GEO-REFERENCED VIDEO RETRIEVAL: TEXT ANNOTATION AND SIMILARITY SEARCH

Ph.DDOCTOR OF PHILOSOPH

Health State Estimation

Life's most valuable asset is health. Continuously understanding the state of our health and modeling how it evolves is essential if we wish to improve it. Given the opportunity that people live with more data about their life today than any other time in history, the challenge rests in interweaving this data with the growing body of knowledge to compute and model the health state of an individual continually. This dissertation presents an approach to build a personal model and dynamically estimate the health state of an individual by fusing multi-modal data and domain knowledge. The system is stitched together from four essential abstraction elements: 1. the events in our life, 2. the layers of our biological systems (from molecular to an organism), 3. the functional utilities that arise from biological underpinnings, and 4. how we interact with these utilities in the reality of daily life. Connecting these four elements via graph network blocks forms the backbone by which we instantiate a digital twin of an individual. Edges and nodes in this graph structure are then regularly updated with learning techniques as data is continuously digested. Experiments demonstrate the use of dense and heterogeneous real-world data from a variety of personal and environmental sensors to monitor individual cardiovascular health state. State estimation and individual modeling is the fundamental basis to depart from disease-oriented approaches to a total health continuum paradigm. Precision in predicting health requires understanding state trajectory. By encasing this estimation within a navigational approach, a systematic guidance framework can plan actions to transition a current state towards a desired one. This work concludes by presenting this framework of combining the health state and personal graph model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin

Top Down Effects and Resource Selection by Coyotes in South Carolina

Top predators play important roles in functioning ecosystems, including regulating the populations of prey species and competing with other predators. However, in the face of global change, many top terrestrial predators have declined in both range and abundance, making room for some smaller predators to expand into new niches. Coyotes (Canis latrans) in North America are a prime example of this – they have rapidly expanded their range in the last 120 years, raising concerns about their impacts on both domestic and wild species. In eastern North America, research has centered around their effects on white-tailed deer (Odocoileus virginianus), which are an important game species and particularly vulnerable to coyotes during the first few weeks of life. Despite efforts by governments and citizens to kill coyotes across much of their new range, they are now established, and managers are looking for ways to quantify and reduce their effects on native species. Critical questions remain about variability within coyote populations and how exactly they respond to temporary foods on the landscape. To address these questions, we studied coyote spatial and community ecology in the Piedmont region of South Carolina, USA. In Chapter 1, we used GPS data to investigate variability in habitat selection and movement. At the population level, coyotes avoided risky areas (i.e., open and developed habitat), especially during risky times. However, we found differences across seasons, behavior states, and sexes, highlighting the importance of both extrinsic and intrinsic factors in predicting movement. In Chapter 2, we quantified coyote diet, focusing on the summer months when fawns were available. Coyotes largely switched to temporarily available foods during the summer and fall, suggesting that alternative foods (i.e., summer fruits) could buffer predation on fawns. In addition, using genetics, we found that most of the individuals in our population switched to these temporary foods, indicating that targeted removal would likely not decrease fawn mortality. In Chapter 3, we investigated whether coyotes changed their foraging tactics for different foods and also described coyote movement surrounding fawn predation events. We compared foraging patterns for fawns, blackberries, and small mammals and found relatively few differences in when and how coyotes moved, yet differences in where they foraged. Linking fawn predation events to coyote GPS data showed that coyotes tended to move relatively fast and linearly prior to killing a fawn, then would quickly move away from the kill site and rest for several hours. In Chapter 4, we broadened our investigation into top-down effects by using a field manipulation to test how coyotes influenced smaller carnivore scavenging behavior, relative to other hypothesized factors. We found that coyotes only directly influenced bobcat behavior, while forest structure (particularly understory cover) seemed to modulate risk from coyotes, highlighting the complexity of interactions among carnivores. Taken together, our findings highlight that 1) coyotes have diverse, yet context-dependent top-down effects and that 2) temporary foods shape their behavior and diet (particularly in the summer). More broadly, our findings suggest that habitat management which promotes alternative foods and accounts for human shields may be a viable strategy to influence the behavior of large-herbivore predators. Generalist carnivores will likely continue to thrive in the Anthropocene, necessitating continued research into their effects on other species and management strategies to best coexist

Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model

Thermal bar is an important phenomenon in large, temperate lakes like Lake Ontario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits the exchange of nutrients. Evolution of the spring thermal bar through Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation is performed from April to July, 2011; on a 2-km grid. The numerical model has been calibrated by specifying: appropriate initial temperature and solar radiation attenuation coefficients. The existing evaporation algorithm in EFDC is updated to modified mass transfer approach to ensure correct simulation of evaporation rate and latent heatflux. Reasonable values for mixing coefficients are specified based on sensitivity analyses. The model simulates overall surface temperature profiles well (RMSEs between 1-2°C). The vertical temperature profiles during the lake mixed phase are captured well (RMSEs < 0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. An update of vertical mixing coefficients is under investigation to improve the summer thermal stratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS

Elephants in a landscape of risk: spatial, temporal, and behavioural responses to anthropogenic risk in African savannah elephants

African savanna elephant (Loxodonta africana) populations have declined due to poaching for the ivory trade. Elephants and humans also increasingly share ranges and resources. This thesis investigates whether and how human-mediated risk influences elephant space use, activity patterns, resource use, grouping patterns, and sex differences in responses to risk, in the Ruaha-Rungwa ecosystem, Tanzania. This area experienced multiple poaching surges and has increasing levels of human activity. I applied occupancy models to elephant occurrence data to investigate space use in relation to risk and environmental factors. Elephant occurrence was negatively associated with human population densities and conversion to agriculture, as well as elephant carcass occurrence (a proxy for poaching risk) and illegal human use. Using camera trap data I compared active periods, grouping patterns, and use of roads and water sources at one low-risk site and three high-risk sites. Male and female elephants were more nocturnal in high-risk versus low-risk sites, including use of water sources; this was more pronounced for cow-calf groups than for lone males. In the high-risk versus low-risk sites, elephants were active for less time overall, avoided movement on roads, and male elephants associated more with males and cow-calf groups. I assessed how risk influences elephant use of water sources using camera trap data. Elephant use of a high-risk resource was driven by seasonal variation in water availability, and use of high-risk water sources was more nocturnal than use of low-risk water sources. Males, but not females, adjusted group size in relation to risk. I discuss costs associated with risk-induced behavioural shifts, including a reduction in total active time and effects on body condition, and show that the consequences of elephant poaching in Ruaha-Rungwa extend beyond effects on population size and structure. I suggest that risk-avoidance behaviour may enable elephants to persist in increasingly human-dominated landscapes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes