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?

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.

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.

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).

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).

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.

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).

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).

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.

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.

## Smile! You’re on Camera!

By Florence Sullivan, MSc. Student, GEMM Lab

Happy Spring everyone!  You may be wondering where the gray whale updates have been all winter – and while I haven’t migrated south to Baja California with them, I have spent many hours in the GEMM Lab processing data, and categorizing photos.

You may recall that one of my base questions for this project is:

Do individual whales have different foraging strategies?

In order to answer this question, we must be able to tell individual gray whales apart. Scientists have many methods for recognizing individuals of different species using tags and bands, taking biopsy samples for DNA analysis, and more. But the method we’re using for this project is perhaps the simplest: Photo-Identification, which relies on the unique markings on individual animals, like fingerprints.  All you need is a camera and rather a lot of patience.

Bottlenose dolphins were some of the first cetaceans to be documented by photo-identification.  Individuals are identified by knicks and notches in their fins. Humpback whales are comparatively easy to identify – the bold black and white patterns on the underside of their frequently displayed flukes are compared.  Orcas, one of the most beloved species of cetaceans, are recognized thanks to their saddle patches – again, unique to each individual. Did you know that the coloration and shape of those patches is actually indicative of the different ecotypes of Orca around the world? Check out this beautiful poster by Uko Gorter to see!

Gray whale photo identification is a bit more subtle since these whales don’t have dorsal fins and do not show the undersides of their fluke regularly.  Because gray whales can have very different patterns on either side of their body, it is also important to get photos of both their right and left sides, as well as the fluke, to be sure of recognizing an individual if it comes around again.   When taking photos of a gray whale, it’s a good idea to include the dorsal hump, where the knuckles start as it dives, as an easy indicator of which side of the body you are looking at when you’re trying to match photos.  Some clues that I often use when identifying an individual include the placement of barnacles, and patterns of pigmentation and scars.  You can see that patience and a talent for pattern recognition come in handy for this sort of work.

While we were in the field, it was important for my team to quickly find reference features to make sure we were always tracking the same whale. If you stopped by to visit our field station, you may have heard use saying things like “68 has white on both fluke-tips”, “70 has a propeller scar on the left side”,  “the barnacles on 54’s head looks like a polyp”, or “27 has a smiley face in front of the first knuckle left side.” Sometimes, if a trait was particularly obvious, and the whale visited our field station more than once, we would give them a name to help us remember them.  These notes were often (but to my frustration, not always!) recorded in our field notebook, and have come in handy this winter as I have systematically gone through the 8000+ photos we took last summer, identifying each individual, and noting whenever one was a repeat visitor. With these individuals labeled, I can now assess their level of behavioral and distribution consistency within and between study sites, and over the course of the summer.

Why don’t you try your luck?  How many individuals are in this photoset? How many repeats?  If I tell you that my team named some of these whales Mitosis, Smiley, Ninja and Keyboard can you figure out which ones they are?

Keep scrolling for the answer key ( I don’t want to spoil it too easily!)

There are 7 whales in this photoset. Smiley and Keyboard both have repeat shots for you to find, and Smiley even shows off both left and right sides.

1. Whale 18 – Mitosis
2. Whale 70 -Keyboard
3. Whale 23 -Smiley
4. Whale 68 – Keyboard
5. Whale 27 -Smiley
6. Whale 67
7. Whale 36 -Ninja
8. Whale 60 – “60”
9. Whale 38 – has no nickname even if we’ve seen it 8 times! Have any suggestions? leave it in the comments!
10. Whale 55 – Smiley

## On learning to Code…

By Amanda Holdman, MSc student, Dept. Fisheries and Wildlife, OSU

I’ve never sworn so much in my life. I stared at a computer screen for hours trying to fix a bug in my script. The cause of the error escaped me, pushing me into a cycle of tension, self-loathing, and keyboard smashing.

The cause of the error? A typo in the filename.

When I finally fixed the error in my filename and my code ran perfectly – my mood quickly changed. I felt invincible; like I had just won the World Cup. I did a quick victory dance in my kitchen and high-fived my roommate, and then sat down and moved on the next task that needed to be conquered with code. Just like that, programming has quickly become a drug that makes me come back for more despite the initial pain I endure.

