Browsing by Author "Johnston, David W"

Pinniped species undergo a life history, unique among marine mammals, that includes discrete periods of occupancy on land or ice within a predominantly marine existence. This makes many pinniped species valuable sentinels of marine ecosystem health and models of marine mammal physiology and behavior. Pinniped research has often progressed hand-in-hand with advances at the technological frontiers of wildlife biology, and drones represent a leap forward in the long-established field of aerial photography, heralding opportunities for data collection and integration at new scales of biological importance. The following chapters employ and evaluate recent and emerging methods of wildlife surveillance that are uniquely enabled and facilitated by drone methods, in applied research and management campaigns with near-polar pinniped species. These methods represent advancements in abundance estimation and distribution modeling of pinniped populations that are dynamically shifting amid climate change, fishing pressure, and recovery from historical depletion.Conventional methods of counting animals from aerial imagery—typically visual interpretation by human analysts—can be time-consuming and limits the practical use of this data type. Deep learning methods of computer vision can ease this burden when applied to drone imagery, but are not yet characterized for practical and generalized use. To this end, I used a common implementation of deep learning for object detection in imagery to train and test models on a variety of datasets describing breeding populations of gray seals (Halichoerus grypus) in the northwest Atlantic Ocean (Chapter 2). I compare standardized performance metrics of models trained and tested on different combinations of datasets, demonstrating that model performance varies depending on both training and testing data choices. We find that models require careful validation to estimate error rates, and that they can be effectively deployed to aid, but not replace, conventional human visual interpretation of novel datasets for gray seal detection, location, age-classification and abundance estimation. Spatial analysis and species distribution modeling can use fine-scale drone-derived data to describe local species–habitat relationships at the scale of individual animals. I applied structure-from-motion methods to a survey of three pinniped species, pacific harbor seals (Phoca vitulina richardii), northern fur seals (Callorhinus ursinus), and Steller sea lions (Eumetopias jubatus), in adjacent non-breeding haul-outs to compare occupancy and habitat selection (Chapter 3). I describe and compare fitted occupancy models of pacific harbor seals and northern fur seals, finding that conspecific attraction is a key driver of habitat selection for each species, and that each species exhibits distinct topographic preferences. These findings illustrate both opportunities and limitations of spatial analysis at the scale of individual pinnipeds. Ease of deployment and rapid data collection make drones a powerful tool for monitoring populations of interest over time, while animal locations, revealed in high-resolution imagery, and contextual habitat products can reveal spatial relationships that persist beyond local contexts. I designed and carried out a campaign of drone surveillance over coastal habitats near Palmer Station, Antarctica, in the austral summer of 2020 to assess the seasonal abundance and habitat use of Antarctic fur seals (Arctocephalus gazella) in the Palmer Archipelago and adjacent regions (Chapter 4). I modeled abundance as a function of date, with and without additional terms to capture variance by site, and used these models to estimate peak abundance near Palmer Station in the 2020 summer season. These findings leverage the spatial and temporal advantages of drone methods to estimate species phenology, distribution and abundance. Together, these chapters describe emerging applications of drone technology that can advance pinniped research and management into new scales of analytical efficiency and ecological interpretation. These studies describe methods that have been proven in concept, but not yet standardized for practical deployment, and their findings reveal new ecological insights, opportunities for methodological advancement, and current limitations of drone methods for the study of pinnipeds in high-latitude environments.

Humpback whales (Megaptera novaeangliae) are one of the biggest animals on the planet and thus require large quantities of dense prey to meet their energetic requirements. They feed using discrete lunges and filtration to capture their euphausiid (krill) and schooling fish prey. They forage independently, in small groups, or in larger coordinated groups of up to 15 or more individuals. This dissertation aims to improve our understanding of the fine-scale foraging behavior of humpback whales in Southeast Alaska. High-resolution biologging tags (DTAGs) were deployed and focal follows were conducted on foraging humpback whales in Sitka Sound, Alaska in September 2012 and Tenakee Inlet, Alaska in April 2013. Concurrently, prey around tagged foraging whales was sampled using a Simrad EK 60 scientific echosounder, and acoustic data were ground-truthed with net tows for krill and hook and line samples for fish. Whale and prey data were then spatially and temporally integrated to determine characteristics of prey patches upon which whales fed. Results indicate that humpbacks targeted the densest layer of krill in Sitka Sound, maximizing their energetic gain by capturing the most prey with each lunge. When foraging together in groups, bubble-net feeding humpback whales repeated specific behaviors within a foraging bout, suggesting that whales were feeding cooperatively using role specialization and a division of labor to improve foraging efficiency. Finally, the group size of humpbacks increased throughout a week-long study as whales exploited a pre-spawning Pacific herring (Clupea pallasii) aggregation in Tenakee Inlet, which responded to the increased predation pressure with changes in school behavior. As the population of North Pacific humpback whales increases and global warming continues to affect marine ecosystems, a better understanding of predator-prey interactions is crucial to best conserve and manage humpback whales and their ecosystem as a whole.

