New publication by GEMM Lab reveals sub-population health differences in gray whales 

Dr. KC Bierlich, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab

In a previous blog, I discussed the importance of incorporating measurement uncertainty in drone-based photogrammetry, as drones with different sensors, focal length lenses, and altimeters will have varying levels of measurement accuracy. In my last blog, I discussed how to incorporate photogrammetric uncertainty when combining multiple measurements to estimate body condition of baleen whales. In this blog, I will highlight our recent publication in Frontiers in Marine Science (https://doi.org/10.3389/fmars.2022.867258) led by GEMM Lab’s Dr. Leigh TorresClara Bird, and myself that used these methods in a collaborative study using imagery from four different drones to compare gray whale body condition on their breeding and feeding grounds (Torres et al., 2022).

Most Eastern North Pacific (ENP) gray whales migrate to their summer foraging grounds in Alaska and the Arctic, where they target benthic amphipods as prey. A subgroup of gray whales (~230 individuals) called the Pacific Coast Feeding Group (PCFG), instead truncates their migration and forages along the coastal habitats between Northern California and British Columbia, Canada (Fig. 1). Evidence from a recent study lead by GEMM Lab’s Lisa Hildebrand (see this blog) found that the caloric content of prey in the PCFG range is of equal or higher value than the main amphipod prey in the Arctic/sub-Arctic regions (Hildebrand et al., 2021). This implies that greater prey density and/or lower energetic costs of foraging in the Arctic/sub-Arctic may explain the greater number of whales foraging in that region compared to the PCFG range. Both groups of gray whales spend the winter months on their breeding and calving grounds in Baja California, Mexico. 

Figure 1. The GEMM Lab field team following a Pacific Coast Feeding Group (PCFG) gray whale swimming in a kelp bed along the Oregon Coast during the summer foraging season. 

In January 2019 an Unusual Mortality Event (UME) was declared for gray whales due to the elevated numbers of stranded gray whales between Mexico and the Arctic regions of Alaska. Most of the stranded whales were emaciated, indicating that reduced nutrition and starvation may have been the causal factor of death. It is estimated that the population dropped from ~27,000 individuals in 2016 to ~21,000 in 2020 (Stewart & Weller, 2021).

During this UME period, between 2017-2019, the GEMM Lab was using drones to monitor the body condition of PCFG gray whales on their Oregon coastal feeding grounds (Fig. 1), while Christiansen and colleagues (2020) was using drones to monitor gray whales on their breeding grounds in San Ignacio Lagoon (SIL) in Baja California, Mexico. We teamed up with Christiansen and colleagues to compare the body condition of gray whales in these two different areas leading up to the UME. Comparing the body condition between these two populations could help inform which population was most effected by the UME.

The combined datasets consisted of four different drones used, thus different levels of photogrammetric uncertainty to consider. The GEMM Lab collected data using a DJI Phantom 3 Pro, DJI Phantom 4, and DJI Phantom 4 Pro, while Christiansen et al., (2020) used a DJI Inspire 1 Pro. By using the methodological approach described in my previous blog (here, also see Bierlich et al., 2021a for more details), we quantified photogrammetric uncertainty specific to each drone, allowing cross-comparison between these datasets. We also used Body Area Index (BAI), which is a standardized relative measure of body condition developed by the GEMM Lab (Burnett et al., 2018) that has low uncertainty with high precision, making it easier to detect smaller changes between individuals (see blog here, Bierlich et al., 2021b). 

While both PCFG and ENP gray whales visit San Ignacio Lagoon in the winter, we assume that the photogrammetry data collected in the lagoon is mostly of ENP whales based on their considerably higher population abundance. We also assume that gray whales incur low energetic cost during migration, as gray whale oxygen consumption rates and derived metabolic rates are much lower during migration than on foraging grounds (Sumich, 1983). 

Interestingly, we found that gray whale body condition on their wintering grounds in San Ignacio Lagoon deteriorated across the study years leading up to the UME (2017-2019), while the body condition of PCFG whales on their foraging grounds in Oregon concurrently increased. These contrasting trajectories in body condition between ENP and PCFG whales implies that dynamic oceanographic processes may be contributing to temporal variability of prey available in the Arctic/sub-Arctic and PCFG range. In other words, environmental conditions that control prey availability for gray whales are different in the two areas. For the ENP population, this declining nutritive gain may be associated with environmental changes in the Arctic/sub-Arctic region that impacted the predictability and availability of prey. For the PCFG population, the increase in body condition across years may reflect recovery of the NE Pacific Ocean from the marine heatwave event in 2014-2016 (referred to as “The Blob”) that resulted with a period of low prey availability. These findings also indicate that the ENP population was primarily impacted in the die-off from the UME. 

Surprisingly, the body condition of PCFG gray whales in Oregon was regularly and significantly lower than whales in San Ignacio Lagoon (Fig. 2). To further investigate this potential intrinsic difference in body condition between PCFG and ENP whales, we compared opportunistic photographs of gray whales feeding in the Northeastern Chukchi Sea (NCS) in the Arctic collected from airplane surveys. We found that the body condition of PCFG gray whales was significantly lower than whales in the NCS, further supporting our finding that PCFG whales overall have lower body condition than ENP whales that feed in the Arctic (Fig. 3). 

Figure 2. Boxplots showing the distribution of Body Area Index (BAI) values for gray whales imaged by drones in San Ignacio Lagoon (SIL), Mexico and Oregon, USA. The data is grouped by phenology group: End of summer feeding season (departure Oregon vs. arrival SIL) and End of wintering season (arrival Oregon vs. departure SIL). The group median (horizontal line), interquartile range (IQR, box), maximum and minimum 1.5*IQR (vertical lines), and outliers (dots) are depicted in the boxplots. The overlaid points represent the mean of the posterior predictive distribution for BAI of an individual and the bars represents the uncertainty (upper and lower bounds of the 95% HPD interval). Note how PCFG whales at then end of the feeding season (dark green) typically have lower body condition (as BAI) compared to ENP whales at the end of the feeding season when they arrive to SIL after migration (light brown).
Figure 3. Boxplots showing the distribution of Body Area Index (BAI) values of gray whales from opportunistic images collected from a plane in Northeaster Chukchi Sea (NCS) and from drones collected by the GEMM Lab in Oregon. The boxplots display the group median (horizontal line), interquartile range (IQR box), maximum and minimum 1.5*IQR (vertical lines), and outlies (dots). The overlaid points are the BAI values from each image. Note the significantly lower BAI of PCFG whales on Oregon feeding grounds compared to whales feeding in the Arctic region of the NCS.

This difference in body condition between PCFG and ENP gray whales raises some really interesting and prudent questions. Does the lower body condition of PCFG whales make them less resilient to changes in prey availability compared to ENP whales, and thus more vulnerable to climate change? If so, could this influence the reproductive capacity of PCFG whales? Or, are whales that recruit into the PCFG adapted to a smaller morphology, perhaps due to their specialized foraging tactics, which may be genetically inherited and enables them to survive with reduced energy stores?

These questions are on our minds here at the GEMM Lab as we prepare for our seventh consecutive field season using drones to collect data on PCFG gray whale body condition. As discussed in a previous blog by Dr. Alejandro Fernandez Ajo, we are combining our sightings history of individual whales, fecal hormone analyses, and photogrammetry-based body condition to better understand gray whales’ reproductive biology and help determine what the consequences are for these PCFG whales with lower body condition.

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References

Bierlich, K. C., Hewitt, J., Bird, C. N., Schick, R. S., Friedlaender, A., Torres, L. G., … & Johnston, D. W. (2021). Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Frontiers in Marine Science, 1729.

Bierlich, K. C., Schick, R. S., Hewitt, J., Dale, J., Goldbogen, J. A., Friedlaender, A.S., et al. (2021b). Bayesian Approach for Predicting Photogrammetric Uncertainty in Morphometric Measurements Derived From Drones. Mar. Ecol. Prog. Ser. 673, 193–210. doi: 10.3354/meps13814

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2018). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science35(1), 108–139.

