Sunny south meets windy west

By Lauren Ashley, senior at Savannah State University and current summer intern in the GEMM Lab

Enjoying South Beach, Oregon. Photo by Katherine Bartels
Enjoying South Beach, Oregon. Photo by Katherine Bartels

My name is Lauren Ashley and I am a rising senior from Savannah State University. I am a marine science major, with dreams of becoming a veterinarian. I would have never thought I would experience a summer on the northwest coast. And let me tell you guys, it is a huge adjustment!

I secured an internship with the Living Marine Resources Cooperative Science Center (LMRCSC). I am working in the GEMM Lab at Oregon State University where I am developing an interactive display for the visitor center at the Hatfield Marine Science Center. This display will convey the results from our LMRCSC funded project about the impacts of environmental and climate change on California sea lions and their prey.

I am processing and creating the interactive maps for display through the software ArcGIS 10.3. The amount of challenges I have run into coincide with the amount of things I have learned about the software. The biggest tool I have in my arsenal for problem solving is patience. Somedays, some of the biggest challenges I face, when processing information, seem to have the most simple of solutions, as unconventional and out of the box as they may be. For example, I needed to add a raster depicting the California sea lions forecasted distribution but the files seemed to be incorrect. I went in the conventional way, several times I may add, trying to correct the data. Nothing seemed to work. Eventually my research mentor showed me that the problem could be solve simply by copying the raw data and pasting it to a blank excel file. In a course of a single day the maps can transform based on feedback and edits. And boy does that take time and thought. I am fortunate to be the intern of such a proficient GIS user. Most of what I have learned so far has come at the grace of her teachings.

As I learn to communicate science to a broad audience, most of which have no science background, I have discovered that people learn and process information in many different ways. The biggest challenge thus far is finding a balance where the map conveys information that is not too overwhelming or too broad that it takes away from the true learning outcome. We don’t want to confuse or bore our audience. The outcome of this display is to inform our audience of how environmental change influences the distribution of not just one species at a time, but a community of species through predator and prey interactions.

The very first map that I made for this project, putting it nicely, was terrible. The map, displayed below, had no labeling besides the title whatsoever. The legend was non-existent so even though I knew what the data was no one else knew. And, even though the green shapes of the Pacific northwest were obvious to me, I was told that many viewers would not know that they we looking at Oregon, Washington and Vancouver Island. As time has passed, the maps I produce have developed quite a bit, though I still have many chafes and challenges ahead of me. It is certainly becoming clear to me that effective science communication is a tricky goal.

My first attempt at a map to relate scientific results on sea lion distribution patterns to a general, non-scientific audience.
My first attempt at a map to relate scientific results on sea lion distribution patterns to a general, non-scientific audience.

Upon hearing that this internship, starting in June, would be in Newport, Oregon, my close family and friends grew excited for me, even though I would be away from them for 10 weeks.  I, on the other hand, was not too excited. Truthfully, I was nervous. I did not want to make any assumptions about a place I had barely even heard of.  The southeast USA is my home, and upon arriving in Newport after my four hour flight and a two hour drive I realized that I was transported to a whole new world. Everything was foreign to me, from the living arrangements to the time zone.

The first adjustment I had to make was a time adjustment. In Oregon, I am three hours behind where I usually am, and let me tell you, it is not fun waking up at 3:45 when you are used to waking up at 6:45 ET. To be honest, even after three weeks, I’m still not sure I am completely adjusted to Pacific Time. I have the dark circles to prove it.

Anyone that has ever been to/lived in Georgia can accurately describe the weather in two simple words: HOT and HUMID. I am used to 100 °F days during the summer and here the highest I have yet to experience is 64°F. In other words: I am freezing my tail off! The cold windy days do not usually agree with my choice of attire. I have resorted to wearing long-sleeve shirts and hoodies on a daily basis.

But all of that aside, Oregon is the MOST breathtakingly beautiful place I have ever been to. There is nothing like the Pacific Northwest coast. After my internship is up, I would not be opposed to taking a road trip to explore this whole coast. This first month has consisted of whale watching, hikes along the big creek trails, and long walks on the beach, lots of beer, and plenty of seafood. The atmosphere of this small town is very refreshing compared to life in the city.

At the Yaquina Lighthouse, Photo by Katherine Bartels
At the Yaquina Lighthouse, Photo by Katherine Bartels

Unmanned Aircraft Systems: keep your distance from wildlife!

By Leila Lemos, Ph.D. Student, Department of Fisheries and Wildlife, OSU

Unmanned aircraft systems (UAS) or “drones” are becoming commonly used to observe natural landscapes and wildlife. These systems can provide important information regarding habitat conditions, distribution and abundance of populations, and health, fitness and behavior of the individuals (Goebel et al. 2015, Durban et al. 2016).

The benefits for the use of UAS by researchers and wildlife managers are varied and include reduced errors of population estimations, reduced observer fatigue, increased observer safety, increased survey effort, and access to remote settings and harsh environments (Koski et al. 2010, Vermeulen et al. 2013, Goebel et al. 2015, Smith et al. 2016). Importantly, data gathered from UAS can provide needed information for the conservation and management of several species. Although it is often assumed that wildlife incur minimal disturbance from UAS due to the reduced noise compared to traditional aircraft used for wildlife monitoring (Acevedo-Whitehouse et al. 2010), the impacts of UAS on most wildlife populations is currently unexplored.

Several studies have tried to comprehend the effects of UAS flights over animals and so far there is no evidence of behavioral disturbance. For instance Vermeulen et al. (2013) conducted a study where authors observed a group of elephants’ reaction or warning behavior while a UAS passed ten times over the individuals at altitudes of 100 and 300 meters, and no disturbance was recorded. Furthermore, a study conducted by Acevedo-Whitehouse et al. (2010) reported that six different species of large cetaceans (Bryde’s whale, fin whale, sperm whale, humpback whale, blue whale and gray whale) did not display avoidance behavior when approached by the UAS for blow sampling, suggesting that the system caused minimal distress (negative stress) to the individuals.

However, the fact that we cannot visually see an effect in the animal does not mean that a stress response is not occurring. A study analyzed the effects of UAS flights on movements and heart rate responses of American black bears in northwestern Minnesota (Ditmer et al. 2015). It was observed that all bears, including an individual that was hibernating, responded to UAS flights with increased heart rates (123 beats per minute above the pre-flight baseline). In contrast, no behavioral response by the bears was recorded (Figure 1).

