Current Projects – Page 17 – Geospatial Ecology of Marine Megafauna Laboratory

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.

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.

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.

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.

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; http://dels.nas.edu/Report/Marine-Mammal-Populations-Ocean-Noise/11147). 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 11^th 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: (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: 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.

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!)

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.

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

Entering in the world of Photogrammetry

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

Hello everybody with the first post of the year from the GEMM Lab!!

The year of 2016 has just begun and with that comes new projects and great expectations about my PhD project.

During this week I am going to learn how to measure gray whales (Eschrichtius robustus) using aerial images that were captured during last summer’s pilot field season along the Oregon Coast led by my advisor Dr. Leigh Torres.

Dr. Torres aimed to test the methodology for our project that will combine these whales’ measurements data with hormonal analysis to assess the overall health of gray whales.

The aerial videos and images were taken through an unmanned aerial system (UAS) that is composed of a flying unit and an on-board camera. An example of this system can be seen below, in Figure 1.

Figure 1: Dr. Leigh Torres re-captures the UAS (DJI Phantom 3) while at sea after an over flight of a gray whale.

Source: Leigh Torres, 2015.

The measurement of the whales through aerial images is known as “photogrammetry” and this method can give us important information about the whales through this unique overhead perspective, such as individual identification using natural markings, sex and reproductive condition based on size estimation, and individual-based changes in growth, health and body condition (nutritive condition) over time through replicate samples.

Perryman and Lynn (2002) used images captured from planes and adopted four different measurements for each photographed whale: the total length (L_t), the width of the whale at its widest point (W_m), the distance from the tip of the rostrum to the widest point (RW_m), and the width of the flukes (F_w), as shown in the Figure 2. Using these methods, this study was able to identify pregnant females and found that southbound migrating gray whales were significantly wider than northbound whales.

Figure 2: Features measured on vertical photographs in gray whales

Source: Perryman and Lynn, 2002.

We plan to build upon this established method by measuring width at multiple points along the whale’s body, in addition to the total length.

Images taken of the same individuals during different temporal periods can reveal variations in their body condition.

We aim to collect images of the same individuals at the beginning and end of a foraging season and hypothesize that due to weight gain and increased blubber mass the width of animals will increase. Additionally, when images of indiviudals are compared between years we hypothesize that body condition changes due to major events such as pregnancy, entanglements, skin lesions, and predation events, will be linked to changes in body condition.

We will relate these photogrammetry data to hormonal data on stress and reproductive status in order to describe individual stress variation as it relates to size, health, location, year, reproductive status and ocean noise levels.

During the pilot field season, six gray whale fecal samples were collected and hormonal levels in these samples were analyzed showing positive results. Based on the success of the pilot field season, I believe my PhD project will produce exciting and informative data about gray whale ecology by linking physiology and morphometrics.

I am excited to begin my thesis research and, until my field season starts next summer, you can find me measuring gray whales!

To illustrate, below are a few aerial images taken of gray whales off Newport, Oregon, using a UAS, which we will use to conduct photogrammetry (all photos taken under NMFS permit 16111 issued to John Calambokidis).

And, just for fun, here is a UAS clip of a foraging gray whale in a kelp bed off the coast of Oregon to give a sense of the unique perspective we can get on animal behavior.

* Taken under NMFS permit 16111 issued to John Calambokidis.

This research is facilitated through the collaboration with OSU’s Aerial Imaging Systems Lab (http://ais.forestry.oregonstate.edu/), and Cascadia Research Collective (http://www.cascadiaresearch.org/).

Until next time and thanks for reading!

Bibliographic Reference:

Perryman WL, Lynn MS. 2002. Evaluation of nutritive condition and reproductive status of migrating gray whales (Eschrichtius robustus) based on analysis of photogrammetric data. J. Cetacean Res. Manage. 4(2):155-164.