I had never opened a computer programming software until my first year of graduate school. Before then Matlab was just the subject of a muttered complaint by my college engineering roommate. As a biology major, I blew it off as something (thank goodness!) I would never need to use. Needless to say, that set me up for a rude awakening just one year later.

The time has finally come for me to, *gulp*, learn how to code. I honestly think I went through all 5 stages of grief before I realized I was at the point where I could no longer put it off.

By now you are familiar with the GEMM Lab updating you with photos of our charismatic study species in our beautiful study areas. However, summer is over. My field work is complete, and I’m enrolled in my last course of my master’s career. So what does this mean? Winter. And with winter comes data analysis. So, instead of spending my days out on a boat in calm seas, watching humpbacks breach, or tagging along with Florence to watch gray whales forage along the Oregon coast, I’ve reached the point of my graduate career that we don’t often tell you about: Figuring out what story our data is telling us. This stage requires lots of coffee and patience.

However, in just two short weeks of learning how to code, I feel like I’ve climbed mountains. I tackle task after task, each allowing me to learn new things, revise old knowledge, and make it just a little bit closer to my goals. One of the most striking things about learning how to code is that it teaches you how to problem solve. It forces you to think in a strategic and conceptual way, and to be honest, I think I like it.

For example, this week I mapped the percent of my harbor porpoise detections over tidal cycles. One of the most important factors explaining the distribution and behavior of coastal marine mammals are tides. Tidal forces drive a number of preliminary and secondary oceanographic processes like changes in water depth, salinity, temperature, and the speed and direction of currents. It’s often difficult to unravel which part of the tidal process is most influential to a species due to the several covariates related to the change in tides , how inter-related those covariates are, and the elusive nature of the species (like the cryptic harbor porpoise). However, while the analysis is preliminary, if we map the acoustic detections of harbor porpoise over the tidal cycle, we can already start to see some interesting trends between the number of porpoise detections and the phases of the tide. Check it out!

Now, I won’t promise that I’ll be an excellent coder by the end of the winter, but I think I might have a good chance at being able to mark the “proficient” box next to Matlab and R on my first job application. Yet, whatever your reason for learning code – whether you are an undergraduate hoping to get ahead for graduate school, a graduate student hoping to escape the inevitable (like me), or just someone who thinks getting a code to work properly is a fun game – my advice to you is this:

Google first. If that fails, take mental breaks. Revisit the problem later. Think through all possible sources of error. Ask around for help. Then, when you finally fix the bug or get the code to work the way you would like it to, throw a mini-party. After it’s all over, take a deep breath and go again. Remember, you are not alone!

Happy coding this winter GEMM Lab readers – and I wish you lots of celebratory dancing!

## Gray whale field work wrap-up; sea you later

Hello everyone,

Florence here with an update about the final numbers from this summer’s gray whale field season.

For folks just hearing about the project, my team of interns and I spent the summer alternating between study sites at Depoe Bay and Port Orford to conduct fine-scale focal follows of gray whales foraging in near-shore Oregon waters using a theodolite.  That is to say, we gathered 10,186 ‘marks’ or ‘locations’ where whales came to the surface, and by connecting the dots, we are able to create tracklines and analyze their movement patterns.  The idea is to document and describe gray whale foraging behavior in order to answer the questions: Are there patterns in how the whales use the space? Is there a relationship between foraging success and proximity to kelp beds? Do behaviors vary between individuals, location, or over time during the season?

While at our study sites, we often received questions about vessel disturbance on the whale’s behavior. Over the course of the summer, we saw whales completely ignore boats, approach boats, and actively avoid boats. Therefore, we documented these vessel interactions in order to ask questions such as: Does vessel disturbance alter behavior? How close is too close? Does the potential for vessel disturbance vary depending on (1) size of motor, (2) speed of approach, (3) type of vessel, i.e. kayak, fishing boat, tour boat, (4) the number of vessels already in the area, (5) amount of time a vessel has been following a whale, (6) time of season, (7) the presence of a calf or other whales? The end goal, once the data have been analyzed, is to bring our results to local vessel operators (commercial and recreational) and work together to write reasonable, effective, and scientifically informed guidelines for vessel operations in the presence of gray whales.