By Leila Lemos, Ph.D. Student, Department of Fisheries and Wildlife, OSU Unmanned aircraft systems (UAS) or “drones” are becoming commonly used to observe natural landscapes and wildlife. These systems can provide important information regarding habitat conditions, distribution and abundance of populations, and health, fitness and behavior of the individuals (Goebel et al. 2015, Durban et al. 2016). The benefits for the use of UAS by researchers and wildlife managers are varied and include reduced errors of population estimations, reduced observer fatigue, increased observer safety, increased survey effort, and access to remote settings and harsh environments (Koski et al. 2010, Vermeulen et al. 2013, Goebel et al. 2015, Smith et al. 2016). Importantly, data gathered from UAS can provide needed information for the conservation and management of several species. Although it is often assumed that wildlife incur minimal disturbance from UAS due to the reduced noise compared to traditional aircraft used for wildlife monitoring (Acevedo-Whitehouse et al. 2010), the impacts of UAS on most wildlife populations is currently unexplored. Several studies have tried to comprehend the effects of UAS flights over animals and so far there is no evidence of behavioral disturbance. For instance Vermeulen et al. (2013) conducted a study where authors observed a group of elephants’ reaction or warning behavior while a UAS passed ten times over the individuals at altitudes of 100 and 300 meters, and no disturbance was recorded. Furthermore, a study conducted by Acevedo-Whitehouse et al. (2010) reported that six different species of large cetaceans (Bryde’s whale, fin whale, sperm whale, humpback whale, blue whale and gray whale) did not display avoidance behavior when approached by the UAS for blow sampling, suggesting that the system caused minimal distress (negative stress) to the individuals. However, the fact that we cannot visually see an effect in the animal does not mean that a stress response is not occurring. A study analyzed the effects of UAS flights on movements and heart rate responses of American black bears in northwestern Minnesota (Ditmer et al. 2015). It was observed that all bears, including an individual that was hibernating, responded to UAS flights with increased heart rates (123 beats per minute above the pre-flight baseline). In contrast, no behavioral response by the bears was recorded (Figure 1).
Figure 1: (A) Movement rates (meters per hour) of an adult female black bear with cubs prior to, during, and after a UAS flight (gray bar); (B) The corresponding heart rate (beats per minute) of the adult female black bear. Source: Modified from Figure 1 from Ditmer et al. 2015.

 

Therefore, behavioral analysis alone may not be able to describe the complete effects of UAS on wildlife, and it is important to consider other possible stress responses of wildlife.

Regarding marine mammals, only a few studies have systematically documented the effects of UAS on these animals. A review of these studies was produced by Smith et al. (2016) and the main factors influencing behavioral disturbance were identified as (1) noise and visual stimulus (from the UAS or its shadow), and (2) flight altitude of the UAS. Thus, studies that approach marine mammals closely with UAS (e.g., blow sampling in cetaceans) should be closely monitored for behavioral reactions because the noise level and visual stimulus will likely be increased.

Fortunately, when UAS work is applied to cetaceans and sirenians (manatees and dugongs) the air-water interface acts as a barrier to sound so these animals are unlikely to be acoustically disturbed by UAS. However, acoustic detection and response are still possible when an animal’s ears are exposed in the air during a surfacing event.

The best way to minimize stress responses in wildlife is to use caution while operating UAS at any altitude. According to National Oceanic and Atmospheric Administration (NOAA), “UAS can also be disruptive to both people and animals if not used safely, appropriately, or responsibly”. Therefore, since 2012, the Federal Aviation Administration (FAA) has required UAS operators in the United States to have a certified and registered aircraft, a licensed pilot, and operational approval, known as Section 333 Exemption (Note: in late August 2016, the 333 will be replaced by a revision to part 107). These authorizations require an air worthiness statement or certificate and registered aircraft. Public entities, like Oregon State University, operate under a certificate of authorization (COA.) As a public entity OSU certifies its own aircraft and sets standards for UAS operators. These permit requirements discourage illegal operations and improves safety.

Regarding marine mammals, all UAS operators should also be aware of The Marine Mammal Protection Act (MMPA) of 1972. This law makes it illegal to harass marine mammals in the wild, which may cause disruption to behavioral patterns, including, but not limited to, migration, breathing, nursing, breeding, feeding, or sheltering. A close UAS approach has the potential to cause harassments to marine mammals, thus federal guidelines recommend keeping a safe distance from these animals in the wild. The required vertical distance is 1000 ft for most marine mammals, but increases for endangered animals such as the North Atlantic right whales with a required buffer of 1500 ft (http://www.nmfs.noaa.gov/pr/uas.html). Therefore, NOAA evaluates all scientific research that use UAS within 1000 ft of marine mammals in order to ensure that the benefits outweigh possible hazards. NOAA distributes research permits accordingly.

Of course, with new technology the rules are always changing. In fact, last week the Department of Transportation (DOT) and the FAA finalized the first operational rules for routine commercial use of small UAS. These new guidelines aim to support new innovations in order to spur job growth, advance critical scientific research and save lives, and are designed to minimize risks to other aircraft and people and property on the ground. These new regulations include several requirements (e.g., height and speed restrictions) and hopefully allow for a streamlined system that enables beneficial and exciting wildlife research.

For my PhD project we are using UAS to collect aerial images from gray whales in order to describe behavioral patterns and apply a photogrammetry methodology. Through these methods we will determine the overall body condition and health of the individuals for comparison to variable ambient ocean noise levels. This project is conducted in collaboration with the NOAA Pacific Marine Environmental Lab.

Since October 2015, we have conducted 31 over-flights of gray whales using our UAS (DJI Phantom 3) and no behavioral disturbance has been observed. When over the whale(s) we generally fly between 25 and 40 m above the animals. We have a FAA certified UAS operator and fly under our NOAA/NMFS permit 16111. Prior to each flight we ensure that the weather conditions are safe, the whales are behaving normally, and that no on-lookers from shore or other boats will be disturbed.

Here is a video showing the launch and retrieval of the UAS system, our research vessel, the surrounding Oregon coastline beauty and gray whale individuals. The video includes some interesting footage of a gray whale foraging over a shallow reef, indicating that this UAS flight did not disturb the animal’s natural behavior patterns.

We all have the responsibility to help keep wildlife safe. Here in the GEMM Lab, we commit to using UAS safely and responsibly, and aim to use this new and exciting technology to continue our efforts to better protect and understand marine mammals.

 

References

Acevedo‐Whitehouse K, Rocha‐Gosselin A and Gendron D. 2010. A novel non‐invasive tool for disease surveillance of free‐ranging whales and its relevance to conservation programs. Anim. Conserv. 13(2):217–225.

Ditmer MA, Vincent JB, Werden LK, Tanner JC, Laske TG, Iaizzo PA, Garshelis DL and Fieberg JR. 2015. Bears Show a Physiological but Limited Behavioral Response to Unmanned Aerial Vehicles. Current Biology 25:2278–2283.

Durban JW, Moore MJ, Chiang G, Hickmott LS, Bocconcelli A, Howes G, Bahamonde PA, Perryman WL and Leroi DJ. 2016. Photogrammetry of blue whales with an unmanned hexacopter. Marine Mammal Science. DOI: 10.1111/mms.12328.

Goebel ME, Perryman WL, Hinke JT, Krause DJ, Hann NA, Gardner S and LeRoi DJ. 2015. A small unmanned aerial system for estimating abundance and size of Antarctic predators. Polar Biol. 38(5):619-630.

Koski WR, Abgrall P and Yazvenko SB. 2010. An inventory and evaluation of unmanned aerial systems for offshore surveys of marine mammals. J. Cetacean Res. Manag. 11(3):239–247.

NOAA. Unmanned Aircraft Systems: Responsible Use to Help Protect Marine Mammals. In: http://www.nmfs.noaa.gov/pr/uas.html. Accessed in: 06/12/2016.

Smith CE, Sykora-Bodie ST, Bloodworth B, Pack SM, Spradlin TR and LeBoeuf NR. 2016. Assessment of known impacts of unmanned aerial systems (UAS) on marine mammals: data gaps and recommendations for researchers in the United States1 J. Unmanned Veh. Syst. 4:1–14.

Vermeulen C, Lejeune P, Lisein J, Sawadogo P and Bouché P. 2013. Unmanned aerial survey of elephants. PLoS One. 8(2):e54700.