Christiansen, F., Rodrı́guez-González, F., Martı́nez-Aguilar, S., Urbán, J., Swartz, S., Warick, H., et al. (2021). Poor Body Condition Associated With an Unusual Mortality Event in Gray Whales. Mar. Ecol. Prog. Ser. 658, 237–252. doi:10.3354/meps13585

Hildebrand, L., Bernard, K. S., and Torres, L. G. (2021). Do Gray Whales Count Calories? Comparing Energetic Values of Gray Whale Prey Across Two Different Feeding Grounds in the Eastern North Pacific. Front. Mar. Sci. 8. doi: 10.3389/fmars.2021.683634

Stewart, J. D., and Weller, D. (2021). Abundance of Eastern North Pacific Gray Whales 2019/2020 (San Diego, CA: NOAA/NMFS)

Sumich, J. L. (1983). Swimming Velocities, Breathing Patterns, and Estimated Costs of Locomotion in Migrating Gray Whales, Eschrichtius Robustus. Can. J. Zoology. 61, 647–652. doi: 10.1139/z83-086

Torres, L.G., Bird, C., Rodrigues-Gonzáles, F., Christiansen F., Bejder, L., Lemos, L., Urbán Ramírez, J., Swartz, S., Willoughby, A., Hewitt., J., Bierlich, K.C. (2022). Range-wide comparison of gray whale body condition reveals contrasting sub-population health characteristics and vulnerability to environmental change. Frontiers in Marine Science. 9:867258. https://doi.org/10.3389/fmars.2022.867258

The many dimensions of a fat whale: Using drones to measure the body condition of baleen whales 

Dr. KC Bierlich, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab

In my last blog, I discussed how to obtain morphological measurements from drone-based imagery of whales and the importance of calculating and considering uncertainty, as different drone platforms have varying levels of measurement uncertainty. But how does uncertainty scale and propagate when multiple measurements are combined, such as when measuring body condition of the whole animal? In this blog, I will discuss the different methods used for measuring body condition of baleen whales from drone-based imagery and how uncertainty differs between these metrics.

Body condition is defined as the energy stored in the body as a result of feeding and is assumed to indicate an animal’s overall health, as it reflects the balance between energy intake and investment toward growth, maintenance and reproduction (Peig and Green, 2009). Thus, body condition reflects the foraging success of an individual, as well as the potential for reproductive output and the quality of habitat. For example, female North American brown bears (Ursus arctos) in high quality habitats were in better body condition, produced larger litter sizes, and lived in greater population densities compared to females in lower quality habitats (Hilderbrand et al., 1999). As Dawn Barlow and Will Kennerley discussed in their recent blog, baleen whales are top predators and serve as ecosystem sentinels that shed light not only on the health of their population, but on the health of their ecosystem. As ocean climate conditions continue to change, monitoring the body condition of baleen whales is important to provide insight on how their population and ecosystem is responding. 

As discussed in a previous blog, drones serve as a valuable tool for obtaining morphological measurements of baleen whales to estimate their body condition. Images are imported into photogrammetry software, such as MorphoMetriX (Torres and Bierlich, 2020), to measure the total length of an individual and that is then divided into perpendicular width segments (i.e., in 5 or 10% increments) down the body (Fig. 1). These total length and width measurements are then used to estimate body condition in either 1-, 2-, or 3-dimensions: a single width (1D), a projected dorsal surface area (2D), or a body volume measure (3D). These 1D, 2D, and 3D measurements of body condition can then be standardized by total length to produce a relative measure of an individual’s body condition to compare among individuals and populations. 

Figure 1. An example of a Pacific Coast Feeding Group (PCFG) gray whale measured in MorphoMetriX (Torres & Bierlich, 2020).

While several different studies have used each of these dimensions to assess whale body condition, it is unclear how these measurements compare amongst each other. Importantly, it is also unclear how measurement uncertainty scales across these multiple dimensions and influences inference, which can lead to misinterpretation of data. For example, the surface area and volume of two geometrically similar bodies of different sizes are not related to their linear dimensions in the same ratio, but rather to the second and third power, respectively (i.e., x2 vs. x3).  Similarly, uncertainty should not be expected to scale linearly across 1D, 2D, and 3D body condition measurements. 

The second chapter of my dissertation, which was recently published in Frontiers in Marine Science and includes Clara Bird and Leigh Torres as co-authors, compared the uncertainty associated with 1D, 2D, and 3D drone-based body condition measurements in three baleen whale species with different ranges in body sizes: blue, humpback, and Antarctic minke whales (Figure 2) (Bierlich et al., 2021). We used the same Bayesian model discussed in my last blog, to incorporate uncertainty associated with each 1D, 2D, and 3D estimate of body condition. 

Figure 2. An example of total length and perpendicular width (in 5% increments of total length) measurements of an individual blue, humpback and Antarctic minke whale. Each image measured using MorphoMetriX (Torres and Bierlich, 2020). 

We found that uncertainty does not scale linearly across multi-dimensional measurements, with 2D and 3D uncertainty increasing by a factor of 1.45 and 1.76 compared to 1D, respectively. This result means that there is an added cost of increased uncertainty when utilizing a multidimensional body condition measurement. Our finding is important to help researchers decide which body condition measurement best suits their scientific question,  particularly when using a drone platform that is susceptible to greater error – as discussed in my previous blog. However, a 1D measurement only relies on a single width measurement, which may be excluding other regions of an individual’s body condition that is important for energy storage. In these situations, a 2D or 3D measure may be more appropriate.

We found that when comparing relative measures of body condition (standardized by total length of the individual), each standardized metric was highly correlated with one another. This finding suggests that 1D, 2D, and 3D metrics will draw similar relative predictions of body condition for individuals, allowing researchers to be confident they will draw similar conclusions relating to the body condition of individuals, regardless of which standardized metric they use. However, when comparing the precision of each of these metrics, the body area index (BAI) – a 2D standardized metric – displayed the highest level of precision. This result highlights how BAI can advantageously detect small changes in body condition, which is useful for comparing individuals or even tracking the same individual over time.

BAI was developed by the GEMM Lab (Burnett et al., 2018) and was designed to be similar to body mass index (BMI) in humans [BMI = mass (kg)/(height (m))2], where BAI uses the calculated surface area as a surrogate for body mass. In humans, a healthy BMI range is generally considered 18.5–24.9, below 18.5 is considered underweight, above 24.9 is considered overweight, and above 30 is considered obese (Flegal et al., 2012). Identifying a healthy range in BAI for baleen whales is challenged by a limited knowledge of what a “healthy” body condition range is for a whale. We found strong evidence that a healthy range of BAI is species-specific, as each species displayed a distinctive range in BAI: blue whales: 11–16; AMW: 17–24; humpback whales: 23–32; humpback whale calves: 23–28 (Fig. 3). These differences in BAI ranges likely reflect differences in the body shape of each species (Fig. 4). For example, humpbacks have the widest range of BAI compared to these other two species, which was also reflected in their larger variation in perpendicular widths (Figs. 2-4). Thus, it seems that BAI offers conditionally “scalefree” comparisons between species, yet it is unreasonable to set a single, all-whale BAI threshold to determine “healthy” versus “unhealthy” body condition.  Collecting a large sample of body condition measurements across many individuals and demographic units over space and time with information on vital rates (e.g., reproductive capacity) will help elucidate a healthy BAI range for each species.

Figure 3. Body area index (BAI) for each species. AMW = Antarctic minke whale.  Figure from Bierlich et al. (2021).
Figure 4. A) Absolute widths (m) and B) relative widths, standardized by total length (TL) to help elucidate the different body shapes of Antarctic minke whales (AMW; n = 40), blue whales (n = 32), humpback whales (n = 40), and humpback whale calves (n = 15). Note how the peak in body width occurs at a different percent body width between species, demonstrating the natural variation in body shape between baleen whales. Figure from Bierlich et al. (2021).

Over the past six years, the GEMM Lab has been collecting drone images of Pacific Coast Feeding Group (PCFG) gray whales off the coast of Oregon to measure their BAI (see GRANITE Project blog). Many of the individuals we encounter are seen across years and throughout the foraging season, providing an opportunity to evaluate how an individual’s BAI is influenced by environmental variation, stress levels, maturity, and reproduction. These data will in turn help determine what the healthy range in BAI for gray whales is. For example, linking BAI to pregnancy – whether a whale is currently pregnant or becomes pregnant the following season – will help determine what BAI is needed to support calf production. We are currently analyzing hundreds of body condition measurements from 2016 – 2021, so stay tuned for upcoming results!

References

Bierlich, K. C., Hewitt, J., Bird, C. N., Schick, R. S., Friedlaender, A., Torres, L. G., … & Johnston, D. W. (2021). Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales. Frontiers in Marine Science, 1729.

Burnett, J. D., Lemos, L., Barlow, D., Wing, M. G., Chandler, T., & Torres, L. G. (2018). Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science35(1), 108–139.

Flegal, K. M., Carroll, M. D., Kit, B. K., & Ogden, C. L. (2012). Prevalence of Obesity and Trends in the Distribution of Body Mass Index Among US Adults, 1999-2010. JAMA307(5), 491. https://doi.org/10.1001/jama.2012.39

Hilderbrand, G. V, Schwartz, C. C., Robbins, C. T., Jacoby, M. E., Hanley, T. A., Arthur, S. M., & Servheen, C. (1999). The importance of meat, particularly salmon, to body size, population productivity, and conservation of North American brown bears. Canadian Journal of Zoology77(1), 132–138.

Peig, J., & Green, A. J. (2009). New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative method. Oikos118(12), 1883–1891.

Torres, W., & Bierlich, K. C. (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software5(45), 1825–1826.

