Frustrating. Exhausting. Time-consuming. Repetitive. Draining. De-Motivating. A sine wave of cautious excitement followed by the crash of disappointment at another rejection. The longer my job search continues, the more adjectives I have to describe it.
Last spring, I got rejected from a marine mammal and bird survey technician position because I didn’t have enough experience identifying birds. I found this immensely frustrating. So, fueled by the desire to prove “them” wrong, I embarked on my journey of revenge. First, I registered for a free online bird ID course at the Cornell Lab of Ornithology. Then, I got my bird books out, and started paying more attention to the species I encountered in my neighborhood. Next, I attended a training session for the Puget Sound Seabird Survey with the Seattle Audubon Society, and joined a citizen science monitoring team. We are responsible for documenting seabird habitat use at 3 beaches in the South Puget Sound on the first Saturday of each month. Most of my team members have been birding for decades, and they have been helpfully pointing out ID tricks like flight patterns, wing shapes, and color bands to distinguish one species from another. I feel like my marine bird ID is coming along nicely, but there are SO MANY bird species out there…. I know I learn better, and am more focused, when I am working for a team effort, so two weeks ago I attended a training for the Secretive Wetland Bird Monitoring project with the Puget Sound Bird Observatory. We’ll be doing playback surveys for species like American Bittern, Virginia Rail, and Green Herons during three survey windows from April to June. I’m excited for this project because even if I don’t learn to ID the birds by sight (they are secretive after all), it’s a chance to improve my ‘birding by ear’ skills! With all this, I think the next time a job application asks about my experience with birds, I’ll be able to give some more informed answers.
In Summer 2018, I had a rather tumultuous field research experience with a very disorganized project leader. I ended up leaving the project after a series of poor safety choices by the leadership culminated in the vessel running aground on a well-marked reef. Several of my colleagues and I were injured in the accident, and it was the first time in my 10 year maritime career that I grabbed my emergency bag and seriously thought I might have to abandon ship. In this case, we made it to shore, and there was a clinic nearby where we got treated, but what if there hadn’t been? The more I reflected on what happened, the more I realized how bad the situation could have been. My revenge on that feeling of helplessness was to sign up for a NOLS Wilderness First Aid Course. During the course, we practiced patient assessment, discussed the most common injuries when adventuring in the remote areas, and played out scenarios, as both patients and first responders. We discussed proper scene assessment, basic wound care and splints (those were fun to practice), situations like hypo and hyperthermia, and how to make a radio call for help that transmits the most relevant information. After this two day course, I feel much more confident in my ability to manage emergency situations for myself and any team I work with. Handily enough, many field technician jobs list ‘Wilderness First Aid/Wilderness First Responder” in their desired qualifications sections, so I can check that bullet off now too!
One of the best bits of finishing my grad degree has been getting my evenings and weekends back from the depths of homework and research fueled need-to-be-productive-all-the-time depression. I like making things. Shortly after turning in my thesis, I traded labor for a sheep fleece & two alpaca fleeces.
An acquaintance needed help shearing his small flock, and I saw the opportunity to try a “Sheep to Shawl” project – where you take the raw fiber, clean it, spin it into thread, and weave it into a shawl. I learned how to weave in high school, but I did not know how to spin my own thread. I borrowed a spinning wheel from my fiber arts mentor, found a spinning group at my local yarn store, and since January have been spinning my own thread!
I started with some practice wool to figure the whole thing out, and have just started to spin the fleeces I helped to harvest. It’s going to take me a while, but I’m more interested in the process than any sort of speed. There’s an unfortunate cultural dichotomy between “art” and “science”, but I find that the sort of thinking needed to plan how the threads will intertwine to make a solid and beautiful cloth, is the same sort of thinking needed to understand the myriad processes that inform how an ecosystem functions. If you think about it sideways, knitting & weaving pattern drafts are the first form of binary computer programs – repetitive patterns that when followed result in a product. The creativity needed to make beautiful art is the same creativity that helps problem solve in the field, and long term project planning, forethought and tenacity are all necessary in both research and in fiber arts. While the art itself may not be relevant to the jobs I apply for, the skills are transferable, and the actions recharge my batteries so I can keep solving problems creatively.