 

SeaBASS 2016

By Samara Haver, MSc student, OSU Fisheries and Wildlife, ORCAA Lab

As a graduate student in bioacoustics (the study of noise produced by biological sources), my education is interdisciplinary. Bioacoustics is a relatively small field, and (together with my peers) I am challenged to find my way through coursework in ecology, physiology, physics, oceanography, statistics, and engineering to learn the background information that I need to develop and answer research questions (since this is my first post for the GEMM lab, here is a little more information about my interests). While this challenge (for all young bioacousticians) presents itself a little differently at all universities, the information gap is essentially the same. Hence, just over 6 years ago, Dr. Jennifer Missis-Old and Dr. Susan Parks recognized a need to fill this gap for graduate students in bioacoustics and created SeaBASS, a BioAcoustics Summer School.

This year, for the 4th iteration of the week-long program, I was lucky to have the opportunity to attend SeaBASS. I first heard about SeaBASS as a research assistant in Dr. Sofie Van Parijs’s passive acoustics group at the Northeast Fisheries Science Center, but the workshop is limited to graduate students only so I had to wait until I was officially enrolled in grad school to apply. My ORCAA lab-mates, Niki, Selene, and Michelle are all alumni of SeaBASS (read Miche’s re-cap from 2014 here ) so by the time I was preparing for my trip to upstate NY this summer to attend, I had a pretty good idea of what was to come.

As expected, the week was packed. I flew to the East Coast a few days early to visit our fearless ORCAA leader, Holger, at the Bioacoustics Research Program at the Cornell Lab of Ornithology, so I was lucky to be somewhat adjusted to EST by the time I arrived at Syracuse on Sunday afternoon. After exploring the campus, it was time for official SeaBASS programming to begin. Our first class, an “Introduction to Acoustics and Proportion”, began early on Monday morning. In the afternoon and through the rest of the week we also learned about active acoustics (creating a sound in the water and using the echo to detect animals or other things) and marine mammal physiology, echolocation, communication, and behavior. We also heard about passive acoustics (listening to existing underwater sounds), including the different types of technology being used and its application for population density estimation. On Friday afternoon, the final lecture covered the effects of noise on marine mammals.

Samara1 Some SeaBASS-ers testing the hypothesis that humans are capable of echolocation.

In addition to the class lectures given by each instructor, we also heard individual opinions about “hot topics” in bioacoustics. This session was my favorite part of the week because we (the students) had the opportunity to hear from a number of accomplished scientists about what they believe are the most pressing issues in the field. Unlike a conference or seminar, these short talks introduced (or reinforced) ideas from researchers in an informal setting, and among our small group it was easy to hear impressions from other SeaBASS-ers afterwards. As a student I spend a lot of my time working alone; my ORCAA labmates are focused on related acoustic projects, but we do not overlap completely. The best part of SeaBASS was sharing ideas, experiences, and general camaraderie with other students that are tackling questions very similar to my own.

Samara2 SeaBASS 2016

Although a full week of class would be plenty to take in by itself, our evenings were also filled with activities. We (students) shared posters (this was mine ) about our individual research projects, listened to advice about life as a researcher in the field, attended a Syracuse Chiefs baseball game, and at the end of each day reflected on our new knowledge and experiences over pints. So, needless to say, I returned home to Oregon completely exhausted, but also with refreshed excitement about my place in the small world of bioacoustics research.

Samara3 Luckily we had beautiful weather for the baseball game!

Samara4

 

The Gray [Whale]s are back in town – Field season 2016 is getting started!

By Florence Sullivan – MSc Student, GEMM Lab

Hello Everyone, and welcome back for season two of our ever-expanding research project(s) about the gray whales of the Oregon coast!

Overall, our goal is document and describe the foraging behavior and ecology of the Pacific Coast Feeding Group of Gray Whales on the Oregon Coast. For a quick recap on the details of this project read these previous posts:

During this summer season, the newest iteration of team ro”buff”stus will be heading back down to Port Orford, Oregon to try to better understand the relationship between gray whales and their mysid prey. Half the team will once again use the theodolite from the top of Graveyard Point to track gray whales foraging in Tichenor Cove, the Port of Port Orford, and the kelp beds near Mill Rocks.  Meanwhile, the other half of the team will use the R/V Robustus (i.e. a tandem ocean kayak named after our study species – Eschrichtius robustus, the gray whale) to repeatedly deploy a GoPro camera at several sampling locations in Tichenor cove. We hope that by filming vertical profiles of the water column, we will be able to create an index of abundance for the mysid to describe their temporal and spatial distribution of their swarms.  We’re particularly interested in the differences between mysid swarm density before and after a whale forages in an area, and how whale behaviors might change based on the relative density of the available prey.

The GEMM lab's new research vessel being launched on her maiden voyage.
Ready to take the R/V Robustus out for her maiden voyage in Port Orford to test some of our new equipment. photo credit: Leigh Torres

In theory, asking these questions seems simple – get in the boat, drop the camera, compare images to the whale tracklines, get an answer!  In reality, this is not the case. A lot of preparatory work has been going on behind the scenes over the last six months. First, we had to decide what kind of camera to use, and decide what sort of weighted frame to build to get it to sink straight to the bottom. Then came the questions of deployment by hand versus using a downrigger,

Example A why it is a bad idea to try to sample during a diatom bloom.
Example A why it is a bad idea to try to sample during a diatom bloom – You can’t see anything but green.

what settings to use on the camera, how fast to send it down and bring it back up, what lens filters are needed (magenta) and other logistical concerns. (Huge thank you to our friends at ODFW Marine Reserves Program for the help and advice they provided on many of these subjects.) We spent some time in late May testing our deployment system, and quickly discovered that sampling during a diatom bloom is completely pointless because visibility is close to nil.

However, this week, we were able to test the camera in non-bloom conditions, and it works!  We were able to capture images of a few small mysid swarms very near the bottom of the water column, and we didn’t need external lights to do it. We were worried that adding extra lights would artificially attract mysid to the camera, and bias our measurements, as well as potentially disturbing the whale’s foraging behavior. (Its also a relief because diving lights are expensive, and would have been one more logistical thing that could go wrong. General advice: Always follow the KISS method when designing a project – keep it simple, ——!)

 

This image is taken at a depth of ~10 meters, with no color corrective filter on the lens
This image is taken at a depth of ~10 meters, with no color corrective filter on the lens – notice how blurry the mysid are.
This is empty water, in the mid water column
This is empty water, in the mid water column
More Mysid! This time with a Magenta filter on the lens to correct the colors for us.
Much clearer Mysid! This time with a magenta filter on the lens to correct the colors for us.

My advisor recently introduced me to the concept of the “7 Ps”; Proper Prior Planning Prevents Piss Poor Performance.  To our knowledge, we are the first group to try to use GoPro cameras to study the spatial and temporal patterns of zooplankton aggregations. With new technology comes new opportunities, but we have to be systematic and creative in how we use them. Trial and error is an integral part of developing new methods – to find the best technique, and so that our work can be replicated by others. Now that we know the GoPro/Kayak set-up is capable of capturing useable imagery, we need to develop a protocol for how to process and quantify the images, but that’s a work in progress and can wait for another blog post.   Proper planning also includes checking last year’s equipment to make sure everything is running smoothly, installing needed computer programs on the new field laptop, editing sampling protocols to reflect things that worked well last year, and expanding the troubleshooting appendixes so that we have a quick reference guide for when things go wrong in the field.  I am sure that we will run into more weird problems like last year’s “Chinese land whale”, but I also know that we would have many more difficulties if we had not been planning this field effort for the last several months.

