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!


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 Conservation191: 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).


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.


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.

Wildlife of the Western Antarctic Peninsula

Erin Pickett, MS Student, Fisheries and Wildlife Department, OSU

This time last week, I was on a research vessel crossing the Drake Passage. The Drake extends from the tip of the Western Antarctic Peninsula to South America’s Cape Horn, and was part of the route I was taking home from Antarctica. Over the past three months I have been working on a long-term ecological research (LTER) project based out of Palmer Station, a U.S. based research facility located on Anvers Island.


While in Antarctica, I was working on the cetacean component of the Palmer LTER project, which I’ve described in previous blog posts. In lieu of writing more about what it is like to work and live on the Antarctic Peninsula, I thought I’d share some photos with you. Working on the water everyday while searching for whales provided me with many opportunities to photograph the local wildlife. I hope you’ll enjoy a few of my favorite shots.

Does ocean noise stress-out whales?

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


We’ve all been stressed. You might be stressed right now. Deadlines, demands, criticism, bills, relationships. It all adds up and can boil over: Chronic stress is linked with poor health, and also increased risk of illness (like cancer; Glaser et al. 2005, Godbout and Glaser 2006).

Biologically, stress manifests itself in vertebrate animals as variable levels of hormones, particularly the hormone cortisol. This academic term I am taking a class at OSU called “vertebrate endocrinology” where I am learning the different types of secretions and organs involved and the mechanisms of hormone action: how secretion, transport and signaling happen. These issues are important for my research because I will be examining stress levels in gray whales.

It may seem strange to study how stressed out a whale is, but there are reasons to believe that the long-term health of animals, including whales, is significantly related to their stress-hormone levels (Jepson et al. 2003, Cox et al. 2006, Wright et al. 2007, Rolland et al. 2012).

So what could be stressing out whales? Probably lots of things including food availability, predators and mating. However, we are mostly interested in describing how much added stress human activities in the oceans are causing, particularly from increased ocean noise. Ambient ocean noise levels have increased considerably over the last decades: according to IUCN, the increase was about 3 dB per decade over the past 60 years and now it seems to be increasing from 3 to 5 dB per decade (Simard and Spadone 2012).

Baleen whales communicate through low-frequency signals, reaching between 20 and 200 Hz and large ships generate noise in the same frequency band, which can mask whale vocalizations and potentially add stress (Rolland et al. 2012), especially in areas with high anthropogenic activities such as big ports, where an intense traffic occurs.

To get a sense of how noisy oceans can disrupt the acoustic lives of whales, the Monterey Institute has created an excellent interactive website where it is possible to listen to the whales’ vocalization and add other sources of sounds, like ship traffic, to compare the difference in noise levels.

We still do not understand the population level consequences of this increased ocean noise on whales, however it has been demonstrated that whales respond behaviorally to increased noise, including changes in vocalization rates and habitat displacement (Morton and Symonds 2002, Nowacek et al. 2007, Weilgart 2007, Rolland et al. 2012). In order to understand how acoustic disturbance may influence marine mammal populations, the population consequences of acoustic disturbance model (PCAD) was developed by the US National Academies of Sciences (National Resource Council 2005; We believe that examining stress levels in whales can provide a useful link between ocean noise and population-level impacts.

One previous study convincingly demonstrated the impacts of ocean noise on whale stress hormones due to the chance experiment caused by the shut-down of all air and vessel traffic during the days following September 11, 2001. Rolland et al. (2012) collected acoustic samples from the Bay of Fundy, Canada, and fecal samples from north Atlantic right whales in the area before and after September 11th over five years. The results found a 6 dB decrease in ocean noise (Figure 1) in the area after this date and an associated decrease in glucocorticoids (GC) metabolite (stress) levels in the whales (Figure 2 – highlighted in red).

