Tag: Photo Identification

Blue whale portraits: pieces of the puzzle

By Dawn Barlow, MSc Student, Oregon State University

Perhaps you’ve read some posts about New Zealand blue whales on this blog from the past field season in the South Taranaki Bight (STB). I know I eagerly awaited updates from the field while the team was in New Zealand and I was in Southern California, finishing undergrad and writing funding proposals and grad school applications. Now that undergrad is done and dusted, I’ve arrived in Newport and begun to settle in to my next chapter as the newest member of the GEMM Lab, joining the blue whale research team as a MSc student in OSU’s Department of Fisheries and Wildlife. Since no blue whale news has made it onto this blog in some time, I’m excited to share what has happened since the team returned from the field!

As you may have heard from Leigh, Callum, and Kristin, 2016 was a fruitful field season. In nearly 1,500 miles of vessel surveys, the team documented blue whale foraging behavior, a pair of racing whales, four mother-calf pairs, what may be the first aerial footage of nursing behavior in baleen whales (video below), and a whale with apparent deformities. Five hydrophones units were deployed, fecal and biopsy samples were collected, oceanographic conditions were measured, and photos were taken.

I was welcomed into the GEMM Lab in early July, and presented with a workspace, a hard drive with thousands of photos, new software programs to learn, wonderfully accessible tea and coffee, and tasked with creating a photo-ID catalog of all the blue whales our team photographed this past field season. Here’s a great thing about blue whales: while they may be tricky to study, when someone sees a blue whale they are often excited to report it. In addition to the data collected by our team during the 2016 season and the 2014 pilot season, we are incorporating many photo-documented sightings of blue whales from all around New Zealand that we have received from collaborative researchers, whale watch organizations, and fishing vessels alike captured between 2004 and 2016. All these photos are precious data to us, as we can use them to better understand their ecology.

There are many unanswered questions about this population of blue whales in New Zealand — How many are there? Just how big are they? Do they stay in New Zealand year-round or are they migratory? Through the photo-ID analysis that I’ve done, we are just beginning to piece together some answers. We have now compiled records of sightings in New Zealand from every month of the year. I’ve identified 94 unique individual blue whales, 26 of which were sighted in the STB during the 2016 season. Five whales were seen in multiple years (Figure 1), including one whale that was seen in three different years, in three different places, and with three different calves! And what might all of this mean? At this point it’s still speculative, but these findings hint at year-round residency and seasonal movement patterns within New Zealand waters… with more data and more analysis I will be able to say these things more conclusively.

New Zealand Blue Whale Photo-ID

NZ Blue Whale Photo-ID
Figure 1. Blue whale photographed off of Westport on 31 January 2013 (above) by the Australian Antarctic Division (data provided by Mike Double), and in the South Taranaki Bight on 2 February 2016 (below). Note how the tear in the dorsal fin has healed over the three-year period.

Perhaps you’ve read Leila’s post about photogrammetry, and how she is able to make measurements using aerial photographs captured using an Unmanned Aerial System (UAS, aka ‘drone’). Using the same method, I will soon be able to tell you how long these whales really are (Figure 2).

sighting 22 UAS reduced
Figure 2. An aerial photograph captured with the UAS during the 2016 season, which will be used to measure the length of these whales using photogrammetry.

How many of them are there? Well, that’s a trickier question. Using a straightforward abundance calculation based on our rate of re-sightings, the estimate I came up with is 594 ± 438. In other words, I can say with 95% confidence that there are between 156 and 1031 blue whales in New Zealand. How helpful is this? Well, not very! The wide confidence intervals in this estimate are problematic, and it is difficult to draw any conclusions when the range of possible numbers is so large. So stay tuned as I will be learning more about modeling population abundance estimates in order to provide a more precise and descriptive answer.

But stepping back for a minute, what does it matter how many whales there are and what they’re doing? In 2014, Leigh demonstrated that the STB is an important foraging ground for these blue whales. However, the STB is also a region heavily used by industry, experiencing active oil and gas extraction (Figure 3), seismic surveying, shipping traffic, and proposed seafloor mining. If we don’t know how the blue whales are using this space, then how can we know what effect the presence of industry will have on their ecology? It is our hope that findings from this study can guide effective conservation and management of these ocean giants as well as the ecosystem they are part of.

Figure 2. A blue whale surfaces in front of an oil rig in the South Taranaki Bight, New Zealand. Photo by Deanna Elvines.
Figure 3. A blue whale surfaces in front of an oil rig. Photo by Deanna Elvines.

Keeping these goals in mind, I’m eagerly awaiting the start of our 2017 field season in the STB. As I look through all these photos I feel like I’m getting to know this group of whales just a little bit and I look forward to being on the water seeing them myself, maybe even recognizing some from the 2016 photos. More time on the water and more data will bring us closer to the piecing together the story of these whales, and inevitably open doors to more questions than we started with. And in the meantime, I’m grateful for the community I’ve found here in the GEMM Lab, at Hatfield Marine Science Center, and in Newport.

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.

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?

#1
#2
#2
#3
#4
#4
#5
#5
#6
#6
#7
#7
#8
#8
#9
#9
#10
#10

 

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

 

 

 

 

 

Answers:

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