Planning our sampling pattern in Tichenor Cove
Planning our sampling pattern in Tichenor Cove.

Team Ro”buff”stus is from all over the place this year – we will have members from Oregon, North Carolina and Michigan – and we are all meeting for the first time this week.  The next two weeks are going to be a whirlwind of introductions, team bonding, and learning how to communicate effectively while using the theodolite, our various computer programs, GoPro, Kayak, and more!  We will keep the blog updated with our progress, and each team member will post at least once over the course of the summer. Wish us luck as we watch for whales, and feel free to join in the fun on pretty much any cliff-side in Oregon (as long as you’ve got a kelp bed nearby, chances are you’ll see them!)

Sonic Sea asks “can we turn down the volume before it’s too late?”

By: Amanda Holdman, MS student, Geospatial Ecology and Marine Megafauna Lab & Oregon State Research Collective for Applied Acoustics, MMI

It was March 15th, 2000; Kenneth Balcomb was drinking coffee with his new summer interns in the Bahamas when a goose-beaked whale stranded on a nearby beach. Balcomb, a whale researcher and former U.S. Navy Officer, gently pushed the whale out to sea but the beaked whale kept returning to the shore. He continued this process until a second beaked whale stranding was reported further down the beach; and then a third. Within hours, 17 cetaceans had stranded in the Bahamas trying to escape ‘something’ in the water, and Kenneth Balcomb was determined to solve the mystery of the mass stranding. The cause, he eventually learned, was extreme noise – sonar tests from Navy Warships.

The world is buzzing with the sounds of Earth’s creatures as they are living, interacting, and communicating with one another, even in the darkest depths of the oceans. Beneath the surface of our oceans lies a finely balanced, living world of sound. To whales, dolphins and other marine life, sound is survival; the key to how they navigate, find mates, hunt for food, communicate over vast distances and protect themselves against predators in waters dark and deep. Yet, this symphony of life is being disrupted and sadly destroyed, by today’s increasing noise pollution (Figure 1). Human activities in the ocean have exploded over the past 5 decades with ocean noise rising by 3db per decade (Halpern et al. 2008). People have been introducing more and more noise into the ocean from shipping, seismic surveys for oil and gas, naval sonar testing, renewable energy construction, and other activities. This increased noise has significant impacts on acoustically active and sensitive marine mammals. However, as the Discovery Chanel’s new documentary Sonic Sea points out “The biggest thing about noise in the ocean is that humans aren’t aware of the sound at all.” The increase of ocean noise has transformed the delicate ocean habitat, and has challenged the ability of whales and other marine life to prosper and survive.

June blogFigure 1: Anthropogenic sources contributing to ocean soundscapes and the impacts on marine megafauna survival (sspa.se)

Like the transformative documentary from 10 years ago, An Inconvenient Truth, which highlighted the reality and dangers of climate change, Sonic Sea aims to inform audiences of increased man-made noise in the oceans and the harm it poses to marine animals. The Hatfield Marine Science Center and Oregon Chapter of the American Cetacean Society offered a free, premier showing of the award-winning documentary followed by a scientific panel discussion. The panel featured Dave Mellinger, Joe Haxel, and Michelle Fournet of Oregon State University’s Cooperative Institute for Marine Resources Studies (CIMRS) marine bioacoustics research along with GEMM Lab leader, Leigh Torres, of the Marine Mammal Institute.

Sonic Sea introduces us to this global problem of ocean noise and offers up solutions for change. The film uncovers how better ship design, speed limits for large ships, quieter methods for under water resource exploration, and exclusion zones for sonar training can work to reduce the noise in our oceans. However, these efforts require continued innovation and regulatory involvement to bring plans to action.

Around the world the scientific community, policymakers and authorities such as The National Oceanic and Atmospheric Administration (NOAA), the European Union (EU), the International Maritime Organization (IMO) and other authorities have increasingly pressed for the reduction of noise.  NOAA, which manages and protects marine life in United States waters, is trying to reduce ocean noise through their newly released Ocean Noise Strategy Roadmap, where the challenge is dealt with as a comprehensive issue rather than a case-by-case basis. This undersea map is a 10-year plan that aims to identify areas of specific importance for cetaceans and the temporal, spatial, and frequency of man-made underwater noise. After obtaining a more comprehensive scientific understanding of the distributions and effects of noise in the ocean, these maps can help to develop better tools and strategies for the management and mitigation of ocean noise.

Sonic Sea states “we must protect what we love” but then asks “how we can love it if we don’t understand it?” Here at GEMM Lab and the Marine Mammal Institute, we are trying to understand marine species ecology, distributions and behavioral responses to anthropogenic impacts. One of the suggestions Sonic Sea makes to reduce the impact of ocean noise is to restrict activity in biologically sensitive habitats. Therefore, we must know where these important areas are. In an ideal world, we would have a good inventory of data on the marine animals present in a region and when these animals breed, birth and feed. Then we could use this information to guide marine spatial planning and management to keep noise out of important habitats. My thesis project aims to provide such baseline information on harbor porpoise distribution patterns within a proposed marine energy development site. By filling knowledge gaps about where marine animals can be found and why certain habitats are critical, conservation efforts can be more directed and effective in reducing threats, such as ocean noise, to marine mammals.

Noise in our oceans is hard to observe, but its effects are visibly traumatic and well-documented. Unlike other sources of pollution to our oceans, (climate change, acidification, plastic pollution), which may take years, decades or centuries to dissipate, reducing ocean noise is rather straight forward. “Like a summer night when the fireworks end, our oceans can quickly return to their natural soundscape.” Ocean noise is a problem we can fix. To quiet the world’s waters, we all need to raise our voices so policy makers hear of this problem. That’s what Sonic Sea is all about: increasing awareness of this growing threat and building a worldwide community of citizen advocates to help us turn down the volume on undersea noise. If we sit back and do nothing to mitigate oceanic noise pollution, the problem will likely worsen. I highly suggest watching Sonic Sea.  Then, together, we can speak up to turn down the noise that threatens our oceans — and threatens us all.

Sonic Sea airs TONIGHT (6/8) for World Ocean’s Day on Animal Planet  at 10pm ET/PT!

On niche partitioning and the Ohio State Buckeyes

By: Erin Pickett, MS student, Biotelemetry and Behavioral Ecology Laboratory & GEMM Lab, MMI

Buckeye anecdote

I recently found myself sitting at a Sunday brunch at the Westin in Washington, D.C., talking to my uncle about my research on the foraging ecology of penguins. Our entire extended family had gathered for a cousin’s wedding, and it was the first family gathering in a long time that I had been able to attend due to always being “out on some island”, as my cousin puts it. In fact, I got a shout-out during one of the dinner reception speeches for coming all the way from Antarctica for the wedding.

My uncle asked me about my research while our surrounding family members sipped their coffee and OJ and recounted the highlights of the previous night’s wedding reception. This conversation with my uncle was the first I’d had with a family member all weekend that had progressed past my ‘elevator speech’ of what I was studying in school. After I described my research questions about resource partitioning between Adelie and gentoo penguins, my uncle glanced around the room full of family members and said to me, “You know what….”? And then he went on to describe his thoughts about how our aunts, uncles, cousins and in-laws all occupied distinct niches within our family.

