What drives individual specialization?

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

When I wrote my first blog on individual specialization well over a year ago, I just skimmed the surface of the literature on this topic and only started to recognize the importance of studying individual specialization. The question, “is there individual specialization in the PCFG of gray whales?” is the focus of my first thesis chapter and the results will affect all my subsequent work. Therefore, the literature and concepts of individual specialization are a focus of my literature review and studies.

In my previous blog I focused on common characteristics of individuals that are similarly specialized as an underlying driver of individual specialization. While these characteristics (often attributable to age, sex, or physical traits) are important to consider, I’ve learned that the list of drivers of individual specialization is long and that many variables are dynamic. Of all the drivers I’ve learned about, competition is among the most common.

Competition is a major driver of individual specialization, and a common driver of competition is resource availability. When resource availability decreases, whether caused by increasing population density or changing environmental conditions, competition for that resource increases. As competition increases, individuals have a choice. They can choose to engage in competition, either by racing, fighting, or sharing [1], which can be costly, or they can diffuse the competition by focusing on a different resource.  This second approach would be considered niche partitioning in the prey dimension. Niche partitioning is a way for individuals to share ecological space by using different resources. Essentially, individuals can share habitat without having to engage in direct competition by pursuing different prey types [2]. 

This switch to different prey types can change the degree of individual specialization present in the population (Figure 1). But the direction of the change is not constant. If all individuals were pursuing the same prey type under low competition conditions but then switched to different alternate prey types under high competition, then individual specialization would increase (Figure 1a). This direction has been observed across a range of species including sharks [3], otters [4]–[7], dolphins [8], [9], stickleback fish [10], [11], largemouth bass [12], banded mongoose [13], fur seals [14], and baleen whales [15].

However, if individuals were pursuing different prey types under low competition conditions (maybe because of underlying differences such as age or sex) but then switched to the same alternate prey types under high competition, diet overlap would increase, and individual specialization would decrease (Figure 1b). Furthermore, an individual might not switch to an entirely new prey type but instead add prey items to its diet [16]. This diet expansion under competition would also decrease individual specialization. Fewer studies have reported this direction but it’s been found in the common bumblebee [17] and in several neotropical vertebrate species [18], [19].

Figure `1. Figure 3 from Araújo et al. 2011 [20]. Illustration of how ecological mechanisms may affect the degree of individual specialization. Arrows linking resources to individual consumers indicate resource consumption (relative thickness indicates proportional contribution). 
Horizontal arrows indicate the sign (positive or negative) of the effect on the degree of individual specialization. (a) Consumers share the same preferred resource (dark gray tangle) but have different alternative resources (white and light gray triangles). As the preferred resource becomes scarce, consumers switch to different alternatives, increasing the degree of individual specialization. (b) Alternatively, consumers have distinct preferred resources, so that as resources become scarce, individuals converge to the alternative resource (dark gray triangle), reducing diet variation.

Interestingly, its hypothesized that individual specialization driven by competition is one of the factors that facilitates the formation and existence of stable groups [21]. For example, a study on resident female dolphins in Sarasota Bay, FL, USA found that females with high spatial overlap used distinct foraging specializations [8](Figure 2). This study illustrates how partitioning prey enabled spatial and social coexistence. A study on banded mongooses reached a similar conclusion [13]. They found that specialization was highest in the biggest groups (with the most competition) and not explained by sex, age, or other inherent differences. They hypothesized that specialization increasing with competition reduced conflict and allowed the groups to remain stable. This study also highlighted the role of learning to determine an individual’s specialization.

Figure 2. A bottlenose dolphin.
Source: https://sarasotadolphin.org

Learning drives the distribution of knowledge throughout a population, which can lead to either specialization or generalization. ‘One-to-one’ learning, where one individual learns from one demonstrator, tends to promote individual specialization [21]. This form of transmission drives specialization because the individuals who learn the specialization tend to then carry on using, and eventually teaching, that specialization [6]. A common example of ‘one-to-one’ learning is vertical transmission from parent to offspring. It has been shown to transmit specializations in dolphins [22] and otters [6]. ‘One-to-one’ learning can occur outside of parent-offspring pairs; non-parent-offspring ‘one-to-one’ learning has been shown to drive specialization in banded mongooses [13](Figure 3).

However, other forms of social learning can promote more generalized foraging strategies. ‘Many-to-one’ or ‘one-to-many’ learning  can reduce the presence of specialization in species [13], [21] as can the presence of conformity in a group [23], [24].

Figure 3. A group of banded mongooses.
Source: http://socialisresearch.org/about-the-banded-mongoose-project/

The multiple drivers of specialization and their dynamic quality means that it is important to contextualize specialization. For example, a study on four species of neotropical frogs found varying degrees of specialization across multiple populations of each species [18]. The degree of specialization was dependent on a variety of drivers including predation and both intra- and inter-specific competition. Notably, the direction of the relationship between degree of specialization and each driver was species specific. This study highlights that one species may not always be more specialized than another, but that a populations’ specialization is context dependent.

Therefore, it is important to not only be aware of the degree of specialization present in a population, but to also understand its dynamics and drivers. These relationships can then be used to understand how, and why, a population may react to competition from other species, predators, and changes in resource availability [20].  A population’s specialization can also affect the specialization of other populations and community dynamics [25], therefore, it’s important to consider and study individual specialization on both the population and community level. I am excited to start using our incredible six-year dataset to start investigating these questions for PCFG gray whales, so stay tuned for results!

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References

[1]       M. Taborsky, M. A. Cant, and J. Komdeur, The Evolution of Social Behaviour. Cambridge: Cambridge University Press, 2021. doi: 10.1017/9780511894794.

[2]       E. R. Pianka, “Niche Overlap and Diffuse Competition,” vol. 71, no. 5, pp. 2141–2145, 1974.

[3]       P. Matich et al., “Ecological niche partitioning within a large predator guild in a nutrient-limited estuary,” Limnol. Oceanogr., vol. 62, no. 3, pp. 934–953, 2017, doi: https://doi.org/10.1002/lno.10477.

[4]       S. D. Newsome et al., “The interaction of intraspecific competition and habitat on individual diet specialization: a near range-wide examination of sea otters,” Oecologia, vol. 178, no. 1, pp. 45–59, May 2015, doi: 10.1007/s00442-015-3223-8.

[5]       M. T. Tinker, G. Bentall, and J. A. Estes, “Food limitation leads to behavioral diversification and dietary specialization in sea otters,” Proc. Natl. Acad. Sci., vol. 105, no. 2, pp. 560–565, Jan. 2008, doi: 10.1073/pnas.0709263105.

[6]       M. T. Tinker, M. Mangel, and J. A. Estes, “Learning to be different: acquired skills, social learning, frequency dependence, and environmental variation can cause behaviourally mediated foraging specializations,” Evol. Ecol. Res., vol. 11, pp. 841–869, 2009.

[7]       M. T. Tinker et al., “Structure and mechanism of diet specialisation: testing models of individual variation in resource use with sea otters,” Ecol. Lett., vol. 15, no. 5, pp. 475–483, 2012, doi: 10.1111/j.1461-0248.2012.01760.x.

[8]       S. Rossman et al., “Foraging habits in a generalist predator: Sex and age influence habitat selection and resource use among bottlenose dolphins (Tursiops truncatus),” Mar. Mammal Sci., vol. 31, no. 1, pp. 155–168, 2015, doi: https://doi.org/10.1111/mms.12143.

