Finding the edge: Preliminary insights into blue whale habitat selection in New Zealand

By Dawn Barlow, MSc student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

I was fortunate enough to spend the Austral summer in the field, and so while the winter rain poured down on Oregon I found myself on the water with the sun and wind on my face, looking for blue whales in New Zealand. This spring I switched gears and spent time taking courses to build my analytical toolbox. In a course on technical writing and communication, I was challenged to present my research using only pictures and words with no written text, and to succinctly summarize the importance of my research in an introduction to a technical paper. I attended weekly seminars to learn about the diverse array of marine science being conducted at Oregon State University and beyond. I also took a course entitled “Advanced Spatial Statistics and Geographic Information Science”. In this skill-building course, we were given the opportunity to work with our own data. Even though my primary objective was to expand the tools in my toolbox, I was excited to explore preliminary results and possible insight into blue whale habitat selection in my study area, the South Taranaki Bight region (STB) of New Zealand (Figure 1).

Figure 1. A map of New Zealand, with the South Taranaki Bight (STB) region delineated by the black box. Farewell Spit is denoted by a star, and Kahurangi point is denoted by an X.

Despite the recent documentation of a foraging ground in the STB, blue whale distribution remains poorly understood in New Zealand. The STB is New Zealand’s most industrially active marine region, and the site of active oil and gas extraction and exploration, busy shipping traffic, and proposed seabed mining. This potential space-use conflict between endangered whales and industry warrants further investigation into the spatial and temporal extent of blue whale habitat in the region. One of my research objectives is to investigate the relationship between blue whales and their environment, and ultimately to build a model that can predict blue whale presence based on physical and biological oceanographic features. For this spring term, the question I asked was:

Is the number of blue whales present in an area correlated with remotely-sensed sea surface temperature and chlorophyll-a concentration?

For the purposes of this exploration, I used data from our 2017 survey of the STB. This meant importing our ship’s track and our blue whale sighting locations into ArcGIS, so that the data went from looking like this:

… to this:

The next step was to get remote-sensed images for sea surface temperature (SST) and chlorophyll-a (chl-a) concentration. I downloaded monthly averages from the NASA Moderate Resolution Imaging Spectrometer (MODIS aqua) website for the month of February 2017 at 4 km2 resolution, when our survey took place. Now, my images looked something more like this:

But, I can’t say anything reliable about the relationships between blue whales and their environment in the places we did not survey.  So next I extracted just the portions of my remote-sensed images where we conducted survey effort. Now my maps looked more like this one:

The above map shows SST along our ship’s track, and the locations where we found whales. Just looking at this plot, it seems like the blue whales were observed in both warmer and colder waters, not exclusively in one or the other. There is a productive plume of cold, upwelled water in the STB that is generated off of Kahurangi point and curves around Farewell Spit and into the bight (Figure 1). Most of the whales we saw appear to be near that plume. But how can I find the edges of this upwelled plume? Well, I can look at the amount of change in SST and chl-a across a spatial area. The places where warm and cold water meet can be found by assessing the amount of variability—the standard deviation—in the temperature of the water. In ArcGIS, I calculated the deviation in SST and chl-a concentration across the surrounding 20 km2 for each 4 km2 cell.

Now, how do I tie all of these qualitative visual assessments together to produce a quantitative result? With a statistical model! This next step gives me the opportunity to flex some other analytical muscles, and practice using another computational tool: R. I used a generalized additive model (GAM) to investigate the relationships between the number of blue whales observed in each 4 km2 cell our ship surveyed and the remote-sensed variables. The model can be written like this:

Number of blue whales ~ SST + chl-a + sd(SST) + sd(chl-a)

In other words, are SST, chl-a concentration, deviation in SST, and deviation in chl-a concentration correlated with the number of blue whales observed within each 4 km2 cell on my map?