Spectrum of the noise in different days along the Bay of Fundy, Canada. Source: Rolland et al. (2012)
Figure 1: Spectrum of the noise in different days along the Bay of Fundy, Canada.
Source: Rolland et al. (2012)


Figure 2
Figure 2: (a) Levels of fecal glucocorticoids metabolites (ng g -1) in North Atlantic right whales before (gray) and after (white) 11 September; (b) Yearly difference in median fecal GC levels. Source: Rolland et al. (2012)


For my PhD research we are attempting to assess how multiple, confounding factors contribute to stress levels in individual whales: prey availability, body condition, location, ocean noise, sex and sexual maturity. My advisor, Dr. Leigh Torres, developed a conceptual pathway diagram that illustrates potential scenarios caused by dichotomous levels of three major ecological components and their hypothesized influence on whale stress levels (Figure 3).


Figure 3
Figure 3: Conceptual pathway diagram of the hypothesized stress response of whales based on high or low levels of the three contributing ecological factors on stress that will be measured (developed by L. Torres).


From this diagram we can generate different hypotheses for our research to test. Distinct levels (high or low) of noise, prey availability, and health condition can lead to varied responses in the amplitude and duration of stress. We will measure prey availability through GoPro camera drops, hormone levels through fecal sample collection, and body condition through photogrammetry measurements of aerial images captured through an Unmanned Aerial System (aka drone).

Watch a video clip filmed via a UAS of a gray whale defecation event, and the field team collecting the sample for analysis.

Our study species is the gray whale, a non endangered species that regularly visits the Oregon coast during summer and fall months to feed, allowing accessibility to whales and repeatability of sighting individual animals. This ability to resight individual whales within and between years is important so that we can evaluate natural stress variability, thus allowing us to identify ‘stressful events’ and potential causes.

Overall, the main aim of my PhD research is to better understand how gray whale hormone levels vary across individual, time, body condition, location, and ambient noise environments. We may then be able to scale-up our results to better understand the population level impacts of elevated ocean noise on reproduction, distribution and abundance of whales.

We plan to study the correlation between stress levels in whales and ocean noise over many years to compile a robust database that allows us to identify how animals may be impacted physiologically at short- and long- term scales. These results will inform environmental management decisions regarding thresholds of ambient ocean noise levels in order to limit harm posed to baleen whales.


Bibliographic References:

Cox T, Ragen T, Read A, Vos E, Baird R, Balcomb K, Barlow J, Caldwell J, Cranford T, Crum L, D’Amico A, D’Spain G, Fernandez A, Finneran J, Gentry R, Gerth W, Gulland F, Hildebrand J, Houser D, Hullar T, Jepson P, Ketten D, MacLeod C, Miller P, Moore S, Mountain D, Palka D, Rommel S, Rowles T, Taylor B, Tyack P, Wartzok D, Gisiner R, Mead J, Benner L. 2006. Understanding the impacts of anthropogenic sound on beaked whales. Journal of Cetacean Research and Management 7:177-187.

Glaser R, Padgett DA, Litsky ML, Baiocchi RA, Yang EV, Chen M, Yeh PE, Klimas NG, Marshall GD, Whiteside T, Herberman R, Kiecolt-Glaser J, Williams MV (2005) Stress-associated changes in the steady-state expression of latent Epstein-Barr virus: implications for chronic fatigue syndrome and cancer. Brain Behav. Immun. 19(2):91-103.

Godbout JP, Glaser R. 2006. Stress-Induced Immune Dysregulation: Implications for Wound Healing, Infectious Disease and Cancer. J. Neuroimmune Pharm. 1:421-427.

Simard F, Spadone A (eds). 2012. An Ecosystem Approach to Management of Seamounts in the Southern Indian Ocean. Volume 2 – Anthropogenic Threats to Seamount Ecosystems and Biodiversity. Gland, Switzerland: IUCN. 64pp.

Jepson PD, Arbelot M, Deaville R, Patterson IAP, Castro P, Baker JR, Degollada E, Ross HM, Herraez P, Pocknell AM, Rodriguez F, Howie II FE, Espinosa A, Reid RJ, Jaber JR, Martin V, Cunningham AA, Fernandez A. 2003. Gas-bubble lesions in stranded cetaceans: Was sonar responsible for a spate of whale deaths after an Atlantic military exercise? Nature 425:575-576.