It’s an easy trap to fall into – the idea that learning only happens in the classroom, and that once you’ve finally finished school and thrown off the trappings of academia you’re done and never have to learn again.
But never learning anything new would get boring quickly, wouldn’t it?
I may be frustrated by how long it is taking me to find ‘a career’, but I can’t regret the lily pads that I have landed on in the mean-time, or the skills that I have had the opportunity to pick up.
Exciting. Inspiring. Educational. Opportunistic. Expanding my network. Hopeful. A sine wave of disappointment followed by renewed determination to keep trying. The longer my job search continues, the more adjectives I have to describe it.
Paul Lask teaches writing at Oregon Coast Community College, and is a faculty fellow with Portland State University’s Institute for Sustainable Solutions. His writing can be found at prlask.com.
I pulled my kayak down to the beach, where a woman stood pointing toward the ocean. A fin rose from the water about a hundred yards offshore.
“It’s an orca,” she said.
“Naw,” the man beside her said. “That’s a gray.”
I recalled a documentary scene of a group of orcas spy-hopping near a seal marooned on an ice chunk. After their pogoing taunts, they left it alone. Another clip showed the orcas band together and charge forward, pushing a big wave over the ice and knocking the seal in.
I brought myself back to the beach. I wanted it to be a gray. It was one of my first solo ocean paddles, and I stood in my dry suit, PFD and helmet, having checked my weather and swell apps, having spent many hours in pools and bays learning rolls and rescues, and many dollars on courses, gear and guidebooks, now arguing a dubious fin into goodness.
It had to be a gray.
I dragged my boat to the water. Small dumping waves sucked back dark gravelly sand. The fin flopped over.
Aspiring rough water sea kayakers are trained in safety and rescue. We learn about dealing with battering surf, longshore currents, T-rescues and re-entry rolls. We don’t learn about sea life. I grew up in northern Illinois, where the nearest sea animal was a river dragon fashioned out of a downed tree that got painted annually, and TV specials on Loch Ness.
I stuffed myself into my boat, suddenly remembering the shark story an instructor told me: They were out near Pacific City when the bad fin emerged. My instructor had a Go Pro on his helmet. His buddy dared him to roll to get a shot of their follower. My instructor declined.
Sealing my spray skirt over the cockpit, I focused on launch prep. I checked my radio. Made sure my extra paddle was secure. Confirmed I hadn’t sealed the skirt over my skeg rope. Here at North Fogarty Creek beach there was a gap between where the fin had been and a rock the size of a two story house. I waited for a set of waves to pass, then pushed off.
I saw the gray whale’s back split the water, heard the great sigh. A misty rainbow evaporated. I darted past the whale into the open sea. Other puffs dotted the horizon.
In time I would learn the kelp forest I had just paddled through hosted galaxies of tiny shrimp-like zooplankton. The gray was “sharking,” a foraging behavior in shallow water wherein it lays on its side with half its tail sticking out. Of the 20,000 gray whales that annually migrate from Mexico to Alaska, about 200 mysteriously break away and feed nearshore in Oregon. Scientists don’t know[i] for sure why this occurs, but the abundance of those shrimp-like animals is one theory.
The mavericks are good for the tourism industry. From late spring through summer Depoe Bay is a frenzy of camera clicks and selfie sticks. A gauntlet of vehicles cram both sides of Hwy 101. Whale watching boats enter and exit the “world’s smallest harbor” through a bottleneck I’ve heard can be sketchy for kayakers.
As I paddled I toyed with wishful thinking—because I was a non-motorized vessel, the whales might better appreciate my presence. I was not there to photograph them. I just liked being in the sway of the water. “No cradle is so comfortable,” Rudyard Kipling wrote, “as the long, rocking swell of the Pacific.”[ii] Especially on an uncharacteristically calm day like this.
