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September-December 2022, CERM (Coastal Ecology and Resource Management) class
It is important to measure animal size and physiology to assess individual and population-level health. Historically, obtaining morphological measurements of whales was limited to opportunistic sampling from strandings, or lethally from commercial and scientific whaling. Later, scientists utilized helicopters and airplanes to observe whales from above, but this proved to be disruptive to the animals, dangerous for the observers, prohibitively expensive, and limited by infrastructure15. The development of drones, or unoccupied aircraft systems (UAS), has revolutionised marine science as a non-invasive means of observing marine mammals1. UAS have been successfully applied to a range of marine mammal research methods including photogrammetry, population counts, recording behaviours and biological sampling2. Photogrammetry has been particularly useful for measuring whale morphology from vertical photographs collected by UAS (Fig.1).
Baleen whales are able to reflect ecological variation across both temporal and spatial scales, and are thus considered ecosystem sentinels6,7. Ecosystem sentinels are species which offer insight into ecosystem health and function, in turn helping to predict future changes8. The body condition of ecosystem sentinels can also be indicative of population health6. Photogrammetry from drones can help track the health of whales and monitor their response to environmental change and anthropogenic stressors16, such as to anticipate trends in survival and reproduction, as well as foraging success and prey availability12.
Whale physiology is determined by intrinsic factors (i.e., reproductive stage, age) and extrinsic factors (i.e., vessel disturbance, prey limitation, seasonality, changes in water temperature)17. Vessel traffic can have long-term deleterious implications on PCFG (Pacific Coast Feeding Group) gray whales due to strikes (fatal or non-fatal) and acoustic disturbance18. Alterations and accentuations in the ocean soundscape from anthropogenic noise can severely impact the physiology and behaviour of acoustically sensitive cetaceans due to their reliance on sound for foraging, communication, and navigation1,19. For example, one study found that PCFGs at Port Orford, Oregon, which came into close proximity with vessels stopped foraging for food and transitioned to travelling instead, which poses risks to acquiring a healthy body weight due to loss of foraging20. The exposure of PCFGs to anthropogenic disturbances has stimulated a drive to better understand the limiting factors affecting their body condition.
Since cetaceans must return to the surface to offload carbon dioxide (CO2) and replenish their oxygen (O2) stores, measuring breath frequency and magnitude is one way to study a whale’s O2 consumption4. Differentiating between breath types (i.e., variations in nares expansion) offers insight into how a whale is conserving its energy levels, and can be used to monitor whales as they are exposed to anthropogenic disturbance and climate change4. Previous studies have measured blowhole expansion as a proxy for breath-to-breath variability9,10,11, for example by using animal-borne video tags4, to predict challenges whales face from anthropogenic threats (i.e., vessel disturbance)4.
In this study we apply a new method for measuring nares expansion in relation to total length using UAS-based photogrammetry. By determining whether we can measure nares expansion from UAS footage, this preliminary investigation will contribute to further studies looking at whether whales are taking larger breaths associated with certain behaviours, such as if individuals are breathing more heavily and frequently when exposed to anthropogenic stressors, increasing their energy expenditure.
This project focuses on measuring the total length and blowhole size (width and length) of a subset of PCFG gray whales using the photogrammetry software MorphoMetriX13. A subset of data from the GEMM (Geospatial Ecology or Marine Megafauna) Laboratory is used to identify whether there is a correlation between total length and blowhole size. Variation in nares expansion is also measured using aspect ratio (blowhole width/ blowhole length).
The ‘Image ID’ (Fig.2) is set to the name of the individual whale, and total length (tip of snout to fluke notch) is measured using the ‘Measure Length’ function. The resulting data is exported as a .csv file using the ‘Export Measurements’ function. The scale for each image is calculated using the equation ‘scale=altitude/lens focal length’21.
The ‘Measure Length’ function is used to measure both the length and width of each blowhole. Both length and width were measured on the muscular flap (the dark outer rim of the blowhole) (Fig.3). Blowhole area was also measured, using the ‘Measure Area’ function, but due to an unknown error the measurements were inaccurate, hence were discarded from analyses.