Drones with lasers: almost as cool as “sharks with laser beams attached to their heads”

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 Biology222(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 Software5(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

Little whale, big whale, swimming in the water: A quick history on how aerial photogrammetry has revolutionized the ability to obtain non-invasive measurements of whales

Dr. KC Bierlich, Postdoctoral Scholar, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

The morphology and body size of an animal is one of the most fundamental factors for understanding a species ecology. For instance, fish body size and fin shape can influence its habitat use, foraging behavior, prey type, physiological performance, and predator avoidance strategies (Fig 1). Morphology and body size can thus reflect details of an individual’s current health, likelihood of survival, and potential reproductive success, which directly influences a species life history patterns, such as reproductive status, growth rate, and energetic requirements. Collecting accurate morphological measurements of individuals is often essential for monitoring populations, and recent studies have demonstrated how animal morphology has profound implications for conservation and management decisions, especially for populations inhabiting anthropogenically-altered environments (Miles 2020) (Fig. 1). For example, in a study on the critically endangered European eel, De Meyer et al. (2020) found that different skulls sizes were associated with different ecomorphs (a local variety of a species whose appearance is determined by its ecological environment), which predicted different diet types and resulted with some ecomorphs having a greater exposure to pollutants and toxins than others. However, obtaining manual measurements of wild animal populations can be logistically challenging, limited by accessibility, cost, danger, and animal disturbance. These challenges are especially true for large elusive animals, such as whales that are often in remote locations, spend little time at the surface of the water, and their large size can preclude safe capture and live handling.

Figure 1. Top) A pathway framework depicting how the morphology of an animal influences its habitat use, behavior, foraging, physiology, and performance. These traits all affect how successful an animal is in its environment and can reflect an individual’s current health, likelihood of survival, and potential reproductive success. This individual success can then be scaled up to assess overall population health, which in turn can have direct implications for conservation. Bottom) an example of morphological differences in fish body size and fin shape from Walker et al. (2013). Fineness ratio (f) = length of body ­÷ max body width. 

Photogrammetry is a non-invasive method for obtaining accurate morphological measurements of animals from photographs. The two main types of photogrammetry methods used in wildlife biology are 1) single camera photogrammetry, where a known scale factor is applied to a single image to measure 2D distances and angles and 2) stereo-photogrammetry, where two or more images (from a single or multiple cameras) are used to recreate 3D models. These techniques have been used on domestic animals to measure body condition and estimate weight of dairy cows and lactating Mediterranean buffaloes (Negretti et al., 2008; Gaudioso et al., 2014) and on wild animals to measure sexual dimorphism in Western gorillas (Breuer et al., 2007), shoulder heights of elephants (Schrader et al., 2006), nutritional status of Japanese macaques (Kurita et al., 2012), and the body condition of brown bears (Shirane et al., 2020). Over 70 years ago, Leedy (1948) encouraged wildlife biologists to use aerial photogrammetry from aircraft for censusing wild animal populations and their habitats, where photographs can be collected at nadir (straight down) or an oblique angle, and the scale can be calculated by dividing the focal length of the camera by the altitude or by using a ratio of selected points in an image of a known size. Indeed, aerial photogrammetry has been wildly adopted by wildlife biologists and has proven useful in obtaining measurements in large vertebrates, such as elephants and whales.

Whitehead & Payne (1978) first demonstrated the utility of using aerial photogrammetry from airplanes and helicopters as a non-invasive technique for estimating the body length of southern right whales. Prior to this technique, measurements of whales were traditionally limited to assessing carcasses collected from scientific whaling operations, or opportunistically from commercial whaling, subsistence hunting, stranding events, and bycatch. Importantly, aerial photogrammetry provides a method to collect measurements of whales without killing them. This approach has been widely adopted to obtain body length measurements on a variety of whale and dolphin species, including bowhead whales (Cubbage & Calambokidis, 1987), southern right whales (Best & Rüther, 1992), fin whales (Ratnaswamy and Wynn, 1993), common dolphins (Perryman and Lynn, 1993), spinner dolphins (Perryman & Westlake 1998), and killer whales (Fearnbach et al. 2012). Aerial photogrammetry has also been used to measure body widths to estimate nutritive condition related to reproduction in gray whales (Perryman and Lynn, 2002) and northern and southern right whales (Miller et al., 2012). However, these studies collected photographs from airplanes and helicopters, which can be costly, limited by weather and infrastructure to support aircraft research efforts and, importantly, presents a potential risk to wildlife biologists (Sasse 2003). 

The recent advancement and commercialization of unoccupied aircraft systems (UAS, or drones) has revolutionized the ability to obtain morphological measurements from high resolution aerial photogrammetry across a variety of ecosystems (Fig. 2). Drones ultimately bring five transformative qualities to conservation science compared to airplanes and helicopters: affordability, immediacy, quality, efficiency, and safety of data collection. Durban et al. (2015) first demonstrated the utility of using drones for non-invasively obtaining morphological measurements of killer whales in remote environments. Since then, drone-based morphological measurements have been applied to a wide range of studies that have increased our understanding on different whale populations. For example, Leslie et al. (2020) used drone-based measurements of the skull to distinguish a unique sub-species of blue whales off the coast of Chile. Groskreutz et al. (2019) demonstrated how long-term nutritional stress has limited body growth in northern resident killer whales, while Stewart et al. (2021) found a decrease in body length of North Atlantic Right whales since 1981 that was associated with entanglements from fishing gear and may be a contributing factor to the decrease in reproductive success for this endangered population. 

Drone imagery is commonly used to estimate the body condition of baleen whales by measuring the body length and width of individuals. Recently, the GEMM Lab used body length and width measurements to quantify intra- and inter-seasonal changes in body condition across individual gray whales (Lemos et al., 2020). Drones have also been used to measure body condition loss in humpback whales during the breeding season (Christiansen et al., 2016) and to compare the healthy southern right whales to the skinnier, endangered North Atlantic right whales (Christiansen et al., 2020). Drone-based assessments of body condition have even been used to measure how calf growth rate is directly related to maternal loss during suckling (Christiansen et al., 2018), and even estimate body mass (Christiansen et al., 2019). 

Drone-based morphological measurements can also be combined with whale-borne inertial sensing tag data to study the functional morphology across several different baleen whale species. Kahane-Rapport et al. (2020) used drone measurements of tagged whales to analyze the biomechanics of how larger whales require longer times for filtering the water through their baleen when feeding. Gough et al. (2019) used size measurements from drones and swimming speeds from tags to determine that a whale’s “walking speed” is 2 meters per second – whether the largest of the whales, a blue whale, or the smallest of the baleen whales, an Antarctic minke whale. Size measurements and tag data were combined by Segre et al. (2019) to quantify the energetic costs of different sized whales when breaching. 

Taken together, drones have revolutionized our ability to obtain morphological measurements of whales, greatly increasing our capacity to better understand how these animals function and perform in their environments. These advancements in marine science are particularly important as these methods provide greater opportunity to monitor the health of populations, especially as they face increased threats from anthropogenic stressors (such as vessel traffic, ocean noise, pollution, fishing entanglement, etc.) and climate change. 

Drone-based photogrammetry is one of the main focuses of the GEMM Lab’s project on Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE). This summer we have been collecting drone videos to measure the body condition of gray whales feeding off the coast of Newport, Oregon (Fig. 2). As we try to understand the physiological stress response of gray whales to noise and other potential stressors, we have to account for the impacts of overall nutritional state of each individual whale’s physiology, which we infer from these body condition estimates. 

Figure 2. Drones can help collect images of whales to obtain morphological measurements using photogrammetry and help us fill knowledge gaps for how these animals interact in their environment and to assess their current health. Bottom photo is an image collected by the GEMM Lab of a gray whale being measured in MorphoMetriX software to estimate its body condition. 

References

Best, P. B., & Rüther, H. (1992). Aerial photogrammetry of southern right whales, Eubalaena australis. Journal of Zoology228(4), 595-614.

Breuer, T., Robbins, M. M., & Boesch, C. (2007). Using photogrammetry and color scoring to assess sexual dimorphism in wild western gorillas (Gorilla gorilla). American Journal of Physical Anthropology134(3), 369–382. https://doi.org/10.1002/ajpa.20678 

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. Marine Ecology Progress Series592, 267–281. 

Christiansen, F. (2020). A population comparison of right whale body condition reveals poor state of North Atlantic right whale, 1–43. 

Christiansen, F., Dujon, A. M., Sprogis, K. R., Arnould, J. P. Y., & Bejder, L. (2016). Noninvasive unmanned aerial vehicle provides estimates of the energetic cost of reproduction in humpback whales. Ecosphere7(10), e01468–18. 

Christiansen, F., Sironi, M., Moore, M. J., Di Martino, M., Ricciardi, M., Warick, H. A., … Uhart, M. M. (2019). Estimating body mass of free-living whales using aerial photogrammetry and 3D volumetrics. Methods in Ecology and Evolution10(12), 2034–2044. 