[9]       L. G. Torres, “A kaleidoscope of mammal , bird and fish : habitat use patterns of top predators and their prey in Florida Bay,” vol. 375, pp. 289–304, 2009, doi: 10.3354/meps07743.

[10]     M. S. Araújo et al., “Network Analysis Reveals Contrasting Effects of Intraspecific Competition on Individual Vs. Population Diets,” Ecology, vol. 89, no. 7, pp. 1981–1993, 2008, doi: 10.1890/07-0630.1.

[11]     R. Svanbäck and D. I. Bolnick, “Intraspecific competition drives increased resource use diversity within a natural population,” Proc. R. Soc. B Biol. Sci., vol. 274, no. 1611, pp. 839–844, Mar. 2007, doi: 10.1098/rspb.2006.0198.

[12]     D. E. Schindler, J. R. Hodgson, and J. F. Kitchell, “Density-dependent changes in individual foraging specialization of largemouth bass,” Oecologia, vol. 110, no. 4, pp. 592–600, May 1997, doi: 10.1007/s004420050200.

[13]     C. E. Sheppard et al., “Intragroup competition predicts individual foraging specialisation in a group-living mammal,” Ecol. Lett., vol. 21, no. 5, pp. 665–673, 2018, doi: 10.1111/ele.12933.

[14]     L. Kernaléguen, J. P. Y. Arnould, C. Guinet, and Y. Cherel, “Determinants of individual foraging specialization in large marine vertebrates, the Antarctic and subantarctic fur seals,” J. Anim. Ecol., vol. 84, no. 4, pp. 1081–1091, 2015, doi: 10.1111/1365-2656.12347.

[15]     E. M. Keen and K. M. Qualls, “Respiratory behaviors in sympatric rorqual whales: the influence of prey depth and implications for temporal access to prey,” J. Mammal., vol. 99, no. 1, pp. 27–40, Feb. 2018, doi: 10.1093/jmammal/gyx170.

[16]     R. H. MacArthur and E. R. Pianka, “On Optimal Use of a Patchy Environment,” Am. Nat., vol. 100, no. 916, pp. 603–609, 1966, doi: 10.1086/282454.

[17]     C. Fontaine, C. L. Collin, and I. Dajoz, “Generalist foraging of pollinators: diet expansion at high density,” J. Ecol., vol. 96, no. 5, pp. 1002–1010, 2008, doi: 10.1111/j.1365-2745.2008.01405.x.

[18]     R. Costa-Pereira, V. H. W. Rudolf, F. L. Souza, and M. S. Araújo, “Drivers of individual niche variation in coexisting species,” J. Anim. Ecol., vol. 87, no. 5, pp. 1452–1464, 2018, doi: 10.1111/1365-2656.12879.

[19]     M. M. Pires, P. R. Guimarães Jr, M. S. Araújo, A. A. Giaretta, J. C. L. Costa, and S. F. dos Reis, “The nested assembly of individual-resource networks,” J. Anim. Ecol., vol. 80, no. 4, pp. 896–903, 2011, doi: 10.1111/j.1365-2656.2011.01818.x.

[20]     M. S. Araújo, D. I. Bolnick, and C. A. Layman, “The ecological causes of individual specialisation,”Ecol. Lett., vol. 14, no. 9, pp. 948–958, 2011, doi: https://doi.org/10.1111/j.1461-0248.2011.01662.x.

[21]     C. E. Sheppard, R. Heaphy, M. A. Cant, and H. H. Marshall, “Individual foraging specialization in group-living species,” Anim. Behav., vol. 182, pp. 285–294, Dec. 2021, doi: 10.1016/j.anbehav.2021.10.011.

[22]     S. Wild, S. J. Allen, M. Krützen, S. L. King, L. Gerber, and W. J. E. Hoppitt, “Multi-network-based diffusion analysis reveals vertical cultural transmission of sponge tool use within dolphin matrilines,” Biol. Lett., vol. 15, no. 7, p. 20190227, Jul. 2019, doi: 10.1098/rsbl.2019.0227.

[23]     L. M. Aplin, D. R. Farine, J. Morand-Ferron, A. Cockburn, A. Thornton, and B. C. Sheldon, “Experimentally induced innovations lead to persistent culture via conformity in wild birds,” Nature, vol. 518, no. 7540, pp. 538–541, Feb. 2015, doi: 10.1038/nature13998.

[24]     E. Van de Waal, C. Borgeaud, and A. Whiten, “Potent Social Learning and Conformity Shape a Wild Primate’s Foraging Decisions,” Science, Apr. 2013, doi: 10.1126/science.1232769.

[25]     D. I. Bolnick et al., “Why intraspecific trait variation matters in community ecology,” Trends Ecol. Evol., vol. 26, no. 4, pp. 183–192, Apr. 2011, doi: 10.1016/j.tree.2011.01.009.

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.

Hope lies in cooperation: the story of a happy whale!

By Solène Derville, Postdoc, OSU Department of Fisheries, Wildlife, and Conservation Science, Geospatial Ecology of Marine Megafauna Lab

I wrote my last blogpost in the midst of winter and feeling overwhelmed as I was trying to fly to the US at the peak of the omicron pandemic… Since then, morale has improved exponentially. I have spent two months in the company of my delightful GEMM lab friends, nerding over statistics, sharing scientific conversations, drinking (good!) beer and enjoying the company of this great group of people. During that stay, I was able to focus on my OPAL project more than I have ever been able to, as I set myself the goal of not getting distracted by anything else during my stay in Newport.

The only one distraction that I do not regret is a post I read one morning on the Cetal Fauna Facebook page, a group of cetacean experts and lovers who share news, opinions, photos… anything cetacean related! Someone was posting a photo of a humpback whale stranded in the 1990s’ on Peregian beach, on the east coast of Australia, which is known as a major humpback whale migratory corridor. The story said that (probably with considerable effort) the whale was refloated by many different individuals and organizations present at the beach on that day, specifically Sea World Research, Rescue & Conservation.

I felt very touched by this story and the photo that illustrated it (Figure 1). Seeing all these people come together in this risky operation to save this sea giant is quite something. And the fact that they succeeded was even more impressive! Indeed, baleen whales strand less commonly than toothed whales but their chances of survival when they do so are minimal. In addition to the actual potential damages that might have caused the whale to strand in the first place (entanglements, collisions, diseases etc.), the beaching itself is likely to hurt the animal in a permanent way as their body collapses under their own weight usually causing a cardiovascular failure (e.g., Fernández et al., 2005)⁠. The rescue of baleen whales is also simply impaired by the sheer size and weight of these animals. Compared to smaller toothed whales such as pilot whales and false killer whales that happen to strand quite frequently over some coastlines, baleen whales are almost impossible to move off the beach and getting close to them when beached can be very dangerous for responders. For these reasons, I found very few reports and publications mentioning successful rescues of beached baleen whales (e.g., Priddel and Wheeler, 1997; Neves et al., 2020).⁠

Figure 1: Stranded humpback whale in Peregian Beach, East Australia, on Aug 16th 1991. Look at the size of the fluke compared to the men who are trying to rescue her! Luckily, that risky operation ended well. Credit: Sea World Research, Rescue and Conservation. Photo posted by P. Garbett on https://www.facebook.com/groups/CetalFauna – February 26, 2022)

Now the story gets even better… the following day I received an email from Ted Cheeseman, director and co-founder of Happywhale, a collaborative citizen science tool to share and match photographes of cetaceans (initially only humpback whales but has extended to other species) to recognize individuals based on the unique patterns of the their fluke or dorsal fin. The fluke of the whale stranded in Australia in 1991 had one and only match within the Happywhale immense dataset… and that match was to a whale seen in New Caledonia (Figure 2). “HNC338” was the one!