And now, the numbers you’ve all been waiting for, here is the tally of our data collection this summer:

 Boiler Bay Graveyard Point Humbug Mountain Whales total 80 73 28 Boats total 307 105 7 Total survey time (HH:MM:SS) 122:22:41 72:49:17 50:22:35 Total survey time with whales (HH:MM:SS) 64:47:54 80:39:57 22:59:00 Total Marks 4744 4334 1108

Table 1. Summary of survey effort for gray whale foraging ecology field season summer 2015

So, what does this all mean?  Well, the unsatisfying answer is of course: we don’t know yet. However, it is my job to find out!  I will spend the fall and winter processing data, writing and running behavioral models, communicating my successes and frustrations, and finally presenting my results to the community.

The human eye is well adapted to pick out patterns. Test yourself – what trends can you see in these images?  Are there areas that the whales seem to prefer over other areas?  In the Port Orford images with Keyboard & our kelp patches, does our theory of a relationship between whale presence and kelp patches seem valid?

This field season would not have been possible without the help of some truly excellent people.  Thank you Cricket and Justin and Sarah for making up the core of Team Ro”buff”stus. It was a pleasure working with you this summer.  Thank you to guest observers and photographers Era, Steven, Diana, Cory, Kelly, Shea and Brittany for filling in when we needed extra help! Thank you to our support network down in Port Orford: Tom, Tyson and the team at the Port Orford Field Station – we appreciate the housing and warm welcome, and to Jim and Karen Auborn and the Port of Port Orford for allowing us access to such a fantastic viewing location. Thank you to Oregon State Parks for allowing us access to the field sites at Boiler Bay and Humbug. Finally, thank you to Depoe Bay Pirate Coffee Company for keeping us warm and caffeinated on many foggy, cold early mornings. This work was funded by the William and Francis McNeil Fellowship Award, the Wild Rivers Coast Alliance, and the American Cetacean Society: Oregon Chapter.

Fair winds,

Florence

## Not Everyday is Gray (just most of them)

As Amanda explains quite nicely in her previous blog post, research is not always glamorous, and we don’t always see the species we’ve come out to the field to study.  However, that doesn’t mean that there aren’t other cool species out there to spot!  Here are some common (and uncommon) visitors to some of our research sites this summer.

Also, if you continue to the bottom, we’ve included some cool videos of (1) gray whale sharking behaviour, (2) Gray whale swimming (top down full body view), and what it looks and sounds like when we’re doing one of our close-in focal follows. Enjoy!

If you remember a few weeks ago, we shared photos of gray whale “sharking” behaviour.  Well, now we have video!  Enjoy:

Here’s what it looks like from the top of Graveyard Bluff when a whale swims by below us!

We get really excited by this behavior because its positive proof that the whales are successfully foraging!

and here is a fluke!

We’ll be back soon with more updates from Port Orford.

Fair winds,

Florence & the rest of Team Ro”buff”stus

## We need all the “Kelp” we can get!

Hello from Hatfield Marine Science Center! This is Justin bringing you the latest and greatest in Gray Whale news. But first, let me fill you folks in with some info about me.  I am an undergraduate student, transitioning into my senior year, with Oregon State University’s Fisheries and Wildlife Department. In addition to my major, I am also minoring in statistics; crazy right? I have hopes and dreams of working in Marine Ecology, and I believe working on this Gray Whale project is a fine start! Which means, this summer, I have had the fortunate opportunity to work alongside the lovely Florence van Tulder, the mastermind behind the project, as well as Cricket and Sarah, the other two charismatic interns.

As we were wrapping up our two week stint in Port Orford, We observed the Gray Whales exhibiting some interesting behavior; they seemed to move from kelp patch to kelp patch, almost as if they were searching for something. What could be hiding under the luscious stands of Nereocystis luetkeana, otherwise known as bull kelp? Well, with the presence of defecation ( whale droppings) left behind from diving whales near many of the floating kelp patches, one culprit came to mind- mysid shrimp. Mysid shrimp are believed to be a primary prey source of the Gray whales.

Naturally, my curiosity got the best me and I ended up spending hours on end conducting literature searches and looking for bathymetry maps, thanks to Florence. All joking aside, I asked Florence if we could use our fancy Theodolite to assess or roughly map the distribution of the kelp patches. We would create polygonal shapes of the kelp on a map and observe how the whales move with respect to the kelp. The idea being, to get a better of picture of the relationship between the whales and the kelp, if any relationship exists at all. It is still a work in progress, due to our survey sites getting all kinds of “fogged” up. When the kinks are worked out and we have some useful visual data, we will post an awesome photo.