Cubbage, J. C., & Calambokidis, J. (1987). Size-class segregation of bowhead whales discerned through aerial stereo-photogrammetry. Marine Mammal Science3(2), 179–185. 

De Meyer, J., Verhelst, P., & Adriaens, D. (2020). Saving the European Eel: How Morphological Research Can Help in Effective Conservation Management. Integrative and Comparative Biology23, 347–349. 

Gaudioso, V., Sanz-Ablanedo, E., Lomillos, J. M., Alonso, M. E., Javares-Morillo, L., & Rodr\’\iguez, P. (2014). “Photozoometer”: A new photogrammetric system for obtaining morphometric measurements of elusive animals, 1–10.

Gough, W. T., Segre, P. S., Bierlich, K. C., Cade, D. E., Potvin, J., Fish, F. E., … Goldbogen, J. A. (2019). Scaling of swimming performance in baleen whales. Journal of Experimental Biology222(20), jeb204172–11. 

Groskreutz, M. J., Durban, J. W., Fearnbach, H., Barrett-Lennard, L. G., Towers, J. R., & Ford, J. K. B. (2019). Decadal changes in adult size of salmon-eating killer whales in the eastern North Pacific. Endangered Species Research40, 1 

Kahane-Rapport, S. R., Savoca, M. S., Cade, D. E., Segre, P. S., Bierlich, K. C., Calambokidis, J., … Goldbogen, J. A. (2020). Lunge filter feeding biomechanics constrain rorqual foraging ecology across scale. Journal of Experimental Biology223(20), jeb224196–8. 

Leedy, D. L. (1948). Aerial Photographs, Their Interpretation and Suggested Uses in Wildlife Management. The Journal of Wildlife Management12(2), 191. 

Lemos, L. S., Burnett, J. D., Chandler, T. E., Sumich, J. L., and Torres, L. G. (2020). Intra- and inter-annual variation in gray whale body condition on a foraging ground. Ecosphere 11.

Leslie, M. S., Perkins-Taylor, C. M., Durban, J. W., Moore, M. J., Miller, C. A., Chanarat, P., … Apprill, A. (2020). Body size data collected non-invasively from drone images indicate a morphologically distinct Chilean blue whale (Balaenoptera musculus) taxon. Endangered Species Research43, 291–304. 

Miles, D. B. (2020). Can Morphology Predict the Conservation Status of Iguanian Lizards? Integrative and Comparative Biology

Miller, C. A., Best, P. B., Perryman, W. L., Baumgartner, M. F., & Moore, M. J. (2012). Body shape changes associated with reproductive status, nutritive condition and growth in right whales Eubalaena glacialis and E. australis. Marine Ecology Progress Series459, 135–156. 

Negretti, P., Bianconi, G., Bartocci, S., Terramoccia, S., & Verna, M. (2008). Determination of live weight and body condition score in lactating Mediterranean buffalo by Visual Image Analysis. Livestock Science113(1), 1–7. https://doi.org/10.1016/j.livsci.2007.05.018 

Ratnaswamy, M. J., & Winn, H. E. (1993). Photogrammetric Estimates of Allometry and Calf Production in Fin Whales, \emph{Balaenoptera physalus}. American Society of Mammalogists74, 323–330. 

Perryman, W. L., & Lynn, M. S. (1993). Idendification of geographic forms of common dolphin(\emph{Delphinus Delphis}) from aerial photogrammetry. Marine Mammal Science9(2), 119–137. 

Perryman, W. L., & Lynn, M. S. (2002). Evaluation of nutritive condition and reproductive status of migrating gray whales (\emph{Eschrichtius robustus}) based on analysisof photogrammetric data. Journal Cetacean Research and Management4(2), 155–164. 

Perryman, W. L., & Westlake, R. L. (1998). A new geographic form of the spinner dolphin, stenella longirostris, detected with aerial photogrammetry. Marine Mammal Science14(1), 38–50. 

Sasse, B. (2003). Job-Related Mortality of Wildlife Workers in the United States, 1937- 2000, 1015–1020. 

Segre, P. S., Potvin, J., Cade, D. E., Calambokidis, J., Di Clemente, J., Fish, F. E., … & Goldbogen, J. A. (2020). Energetic and physical limitations on the breaching performance of large whales. Elife9, e51760.

Shirane, Y., Mori, F., Yamanaka, M., Nakanishi, M., Ishinazaka, T., Mano, T., … Shimozuru, M. (2020). Development of a noninvasive photograph-based method for the evaluation of body condition in free-ranging brown bears. PeerJ8, e9982. https://doi.org/10.7717/peerj.9982 

Shrader, A. M., M, F. S., & Van Aarde, R. J. (2006). Digital photogrammetry and laser rangefinder techniques to measure African elephants, 1–7. 

Stewart, J. D., Durban, J. W., Knowlton, A. R., Lynn, M. S., Fearnbach, H., Barbaro, J., … & Moore, M. J. (2021). Decreasing body lengths in North Atlantic right whales. Current Biology.

Walker, J. A., Alfaro, M. E., Noble, M. M., & Fulton, C. J. (2013). Body fineness ratio as a predictor of maximum prolonged-swimming speed in coral reef fishes. PloS one8(10), e75422.

Learning from teaching

Clara Bird, PhD Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Based on my undergrad experience I assumed that most teaching in grad school would be as a teaching assistant, and this would consist of teaching labs, grading, leading office hours, etc. However, now that I’m in graduate school, I realize that there are many different forms of teaching as a graduate student. This summer I worked as an instructor for an e-campus course, which mainly involved grading and mentoring students as they developed their own projects. Yet, this past week I was a guest teacher for Physiology and Behavior of Marine Megafauna, which was a bit more involved.

I taught a whale photogrammetry lab that I originally developed as a workshop with a friend and former lab mate, KC Bierlich, at the Duke University Marine Robotics and Remote Sensing (MaRRS) lab when I worked there. Similar to Leila’s work, we were using photogrammetry to measure whales and assess their body condition. Measuring a whale is a deceivingly simple task that gets complicated when taking into account all the sources of error that might affect measurement accuracy. It is important to understand the different sources of error so that we are sure that our results are due to actual differences between whales instead of differences in errors.

Error can come from distortion due to the camera lens, inaccurate altitude measurements from the altimeter, the whale being arched, or from the measurement process. When we draw a line on the image to make a measurement (Image 1), measurement process errors come from the line being drawn incorrectly. This potential human error can effect results, especially if the measurer is inexperienced or rushing. The quality of the image also has an effect here. If there is glare, wake, blow or refraction covering or distorting the measurer’s view of the full body of the whale then the measurer has to estimate where to begin and end the line. This estimation is subjective and, therefore, a source of error. We used the workshop as an opportunity to study these measurement process errors because we could provide a dataset including images of varying qualities and collect data from different measurers.

Image 1. Screenshot of measuring the widths along a minke whale in MorphoMetriX. Source: https://github.com/wingtorres/morphometrix/blob/master/images/Picture4.png

This workshop started as a one-day lecture and lab that we designed for the summer drone course at the Duke Marine Lab. The idea was to simultaneously teach the students about photogrammetry and the methods we use, while also using all the students’ measurements to study the effect of human error and image quality on measurement accuracy. Given this one-day format, we ambitiously decided to teach and measure in the morning, compile and analyze the students’ measurements over lunch, and then present the results of our error analysis in the afternoon. To accomplish this, we prepared as much as we could and set up all the code for the analysis ahead of time. This preparation meant several days of non-stop working, discussing, and testing, all to anticipate any issues that might come up on the day of the class.  We used the measuring software MorphoMetriX (Torres & Bierlich, 2020) that was developed by KC and a fellow Duke Marine Lab grad student Walter Torres. MorphoMetriX was brand new at the time, and this newness of the software meant that we didn’t yet know all the issues that might come up and we did not have time to troubleshoot. We knew this meant that helping the students install the software might be a bit tricky and sure enough, all I remember from the beginning of that first lab is running around the room helping multiple people troubleshoot at the same time, using use all the programming knowledge I had to discover new solutions on the fly.

While troubleshooting on the fly can be stressful and overwhelming, I’ve come to appreciate it as good practice. Not only did we learn how to develop and teach a workshop, we also used what we had learned from all the troubleshooting to improve the software. I also used the code we developed for the analysis as the starting blocks for a software package I then wrote, CollatriX (Bird & Bierlich, 2020), as a follow up software to MorphoMetriX. Aside from the initial troubleshooting stress, the workshop was a success, and we were excited to have a dataset to study measurement process errors. Given that we already had all the materials for the workshop prepared, we decided to run a few more workshops to collect more data.

