The pandemic has taught me that certain skills – including ones I never recognized as such – can atrophy. How do I construct an outfit that involves actual pants instead of gym shorts? How do I make a lunch that is portable and can be eaten outside my home?
These are things that I’ve had to relearn over the last year, as I increasingly leave my virtual work world and move back into the physical world. Recently, the new ways in which the world is opening up again have pushed me to brush off another skill – how do I talk to other people about my work?
The pandemic has necessarily made the world a bit more insular. A year and a half into my graduate career, I’ve mostly discussed my work within the cozy cocoon of my lab groups and cohort. In particular, I’ve lived the last few months in that realm of research that is so specific and internal that almost no one else fully understands or cares about what I’m doing: I’ve spent days tangled up in oodles of models, been woken up at night by dreams about coding, and sweated over the decimal points of statistical deviance-explained values.
This period of scientific navel gazing abruptly ended this February. In the space of ten days, I presented at my first in-person conference during graduate school, gave a short talk at my first international conference, and gave my longest talk yet to a public audience. After reveling in the minutiae of research for months, it was so valuable to be forced to take a step back, think about the overarching narrative of this work, and practice telling that story to different audiences.
A February talk for the Oregon chapter of the American Cetacean Society gave me the chance to tell the story of my research to a broad audience.
Presenting this work to an in-person audience for the first time was especially rewarding. With a physical (!) poster in hand, I headed out to Newport for the annual meeting of the Oregon Chapter of The Wildlife Society. The GEMM Lab really took this conference by storm – Leigh gave a plenary talk on the meeting’s theme of “Dynamic Oceans, Shifting Landscapes”, Lisa chaired a session and gave a talk about trophic relationships between kelp and whales, and Miranda presented a poster on the new Holistic Assessment of Living marine resources off the Oregon coast (HALO) project.
This great GEMM Lab presence gave me the opportunity to reference everyone else’s work as I shared my own, and to think about the body of work we do as a group and the coherence in research themes that different projects share. I almost lost my voice by talking for the entire duration of the poster session, and was energized by the opportunity to share this work with so many interested people.
The GEMM Lab and other OSU Marine Mammal Institute members presented alongside terrestrial researchers on the theme of “Dynamic Oceans, Shifting Landscapes”.
Originally scheduled for Hawaii, this meeting was instead held virtually as a safety precaution against Covid-19. Nevertheless, the diversity of talks and time spent gathering online still gave me the sense of being part of an international ocean science community. People attended from every time zone, and watching early-morning talks while wearing pajamas with Solene, Dawn, and Quin the dog is officially one of my new favorite conference experiences.
In addition to the chance to discuss science with other students and researchers, it was great to have the opportunity to step back from our normal routines a bit. The Krill Seeker Lab did the conference-organized 5K walk together (in intermittent rain, of course) and our team even came within one point of winning the trivia contest. All the while, we were hopping in and out of poster sessions and talks, realizing that virtual conferences can be just as busy as in-person ones.
Taking a 5k-long break from watching talks! From left to right: Rachel Kaplan, Kim Bernard, Giulia Wood, and Kirsten Steinke.
Over the last two years, one of the things the pandemic has made me appreciate the most is the ability to gather. Dinner with friends, holidays with family – the ability to be together is far more tentative and precious than I realized during the “before times.” Now, as we start tiptoeing back into normal life a bit more, I’m appreciating the ability to gather for science and looking forward to more conferences in the future.
Did you enjoy this blog? Want to learn more about marine life, research and conservation? Subscribe to our blog and get a weekly email when we make a new post! Just add your name into the subscribe box on the left panel.
By Allison Dawn, GEMM Lab Master’s student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab
During my second term of graduate school, I have been preparing to write my research proposal. The last two months have been an inspiring process of deep literature dives and brainstorming sessions with my mentors. As I discussed in my last blog, I am interested in questions related to pattern and scale (fine vs. mesoscale) in the context of the Pacific Coast Feeding Group (PCFG) of gray whales, their zooplankton prey, and local environmental variables.
My work currently involves exploring which scales of pattern are important in these trophic relationships and whether the dominant scale of a pattern changes over time or space. I have researched which analysis tools would be most appropriate to analyze ecological time series data, like the impressive long-term dataset the GEMM lab has collected in Port Orford as part of the TOPAZ project, where we have monitored the abundance of whales and zooplankton, as well as environmental variables since 2016.
A useful analytical tool that I have come across in my recent coursework and literature review is called wavelet analysis. Importantly, wavelet analysis can handle non-stationarity and edge detection in time series data. Non-stationarity is when a dataset’s mean and/or variance can change over time or space, and edge detection is the identification of the change location (in time or space). For example, it is not just the cycles or “ups and downs” of zooplankton abundance I am interested in, but when in time or where in space these cycles of “ups and downs” might change in relation to what their previous values, or distances between values, were. Simply stated, non-stationarity is when what once was normal is no longer normal. Wavelet analysis has been applied across a broad range of fields, such as environmental engineering (Salas et al. 2020), climate science (Slater et al. 2021), and bio-acoustics (Buchan et al. 2021). It can be applied to any time series dataset that might violate the traditional statistical assumption of stationarity.
In a recent review of climate science methodology, Slater et al. (2021) outlined the possible behavior of time series data. Using theoretical plots, the authors show that data can a) have the same mean and variance over time, or b) have non-stationarity that can be broken into three major groups – trend, step change, or shifts in variance. Figure 1 further demonstrates the difference between stationary vs. non-stationary data in relation to a given variable of interest over time.
Figure 1. Plots showing the possible magnitude of a given variable across a time series: a) Stationary behavior, b) Non-stationary trend, step-change, and a shift in variance. [Taken from Slater et. al(2021)].
Traditional correlation statistics assumes stationarity, but it has been shown that ecological time series are often non-stationary at certain scales (Cazelles & Hales, 2006). In fact, ecological data rarely meets the requirements of a controlled experiment that traditional statistics require. This non-stationarity of ecological data means that while widely-used methods like generalized linear models and analyses of variances (ANOVAs) can be helpful to assess correlation, they are not always sufficient on their own to describe the complex natural phenomena ecologists seek to explain. Non-stationarity occurs frequently in ecological time series, so it is appropriate to consider analysis tools that will allow us to detect edges to further investigate the cause.
Wavelet analysis can also be conducted across a time series of multiple response variables to assess if these variables share high common power (correlation). When data is combined in this way it is called a cross-wavelet analysis. An interesting paper used cross-wavelet analysis to assess the seasonal response of zooplankton life history in relation to climate warming (Winder et. al 2009). Results from their cross-wavelet analysis showed that warming temperatures over the past two decades increased the voltinism (number of broods per year) of copepods. The authors show that where once annual recruitment followed a fairly stationary pattern, climate warming has contributed to a much more stochastic pattern of zooplankton abundance. From these results, the authors contribute to the hypothesis that climate change has had a temporal impact on zooplankton population dynamics, and recruitment has increasingly drifted out of phase from the original annual cycles.
Figure 2. Cross-wavelet spectrum for immature and adult Leptodiaptomus ashlandi for 1965 through either 2000 or 2005. Plots show a) immatures and temperature, b) adults and temperature, c) immatures and phytoplankton, and d) adults and phytoplankton. Arrows indicate phase between combined time series. 0 degrees is in-phase and 180 degrees is anti-phase. Black contour lines show “cone of influence” or the 95% significance level, every value within the cone is considered significant. Left axis shows the temporal period, and the color legend shows wavelet frequency power, with low frequencies in blue and high frequencies in red. Plots show strong covariation of high common power at the 12-month period until the 1980s. This pattern is especially evident in plot c) and d). [Taken from (Winder et. al 2009)].
While wavelet and cross-wavelet analyses should not be the only tool used to explore data, due to its limitations with significance testing, it is still worth implementing to gain a better understanding of how time series variables relate to each other over multiple spatial and/or temporal scales. It is often helpful to combine multiple methods of analysis to get a larger sense of patterns in the data, especially in spatio-temporal research.
When conducting research within the context of climate change, where the concentration of CO2 in ppm in the atmosphere is a non-stationary time series itself (Figure 3), it is important to consider how our datasets might be impacted by climate change and wavelet analysis can help identify the scales of change.
When considering our ecological time series of data in Port Orford, we want to evaluate how changing ocean conditions may be related to data trends. For example, has the annual mean or variance of zooplankton abundance changed over time, and where has that change occurred in time or space? These changes might have occurred at different scales and might be invisible at other scales. I am eager to see if wavelet analysis can detect these sorts of changes in the abundance of zooplankton across our time series of data, particularly during the seasons of intense heat waves or upwelling.
Did you enjoy this blog? Want to learn more about marine life, research and conservation? Subscribe to our blog and get a weekly email when we make a new post! Just add your name into the subscribe box on the left panel.
References
Buchan, S. J., Pérez-Santos, I., Narváez, D., Castro, L., Stafford, K. M., Baumgartner, M. F., … & Neira, S. (2021). Intraseasonal variation in southeast Pacific blue whale acoustic presence, zooplankton backscatter, and oceanographic variables on a feeding ground in Northern Chilean Patagonia. Progress in Oceanography, 199, 102709.
Cazelles, B., & Hales, S. (2006). Infectious diseases, climate influences, and nonstationarity. PLoS Medicine, 3(8), e328.
Salas, J. D., Anderson, M. L., Papalexiou, S. M., & Frances, F. (2020). PMP and climate variability and change: a review. Journal of Hydrologic Engineering, 25(12), 03120002.
Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., … & Wilby, R. L. (2021). Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management. Hydrology and Earth System Sciences, 25(7), 3897-3935.
Winder, M., Schindler, D. E., Essington, T. E., & Litt, A. H. (2009). Disrupted seasonal clockwork in the population dynamics of a freshwater copepod by climate warming. Limnology and Oceanography, 54(6part2), 2493-2505.
Dr. Alejandro A. Fernández Ajó, Postdoctoral Scholar, Marine Mammal Institute – OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab.
I just returned to my home country, Argentina, after over 2 years without leaving the USA due to COVID-19 travel restrictions. Being back with my family, my friends, my culture, and speaking my native language feels great and relaxing. However, I returned to a country struggling to rebound from the coronavirus pandemic. I am afraid this post pandemic scenario places Argentina in a vulnerable situation. The need for economic growth could result in decisions or policies that, in the long term, hurt the country, leaving environmental damage for potential economic growth.