Figure 2: Happy whale page showing the match of HNC338 between East Australia and New Caledonia. https://happywhale.com/individual/78069;enc=284364?fbclid=IwAR1QEG_6JkpH_k2UrF-qp-9qrOboHYakKjlTj0lLbDFygjN5JugkkKVeMQw

Since I conducted my PhD on humpback whale spatial ecology in New Caledonia, I have continued working on a number of topics along with my former PhD supervisor, Dr Claire Garrigue, in New Caledonia. Although I do not remember each and every whale from her catalogue (composed of more than 1600 humpback whales as of today), I do love a good “whale tale” and I was eager to know who this HNC338 was. I quickly looked into Claire’s humpback whale database and sure enough I found it there: encountered at the end of the 2006 breeding season on September 12th, at a position of 22°26.283’S and 167°01.991’E and followed for an hour. Field notes reported a shy animal that kept the boat at a distance. But most of all, HNC338 was genetically identified as a female and was accompanied by a calf during that season! The calf was particularly big, as expected at this time of the season. What an inspiring thing to think that this whale, stranded in 1991, was resighted 15 years later in a neighboring breeding ground, apparently healthy and raising a calf of her own.

As genetic paternity analysis have been conducted on many New Caledonia calf biopsy samples as part of the Sexy Singing project conducted with our colleagues from St Andrews University in Scotland, we might be able to identify the calf’s father in this breeding stock. Thanks to the great amount of data shared and collected through Happywhale, we are discovering more and more about whale migratory patterns and behavior. It might as well be that this calf’s father was one of those whales that seem to roam over several different breeding grounds (New Caledonia and East Australia). This story is far from finished…

Figure 3: A (pretty bad!) photo of HNC338’s fluke. Luckily the Happywhale matching algorithm is very efficient and was able to detect the similarities of the fluke’s trailing edge compared to figure 1 (Cheeseman et al., 2021)⁠. Also of note, see that small dorsal fin popping out of the waters behind big mama’s fluke? That’s her calf!

From the people who pulled this whale back into the water in 1991, to the scientists and cetacean enthusiasts who shared their data and whale photos online, this story once again shows us that hope lies in cooperation! Happywhale was only created in 2015 but since then it has brought together the general public and the scientists to contribute over 465,000 photos allowing the identification of 75,000 different individuals around the globe. In New Caledonia, in Oregon and elsewhere, I hope that these collective initiatives grow more and more in the future, to the benefit of biodiversity and people.

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References

Cheeseman, T., Southerland, K., Park, J., Olio, M., Flynn, K., Calambokidis, J., et al. (2021). Advanced image recognition: a fully automated, high-accuracy photo-identification matching system for humpback whales. Mamm. Biol. doi:10.1007/s42991-021-00180-9.

Fernández, A., Edwards, J. F., Rodríguez, F., Espinosa De Los Monteros, A., Herráez, P., Castro, P., et al. (2005). “Gas and fat embolic syndrome” involving a mass stranding of beaked whales (Family Ziphiidae) exposed to anthropogenic sonar signals. Vet. Pathol. 42, 446–457. doi:10.1354/vp.42-4-446.

Neves, M. C., Neto, H. G., Cypriano-Souza, A. L., da Silva, B. M. G., de Souza, S. P., Marcondes, M. C. C., et al. (2020). Humpback whale (megaptera novaeangliae) resighted eight years after stranding. Aquat. Mamm. 46, 483–487. doi:10.1578/AM.46.5.2020.483.

Priddel, D., and Wheeler, R. (1997). Rescue of a Bryde’s whale Balaenoptera edeni entrapped in the Manning River, New South Wales: Unmitigated success or unwarranted intervention? Aust. Zool. 30, 261–271. doi:10.7882/AZ.1997.002.

Cross-taxa collaborations: a look at the value of human and cetacean partnerships.

Miranda Mayhall, Graduate student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab.

For marine science to be successful and impactful, it is crucial for collected data and results of analyses to be shared as widely as possible. This sharing should occur with the research community itself (which of course saves time and helps ignite the big, impactful ideas), and also amongst the public, in government, the fishing industry, big energy businesses, the military, and shipping industries as well. All these entities can relate in some way to the use of the oceans. Our increased collective knowledge can help us make conscious and intelligent management choices that will promote healthy oceans and in turn provide more resources to humans as well.

Though I am only just breaking the ice in my marine science education, I am already experiencing my first tastes of what this collaboration can look like. My graduate thesis focuses on the acoustic and observational detections of fin whales, an endangered species, as they relate to environmental characteristics in the NE Pacific. I am still in the early stages collecting data with the HALO project, but for now it is important to get started reviewing what’s currently available in the field. GEMM lab’s OPAL project, led by Dr. Leigh Torres and Dr. Solene Derville, was quick to provide me with their fin whale sightings data collected over the past few years, as well as share some of their great fin whale photos (Fig. 1). Clearly, I am already becoming rich through this association.

Figure 1. Two fin whales surface off the Oregon coast. Photographed by Leigh Torres during an OPAL helicopter survey in September 2021 under NMFS permit # 21678.

My career interests revolve around filling knowledge gaps of cetacean behaviors, so I often find myself associating what’s happening in my life to what I am reading currently as it relates to this field of research. My most recent blog, highlighted my need to relax occasionally with play and prompted me to consider how play is defined in cetacean behavior. So, with the ignition of my graduate research and this first aforementioned taste of scientific collaboration, I synaptically thought about a recent study of interspecies collaborative hunting between dolphins and humans that was co-authored by the Marine Mammal Institute’s Dr. Mauricio Cantor. Here, bottlenose dolphins who have learned to herd fish to shore, stick together and use their skills to move schools of fish toward local fishermen standing by with nets. The dolphins then provide a signal to the fishermen, the nets are cast at just the right time, and the dolphins forage on the fish trapped between the fishermen and the nets (Daura-Jorge, Cantor, et al., 2012). Both the dolphins and the fishermen greatly benefit by working together. I found this study thought-provoking; I have not seen anything quite like this interspecies association.

National Geographic video provides close perspective of the Laguna, Brazil fishermen working together with dolphins to net fish. https://www.youtube.com/watch?v=8kMGJ8T3-Pg.

In the interest of potentially finding more cross-taxa cetacean relationships, I dug into the literature and found a few more interspecies associations to note. The first article that took me aback was a 2017 report detailing humpback whales defending other marine mammal species by interfering with the hunting practices of transient killer whales (Pitman et al., 2017). Killer whales are apex predators who hunt marine mammals, to include pinnipeds, adult baleen whales and often the calves of baleen whales. Slow, rotund baleen whales (right whales, gray whales, and humpbacks) are known to use their immense size and large appendages to fight off killer whales. What is unique with this study is that humpback whales were observed not only protecting their own calves from predation but also using a mobbing tactic to protect other cetacean species (minke whales, gray whales, Dall’s porpoises, and others) and pinnipeds (Steller sea lions, California sea lions, Weddell seals, and others; Fig. 2) as well, showing acts of potential altruism in cetaceans (Pitman et al., 2017).

