“…We used MorphoMetriX open-source photogrammetry software to measure (in pixels) total length, from the tip of the rostrum to the fluke notch ( figure 4 ) [ 51 ]. MorphoMetriX outputs were collated using CollatriX open-source software [ 52 ]. Figure 4 An example of a UAS image of an Antarctic minke whale (AMW).…”
Antarctic minke whales (
Balaenoptera bonaerensis
, AMW) are an abundant, ice-dependent species susceptible to rapid climatic changes occurring in parts of the Antarctic. Here, we used remote biopsy samples and estimates of length derived from unoccupied aircraft system (UAS) to characterize for the first time the sex ratio, maturity, and pregnancy rates of AMWs around the Western Antarctic Peninsula (WAP). DNA profiling of 82 biopsy samples (2013–2020) identified 29 individual males and 40 individual females. Blubber progesterone levels indicated 59% of all sampled females were pregnant, irrespective of maturity. When corrected for sexual maturity, the median pregnancy rate was 92.3%, indicating that most mature females become pregnant each year. We measured 68 individuals by UAS (mean = 8.04 m) and estimated that 66.5% of females were mature. This study provides the first data on the demography of AMWs along the WAP and represents the first use of non-lethal approaches to studying this species. Furthermore, these results provide baselines against which future changes in population status can be assessed in this rapidly changing marine ecosystem.
“…We used MorphoMetriX open-source photogrammetry software to measure (in pixels) total length, from the tip of the rostrum to the fluke notch ( figure 4 ) [ 51 ]. MorphoMetriX outputs were collated using CollatriX open-source software [ 52 ]. Figure 4 An example of a UAS image of an Antarctic minke whale (AMW).…”
Antarctic minke whales (
Balaenoptera bonaerensis
, AMW) are an abundant, ice-dependent species susceptible to rapid climatic changes occurring in parts of the Antarctic. Here, we used remote biopsy samples and estimates of length derived from unoccupied aircraft system (UAS) to characterize for the first time the sex ratio, maturity, and pregnancy rates of AMWs around the Western Antarctic Peninsula (WAP). DNA profiling of 82 biopsy samples (2013–2020) identified 29 individual males and 40 individual females. Blubber progesterone levels indicated 59% of all sampled females were pregnant, irrespective of maturity. When corrected for sexual maturity, the median pregnancy rate was 92.3%, indicating that most mature females become pregnant each year. We measured 68 individuals by UAS (mean = 8.04 m) and estimated that 66.5% of females were mature. This study provides the first data on the demography of AMWs along the WAP and represents the first use of non-lethal approaches to studying this species. Furthermore, these results provide baselines against which future changes in population status can be assessed in this rapidly changing marine ecosystem.
“…We used MorphoMetriX open-source photogrammetry software to measure (in pixels) the total length (TL, tip of rostrum to fluke notch) and perpendicular widths in 5% increments of the total length measurement (Torres and Bierlich, 2020) (Figure 2). MorphoMetriX outputs were collated using CollatriX (Bird and Bierlich, 2020).…”
Section: Photogrammetry and Uncertainty Quantificationmentioning
confidence: 99%
“…(B) Posterior predictive distributions for each 5% width included in the Head-Tail Range are generated and used to calculate BAI. (C) BAI is calculated for each iteration in the Markov Chain Monte Carlo output of each posterior predictive distribution for TL and widths using CollatriX(Bird and Bierlich, 2020). (D) An example of a posterior predictive distribution for TL and BAI for a single individual.…”
Most baleen whales are capital breeders that use stored energy acquired on foraging grounds to finance the costs of migration and reproduction on breeding grounds. Body condition reflects past foraging success and can act as a proxy for individual fitness. Hence, monitoring the seasonal gain in body condition of baleen whales while on the foraging grounds can inform how marine mammals support the costs of migration, growth, and reproduction, as well as the nutritional health of the overall population. Here, we use photogrammetry from drone-based imagery to examine how the body condition of humpback whales (Megaptera novaeangliae) changed over the foraging season (November to June) along the Western Antarctic Peninsula (WAP) from 2017 to 2019. This population (IWC stock G) is recovering from past whaling and is growing rapidly, providing an opportunity to study how whales store energy in a prey-rich environment. We