Morton AB, Symonds HK. 2002. Displacement of Orcinus orca (L.) by high amplitude sound in British Columbia, Canada. ICES Journal of Marine Science 59:71-80.

National Resource Council. 2005. Marine Mammal Populations and Ocean Noise: Determining When Noise Causes Biologically Significant Effects. National Academies Press, Washington D.C.

Nowacek DP, Thorne LH, Johnston DW, Tyack PL. 2007. Responses of cetaceans to anthropogenic noise. Mammal Rev. 37, 81–115.

Rolland RM, Parks SE, Hunt KE, Castellote M, Corkeron PJ, Nowacek DP, Wasser SK, Kraus SD. 2012. Evidence that ship noise increases stress in righ whales. Proc. R. Soc. B 279:2363-2368.

Weilgart LS. 2007 The impacts of anthropogenic ocean noise on cetaceans and implications for management. Can. J. Zool. 85, 1091–1116.

Wright AJ, Soto NA, Baldwin AL, Bateson M, Beale CM, Clark C, Deak T, Edwards EF, Fernández A, Godinho A. 2007. Do Marine Mammals Experience Stress Related to Anthropogenic Noise? International Journal of Comparative Psychology 20.


A porpoise-full lesson on cetacean identification

By Amanda Holdman, M.S. student

The rain is beginning to lighten, the heavy winds are starting  to dissipate, and the sun is beginning to shine. Seabirds are starting to fill the air and marine mammals are starting to fill the coastline, making this week a perfect time to learn about some of the small, cryptic cetaceans that consider the Oregon coast home year round.

While I was walking my dog on South Beach in Newport last week, I heard the mother of a small family point and shout that she had just seen an animal that she referred to as a “porpoise/dolphin/small whale.” Upon a second sighting of it, she ruled against the small whale and decided on a dolphin. In reality, she had just sighted a harbor porpoise.

Throughout the duration of my work with Oregon State studying the patterns of harbor porpoise occurrence, one of the most frequently asked questions I get is “What is the difference between a porpoise and a dolphin?”

Differentiating between a dolphin and porpoise is probably the most common identification mistake when it comes to cetaceans. Understandably, there is significant confusion between the two species. The words dolphin and porpoise were, colloquially, used as synonyms until the 1970’s. Unlike lions and tigers that are not only in the same family, but also the same genus, dolphins and porpoises are in different families, having diverged evolutionarily about 15 million years ago! Therefore, dolphins and porpoises are more distinct than lions and tigers. These differences span from head and fin shape, to behavior, group size and vocals.

Physical Differences

Most people are quite certain they are seeing a dolphin mainly because dolphins are more prevalent than porpoises; over 30 species of dolphins are known to exist, but only 6 porpoise species have been identified worldwide. Unless, you’ve seen dolphins and porpoises side by side, nose to fin, it is quite difficult to tell the difference at first glance. In the natural history of cetacean’s course at Oregon State, we are taught that the three main visual differences are in the shape of the teeth, snout, and dorsal fin. But in reality, the first two characteristics aren’t likely to help you spot them from shore. In addition to fin size, the behavior and group size is more likely to cue you in on what animal you are seeing. The picture below does a pretty good job summarizing their physical characteristics. Porpoise have a small triangle fin, while dolphins have more of a curved, pointy fin.

identificationDrawing by Mike Rock, 2009.

Size Differences

The lengths and widths of dolphins vary anywhere from 4 feet to 30 feet. Killer whales, the largest dolphin species and known predator to the harbor porpoise, can weigh up to ten tons, while the harbor porpoise is about five feet and rarely weighs in over 150 pounds.  Porpoises are one of the smallest cetaceans, and because of their small size, they lose body heat to the water more quickly than other cetaceans. Their blunt snout is likely an adaptation to minimize surface area to conserve heat. The small sizes of porpoise require them to eat frequently, rather than depending on fat reserves, making them more of an opportunistic feeder. The need to constantly forage also keeps harbor porpoise from migrating on a large scale. Harbor porpoise are known to move from onshore to offshore waters with changing water temperatures and prey distributions, but not known to make long migration trips.