Argentina holds extensive oil and gas deposits, including the world’s second largest gas formation, Vaca Muerta. Although offshore (i.e., in the ocean) oil exploration and exploitation are not yet extensively developed, the intention of offshore gas and oil drilling is on the agenda. In July 2021, a public hearing was held with the purpose to consider the environmental impact assessment for carrying out seismic exploration in the North Argentinian basin off the southern coast of the Buenos Aires province. Over 90% of the participants, including scientists, researchers, technicians from various institutions, non-governmental organizations and representatives of the fishing sector spoke against the project and highlighted the negative impacts that such activity can generate on marine life, and to other socioeconomic activities such as tourism and fishing, not only in Argentina but at the regional level.
Thousands of people marched along the beaches and the main coastal cities of Argentina to protest against the approval for a seismic explorations project in the Argentinian basin. Photo source: prensaobrera.com
Seismic prospections are usually done with the purpose for oil and gas exploitation and less frequently for research purposes. In seismic prospections, ships carry out explosions with airguns, whose sound waves reach the seabed, bounce back and are captured by receivers on the ships to map the petroleum deposits in seafloor and identify potential areas for hydrocarbon extractions. The sound emitted by the seismic airguns can reach extremely loud levels of sounds that travel for thousands of miles underwater. Such extreme high levels of sound can alter the behavior of many marine species, from the smallest planktonic species, to the largest marine mammals, masking their communication, causing physical and physiological stress, interfering with their vital functions, and reducing the local availability of prey (Di Iorio & Clark, 2010; Hildebrand, 2009; Weilgart, 2018).
Here you can listen to a short audio clip of a seismic airgun firing every ~8 seconds, a typical pattern. Close your eyes and imagine you are a whale living in this environment. Now, put the clip on loop and play it for three months straight. This would be the soundscape that whales living in a region of oil and gas exploration hear, as seismic surveys often last 1-4 months (see our previous post “Hearing is believing” for more details).
Despite the public rejection and the mounting evidence about the negative impacts and environmental risks associated with such activities, the government approved the initiation of the seismic prospection off the southern coast of Buenos Aires. In response, thousands of people marched along the beaches and the main coastal cities of Argentina to protest against the oil exploration project. The areas where the seismic surveys will be carried out overlap largely with the southern right whale’s migration routes and feeding areas during their spring and summer (Figure 1). Likewise, the area overlaps with highly productive areas in the ocean that hosts great biodiversity of species of ecological and commercial importance, including the feeding areas of seabirds, turtles and other marine mammals. Additionally, the seismic activity will endanger the health of the beaches of the coast of Buenos Aires and Uruguay where thousands of tourists spend the summer to escape from the large cities.
Figure 1. The map on the left is showing (light blue squares CAN_100, CAN_108, and CAN_114) the areas where seismic prospections are proposes. The map on the right is showing the individual satellite track lines for eleven individual southern right whales (SRW) during the feeding season. You can observe that the proposed area for seismic explorations overlaps with critical feeding habitat for the SRW. Source: Whale Conservation Institute of Argentina (ICB).
The impacts of these activities to marine wildlife are difficult to control and monitor (Elliott et al. 2019, Gordon et al, 2003), especially for large whales that are a very challenging taxa to study (Hunt et al. 2013). We know that the ability to perceive biologically important sounds is critical to marine mammals, and acoustic disturbance through human-generated noise can interfere with their natural functions. Sounds from seismic surveys are intense and have peak frequency bands overlapping those used by baleen whales (Di Lorio & Clark, 2010); however, evidence of interference with baleen whale acoustic communication, and the effects on their health and physiology are sparse. In this context, the GEMM Lab project GRANITE (Gray Whale Response to Ambient Noise Informed by Technology and Ecology), plans to generate information to fulfill these knowledge gaps and provide tools to aid conservation and management decisions in terms of allowable noise level in whale habitats. I am hopeful such information will reach decision makers and influence their decisions, however, sometimes it is frustrating to see how evidence-based information generated with high quality standards are often ignored.
The recent approval of the seismic exploration in Argentina is an example of my frustration. There is no way that the oil industry can guarantee a low-risk of impact on biodiversity and the environment. There are too many examples of environmental catastrophes related to the oil industries at sea that speak for themselves. Moreover, the promotion of such activities goes against the compromises assumed by the country to work to mitigate the effects of Climate Change, and to achieve the reductions of the greenhouse gas emissions to comply with the Paris Agreement. Decades of research help recognized the areas that would be impacted by these seismic activities as key habitat for the life cycle of whales, penguins, seals and more. But, apparently all these scientific data are ignored at the time of weighing the tradeoffs between “economic development” and environmental impacts. As a conservation biologist, I am questioning what can be done in order to be heard and significantly influence such decisions.
Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly alert when we make a new post! Just add your name into the subscribe box on the left panel.
References:
Di Iorio, L., & Clark, C. W. (2010). Exposure to seismic survey alters blue whale acoustic communication. Biology Letters, 6(1), 51–54. https://doi.org/10.1098/rsbl.2009.0651
Weilgart, L. (2018). The impact of ocean noise pollution on fish and invertebrates. Report for OceanCare, Switzerland.
Elliott, B. W., Read, A. J., Godley, B. J., Nelms, S. E., & Nowacek, D. P. (2019). Critical information gaps remain in understanding impacts of industrial seismic surveys on marine vertebrates. In Endangered Species Research (Vol. 39, pp. 247–254). Inter-Research. https://doi.org/10.3354/esr00968
Gordon, J., Gillespie, D., Potter, J., Frantzis, A., Simmonds, M. P., Swift, R., & Thompson, D. (2003). A review of the effects of seismic surveys on marine mammals. Marine Technology Society Journal, 37(4), 16-34.
Hunt, K. E., Moore, M. J., Rolland, R. M., Kellar, N. M., Hall, A. J., Kershaw, J., Raverty, S. A., Davis, C. E., Yeates, L. C., Fauquier, D. A., Rowles, T. K., & Kraus, S. D. (2013). Overcoming the challenges of studying conservation physiology in large whales: a review of available methods. Conservation Physiology, cot006–cot006. https://doi.org/10.1093/conphys/cot006
Dr. KC Bierlich, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab
The recent advancement in drones (or unoccupied aircraft systems, UAS) has greatly enhanced opportunities for scientists across a broad range of disciplines to collect high-resolution aerial imagery. Wildlife researchers in particular have utilized this technology to study large elusive animals, such as whales, to observe their behavior (see Clara Bird’s blog) and obtain morphological measurements via photogrammetry (see previous blog for a brief history on photogrammetry and drones). However, obtaining useful measurement data is not as easy as flying the drone and pressing record. For this blog, I will provide a brief overview on the basics of using photogrammetry to extract morphological measurements from images collected with drones, as well as the associated uncertainty from using different drone platforms.
During my PhD at Duke University, I co-developed an open-source photogrammetry software called MorphoMetriX to measure whales in images I collected using drones (Fig. 1) (Torres and Bierlich, 2020) (see this blog for some fieldwork memoirs!). The software is designed to be flexible, simple to use, and customizable without knowledge of scripting languages. Using MorphoMetriX, measurements are made in pixels and then multiplied by the ground sampling distance (GSD) to convert to standard units (e.g., meters) (Fig. 2A). GSD represents the distance on the ground each pixel represents (i.e., the linear size of the pixel) and therefore sets the scale of the image (i.e., cm per pixel). Figure 2A describes how GSD is dependent on the camera sensor, focal length lens, and altitude. Thus, drones equipped with different cameras and focal length lenses will have inherent differences in GSD as altitude increases (Fig. 2B). A larger GSD increases the length each pixel represents in a photo and results in a lower resolution image, potentially obscuring important features in the photo and introducing measurement error.
Figure 1. An example of a Pacific Coast Feeding Group gray whale measured in MorphoMetriX (Torres & Bierlich, 2020).Figure 2: Overview of photogrammetry methods and calculating ground sampling distance (GSD). A) Photogrammetry methods for how each image is scaled to convert measurements in pixels to standard units (e.g., meters). Altitude is the distance between the camera lens and whale (usually at the surface of the water). Figure from Torres and Bierlich (2020). B) The exact (not accounting for distortion or altitude error) ground sampling distance (GSD) for two drone platforms commonly used to obtain morphological measurements of cetaceans. The difference in GSD between the P4Pro and Inspire 1 is due to the difference in sensor width and focal lengths of the cameras used. Figure from Bierlich et al. (2021).
Obtaining accurate altitude information is a key component in obtaining accurate measurements. All drones are equipped with a barometer, which measures altitude from changes in pressure. In general, barometers usually yield low accuracy in the altitude recorded, particularly for low-cost sensors commonly found on small, off-the-shelf drones (Wei et al., 2016). Dawson et al. (2017) added a laser altimeter (i.e., LightWare SF11/C, https://www.mouser.com//datasheet//2//321//28054-SF11-Laser-Altimeter-Manual-Rev8-1371857.pdf) to a drone, which yields higher accuracy in the altitude recorded. Since then, several studies have adopted use of a laser altimeter to study different species of baleen whales (i.e., Gough et al., 2019; Christiansen et al., 2018).
The first chapter of my dissertation, which was published last year in Marine Ecology Progress Series, compared the accuracy of several drones equipped with different camera sensors, focal length lenses, and a barometer vs. laser altimeter (Bierlich et al., 2021). We flew each drone over a known sized object floating at the surface and collected images at various altitudes (between 10 – 120 m). We used the known size of the floating object to determine the percent error of each measurement at each altitude. We found that 1) there is a lot of variation in measurement error across the different drones when using a barometer to measure altitude and 2) using a laser altimeter dramatically reduces measurement error for each drone (Fig. 3).
Figure 3. The % error for measurements from different drones. Black dashed line represents 0% error (true length = 1.48 m). The gray dashed lines represent under- and over-estimation of the true length by 5% (1.41 and 1.55 m, respectively).
These findings are important because if a study is analyzing measurements that are from more than one drone, the uncertainty associated with those measurements must be taken into account to know if measurements are reliable and comparable. For instance, let’s say we are comparing the body length of two different populations and found that population A is significantly longer than population B. From looking at Figure 3, that significant difference in length between population A and B could be unreliable as the difference may be due to the bias introduced by the type of drone, camera sensor, focal length lens, and whether a barometer or laser altimeter was used for recording altitude. In other words, without incorporating uncertainty associated with each measurement, how do you trust your measurement?
