Increasingly, drone-based photogrammetry has been used to measure size and body condition changes in marine megafauna. A broad range of platforms, sensors, and altimeters are being applied for these purposes, but there is no unified way to predict photogrammetric uncertainty across this methodological spectrum. As such, it is difficult to make robust comparisons across studies, disrupting collaborations amongst researchers using platforms with varying levels of measurement accuracy. Here we built off previous studies quantifying uncertainty and used an experimental approach to train a Bayesian statistical model using a known-sized object floating at the water’s surface to quantify how measurement error scales with altitude for several different drones equipped with different cameras, focal length lenses, and altimeters. We then applied the fitted model to predict the length distributions and estimate age classes of unknown-sized humpback whales Megaptera novaeangliae , as well as to predict the population-level morphological relationship between rostrum to blowhole distance and total body length of Antarctic minke whales Balaenoptera bonaerensis . This statistical framework jointly estimates errors from altitude and length measurements from multiple observations and accounts for altitudes measured with both barometers and laser altimeters while incorporating errors specific to each. This Bayesian model outputs a posterior predictive distribution of measurement uncertainty around length measurements and allows for the construction of highest posterior density intervals to define measurement uncertainty, which allows one to make probabilistic statements and stronger inferences pertaining to morphometric features critical for understanding life history patterns and potential impacts from anthropogenically altered habitats.
more »
« less
Comparing Uncertainty Associated With 1-, 2-, and 3D Aerial Photogrammetry-Based Body Condition Measurements of Baleen Whales
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.
more »
« less
- Award ID(s):
- 1643877
- NSF-PAR ID:
- 10315595
- Date Published:
- Journal Name:
- Frontiers in Marine Science
- Volume:
- 8
- ISSN:
- 2296-7745
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Despite their enormous size, whales make their living as voracious predators. To catch their much smaller, more maneuverable prey, they have developed several unique locomotor strategies that require high energetic input, high mechanical power output and a surprising degree of agility. To better understand how body size affects maneuverability at the largest scale, we used bio-logging data, aerial photogrammetry and a high-throughput approach to quantify the maneuvering performance of seven species of free-swimming baleen whale. We found that as body size increases, absolute maneuvering performance decreases: larger whales use lower accelerations and perform slower pitch-changes, rolls and turns than smaller species. We also found that baleen whales exhibit positive allometry of maneuvering performance: relative to their body size, larger whales use higher accelerations, and perform faster pitch- changes, rolls and certain types of turns than smaller species. However, not all maneuvers were impacted by body size in the same way, and we found that larger whales behaviorally adjust for their decreased agility by using turns that they can perform more effectively. The positive allometry of maneuvering performance suggests that large whales have compensated for their increased body size by evolving more effective control surfaces and by preferentially selecting maneuvers that play to their strengths.more » « less
-
Cooke, Steven (Ed.)Abstract Baleen whales are subject to a myriad of natural and anthropogenic stressors, but understanding how these stressors affect physiology is difficult. Measurement of adrenal glucocorticoid (GC) hormones involved in the vertebrate stress response (cortisol and corticosterone) in baleen could help fill this data gap. Baleen analysis is a powerful tool, allowing for a retrospective re-creation of multiple years of GC hormone concentrations at approximately a monthly resolution. We hypothesized that whales that died from acute causes (e.g. ship strike) would have lower levels of baleen GCs than whales that died from extended illness or injury (e.g. long-term entanglement in fishing gear). To test this hypothesis, we extracted hormones from baleen plates of four humpback whales (Megaptera novaeangliae) with well-documented deaths including multiple and chronic entanglements (n = 1, female), ship strike (n = 2, male and female) and chronic illness with nutritional stress (n = 1, male). Over ~3 years of baleen growth and during multiple entanglements, the entangled whale had average corticosterone levels of 80–187% higher than the other three whales but cortisol levels were similar to two of the other three whales. The nutritionally stressed and chronically ill whale showed a slow increase in both cortisol and corticosterone spanning ~3 years, followed by a sharp decline in both hormones before death, possibly indicative of adrenal failure in this moribund individual. This whale’s correlation between cortisol and corticosterone was significant but there were no correlations in the other three whales. Our results show that cortisol and corticosterone concentrations vary according to the type and duration of illness or injury. Single-point GC concentrations should be interpreted with caution as low values can occur in whales experiencing pronounced stress and individual baselines can be highly variable. Baleen analysis is a promising tissue type for retrospective analyses of physiological responses to various stressors affecting baleen whales.more » « less
-
Fundamental scaling relationships influence the physiology of vital rates, which in turn shape the ecology and evolution of organisms. For diving mammals, benefits conferred by large body size include reduced transport costs and enhanced breath-holding capacity, thereby increasing overall foraging efficiency. Rorqual whales feed by engulfing a large mass of prey-laden water at high speed and filtering it through baleen plates. However, as engulfment capacity increases with body length (Engulfment Volume ∝ Body Length 3.57), the surface area of the baleen filter does not increase proportionally (Baleen Area ∝ Body Length1.82), and thus the filtration time of larger rorquals predictably increases as the baleen surface area must filter a disproportionally large amount of water. We predicted that filtration time should scale with body length to the power of 1.75 (Filter Time ∝ Body Length1.75). We tested this hypothesis on four rorqual species using multi-sensor tags with corresponding unoccupied aircraft systems (UAS) -based body length estimates. We found that filter time scales with body length to the power of 1.79 (95% CI: 1.61 - 1.97). This result highlights a scale-dependent trade-off between engulfment capacity and baleen area that creates a biomechanical constraint to foraging through increased filtration time. Consequently, larger whales must target high density prey patches commensurate to the gulp size to meet their increased energetic demands. If these optimal patches are absent, larger rorquals may experience reduced foraging efficiency compared to smaller whales if they do not match their engulfment capacity to the size of targeted prey aggregations.more » « less
-
more » « less
Abstract The flourishing application of drones within marine science provides more opportunity to conduct photogrammetric studies on large and varied populations of many different species. While these new platforms are increasing the size and availability of imagery datasets, established photogrammetry methods require considerable manual input, allowing individual bias in techniques to influence measurements, increasing error and magnifying the time required to apply these techniques.
Here, we introduce the next generation of photogrammetry methods utilizing a convolutional neural network to demonstrate the potential of a deep learning‐based photogrammetry system for automatic species identification and measurement. We then present the same data analysed using conventional techniques to validate our automatic methods.
Our results compare favorably across both techniques, correctly predicting whale species with 98% accuracy (57/58) for humpback whales, minke whales, and blue whales. Ninety percent of automated length measurements were within 5% of manual measurements, providing sufficient resolution to inform morphometric studies and establish size classes of whales automatically.
The results of this study indicate that deep learning techniques applied to survey programs that collect large archives of imagery may help researchers and managers move quickly past analytical bottlenecks and provide more time for abundance estimation, distributional research, and ecological assessments.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fmars.2021.749943