I have met paddlers who are indifferent to our resident grays. One referred to them as squirrels. Another claimed he got too near a spout, and was covered in the slime geyser, which he’d found disgusting. Others want to get close. A friend is interested in bringing snorkeling gear out next season, and a non-paddling acquaintance wants to get a kayak so he can sneak up and swim with one.
Dr. Roger Payne, the biologist famous for discovering that humpbacks sing, discusses Baja’s “‘friendly gray whale phenomenon’, wherein gray whales come so close to whale-watching boats that the tourists can reach out and pat them.”[iii] Grays weren’t always treated like housecats. When whaling was in full swing, Dr. Payne continues, they were referred to as “devil fish” by whalers in Scammon’s Lagoon in Baja. The whales were being routinely harpooned, so they fought back, earning a fierce reputation. Their numbers plummeted. Federal protections helped them recover, and in 1994 eastern Pacific gray whales were removed from the U.S. Endangered Species List.
U.S. federal law requires people keep a hundred yards away from whales. Natural law supports this precaution. Once paddling through my shark and orca anxiety, I developed an ambivalence about my proximity to the grays. It was not fear of aggression, but indifference. I was sneaking around the living room of 35-ton animals. Despite their boxcar bulk, they moved with quick snaky grace; regardless of my attempts at putting a football field between us, what was to keep one from accidentally rolling over me or smashing me with its tail?
With shipwrecks in mind, Herman Melville pondered the power of a whale fluke: “But as if this vast local power in the tendinous tail were not enough, the whole bulk of the leviathan is knit over with a warp and woof of muscular fibers and filaments, which passing on either side of the loins and running down into the flukes, insensibly blend with them, and largely contribute to their might; so that in the tail the confluent measureless force of the whole whale seems concentrated to a point. Could annihilation occur to matter, this were the thing to do it.”[iv]
Whale-caused shipwrecks didn’t end in the nineteenth century. Contemplating how his sloop went down, Steven Callahan, a sailor lost at sea for 76 days, recalls how his nineteen-ton, forty-three-foot schooner and a heavy cruiser were both sunk by whales in the 1970s.[v] Dr. Payne also has boat breaching stories. “There’s a woman who works in my laboratory who had a whale breach directly on top of her boat. Not a glancing blow, but a direct hit across the bow. The boat was totaled…”
In 2015, a 33-ton humpback breached onto a tandem kayak in Monterey Bay, California. Reanalyzing video footage, Tom Mustill, one of the struck kayakers, believes he can see the whale “sticking its eyes out and taking a look at us while he’s in the air.” He speculates that the whale may have calculated its landing so as to avoid full body impact. Mustill is currently making a BBC2 documentary about the incident titled “Humpback Whales: A Detective Story.”
How whales behave around vessels is still an open scientific question. OSU whale mammologist Dr. Leigh Torres asks: “Are there behavior differences based on boat traffic and composition? Whales might react to some boats, but perhaps not others based on speed, approach, motor type, etc.”[vi] The ocean is also getting noisier. One study shows that over the last sixty years ambient noise in the ocean has increased about three to five decibels per decade.[vii] To what extent is this noise stressing out whales, and what kind of reactions will we begin to see?
Dr. Torres told me whales were like a gateway drug for getting people hooked on marine ecology. Since that tricky fin at Fogarty Creek I’ve given them a good amount of thought. It’s partially their size that inspires awe and reflection. Writer Julia Whitty gets at their enormity by thinking about their deaths, comparing whales to old growth trees. She describes whalefall beautifully:
“…the downward journey takes place in the slow motion of the underwater world, as the processes of decomposition produce buoyant gases that duel with the force of gravity in such a way that the carcass rides a gentle elevator up and down on its way down” (178). Once the body hits the ocean floor it provides a “nutritional bonanza of a magnitude that might otherwise take thousands of years to accumulate from the background flow of small detritus from the surface.” A gray takes a year and a half to be “stripped to the bone by the scalpels and stomachs of the deep.” A blue whale can take as long as eleven years. [viii]
But I don’t think it’s just their size that hooks us. They’re mammals, nurse their young, sing to one another. “Flowing like breathing planets,” Gary Snyder writes,[ix] we can only wonder what a whale might know.