The definition of the word niche is broad, and for this reason it can be used to describe the roles of younger siblings, matriarchs, sisters, and Ohio State Buckeye fans within their families or communities. Take for example my entire family on the dance floor chanting O-H-I-O during the bands requisite rendition of “Hang on Sloopy” at the wedding reception. As Buckeyes, we were occupying a role distinct from that of the bride’s family, who are Notre Dame Fans. Within our immediate families, the roles of every sibling and parent are further differentiated. My uncle and I looked around the room and saw a family who despite a wide range of personalities and football allegiances, was managing to enjoy a pretty good time together!

Ecological niche theory and sympatric penguins

In ecology, the term niche is used to describe the ecological role that a species occupies within an ecosystem (Hutchinson 1957). The concept of an ecological niche is typically used in ecology to describe how similar species coexist within the same space. This coexistence is made possible through segregation mechanisms that facilitate resource partitioning, such as spatial or temporal differences in foraging location, or dietary segregation (Pianka 1974). With this in mind, the main objective of my master’s research is to quantify the ecological niches of Adelie and gentoo penguins in terms of space, time and diet, in order to investigate whether foraging competition is occurring between these two species. You’ll find more background on this project here.

The first step in my investigation of resource partitioning was to assess the extent and consistency of dietary overlap between these two species. The diets of Adelie and gentoo penguins vary regionally, but along the Antarctic Peninsula the prey of both species is typically dominated by Antarctic krill. This was the case when I studied the diets of these two species at Palmer Station in Antarctica. I also found that both species consume the same size classes of krill and that this was consistent across both low and high prey availability years (Figure 1).

Size class frequency distribution of Antarctic krill found in penguin diet samples (2010-2015). Krill size class bins shown on x-axis and proportions depicted on y-axis
Figure 1. Length-frequency distribution of Antarctic krill found in penguin diet samples (2010-2015). Krill size class bins shown on x-axis with the proportion of those size classes depicted on the y-axis. Palmer LTER unpublished data.

The next step of my project is to assess the foraging habits and space-use patterns of these two species. They share food, but do they forage in the same areas? I am in the process of analyzing spatial data obtained from satellite and TDR (time depth recording) tags temporarily attached to Adelie and gentoo penguins during the breeding season to determine the core foraging areas. I am using kernel density estimate (KDE) techniques to visually and quantitatively determine the size and extent of spatial overlap between both species foraging areas (Figure 2).

Figure 2.
Figure 2. An example plot of 3D kernel density estimates outlining 95% and 50% volume contours of foraging penguins during the 2010 breeding season. Orange and green depict the core foraging areas of gentoo and Adelies, respectively. Horizontal axes show northing and easting values and depth is shown in meters on the vertical axis.

The KDE method allows me to turn hundreds of satellite tag derived location points into a probability density surface which depicts where an animal is most likely to be found (Kie et al. 2010).  2D KDEs are sufficient to describe the ranges of many terrestrial animals, however, 3D KDEs are a more appropriate description of the space-use patterns of diving seabirds. By failing to incorporate the depth at which these two species are foraging, 2D KDEs might overestimate the extent of spatial overlap between two species who are foraging in the same location but at different depths. Similar to other studies (Cimino et al. 2016 & Wilson 2010), I am finding that Adelie and gentoo penguins may be partitioning resources by foraging at different depths, with gentoo penguins diving deeper than Adelies. By foraging at different depths, these two species are limiting foraging competition.

While I am working on these analyses, I am also thinking about my next step, which will be to determine whether foraging niche overlap between Adelie and gentoo penguins is a function of prey availability. Resource availability is a critical component of niche segregation. When resources are abundant, there is typically a higher tolerance for niche overlap (Pianka 1974, Torres 2009). Conversely, niches may become more distinct as resources decrease and successfully partitioning these resources will become more important to minimize competition. In order to address the effect of resource availability on niche partitioning between Adelie and gentoo penguins, I will be comparing their foraging niches during years of both low and high prey availability. This will allow me to truly evaluate the potential occurrence of foraging competition between these two species.

Conclusion

I’ll keep you updated on my progress with data analysis in future blogs, but before I go I’ll share one last piece of wisdom about niche theory that I’ve learned from my family. There is a niche for everyone unless you are a Michigan fan, then no amount of spatial or dietary partitioning in a room full of Ohio State Buckeyes will save you.

References 

Cimino, Megan A., et al. “Climate-driven sympatry may not lead to foraging competition between congeneric top-predators.” Scientific reports 6 (2016).

Hutchinson, G.E. “Concluding remarks. Population Studies: Animal Ecology and Demography.” Cold Spring Harbor Symposia on Quantitative Biology 22 (1957): 415-427.

Kie, John G., et al. “The home-range concept: are traditional estimators still relevant with modern telemetry technology?” Philosophical Transactions of the Royal Society of London B: Biological Sciences 365.1550 (2010): 2221-2231.

Pianka, Eric R. “Niche overlap and diffuse competition.” Proceedings of the National Academy of Sciences 71.5 (1974): 2141-2145.

Torres, Leigh G. “A kaleidoscope of mammal, bird and fish: habitat use patterns of top predators and their prey in Florida Bay.” Marine Ecology Progress Series 375 (2009): 289-304.

How can we reconstruct life-history pathways of whales?

By Leila Lemos, Ph.D. Student, Department of Fisheries and Wildlife, OSU

 

Have you ever heard of statistical modeling? What about Hierarchical Bayes Models?

Hard words, I know…

Modeling is when known data (previously collected) is analyzed using sophisticated computer algorithms to look for patterns in these data. Models can be very useful for filling in data gaps where and when no sampling occurred. Hierarchical Bayes model is a type of statistical model that hierarchically integrates the observed data to estimate parameters. This type of model can analyze long-term data from individual animals to predict into data gaps and inform us about population dynamics.

When studying wild animals we often only collect data from brief and random encounters. Therefore, many researchers struggle with the reconstruction of possible pathways that could connect different sightings of wild animals to determine where, when and how the animal was doing in between sightings.

For instance, consider an animal that was observed in healthy condition at one sighting but in a subsequent sighting it was in poor health. How can we estimate what happened to this animal between sightings? Can we estimate where, when and how health deteriorated?

This is where the modeling comes in! It is a powerful tool used by many researchers to fill in gaps in our scientific knowledge using data that we do have. We use these ‘known data’ to estimate patterns and determine probabilities. The hierarchical Bayes model is a type of modeling that can be used to estimate the probability of pathways between known events. Schick et al. (2013) used hierarchical Bayes models to estimate the many factors that impact whale health and survivorship including distribution and movement patterns, true health condition of the individual and survival rates.

Modeling is very advantageous when studying aquatic animals like dolphins and whales that are very hard to spot since they spend a higher proportion of their lives submerged than above water. Also, sea conditions can hamper visual detection.

Schick et al. (2013) analyzed decades of data from photo-identifications of North Atlantic right whale resightings along the east coast of North America. They assessed different information from these pictures including body condition, infestation of cyamids, presence of fishing gear entanglements, rake marks and skin condition. The authors also used information of age and calving of the individuals. A model using these data was constructed and a more complete scenario of health and movement patterns of individuals and the populations were estimated. Survival rates of each individual were also estimated using this model. This is an example of a well-informed model and is important to notice that a model is only as good as the data you put into the model.