Port Orford didn’t just bring us sweet whales, it brought the heat! Temperatures were up to almost the nineties the last week in July! We beat the heat with plenty of hydration and sun block and the predicable wind patterns became a savior on those sweltering days giving us temporary relief.  The heat seemed to tease out other critters as well. We saw a variety of birds, from turkey vultures, Peregrine Falcons, Ospreys, Bald Eagles, and even Egrets!  In the water we saw baby Harbor seals, and some bonus River Otters.

In more recent news, August 8th marked our first full month of surveying between our two whale hotspots. However, the term “hotspot” doesn’t always seem to be fitting. This past week has been a tough one for the team and I up in Boiler Bay due to less than optimal weather conditions and our survey site has been exposed to an abnormal cycle of fog. Our friendly “neighborhood” grays have been a bit sparse, and yet, we have had Humpback Whales grace us with their presence and these whales have been spotted during several survey days this week! ( In the tradition of opportunistic data, we even tracked one of them.)

This summer has been very fun because not only do we get to watch whales every day, but when we are in Boiler Bay, we have the opportunity to meet fascinating people from all over the world! The positive support for the project coming from the community is quite a nice touch to our days in the field. If you are ever in the neighborhood, stop by and say hello, maybe share a whale’s tale or two!

## Gray Whale Goofs

Hello there!  Florence here, signing in from Newport.  We had a fantastic trip south to Port Orford, and tracked another 53 whales bringing our season total up to 117 so far! This morning, we were back out at Boiler Bay and spent 5 hours staring at empty water – in keeping with the theme of this post, field work does not always go as planned.

Our two study areas couldn’t be more different.  At the Boiler Bay State Wayside, we are approximately 18 meters off the water.  In Port Orford, we are perched on the side of a 63 meter tall cliff. This extra height greatly increases our range and accuracy as well as changing the angle of our photography and the type of photo analysis we can do.  We’re quite excited to have a top down view of our whales, because the photos we are capturing will allow us to use certain photogrammetry techniques to measure the length and girth of the individuals.  With luck, when we compare the photos from the beginning of the season (now) to the end of our study (September) we may be able to see a change in the height of the post-cranial fat deposit, which would indicate a successful foraging season.  Gray whales do not eat from the beginning of their southward migration, through the breeding and calving season, until they reach productive foraging grounds at the end of their northward migration.  This means that all their sustenance for 6+ months is derived from their summer foraging success.  Did you know that they even generate their own water through an oxidation reaction which creates ‘metabolic water’ from their blubber stores?  So it will be rather fantastic if we manage to measure the change in whale body condition over the course of the summer – particularly if we are able to spot any mother-calf pairs who will have had an especially grueling journey north.

So, while our photo database is advancing nicely, technical difficulties are to be expected when you’re in the field, and sometimes, troubleshooting takes longer than you would like it to.  This evening, let me introduce you to the elusive species known as ‘the Chinese land whale.’  It is a very rare breed which spontaneously generates itself from misaligned computer files.

When the theodolite beeps as we ‘mark’ a whale, a pair of horizontal and vertical angles are getting sent from the machine to a program called ‘Pythagoras’ on the laptop. Given our starting coordinates and a few other variables, the program auto-calculates for us the latitude and longitude of that whale.  While we hoped it would be a simple matter to upload these coordinates to Google Earth to visualize the tracklines, it turns out that Pythagoras stores the East/West hemisphere information in a separate column, so if we just plot the raw numbers, our whale tracks end up in the middle of a field in rural China! Hence, the rare ‘Chinese land whale’.  Now that we know the trick, it is not so difficult to fix, but we were quite surprised the first time it happened!

Of course, that is not the only thing that has gone wrong with visualizing the tracklines.  When we first got to Graveyard Point survey site, it turns out that we had set our azimuth (our reference angle) the wrong direction from true north, so all our whales seemed to be foraging near the fish and chips restaurant in the middle of town.

After discovering that in order to rotate something 180degrees, you simply need to alter the azimuth angle by 90degrees, (we’re still not sure why this is working), the whales left the fish and chips to us and returned to the harbor.  Anyways, now that we’ve figured out these glitches, we can focus on identifying individual whales, and figuring out which track-lines might be repeat visitors.