This model found that the most important predictor was the deviation in SST. In other words, these New Zealand blue whales may be seeking the edges of the upwelling plume, honing in on places where warm and cold water meet. Thinking back on the time I spent in the field, we often saw feeding blue whales diving along lines of mixing water masses where the water column was filled with aggregations of krill, blue whale prey. Studies of marine mammals in other parts of the world have also found that eddies and oceanic fronts—edges between warm and cold water masses—are important habitat features where productivity is increased due to mixing of water masses. The same may be true for these New Zealand blue whales.

These preliminary findings emphasize the benefit of having both presence and absence data. The analysis I have presented here is certainly strengthened by having environmental measurements for locations where we did not see whales. This is comforting, considering the feelings of impatience generated by days on the water spent like this with no whales to be seen:

Moving forward, I will include the blue whale sighting data from our 2014 and 2016 surveys as well. As I think about what would make this model more robust, it would be interesting to see if the patterns become clearer when I incorporate behavior into the model—if I look at whales that are foraging and traveling separately, are the results different? I hope to explore the importance of the upwelling plume in more detail—does the distance from the edge of the upwelling plume matter? And finally, I want to adjust the spatial and temporal scales of my analysis—do patterns shift or become clearer if I don’t use monthly averages, or if I change the grid cell sizes on my maps?

I feel more confident in my growing toolbox, and look forward to improving this model in the coming months! Stay tuned.

Making a Splash

By: Cathryn Wood, Lawrence University ’17, summer REU in the GEMM Lab

Greetings from Port Orford! My name is Cathryn, and I am the fourth member of the GEMM Lab’s gray whale foraging ecology research team, which includes Florence, Kelli, and the other Catherine (don’t worry, I go by Cat). Nearly 5 weeks into field season, I am still completely amazed with my first West Coast experience and doing what I’ve always dreamt of: studying marine mammals. Coming from Michigan’s Upper Peninsula, this may seem slightly out of place, but my mom can attest; she read “Baby Beluga” to me every night when I was a toddler. Now a rising senior majoring in biology at Lawrence University, I’ve been focusing my coursework on aquatic and marine ecology to prepare for graduate school where I plan to specialize in marine science. Being part of this research is a very significant step for me into the field.

So how did I end up here, as part of this amazing project and dream, women-in-science team? I am interning through OSU’s Ocean Sciences REU program at the Hatfield Marine Science Center, where the GEMM Lab is located. REU stands for “Research Experience for Undergraduates ”, and is an NSF-funded research internship program found in numerous universities around the country. These internships allow undergrads to conduct independent research projects under the guidance of a faculty mentor at the program’s institution. I applied to several REUs this past winter, and was one of 12 undergrads accepted for the program at HMSC. Each of us is paired with different faculty members to work on various projects that cover a diverse range of topics in the marine sciences; everything from estuarine ecology, to bioacoustics. I was ecstatic to learn that I had been paired with Dr. Torres as my faculty mentor to work on Florence’s gray whale project, which had been my first choice during the application process.

My particular research this summer is going to complement Florence’s master’s thesis work by asking new questions regarding the foraging data. While her project focuses on the behavioral states of foraging whales, I will be looking at the whale tracks to see if there are patterns in their foraging behavior found at the individual level. Traditionally, ecological studies have accepted classical niche theory, treating all individuals within a population as ecological equivalents with the same niche width. Any variances present among individuals are often disregarded as having an insignificant consequence on the population dynamics as a whole, but this simplification can overlook the true complexity of that population . The presence of niche variation among conspecifics is known to occur in at least 93 species across a diverse array of taxa, so the concept of individual specialization, and how it can affect ecological processes is gaining recognition progressively in the field (Bolnick et al., 2003). My goal is to determine whether or not the gray whales in this study, and presumably others in the Pacific Coast Feeding Group (PCFG), exhibit individual specialization in their foraging strategies . There are many ways in which individuals can specialize in foraging, but I will be specifically determining if fine scale spatial patterns in the location of foraging bouts exists, regardless of time.