That brings me to my time at here at OSU. I left the Duke MaRRS lab to start graduate school shortly after we taught the workshop. Interested in running the workshop here, I reached out to a few different people. I first ran the workshop here as an event organized by the undergraduate club Ocean11 (Image 2). It was fun running the workshop a second time, as I used what I learned from the first round; I felt more confident, and I knew what the common issues would likely be and how to solve them. Sure enough, while there were still some troubleshooting issues, the process was smoother and I enjoyed teaching, getting to know OSU undergraduate students, and collecting more data for the project.

Image 2. Ocean11 students measuring during the workshop (Feb 7, 2020).
Image credit: Clara Bird

The next opportunity to run the lab came through Renee Albertson’s physiology and behavior of marine megafauna class, but during the COVID era this class had other challenges. While it’s easier to teach in person, this workshop was well suited to be converted to a remote activity because it only requires a computer, the data can be easily sent to the students, and screen sharing is an effective way to demonstrate how to measure. So, this photogrammetry module was a good fit for the marine megafauna class this term that has been fully remote due to COVID-19.  My first challenge was converting the workshop into a lab assignment with learning outcomes and analysis questions. The process also involved writing R code for the students to use and writing step-by-step instructions in a way that was clear and easy to understand. While stressful, I appreciated the process of developing the lab and these accompanying materials because, as you’ve probably heard from a teacher, a good test of your understanding of a concept is being able to teach it. I was also challenged to think of the best way to communicate and explain these concepts. I tried to think of a few different explanations, so that if a student did not understand it one way, I could offer an alternative that might work better. Similar to the preparation for the first workshop, I also prepared for troubleshooting the students’ issues with the software. However, unlike my previous experiences, this time I had to troubleshoot remotely.

After teaching this photogrammetry lab last week my respect for teachers who are teaching remotely has only increased. Helping students without being able to sit next to them and walk them through things on their computer is not easy. Not only that, in addition to the few virtual office hours I hosted, I was primarily troubleshooting over email, using screen shots from the students to try and figure out what was going on. It felt like the ultimate test of my programming knowledge and experience, having to draw from memories of past errors and solutions, and thinking of alternative solutions if the first one didn’t work. It was also an exercise in communication because programming can be daunting to many students; so, I worked to be encouraging and clearly communicate the instructions. All in all, I ended this week feeling exhausted but accomplished, proud of the students, and grateful for the reminder of how much you learn when you teach.

References

Bird, C. N., & Bierlich, K. (2020). CollatriX: A GUI to collate MorphoMetriX outputs. Journal of Open Source Software, 5(51), 2328. https://doi.org/10.21105/joss.02328

Torres, W., & Bierlich, K. (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

Photogrammetry Insights

By Leila Lemos, PhD Candidate, Fisheries and Wildlife Department, Oregon State University

After three years of fieldwork and analyzing a large dataset, it is time to finally start compiling the results, create plots and see what the trends are. The first dataset I am analyzing is the photogrammetry data (more on our photogrammetry method here), which so far has been full of unexpected results.

Our first big expectation was to find a noticeable intra-year variation. Gray whales spend their winter in the warm waters of Baja California, Mexico, period while they are fasting. In the spring, they perform a big migration to higher latitudes. Only when they reach their summer feeding grounds, that extends from Northern California to the Bering and Chukchi seas, Alaska, do they start feeding and gaining enough calories to support their migration back to Mexico and subsequent fasting period.

 

Northeastern gray whale migration route along the NE Pacific Ocean.
Source: https://journeynorth.org/tm/gwhale/annual/map.html

 

Thus, we expected to see whales arriving along the Oregon coast with a skinny body condition that would gradually improve over the months, during the feeding season. Some exceptions are reasonable, such as a lactating mother or a debilitated individual. However, datasets can be more complex than we expect most of the times, and many variables can influence the results. Our photogrammetry dataset is no different!

In addition, I need to decide what are the best plots to display the results and how to make them. For years now I’ve been hearing about the wonders of R, but I’ve been skeptical about learning a whole new programming/coding language “just to make plots”, as I first thought. I have always used statistical programs such as SPSS or Prism to do my plots and they were so easy to work with. However, there is a lot more we can do in R than “just plots”. Also, it is not just because something seems hard that you won’t even try. We need to expose ourselves sometimes. So, I decided to give it a try (and I am proud of myself I did), and here are some of the results:

 

Plot 1: Body Area Index (BAI) vs Day of the Year (DOY)

 

In this plot, we wanted to assess the annual Body Area Index (BAI) trends that describe how skinny (low number) or fat (higher number) a whale is. BAI is a simplified version of the BMI (Body Mass Index) used for humans. If you are interested about this method we have developed at our lab in collaboration with the Aerial Information Systems Laboratory/OSU, you can read more about it in our publication.

The plots above are three versions of the same data displayed in different ways. The first plot on the left shows all the data points by year, with polynomial best fit lines, and the confidence intervals (in gray). There are many overlapping observation points, so for the middle plot I tried to “clean up the plot” by reducing the size of the points and taking out the gray confidence interval range around the lines. In the last plot on the right, I used a linear regression best fit line, instead of polynomial.

We can see a general trend that the BAI was considerably higher in 2016 (red line), when compared to the following years, which makes us question the accuracy of the dataset for that year. In 2016, we also didn’t sample in the month of July, which is causing the 2016 polynomial line to show a sharp decrease in this month (DOY: ~200-230). But it is also interesting to note that the increasing slope of the linear regression line in all three years is very similar, indicating that the whales gained weight at about the same rate in all years.

 

Plot 2: Body Area Index (BAI) vs Body Condition Score (BCS)

 

In addition to the photogrammetry method of assessing whale body condition, we have also performed a body condition scoring method for all the photos we have taken in the field (based on the method described by Bradford et al. 2012). Thus, with this second set of plots, we wanted to compare both methods of assessing whale body condition in order to evaluate when the methods agree or not, and which method would be best and in which situation. Our hypothesis was that whales with a ‘fair’ body condition would have a lower BAI than whales with a ‘good’ body condition.

The plots above illustrate two versions of the same data, with data in the left plot grouped by year, and the data in the right plot grouped by month. In general, we see that no whales were observed with a poor body condition in the last analysis months (August to October), with both methods agreeing to this fact. Additionally, there were many whales that still had a fair body condition in August and September, but less whales in the month of October, indicating that most whales gained weight over the foraging seasons and were ready to start their Southbound migration and another fasting period. This result is important information regarding monitoring and conservation issues.

However, the 2016 dataset is still a concern, since the whales appear to have considerable higher body condition (BAI) when compared to other years.

 

Plot 3:Temporal Body Area Index (BAI) for individual whales

 

In this last group of plots, we wanted to visualize BAI trends over the season (using day of year – DOY) on the x-axis) for individuals we measured more than once. Here we can see the temporal patterns for the whales “Bit”, “Clouds”, “Pearl”, “Scarback, “Pointy”, and “White Hole”.

We expected to see an overall gradual increase in body condition (BAI) over the seasons, such as what we can observe for Pointy in 2018. However, some whales decreased their condition, such as Bit in 2018. Could this trend be accurate? Furthermore, what about BAI measurements that are different from the trend, such as Scarback in 2017, where the last observation point shows a lower BAI than past observation points? In addition, we still observe a high BAI in 2016 at this individual level, when compared to the other years.

My next step will be to check the whole dataset again and search for inconsistencies. There is something causing these 2016 values to possibly be wrong and I need to find out what it is. The overall quality of the measured photogrammetry images was good and in focus, but other variables could be influencing the quality and accuracy of the measurements.

For instance, when measuring images, I often struggled with glare, water splash, water turbidity, ocean swell, and shadows, as you can see in the photos below. All of these variables caused the borders of the whale body to not be clearly visible/identifiable, which may have caused measurements to be wrong.

 

Examples of bad conditions for performing photogrammetry: (1) glare and water splash, (2) water turbidity, (3) ocean swell, and (4) a shadow created in one of the sides of the whale body.
Source: GEMM Lab. Taken under NMFS permit 16111 issued to John Calambokidis.

 

Thus, I will need to check all of these variables to identify the causes for bad measurements and “clean the dataset”. Only after this process will I be able to make these plots again to look at the trends (which will be easy since I already have my R code written!). Then I’ll move on to my next hypothesis that the BAI of individual whales varied by demographics including sex, age and reproductive state.

To carry out robust science that produces results we can trust, we can’t simply collect data, perform a basic analysis, create plots and believe everything we see. Data is often messy, especially when developing new methods like we have done here with drone based photogrammetry and the BAI. So, I need to spend some important time checking my data for accuracy and examining confounding variables that might affect the dataset. Science can be challenging, both when interpreting data or learning a new command language, but it is all worth it in the end when we produce results we know we can trust.