used a body area index (BAI) to estimate changes in body condition and applied a Bayesian approach to incorporate measurement uncertainty associated with different drone types used for data collection. We used biopsy samples to determine sex and pregnancy status, and a length-based maturity classification to assign reproductive classes (n = 228; calves = 31, juveniles = 82, lactating females = 31, mature males = 12, mature unknown sex = 56, non-pregnant females = 12, pregnant females = 3, pregnant & lactating females = 1). Average BAI increased linearly over the feeding season for each reproductive class. Lactating females had lower BAI compared to other mature whales late in the season, reflecting the high energetic costs of nursing a calf. Mature males and non-pregnant females had the highest BAI values. Calves and juvenile whales exhibited an increase in BAI but not structural size (body length) over the feeding season. The body length of lactating mothers was positively correlated with the body length of their calves, but no relationship was observed between the BAI of mothers and their calves. Our study establishes a baseline for seasonal changes in the body condition for this humpback whale population, which can help monitor future impacts of disturbance and climate change.
“…We used MorphoMetriX (v1.0.2) open-source photogrammetry software (Torres and Bierlich, 2020) to measure (in pixels) the TL (tip of rostrum to fluke notch) and perpendicular widths in 5% increments of the TL measurement (Figure 1). MorphoMetriX outputs were collated using CollatriX (v1.0.7) (Bird and Bierlich, 2020), and then input into the uncertainty model. As demonstrated by Christiansen et al (2018), initial analysis of individuals with measurements from multiple images (see section "Data Filtering") confirmed that filtering for images with quality scores of 1 or 2 was robust to potential biases of width measurements related to variation in TL measurements, such as from any slight bending or arching of the individual (Supplementary Figure 1).…”
Section: Photogrammetrymentioning
confidence: 99%
“…(B) Posterior predictive distributions for each 5% width included in the Head-Tail Range (20-90%) that will be used to calculate each body condition metric. (C) One-dimensional (1D), 2D, and 3D body condition metrics are calculated using CollatriX (Bird and Bierlich, 2020) for each iteration in the MCMC output of the posterior predicted widths. (D) The posterior predictive distributions for each body condition metric calculated for a single individual.…”
Body condition is a crucial and indicative measure of an animal’s fitness, reflecting overall foraging success, habitat quality, and balance between energy intake and energetic investment toward growth, maintenance, and reproduction. Recently, drone-based photogrammetry has provided new opportunities to obtain body condition estimates of baleen whales in one, two or three dimensions (1D, 2D, and 3D, respectively) – a single width, a projected dorsal surface area, or a body volume measure, respectively. However, no study to date has yet compared variation among these methods and described how measurement uncertainty scales across these dimensions. This associated uncertainty may affect inference derived from these measurements, which can lead to misinterpretation of data, and lack of comparison across body condition measurements restricts comparison of results between studies. Here we develop a Bayesian statistical model using known-sized calibration objects to predict the length and width measurements of unknown-sized objects (e.g., a whale). We use the fitted model to predict and compare uncertainty associated with 1D, 2D, and 3D photogrammetry-based body condition measurements of blue, humpback, and Antarctic minke whales – three species of baleen whales with a range of body sizes. The model outputs a posterior predictive distribution of body condition measurements and allows for the construction of highest posterior density intervals to define measurement uncertainty. We find 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. Each standardized body condition measurement is highly correlated with one another, yet 2D body area index (BAI) accounts for potential variation along the body for each species and was the most precise body condition metric. We hope this study will serve as a guide to help researchers select the most appropriate body condition measurement for their purposes and allow them to incorporate photogrammetric uncertainty associated with these measurements which, in turn, will facilitate comparison of results across studies.
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