Social Differences

Porpoises are also less social and talkative than dolphins. Dolphins are typically found in large groups, can be highly acrobatic, and often seen bow-riding. Porpoise, specifically harbor porpoise, are often found singularly or in groups of two to three, and shy away from vessels, making them difficult to observe at sea. While both species have large melon heads for echolocation purposes, dolphins make whistles through there blow holes to communicate with each other underwater. Evolutionary scientists believe porpoises do not whistle due to structural differences in their blowhole. (This is why acoustics is such a great way to learn about the occurrence patterns of harbor porpoise – their echolocation is very distinct!) Porpoise echolocation signals have evolved into a very narrow frequency range – theoretically to protect themselves from killer whale predation by echolocating at a frequency killer whales cannot hear.  Dolphins have evolved other strategies to avoid predators such as large group size and fast speed.

While differentiating between porpoises and dolphins takes a bit of practice, it is important to differentiate between the two species because we manage them differently due to some of their morphological differences. Their different adaptations between the species make them more sensitive to certain stressors. For example, for harbor porpoise, the sound produced from boat noise or renewable energy devices is more likely to impact them than other cetaceans. The sensitivity of the nerve cells in the ears of animals (including humans) generally corresponds to the frequencies that each animal produces. So animals like the harbor porpoise have more nerves in their ears that are tuned to very high frequencies (since they make high frequency sounds). If the nerve cells in the harbor porpoise ears become damaged, their ability to communicate, navigate and find food is seriously affected. In addition to their small home ranges and moderately high position in the food web, the sensitivity of harbor porpoise to ocean noise levels make harbor porpoise an important indicator species for ecosystem health, and an important species to study on the Oregon Coast.

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


Midway Atoll: Two weeks at the largest albatross colony in the world

By Rachael Orben, Postdoctoral Scholar, Seabird Oceanography Lab & Geospatial Ecology of Marine Megafauna Lab, Oregon State University

In January I was extremely lucky to accompany my former PhD advisor, Scott Shaffer to Midway Atoll National Wildlife Refuge in the Papahānaumokuākea Marine National Monument as part of my job as a postdoc working in Rob Suryan’s Seabird Oceanography LabWe were there with the dual purpose of GPS tracking Laysan and Black-footed albatrosses as part of Scott’s long-term research and to collect fine-scale data on flight behavior to develop collision risk models for wind energy development (in other areas of the species ranges such as Oregon). Here are my impressions of this amazing island.

So many albatrosses! Our approximately four hour flight from Honolulu to Midway landed at night and as we stood around on the dark tarmac greeting the human island residents I could just make out the ghostly glistening outlines of albatrosses by moonlight. But I had to wait until the following morning to really take stock of where I had suddenly landed: Midway Atoll, the largest albatross colony in the world. This was my first trip to the Northwestern Hawaiian Islands, but I have been to other albatross colonies before and Midway is most definitely different.

First of all, it was hot(ish)!

Secondly, I was amazed to see albatrosses nesting everywhere. Unlike the southern hemisphere colonies I have visited, the albatrosses aren’t restricted to their section of the island or even nesting as close to each other as possible. Instead there are nests literally everywhere there might be enough loose substrate! Birds nest in the middle of the roads, in the bike racks (bikes are an easy quick means of transportation), along the paths, next to the extremely loud generator, near piles of old equipment, and around buildings. Hawaiian albatross nests are not much to look at compared to the mud pedestal nests of the southern hemisphere mollymawks (see the photos below) and are often made of just enough sand and vegetation to keep the egg in place. There are no aerial predators of these birds, beyond the occasional vagrant peregrine, and certainly nothing that might rival the tenacity of the skuas in the southern hemisphere. Perhaps it is this naiveté that has lead to their willingness to nest anywhere.