Figure 2. Humpback whale moving in to interfere with a killer whale hunting a seal. Photo credit: Robert Pitman, https://whalescientists.com/humpback-whales-altruism/.

The next interspecies association catching my eye came from studies detailing the two largest marine mammals, blue and fin whales, reproducing together. Though the two species are relatively alike in having large sleek physiques, they are very different in their known migratory and acoustic behaviors, so it doesn’t seem obvious or likely the two would mate. However, following the genetic testing of a whale near Iceland that displayed an unusual phenotype, researchers were able to determine that the whale did in fact contain the DNA of both species (Pampoulie et al., 2020). These blue/fin hybrids have been spotted in several locations worldwide and they are even found to be fertile. A recent study of a successfully tagged and observed blue/fin hybrid called, “Flue” (Fig. 3), co-authored by Dr. Daniel Palacios of MMI’s WHET Lab, found that though the animal possessed a phenotype mostly descriptive of fin whale, Flue appeared to follow blue whale migratory behavior (moving farther north along the California coast to forage in the summer and then moving to southern breeding ground waters along the coast of Mexico). These researchers suggest that blue/fin hybrid whales are common and postulate whether these animals are the source of an unmatched 52 Hz whale call sometimes recorded in the North Pacific (Jefferson et al., 2021).

Figure 3. Highly observed and documented blue/fin whale hybrid, called “Flue”, spotted off the coast of Santa Barbara, CA, USA. Photo credit: Adam Ernster, Condor Express Media, https://www.youtube.com/watch?v=4LjH2-naRPE&feature=youtu.be&app=desktop.

Lastly (and perhaps my favorite of the papers of the collection), there is a report published in 2019 detailing a closely followed bottlenose dolphin female who adopted a young melon-headed whale calf near French Polynesia in the South Pacific (Fig. 4). Though cetaceans have been known to participate in allonursing, a form of alloparental care in which adult females will nurse another’s offspring of the same species, an interspecific adoption has rarely been reported. This mother-calf interspecies pair were observed together just after the adoptive mother gave birth to another calf, so it was impossible that the adopted calf was a potential hybrid. Furthermore, the two species have overlapping populations in this area of the South Pacific and thus it was concluded that the female dolphin had accepted a lost calf as her own (Carzon et al., 2019). Lactation is energetically costly, and considering the dolphin already had another calf to feed, the fact that she accepted the adopted calf, was observed nursing it, and developed a lengthy bond with it is remarkable.

Figure 4. Bottlenose dolphin female with her adopted melon-headed whale calf near French Polynesia in the South Pacific (Carzon et al., 2019).

I admit it was more fun than work to dig into these interspecies associations this week, because they depict how rich our world can be when animals (including humans) evoke positive associations across taxa. Reverting into my fin whale research, I cannot wait to see where my analysis will lead. I am eager to share my results, begin collaborations with other researchers and eventually present it to the public with the hopes of developing positive associations between humans and the marine world.

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Literature Cited

Carzon, P., Delfour, F., Dudzinski, K. et al. 2019. Cross-genus adoption in delphinids: One example with taxonomic discussion. Ethology: Behavioral Notes, 125: 669-676.

Daura-Jorge, F., Cantor, M., Ingram, S. et al. 2012. The structure of a bottlenose dolphin society is coupled to a unique foraging cooperation with artisanal fisherman. Biology Letters, 8: 702-705.

Jefferson, T., Palacios, D., Calambokidis, J. et al. 2021. Sightings and satellite tracking of a blue/fin whale hybrid in its wintering and summering ranges in the eastern north pacific. Advances in Oceanography & Marine Biology, 2 (4). http://dx.doi.org/10.33552/AOMB.2021.02.000545.  

Pampoulie, C., Gislason, D., Olafsdottir, G. et al. 2020. Evidence of unidirectional hybridization and second-generation adult hybrid between the two largest animals on Earth, the fin and blue whales. Evolutionary Applications, 14: 314-321.

Pitman, R., Deecke, V., Gabriele, C., et al. 2016. Humpback whales interfering when mammal-eating killer whales attack other species: Mobbing behavior and interspecific altruism? Marine Mammal Science, 33 (1): 7-58. https://doi.org/10.1111/mms.12343.

Back to the Future: The return of scientific conferences

By Rachel Kaplan, PhD student, Oregon State University College of Earth, Ocean, and Atmospheric Sciences and Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

The pandemic has taught me that certain skills – including ones I never recognized as such – can atrophy. How do I construct an outfit that involves actual pants instead of gym shorts? How do I make a lunch that is portable and can be eaten outside my home?

These are things that I’ve had to relearn over the last year, as I increasingly leave my virtual work world and move back into the physical world. Recently, the new ways in which the world is opening up again have pushed me to brush off another skill – how do I talk to other people about my work?

The pandemic has necessarily made the world a bit more insular. A year and a half into my graduate career, I’ve mostly discussed my work within the cozy cocoon of my lab groups and cohort. In particular, I’ve lived the last few months in that realm of research that is so specific and internal that almost no one else fully understands or cares about what I’m doing: I’ve spent days tangled up in oodles of models, been woken up at night by dreams about coding, and sweated over the decimal points of statistical deviance-explained values. 

This period of scientific navel gazing abruptly ended this February. In the space of ten days, I presented at my first in-person conference during graduate school, gave a short talk at my first international conference, and gave my longest talk yet to a public audience. After reveling in the minutiae of research for months, it was so valuable to be forced to take a step back, think about the overarching narrative of this work, and practice telling that story to different audiences. 

A February talk for the Oregon chapter of the American Cetacean Society gave me the chance to tell the story of my research to a broad audience.

Presenting this work to an in-person audience for the first time was especially rewarding. With a physical (!) poster in hand, I headed out to Newport for the annual meeting of the Oregon Chapter of The Wildlife Society. The GEMM Lab really took this conference by storm – Leigh gave a plenary talk on the meeting’s theme of “Dynamic Oceans, Shifting Landscapes”, Lisa chaired a session and gave a talk about trophic relationships between kelp and whales, and Miranda presented a poster on the new Holistic Assessment of Living marine resources off the Oregon coast (HALO) project.

This great GEMM Lab presence gave me the opportunity to reference everyone else’s work as I shared my own, and to think about the body of work we do as a group and the coherence in research themes that different projects share. I almost lost my voice by talking for the entire duration of the poster session, and was energized by the opportunity to share this work with so many interested people.

The GEMM Lab and other OSU Marine Mammal Institute members presented alongside terrestrial researchers on the theme of “Dynamic Oceans, Shifting Landscapes”.

Just a few days later, the biennial Ocean Sciences Meeting began. Dawn presented on forecasting the distribution of blue whales in New Zealand’s South Taranaki Bight region, and several members of the Krill Seeker Lab, led by my co-advisor Dr. Kim Bernard, presented their own zooplankton ecology research.

Originally scheduled for Hawaii, this meeting was instead held virtually as a safety precaution against Covid-19. Nevertheless, the diversity of talks and time spent gathering online still gave me the sense of being part of an international ocean science community. People attended from every time zone, and watching early-morning talks while wearing pajamas with Solene, Dawn, and Quin the dog is officially one of my new favorite conference experiences.