Blowhole images were ranked depending on their image quality (i.e., ‘Is the image blurry or high quality?’) and dilation (i.e., ‘How open is the blowhole?’ ‘Can we detect the blowhole?’). Good quality and dilation = 1. Poor quality and dilation = 2. See below for examples of images given poor quality and dilation scores. All images with scores of 2 were discarded from analyses.
Both left and right blowhole were measured to detect variation between the two. By performing linear regression, we found that left and right blowholes were significantly positively correlated. We decided to use only right blowhole for analyses since they approximate one another. However, there were some unexpected outliers which could be due to a range of factors, such as the amount of water intake to the blowhole in the last inhalation. Determining the reason driving these unusual outliers requires further investigation.
Three data frames were combined in R: blowhole measurements, total length measurements, and blowhole meta data (descriptive notes, quality and dilation rankings). Blowhole measurements were converted from pixels to meters using the following equation in R: Total Length = (altitude/ focal length) x (sensor width/ image width) x pixel dimensions13(Fig.5).
Data was then filtered (quality == 1, dilation ==1), and linear regression analyses were carried out between: i) right blowhole length vs total length, ii) right blowhole width vs total length (Fig.6, Fig.7). A box and whisker plot was also made as a means to visualise aspect ratio (Fig.8).
This study demonstrates the effectiveness of using MorphoMetriX13 for measuring nares expansion, and adds to the existing groundwork of previous studies4. Having detected a significant positive correlation between blowhole length and blowhole width with total body length, we went on to using aspect ratio14 and were able to identify variation of nares expansion amongst individuals. Increasing sample size for each individual will help characterize variation in blowhole expansion amongst individuals. Understanding how nares expansion is influences by behaviour can help increase our current understanding of oxygen consumption estimates and energetics of PCFGs.
This method for detecting oxygen inhalation offers an opportunity for future research, which should focus on associating breath types with behavioural characteristics (i.e., resting, foraging, traveling) in order to gauge cetacean energy expenditure in relation to eco-physiological challenges, such as anthropogenic stressors and climate change4 (i.e., do whales use higher quantities of O2 when exposed to more vessels?). This will improve our understanding of the consistency of nares expansion across surfacing sequences, as well as the entire foraging season.
This research project contributes to the work of PhD candidate Clara Bird, who is analizing gray whale behaviour and physiology and how their breathing relates to their foraging tactics, with the ultimate aim to monitor whale responses to future climate change and anthropogenic stressors. Furthermore, this work could embellish the groundwork of the existing multifaceted GRANITE (Gray Whale Response to Ambient Noise Informed by Technology and Ecology) Project.
A special thanks to Scarlett Arbuckle for organizing and teaching such a wonderfully unique class (FW 426, 2022), to Mauricio Cantor for his fantastic contributions and inspiration to the learning environment, and to the GEMM Lab’s Gray whale Response to Ambient Noise Informed by Technology and Ecology (GRANITE) fieldwork team. Data collected by NOAA/NMFS permits #16011 and #21678.
Note: references are ordered in accordance to the poster and to this blog (from top to bottom).