Hence, the National Institute of Standards and Technology (NIST) states that a measurement is complete only when accompanied by a quantitative statement of its uncertainty (Taylor & Kuyatt, 1994). In our Bierlich et al. (2021) study, we develop a Bayesian statistical model where we use the measurements of the known-sized object floating at the surface (what was used for Fig. 3) as training data to predict the lengths of unknown-sized whales. This Bayesian approach views data and the underlying parameters that generated the data (such as the mean or standard deviation) as random, and thus can be described by a statistical distribution. Using Bayes’ Theorem, a model of the observed data (called the likelihood function), is combined with prior knowledge pertaining to the underlying parameters (called the prior probability distribution) to form the posterior probability distribution, which serves as updated knowledge about the underlying parameter. For example, if someone told me they saw a 75 ft blue whale, I would not be phased. But if someone told me they saw a 150 ft blue whale, I would be skeptical – I’m using prior knowledge to determine the probability of this statement being true.
The posterior probability distribution produced by this Bayesian approach can also serve as new prior information for subsequent analyses. Following this framework, we used the known-sized objects to first estimate the posterior probability distribution for error for each drone. We then used that posterior probability distribution for error specific to each drone platform as prior information to form a posterior predictive distribution for length of unknown-sized whales. The length of an individual whale can then be described by the mean of this second posterior predictive distribution, and its uncertainty defined as the variance or an interval around the mean (Fig. 4).
Figure 4. An example of a posterior predictive distribution for total length of an individual blue whale. The black bars represent the uncertainty around the mean value (the black dot) – the longer black bars represent the 95% highest posterior density (HPD) interval, and the shorter black bars represent the 65% HPD interval.
For over half a decade, the GEMM Lab has been collecting drone images of Pacific Coast Feeding Group (PCFG) of gray whales off the coast of Oregon to measure their morphology and body condition (see GRANITE Project Blog). We have been using several different types of drones equipped with different cameras, focal length lenses, barometers, and laser altimeters. These measurements from different drones will inherently have different levels of error associated with them, so adapting these methods for incorporating uncertainty will be key to ensure our measurements are comparable and analyses are robust. To do this, we fly over a known-sized board (1 m) at the start of each flight to use as training data to generate a posterior predictive distribution for length of the an unknown-sized PCFG gray whale that we fly over (Fig. 5). Likewise, we are working closely with several other collaborators who are also using different drones. Incorporating measurement uncertainty from drones used across research labs and in different environments will help ensure robust analyses and provide great opportunity for some interesting comparisons – such as differences in gray whale body condition on their feeding grounds in Oregon vs. their breeding grounds in Baja, Mexico, and morphological comparisons with other baleen whale species, such as blue and humpback whales. We are currently wrapping up measurement from thousands of boards (Fig. 5) and whales (Fig. 1) from 2016 – 2021, so stay tuned for the results!
Figure 5. An example of a known-sized object (1 m long board) used as training data to assess measurement uncertainty.
References
Bierlich, K.C., Schick, R.S., Hewitt, J., Dale, J., Goldbogen, J.A., Friedlaender, A.S., Johnston D.J. (2021). A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS. Marine Ecology Progress Series. DOI: https://doi.org/10.3354/meps13814
Christiansen F, Vivier F, Charlton C, Ward R, Amerson A, Burnell S, Bejder L (2018) Maternal body size and condition determine calf growth rates in southern right whales. Mar Ecol Prog Ser 592: 267−281
Dawson SM, Bowman MH, Leunissen E, Sirguey P (2017) Inexpensive aerial photogrammetry for studies of whales and large marine animals. Front Mar Sci 4: 366
Gough, W.T., Segre, P.S., Bierlich, K.C., Cade, D.E., Potvin, J., Fish, F. E., Dale, J., di Clemente, J., Friedlaender, A.S., Johnston, D.W., Kahane-Rapport, S.R., Kennedy, J., Long, J.H., Oudejans, M., Penry, G., Savoca, M.S., Simon, M., Videsen, S.K.A., Visser, F., Wiley, D.N., Goldbogen, J.A. (2019). Scaling of swimming performance in baleen whales. Journal of Experimental Biology, 222(20).https://doi.org/10.1242/jeb.204172
Taylor, B. N., and Kuyatt, C. E. (1994). Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results. Washington, DC: National Institute of Standards and Technology. 1–25.
Torres, W.I., & Bierlich, K.C. (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), 1825. https://doi.org/10.21105/joss.01825
Wei S, Dan G, Chen H (2016) Altitude data fusion utilizing differential measurement and complementary filter. IET Sci Meas Technol (Singap) 10: 874−879
“Why don’t you just automate it?” This is a question I am frequently asked when I tell someone about my work. My thesis involves watching many hours of drone footage of gray whales and meticulously coding behaviors, and there are plenty of days when I have asked myself that very same question. Streamlining my process is certainly appealing and given how wide-spread and effective machine learning methods have become, it is a tempting option to pursue. That said, machine learning is only appropriate for certain research questions and scales, and it’s important to consider these before investing in using a new tool.
The application of machine learning methods to behavioral ecology is called computational ethology (Anderson & Perona, 2014). To identify behaviors from videos, the model tracks individuals across video frames and identifies patterns of movement that form a behavior. This concept is similar to the way we identify a whale as traveling if it’s moving in a straight line and as foraging if it’s swimming in circles within a small area (Mayo & Marx, 1990, check out this blog to learn more). The level of behavioral detail that the model is able to track depends on the chosen method (Figure 1, Pereira et al., 2020). These methods range from tracking each animal as a simple single point (called a centroid) to tracking the animal’s body positioning in 3D (this method is called pose estimation), which range from providing less detailed to more detailed behavior definitions. For example, tracking an individual as a centroid could be used to classify traveling and foraging behaviors, while pose estimation could identify specific foraging tactics.
Figure 1. Figure from Pereira et al. (2020) illustrating the different methods of animal behavior tracking that are possible using machine learning.
Pose estimation involves training the machine learning algorithm to track individual anatomical features of an individual (e.g., the head, legs, and tail of a rat), meaning that it can define behaviors in great detail. A behavior state could be defined as a combination of the angle between the tail and the head, and the stride length.
For example, Mearns et al. (2020) used pose estimation to study how zebrafish larvae in a lab captured their prey. They tracked the tail movements of individual larvae when presented with prey and classified these movements into separate behaviors that allowed them to associate specific behaviors with prey capture (Figure 2). The authors found that these behaviors occurred in a specific sequence, that the behaviors kept the prey within the larvae’s line of sight, and that the sequence was triggered by visual cues. In fact, when they removed the visual cue of the prey, the larvae terminated the behavior sequence, meaning that the larvae are continually choosing to do each behavior in the sequence, rather than the sequence being one long behavior event that is triggered only by the initial visual cue. This study is a good example of the applicability of machine learning models for questions aimed at kinematics and fine-scale movements. Pose estimation has also been used to study the role of facial expression and body language in rat social communication (Ebbesen & Froemke, 2021).
Figure 2. Excerpt from figure 1 of Mearns et al. (2020) illustrating (A) the camera set up for their experiment, (B) how the model tracked the eye angles and tail of the larvae fish, (C) the kinematics extracted from the footage. In panel (C) the top plot shows how the eyes converged on the same object (the prey) during prey capture event, the middle plot shows when the tail was curved to the left or the right, and the bottom plot shows the angle of the tail tip relative to the body.
While previous machine learning methods to track animal movements required individuals to be physically marked, the current methods can perform markerless tracking (Pereira et al., 2020). This improvement has broadened the kinds of studies that are possible. For example, Bozek et al., (2021) developed a model that tracked individuals throughout an entire honeybee colony and showed that certain individual behaviors were spatially distributed within the colony (Figure 3). Machine learning enabled the researchers to track over 1000 individual bees over several months, a task that would be infeasible for someone to do by hand.
Figure 3. Excerpt from figure 1 of Bozek et al., (2021) showing how individual bees and their trajectories were tracked.
These studies highlight that the potential benefits of using machine learning when studying fine scale behaviors (like kinematics) or when tracking large groups of individuals. Furthermore, once it’s trained, the model can process large quantities of data in a standardized way to free up time for the scientists to focus on other tasks.
While machine learning is an exciting and enticing tool, automating behavior detection via machine learning could be its own PhD dissertation. Like most things in life, there are costs and benefits to using this technique. It is a technically difficult tool, and while applications exist to make it more accessible, knowledge of the computer science behind it is necessary to apply it effectively and correctly. Secondly, it can be tedious and time consuming to create a training dataset for the model to “learn” what each behavior looks like, as this step involves manually labeling examples for the model to use.
As I’ve mentioned in a previous blog, I came quite close to trying to study the kinematics of gray whale foraging behaviors but ultimately decided that counting fluke beats wasn’t necessary to answer my behavioral research questions. It was important to consider the scale of my questions (as described in Allison’s blog) and I think that diving into more fine-scale kinematics questions could be a fascinating follow-up to the questions I’m asking in my PhD.
For instance, it would be interesting to quantify how gray whales use their flukes for different behavior tactics. Do gray whales in better body condition beat their flukes more frequently while headstanding? Does the size of the fluke affect how efficiently they can perform certain tactics? While these analyses would help quantify the energetic costs of different behaviors in better detail, they aren’t necessary for my broad scale questions. Consequently, taking the time to develop and train a pose estimation machine learning model is not the best use of my time.
That being said, I am interested in applying machine learning methods to a specific subset of my dataset. In social behavior, it is not only useful to quantify the behaviors exhibited by each individual but also the distance between them. For example, the distance between a mom and her calf can be indicative of the calves’ dependence on its mom (Nielsen et al., 2019). However, continuously measuring the distance between two individuals throughout a video is tedious and time intensive, so training a machine learning model could be an effective use of time. I plan to work with an intern this summer to develop a machine learning model to track the distance between pairs of gray whales in our drone footage and then relate this distance data with the manually coded behaviors to examine patterns in social behavior (Figure 4). Stay tuned to learn more about our progress!
Figure 4. A mom and calf pair surfacing together. Image collected under NOAA/NMFS permit #21678
Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get a weekly alert when we make a new post! Just add your name into the subscribe box on the left panel.