In addition to the chance to discuss science with other students and researchers, it was great to have the opportunity to step back from our normal routines a bit. The Krill Seeker Lab did the conference-organized 5K walk together (in intermittent rain, of course) and our team even came within one point of winning the trivia contest. All the while, we were hopping in and out of poster sessions and talks, realizing that virtual conferences can be just as busy as in-person ones.

Taking a 5k-long break from watching talks! From left to right: Rachel Kaplan, Kim Bernard, Giulia Wood, and Kirsten Steinke.

Over the last two years, one of the things the pandemic has made me appreciate the most is the ability to gather. Dinner with friends, holidays with family – the ability to be together is far more tentative and precious than I realized during the “before times.” Now, as we start tiptoeing back into normal life a bit more, I’m appreciating the ability to gather for science and looking forward to more conferences in the future.

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Marine megafauna as ecosystem sentinels: What animals can tell us about changing oceans

By Dawn Barlow1 and Will Kennerley2

1PhD Candidate, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

2MS Student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Seabird Oceanography Lab

The marine environment is dynamic, and mobile animals must respond to the patchy and ephemeral availability of resource in order to make a living (Hyrenbach et al. 2000). Climate change is making ocean ecosystems increasingly unstable, yet these novel conditions can be difficult to document given the vast depth and remoteness of most ocean locations. Marine megafauna species such as marine mammals and seabirds integrate ecological processes that are often difficult to observe directly, by shifting patterns in their distribution, behavior, physiology, and life history in response to changes in their environment (Croll et al. 1998, Hazen et al. 2019). These mobile marine animals now face additional challenges as rising temperatures due to global climate change impact marine ecosystems worldwide (Hazen et al. 2013, Sydeman et al. 2015, Silber et al. 2017, Becker et al. 2019). Given their mobility, visibility, and integration of ocean processes across spatial and temporal scales, these marine predator species have earned the reputation as effective ecosystem sentinels. As sentinels, they have the capacity to shed light on ecosystem function, identify risks to human health, and even predict future changes (Hazen et al. 2019). So, let’s explore a few examples of how studying marine megafauna has revealed important new insights, pointing toward the importance of monitoring these sentinels in a rapidly changing ocean.

Cairns (1988) is often credited as first promoting seabirds as ecosystem sentinels and noted several key reasons why they were perfect for this role: (1) Seabirds are abundant, wide-ranging, and conspicuous, (2) although they feed at sea, they must return to land to nest, allowing easier observation and quantification of demographic responses, often at a fraction of the cost of traditional, ship-based oceanographic surveys, and therefore (3) parameters such as seabird reproductive success or activity budgets may respond to changing environmental conditions and provide researchers with metrics by which to assess the current state of that ecosystem.

The unprecedented 2014-2016 North Pacific marine heatwave (“the Blob”) caused extreme ecosystem disruption over an immense swath of the ocean (Cavole et al. 2016). Seabirds offered an effective and morbid indication of the scale of this disruption: Common murres (Uria aalge), an abundant and widespread fish-eating seabird, experienced widespread breeding failure across the North Pacific. Poor reproductive performance suggested that there may have been fewer small forage fish around and that these changes occurred at a large geographic scale. The Blob reached such an extreme as to kill immense numbers of adult birds, which professional and community scientists found washed up on beach-surveys; researchers estimate that an incredible 1,200,000 murres may have died from starvation during this period (Piatt et al. 2020). While the average person along the Northeast Pacific Coast during this time likely didn’t notice any dramatic difference in the ocean, seabirds were shouting at us that something was terribly wrong.

Happily, living seabirds also act as superb ecosystem sentinels. Long-term research in the Gulf of Maine by U.S. and Canadian scientists monitors the prey species provisioned by adult seabirds to their chicks. Will has spent countless hours over five summers helping to conduct this research by watching terns (Sterna spp.) and Atlantic puffins (Fratercula arctica) bring food to their young on small islands off the Maine coast. After doing this work for multiple years, it’s easy to notice that what adults feed their chicks varies from year to year. It was soon realized that these data could offer insight into oceanographic conditions and could even help managers assess the size of regional fish stocks. One of the dominant prey species in this region is Atlantic herring (Clupea harengus), which also happens to be the focus of an economically important fishery.  While the fishery targets four or five-year-old adult herring, the seabirds target smaller, younger herring. By looking at the relative amounts and sizes of young herring collected by these seabirds in the Gulf of Maine, these data can help predict herring recruitment and the relative number of adult herring that may be available to fishers several years in the future (Scopel et al. 2018).  With some continued modelling, the work that we do on a seabird colony in Maine with just a pair of binoculars can support or maybe even replace at least some of the expensive ship-based trawl surveys that are now a popular means of assessing fish stocks.

A common tern (Sterna hirundo) with a young Atlantic herring from the Gulf of Maine, ready to feed its chick (Photo courtesy of the National Audubon Society’s Seabird Institute)

For more far-ranging and inaccessible marine predators such as whales, measuring things such as dietary shifts can be more challenging than it is for seabirds. Nevertheless, whales are valuable ecosystem sentinels as well. Changes in the distribution and migration phenology of specialist foragers such as blue whales (Balaenoptera musculus) and North Atlantic right whales (Eubalaena glacialis) can indicate relative changes in the distribution and abundance of their zooplankton prey and underlying ocean conditions (Hazen et al. 2019). In the case of the critically endangered North Atlantic right whale, their recent declines in reproductive success reflect a broader regime shift in climate and ocean conditions. Reduced copepod prey has resulted in fewer foraging opportunities and changing foraging grounds, which may be insufficient for whales to obtain necessary energetic stores to support calving (Gavrilchuk et al. 2021, Meyer-Gutbrod et al. 2021). These whales assimilate and showcase the broad-scale impacts of climate change on the ecosystem they inhabit.

Blue whales that feed in the rich upwelling system off the coast of California rely on the availability of their krill prey to support the population (Croll et al. 2005). A recent study used acoustic monitoring of blue whale song to examine the timing of annual population-level transition from foraging to breeding migration compared to oceanographic variation, and found that flexibility in timing may be a key adaptation to persistence of this endangered population facing pressures of rapid environmental change (Oestreich et al. 2022). Specifically, blue whales delayed the transition from foraging to breeding migration in years of the highest and most persistent biological productivity from upwelling, and therefore listening to the vocalizations of these whales may be valuable indicator of the state of productivity in the ecosystem.

Figure reproduced from Oestreich et al. 2022, showing relationships between blue whale life-history transition and oceanographic phenology of foraging habitat. Timing of the behavioral transition from foraging to migration (day of year on the y-axis) is compared to (a) the date of upwelling onset; (b) the date of peak upwelling; and (c) total upwelling accumulated from the spring transition to the end of the upwelling season.

In a similar vein, research by the GEMM Lab on blue whale ecology in New Zealand has linked their vocalizations known as D calls to upwelling conditions, demonstrating that these calls likely reflect blue whale foraging opportunities (Barlow et al. 2021). In ongoing analyses, we are finding that these foraging-related calls were drastically reduced during marine heatwave conditions, which we know altered blue whale distribution in the region (Barlow et al. 2020). Now, for the final component of Dawn’s PhD, she is linking year-round environmental conditions to the occurrence patterns of different blue whale vocalization types, hoping to shed light on ecosystem processes by listening to the signals of these ecosystem sentinels.

A blue whale comes up for air in the South Taranaki Bight of New Zealand. photo by L. Torres.