In other outreach news, the OSU media department came out to the field and interviewed us a few weeks ago (on a day that the theodolite and computer were refusing to talk to each other due to a faulty connector cable – which is always delightful when one is trying to showcase research in progress). The resulting article has been posted should you wish to take a look:

http://oregonstate.edu/ua/ncs/archives/2015/aug/researchers-studying-oregon%E2%80%99s-%E2%80%9Cresident-population%E2%80%9D-gray-whales

Until next time,

Team Ro”buff”stus

## Following Tracks: A Summer of Research in Quantitative Ecology

**GUEST POST** written by Irina Tolkova from the University of Washington.

R, a programming language and software for statistical analysis, gives me an error message.

I mull it over. Revise my code. Run it again.

Hey, look! Two error messages.

I’m Irina, and I’m working on summer research in quantitative ecology with Dr. Leigh Torres in the GEMM Lab. Ironically, as much as I’m interested in the environment and the life inhabiting it, my background is actually in applied math, and a bit in computer science.

(Also, my background is the sand dunes of Florence, OR, which are downright amazing.)

When I mention this in the context of marine research, I usually get a surprised look. But from firsthand experience, the mindsets and skills developed in those areas can actually be very useful for ecology. This is partly because both math and computer science develop a problem-solving approach that can apply to many interdisciplinary contexts, and partly because ecology itself is becoming increasingly influenced by technology.

Personally, I’m fascinated by the advancement in environmentally-oriented sensors and trackers, and admire the inventors’ cleverness in the way they extract useful information. I’ve heard about projects with unmanned ocean gliders that fly through the water, taking conductivity, temperature, depth measurements (Seaglider project by APL at the University of Washington), which can be used for oceanographic mapping. Arrays of hydrophones along the coast detect and recognize marine mammals through bioacoustics (OSU Animal Bioacoustics Lab), allowing for analysis of their population distributions and potentially movement. In the GEMM lab, I learned about light and small GPS loggers, which can be put on wildlife to learn about their movement, and even smaller lighter ones that determine the animal’s general position using the time of sunset and sunrise. Finally, scientists even made artificial nest mounds which hid a scale for recording the weight of breeding birds — looking at the data, I could see a distinctive sawtooth pattern, since the birds lost weight as they incubated the egg, and gained weight after coming home from a foraging trip…

On the whole, I’m really hopeful for the ecological opportunities opened up by technology. But the information coming in from sensors can be both a blessing and a curse, because — unlike manually collected data — the sample sizes tend to be massive. For statistical analysis, this is great! For actually working with the data… more difficult. For my project, this trade-off shows as R and Excel crash over the hundreds of thousands of points in my dataset… what dataset, you might ask? Albatross GPS tracking data.

In 2011, 2012, and 2013, a group of scientists (including Dr. Leigh!) tagged grey-headed albatrosses at Campbell Island, New Zealand, with small GPS loggers. This was done in the summer months, when the birds were breeding, so the GPS tracks represent the birds’ flights as they incubated and raised their chicks. A cool fact about albatrosses: they only raise one chick at a time! As a result, the survival of the population is very dependent on chick survival, which means that the health of the albatrosses during the breeding season, and in part their ability to find food, is critical for the population’s sustainability. So, my research question is: what environmental variables determine where these albatrosses choose to forage?

The project naturally breaks up into two main parts.

• How can we quantify this “foraging effort” over a trajectory?
• What is the statistical relationship between this “foraging effort metric” and environmental variables?

Luckily, R is pretty good for both data manipulation and statistical analysis, and that’s what I’m working on now. I’ve just about finished part (1), and will be moving on to part (2) in the coming week. For a start, here are some color-coded plots showing two different ways of measuring the “foraging value” over one GPS track:

Most of my time goes into writing code, and, of course, debugging. This might sound a bit dull, but the anticipation of new results, graphs, and questions is definitely worth it. Occasionally, that anticipation is met with a result or plot that I wasn’t quite expecting. For example, I was recently attempting to draw the predicted spatial distribution of an albatross population. I fixed some bugs. The code ran. A plot window opened up. And showed this:

I stared at my laptop for a moment, closed it, and got some hot tea from the lab’s electronic kettle, all the while wondering how R came up with this abstract art.

All in all, while I spend most of my time programming, my motivation comes from the wildlife I hope to work for. And as any other ecologist, I love being out there on the Oregon coast, with the sun, the rain, sand, waves, valleys and mountains, cliff swallows and grey whales, and the rest of our fantastic wild outdoors.