References
Anderson, D. J., & Perona, P. (2014). Toward a Science of Computational Ethology. Neuron, 84(1), 18–31. https://doi.org/10.1016/j.neuron.2014.09.005
Bozek, K., Hebert, L., Portugal, Y., Mikheyev, A. S., & Stephens, G. J. (2021). Markerless tracking of an entire honey bee colony. Nature Communications, 12(1), 1733. https://doi.org/10.1038/s41467-021-21769-1
Ebbesen, C. L., & Froemke, R. C. (2021). Body language signals for rodent social communication. Current Opinion in Neurobiology, 68, 91–106. https://doi.org/10.1016/j.conb.2021.01.008
Mayo, C. A., & Marx, M. K. (1990). Surface foraging behaviour of the North Atlantic right whale, Eubalaena glacialis , and associated zooplankton characteristics. Canadian Journal of Zoology, 68(10), 2214–2220. https://doi.org/10.1139/z90-308
Mearns, D. S., Donovan, J. C., Fernandes, A. M., Semmelhack, J. L., & Baier, H. (2020). Deconstructing Hunting Behavior Reveals a Tightly Coupled Stimulus-Response Loop. Current Biology, 30(1), 54-69.e9. https://doi.org/10.1016/j.cub.2019.11.022
Nielsen, M., Sprogis, K., Bejder, L., Madsen, P., & Christiansen, F. (2019). Behavioural development in southern right whale calves. Marine Ecology Progress Series, 629, 219–234. https://doi.org/10.3354/meps13125
Pereira, T. D., Shaevitz, J. W., & Murthy, M. (2020). Quantifying behavior to understand the brain. Nature Neuroscience, 23(12), 1537–1549. https://doi.org/10.1038/s41593-020-00734-z
Over six field seasons the GEMM lab team has conducted nearly 500 drone flights over gray whales, equaling over 100 hours of footage. These hours of footage are the central dataset for my PhD dissertation, so it’s up to me to process them all. This process can be challenging, tedious, and daunting, but it is also quite fun and a privilege to be the one person who gets to watch all the footage. It’s fascinating to get to know the whales and their behaviors and pick up on patterns. It motivates me to get through this video processing step and start doing the data analysis. Recently, it’s been especially fun to notice patterns that I’ve seen mentioned in the literature. One example is adult social behavior.
There are two categories of social behavior that I’m interested in studying: maternal behavior, defined as interactions between a mom and its calf, and general social behaviors, defined as social interactions between non-mom/calf pairs. In this blog I’ll focus on general social behaviors, but if you’re interested in maternal behavior check out this blog. General social behavior, which I’ll refer to as social behavior moving forward, includes tactile interactions and promiscuous behaviors (Torres et al. 2018; Clip 1). While gray whales in the PCFG range are primarily foraging, researchers have observed increases in social behavior towards the end of the foraging season (Stelle et al., 2008; Torres et al., 2018). We think that this indicates that the whales are starting to focus less on feeding and more on breeding. This tradeoff of foraging vs. socializing time is interesting because it comes at an energetic cost.
Clip 1. Example of social interaction between a male and female gray whale off the coast of Oregon, USA. Collected under NOAA/NMFS permit #21678
Broadly, animals need to balance the energetic demands of survival with those of reproduction. They need to reproduce to pass on their genes, but reproduction is energetically demanding, and animals also need to survive and grow to be able to reproduce. The decision to reproduce is costly because reproduction requires energetic investment and time investment since animals do not forage (gaining energy) when they are socializing. Consequently, only animals with sufficient energy reserves (i.e., body condition) to invest in reproduction actually engage in reproduction. Given these costs associated with reproduction, we expect to see a relationship between social behavior and body condition (Green, 2001) with mainly animals in good body condition engaging in social behavior because these animals have sufficient reserves to sustain the cost. Furthermore, since body condition is an indicator of foraging success and prey availability, environmental conditions can also affect social behavior and reproduction through this pathway.
Rahman et al. (2014) used a lab experiment to study the relationship between nutritional stress and male guppy courtship behavior (Figure 1). In their experiment they tested for the effects of both decreased diet quantity and quality on the frequency of male courtship behaviors. Rahman et al (2014) found that individuals in the low-quantity group were significantly smaller than those in the high-quality group and that diet quantity had a significant effect on the frequency of courtship behaviors. Males fed a low-quantity diet performed fewer courtship behaviors. Interestingly, there was no significant effect of diet quality on courtships behavior, although there was some evidence of an interaction effect, which suggests that within the low-quantity group, males fed with high-quality food performed more courtship behaviors that those fed with low-quality food. This study is interesting because it shows how foraging success (diet quantity and quality) can affect courting behavior.
Figure 1. A guppy (Rahman et al., 2013)
However, guppies are not the ideal species for comparison to gray whales because gray whales and guppies have quite different life history traits. A more fitting comparison would be with an example species with more in common with gray whales, such as viviparous capital breeders. Viviparous animals develop the embryo inside the body and give live birth. Capital breeders forage to build energy reserves and then rely on those energy reserves during reproduction. Surprisingly, I found asp vipers to be a good example species for comparison to gray whales.
Asp vipers (Figure 2) are viviparous snakes who are considered capital breeders because they forage prior to hibernation, and then begin reproduction immediately following hibernation without additional foraging. Naulleau & Bonnet (1996) conducted a field study on female asp vipers to determine if there was a difference in body condition at the start of the breeding season between females who reproduced or not during that season. To do this they marked individuals and measured their body condition at the start of the breeding season and then recaptured those individuals at the end of the breeding season and recorded whether the individual had reproduced. Interestingly, they found that there was a strongly significant difference in body condition between females that did and did not reproduce. In fact, they discovered that no female below a certain body condition value reproduced, meaning that they found a body condition threshold for reproduction.
Figure 2. An asp viper
Additionally, a study on water pythons found that their body condition threshold for reproduction shifted over time in response to prey availability (Madsen & Shine, 1999). These authors found that females lowered their threshold after several consecutive years of poor prey availability. These studies are really exciting to me because they address questions that the GRANITE project team is interested in tackling.
Understanding the relationship between body condition and reproduction in gray whales is an important puzzle piece for our work. The aim of the GRANITE project is to understand how the effects of stressors on individual whales scales up to population level impacts (read Lisa’s blog to learn more). Reproduction rates play a big role in population dynamics, so it is important to understand what factors affect reproduction. Since we’re studying these whales on their foraging grounds, assessing body condition provides an important link between foraging behavior and reproduction.
For example, if an individual’s response to a stressor is to forage less, that may lead to poorer body condition, meaning that they may be less likely to reproduce. While reduced reproduction in one individual may not have a big effect on the population, the same response from multiple individuals could impact the population’s dynamics (i.e., increasing or decreasing abundance). Understanding these different relationships between behavior, body condition, and reproduction rates is a big undertaking, but it’s exciting to be a member of the GRANITE team as this strong group of scientists works to bring together different data streams to work on this big picture question. We’re all deep into data processing right now so stay tuned over the next few years to learn more about gray whale social behavior and to find out if fat whales are more social than skinny whales.
Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get weekly updates and more! Just add your name into the subscribe box on the left panel.
References
Green, A. J. (2001). Mass/Length Residuals: Measures of Body Condition or Generators of Spurious Results? Ecology, 82(5), 1473–1483. https://doi.org/10.1890/0012-9658(2001)082[1473:MLRMOB]2.0.CO;2
Madsen, T., & Shine, R. (1999). The adjustment of reproductive threshold to prey abundance in a capital breeder. Journal of Animal Ecology, 68(3), 571–580. https://doi.org/10.1046/j.1365-2656.1999.00306.x
Naulleau, G., & Bonnet, X. (1996). Body Condition Threshold for Breeding in a Viviparous Snake. Oecologia, 107(3), 301–306.
Rahman, M. M., Kelley, J. L., & Evans, J. P. (2013). Condition-dependent expression of pre- and postcopulatory sexual traits in guppies. Ecology and Evolution, 3(7), 2197–2213. https://doi.org/10.1002/ece3.632
Rahman, M. M., Turchini, G. M., Gasparini, C., Norambuena, F., & Evans, J. P. (2014). The Expression of Pre- and Postcopulatory Sexually Selected Traits Reflects Levels of Dietary Stress in Guppies. PLOS ONE, 9(8), e105856. https://doi.org/10.1371/journal.pone.0105856
Stelle, L. L., Megill, W. M., & Kinzel, M. R. (2008). Activity budget and diving behavior of gray whales (Eschrichtius robustus) in feeding grounds off coastal British Columbia. Marine Mammal Science, 24(3), 462–478. https://doi.org/10.1111/j.1748-7692.2008.00205.x
Torres, L. G., Nieukirk, S. L., Lemos, L., & Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science, 5(SEP). https://doi.org/10.3389/fmars.2018.00319
Dr. Alejandro Fernández Ajó, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab
Rises in ocean temperatures can lead to multiple alterations in marine ecosystems, including the increase and the frequency of Harmful Algal Blooms (HABs). HABs are characterized by the rapid growth of toxin-producing species of algae that can be harmful to people, animals, and the local ecology, even causing death in severe cases. Species of marine diatom within the genus Pseudo-nitzschia and Nitzschia can form HABs when they produce domoic acid (DA), a potent neurotoxin responsible for amnesic shellfish poisoning (D’Agostino et al., 2018, 2017).
Figure 1. Southern right whale (E. australis) mother and calf swimming at the gulfs of Peninsula Valdes, Argentina, during a phytoplankton bloom. Photo: Mariano Sironi / Instituto de Conservacion de Ballenas de Argentina.
During HABs, DA is transferred to higher organisms through the pelagic food web and is accumulated by intermediate vectors, such as copepods, euphausiids (i.e., krill), shellfish, and fish. As this neurotoxin affects top predators, DA poisoning poses a risk to the safety and health of humans and wildlife. This neurotoxin has caused mortality in many marine mammal species, including both pinnipeds and cetaceans (Gulland 1999; Lefebvre et al. 1999; Fire et al. 2010, 2021; Broadwater et al. 2018). In addition, the exposure to DA constitutes a stressor that may affect glucocorticoids (hormones involved in the stress response) concentrations.
The glucocorticoids (GCs; cortisol and corticosterone) are adrenal steroid hormones that maintain the essential functions of metabolism and energy balance in mammals. GCs can increase sharply in response to environmental stressors to elicit physiological and behavioral adaptations by individuals to support survival (Sapolsky et al. 2000; Bornier et al. 2009). However, with the chronic exposure to a stressor, this relationship can reverse, with GCs sometimes declining below its baseline levels (Dickens and Romero, 2013; Fernández Ajó et al., 2018). Moreover, DA can interfere with the stress response in mammals, and cause alterations in their physiological response. DA is an excitatory amino acid analog of glutamate (Pulido 2008), a well-known brain neurotransmitter that play an important role in the activation of the adrenal axis (which in turn regulate the production and secretion of the GCs) and regulate many of the pituitary hormones involved in the stress response (Brann and Mahesh 1994; Johnson et al. 2001). Hence, monitoring GC levels in marine mammals can be a potential useful metric for assessing the physiological impacts of exposure to DA.