It may also be this naiveté that has facilitated the following unfortunate turn of events. Just before I arrived, the USFWS and a crew of volunteers had just finished up the annual albatross count. During their counting sweeps they noticed injured adults incubating eggs. After setting out trail cams, suspicions were confirmed. The introduced mice on Midway have discovered that albatrosses are a source of food. House mice are known to prey on albatross chicks on Gough and Marion Islands in the South Atlantic (more information here – warning graphic photos), but to my knowledge this is the first time that they have started eating adult birds. You can read the USFWS announcement here. The plane that I flew out on brought in people, traps, and resources to deal with the situation, but stay tuned as I fear this saga is just beginning.

Finally, and on a further less than positive note, I went to Midway fully aware of the problem that plastics pose to these birds and our marine ecosystem, but there is something to be said for seeing it first hand. The chicks were very small when I was there so I didn’t see any direct impacts on them, but see below for photos of carcasses of last year’s fledglings with plastic filled stomachs. Instead, it was the shear amount of random plastic bits strewn around the island and buried layers deep into the sand that struck me. I learned that sometimes the plastic bits are glow-in-the-dark! Sometimes fishing lures have batteries in them – I am not sure what they are used to catch – do you know? And toothbrushes are very common. All of the plastic that I saw among the birds arrived in the stomach of an adult albatross. All-in-all the experience gave me renewed inspiration for continuing to reduce the amount of plastic that I use (click here for more information on albatrosses and plastic, and here and here for info on marine plastic pollution in general). I collected interesting pieces to bring home with me (see the photos below), but it is a non-random sampling of what caught my eye. I left many many plastic shards where they were.

I have written mostly about the birds, but Midway is full of human history. As I biked along the runway, or past the old officer quarters, I often found myself wondering what all these albatrosses have seen over the years and what they might witness in the future. Two weeks was really just a blink-of-an-eye for an albatross that can live over 40 years (or longer like Wisdom the albatross). I was terribly sad to leave such a beautiful place, but I came home with amazing memories, photos, and gigabytes of data that are already giving me a glimpse into the world of albatrosses at sea.

Scratching the Surface

By Dr. Leigh Torres, Assistant Professor, Oregon State University, Geospatial Ecology of Marine Megafauna Lab

I have been reminded of a lesson I learned long ago: Never turn your back on the sea – it’s always changing.

The blue whales weren’t where they were last time. I wrongly assumed oceanographic patterns would be similar to our last time out in 2014 and that the whales would be in the same area. But the ocean is dynamic – ever changing. I knew this. And I know it better now.

Below (Fig. 1) are two satellite images of sea surface temperature (SST) within the South Taranaki Bight and west coast region of New Zealand that we surveyed in Jan-Feb 2014 and again recently during Jan-Feb 2016. The plot on the left describes ocean surface conditions in 2014 and illustrates how SST primarily ranged between 15 and 18 ⁰C. By comparison, the panel on the right depicts the sea surface conditions we just encountered during the 2016 field season, and a huge difference is apparent: this year SST ranged between 18 and 23 ⁰C, barely overlapping with the 2014 field season conditions.

Figure 1. A comparison of satellite images of sea surface temperature (SST) in the South Taranaki Bight region of New Zealand between late January 2014 and early February 2016. The white circles on each image denote where the majority of blue whales were encountered during each field season.
Figure 1. A comparison of satellite images of sea surface temperature (SST) in the South Taranaki Bight region of New Zealand between late January 2014 and early February 2016. The white circles on each image denote where the majority of blue whales were encountered during each field season.