 

 

 

Remote Sensing Applications

By Leila Lemos, PhD candidate

Fisheries and Wildlife Department, OSU

 

I am finally starting my 3rd and last year of my PhD. Just a year left and yet so many things to do. As per department requirements, I still need to take some class credits, but what classes could I take? In this short amount of time it is important to focus on my research project and on what could help me better understand the many branches of the project and what could improve my analyses. Thinking of that, both my advisor (Dr. Leigh G. Torres) and I agreed that it would be useful for me to take a class on remote sensing. So, I could learn more about this field, as well as try to include some remote sensing analyses in my project, such as sea surface temperature (SST) and chlorophyll (i.e., as a productivity indicator) conditions over the years we have collected data on gray whales off the Oregon coast.

 

Our photogrammetry data indicates that whales gradually increased their body condition over the feeding seasons of 2016 and 2018, while 2017 is different. Whales were still looking skinny in the middle of the season, and we were not collecting many fecal samples up to that point (indicating not much feeding). These findings made us wonder if this was related to delayed seasonal upwelling events and consequently low prey availability. These questions are what motivated me the most to join this class so that we might be able to link environmental correlates with our observations of gray whale body condition.

Figure 01: Skinny body condition state of the gray whale “Pancake” in August 2017.
Source: Leila S. Lemos

 

If we stop to think about what remote sensing is, we have already been implementing this method in our project since the beginning, as my favorite definition for remote sensing is “the art of collecting information of objects or phenomenon without touching it”. So, yes, the drone is a type of sensor that remotely collects information of objects (in this case, whales).

Figure 02: Drone remotely collecting information of a whale in September 2018. Drone in detail. Collected under NOAA/NMFS permit #16111.
Source: Leila Lemos

 

However, satellites, all the way up in the space, are also remotely sensing the Earth and its objects and phenomena. Even from thousands of km above Earth, these sensors are capable of generating a great amount of detailed data that is easily and freely accessible (i.e., NASA, NOAA), and can be used for multiple applications in different fields of study. Satellites are also able to collect data from remote areas like the Antarctica and the Arctic, as well as other areas that are not easily reached by humans. One important application of the use of satellite imagery is wildlife monitoring.

For example, satellite data was used to detect variation in the abundance of Weddell seals (Leptonychotes weddellii) in Erebus Bay, Antarctica (LaRue et al., 2011). Because this is a well-studied seal population, the object of this study was to test if satellite imagery could produce reliable abundance estimates. The authors used high-resolution (0.6 m) satellite imagery (from satellites Quick-Bird-2 and WorldView-1) to compare counts from the ground with counts from satellite images in the same locations at the same time. This study demonstrated a reliable methodology for further studies to replicate.

Figure 03: WorldView-1 image (0.6 m resolution) of Weddell seals hauled out east of Inaccessible Island, Erebus Bay, Antarctica.
Source: LaRue et al. (2011).

 

Satellite imagery was also applied to estimate colony sizes of Adélie penguins in Antarctica (LaRue et al., 2014). High-resolution (0.6 m) satellite imagery combined with spectral analysiswas used to estimate the sizes of the penguin breeding colonies. Ground counts were also used in order to check the reliability of the applied method. The authors then created a model to predict the abundance of breeding pairs as a function of the habitat, which was identified terrain slope as an important component of nesting density.

The identification of whales using satellite imagery is also possible. Fretwell et al. (2014)pioneered this method by successfully identifing Southern Right Whales (Eubalaena australis) in the Golfo Nuevo, Península Valdés, in Argentina in satellite images. By using very high-resolution satellite imagery (50 cm resolution) and a water penetrating coastal band that was able to see deeper into the water column, the researchers were able to successfully identify and count the whales (Fig. 04). The importance of this study was very significant, since this species was extensively hunted from the 17ththrough to the 20thcentury. Since then, the species has shown a strong recovery, but population estimates are still at <15% of historical estimates. Thus, being able to use new tools to identify, count and monitor individuals in this recovering population is a great development, especially in remote and hard to reach areas.

Figure 04: Identification of Southern Right Whales by using imagery from the WorldView2 satellite in the Golfo Nuevo Bay, Península Valdés, Argentina.
Source: Fretwell et al. (2014).

 

Polar bears (Ursus maritimus) have also been studied in the Foxe Basin, in Nunavut and Quebec, Canada (LaRue et al., 2015). Researchers used high-resolution satellite imagery in an attempt to identify and count the bears, but spectral signature differences between bears and other objects were insufficient to yield useful results. Therefore, researchers developed an automated image differencing, also known as change detection, that identifies differences between remotely sensed images collected at different times and “subtract of one image from another”. This method correctly identified nearly 90% of the bears. The technique also generated false positives, but this problem can be corrected by a manual review.

Figure 05 shows the difference in resolution of two types of satellite imagery, the panchromatic (0.6 m resolution) and the multispectral (2.4 m resolution). LaRue et al. (2015)decided not to use the multispectral imagery due to resolution constraints.

Figure 05: Polar Bears on panchromatic (0.6 m resolution) and multispectral (2.4 m resolution) imagery.
Source: LaRue et al. (2015).

 

A more recent study is being conducted by my fellow OSU Fisheries and Wildlife graduate student, Jane Dolliveron breeding colonies of three species of North Pacific albatrosses (Phoebastria immutabilis, Phoebastria nigripes, and Phoebastria albatrus)(Dolliver et al., 2017). Jane is using high-resolution multispectral satellite imagery (DigitalGlobe WorldView-2 and -3) and image processing techniques to enumerate the albatrosses. They are also using albatross species at multiple reference colonies in Hawaii and Japan (Fig. 06) to determine species identification accuracy and required correction factor(s). This will allow scientists to accurately count unknown populations on the Senkakus, which are uninhabited islands controlled by Japan in the East China Sea.

Figure 06: Satellite image of a colony of short-tailed albatrosses (Phoebastria albatrus) in Torishima, Japan, 2016.
Source: Satellite image provided by Jane Dolliver.

 

Using satellite imagery to count seals, penguins, whales, bears and albatrosses is just the start of this rapidly advancing technology. Techniques and resolutions are continuously improving. Methods can also be applied to many other endangered species, especially in remote areas, providing data on presence, abundance, annual productivity, population estimates and trends, changes in distribution, and breeding ground usage.

Other than directly monitoring wildlife, satellite images can also provide information on the environmental variables that can be related to wildlife presence, abundance, productivity and distribution.

Gentemann et al. (2017), for example, used satellite data from NASA to analyze SST variations along the west coast of the United States from 2002 to 2016. The NASA Jet Propulsion Laboratory produces global, daily, 1 km, multiscale ultra-high resolution, motion-compensated analysis of SST, and incorporates SSTs from eight different satellites. Researchers were able to identify warmer than usual SSTs (also called anomalies) along the Washington, Oregon, and California coasts from January 2014 to August 2016 (Fig.07) relative to previous years. This marine heat wave started in the Gulf of Alaska and ended in Southern California, where SST reached a maximum temperature anomaly of 6.2°C, causing major disturbances and substantial economic impacts.

Figure 07: Monthly SST anomalies in the West Coast of United States, from January 2014 to August 2016.
Source: Gentemann et al. (2017).

 

Changes in SST and winds may alter events such as the coastal upwelling that supplies nutrients to sustain a whole food chain. A marine heat-wave event as described by Gentemann et al. (2017)could have significant impacts on the health of the marine ecosystem in the subsequent season (Gentemann et al., 2017).

These findings may even relate to our questions regarding the poor gray whale body condition we noticed in 2017: this marine heat wave that lasted until August 2016 along the US west coast could have impacted the ecosystem in the subsequent season. However, I must conduct a more detailed study to determine if this heat wave was related or if another oceanographic process was involved.

So, whether remotely sensed data is generated by satellites, drones, thermal imagery, robots (as I previously wrote about), or another type of technology, it can have important  and informative applications to monitor wildlife or environmental variables associated with their ecology and biology. We can take advantage of remotely sensed technology to aid wildlife conservation efforts.

 

References

Dolliver, J., et al., Multispectral processing of high resolution satellite imagery to determine the abundance of nesting albatross. Ecological Society of America, Portland, OR, United States., 2017.

Fretwell, P. T., et al., 2014. Whales from Space: Counting Southern Right Whales by Satellite. Plos One. 9,e88655.

Gentemann, C. L., et al., 2017. Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophysical Research Letters. 44,312-319.

LaRue, M. A., et al., 2014. A method for estimating colony sizes of Adélie penguins using remote sensing imagery. Polar Biology. 37,507-517.

LaRue, M. A., et al., 2011. Satellite imagery can be used to detect variation in abundance of Weddell seals (Leptonychotes weddellii) in Erebus Bay, Antarctica. Polar Biology. 34,1727–1737.

LaRue, M. A., et al., 2015. Testing Methods for Using High-Resolution Satellite Imagery to Monitor Polar Bear Abundance and Distribution. Wildlife Society Bulletin. 39,772-779.