Glucocorticoids are traditionally measured in plasma, but given that plasma sampling from free-ranging large whales is currently impossible, alternative sample types such as fecal samples, among others, can be utilized to quantify GCs in large whales (Ajó et al., 2021; Burgess et al., 2018, 2016; Fernández Ajó et al., 2020, 2018; Hunt et al., 2019, 2014, 2006; Rolland et al., 2017, 2005)(Figure 2). The analyses of fecal glucocorticoid metabolites (fGCm) is particularly useful for endocrine assessments of free-swimming whales, with several studies showing that fGCm correlate in meaningful ways with presumed stressors. For example, high levels of fGCm in North Atlantic right whales (NARW, Eubalaena glacialis) and in gray whales (Eschrichtius robustus) correlate with poor body condition (Hunt et al., 2006; Lemos et al., 2021), and fGCm increases were associated with whale entanglements and ship strikes (i.e., Lemos et al., 2020; Rolland et al., 2017).
Figure 2. Alternative samples types can be used to study hormones in large whales. 1-2-3 are sample types that can be obtained from free-living whales and provide a more instantaneous and acute measurement of the whales´ physiology. 4-5 can be obtained at necropsy when the whale is found dead at the beach and provide an integrated measure of the whale physiology that can expand through years or even the lifespan of an individual.
In Península Valdés, Argentina, southern right whales (SRW, E. australis) gather in large numbers to mate and nurse their calves during the austral winter months (Bastida and Rodríguez, 2009). SRWs are capital breeders, largely fasting during the breeding season and instead relying on stored blubber fuel reserves. However, they can occasionally feed on calanoid copepods (D’Agostino et al., 2018, 2016), particularly during the phytoplankton blooms that are dominated by diatoms of the genus Pseudo-nitzschia (Sastre et al. 2007; D’Agostino et al. 2015, 2018). Therefore, feeding SRWs in Península Valdés temporally overlap with these Pseudo-nitzschia blooms (D’Agostino et al. 2018, 2015) and represents a test case for assessing the relationship of DA exposure with GC levels (Figure 3).
Figure 3. Southern right whale (E. australis) skim feeding at the Peninsula Valdes breeding ground. Photo: Lucas Beltranino.
In our recent scientific publication (D’Agostino et al. 2021), we investigate SRW exposure to DA at their breeding ground in Peninsula Valdes and assessed its effects on fecal glucocorticoid concentrations. Although the sample size of this study is unavoidably small due to the difficulties of obtaining fecal samples from whales at their calving grounds where defecation is infrequent, we observed significantly lower fGCm in samples from whales exposed to DA (Figure 4). Our results agree with findings from a previous study in California sea lions (Zalophus californianus) exposed to DA, where these authors found a significant association of DA exposure with reduced serum cortisol (Gulland et al., 2009), which can be tentatively attributed to abnormal function of the adrenal axis due to the exposure.
Figure 4. Fecal glucocorticoid metabolite levels in southern right whales exposed (YES, solid triangles) and not-exposed (NO, open circles) to DA. Left panel: immunoreactive fecal corticosterone metabolites. Right panel: immunoreactive fecal cortisol metabolites. Hormone concentrations are expressed in ng of immunoreactive hormone per gram of dry fecal sample. Significant differences between groups are denoted with an asterisk (P<0.05). The black solid line indicates the mean for each group, and in parenthesis is the sample size for each group. Adapted from D’Agostino et al. 2021.
If ingestion of toxins produced by phytoplankton can result in long-term suppression of baseline GCs, whales and marine mammals in general, could suffer reduced ability to cope with additional stressors. The adrenal function is essential to maintain circulating blood glucose and other aspects of metabolism within normal bounds. Additionally, the ability to elevate GCs facilitates energy mobilization to physiologically cope with a stressful event and to initiate appropriate behavioral responses (i.e., flee from predators, heal wounds). Various toxicants have been shown to reduce adrenal function across taxa (Romero and Wingfield, 2016) and could have negative consequences on the ability of cetaceans to respond and adapt to ongoing environmental and anthropogenic changes. Compounding this problem, whales are exposed to an increasing number of stressors from multiple sources and with cumulative effects and they need to be able to physiologically respond to continue to reproduce and survive.
To our knowledge, this study provides the first quantification of fGCm levels in whales exposed to DA; and we hope this effort starts a growing dataset to which other researchers can add. Sampling and analysis of non-traditional matrices, such as feces, blubber, baleen and others, would likely increase sample sizes and thus our understanding of the interrelationships among DA exposure and age, sex, and reproductive status of cetaceans. Given that chronic exposure to DA could alter the capacity of animals to respond to stress, and indications that HABs are becoming more frequent and intense world-wide (Van Dolah 2000; Masó et al. 2006; Erdner et al. 2008), we believe that research evaluating the health status of marine mammal populations should include the assessment of stress physiology relative to natural and anthropogenic stressors including exposure to toxicants.
Did you enjoy this blog? Want to learn more about marine life, research, and conservation? Subscribe to our blog and get weekly updates and more! Just add your name into the subscribe box on the left panel.
References
Bastida R, Rodríguez D (2009) Ballena franca austral [Southern right whale]. In: Mazzini V (ed) Mamíferos marinos de Patagonia y Antártida [Marine mammals of Patagonia and Antarctica]. Zagier & Urruty Publications, Buenos Aires, pp 72–84.
Bonier F, Moore IT, Martin PR, Robertson RJ (2009) The relationship between fitness and baseline glucocorticoids in a passerine bird. Gen Comp Endocrinol 163:208–213. https:// doi. org/ 10. 1016/j.ygcen. 2008. 12. 013.
Brann DW, Mahesh VB (1994) Excitatory amino acids: function and significance in reproduction and neuroendocrine regulation. Front Neuroendocrinol 15:3–49. https:// doi. org/ 10. 1006/ frne. 1994. 1002.
Broadwater MH, Van Dolah FM, Fire SE (2018) Vulnerabilities of marine mammals to harmful algal blooms. Harmful Algal Blooms 2:191–222.
Burgess, E.A., Hunt, K.E., Kraus, S.D., Rolland, R.M., 2016. Get the most out of blow hormones: Validation of sampling materials, field storage and extraction techniques for whale respiratory vapour samples. Conserv. Physiol. 4, cow024. https://doi.org/10.1093/conphys/cow024
Burgess, E.A., Hunt, K.E., Kraus, S.D., Rolland, R.M., 2018. Quantifying hormones in exhaled breath for physiological assessment of large whales at sea. Sci. Rep. 8, 10031. https://doi.org/10.1038/s41598-018-28200-8
D’Agostino VC, Hoffmeyer MS, Almandoz GO, Sastre V, Degrati M., 2015. Potentially toxic Pseudo-nitzschia species in plankton and fecal samples of Eubalaena australis from Península Valdés calv-ing ground, Argentina. J Sea Res 106:39–43. https:// doi. org/ 10.1016/j. seares.
D’Agostino, V.C., Degrati, M., Santinelli, N., Sastre, V., Dans, S.L., Hoffmeyer, M.S., 2018. The seasonal dynamics of plankton communities relative to the foraging of the southern right whale (Eubalaena australis) in northern Patagonian gulfs, Península Valdés, Argentina. Cont. Shelf Res. 164, 45–57. https://doi.org/10.1016/j.csr.2018.06.003
D’Agostino, V.C., Degrati, M., Sastre, V., Santinelli, N., Krock, B., Krohn, T., Dans, S.L., Hoffmeyer, M.S., 2017. Domoic acid in a marine pelagic food web: Exposure of southern right whales Eubalaena australis to domoic acid on the Península Valdés calving ground, Argentina. Harmful Algae 68, 248–257. https://doi.org/10.1016/j.hal.2017.09.001
D’Agostino, V.C., Hoffmeyer, M.S., Degrati, M., 2016. Faecal analysis of southern right whales (Eubalaena australis) in Península Valdés calving ground, Argentina: Calanus australis, a key prey species. J. Mar. Biol. Assoc. United Kingdom 96, 859–868. https://doi.org/10.1017/S0025315415001897
Dickens, M.J., Romero, L.M., 2013. A consensus endocrine profile for chronically stressed wild animals does not exist. Gen. Comp. Endocrinol. 191, 177–189. https://doi.org/10.1016/j.ygcen.2013.06.014
Erdner DL, Dyble J, Parsons ML, Stevens RC, Hubbard KA, Wrabel ML, Moore SK, Lefebvre KA, Anderson DM, Bienfang P, Bidi-gare RR, Parker MS, Moeller P, Brand LE, Trainer VL (2008) Centers for Oceans and Human Health: a unified approach to the challenge of harmful algal blooms. Environ Health 7:S2. https://doi. org/ 10. 1186/ 1476- 069X-7- S2- S2.
Fernández Ajó, A., Hunt, K.E., Dillon, D., Uhart, M., Sironi, M., Rowntree, V., Buck, C.L., 2021. Optimizing hormone extraction protocols for whale baleen: tackling questions of solvent:sample ratio and variation. Gen. Comp. Endocrinol. 113828. https://doi.org/10.1016/j.ygcen.2021.113828
Fernández Ajó, A.A., Hunt, K.E., Giese, A.C., Sironi, M., Uhart, M., Rowntree, V.J., Marón, C.F., Dillon, D., DiMartino, M., Buck, C.L., 2020. Retrospective analysis of the lifetime endocrine response of southern right whale calves to gull wounding and harassment: A baleen hormone approach. Gen. Comp. Endocrinol. 296, 113536. https://doi.org/10.1016/j.ygcen.2020.113536
Fernández Ajó, A.A., Hunt, K.E., Uhart, M., Rowntree, V., Sironi, M., Marón, C.F., Di Martino, M., Buck, C.L., 2018. Lifetime glucocorticoid profiles in baleen of right whale calves: potential relationships to chronic stress of repeated wounding by Kelp Gulls. Conserv. Physiol. 6, 1–12. https://doi.org/10.1093/conphys/coy045
Fire SE, Bogomolni A, DiGiovanni RA Jr, Early G, Leighfield TA, Matassa K, Miller GA, Moore KM, Moore M, Niemeyer M, Pugliares K (2021) An assessment of temporal, spatial and taxonomic trends in harmful algal toxin exposure in stranded marine mammals from the US New England coast. PLoS ONE 16(1):e0243570. https:// doi. org/ 10. 1371/ journ al. pone. 02435 70
Fire SE, Wang Z, Berman M, Langlois GW, Morton SL, Sekula-Wood E, Benitez-Nelson CR (2010) Trophic transfer of the harmful algal toxin domoic acid as a cause of death in a minke whale (Balaenoptera acutorostrata) stranding in southern California. Aquat Mamm 36(4):342–350. https:// doi. org/ 10. 1578/ AM. 36.4.2010. 342.