As I continue exploring our coast by kayak, I occasionally talk to whales. It no longer seems strange to want to hug one. I attempt to maintain the lawful distance, though now and then one rises close enough to see the individual barnacles studded among old scratches and scribbles. This wordless poetry is like a map into deep time. I realize I want to keep being humbled and a little afraid. I realize I’m hooked.
[i] Oregon State University. (2015, August 4). Researchers studying Oregon’s “resident population” of gray whales. Retrieved from https://today.oregonstate.edu/archives/2015/aug/researchers-studying-oregon’s-“resident-population”-gray-whales
[ii] Kipling, R. (1914). The Jungle Book (p. 145). New York, NY: Double Day. Retrieved from https://play.google.com/store/books/details?id=LO88AQAAIAAJ&rdid=book-LO88AQAAIAAJ&rdot=1
[iii] White, J. (2016). Talking on the Water (pp. 25-26). San Antonio, TX: Trinity University Press.
[iv] Friends of the Earth. (1970). Wake of the Whale (p. 71). San Francisco, CA: Friends of the Earth, Inc.
[v] Steven, C. (2002). Adrift (p. 37). New York, NY: First Mariner Books.
[vi]Oregon State University. (2015, August 4). Researchers studying Oregon’s “resident population” of gray whales. Retrieved from
[vii] Lemos, L. (2016, April 6). Does ocean noise stress-out whales?. In Geospatial Ecology of Marine Megafauna Laboratory. Retrieved from http://blogs.oregonstate.edu/gemmlab/2016/04/06/does-ocean-noise-stress-out-whales/
[viii] Whitty, J. (2010). Deep Blue Home (pp. 178-181). New York, NY: Houghton Mifflin Harcourt.
[ix] Snyder, G. (1974). Turtle Island. New York, NY: New Directions Publishing Group. Retrieved from https://www.poets.org/poetsorg/poem/mother-earth-her-whales-0
In our modern world we often share space with people, but never really interact with them. Like right now, I am on a train in France with a bunch of people but I’m not interacting with any of them (maybe because I don’t speak French…). This situation extends to our efforts to understand the bycatch of marine predators in fisheries.
Productivity in the ocean is patchy, so both fishing vessels and marine predators, like seabirds and dolphins, may target the same areas to get their prey. This scenario can be considered spatial overlap, but not necessarily interaction because the two entities (predator and vessel) can independently chose to be in the same place at the same time. Also, overlap can happen at larger spatial and temporal scales than interaction events, which typically must occur at small scales. Again, consider me on this train: all my fellow passengers and I are overlapping on a 500 m long train for 2.5 hours (larger scale) but I only interact with the passenger in the seat 1 m across from me for a minute (smaller scale) while I explain that I don’t understand what they are saying.
Distinguishing overlap from interaction between seabirds and fishing vessels is important to help managers determine how to best direct their efforts to reduce bycatch. Different management approaches can be applied depending on whether seabirds are using the same habitat as fishing vessels (overlap) or are attracted to vessels for feeding opportunities (interaction) and then incidentally caught/injured in the fishing gear. Furthermore, if we can describe discrete interaction events we may also be able to identify the individual fishing vessel, fishing gear used, country of origin, and other such specific information that can help direct bycatch reduction efforts.