 

 

 

 

 

Over the Ocean and Under the Bridges: STEM Cruise on the R/V Oceanus

By Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

From September 22nd through 30th, the GEMM Lab participated in a STEM research cruise aboard the R/V Oceanus, Oregon State University’s (OSU) largest research vessel, which served as a fully-functioning, floating, research laboratory and field station. The STEM cruise focused on integrating science, technology, engineering and mathematics (STEM) into hands-on teaching experiences alongside professionals in the marine sciences. The official science crew consisted of high school teachers and students, community college students, and Oregon State University graduate students and professors. As with a usual research cruise, there was ample set-up, data collection, data entry, experimentation, successes, and failures. And because everyone in the science party actively participated in the research process, everyone also experienced these successes, failures, and moments of inspiration.

The science party enjoying the sunset from the aft deck with the Astoria-Megler bridge in the background. (Image source: Alexa Kownacki)

Dr. Leigh Torres, Dr. Rachael Orben, and I were all primarily stationed on flybridge—one deck above the bridge—fully exposed to the elements, at the highest possible location on the ship for best viewing. We scanned the seas in hopes of spotting a blow, a splash, or any sign of a marine mammal or seabird. Beside us, students and teachers donned binoculars and positioned themselves around the mast, with Leigh and I taking a 90-degree swath from the mast—either to starboard or to port. For those who had not been part of marine mammal observations previously, it was a crash course into the peaks and troughs—of both the waves and of the sightings. We emphasized the importance of absence data: knowledge of what is not “there” is equally as important as what is. Fortunately, Leigh chose a course that proved to have surprisingly excellent environmental conditions and amazing sightings. Therefore, we collected a large amount of presence data: data collected when marine mammals or seabirds are present.

High school student, Chris Quashnick Holloway, records a seabird sighting for observer, Dr. Rachael Orben. (Image source: Alexa Kownacki).

When someone sighted a whale that surfaced regularly, we assessed the conditions: the sea state, the animal’s behavior, the wind conditions, etc. If we deemed them as “good to fly”, our licensed drone pilot and Orange Coast Community College student, Jason, prepared his Phantom 4 drone. While he and Leigh set up drone operations, I and the other science team members maintained a visual on the whale and stayed in constant communication with the bridge via radio. When the drone was ready, and the bridge gave the “all clear”, Jason launched his drone from the aft deck. Then, someone tossed an unassuming, meter-long, wood plank overboard—keeping it attached to the ship with a line. This wood board serves as a calibration tool; the drone flies over it at varying heights as determined by its built-in altimeter. Later, we analyze how many pixels one meter occupied at different heights and can thereby determine the body length of the whale from still images by converting pixel length to a metric unit.

High school student, Alishia Keller, uses binoculars to observe a whale, while PhD student, Alexa Kownacki, radios updates on the whale’s location to the bridge and the aft deck. (Image source: Tracy Crews)

Finally, when the drone is calibrated, I radio the most recent location of our animal. For example, “Blow at 9 o’clock, 250 meters away”. Then, the bridge and I constantly adjust the ship’s speed and location. If the whale “flukes” (dives and exposes the ventral side of its tail), and later resurfaced 500 meters away at our 10 o’clock, I might radio to the bridge to, “turn 60 degrees to port and increase speed to 5 knots”. (See the Hidden Math Lesson below). Jason then positions the drone over the whale, adjusting the camera angle as necessary, and recording high-quality video footage for later analysis. The aerial viewpoint provides major advantages. Whales usually expose about 10 percent of their body above the water’s surface. However, with an aerial vantage point, we can see more of the whale and its surroundings. From here, we can observe behaviors that are otherwise obscured (Torres et al. 2018), and record footage that to help quantify body condition (i.e. lengths and girths). Prior to the batteries running low, Jason returns the drone back to the aft deck, the vessel comes to an idle, and Leigh catches the drone. Throughout these operations, those of us on the flybridge photograph flukes for identification and document any behaviors we observe. Later, we match the whale we sighted to the whale that the drone flew over, and then to prior sightings of this same individual—adding information like body condition or the presence of a calf. I like to think of it as whale detective work. Moreover, it is a team effort; everyone has a critical role in the mission. When it’s all said and done, this noninvasive approach provides life history context to the health and behaviors of the animal.

Drone pilot, Jason Miranda, flying his drone using his handheld ground station on the aft deck. (Photo source: Tracy Crews)

Hidden Math Lesson: The location of 10 o’clock and 60 degrees to port refer to the exact same direction. The bow of the ship is our 12 o’clock with the stern at our 6 o’clock; you always orient yourself in this manner when giving directions. The same goes for a compass measurement in degrees when relating the direction to the boat: the bow is 360/0. An angle measure between two consecutive numbers on a clock is: 360 degrees divided by 12-“hour” markers = 30 degrees. Therefore, 10 o’clock was 0 degrees – (2 “hours”)= 0 degrees- (2*30 degrees)= -60 degrees. A negative degree less than 180 refers to the port side (left).

Killer whale traveling northbound.

Our trip was chalked full of science and graced with cooperative weather conditions. There were more highlights than I could list in a single sitting. We towed zooplankton nets under the night sky while eating ice cream bars; we sang together at sunset and watched the atmospheric phenomena: the green flash; we witnessed a humpback lunge-feeding beside the ship’s bow; and we saw a sperm whale traveling across calm seas.

Sperm whale surfacing before a long dive.

On this cruise, our lab focused on the marine mammal observations—which proved excellent during the cruise. In only four days of surveying, we had 43 marine mammal sightings containing 362 individuals representing 9 species (See figure 1). As you can see from figure 2, we traveled over shallow, coastal and deep waters, in both Washington and Oregon before inland to Portland, OR. Because we ventured to areas with different bathymetric and oceanographic conditions, we increased our likelihood of seeing a higher diversity of species than we would if we stayed in a single depth or area.

Humpback whale lunge feeding off the bow.

Number of sightings Total number of individuals
Humpback whale 22 40
Pacific white-sided dolphin 3 249
Northern right whale dolphin 1 9
Killer whale 1 3
Dall’s porpoise 5 49
Sperm whale 1 1
Gray whale 1 1
Harbor seal 1 1
California sea lion 8 9
Total 43 362

Figure 1. Summary table of all species sightings during cruise while the science team observed from the flybridge.

Pacific white-sided dolphins swimming towards the vessel.

Figure 2. Map with inset displaying study area and sightings observed by species during the cruise, made in ArcMap. (Image source: Alexa Kownacki).

Even after two days of STEM outreach events in Portland, we were excited to incorporate more science. For the transit from Portland, OR to Newport, OR, the entire science team consisted two people: me and Jason. But even with poor weather conditions, we still used science to answer questions and help us along our journey—only with different goals than on our main leg. With the help of the marine technician, we set up a camera on the bow of the ship, facing aft to watch the vessel maneuver through the famous Portland bridges.

Video 1. Time-lapse footage of the R/V Oceanus maneuvering the Portland Bridges from a GoPro. Compiled by Alexa Kownacki, assisted by Jason Miranda and Kristin Beem.

Prior to the crossing the Columbia River bar and re-entering the Pacific Ocean, the R/V Oceanus maneuvered up the picturesque Columbia River. We used our geospatial skills to locate our fellow science team member and high school student, Chris, who was located on land. We tracked each other using GPS technology in our cell phones, until the ship got close enough to use natural landmarks as reference points, and finally we could use our binoculars to see Chris shining a light from shore. As the ship powered forward and passed under the famous Astoria-Megler bridge that connects Oregon to Washington, Chris drove over it; he directed us “100 degrees to port”. And, thanks to clear directions, bright visual aids, and spatiotemporal analysis, we managed to find our team member waving from shore. This is only one of many examples that show how in a few days at sea, students utilized new skills, such as marine mammal observational techniques, and honed them for additional applications.

On the bow, Alexa and Jason use binoculars to find Chris–over 4 miles–on the Washington side of the Columbia River. (Image source: Kristin Beem)

Great science is the result of teamwork, passion, and ingenuity. Working alongside students, teachers, and other, more-experienced scientists, provided everyone with opportunities to learn from each other. We created great science because we asked questions, we passed on our knowledge to the next person, and we did so with enthusiasm.

High school students, Jason and Chris, alongside Dr. Leigh Torres, all try to get a glimpse at the zooplankton under Dr. Kim Bernard’s microscope. (Image source: Tracy Crews).

Check out other blog posts written by the science team about the trip here.

How to apply my PhD?

By Leila Lemos, PhD candidate, Fisheries and Wildlife Department

Time has flown. It seems that it was like a month ago that I received the news that I was approved in a public notice from the Brazilian government to study abroad, and began the process of moving to Oregon. But actually almost three years have now passed, and I am starting to wrap up my PhD, since I need to defend it in a little bit more than a year.

Our team is now starting the third and last fieldwork season for my PhD project. I am also working on my study plan to determine the last classes I need to take, and our first manuscripts are ‘in press’ or ‘in prep’ for submission to journals. So, it’s time for me to think about what comes next.