Gulland F.M., 1999. Domoic acid toxicity in California sea lions stranded along the central California Coast, May-October 1998. NOAA Tech. Memo. NMFS-OPR-8. USA National Marine Fisheries Service, US Department of Commerce.
Gulland FMD, Hall AJ, Greig DJ, Fram ER, Colegrove KM, Booth RKN, Wasser SK, S.-M.C., 2009. Gulland, Hall – 2012 – Evaluation of circulating eosinophil count and adrenal gland function in California sea lions naturally exposed t. J. Am. Vet. Med. Assoc. 241, 943–949.
Gulland, F., Pérez-Cortés, H., Urbán, J.R., Rojas-Bracho, L., Ylitalo, G., Weir, J., Norman, S., Muto, M., Rugh, D., Kreuder, C., Rowles, T., 2005. Eastern North Pacific gray whale (Eschrichtius robustus) unusual mortality event, 1999-2000. U.S. Dep. Commer. NOAA Tech. Memo. NMFS-AFSC-150. 33 pp.
Hunt, K.E., Robbins, J., Buck, C.L., Bérubé, M., Rolland, R.M., 2019. Evaluation of fecal hormones for noninvasive research on reproduction and stress in humpback whales (Megaptera novaeangliae). Gen. Comp. Endocrinol. 280, 24–34. https://doi.org/10.1016/j.ygcen.2019.04.004
Hunt, K.E., Rolland, R.M., Kraus, S.D., Wasser, S.K., 2006. Analysis of fecal glucocorticoids in the North Atlantic right whale (Eubalaena glacialis). Gen. Comp. Endocrinol. 148, 260–272. https://doi.org/10.1016/j.ygcen.2006.03.012
Hunt, K.E., Stimmelmayr, R., George, C., Hanns, C., Suydam, R., Brower, H., Rolland, R.M., 2014. Baleen hormones: a novel tool for retrospective assessment of stress and reproduction in bowhead whales (Balaena mysticetus). Conserv. Physiol. 2, cou030–cou030. https://doi.org/10.1093/conphys/cou030
Johnson MP, Kelly G, Chamberlain M (2001) Changes in rat serum corticosterone after treatment with metabotropic glutamate receptor agonists or antagonists. J Neuroendocrinol 13:670–677. https:// doi. org/ 10. 1046/j. 1365- 2826. 2001. 00678.x.
Lefebvre KA, Powell CL, Busman M, Doucette GJ, Moeller PDR, Sliver JB, Miller PE, Hughes MP, Singaram S, Silver MW, Tjeer-dema RS (1999) Detection of domoic acid in northern anchovies and California sea lions associated with an unusual mortality event. Nat Toxins 7(3):85–92. https:// doi. org/ 10. 1002/ (SICI) 1522-7189(199905/ 06)7: 3% 3C85:: AID- NT39% 3E3.0. CO;2-Q.
Lefebvre, K.A., Kendrick, P.S., Ladiges, W., Hiolski, E.M., Ferriss, B.E., Smith, D.R., Marcinek, D.J., 2017. Chronic low-level exposure to the common seafood toxin domoic acid causes cognitive deficits in mice. Harmful Algae 64, 20–29. https://doi.org/10.1016/j.hal.2017.03.003
Lemos, L.S., Olsen, A., Smith, A., Burnett, J.D., Chandler, T.E., Larson, S., Hunt, K.E., Torres, L.G., 2021. Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Mar. Mammal Sci. 1–11. https://doi.org/10.1111/mms.12877
Lemos, L.S., Olsen, A., Smith, A., Chandler, T.E., Larson, S., Hunt, K., Torres, L.G., 2020. Assessment of fecal steroid and thyroid hormone metabolites in eastern North Pacific gray whales. Conserv. Physiol. 8. https://doi.org/10.1093/conphys/coaa110
Masó M, Garcés E., 2006. Harmful microalgae blooms (HAB); prob-lematic and conditions that induce them. Mar Pollut Bull 53:620–630. https:// doi. org/ 10. 1016/j. marpo lbul. 2006. 08. 006.
Pulido O.M., 2008. Domoic acid toxicologic pathology: a review. Mar Drugs 6(2):180–219. https:// doi. org/ 10. 3390/ md602 0180.
Rolland, R., McLellan, W., Moore, M., Harms, C., Burgess, E., Hunt, K., 2017. Fecal glucocorticoids and anthropogenic injury and mortality in North Atlantic right whales Eubalaena glacialis. Endanger. Species Res. 34, 417–429. https://doi.org/10.3354/esr00866
Rolland, R.M., Hunt, K.E., Kraus, S.D., Wasser, S.K., 2005. Assessing reproductive status of right whales (Eubalaena glacialis) using fecal hormone metabolites. Gen. Comp. Endocrinol. 142, 308–317. https://doi.org/10.1016/j.ygcen.2005.02.002
Romero, M.L., Wingfield, J.C., 2016. Oxford series in behavioral neuroendocrinology. Tempests, poxes, predators, and people: stress in wild animals and how they cope 1–2.
Sastre V, Santinelli N, Marino G, Solís M, Pujato L, Ferrario M., 2007. First detection of domoic acid produced by Pseudo-nitzschia spe-cies, Chubut coastal waters, Patagonia, Argentina. Harmful Algae News 34:12–14.
Van Dolah FM., 2000. Marine algal toxins: origins, health effects, and their increased occurrence. Environ Health Perspect 108:133–141. https:// doi. org/ 10. 1289/ ehp. 00108 s1133
By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
Humans are fascinated by food. We want to know its source, its nutrient content, when it was harvested and by whom, and so much more. Since childhood, I was the nagging child who interrogated wait staff about the seafood menu because I cared about the sustainability aspect as well as consuming ethically-sourced seafood. Decades later I still do the same: ask a myriad of questions from restaurants and stores in order to eat as sustainably as possible. But in addition to asking these questions about my food, I also question what my study species eats and why. My study populations, common bottlenose dolphins, are described as top opportunistic predators (Norris and Prescott 1961, Shane et al. 1986, Barros and Odell 1990). In my study area off of California, this species exists in two ecotypes. The coastal ecotype off of California, USA are generalist predators, feeding on many different species of fish using different foraging techniques (Ballance 1992, Shane 1990). The offshore ecotype, on the other hand, is less well-studied, but is frequently observed in association with sperm whales, although the reason is still unknown (Díaz-Gamboa et al. 2018). Stable isotope analysis from skin samples from the two ecotypes indicates that the ecotypes exhibit different foraging strategies based on different isotopic carbon and nitrogen levels (Díaz-Gamboa et al. 2018).
Growing up, I kept the Monterey Bay Aquarium’s Seafood Watch Guide with me to choose the most sustainably-sourced seafood at restaurants. Today there is an easy-to-use application for mobile phones that replaced the paper guide. (Image Source: https://www.seafoodwatch.org/)
Preliminary and historical data on common bottlenose dolphins (Tursiops truncatus) suggest that the coastal ecotype spend more time near estuary mouths than offshore dolphins (Ballance 1992, Kownacki et al. unpublished data). Estuaries contain large concentrations of nutrients from runoff, which support zooplankton and fishes. It is for this reason that these estuaries are thought to be hotspots for bottlenose dolphin foraging. Some scientists hypothesize that these dolphins are estuarine-based prey specialists (Barros and Odell 1990), or that the dolphins simply aggregate in estuaries due to higher prey abundance (Ballance 1992).
Coastal bottlenose dolphins traveling near an estuary mouth in San Diego, CA. (Photographed under NOAA NMFS Permit # 19091).
In an effort to understand diet compositions of bottlenose dolphins, during coastal surveys seabirds were recorded in association with feeding groups of dolphins. Therefore, it is reasonable to believe that dolphins were feeding on the same fishes as Brown pelicans, blue-footed and brown boobies, double-crested cormorants, and magnificent frigatebirds, seeing as they were the most common species associated with bottlenose dolphin feeding groups (Ballance 1992). A shore-based study by Hanson and Defran (1993) found that coastal dolphins fed more often in the early morning and late afternoon, as well as during periods of high tide current. These patterns may have to do with the temporal and spatial distribution of prey fish species. From the few diet studies conducted on these bottlenose dolphins in this area, 75% of the prey were species from the families Ebiotocidae (surf perches) and Sciaendae (croakers) (Norris and Prescott 1961, Walker 1981). These studies, in addition to optimal foraging models, suggest this coastal ecotype may not be as much of a generalist as originally suggested (Defran et al. 1999).
A redtail surfperch caught by a fisherman from a beach in San Diego, CA. These fish are thought to be common prey of coastal bottlenose dolphins. (Image Source: FishwithJD)
Diet studies on the offshore ecotype of bottlenose dolphins worldwide show a preference for cephalopods, similar to other toothed cetaceans who occupy similar regions, such as Risso’s dolphin, sperm whales, and pilot whales (Clarke 1986, Cockcroft and Ross 1990, Gonzalez et al. 1994, Barros et al. 2000, Walker et al. 1999). Because these animals seldom strand on accessible beaches, stomach contents analyses are limited to few studies and isotope analysis is more widely available from biopsies. We know these dolphins are sighted in deeper waters than the habitat of coastal dolphins where there are fewer nutrient plumes, so it is reasonable to hypothesize that the offshore ecotype consumes different species and may be more specialized than the coastal ecotype.
An bottlenose dolphin forages on an octopus. (Image source: Mandurah Cruises)
For a species that is so often observed from shore and boats, and is known for its charisma, it may be surprising that the diets of both the coastal and offshore bottlenose dolphins are still largely unknown. Such is the challenge of studying animals that live and feed underwater. I wish I could simply ask a dolphin, much like I would ask staff at restaurants: what is on the menu today? But, unfortunately, that is not possible. Instead, we must make educated hypotheses about the diets of both ecotypes based on necropsies and stable isotope studies, and behavioral and spatial surveys. And, I will continue to look to new technologies and creative thinking to provide the answers we are seeking.