While whales can live in a wide range of water temperatures, their prey is much pickier. Krill, tiny zooplankton that blue whales seek and devour in large quantities, tend to aggregate in pockets of nutrient-rich, cool water in this region of New Zealand. During the 2014 field season, we encountered most blue whales in an area where SST was about 15 ⁰C (within the white circle in the left panel of Fig. 1). This year, there was no cool water anywhere and we mainly found the whales off the west coast of Kahurangi shoals in about 21 ⁰C water (within the white circle in the right panel of Fig. 1. NB: the cooler water in the Cook Strait in the southeast region of the right panel is a different water mass than preferred by blue whales and does not contain their prey.)

The hot water we found this year across the survey region can likely be attributed, at least in part, to the El Niño conditions that are occurring across the Pacific Ocean currently. El Niño has brought unusually settled conditions to New Zealand this summer, which means relatively few high wind events that normally churn up the ocean and mix the cool, nutrient rich deep water with the hot surface layer water. These are ideal conditions for Kiwi sun-bathers, but the ocean remains highly stratified with a stable layer of hot water on top. However, this stratification does not necessarily mean the ocean is un-productive – it only means that the SST satellite images are virtually useless for helping us to find whales this year.

Although SST data can be informative about ocean conditions, it only reflects what is happening in the thin, top slice of the ocean. Sub-surface conditions can be very different. Ocean conditions during our two survey periods in 2014 and 2016 could be more similar when compared underwater than when viewed from above. This is why sub-surface sensors and data collection is critical to marine studies. Ocean conditions in 2014 and 2016 could both potentially provide good habitat for the whales. In fact, where and when we encountered whales during both 2014 and 2016 we also detected high densities of krill through hydro-acoustics (Fig. 2). However, in 2014 we observed many surface swarms of krill that we rarely saw this recent field season, which could be due to elevated SST. But, we did capture cool drone footage this year of a brief sub-surface foraging event:

An overhead look of a blue whale foraging event as the animal approaches the surface. Note how the distended ventral (throat) grooves of the buccal cavity (mouth) are visible. This is a big gulp of prey (krill) and water. The video was captured using a DJI Phantom 3 drone in the South Taranaki Bight of New Zealand in on February 2, 2016 under a research permit from the New Zealand Department of Conservation (DOC) permit # 45780-MAR issued to Oregon State University.

Figure 2. An echo-sounder image of dense krill patches at 50-80 m depth captured through hydroacoustics in the South Taranaki Bight region of New Zealand.
Figure 2. An echo-sounder image of dense krill patches at 50-80 m depth captured through hydroacoustics in the South Taranaki Bight region of New Zealand.

Below are SST anomaly plots of January 2014 and January 2016 (Fig. 3). These anomaly plots show how different the SST was compared to the long-term average SST across the New Zealand region. As you can see, in 2014 (left panel) SST conditions in our study area were ~1 ⁰C below average, while in 2016 (right panel) SST conditions were ~1 ⁰C above average. So, what are normal conditions? What can we expect next year when we come back to survey again for blue whales across this region? These are challenging questions and illustrate why marine ecology studies like this one must be conducted over many years. One year is just a snap shot in the lifetime of the oceans.

Figure 3. Comparison of sea surface temperature (SST) anomaly plots of the New Zealand region between January 2014 (left) and January 2016 (right). The white box in both plots denotes the general location of our blue whale study region. (Apologies for the different formats of these plots - the underlying data is directly comparable.)
Figure 3. Comparison of sea surface temperature (SST) anomaly plots of the New Zealand region between January 2014 (left) and January 2016 (right). The white box in both plots denotes the general location of our blue whale study region. (Apologies for the different formats of these plots – the underlying data is directly comparable.)

Like all marine megafauna, blue whales move far and fast to adjust their distribution patterns according to ocean conditions. So, I can’t tell you what the ocean will be like in January 2017 or where the whales will be, but as we continue to study this marine ecosystem and its inhabitants our understanding of ocean patterns and whale ecology will improve. With every year of new data we will be able to better predict ocean and blue whale distribution patterns, providing managers with the tools they need to protect our marine environment. For now, we are just beginning to scratch the (sea) surface.