1. Christiansen, F., Rojano-Doñate, L., Madsen, P. T., & Bejder, L. (2016). Noise Levels of Multi-Rotor Unmanned Aerial Vehicles with Implications for Potential Underwater Impacts on Marine Mammals. Frontiers in Marine Science, 3. https://doi.org/10.3389/fmars.2016.00277
2. Durban, J. W., Fearnbach, H., Barrett-Lennard, L. G., Perryman, W. L., & Leroi, D. J. (2015). Photogrammetry of killer whales using a small hexacopter launched at sea. Journal of Unmanned Vehicle Systems, 3(3), 131–135. https://doi.org/10.1139/juvs-2015-0020
3. Bierlich, K. C., Hewitt, J., Schick, R. S., Pallin, L., Dale, J., Friedlaender, A. S., Christiansen, F., Sprogis, K. R., Dawn, A. H., Bird, C. N., Larsen, G. D., Nichols, R., Shero, M. R., Goldbogen, J., Read, A. J., & Johnston, D. W. (2022). Seasonal gain in body condition of foraging humpback whales along the Western Antarctic Peninsula. Frontiers in Marine Science, 9, 1036860. https://doi.org/10.3389/fmars.2022.1036860
4. Nazario, E. C., Cade, D. E., Bierlich, K. C., Czapanskiy, M. F., Goldbogen, J. A., Kahane-Rapport, S. R., van der Hoop, J. M., San Luis, M. T., & Friedlaender, A. S. (2022). Baleen whale inhalation variability revealed using animal-borne video tags. PeerJ, 10, e13724. https://doi.org/10.7717/peerj.13724
5. Martins, M. C. I., Miller, C., Hamilton, P., Robbins, J., Zitterbart, D. P., & Moore, M. (2020). Respiration cycle duration and seawater flux through open blowholes of humpback ( Megaptera novaeangliae ) and North Atlantic right ( Eubalaena glacialis ) whales. Marine Mammal Science, 36(4), 1160–1179. https://doi.org/10.1111/mms.12703
6. Bierlich, K. C., Hewitt, J., Bird, C. N., Schick, R. S., Friedlaender, A., Torres, L. G., Dale, J., Goldbogen, J., Read, A. J., Calambokidis, J., & 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, 8, 749943. https://doi.org/10.3389/fmars.2021.749943
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9. Sumich, J. L., & May, M. A. (2009). Scaling and remote monitoring of tidal lung volumes of young gray whales, Eschrichtius robustus. Marine Mammal Science, 25(1), 221–228. https://doi.org/10.1111/j.1748-7692.2008.00272.x
10. Fahlman, A., Loring, S. H., Levine, G., Rocho-Levine, J., Austin, T., & Brodsky, M. (2015). Lung mechanics and pulmonary function testing in cetaceans. Journal of Experimental Biology, 218(13), 2030–2038. https://doi.org/10.1242/jeb.119149
11. Sumich, J. L. (2001). Direct and indirect measures of oxygen extraction, tidal lung volumes and respiratory rates in a rehabilitating gray whale calf. 27.3, 279–283.
12. Soledade Lemos, L., Burnett, J. D., Chandler, T. E., Sumich, J. L., & Torres, L. G. (2020). Intra‐ and inter‐annual variation in gray whale body condition on a foraging ground. Ecosphere, 11(4). https://doi.org/10.1002/ecs2.3094
13. 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
14. Pavlov, V., Vincent, C., Mikkelsen, B., Lebeau, J., Ridoux, V., & Siebert, U. (2021). Form, function, and divergence of a generic fin shape in small cetaceans. PLOS ONE, 16(8), e0255464. https://doi.org/10.1371/journal.pone.0255464
15. Johnston, D. W. (2019). Unoccupied Aircraft Systems in Marine Science and Conservation. Annual Review of Marine Science, 11(1), 439–463. https://doi.org/10.1146/annurev-marine-010318-095323
16. Torres, L. G., Bird, C. N., Rodríguez-González, F., Christiansen, F., Bejder, L., Lemos, L., Urban R, J., Swartz, S., Willoughby, A., Hewitt, J., & Bierlich, Kc. (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
17. Lemos, L. S., Olsen, A., Smith, A., Burnett, J. D., Chandler, T. E., Larson, S., Hunt, K. E., & Torres, L. G. (2022). Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Marine Mammal Science, 38(2), 801–811. https://doi.org/10.1111/mms.12877
18. Silber, G., Weller, D., Reeves, R., Adams, J., & Moore, T. (2021). Co-occurrence of gray whales and vessel traffic in the North Pacific Ocean. Endangered Species Research, 44, 177–201. https://doi.org/10.3354/esr01093
19. Lemos, L. S., Haxel, J. H., Olsen, A., Burnett, J. D., Smith, A., Chandler, T. E., Nieukirk, S. L., Larson, S. E., Hunt, K. E., & Torres, L. G. (2021). Sounds of Stress: Assessment of Relationships between Ambient Noise, Vessel Traffic, and Gray whale Stress Hormone [Preprint]. In Review. https://doi.org/10.21203/rs.3.rs-923450/v1
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21. Pitman, R. L., Perryman, W. L., LeRoi, D., & Eilers, E. (2007). A Dwarf Form of Killer Whale in Antarctica. Journal of Mammalogy, 88(1), 43–48. https://doi.org/10.1644/06-MAMM-A-118R1.1