Successfully a Master, or at Least a Bit More Enlightened

By Courtney Hann (M.S. Marine Resource Management)

A week ago, I successfully defended my Masters of Science thesis on “Citizen Science Research: A Focus on Historical Whaling Data and a Current Citizen Science Project, Whale mAPP”, which included a 60 minute presentation to my committee, colleagues, friends, and family. Although a bit nervous at the start, my two weeks of revisions and practice prepared me to enjoy the experience once it started, and be thankful for all of the guidance and knowledge I have gained while at Oregon State University and with the Geospatial Ecology of Marine Megafauna Lab.

My thesis focused on the value of collaboration and creativity in developing new methods for gathering and analyzing marine mammal data; and was driven by the overall question of

How do we study marine mammals over vast spatial and temporal scales without breaking the bank, while still being scientifically rigorous?

This is important because marine mammal data collected over large spatial and temporal scales is relatively rare, and requires extensive collaboration and funding (Calambokidis et al. 2008; Dahlheim et al. 2009). A majority of marine mammal research is conducted over limited time frames (weeks to months) and on local spatial scales, requiring the data to be extrapolated out in order to understand regional patterns (Baker et al. 1985; Rosa et al. 2012). As a result, ecological modeling and other analyses are limited by geographic and temporal scale (Hamazaki 2002; Redfern et al. 2006).

I presented two potential approaches to the use of citizen science data to cost-effectively study marine mammal distributions across vast spatial and temporal scales. The first method is described below:

(1) Use the oldest form of large cetacean citizen science data, historical whaling records, to analyze species trends across extensive spatial and temporal scales. Amazingly, these 200-year-old records provide some of the most informative data for highlighting regional and global marine mammal distributions and abundance estimates (Gregr and Trites 2001; Torres et al. 2013). This information is vital for adapting management strategies as populations recover, change their distribution due to climate changes, or undergo various interactions with humans (net entanglements, ship strikes, competition for commercially important fish and invertebrate species, etc.).

Replicating such datasets today is not fiscally feasible with traditional research methods, but distribution data is still vital for understanding how populations have changed over time and how they are responding to large-scale climate and anthropogenic changes. Modern day citizen science research may be the solution to collecting such baseline data. Therefore, the following second method was evaluated:

(2) Data collected by 39 volunteers using the marine mammal citizen science app, Whale mAPP (www.whalemapp.org), over the summer of 2014 was examined to interpret various spatial, users, and species biases present in the dataset. In addition, the educational benefits, user motivations, and suggestions for revisions to the citizen science project were investigated with two user surveys. Results were used to revise Whale mAPP and highlight both the potential and limitations of citizen science data collected with Whale mAPP.

While I believe in the power of citizen science research for expanding our knowledge of large-scale marine mammal distributions, it is important to continue to interpret the biases in the dataset and truly examine how we can use the results for research. For, although collecting an abundance of data may be fun and exciting, careful examination of the methods and analyses techniques are vital if we hope to one day use the data to inform management and conservation decisions. I hope that my research contributes not only to this knowledge, but also to opening our eyes to the value of embracing a new method of data collection. Such a method relies on collaboration across various disciplines including biologists, managers, educators, app developers, volunteers, and statisticians. Maybe someday a current citizen science project, such as Whale mAPP, will provide a dataset as vast, abundant, and valuable as historical whaling records. Even the possibility of accomplishing such a goal is worth fighting for.

Literature Cited

Baker, C. S., L.M. Herman, A. Perry, et al. 1985. Population characteristics and migration of summer and late-season humpback whales (Megaptera novaengliae) in Southeastern Alaska. Marine Mammal Science 1:304–323.

Calambokidis, J., E.A. Falcone, T.J. Quinn, et al. 2008. SPLASH: Structure of Populations, Levels of Abundance and Status of Hump- back Whales in the North Pacific. Final Report for Contract AB133F-03-RP- 00078 prepared by Cascadia Research for U.S. Department of Commerce.