I am from Rio de Janeiro, Brazil, and I am studying in the US through a Brazilian government program called Science Without Borders. This program aims to send students abroad to learn new techniques and to develop innovative projects. The projects needed to be original to be approved by the public notice. The main idea is to bring these students back to Brazil, after their PhD completion, to disseminate the acquired knowledge by applying the learned techniques.

My project includes a few novel aspects that allowed for funding by this program. The main focus of my thesis is to develop an endocrinology study of a cetacean species. This was (and still is) a critical field in Brazil, as reported by the “National Action Plan for the conservation of aquatic mammals: Small cetaceans” (2010). According to this Action Plan, cetacean hormonal analyses are rare and of high priority, but there are limited labs with the capacity to study cetacean endocrinology in Brazil. Other limiting factors are the associated analysis costs and a lack of human knowledge and skills. In addition to the hormonal analyses (Figure 1), I am also using other ‘new technologies’ in the project: drones (Figure 2; Video 1) and GoPros (Video 2).

Figure 1: Learning how to perform hormonal analysis at the Seattle Aquarium, WA.
Source: Angela Smith

 

Figure 2: Learning how to fly a drone in Newport, OR.
Source: Florence Sullivan

 

Video 1: Drone flights performed in Newport, OR, during fieldwork in 2016.

* Taken under NOAA/NMFS permit #16111 to John Calambokidis.

 

Video 2: Video of mysid swarms during a GoPro deployment conducted in Port Orford, OR, during fieldwork in 2016.

 

The importance of studying cetacean hormones includes a better understanding of their reproductive cycles (i.e., sex hormones such as progesterone, testosterone and estradiol) and their physiological stress response (i.e., cortisol) to possible threats (e.g., acoustic pollution, contaminants, lack of prey). In addition, through photographs and videos recorded by drones we can conduct photogrammetry analysis to monitoring cetacean body condition, and through GoPro recordings of the water column we can assess prey availability. Changes in both body condition and prey can help us explaining how and why hormone levels vary.

Through my PhD I have obtained skills in hormone analysis, photogrammetry and video prey assessment by studying the logistically accessible and non-threatened gray whale (Eschrichtius robustus). During method development, these features are important to increase sample size and demonstrate feasibility. But now that the methodologies have proven successful, we can start applying them to other species and regions, and under different circumstances, to improve conservation efforts of threatened populations.

Many cetacean species along the Brazilian coast are threatened, particularly from fishing gear and vessel interactions, chemical and noise pollution. By applying the methods we have developed in the GEMM Lab during my PhD to cetacean conservation issues in Brazil, we could enable a great expansion in knowledge across many fields (i.e., endocrinology, behavior, photogrammetry, diet). Additionally, these skills can promote safer work environments (for the scientist and for the object of study) and cheaper work processes. However, many countries, such as Brazil, do not have the infrastructure and access to technologies to conduct these same analyses, as in developed countries like the USA. These technologies, when sold in Brazil, have many taxes on the top of the product that they can become an extra hurdle, due to budget constraints. Thus, there is a need for researchers to adapt these skills and technologies, in the best manner possible, to the reality of the country.

Now that I am starting to think about ‘life after PhD’, I can see myself returning to my country to spread the knowledge, technologies and skills I have gained through these years at OSU to new research projects so that I am able to assist with conservation efforts for the ocean and marine fauna in Brazil.

 

References:

PAN, 2010. Plano de ação nacional para a conservação dos mamíferos aquáticos: pequenos cetáceos / André Silva Barreto … [et al.]; organizadores Claudia Cavalcante Rocha-Campos, Ibsen de Gusmão Câmara, Dan Jacobs Pretto. – Brasília: Instituto Chico Mendes de Conservação da Biodiversidade, Icmbio, 132 p. Em: <http://www.icmbio.gov.br/portal/images/ stories/docs-plano-de-acao/pan-peqs-cetaceos/pan_pequenoscetaceos_web.pdf> Acessado em: 27 de Maio de 2015.

 

Diving Deeper

By Taylor Mock, GEMM Lab intern

Greetings, all!

My name is Taylor Mock. Since February I have been volunteering in the GEMM Lab and am ecstatic to make my online debut as part of the team!

For many years, I had a shallow relationship with Hatfield Marine Science Center. As a Newport native, I would spend mornings and evenings glancing over at the Hatfield buildings while driving over the bridge to and from school. I was always intrigued. Sure, I would hear snippets of research from my peers about what projects their parents were involved in, but the inner workings of the complex mystified me.

Toward the end of my Freshman year in 2012 at Westmont College in Santa Barbara, California, my mom asked me what my summer plans were. I replied with the typical “I don’t know… Get a job?” She insisted that instead of a job I think about getting an internship; experience that will last more than a summer. I inquired through a family friend (because every person in this little community is woven together some way or another) if any internships or volunteer opportunities were available at Hatfield. She pointed me in the direction of the Environmental Protection Agency and thus began my Hatfield volunteering saga. I worked that summer, and the next, at the EPA under the direction of Ted DeWitt and Jody Stecher on denitrification studies in estuarine marshes. That summer provided me a glorious front row seat to field research and a greater understanding of my potential as a person and as a scientist. Now, this experience was marvelous, but I knew shortly after starting that my heart was elsewhere.

It was during my study abroad semester in Belize as part of my internship at the Toledo Institute for Development and Environment (TIDE) that I realized I wanted to work with marine macroorganisms. At TIDE, I engaged in radio telemetry conservation efforts tracking Hicatee (Dermatemys mawii) aquatic turtles. We would spend days on a small boat floating through canals and setting nets in hopes of capturing individuals of this small population to outfit them with radio tracking devices. These would be later used to track foraging, mating, and travel patterns in the region. It was an amazing time, to say the least. I remember waking up on my 21st birthday from my camping hammock and staring up at the lush rainforest above my head with a warm breeze across my face, followed by spending the day in the presence of these glorious creatures. It was heaven. I returned to Westmont the following term and took a Marine Mammal Eco-Physiology course and absolutely fell in love with Cetacea. Yes, I had always been captivated by this clade of beings (and truthfully when I was eight years old had a book on “How to Become a Marine Mammal Trainer”), but this was deeper. Of course, pinnipeds and otters and polar bears and manatees were enjoyable to learn about. There was something about the Cetacea though and how they migrated up and down the coast (just like me!) that I really connected with. My time learning about these animals created an intimate understanding of another group of species that developed into a rich, indescribable empathetic connection. I had to take a couple years away from scholastics and away from biology for health and wellness reasons. One day, though, a couple years after graduating and returning to Newport I rekindled with Jody from the EPA. He asked me if I would like to volunteer under Leigh Torres in the Marine Mammal Institute at HMSC. I do not think I could have possibly said no. I have been enjoying my time in the GEMM Lab ever since!

Though I am available to help anyone with any task they need, the work I do mostly centers around photogrammetry.

Using photogrammetry skills to measure gray whales in the GEMM Lab.

Photogrammetry, essentially, means geo-spatially measuring objects using photographs. What that looks like for me is taking an aerial photograph (extracted from overhead drone video footage) of a whale, running the image through a computer program called “Matlab”, taking a series of measurements from the whale (e.g., tip of the mandible to the notch of the fluke, distance between each tip of the fluke, and several measurements across the midsection of the whale). Several images of individuals are processed in order to find an average set of measurements for each whale.

Final result of the photogrammetry method on a gray whale

You might be wondering, “How can one measure the distance accurately from just a photograph?” I am glad you asked! The drones are outfitted with a barometer to measure the atmospheric pressure and, in turn, altitude. The changing altitudes are recorded in a separate program that is run simultaneously with the video footage. Thus, we have the altitudinal measurements for every millisecond of the drone’s flight. To monitor the accuracy and functionality of the barometer, calibrations are completed upon deployment and retrieval of each drone flight. To calibrate: the initial takeoff height is measured, a board of known length is thrown into the water, the drone will then rise or lower slowly above the board between 10 and 40 m, photographs of the board are then taken from varying altitudes, and are processed in Matlab.

During my time in the GEMM Lab, I have had the pleasure of completing photogrammetry assignments for both Leila on the Oregon Coast gray whale and for Dawn on the New Zealand blue whale projects. These ladies, and the other members of the GEMM Lab, have been so patient and gracious in educating me on the workings of Matlab and the video processing systems. It is a distinct honor working with them and to delight in the astounding nature of these creatures together. Each day I am struck in sheer awe of how beautiful and powerful these whales truly are. Their graceful presence and movement through the water rivals even the most skillful dancer.

Over the last 6 years, I am delighted to say that my relationship with Hatfield has become much deeper. The people and the experiences I have encountered during my time here, especially in the GEMM Lab, have been nothing short of incredible. I am sincerely grateful for this continued opportunity. It fills my soul with joy to engage in work that contributes to the well being of the ocean and its inhabitants.

Thank you, Leigh and all of the GEMM Lab members. I hope to continue volunteering with you for as long as you will have me.