However, studying the spatial and temporal relationships between seabirds and fishing vessels is challenging, and highly dependent on the quality of data we have, or can collect, about the movements of birds and boats at-sea. Tracking the movements of seabirds has evolved rapidly with the development of tagging technology and miniaturization, so that over the past 10 years seabird ecologists have collected a large amount of high-resolution GPS data of seabird foraging. While these data reveal fascinating patterns of seabird ecology, our ability to relate these seabird distribution data to fishing vessels has remained limited due to limited access to fishing vessel location data. Historically, fishermen have not wanted to divulge their fishing locations for fear of losing their ‘secret sweet spot’ or regulatory infractions. So, where fishing vessels fish has often been a mystery, at least fine scales. For a long time fishing effort data was only released at scales of 5 x 5 degree grid cells and monthly scales (Fig. 1) (Phillips et al. 2006), which is only broadly useful for assessment of overlap and not useful for assessing interaction events. The situation has improved in some countries where Vessel Monitoring Systems (VMS) data are available but even these GPS data are often too coarse to reveal interaction events (although it’s much better than what was previously available!). In fact, I wrote a paper about this topic in 2013 called “Scaling down the analysis of seabird-fisheries analysis” that called for higher resolution vessel position data to better evaluate and manage seabird and fishing vessel interactions (Torres et al. 2013).
Progress was made in 2016 with the release of Global Fishing Watch (globalfishingwatch.org) that has significantly increased transparency in the fishing industry and revolutionized our ability to monitor fishing vessel activities (Robards et al. 2016). Almost every fishing vessel in the world is required to use the Automated Identification System (AIS) that pings GPS quality position data to satellite and shore receiving stations around the world. AIS was originally developed to increase maritime safety by reducing collision risk, but Global Fishing Watch has developed methods to acquire these AIS data globally, distinguish fishing vessels (from cargo ships or sailing vessels), classify fishing vessels by fishing method, and disseminate these data in an accessible and visually understandable able format (de Souza et al. 2016; Kroodsma et al. 2018). When I saw the Global Fishing Watch website for the first time I actually let out a ‘Woohoo!’ because I knew this was the missing piece I needed to move from overlap to interaction.
So, I assembled a great team of collaborators including Dr. Rachael Orben – seabird movement ecologist extraordinaire – and colleagues who have collected GPS tracking data from three species of albatross in the North Pacific Ocean. Another important step was acquiring funding to support the research effort from the NOAA Bycatch Reduction Engineering Program, and to establish a collaboration with Global Fishing Watch. Fast forward a year and through many data analysis and R coding puzzles, and we have made the jump from overlap to interaction, with some preliminary results to share.
We compiled GPS tracks representing foraging trips conducted by Laysan (Phoebastria immutabilis) and black-footed (P. nigripes) albatrosses breeding in the Hawaiian islands, and juvenile short-tailed albatross (P. albatrus) from Japan. First we identified overlap between bird and boat at daily and 80 km scales. Next, we quantified encounter events at scales of 10 minutes and between 30 and 3 km, which was the assumed distance at which birds are able to perceive a boat. Finally, interaction events were identified when birds and boats were within 3 km and 10 minutes of each other.
At an absolute level, short-tailed albatross overlapped, encountered and interacted with many more fishing vessels than black-footed and Laysan albatross. However, it is important to point out that these results may be biased by the temporal sampling resolution of the GPS tracking data (how often a location was recorded), which we have not accounted for yet. Nevertheless, what is interesting is that when the percent of interaction events that derived from encounter events is assessed, black-footed and Laysan albatross demonstrate much higher rates of fisheries interactions. These results indicate that when a black-footed albatross encountered a fishing vessel engaged in fishing, nearly 50% of these opportunities turned into an interaction event. This rate was 39 and 26 percent for Laysan and short-tailed albatross respectively. This species-level difference between absolute and relative (percentage) interaction with fisheries may be due to the overall distribution patterns of the different albatross species, with short-tailed albatross using areas that overlap with fishing activity more frequently (coastal margins). Furthermore, these results indicate that short-tailed albatross may be more ‘vessel-shy’ than black-footed and Laysan albatross. The high black-footed albatross percent interaction rate aligns with the high by-catch rate of this species, and emphasizes the need to better understand and manage their interactions with fishing vessels.