Using this model, Schick et al. documented variations in annual spatial distribution patterns between sexes (Fig. 1). For example, females arrive earlier to the BOF region than males, and have greater estimated transitions to SEUS region at the end of the year. It is also possible to see that there is a lack of information for the region MIDA, characterizing another advantage of modeling since it can highlight areas where effort should be increased.

Figure 1: Movement transition estimates from North to South regions in the western Atlantic Ocean for male and female right whales over the course of a year. Size of the circles in each region at each month corresponds to the actual number of right whales observed. Lines connecting regions indicate probability of transition. Magnitude of probability is depicted by line thickness. (NRTH: North region; BOF: Bay of Fundy; JL: Jeffreys Ledge; GOM: Gulf of Maine; RB: Roseway Basin; NE: Northeast; GSC: Great South Channel; MIDA: Mid-Atlantic; and SEUS: Southeastern US). Source: Figures 5 and 6 from Schick et al. 2013.
Figure 1: Movement transition estimates from North to South regions in the western Atlantic Ocean for male and female right whales over the course of a year. Size of the circles in each region at each month corresponds to the actual number of right whales observed. Lines connecting regions indicate probability of transition. Magnitude of probability is depicted by line thickness.
(NRTH: North region; BOF: Bay of Fundy; JL: Jeffreys Ledge; GOM: Gulf of Maine; RB: Roseway Basin; NE: Northeast; GSC: Great South Channel; MIDA: Mid-Atlantic; and SEUS: Southeastern US).
Source: Figures 5 and 6 from Schick et al. 2013.

 

When the model is applied to individual whales, the authors were able to estimate survival and health rates across the whale’s life-span (Fig. 2). Whale #1077 was a rarely seen adult male, with a sparse sighting history over 25 years. The last sighting of this whale was in 2004 when its health status was poor due to a poor body condition. According with his condition in the last sighting, the model predicted a high decrease in his health over time and since the whale was not seen for more than six years, so was presumed dead, following the standards set by the North Atlantic Right Whale Consortium.

Figure 2: Health time series for whale #1077. Time series of health observations for body condition, cyamids, entanglements, rake marks and skin condition (circles), estimates with uncertainty of health (thick line and dashed lines) and estimates of survivals (height rectangle at bottom). Photographic observations are color and size coded by class (three categories for body condition: green is good, orange is fair and purple is poor; and two categories for skin condition: green is good and orange is poor). Source: Figure 11 from Schick et al. 2013.
Figure 2: Health time series for whale #1077. Time series of health observations for body condition, cyamids, entanglements, rake marks and skin condition (circles), estimates with uncertainty of health (thick line and dashed lines) and estimates of survivals (height rectangle at bottom). Photographic observations are color and size coded by class (three categories for body condition: green is good, orange is fair and purple is poor; and two categories for skin condition: green is good and orange is poor).
Source: Figure 11 from Schick et al. 2013.

 

As I begin data collection for my thesis project to examine gray whale health along the Oregon coast in relation to ocean noise and inter-annual variability, I am considering how to apply a similar modeling approach to enhance our understanding of what influences individual gray whale health and also connect pathways between our resightings.

The marine environment is constantly changing, across space and over time. Therefore, distinguishing what contributes most significantly to whale stress levels can be very challenging. However, through a model we may be able to decipher the contributions of several factors to individual stress among the many parameters we are monitoring: ocean noise, prey availability, environmental patterns, season, sex, age, geographic area, reproductive status and body condition.

Marine ecology is a complex world, and sometimes complex models are needed to help us to find patterns in our data! Once estimates of these ecological processes are created and different hypotheses are explored, information can then be provided to conservation and environmental management to aid decision making, such as defining thresholds of ambient ocean noise levels in the vicinity of baleen whales.

 

Bibliographic Reference:

Schick RS, Kraus SD, Rolland RM, Knowlton AR, Hamilton PK, Pettis HM, Kenney RD and Clark JS. 2013. Using Hierarchical Bayes to Understand Movement, Health, and Survival in the Endangered North Atlantic Right Whale. PLOS ONE 8(6):e64166.

Are Oregon gulls trash birds?

By Stephanie Loredo, MSc student

“Violent” and “greedy” are words often used to describe gulls in populous areas where food or trash are readily available.  Humans are used to seeing gulls in parking lots, parks, and plazas eating left over crumbs. Many people have even experienced menacing gulls ripping food away from their hands. Anecdotes like these have caused people to have negative perceptions of gulls. But could the repulsive attitude towards these birds be changed with evidence that not all gulls are the same? Well, Oregon may be home to an odd bunch.

Last year, the Seabird Oceanography Lab in conjunction with the GEMM Lab began putting GPS trackers on western gulls (Laurus occidentalis) off the Oregon Coast. One of the goals was to determine where gulls scavenge for food while raising chicks: at sea or on land in association with humans. We were particularly interested to see if western gulls in Oregon would behave similarly to western gulls in California, some of which make trips to the nearest landfill during the breeding season to bring not only food but also potentially harmful pathogens back to the colony.

During the 2015 breeding season, 10 commercially brand ‘i-gotU’ GPS data loggers were placed on gulls from ‘Cleft-in-the-Rock’ colony in Yachats, Oregon. The tags provided GPS locations at intervals of two minutes that determined the general habitat use areas (marine vs. terrestrial). After a two-week period, we were able to recapture six birds, remove tags, and download the data.   We found that these western gulls stayed close to the colony and foraged in nearby intertidal and marine zones (Figure 1). Birds showed high site faithfulness by visiting the same foraging spots away from colony. It was interesting to see that inland habitat use did not extend past 1.3 miles from shore and the only waste facility within such boundaries did not attract any birds (Figure 1). Tagged birds never crossed the 101 Highway, but rather occurred at beaches in state parks such as Neptune and Yachats Ocean Road.

Figure 1. Tracks from 6 western gulls, each color representing a unique bird, from the Cleft-in-the-Rock colony carrying micro-GPS units.
Figure 1. Tracks from 6 western gulls, each color representing a unique bird, from the Cleft-in-the-Rock colony carrying micro-GPS units.

While it is hard to determine whether gulls avoided anthropogenic sources of food at the beach, preliminary analysis shows a high percentage of time spent in marine and intertidal habitat zones by half of the individuals (Figure 2). At a first glance, this is not as much as it seemed on the tracking map (Figure 1), but it nonetheless confirms that these gulls seek food in natural areas. Moreover, time spent at the colony is represented as time spent on coastal habitat on the graph, and thus “coastal” foraging values are over represented. To get a more exact estimate of coastal habitat use, future analysis will have to exclude colony locations and distinguish foraging versus resting behaviors.

Figure 2. Bar plot of the percentage of time spent in three distinct habitats for each gull carrying a GPS unit. The three-letter code represents the unique Bird ID.
Figure 2. Bar plot of the percentage of time spent in three distinct habitats for each gull carrying a GPS unit. The three-letter code represents the unique Bird ID.