During my PhD journey, I have lived at the intersection of a previous military career, leadership as an elected official, and a student passionate about robotics and protecting our beautiful coastal ecosystem. As a non-traditional student, Duke University has presented me with experiences I could not have imagined. With the Duke Marine Robotics and Remote Sensing (MaRRS) Lab drones, I have had the opportunity to survey the mass nesting of thousands of olive ridley sea turtles in Costa Rica, hundreds of gray seals in Massachusetts, endangered right whales off the coast of Florida, dozens of World War I shipwrecks in Maryland, Etruscan and Roman archeological sites in Italy, and hundreds of seals in the Bering Sea. And there have been many more multi-domain surveys of our glorious coastal ecosystem in Carteret County. There have been more than our fair share of challenges during this time frame to include preparing and responding to Hurricanes Florence, Dorian, and Isaias, plus the COVID-19 pandemic. These events took a toll on many fronts, but also presented leadership opportunities. With our drones, we have been able to survey before and after storms, and we’re watching barrier islands move at centimeter scale. The increasing effects of climate change are very personal for those of us living in eastern North Carolina, but in the MaRRS Lab we are well postured with our robotics to air-, sea-, and ground-truth these effects. Perhaps most importantly, the knowledge I gained during my PhD program informed my policy positions during my tenure as the Mayor of the Town of Beaufort, NC. I am very proud of the progress that we made to include a massive clean-up of our waterways following Hurricane Florence, a Harbor Management Ordinance to better manage our waterways, expanded municipal jurisdiction to further manage our ecosystem, unprecedented repairs of infrastructure that were neglected for decades and have negatively affected our water quality, investment in the community, and a five-year budgeting plan to provide greater stability for Beaufort.

This dissertation is a summation of some of the work performed during my Duke PhD experience. In Chapter 1, I describe the evolution of autonomous drones, define distinct generations of this technology, and articulate the negative impacts of a regulatory system that is stifling critical research. For Chapter 2, I discuss the lexicon, taxonomy, and ontology of small autonomous drones, the critical importance of situational awareness, and a framework of considerations and best practices for those interested in pursuing autonomous mobile robots to enhance their research. With Chapters 1 and 2 as a foundation, I next highlight my expansion to the marine domain for water quality research with autonomous surface vessels (Chapter 3) and multi-disciplinary archeological drone surveys in Vulci, Italy (Chapter 4). Finally in Chapter 5, I address scientific research that informed policy successes during my time as a mayor and PhD student. What a great journey!

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.

In this dissertation, I evaluate the major drivers of photogrammetric error and develop a framework to easily quantify and incorporate uncertainty associated with different UAS platforms. To do this, I take 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. I then use the fitted model to predict the length distributions of unknown-sized humpback whales and assess how predicted uncertainty can affect quantities derived from photogrammetric measurements such as the age class of an animal (Chapter 1). I also use the fitted model to predict body condition of blue whales, humpback whales, and Antarctic minke whales, providing the first comparison of how uncertainty scales across commonly used 1-, 2-, and 3-dimensional (1D, 2D, and 3D, respectively) body condition measurements (Chapter 2). This statistical framework jointly estimates errors from altitude and length measurements and accounts for altitudes measured with both barometers and laser altimeters while incorporating errors specific to each. This Bayesian statistical model outputs a posterior predictive distribution of measurement uncertainty around length and body condition 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. From these studies, I find that altimeters can greatly influence measurement predictions, with measurements using a barometer producing larger and greater uncertainty compared to using a laser altimeter, which can influence age classifications. I also find that while the different body condition measurements are highly correlated with one another, uncertainty does not scale linearly across 1D, 2D, and 3D body condition measurements, with 2D and 3D uncertainty increasing by a factor of 1.44 and 2.14 compared to 1D measurements, respectively. I find that body area index (BAI) accounts for potential variation along the body for each species and was the most precise body condition measurement.

I then use the model to incorporate uncertainty associated with different drone platforms to measure how body condition (as BAI) changes over the course of the foraging season for humpback whales along the Western Antarctic Peninsula (Chapter 3). I find that BAI increases curvilinearly for each reproductive class, with rapid increases in body condition early in the season compared to later in the season. Lactating females had the lowest BAI, reflecting the high energetic costs of reproduction, whereas mature whales had the largest BAI, reflecting their high energy stores for financing the costs of reproduction on the breeding grounds. Calves also increased BAI opposed to strictly increasing length, while immature whales may increase their BAI and commence an early migration by mid-season. These results set a baseline for monitoring this healthy population in the future as they face potential impacts from climate change and anthropogenic stresses. This dissertation concludes with a best practices guide for minimizing, quantifying, and incorporating uncertainty associated with photogrammetry data. This work provides novel insights into how to obtain more accurate morphological measurements to help increase our understanding of how animals perform and function in their environment, as well as better track the health of populations over time and space.