While these results from our novel analysis are an interesting start to helping inform bycatch mitigation efforts, perhaps the most illustrative (and coolest!) output so far are the below animations that show the fine-scale movement tracks of an albatross and fishing vessel (Fig. 2 and 3). Both animations are a 24 hour period and show an albatross (red dot) and a fishing vessel (yellow dot). But, Figure 2 illustrates an overlap event, where the bird and boat clearly overlap spatially and temporally but do not interact. However, in Figure 3 we see how the albatross flies directly to the vessel and the bird and vessel remain spatially and temporally linked, demonstrating an interaction event. Our next steps are to improve our ability to distinguish these interaction events (assessment of duration and trajectory correspondence) and to describe the driving factors (e.g., albatross species, fishing vessel method and flag nation, environmental variables) that lead an albatross to move from overlap to interaction.
Figure 2. Fine-scale animation of overlap between the movement path of a Laysan albatross GPS track and the AIS track of a fishing vessel, overlaid on bathymetry. While the bird and boat overlap at this scale, the animation illustrates how the bird and boat do not interact with each other.
Figure 3. Fine-scale animation of overlap between the movement path of a Laysan albatross GPS track and the AIS track of a fishing vessel, overlaid on bathymetry. This animation illustrates how the bird and boat act independently at the start, and then the bird travels directly to the vessel’s location and the movements of the two entities corresponded spatially and temporally, demonstrating a clear interaction event.
de Souza, Erico N., Kristina Boerder, Stan Matwin, and Boris Worm. 2016. ‘Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning’, PLoS ONE, 11: e0158248.
Kroodsma, David A., Juan Mayorga, Timothy Hochberg, Nathan A. Miller, Kristina Boerder, Francesco Ferretti, Alex Wilson, Bjorn Bergman, Timothy D. White, Barbara A. Block, Paul Woods, Brian Sullivan, Christopher Costello, and Boris Worm. 2018. ‘Tracking the global footprint of fisheries’, Science, 359: 904-08.
Phillips, R. A., J. R. D. Silk, J. P. Croxall, and V. Afanasyev. 2006. ‘Year-round distribution of white-chinned petrels from South Georgia: Relationships with oceanography and fisheries’, Biological Conservation, 129: 336-47.
Robards, MD, GK Silber, JD Adams, J Arroyo, D Lorenzini, K Schwehr, and J Amos. 2016. ‘Conservation science and policy applications of the marine vessel Automatic Identification System (AIS)—a review’, Bulletin of Marine Science, 92: 75-103.
Torres, Leigh G., P. M. Sagar, D. R. Thompson, and R. A. Phillips. 2013. ‘Scaling-down the analysis of seabird-fishery interactions’, Marine Ecology Progress Series, 473.
By Erin Pickett, M.Sc. (GEMM Lab member 2014-2016)
Field Assistant, Kaua’i Endangered Seabird Recovery Project
I heaved my body up with both arms, swung one leg up and attempted to muster any remaining energy I had into standing on the ridgeline of the valley that I had just crawled out of. Soaked from the rain, face covered with bits of dirt and with ferns sticking out of my hair I probably resembled a creature crawling out of a swamp. I smiled at this thought knowing that my dramatic emergence from the swamp might have been captured on a nearby motion-sensing trail camera.
I surveyed my surroundings to gain my bearings. I was searching for seabird burrows in a densely vegetated valley called Upper Limahuli Preserve in the mountains of Kaua’i, Hawaii. I was looking for the nests of the endangered Hawaiian Petrel (or ‘Ua’u in Hawaiian) and the threated Newell’s Shearwater (A’o), Hawaii’s only two endemic (found nowhere else in the world) Procellarid species. I registered the trail, the nearby fence line and the two valleys on either side of the ridge I was standing on. If a drone had photographed me from above, the scene of lush green mountains, waterfalls and rugged cliffs would not only look like the views from the helicopter arrival scene in the movie Jurassic Park, but indeed was the same Nā Pali coastline.
