing the Decision Making Committee to fully appreciate the importance of the STB habitat to NZ blue whales, and appropriately consider the potential impacts of TTR’s proposed mining activities on this unique population.
Join us for a couple boat rides as we study blue whales in the South Taranaki Bight of New Zealand.
In both videos below you can see and hear the field team coordinate to capture photo-identification images of the whale(s) while also obtaining a small tissue biopsy sample. It is important to match the individual whale to the sample so we can link biological data obtained from the sample (genetics, hormones, stable isotopes) to the individual whale. We also carefully take notes on where, when and what we collect in order to help us keep track of our data.
In this video clip you can watch as we gently approach two blues surfacing off the starboard bow of the RV Star Keys in order to capture photo-identification images and a small tissue biopsy sample. Callum Lilley (DOC) on the bow; Leigh Torres, Dawn Barlow, and Todd Chandler (OSU) photographing and coordinating from the flying bridge.
We are in the small boat here collecting data on a pair of blue whales. Callum Lilley (DOC) is on the rifle; Leigh Torres (OSU) is on the camera and taking notes; Todd Chandler (OSU) is on the helm.
After four full-on days at sea covering 873 nautical miles, we are back in port as the winds begin to howl again and I now sip my coffee with a much appreciated still horizon. Our dedicated team worked the available weather windows hard and it paid off with more great absence data and excellent presence data too: blue whales, killer whales, common dolphins, and happily swimming pilot whales not headed to nearby Farewell Spit where a sad, massive stranding has occurred. It has been an exhausting, exhilarating, frustrating, exciting, and fulfilling time. As I reflect on all this work and reward, I can’t help but feel gratified for our persistent and focused planning that made it happen successfully. So, as we clean-up, organize data, process samples, and sit in port for a few days I would like to share some of our highlights over the past four days. I hope you enjoy them as much as we did.