Literature cited:
Ballance, L. T. (1992). Habitat use patterns and ranges of the bottlenose dolphin in the Gulf of California, Mexico. Marine Mammal Science, 8(3), 262-274.
Barros, N.B., and D. K. Odell. (1990). Food habits of bottlenose dolphins in the southeastern United States. Pages 309-328 in S. Leatherwood and R. R. Reeves, eds. The bottlenose dolphin. Academic Press, San Diego, CA.
Barros, N., E. Parsons and T. Jefferson. (2000). Prey of bottlenose dolphins from the South China Sea. Aquatic Mammals 26:2–6.
Clarke, M. 1986. Cephalopods in the diet of odontocetes. Pages 281–321 in M. Bryden and R. Harrison, eds. Research on dolphins. Clarendon Press, Oxford, NY.
Cockcroft, V., and G. Ross. (1990). Food and feeding of the Indian Ocean bottlenose dolphin off southern Natal, South Africa. Pages 295–308 in S. Leatherwood and R. R. Reeves, eds. The bottlenose dolphin. Academic Press, San Diego, CA.
Defran, R. H., Weller, D. W., Kelly, D. L., & Espinosa, M. A. (1999). Range characteristics of Pacific coast bottlenose dolphins (Tursiops truncatus) in the Southern California Bight. Marine Mammal Science, 15(2), 381-393.
Díaz‐Gamboa, R. E., Gendron, D., & Busquets‐Vass, G. (2018). Isotopic niche width differentiation between common bottlenose dolphin ecotypes and sperm whales in the Gulf of California. Marine Mammal Science, 34(2), 440-457.
Gonzalez, A., A. Lopez, A. Guerra and A. Barreiro. (1994). Diets of marine mammals stranded on the northwestern Spanish Atlantic coast with special reference to Cephalopoda. Fisheries Research 21:179–191.
Hanson, M. T., and Defran, R. H. (1993). The behavior and feeding ecology of the Pacific coast bottlenose dolphin, Tursiops truncatus. Aquatic Mammals, 19, 127-127.
Norris, K. S., and J. H. Prescott. (1961). Observations on Pacific cetaceans of Californian and Mexican waters. University of California Publications of Zoology 63:29, 1-402.
Shane, S. H. (1990). Comparison of bottlenose dolphin behavior in Texas and Florida, with a critique of methods for studying dolphin behavior. Pages 541-558 in S. Leatherwood and R. R. Reeves, eds. The bottlenose dolphin. Academic Press, San Diego, CA.
Shane, S., R. Wells and B. Wursig. (1986). Ecology, behavior and social organization of bottlenose dolphin: A review. Marine Mammal Science 2:34–63.
Walker, W.A. (1981). Geographical variation in morphology and biology of the bottlenose dolphins (Tursiops) in the eastern North Pacific. NMFS/SWFC Administrative Report. No, LJ-91-03C.
Walker, J., C. Potter and S. Macko. (1999). The diets of modern and historic bottlenose dolphin populations reflected through stable isotopes. Marine Mammal Science 15:335–350.
Another year has come and gone, and with the final days of 2019 upon us, it is fulfilling to look back and summarize all of the achievements in the GEMM Lab this year. So, snuggle up with your favorite holiday drink and enjoy our recap of 2019!
We wrapped up two intense but rewarding gray whale field seasons this summer. Our project investigating the health of Pacific Coast Feeding Group (PCFG) gray whales through fecal hormone and body condition sampling in the context of ocean noise went into its fourth year, while the Port Orford project where we track whales and prey at a very fine-scale celebrated its wood anniversary (five years!). The dedication and hard work of lots of people to help us collect our data meant that we were able to add a considerable amount of samples to our growing gray whale datasets. Our trusty red RHIB Ruby zipped around the Pacific and enabled us to collect 58 fecal samples, fly the drone 102 times, undertake 105 GoPro drops and record 141 gray whale sightings. Our Newport crew was a mix of full-time GEMMers (Leigh, Todd, Dawn, Leila, Clara, and myself) as well as part-time summer GEMMers (Ale, Sharon, and Cassy). Further south in Port Orford, my team of undergraduate and high school students and I had an interesting field season. We only encountered four different individuals (Buttons, Glacier, Smudge, and Primavera), however saw them repeatedly throughout the month of August, resulting in as many as 15 tracklines for one individual. Furthermore, we collected 249 GoPro drops and 248 zooplankton net samples.
Leila taking photos of gray whales from Ruby’s bow pulpit. Photo: Leigh Torres
2019 Port Orford team members Anthony & Lisa collecting prey samples from research kayak ‘Robustus’.
Gray whale fluke. Photo: Lisa Hildebrand.
The GEMM Lab’s fieldwork was not just restricted to gray whales. After last year’s successes aboard the NOAA ship Bell M. Shimada, Alexa and Dawn both boarded the ship again this year as marine mammal observers for the May and September cruises, respectively. They spied humpback, blue, sperm, and fin whales, as well as many dolphins and seabirds, adding to the GEMM Lab’s growing database of megafauna distribution off the Oregon coast.
Alexa observing on the R/V Shimada in May 2019, all bundled up. Image Photo: Alexa Kownacki
Dawn Barlow on the flying bridge of NOAA Ship Bell M. Shimada, heading out to sea with the Newport bridge in the background. Photo: Anna Bolm.
After winning the prestigious L’Oréal-UNESCO For Women in Science fellowship and the inaugural Louis Herman Scholarship, GEMM Lab grad Solène Derville lead her first research cruise aboard the French R/V Alis. She and her team conducted line transect surveys and micronekton/oceanographic sampling over several seamounts to try to solve the mystery of why humpbacks hang out there. We are also very excited to announce that Solène will be returning to the GEMM Lab as a post-doc in 2020! She will be creating distribution models of whales off the coast of Oregon with the data collected by Leigh during helicopter flights with the US Coast Guard. The primary aim of this work is to identify potential whale hotspots in an effort to avoid spatial overlap with fisheries gear and reduce entanglement risk.
Solène soaking wet after spending several hours observing cetaceans and seabirds on R/V Alis. Photo: Jérôme Jambou
A group of bottlenose dolphins observed over one of the seamounts. Photo: Elodie Vourey
Solène at the L’Oréal ceremony in the French National Museum of Natural History in Paris. Photo: Jean-Charles Caslot
Switching the focus from marine mammals to seabirds, Rachael has had an extremely busy year of field work all across the globe. She island-hopped from Midway (Hawaiian Northwest island) to the Falkland Islands in the first half of the year, and is currently overwintering on South Georgia, where she will be until end of February. Rachael is tracking albatross at all three locations by tagging individual birds to understand movements relative to fishing vessels and flight energetics.
Albatross chick. Photo: Rachael Orben
Recording data. Photo: V. Ternisien
Albatross chick and mother. Photo: Rachael Orben.
Besides several field efforts, the GEMM Lab was also busy disseminating our research and findings to various audiences. Our conferences kicked off in late February when Leigh and Rachael both flew to Kauai to present at the Pacific Seabird Group’s 46th Annual Meeting. In the spring, Leila, Dawn, Alexa, Dom, and myself drove to Seattle where the University of Washington hosted the Northwest Student Society of Marine Mammalogy chapter meeting and we all gave talks. Additionally, the Fisheries & Wildlife grad students in the lab also presented at the department’s annual Research Advances in Fisheries, Wildlife, and Ecology conference. Later in the year, Dom and I attended the State of the Coast conference where Dom was invited to participate in a panel about the holistic approaches to management in the nearshore while I presented a poster on preliminary findings of my Master’s thesis. Most recently, the entire GEMM Lab (bar Rachael) flew to Barcelona to present at the World Marine Mammal Conference (WMMC).
GEMM Lab at the WMMC. Photo: Karen Lohman
Our science communication and outreach efforts were not just restricted to conferences though. Over the course of this year, the GEMM Lab supervised a total of 10 undergraduate and high school interns that assisted in a variety of ways (field and/or lab work, data analyses, independent projects) on a number of projects going on in the lab. Leigh and Dawn boarded the R/V Oceanus in the fall to co-lead a STEM research cruise aimed at providing high school students and teachers hands-on marine research. Dawn and I were guests on Inspiration Dissemination, a live radio show run by graduate students about graduate research going on at OSU. Our weekly blog, now in its fifth year, reached its highest viewership with a total of 14,814 views this year!
The GEMMers were once again prolific writers too! The 13 new publications in 10 scientific journals include contributions from Leigh (7), Rachael (6), Solène (2), Dawn (2), and Leila (1). Scroll down to the end of the post to see the list.
Academic milestones were also reached by several of us. Most notably and recently, Dom successfully defended his Master’s thesis this past week – congratulations Dom!! Unsurprisingly, he already has a job lined up starting in January as a Science Officer with the California Ocean Science Trust. Dom is the 6th GEMM Lab graduate, which after just five years of the GEMM Lab existing is a huge testament to Leigh as an advisor. Leila, who is in the 4th year of her PhD, anticipates finishing this coming March. We also had three successful research reviews – I met with my committee in late March to discuss my Master’s proposal, while Alexa and Dawn met with their committees in the summer to review their PhD proposals. All three reviews were fruitful and successful. And we want to highlight the success of a GEMM Lab grad, Florence Sullivan, who started a job in Maui with the Pacific Whale Foundation in September as a Research Analyst.
Dom during his MS seminar. Photo: Leila Lemos
Post-defense happiness. Photo: Karen Lohman
Leigh was recognized for her expertise in gray whale ecology and was appointed to the IUCN Western Gray Whale Advisory Panel (WGWAP). The western gray whales are a critically endangered population. At one point in the 1960s, the population was so scarce that they were believed to have been extinct. While this concern did not prove to be the case, the population still is not doing well, which is why the IUCN formed WGWAP to provide advice on the conservation of the western gray whales. Leigh was appointed to the panel this year and traveled to Switzerland and Russia for meetings.
Clara aboard Ruby on her first day of gray whale field work in Oregon. Photo: Leigh Torres
We are excited about a new addition to the lab. Clara Bird started her MS in Wildlife Science in the Department of Fisheries & Wildlife this fall. She jumped straight into field work when she came in early September and got a taste of the Pacific. Clara joins us from the Duke University where she did her undergraduate degree and worked for the past year in their Marine Robotics and Remote Sensing Lab. Clara is digging into the gray whale drone footage collected over the last four field seasons and scrutinize them from a behavioral point of view.