When I finished my graduate program at Oregon State University in 2017, I began working for a project called the Kaua’i Endangered Seabird Recovery Project (KESRP). Our work at KESRP focuses on monitoring Kauai’s populations of breeding a’o and ‘ua’u, mitigating on-land threats through recovery activities and conducting research (e.g. habitat modeling & at-sea tracking) to learn more about the two species.
An estimated 90% of the Newell’s Shearwater population breeds on the island of Kaua’i, as does a large portion of the Hawaiian Petrel population. Both populations have declined rapidly on Kaua’i over the past two decades, where radar surveys found a 78% decrease of Hawaiian Petrels and a 94% decrease in overall numbers of Newell’s Shearwaters (Raine et al., 2017). Light pollution, collision with electrical power lines, and invasive vertebrate predators represent primary threats to both the a’o and ‘ua’u while on land during the breeding season. As with all seabirds that nest on islands, the a’o and ‘ua’u are easy prey for invasive species such as feral cats and black rats, thus, there is a large effort within our study area to alleviate the threat of these predators.
The purpose of my burrow search effort on this day was to find suitable candidate burrows for a translocation project that KESRP has undertaken since 2015. This fall, we will attempt to relocate via helicopter up to 20 a’o and ‘ua’u chicks from the mountains of Kaua’i, where they are vulnerable to invasive predators, to a predator-proof fenced area located within nearby Kīlauea National Wildlife Refuge. The ultimate aim of our translocation project, a critical component of the Nihokū Ecosystem Restoration Project, is to establish successful breeding colonies of a’o and ‘ua’u within the protected boundaries of a fence that is impermeable to rats, cats, and pigs.
On Kaua’i, the imperiled a’o and ‘ua’u nest on verdant cliffs amid native Hawaiian uluhe ferns and ‘ohi‘a lehua trees. Both species raise their chicks in burrows that can only be located by humans after an extensive search effort that involves scanning the densely vegetated forest floor for tiny feathers and guano trails, and following the musty scent of seabirds until an underground tunnel is found, sometimes with a bird nestled inside.
My afternoon of burrow searching had been strenuous, and being day three it had already been a long week in the field so I sighed and started heading in the direction that would lead me back to our field camp. Though, after a few steps I caught the musty smell of seabird in the air and immediately stopped walking. Like an animal, I followed my nose and turned my head over my right shoulder and sniffed the air. I climbed over the fence that separated the trail I was hiking on from the 3,000 foot drop into the valley below, carefully positioned my feet on the fragile cliff side and lifted a large tuft of grass to find a freshly dug hole that smelled unmistakably like a seabird.
Either a prospecting Hawaiian Petrel or Newell’s Shearwater had broken ground on this new burrow the night before. The birds had been busy digging into the cliff side while I had been conducting an auditory survey a few hundred meters away. The auditory survey had begun at sunset and over the course of the next two hours I listened for and recorded the locations of seabirds transiting overhead, heading from the sea to the mountains and calling from their burrows nearby. Ideally, this auditory survey would help me pinpoint locations of ‘ground callers’ who’s raucous would lead me to their burrows the next day.
Finding a burrow is not often as easy as pinpointing the location of a ground caller, catching a whiff of seabird near that location and immediately locating a hole in the ground. Yet, finding a burrow that is ‘reachable’ and that is reasonably close to a helicopter landing zone, is even more difficult. And this task is one of our objectives throughout the field season this year.
Raine, A. F., Holmes, N. D., Travers, M., Cooper, B. A., & Day, R. H. (2017). Declining population trends of Hawaiian Petrel and Newell’s Shearwater on the island of Kaua‘i, Hawaii, USA. The Condor, 119(3), 405-415.
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.