To address my question, I am using the whale tracking data from both 2015 and 2016, and learning to use some very important software in the spatial ecology world along the way through a method that Dr. Torres introduced to me. Starting in ArcGIS, I generate a kernel density layer of a raw track (Fig. 1 ), which describes the relative distribution of where the tracked whale spent time (Fig. 2 ). Next, using the isopleth function in the software Geospatial Modelling Environment, I generate a 50% density contour line that distinguishes where the whale spent at least 50% of its time during the track (Fig. 3 ). Under the assumption that foraging took place in these high density areas, we use these 50% contour lines to describe foraging bout locations. I now go back to ArcGIS to make centroids within each 50% line, which mark the exact foraging bout locations (Fig. 4 ).

Fig.1 Raw individual whale track.
Fig. 1 Raw individual whale track.
Fig. 2 Kernel Density map of whale track.
Fig. 2 Kernel Density map of whale track.
Fig. 3 50% isopleth contours of locations with highest foraging densities
Fig. 3 50% isopleth contours of locations with highest foraging densities
Fig. 4 Final centroids to signify foraging bouts
Fig. 4 Final centroids to signify foraging bouts

These centroids will be determined for every track by an individual whale, and then compared relative to foraging locations of all tracked whales to determine if the individual is foraging in different locations than the population. Then, the tracks of individuals who repeatedly visit the site at least three times will be compared with one another to determine if the repeat whales show spatial and/or temporal patterns in their foraging bout locations, and if specialization at a fine scale is occurring in this population. If you did not quite follow all those methods, no worries, it was a lot for me to take in at first too. I’ve finally gotten the hang of it though, and am grateful to now have these skills going into grad school.

Because I am interested in behavioral ecology and the concept of individuality in animal populations, I am extremely excited to see how this research plays out. Results could be very eye-opening into the fine scale foraging specialization of the PCFG sub-population because they already demonstrate diet specialization on mysid (as opposed to their counterparts in the Bering Sea who feed on benthic organisms) and large scale individual residency patterns along the Pacific Northwest (Newell, 2009; Calambokidis et al., 2012). Most significantly, understanding how individuals vary in their feeding strategies could have very important implications for future conservation measures for the whales, especially during this crucial foraging season where they replenish their energy reserves.  Management efforts geared for an “average population” of gray whales could ultimately be ineffective if in fact individuals vary from one another in their foraging strategies. Taking into account the ways in which variation occurs amongst individuals is therefore crucial knowledge for successful conservation approaches.

My project is unique from those of the other REUs because I am simultaneously in the midst of assisting in field season number two of Florence’s project. While most of the other interns are back at Hatfield spending their days in the lab and doing data analyses like a 9-5 job, I am with the team down in Port Orford for field season. This means we’re out doing research every dawn as weather allows. Though I may never have an early bird bone in my body, the sleepy mornings are totally worth it because ecology field work is my favorite part of research. To read more about our methods in the field, check out Florence’s post.

Since Catherine’s last update, we’ve had an eventful week. To our dismay, Downrigger Debacle 2.0 occurred. (To read about the first one, see Kelli’s post). This time it was not the line – our new line has been great. It was a little wire that connected the downrigger line to the pipe that the GoPro and TDR are connected to. It somehow snapped due to what I presume was stress from the currents.   Again, it was Catherine and I in the kayak, with a very successful morning on the water coming to a close when it happened. Again, I was in the bow, and she was in the stern deploying the equipment – very déjà vu. When she reeled in an equipment-less line, we at first didn’t know how to break it to Florence and Kelli who were up on the cliff that day. Eventually, Catherine radioed “Brace yourselves…” and we told them the bad news. Once again, they both were very level-headed, methodical, and un-blaming in the moments to follow. We put together the same rescue dive team as last time, and less than a week later, they set off on the mission using the GPS coordinates I had marked while in the kayak. Apparently, between the dredging taking place in the harbor and the phytoplankton bloom, visibility was only about 2 feet during the dive, but they still recovered the equipment, with nothing but baked goods and profuse thanks as payment. We are very grateful for another successful recovery, and are confident that our new attachment mechanism for the downrigger will not require a third rescue mission (Fig. 6-8). Losing the equipment twice now has taught us some very important things about field work. For one, no matter how sound you assume your equipment to be, it is necessary to inspect it for weak points frequently – especially when salt water and currents are in the picture. Perhaps even more importantly, we’ve gotten to practice our problem solving skills and see firsthand how necessary it is to act efficiently and calmly when something goes wrong. In ecological field research you have to be prepared for  anything.