If you are reading this post, we would like to say that we really appreciate your support and interest in our work! We hope you will continue to join us on our journeys in 2020. Until then, happy holidays from the GEMM Lab!
GEMM Lab at the beginning of June with some permanents GEMMs and some temporary summer GEMM helpers.
Barlow, D. R., M. Fournet, and F. Sharpe. 2019. Incorporating tides into the acoustic ecology of humpback whales. Marine Mammal Science 35:234-251.
Barlow, D. R., A. L. Pepper, and L. G. Torres. 2019. Skin deep: an assessment of New Zealand blue whale skin condition. Frontiers in Marine Science doi.org/10.3389/fmars.2019.00757.
Baylis, A. M. M., R. A. Orben, A. A. Arkhipkin, J. Barton, R. L. Brownell Jr., I. J. Staniland, and P. Brickle. 2019. Re-evaluating the population size of South American fur seals and conservation implications. Aquatic Conservation: Marine and Freshwater Ecosystems 29(11):1988-1995.
Baylis, A. M. M., M. Tierney, R. A. Orben, et al. 2019. Important at-sea areas of colonial breeding marine predators on the southern Patagonian Shelf. Scientific Reports 9:8517.
Cockerham, S., B. Lee, R. A. Orben, R. M. Suryan, L. G. Torres, P. Warzybok, R. Bradley, J. Jahncke, H. S. Young, C. Ouverney, and S. A. Shaffer. 2019. Microbial biology of the western gull (Larus occidentalis). Microbial Ecology 78:665-676.
Derville, S., L. G. Torres, R. Albertson, O. Andrews, C. S. Baker, P. Carzon, R. Constantine, M. Donoghue, C. Dutheil, A. Gannier, M. Oremus, M. M. Poole, J. Robbins, and C. Garrigue. 2019. Whales in warming water: assessing breeding habitat diversity and adaptability in Oceania’s changing climate. Global Change Biology 25(4):1466-1481.
Derville, S., L. G. Torres, R. Dodémont, V. Perard, and C. Garrigue. 2019. From land and sea, long-term data reveal persistent humpback whale (Megaptera novaeangliae) breeding habitat in New Caledonia. Aquatic Conservation: Marine and Freshwater Ecosystems 29(10):1697-1711.
Fleischman, A. B., R. A. Orben, N. Kokubun, A. Will, R. Paredes, J. T. Ackerman, A. Takahashi, A. S. Kitaysky, and S. A. Shaffer. 2019. Wintering in the western Subantarctic Pacific increases mercury contamination of red-legged kittiwakes. Environmental Science & Technology 53(22):13398-13407.
Holdman, A. K., J. H. Haxel, H. Klinck, and L. G. Torres. 2019. Acoustic monitoring reveals the times and tides of harbor porpoise (Phocoena phocoena) distribution off central Oregon, U.S.A. Marine Mammal Science 35:164-186.
Kroeger, C., D. E. Crocker, D. R. Thompson, L. G. Torres, P. Sagar, and S. A. Shaffer. 2019. Variation in corticosterone levels in two species of breeding albatrosses with divergent life histories: responses to body condition and drivers of foraging behavior. Physiological and Biochemical Zoology 92(2):223:238.
Loredo, S. A., R. A. Orben, R. M. Suryan, D. E. Lyons, J. Adams, and S. W. Stephensen. 2019. Spatial and temporal diving behavior of non-breeding common murres during two summers of contrasting ocean conditions. Journal of Experimental Biology and Ecology 517:13-24.
Monteiro, F., L. S. Lemos, J. Fulgêncio de Moura, R. C. C. Rocha, I. Moreira, A. P. Di Beneditto, H. A. Kehrig, I. C. A. C. Bordon, S. Siciliano, T. D. Saint’Pierre, and R. A. Hauser-Davis. 2019. Subcellular metal distributions and metallothionein associations in rough-toothed dolphins (Steno bredanensis) from southeastern Brazil. Marine Pollution Bulletin 146:263-273.
Orben, R. A., A. B. Fleischman, A. L. Borker, W. Bridgeland, A. J. Gladics, J. Porquez, P. Sanzenbacher, S. W. Stephensen, R. Swift, M. W. McKown, and R. M. Suryan. 2019. Comparing imaging, acoustics, and radar to monitor Leach’s storm-petrel colonies. PeerJ 7:e6721.
Yates, K. L., …, L. G. Torres, et al. 2019. Outstanding challenges in the transferability of ecological models. Trends in Ecology & Evolution 33(10):790-802.
By Clara Bird, Masters Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
Hi everyone! My name is Clara Bird and I am
the newest graduate student in the GEMM lab. For my master’s thesis I will be
using drone footage of gray whales to study their foraging ecology. I promise
to talk about how cool gray whales in a following blog post, but for my first effort
I am choosing to write about something that I have wanted to explain for a
while: algorithms. As part of previous research projects, I developed a few semi-automated
image analysis algorithms and I have always struggled with that jargon-filled
phrase. I remember being intimidated by the term algorithm and thinking that I
would never be able to develop one. So, for my first blog I thought that I
would break down what goes into image analysis algorithms and demystify a term
that is often thrown around but not well explained.
What is an algorithm?
The dictionary broadly defines an
algorithm as “a step-by-step procedure for solving a problem or accomplishing
some end” (Merriam-Webster). Imagine an algorithm as a flow chart (Fig. 1), where
each step is some process that is applied to the input(s) to get the desired
output. In image analysis the output is usually isolated sections of the image
that represent a specific feature; for example, isolating and counting the
number of penguins in an image. Algorithm development involves figuring out
which processes to use in order to consistently get desired results. I have
conducted image analysis previously and these processes typically involve figuring
out how to find a certain cutoff value. But, before I go too far down that
road, let’s break down an image and the characteristics that are important for
image analysis.
Figure 1. An example of a basic algorithm flow chart. There are two inputs: variables A and B. The process is the calculation of the mean of the two variables.
What is an image?
Think of an image as a spread sheet,
where each cell is a pixel and each pixel is assigned a value (Fig. 2). Each
value is associated with a color and when the sheet is zoomed out and viewed as
a whole, the image comes together. In
color imagery, which is also referred to as RGB, each pixel is associated with
the values of the three color bands (red, green, and blue) that make up that
color. In a thermal image, each pixel’s value is a temperature value. Thinking
about an image as a grid of values is helpful to understand the challenge of
translating the larger patterns we see into something the computer can interpret.
In image analysis this process can involve using the values of the pixels
themselves or the relationships between the values of neighboring pixels.
Our brains take in the whole
picture at once and we are good at identifying the objects and patterns in an
image. Take Figure 3 for example: an astute human eye and brain can isolate and
identify all the different markings and scars on the fluke. Yet, this process
would be very time consuming. The trick to building an algorithm to conduct
this work is figuring out what processes or tools are needed to get a computer
to recognize what is marking and what is not. This iterative process is the algorithm
development.
Figure 3. Photo ID image of a gray whale fluke.
Development
An image analysis algorithm will
typically involve some sort of thresholding. Thresholds are used to classify an
image into groups of pixels that represent different characteristics. A
threshold could be applied to the image in Figure 3 to separate the white color
of the markings on the fluke from the darker colors in the rest of the image.
However, this is an oversimplification, because while it would be pretty simple
to examine the pixel values of this image and pick a threshold by hand, this threshold
would not be applicable to other images. If a whale in another image is a
lighter color or the image is brighter, the pixel values would be different
enough from those in the previous image for the threshold to inaccurately
classify the image. This problem is why a lot of image analysis algorithm
development involves creating parameterized processes that can calculate the
appropriate threshold for each image.
One successful method used to
determine thresholds in images is to first calculate the frequency of color in
each image, and then apply the appropriate threshold. Fletcher et al. (2009)
developed a semiautomated algorithm to detect scars in seagrass beds from
aerial imagery by applying an equation to a histogram of the values in each
image to calculate the threshold. A histogram is a plot of the frequency of
values binned into groups (Fig. 4). Essentially, it shows how many times each value
appears in an image. This information can be used to define breaks between
groups of values. If the image of the fluke were transformed to a gray scale, then
the values of the marking pixels would be grouped around the value for white
and the other pixels would group closer to black, similar to what is shown in
Figure 4. An equation can be written that takes this frequency information and
calculates where the break is between the groups. Since this method calculates
an individualized threshold for each image, it’s a more reliable method for
image analysis. Other characteristics could also be used to further filter the
image, such as shape or area.
However, that approach is not the
only way to make an algorithm applicable to different images; semi-automation
can also be helpful. Semi-automation involves some kind of user input. After
uploading the image for analysis, the user could also provide the threshold, or
the user could crop the image so that only the important components were maintained.
Keeping with the fluke example, the user could crop the image so that it was
only of the fluke. This would help reduce the variety of colors in the image
and make it easier to distinguish between dark whale and light marking.
Figure 4. Example histogram of pixel values. Source: Moallem et al. 2012
Why algorithms are important
Algorithms are helpful because they
make our lives easier. While it would be possible for an analyst to identify
and digitize each individual marking from a picture of a gray whale, it would
be extremely time consuming and tedious. Image analysis algorithms significantly
reduce the time it takes to process imagery. A semi-automated algorithm that I
developed to count penguins from still drone imagery can count all the penguins
on a one km2 island in about 30 minutes, while it took me 24 long hours
to count them by hand (Bird et al. in prep). Furthermore, the process
can be repeated with different imagery and analysts as part of a time series
without bias because the algorithm eliminates human error introduced by
different analysts.
Whether it’s a simple combination
of a few processes or a complex series of equations, creating an algorithm requires
breaking down a task to its most basic components. Development involves
translating those components step by step into an automated process, which after
many trials and errors, achieves the desired result. My first algorithm project
took two years of revising, improving, and countless trials and errors. So, whether creating an algorithm or working
to understand one, don’t let the jargon nor the endless trials and errors stop
you. Like most things in life, the key is to have patience and take it one step
at a time.
References
Bird, C. N., Johnston, D.W., Dale, J. (in prep).
Automated counting of Adelie penguins (Pygoscelis adeliae) on Avian and
Torgersen Island off the Western Antarctic Peninsula using Thermal and
Multispectral Imagery. Manuscript in preparation
Fletcher, R. S., Pulich, W. ‡, & Hardegree, B. (2009). A Semiautomated Approach for Monitoring Landscape Changes in Texas Seagrass Beds from Aerial Photography. https://doi.org/10.2112/07-0882.1
Moallem, Payman & Razmjooy, Navid. (2012). Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization. Journal of Applied Research and Technology. 703.