Dahlheim, M. E., P.A. White and J.M. Waite. 2009. Cetaceans of Southeast Alaska: distribution and seasonal occurrence. J. Biogeogr 36:410–426

Gregr, E.J., A.W. Trites. 2001. Predictions of critical habitat for five whale species in the waters of coastal British Columbia. Canadian Journal of Fisheries and Aquatic Sciences 58:1265–1285

Hamazaki, T. 2002. Spatiotemporal prediction models of cetacean habitats in the mid-western North Atlantic Ocean (from Cape Hatteras, North Carolina, USA to Nova Scotia, Canada). Marine Mammal Science 18:920–939.

Redfern, J.V., M.C. Ferguson, E.A. Becker, et al. 2006. Techniques for cetacean-habitat modeling. Marine Ecology Progress Series 310: 271–295.

Rosa, L. D., J.K. Ford and A.W. Trites. 2012. Distribution and relative abundance of humpback whales in relation to environmental variables in coastal British Columbia and adjacent waters. Cont. Shelf Res. 36:89–104.

Torres, L. G., T. D. Smith, P. Sutton, A. MacDiarmid, J. Bannister, and T. Miyashita. 2013. From exploitation to conservation: habitat models using whaling data predict distribution patterns and threat exposure of an endangered whale. Diversity and Distributions 19:1138-1152.

Looking back on a busy field season

Solène Derville, EnTroPie Lab, Institute of Research for Development, Nouméa, New Caledonia (Ph.D. student under the co-supervision of Dr. Leigh Torres)

After one month and a half in the field, I am now comfortably sitting at my desk in the Institute of Research for Development (IRD) in Nouméa and I am finally finding the time to look back on my first marine mammal field experience.

The New Caledonian South Lagoon is certainly not the worst place on earth to study whales. While some people spend hours trying to spot extremely rare and shy species living in freezing cold polar waters, I have to endure a 25°C temperature, turquoise waters and a study species desperate for attention (series of a dozen breaches are not uncommon). As with all field work, there were ups and downs but following humpback whales during the 2015 breeding season was by far the most exhilarating field experience I’ve ever had.

During the austral winter, humpback whales are thought to travel and stay in different areas of the New Caledonian Economic Exclusive Zone. Using satellite telemetry, several seamounts (e.g. Antigonia), banks (e.g. Torche bank) and shallow areas have been shown to play an important role for breeding and migrating humpback whales (Garrigue et al. In Press). However, as much as we would like to study whales in these areas, offshore field missions are logistically and financially hard to conduct. This is why most of the data on humpback whales in New Caledonian waters have been collected in coastal waters, and more specifically in the South Lagoon. Opération Cétacés, a local NGO, has been studying whales in this area for about two decades and I was lucky to participate in this year’s field season with their experienced team.

The usual day in Prony (the village that we live in during the whale season) usually starts early. We get up at about 5:30, and start by engulfing a bowl of porridge (nicknamed “globi” and considered as a highly exotic dish). By 6:30 everyone is standing in our rigid-hulled inflatable boat, listening to the weather forecast on the radio. After a 15 minute trip across the bay of Prony, two people disembark and climb to a land-based lookout, the N’Doua Cape, where they will spend the day trying to spot humpback whales and guiding the boat towards their location via VHF radio communication. The vessel-based team slowly approaches the whale groups to do photo-identification (using the unique marks on the ventral surface of the tail flukes), biopsy collection, and behavioral activity monitoring. The particular coastal geography of this study area (see previous post: Crossing Latitudes) allows us to uniquely combine land-based and boat-based surveying. These methods increase our encounter rate and allow us to collect more individual-based data. Yet, compared to a standardized boat-based surveys, our survey effort is much more complex to estimate and account for in a spatial distribution model.

This season, the number of whale encounters was particularly high. We spent 31 days at sea and observed a total of 99 groups. Using photo-identification, we documented 113 different individuals, some of which were first observed more than 15 years ago! Biopsy samples were collected from 139 different individuals and we managed to record 4h of songs performed by six different whales. Given that the size of the New Caledonian population is currently thought to be less than 1000 individuals, our sampling is not too bad!

A calf breaching out of the water on a late afternoon. No wonder humpback whales are favored by whale-watching companies, they can be very active at the surface!