Fig. 5 Original setup of GoPro and TDR.
Fig. 5 Original setup of GoPro and TDR.
Fig. 6 Photo taken after the wire that connected the pole to the downrigger line snapped.
Fig. 6 Photo taken after the wire that connected the pole to the downrigger line snapped.
Fig. 7 New mechanism for attaching the pole to the downrigger line.
Fig. 7 New mechanism for attaching the pole to the downrigger line.
Fig. 8 Equipment rescue team: Aaron Galloway and Taylor Eaton diving, Greg Ryder operating the boat, and Florence on board to direct the GPS location of where the equipment was lost.
Fig. 8 Equipment rescue team: Aaron Galloway and Taylor Eaton diving, Greg Ryder operating the boat, and Florence on board to direct the GPS location of where the equipment was lost.

In other news, unlike our slow-whale days during the first two weeks of the project, we have recently had whales to track nearly every day from the cliff! In fact, the same, small, most likely juvenile, whale pictured in Catherine’s last post has returned several times, and we’ve nicknamed her “Buttons” due to two distinguishing white spots on her tail peduncle near the fluke. Though we tend to refer to Buttons as “her”, we cannot actually tell what the sex is definitively…until now. Remember in Catherine’s post when she described how Buttons defecated a lot, and how our team if, given the opportunity, is supposed to collect the feces when we’re out in the kayak for Leila’s project?  Everything from hormone levels to reproductive status to, yes, sex, is held in that poop! Well, Miss (or Mr.) Buttons was in Tichenor Cove today, and to our delight, she performed well in the defecation department once again. Florence and I were on cliff duty tracking her and Kelli and Catherine were in Tichenor on the kayak when we first noticed the defecation.  I then radioed down to the kayak team to stop what they were doing and paddle quickly to go collect it before it sank (Fig. 9).  Even in these situations, it is important to stay beyond 100 yards of the animal, as required by the MMPA. Florence and I cheered them on and our ladies did indeed get the poop sample, without disturbing the whale (Fig. 10). It was a sight to behold.

Fig. 9 Kelli and Catherine on a mission.
Fig. 9 Kelli and Catherine on a mission.
Fig. 10 Kelli and Catherine collecting the feces.
Fig. 10 Kelli and Catherine collecting the feces.

We were able to track Buttons for the remainder of our time on the cliff, and were extremely content with the day’s work as we packed all the gear up later in the afternoon. Right before we were about to leave, however, Buttons had one more big treat for us. As we looked to the harbor before starting the trek back to the truck, we paused briefly after noticing a large, white splash in the middle of the harbor, not far from the dock. We paused for a second and thought “No, it can’t be, was that —?” and then we see it again and unanimously yelled “BREACH!” Buttons breached about five times on her way back to Tichenor Cove from where she had been foraging in Mill Rocks. It is rare to see a gray whale breach, so this was really special. Florence managed to capture one of the breaches on video:

At first I thought a big ole humpback had arrived, but nope, it was our Buttons! I am in awe of this little whale, and am forever-grateful to be in the presence of these kinds of moments. She’s definitely made her splash here in Port Orford. I think our team has started to as well.

 

Bolnick, D. I., Svanback, R., Fordyce, J. A., Yang, L. H., Davis, J. M., Hulsey, C. D., & Forrister, M. L. (2003). Ecology of Individuals: Incidence and Implications of Individual Specialization. The American Naturalist, 161(1), 28.

Calambokidis, J., Laake, J. L., & Klimek, A. (2012). Updated analysis of abundance and population structure of seasonal gray whales in the Pacific Northwest, 1998-2010 (Vol. 2010).

Newell, C. (2009). Ecological Interrelationships Between Summer Resident Gray Whales (Eschrichtius robustus) and Their Prey, Mysid Shrimp (Holmesimysis sculpta and Neomysis rayi) along the Central Oregon Coast.