‘Cleft-in-the-Rock’ is unique and its surroundings may explain why there was high foraging in intertidal and marine zones rather than within city limits. (The Cleft colony can also be tricky to get to, with a close eye on the tide at all times – See video below).  The colony site is close to the Cape Perpetua Scenic Area and surrounded by recently established conservation zones: the Cape Perpetua Marine Reserve Area, Marine Protected Area, and Seabird Protected Area (Figure 1).  Each of these areas has different regulatory rules on what is allowed to take, which you can read about here. The implication of these protected areas in place means there is more food for wildlife!  Moreover, the city of Yachats has a small population of 703 inhabitants (based on 2013 U.S Census Bureau). The small population allows the city to be relatively clean, and the waste facility is not spewing rotten odors into the air like in many big cities such as Santa Cruz (population of 62,864) where our collaborative gull study takes place. Thus, in Yachats, there is more limited odor or visual incentive to attract birds to landfills.

Field crew descends headland slope to reach ‘Cleft-in-the-Rock’ gull island in Yachats, OR (colony can be seen in distance across the water). The team must wear wetsuits and carry equipment in dry bags for protection during water crossing.

In order to determine whether gull habitat use in Yachats is a trend for all western gulls in Oregon, we need to track birds at more sites and for a longer time. That is why during the breeding season of 2016, we will be placing 30 new tags on gulls and include a new colony into the study, ‘Hunters Island’. The new colony is situated near the Pistol River, between Gold Beach and Brookings in southern Oregon, and it is part of the Oregon Islands Wildlife Refuge.

We will have 10 ‘i-gotU’ tags (Figure 3) and 20 CATS tags (Figure 4), the latter are solar powered and can collect data for several weeks, months, and hopefully even years! These tags do not need to be retrieved for data download; rather data can be accessed remotely, providing minimal disturbance to the gulls and colony. With long-term data, we can explore further into the important feeding areas for western gulls, examine rates of foraging in different habitats, and determine how extensive intertidal and marine foraging is throughout the year.

Figure 3. Taping an i-gotU tag for temporary attachment on the tail feathers of a gull.
Figure 3. Taping an i-gotU tag for temporary attachment on the tail feathers of a gull.

 

Figure 4. Rehearsing the placement and harness attachment of a CATS tag which must be secured on the bird‘s back, looping around the wings and hips.

We are excited to kick start our field season in the next couple of weeks and see how well the new tags work. We know that some questions will be solved and many new questions will arise; and we cannot wait to start this gull-filled adventure!

References

Osterback, A.M., Frechette, D., Hayes, S., Shaffer, S., & Moore, J. (2015). Long-term shifts in anthropogenic subsidies to gulls and implications for an imperiled fish. Biological Conservation, 191: 606–613.

Grad School Headaches

By Florence Sullivan, MSc student GEMM lab

Over the past few months I have been slowly (and I do mean SLOWLY – I don’t believe I’ve struggled this much with learning a new skill in a long, long time) learning how to work in “R”.  For those unfamiliar with why a simple letter might cause me so much trouble, R is a programming language and free software environment suitable for statistical computing and graphing.

My goal lately has been to interpolate my whale tracklines (i.e. smooth out the gaps where we missed a whale’s surfacing by inserting artificial locations).  In order to do this I needed to know (1) How long does a gap between fixes need to be to identify a missed surfacing? (2) How many artificial points should be used to fill a given gap?

The best way to answer these queries was to look at a distribution of all of the time steps between fixes.  I started by importing my dataset – the latitude and longitude, date, time, and unique whale identifier for each point (over 5000 of them) we recorded last summer. I converted the locations into x & y coordinates, adjusted the date and time stamp into the proper format, and used the package adehabitatLT  to calculate the difference in times between each fix.  A package known as ggplot2 was useful for creating exploratory histograms – but my data was incredibly skewed (Fig 1)! It appeared that the majority of our fixes happened less than a minute apart from each other. When you recall that gray whales typically take 3-4 short breathes at the surface between dives, this starts to make a lot of sense, but we had anticipated a bimodal distribution with two peaks: one for the quick surfacings, and one for the surfacings between 4-5 minutes dives. Where was this second peak?

Histogram of the difference in time (in seconds) between whale fixes.
Fig. 1.  Histogram of the difference in time (in seconds on x-axis) between whale fixes.

Sometimes, calculating the logarithm of one of your axes can help tease out more patterns in your data  – particularly in a heavily skewed distribution like Fig. 1. When I logged the time interval data, our expected bimodal distribution pattern became evident (Fig. 2). And, when I back-calculate from the center of the two peaks we see that the first peak occurs at less than 20 seconds (e^2.5 = 18 secs) representing the short, shallow blow intervals, or interventilation dives, and that the second peak of dives spans ~2.5 minutes to  ~5 minutes (e^4.9 = 134 secs, e^5.7 = 298 secs). Reassuringly, these dive intervals are in agreement with the findings of Stelle et al. (2008) who described the mean interval between blows as 15.4 ± 4.73 seconds, and overall dives ranging from 8 seconds to 11 minutes.

Fig. 2. Histogram of the log of time difference between whale fixes.
Fig. 2. Histogram of the log of time difference between whale fixes.

So, now that we know what the typical dive patterns in this dataset are, the trick was to write a code that would look through each trackline, and identify gaps of greater than 5 minutes.  Then, the code calculates how many artificial points to create to fill the gap, and where to put them.

Fig. 3. A check in my code to make sure the artificial points are being plotted correctly. The blue points are the originals, and the red ones are new.
Fig. 3. A check in my code to make sure the artificial points are being plotted correctly. The blue points are the originals, and the red ones are new.

One of the most frustrating parts of this adventure for me has been understanding the syntax of the R language.  I know what calculations or comparisons I want to make with my dataset, but translating my thoughts into syntax for the computer to understand has not been easy.  With error messages such as:

Error in match.names(clabs, names(xi)) :

  names do not match previous names

Solution:  I had to go line by line and verify that every single variable name matched, but turned out it was a capital letter in the wrong place throwing the error!

Error in as.POSIXct.default(time1) :

  do not know how to convert ‘time1’ to class “POSIXct”

Solution: a weird case where the data was in the correct time format, but not being recognized, so I had to re-import the dataset as a different file format.

Error in data.frame(Whale.ID = Whale.ID, Site = Site, Latitude = Latitude,  :   arguments imply differing number of rows: 0, 2, 1

Solution: HELP! Yet to be solved….

Is it any wonder that when a friend asks how I am doing, my answer is “R is kicking my butt!”?

Science is a collaborative effort, where we build on the work of researchers who came before us. Rachael, a wonderful post-doc in the GEMM Lab, had already tackled this time-based interpolation problem earlier in the year working with albatross tracks. She graciously allowed me to build on her previous R code and tweak it for my own purposes. Two weeks ago, I was proud because I thought I had the code working – all that I needed to do was adjust the time interval we were looking for, and I could be off to the rest of my analysis!  However, this weekend, the code has decided it doesn’t work with any interval except 6 minutes, and I am lost.

Many of the difficulties encountered when coding can be fixed by judicious use of google, stackoverflow, and the CRAN repository.

But sometimes, when you’ve been staring at the problem for hours, what you really need is a little praise for trying your best. So, if you are an R user, go download this package: praise, load the library, and type praise() into your console. You won’t regret it (See Fig. 4).

Screenshot (74)
Fig. 4. A little compliment goes a long way to solving a headache.

Thank you to Rachael who created the code in the first place, thanks to Solene who helped me trouble shoot, thanks to Amanda for moral support. Go GEMM Lab!