These two adult whales were part of a very active competitive group of eight individuals and displayed a peculiar behavior that included gently rolling and rubbing themselves against each other.

Another great achievement of this season was the tagging of two adult humpback whales with ARGOS satellite-tracking devices. It was a thrilling experience to be part of this procedure and witness the level of concentration and experience required to place a tag on a whale. Our two individuals, one a presumed male and the other a female with calf, were respectively baptized Lutèce (the name Romans gave to Paris) and Ovalie (an old fashioned way to call rugby in France). Their tags transmitted for 15 and 20 days respectively, which was not long enough to follow their migration south towards Antarctica. Yet, both whales spent time on seamounts that are known to play an important role for humpback whales in the region. We were very interested in Ovalie’s track (map given below), as she travelled along the Loyalty ridge, a seafloor structure of great interest to us. We suspect that whales could be using this ridge as a navigational aid and/or using shallow areas (seamounts and banks) along the ridge as resting or breeding habitats. The amount of humpback whales present in this area and the eventual role played by oceanic features along the Loyalty ridge will be the subject of my future research.

But now that we have all this data, let’s get back to work! As much as I love being in the field, there comes a time when you have to sit in front of your computer and try to make sense of all this information you collected.

And that is where my collaboration with the GEMM Lab comes in! I am looking forward to visiting Newport once again in December and to start shedding a light on the ‘How’s and ‘Why’s of New Caledonian humpback whales’ space use.

Literature cited:

Garrigue, C., Clapham, P. J., Geyer, Y., Kennedy, A. S., & Zerbini, A. N. (In Press). Satellite tracking reveals novel migratory patterns and the importance of seamounts for endangered South Pacific Humpback Whales. Royal Society Open Science.

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!

New Zealand’s mega-fauna come to Newport, Oregon.

By Olivia Hamilton, PhD Candidate, University of Auckland, New Zealand.

The week leading up to my departure from New Zealand was an emotional rollercoaster. Excited, nervous, eager, reluctant… I did not feel like the fearless adventurer that I thought I was. D-day arrived and I said my final goodbyes to my boyfriend and mother at the departure gate. Off I went on my three-month research stint at the Hatfield Marine Science Center.

Some thirty hours later I touched down in Portland. I collected my bags and headed towards the public transport area at the airport. A young man greeted me, “Would you like to catch a taxi or a shuttle, ma’am?” “A taxi please! I have no idea where I am”, I responded. He nodded and smiled. I could see the confusion all over his face… My thick kiwi accent was going to make for some challenging conversations.

After a few days in Portland acclimatizing to the different way of life in Oregon, it was time to push on to Newport. I hit a stroke of luck and was able take the scenic route with one of the girls in the GEMM lab, Rachael Orben. With only one wrong turn we made it to the Oregon coast. I was instantly hit with a sense of familiarity. The rugged coastline and temperate coastal forest resembled that of the west coast of New Zealand. However, America was not shy in reminding me of where I was with its big cars, drive-through everything, and RVs larger than some small kiwi houses.

The Oregon Coast. Photo by Olivia Hamilton.

We arrived at Hatfield Marine Science Center: the place I was to call home for the next quarter of a year.

So, what am I doing here?

In short, I have come to do computer work on the other side of the world.

Dr. Leigh Torres is on my PhD committee and I am lucky enough to have been given the opportunity to come to Newport and analyze my data under her guidance.

My PhD has a broad interest in the spatial ecology of mega-fauna in the Hauraki Gulf, New Zealand. For my study, megafauna includes whales, dolphins, sharks, rays, and seabirds. The Hauraki Gulf is adjacent to Auckland, New Zealand’s most populated city and home to one of our largest commercial ports. The Hauraki Gulf is a highly productive area, providing an ideal habitat for a number of fish species, thus supporting a number of top marine predators. As with many coastal areas, anthropogenic activities have degraded the health of the Gulf’s ecosystem. Commercial and recreational fishing, run-off from surrounding urban and rural land, boat traffic, pollution, dredging, and aquaculture are some of the main activities that threaten the Gulf and the species that inhabit it. For instance, the Nationally Endangered Bryde’s whale is a year-round resident in the Hauraki Gulf and these whales spend much of their time close to the surface, making them highly vulnerable to injury or death from ship-strikes. In spite of these threats, the Gulf supports a number of top marine predators. Therefore it is important that we uncover how these top predators are using the Gulf, in both space and time, to identify ecologically important parts of their habitat. Moreover, this study presents a unique opportunity to look at the relationships between top marine predators and their prey inhabiting a common area.