It is important to understand the widespread implications of the rapidly warming climate and changing ocean conditions on valuable and vulnerable marine ecosystems. The cases explored here in this blog exemplify the importance of monitoring these marine megafauna sentinel species, both now and into the future, as they reflect the health of the ecosystems they inhabit.

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References:

Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG (2020) Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar Ecol Prog Ser 642:207–225.

Barlow DR, Klinck H, Ponirakis D, Garvey C, Torres LG (2021) Temporal and spatial lags between wind, coastal upwelling, and blue whale occurrence. Sci Rep 11:1–10.

Becker EA, Forney KA, Redfern J V., Barlow J, Jacox MG, Roberts JJ, Palacios DM (2019) Predicting cetacean abundance and distribution in a changing climate. Divers Distrib 25:626–643.

Cairns DK (1988) Seabirds as indicators of marine food supplies. Biol Oceanogr 5:261–271.

Cavole LM, Demko AM, Diner RE, Giddings A, Koester I, Pagniello CMLS, Paulsen ML, Ramirez-Valdez A, Schwenck SM, Yen NK, Zill ME, Franks PJS (2016) Biological impacts of the 2013–2015 warm-water anomaly in the northeast Pacific: Winners, losers, and the future. Oceanography 29:273–285.

Croll DA, Marinovic B, Benson S, Chavez FP, Black N, Ternullo R, Tershy BR (2005) From wind to whales: Trophic links in a coastal upwelling system. Mar Ecol Prog Ser 289:117–130.

Croll DA, Tershy BR, Hewitt RP, Demer DA, Fiedler PC, Smith SE, Armstrong W, Popp JM, Kiekhefer T, Lopez VR, Urban J, Gendron D (1998) An integrated approch to the foraging ecology of marine birds and mammals. Deep Res Part II Top Stud Oceanogr.

Gavrilchuk K, Lesage V, Fortune SME, Trites AW, Plourde S (2021) Foraging habitat of North Atlantic right whales has declined in the Gulf of St. Lawrence, Canada, and may be insufficient for successful reproduction. Endanger Species Res 44:113–136.

Hazen EL, Abrahms B, Brodie S, Carroll G, Jacox MG, Savoca MS, Scales KL, Sydeman WJ, Bograd SJ (2019) Marine top predators as climate and ecosystem sentinels. Front Ecol Environ 17:565–574.

Hazen EL, Jorgensen S, Rykaczewski RR, Bograd SJ, Foley DG, Jonsen ID, Shaffer SA, Dunne JP, Costa DP, Crowder LB, Block BA (2013) Predicted habitat shifts of Pacific top predators in a changing climate. Nat Clim Chang 3:234–238.

Hyrenbach KD, Forney KA, Dayton PK (2000) Marine protected areas and ocean basin management. Aquat Conserv Mar Freshw Ecosyst 10:437–458.

Meyer-Gutbrod EL, Greene CH, Davies KTA, Johns DG (2021) Ocean regime shift is driving collapse of the north atlantic right whale population. Oceanography 34:22–31.

Oestreich WK, Abrahms B, Mckenna MF, Goldbogen JA, Crowder LB, Ryan JP (2022) Acoustic signature reveals blue whales tune life history transitions to oceanographic conditions. Funct Ecol.

Piatt JF, Parrish JK, Renner HM, Schoen SK, Jones TT, Arimitsu ML, Kuletz KJ, Bodenstein B, Garcia-Reyes M, Duerr RS, Corcoran RM, Kaler RSA, McChesney J, Golightly RT, Coletti HA, Suryan RM, Burgess HK, Lindsey J, Lindquist K, Warzybok PM, Jahncke J, Roletto J, Sydeman WJ (2020) Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016. PLoS One 15:e0226087.

Scopel LC, Diamond AW, Kress SW, Hards AR, Shannon P (2018) Seabird diets as bioindicators of atlantic herring recruitment and stock size: A new tool for ecosystem-based fisheries management. Can J Fish Aquat Sci.

Silber GK, Lettrich MD, Thomas PO, Baker JD, Baumgartner M, Becker EA, Boveng P, Dick DM, Fiechter J, Forcada J, Forney KA, Griffis RB, Hare JA, Hobday AJ, Howell D, Laidre KL, Mantua N, Quakenbush L, Santora JA, Stafford KM, Spencer P, Stock C, Sydeman W, Van Houtan K, Waples RS (2017) Projecting marine mammal distribution in a changing climate. Front Mar Sci 4:413.

Sydeman WJ, Poloczanska E, Reed TE, Thompson SA (2015) Climate change and marine vertebrates. Science 350:772–777.

Wavelet analysis to describe biological cycles and signals of non-stationarity

By Allison Dawn, GEMM Lab Master’s student, OSU Department of Fisheries, Wildlife, and Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab 

During my second term of graduate school, I have been preparing to write my research proposal. The last two months have been an inspiring process of deep literature dives and brainstorming sessions with my mentors. As I discussed in my last blog, I am interested in questions related to pattern and scale (fine vs. mesoscale) in the context of the Pacific Coast Feeding Group (PCFG) of gray whales, their zooplankton prey, and local environmental variables.

My work currently involves exploring which scales of pattern are important in these trophic relationships and whether the dominant scale of a pattern changes over time or space. I have researched which analysis tools would be most appropriate to analyze ecological time series data, like the impressive long-term dataset the GEMM lab has collected in Port Orford as part of the TOPAZ  project, where we have monitored the abundance of whales and zooplankton, as well as environmental variables since 2016. 

A useful analytical tool that I have come across in my recent coursework and literature review is called wavelet analysis. Importantly, wavelet analysis can handle non-stationarity and edge detection in time series data. Non-stationarity is when a dataset’s mean and/or variance can change over time or space, and edge detection is the identification of the change location (in time or space). For example, it is not just the cycles or “ups and downs” of zooplankton abundance I am interested in, but when in time or where in space these cycles of “ups and downs” might change in relation to what their previous values, or distances between values, were. Simply stated, non-stationarity is when what once was normal is no longer normal. Wavelet analysis has been applied across a broad range of fields, such as environmental engineering (Salas et al. 2020), climate science (Slater et al. 2021), and bio-acoustics (Buchan et al. 2021). It can be applied to any time series dataset that might violate the traditional statistical assumption of stationarity. 

In a recent review of climate science methodology, Slater et al. (2021) outlined the possible behavior of time series data. Using theoretical plots, the authors show that data can a) have the same mean and variance over time, or b) have non-stationarity that can be broken into three major groups – trend, step change, or shifts in variance. Figure 1 further demonstrates the difference between stationary vs. non-stationary data in relation to a given variable of interest over time. 

Figure 1. Plots showing the possible magnitude of a given variable across a time series: a) Stationary behavior, b) Non-stationary trend, step-change, and a shift in variance. [Taken from Slater et. al (2021)].

Traditional correlation statistics assumes stationarity, but it has been shown that ecological time series are often non-stationary at certain scales (Cazelles & Hales, 2006). In fact, ecological data rarely meets the requirements of a controlled experiment that traditional statistics require. This non-stationarity of ecological data means that while widely-used methods like generalized linear models and analyses of variances (ANOVAs) can be helpful to assess correlation, they are not always sufficient on their own to describe the complex natural phenomena ecologists seek to explain. Non-stationarity occurs frequently in ecological time series, so it is appropriate to consider analysis tools that will allow us to detect edges to further investigate the cause.