Why do pirates have a hard time learning the alphabet?  It’s not because they love aaaR so much, it’s because they get stuck at “c”!

Stelle, L. L., W. M. Megill, and M. R. Kinzel. 2008. Activity budget and diving behavior of gray whales (Eschrichtius robustus) in feeding grounds off coastal British Columbia. Marine mammal science 24:462-478.

An update on Oregon’s sound sensitive marine mammal, the harbor porpoise.

By Amanda Holdman, M.S. Student

Marine renewable energy is developing at great speeds all around the world. In 2013, the Northwest Marine Renewable Energy Center (NMREC) chose Newport, Oregon as the future site of first utility-scale, grid-connected wave energy test site in the United States – The Pacific Marine Energy Center (PMEC). The development of marine energy holds great potential to help meet our energy needs – it is renewable, and it is predicted that marine energy sources could fulfill nearly one-third of the United States energy demands.

Wave energy construction in Newport could begin as early as 2017. Therefore, it is important to fully understand the potential risks and benefits of wave energy as the industry moves forward. Currently, there is limited information on wave energy devices and the potential ecological impacts that they may have on marine mammals and their habitats. In order to assess the effects of wave energy, pertinent information needs to be collected prior to the installation of the devices.

This is where I contribute to the wave energy industry in Oregon.

Harbor porpoise are a focal species when it comes to renewable energy management; they are sensitive to a range of anthropogenic sounds at very low levels of exposure and may show behavioral responses before other marine mammals, making them a great indicator species for potential problems with wave energy. Little is known about harbor porpoise in Oregon, necessitating the need to look at the fine scale habitat use patterns of harbor porpoise within the proposed wave energy sites.

I used two methods to study harbor porpoise presence and activity in coastal waters: visual boat surveys, and passive acoustic monitoring. Visual surveys have a high spatial resolution and a low temporal resolution, meaning you can conduct visual boat surveys over a wide area, but only during daylight hours. Whereas acoustic surveys have opposite characteristics; you can conduct surveys during all hours of the day, however, the range of the acoustic device is only a few hundred meters. Therefore, these methods work well together to gain complimentary information about harbor porpoise. These methods are crucial for collecting baseline data on harbor porpoise distribution, and providing valuable information for understanding, managing, and mitigating potential impacts.

Bi-monthly standard visual line-transect surveys were conducted for two full years (October 2013-2015), while acoustic devices were deployed May – October 2014. Field work ended last October, and since then, data analysis efforts have uncovered  seasonal, diel, and tidal patterns in harbor porpoise occurrence and activity.

Harbor porpoises in Oregon are thought to be seasonally migratory. With the onset of spring, coinciding with the start of the upwelling season, porpoise are thought to move inshore and abundance increases into the summer. Most births also occur during the late spring and summer. With the return of winter, porpoise are thought to leave the coastal waters and head out to the deeper waters (Dohl 1983, Barlow 1988, Green et al. 1992).

Results from my data support this seasonal trend. Both visual survey and acoustic recording data document the general pattern of peak porpoise presence occurring in the summer months of June and July, with a gradual decline of detections into the fall (Fig. 1 & 2).

1

Figure 1: Overall, from our acoustic surveys we see a large increase from May to June, suggesting the arrival of harbor porpoise to coastal waters. From July, we see a slow decline into the fall months, suggestive of harbor porpoise moving offshore.

2

Figure 2: Our data from visual surveys mimic those of our acoustic surveys. We see a large increase of porpoises from May to June and then a decline into the fall. We had very low survey effort in July, due to rough seas.  If we were able to survey more, it is likely we would have seen more harbor porpoise during this time.

Using acoustic recorders, we are able to get data on harbor porpoise occurrence throughout all hours of the day, regardless of weather conditions. We deployed hydrophones in two locations – one in a near-shore REEF habitat located 4 km from shore, and the second in the middle of the South Energy Testing Site (SETS) 12 km off-shore. These two sites differ in depth and habitat type. The REEF habitat is 30 m deep and has a rocky bottom as a habitat type, while SETS is 60 m deep and has a sandy bottom. When we compare the two sites (Figure 3), we can see that harbor porpoise have a preference for the REEF site.

Additionally, we are also able to get some indices of behavior from acoustic recordings. Equivalent to sonar or radar, marine mammals use echolocation (high frequency sounds) to communicate and navigate. Marine mammals, specifically odonotocetes, also use echolocation to locate prey at depth when there is very little or no light. Porpoises use a series of clicks during their dives, and as the porpoise approach their prey, the clicks become closer and closer together so they sound like a continuous buzz. When studying echolocation patterns in odontocetes we typically look at the inter-click-intervals (ICIs) or the time between clicks. When ICIs are very close together (less than 10 ms apart) it is considered a foraging behavior or a buzz. Anything greater than 10 ms is classified as other (or clicks in this figure).

Click_Buzz_bargraph.

Figure 3: We see harbor porpoise clicks were detected about 27% of the time at the REEF, but only 18% at SETS. Potential feeding was also higher at the REEF site (14%) compared to (4%) at SETS.

Not only did we find patterns in foraging behavior between the two sites, we also found foraging patterns across diel cycles and tidal cycles:

  1. We found a tendency for harbor porpoise to forage more at night (Figure 4).
  2. The diel pattern of harbor porpoise foraging is stronger at the SETS than the REEF site (Figure 4). This result may be due to the prey at the SETS (sandy bottom) exhibiting vertical migration with the day and night cycles since prey there do not have alternative cover, as they would in the rocky reef habitat.
  3. At the reef site, we see a relationship between increased foraging behavior and low tide (Figure 5).

ratio

Figure 4: When analyzing data for trends in foraging behavior across different sites and diel cycles, we use a ratio of buzzes to clicks, so that we incorporate both echolocation behaviors in one value. This figure shows us that the ratio of buzzes to clicks is pretty similar at the REEF site across diel periods, but there is more variation at the SETS site, with more detections at night and during sunrise.

blog_5

Figure 5: Due to the circular nature of tides rotating between high tide and low tide, circular histograms help to observe patterns. In this figure, we see a large preference for harbor porpoise to feed during low tide. We are unclear why harbor porpoise may prefer low tide, but the relationship may be due to minimal current movement that could enhance feeding opportunities for porpoises.

Overall, the combination of visual surveys and passive acoustic monitoring has provided high quality baseline data on harbor porpoise occurrence patterns. It is results like these that can help with decisions regarding wave energy siting, operation and permitting off of the Oregon Coast.

REFERENCES

Barlow, J. 1987. Abundance estimation for harbor porpoise (Phocoena phocoena) based on ship surveys along the coasts of California, Oregon and Washington. SWFC Administrative Report LJ-87-05. Southwest Fishery Center, La Jolla, CA. 36pp.

Dohl, T.P., Guess, R.C., Dunman, M.L. and Helm, R.C. 1983, Cetaceans of central and northern California, 1980-83: status, abundance, and distribution. Final Report to the Minerals Management Service, Contract 14-12-0001-29090. 285pp.

Green, G.A., Brueggeman, J. J., Grotefendt, R.A., Bowlby, C.E., Bonnel, M. L. and Balcomb, K.C. 1992. Cetacean distribution and abundance off Oregon and Washington, 1989-1990. Chapter 1 In Oregon and Washington Marine Mammal and Seabird Surveys. Ed. By J. J. Brueggeman. Minerals Management Service Contract Report 14-12-0001-30426.