The Hauraki Gulf, New Zealand. The purple lines represent the track lines that aerial surveys were conducted along.

Common dolphins in the Hauraki Gulf. Photo by Olivia Hamilton

A Bryde’s whale, common dolphins, and some opportunistic seabirds foraging in the Hauraki Gulf. Photo by Isabella Tortora Brayda di Belvedere.

Australisian Gannets and shearwaters foraging on a bait ball in the Hauraki Gulf. Photo by Olivia Hamilton.

To collect the data needed to understand the spatial ecology of these megafauna, we conducted 22 aerial surveys over a year-long period along pre-determined track lines within the Hauraki Gulf. On each flight we had four observers that collected sightings data for cetaceans, sharks, predatory fish, prey balls, plankton, and other rare species such as manta ray. An experienced seabird observer joined us approximately once a month to identify seabirds. We collected environmental data for each sighting including Beaufort Sea State, glare, and water color.

The summary of our sightings show that common dolphins were indeed common, being the most frequent species we observed. The most frequently encountered sharks were bronze whalers, smooth hammerhead sharks, and blue sharks. Sightings of Bryde’s whales were lower than we had hoped, most likely an artifact of our survey design relative to their distribution patterns. In addition, we counted a cumulative total of 11,172 individual seabirds representing 16 species.

Summary of sightings of megafauna in the Hauraki Gulf.

My goal while here at OSU is to develop habitat models for the megafauna species to compare the drivers of their distribution patterns. But, at the moment I am in the less glamorous, but highly important, data processing and decision-making stage. I am grappling with questions like: What environmental variables affected our ability to detect which species on surveys? How do we account for this? Can we clump species that are functionally similar to increase our sample size? These questions are important to address in order to produce reliable results that reflect the megafauna species true distribution patterns.

Once these questions are addressed, we can get on to the fun stuff – the habitat modeling and interpretation of the results. I will hopefully be able to start addressing these questions soon: What environmental and biological variables are important predictors of habitat use for different taxa? Are there interactions (attraction or repulsion) between these top predators? What is driving these patterns? Predator avoidance? Competition? So many questions to ask! I am looking forward to answering these questions and reporting back.

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?

All these tracklines are from one whale, Keyboard, visiting the same area multiple times over the course of a month. I'll break this figure down a little later in the post. Notice how the whale consistently returns to the bay just west of the port jetty — All these tracklines are from one whale, Keyboard, visiting the same area multiple times over the course of a month. I’ll break this figure down a little later in the post. Notice how the whale consistently returns to the bay just west of the port jetty

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

Whale named "Keyboard" visits graveyard head multiple times. Green track: 7.21.15, Pink track: 7.21.15, Teal track: 7.30.15. The orange polygons are approximate locations of kelp patches. — Whale named “Keyboard” visits graveyard head multiple times. Green track: 7.21.15, Pink track: 7.21.15, Teal track: 7.30.15. The orange polygons are approximate locations of kelp patches.

"Keyboard" continues to visit. Red trackline: 8.27.15, white trackline: 8.28.15, purple trackline: 8.28.15 — “Keyboard” continues to visit. Red trackline: 8.27.15, white trackline: 8.28.15, purple trackline: 8.28.15

Whale 130 foraged near Boiler Bay for 5.5 hours on Aug 12. Trying to look at the whole trackline in one go is a little complicated, so let’s break it down by hour.

This panel shows hours 4-6 of the track. Things get more complex as various vessels use the same area. Whale 130 is always in red.

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

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