Wavelet analysis can also be conducted across a time series of multiple response variables to assess if these variables share high common power (correlation). When data is combined in this way it is called a cross-wavelet analysis. An interesting paper used cross-wavelet analysis to assess the seasonal response of zooplankton life history in relation to climate warming (Winder et. al 2009). Results from their cross-wavelet analysis showed that warming temperatures over the past two decades increased the voltinism (number of broods per year) of copepods. The authors show that where once annual recruitment followed a fairly stationary pattern, climate warming has contributed to a much more stochastic pattern of zooplankton abundance. From these results, the authors contribute to the hypothesis that climate change has had a temporal impact on zooplankton population dynamics, and recruitment has increasingly drifted out of phase from the original annual cycles. 

Figure 2. Cross-wavelet spectrum for immature and adult Leptodiaptomus ashlandi for 1965 through either 2000 or 2005. Plots show a) immatures and temperature, b) adults and temperature, c) immatures and phytoplankton, and d) adults and phytoplankton. Arrows indicate phase between combined time series. 0 degrees is in-phase and 180 degrees is anti-phase. Black contour lines show “cone of influence” or the 95% significance level, every value within the cone is considered significant. Left axis shows the temporal period, and the color legend shows wavelet frequency power, with low frequencies in blue and high frequencies in red. Plots show strong covariation of high common power at the 12-month period until the 1980s. This pattern is especially evident in plot c) and d). [Taken from (Winder et. al 2009)].

While wavelet and cross-wavelet analyses should not be the only tool used to explore data, due to its limitations with significance testing, it is still worth implementing to gain a better understanding of how time series variables relate to each other over multiple spatial and/or temporal scales. It is often helpful to combine multiple methods of analysis to get a larger sense of patterns in the data, especially in spatio-temporal research.

When conducting research within the context of climate change, where the concentration of CO2 in ppm in the atmosphere is a non-stationary time series itself (Figure 3), it is important to consider how our datasets might be impacted by climate change and wavelet analysis can help identify the scales of change. 

Figure 3. Plot showing the historic fluctuations of CO^2 and the recent deviation from normal levels. Source: https://globalclimate.ucr.edu/resources.html

When considering our ecological time series of data in Port Orford, we want to evaluate how changing ocean conditions may be related to data trends. For example, has the annual mean or variance of zooplankton abundance changed over time, and where has that change occurred in time or space? These changes might have occurred at different scales and might be invisible at other scales. I am eager to see if wavelet analysis can detect these sorts of changes in the abundance of zooplankton across our time series of data, particularly during the seasons of intense heat waves or upwelling. 

Did you enjoy this blog? Want to learn more about marine life, research and conservation? Subscribe to our blog and get a weekly email when we make a new post! Just add your name into the subscribe box on the left panel.

References

Buchan, S. J., Pérez-Santos, I., Narváez, D., Castro, L., Stafford, K. M., Baumgartner, M. F., … & Neira, S. (2021). Intraseasonal variation in southeast Pacific blue whale acoustic presence, zooplankton backscatter, and oceanographic variables on a feeding ground in Northern Chilean Patagonia. Progress in Oceanography, 199, 102709.

Cazelles, B., & Hales, S. (2006). Infectious diseases, climate influences, and nonstationarity. PLoS Medicine, 3(8), e328.

Salas, J. D., Anderson, M. L., Papalexiou, S. M., & Frances, F. (2020). PMP and climate variability and change: a review. Journal of Hydrologic Engineering, 25(12), 03120002.

Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., … & Wilby, R. L. (2021). Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management. Hydrology and Earth System Sciences, 25(7), 3897-3935.

Winder, M., Schindler, D. E., Essington, T. E., & Litt, A. H. (2009). Disrupted seasonal clockwork in the population dynamics of a freshwater copepod by climate warming. Limnology and Oceanography, 54(6part2), 2493-2505.

Introducing IndividuWhale!

By Lisa Hildebrand, PhD student, OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna Lab

If you are an avid reader of our blog, you probably know quite a bit about gray whales, specifically the Pacific Coast Feeding Group (PCFG) of gray whales. Of the just over 50 GEMM Lab blogs written in 2021, 43% of them were about PCFG gray whales (or at least mentioned gray whales in some way). I guess this statistic is not too surprising when you consider that six of the 10 GEMM Lab members conduct gray whale-related research. You might think that we would have reached our annual limit of online gray whale content with that many blogs featuring these gentle giants, but you would in fact be wrong…

At the end of 2021, we launched a brand new website all about gray whales called IndividuWhale! It features stories of some of the Oregon coast’s most iconic gray whales, as well as information about how we study them, stressors they experience in our waters, and even a game to test your gray whale identification skills. IndividuWhale is a true labor of love that took over a year to create and that we are extremely proud to share with you today. Before I tell you more about the website, I want to take a moment to give a huge shout out to Erik Urdahl who was instrumental in getting this website off the ground and making it as interactive and beautiful as it is – hurrah Erik!

Equal‘s right side with visible boat propeller scars. Source: GEMM Lab.

Like us humans, gray whales have individual personalities and stories. They experience life-altering events, go through periods of stress, must provide for their offspring, and can behave differently to one another. Since Leigh & co. have been conducting in-depth research about PCFG gray whales in Oregon waters since 2016, we have been able to document several fascinating stories and events that these individuals have experienced. Take Equal, for example, a male whale that is at least 7 years old. The GEMM Lab observed Equal on consecutive days in June 2018, where on the first day he looked healthy and normal, but on the second day had fresh boat propeller-like scars on his back. Not only did we document these scars in photographs, but we were also able to collect a fecal sample from Equal the day we observed him with these scars. After analyzing his fecal sample for stress hormones, we discovered that Equal had very high stress levels compared to previous samples collected – unsurprising seeing as he had been hit by a boat! While this event was certainly sad for Equal (although don’t worry – we have seen him many times again in the years after this event looking healthy & normal once again), it was a very fortuitous occurrence for us since we were able to “validate” our stress hormone data relative to the value from Equal when he was clearly stressed out. Find out more about Equal as well as seven other gray whales here!

You might be wondering, how we knew that the whale with the boat propeller scar was Equal and how we recognize him again years after the incident. Gray whales have unique pigmentation patterns on their bodies and flukes that allow us to re-identify individuals between years and distinguish them from one another. Additionally, scars, such as those that Equal now carries on his back, can also be useful in telling whales apart. Therefore, we take photographs of every whale we see to match markings and identify whales. This process is called photo ID. Some individuals can have very distinctive markings, such as Roller Skate who has two big white dots on her right side, while others can look more inconspicuous, like Clouds. Therefore, conducting photo ID requires a lot of attention to detail and perseverance. To learn more about the different features we use to identify individuals, check out the “Studying Whales With Photographs” page. Do you think you have what it takes to tell individuals apart? Then try your luck at our photo ID game after!

Test your photo ID skills in our whale match game!

Unfortunately, these whales do not live in a pristine environment, as is evidenced by Equal’s story. Their main objective during the summer when in Oregon waters is to gain weight (energy stores) by consuming large amounts of prey, which is made more difficult by a number of stressors, including potential fishery entanglements, ocean noise, vessel traffic, and habitat changes. We describe these four stressors on the IndividuWhale website since we are trying to assess the impacts of them on gray whales through our research, however they are certainly not the only stressors that these whales experience. Little is known about the level at which these stressors may have a negative impact on the whales, and how whales react when they experience some of these stressors in concert. The answers to these questions are difficult to tease apart but the GRANITE project is aiming to do so through a framework called Population Consequences of Multiple Stressors (read more about it here). This approach requires a lot of data on a lot of individuals in a population and as you can see from the IndividuWhale website, we are slowly starting to get there! 2022 will certainly bring many more gray whale-themed blogs to this website, and you can share in our journey of learning about the lives of PCFG gray whales by exploring the IndividuWhale website (https://www.individuwhale.com).

Social turmoil due to the approval of an offshore oil exploration project off the coast of Argentina.

Dr. Alejandro A. Fernández Ajó, Postdoctoral Scholar, Marine Mammal Institute – OSU Department of Fisheries, Wildlife, & Conservation Sciences, Geospatial Ecology of Marine Megafauna (GEMM) Lab.

I just returned to my home country, Argentina, after over 2 years without leaving the USA due to COVID-19 travel restrictions. Being back with my family, my friends, my culture, and speaking my native language feels great and relaxing. However, I returned to a country struggling to rebound from the coronavirus pandemic. I am afraid this post pandemic scenario places Argentina in a vulnerable situation. The need for economic growth could result in decisions or policies that, in the long term, hurt the country, leaving environmental damage for potential economic growth.

Argentina holds extensive oil and gas deposits, including the world’s second largest gas formation, Vaca Muerta. Although offshore (i.e., in the ocean) oil exploration and exploitation are not yet extensively developed, the intention of offshore gas and oil drilling is on the agenda. In July 2021, a public hearing was held with the purpose to consider the environmental impact assessment for carrying out seismic exploration in the North Argentinian basin off the southern coast of the Buenos Aires province. Over 90% of the participants, including scientists, researchers, technicians from various institutions, non-governmental organizations and representatives of the fishing sector spoke against the project and highlighted the negative impacts that such activity can generate on marine life, and to other socioeconomic activities such as tourism and fishing, not only in Argentina but at the regional level.

Thousands of people marched along the beaches and the main coastal cities of Argentina to protest against the approval for a seismic explorations project in the Argentinian basin. Photo source: prensaobrera.com

Seismic prospections are usually done with the purpose for oil and gas exploitation and less frequently for research purposes. In seismic prospections, ships carry out explosions with airguns, whose sound waves reach the seabed, bounce back and are captured by receivers on the ships to map the petroleum deposits in seafloor and identify potential areas for hydrocarbon extractions. The sound emitted by the seismic airguns can reach extremely loud levels of sounds that travel for thousands of miles underwater. Such extreme high levels of sound can alter the behavior of many marine species, from the smallest planktonic species, to the largest marine mammals, masking their communication, causing physical and physiological stress, interfering with their vital functions, and reducing the local availability of prey (Di Iorio & Clark, 2010; Hildebrand, 2009; Weilgart, 2018).

Here you can listen to a short audio clip of a seismic airgun firing every ~8 seconds, a typical pattern. Close your eyes and imagine you are a whale living in this environment. Now, put the clip on loop and play it for three months straight. This would be the soundscape that whales living in a region of oil and gas exploration hear, as seismic surveys often last 1-4 months (see our previous post “Hearing is believing” for more details).

Despite the public rejection and the mounting evidence about the negative impacts and environmental risks associated with such activities, the government approved the initiation of the seismic prospection off the southern coast of Buenos Aires. In response, thousands of people marched along the beaches and the main coastal cities of Argentina to protest against the oil exploration project. The areas where the seismic surveys will be carried out overlap largely with the southern right whale’s migration routes and feeding areas during their spring and summer (Figure 1). Likewise, the area overlaps with highly productive areas in the ocean that hosts great biodiversity of species of ecological and commercial importance, including the feeding areas of seabirds, turtles and other marine mammals. Additionally, the seismic activity will endanger the health of the beaches of the coast of Buenos Aires and Uruguay where thousands of tourists spend the summer to escape from the large cities.

Figure 1. The map on the left is showing (light blue squares CAN_100, CAN_108, and CAN_114) the areas where seismic prospections are proposes. The map on the right is showing the individual satellite track lines for eleven individual southern right whales (SRW) during the feeding season. You can observe that the proposed area for seismic explorations overlaps with critical feeding habitat for the SRW. Source: Whale Conservation Institute of Argentina (ICB).

The impacts of these activities to marine wildlife are difficult to control and monitor (Elliott et al. 2019, Gordon et al, 2003), especially for large whales that are a very challenging taxa to study (Hunt et al. 2013). We know that the ability to perceive biologically important sounds is critical to marine mammals, and acoustic disturbance through human-generated noise can interfere with their natural functions. Sounds from seismic surveys are intense and have peak frequency bands overlapping those used by baleen whales (Di Lorio & Clark, 2010); however, evidence of interference with baleen whale acoustic communication, and the effects on their health and physiology are sparse. In this context, the GEMM Lab project GRANITE (Gray Whale Response to Ambient Noise Informed by Technology and Ecology), plans to generate information to fulfill these knowledge gaps and provide tools to aid conservation and management decisions in terms of allowable noise level in whale habitats. I am hopeful such information will reach decision makers and influence their decisions, however, sometimes it is frustrating to see how evidence-based information generated with high quality standards are often ignored.

The recent approval of the seismic exploration in Argentina is an example of my frustration. There is no way that the oil industry can guarantee a low-risk of impact on biodiversity and the environment. There are too many examples of environmental catastrophes related to the oil industries at sea that speak for themselves. Moreover, the promotion of such activities goes against the compromises assumed by the country to work to mitigate the effects of Climate Change, and to achieve the reductions of the greenhouse gas emissions to comply with the Paris Agreement. Decades of research help recognized the areas that would be impacted by these seismic activities as key habitat for the life cycle of whales, penguins, seals and more. But, apparently all these scientific data are ignored at the time of weighing the tradeoffs between “economic development” and environmental impacts. As a conservation biologist, I am questioning what can be done in order to be heard and significantly influence such decisions.

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References:

  • Di Iorio, L., & Clark, C. W. (2010). Exposure to seismic survey alters blue whale acoustic communication. Biology Letters, 6(1), 51–54. https://doi.org/10.1098/rsbl.2009.0651
  • Weilgart, L. (2018). The impact of ocean noise pollution on fish and invertebrates. Report for OceanCare, Switzerland.
  • Elliott, B. W., Read, A. J., Godley, B. J., Nelms, S. E., & Nowacek, D. P. (2019). Critical information gaps remain in understanding impacts of industrial seismic surveys on marine vertebrates. In Endangered Species Research (Vol. 39, pp. 247–254). Inter-Research. https://doi.org/10.3354/esr00968
  • Gordon, J., Gillespie, D., Potter, J., Frantzis, A., Simmonds, M. P., Swift, R., & Thompson, D. (2003). A review of the effects of seismic surveys on marine mammals. Marine Technology Society Journal37(4), 16-34.
  • Hunt, K. E., Moore, M. J., Rolland, R. M., Kellar, N. M., Hall, A. J., Kershaw, J., Raverty, S. A., Davis, C. E., Yeates, L. C., Fauquier, D. A., Rowles, T. K., & Kraus, S. D. (2013). Overcoming the challenges of studying conservation physiology in large whales: a review of available methods. Conservation Physiology, cot006–cot006. https://doi.org/10.1093/conphys/cot006

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