Modelling animal movement in heterogeneous environments is challenging because organisms experience a complex suite of internal and external stimuli that operate hierarchically over multiple temporal and spatial scales. A fundamental goal of movement ecology is to relate the behaviour of animals to their environments, especially with respect to how external information is perceived and processed in the decisions that guide their movements. With the continued development of tracking devices movement data are becoming available at increasingly higher spatio-temporal resolution, and sophisticated analytical methods developed with which to analyse them. However, with these advances in data-capture technologies, it is becoming increasingly difficult to match research questions to the analytical tools that are appropriate for interrogating complex, serially-dependent, multivariate movement data. The methods developed in my dissertation are designed to bridge the gaps between the underlying processes and observed patterns of movement behaviour, as well as the observational and process scales of movement models. First I extended the conceptual framework of movement ecology developed by Nathan et al. (2008) which depicts the interplay among four basic mechanistic components of movement (the internal state, motion, and navigation capacities of the individual and the external factors). I investigated the influences of environmental factors on movement by categorising them into two general classes: environmental stimuli perceived and responded to by animals, and environmental forces such as wind and water currents that physically displace animals. Using data describing grey-faced petrels (Pterodroma macroptera gouldi) movements, a Markov Chain Monte Carlo (MCMC) model, and a vector analysis, I illustrated that the behavioural states categorised by the MCMC model were actually a combination of movement behaviour and wind displacement. This analysis demonstrated how displacement by fluid external forces can change the interpretation of behavioural states inferred by statistical models of movement. I recommend that to realistically describe the movement behaviour of animals in fluid media, environmental factors, whenever possible, should be incorporated in statistical inferrentail movement models (IMMs). This can be achieved either by addition of environmental covariates directly into the model, or from a post hoc approach, such as by vector analysis. A strong criticism of using state-space approaches to infer behaviour from movement data is that such models assume that the behaviours underlying the observed movement can be adequately represented by combinations of correlated random walks. Gurarie et al. (2009) developed a likelihood-based technique (behaviour change point analysis, or BCPA) to identify behavioural bouts within movement trajectories that are not limited to specific movement mechanisms. I extended the BCPA approach into three sequentially applied statistical procedures: (1) BCPA to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3) k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. I demonstrate application of the method by analysing synthetic trajectories of known ‘artificial behaviours’ comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor) obtained by GPS telemetry. The modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified. Researchers are increasingly using individual-based models (IBMs) to explore ecological systems and, in particular, the emergent outcomes of individual-level processes. A major challenge in developing IBMs for study of movement ecology is that such models often seek to characterise complex phenomena, and thus must represent and parameterise multiple hierarchical levels of unobserved behaviours. Approaches based on Approximate Bayesian computation (ABC) methods have been used to support the parameterisation, calibration and evaluation of IBMs. However, the ABC approach requires selection and use of data to exclude parameter sets and unrealistic model structures that generate atypical or improbable patterns. I propose a modelling framework that integrates information derived from statistical inferential models to describe the behaviour of moving animals with ABC methodologies for parameterisation and analysis of IBMs. To demonstrate its application, I apply such a framework in an exemplar analysis to high-resolution movement trajectories of the foraging trips of black petrels (Procellaria parkinsoni), an endangered seabird endemic to New Zealand. Outcomes of this study show that use of inferential statistical models to summarise movement data can inform model selection and parameterisation procedures via ABC, and enable IBM to produce biologically relaistic movement patterns, and yield valuable insights regarding the movement ecology and behaviour of animals. Movement behaviour is shaped by the cognitive abilities and the experiences of individual animals. As a result, how animals perceive and process intrisnsic and extrinsic information is a central question in ecology. Determining what an animal knows about its environment, and how this information is translated into specific movement behaviours, is a significant conceptual challenge for movement ecology. I explored the functionality of cognition in relation to foraging movements, using a continuous-space IBM of animal movement that incorporated perception, memory and site fidelity. Using the IBM, I assessed the foraging efficiency of individuals with different combinations of cognitive parameters in 18 different landscape types with different combinations of resource amount and aggregation. Results show that in landscapes where resources were limited and aggregated in space, high memory accuracy and persistence were favoured for optimal foraing, and site-fidelity contributed most to foraging efficiency. As resources became more abundant, individuals with better perception were favoured. Compared to the null-model of a correlated random walk, cognition increased foraging efficiency and reduced space-use of indiviudals. These findings provide quantitative insights into the effects of spatial cognition and dynamic information on animal movement decisions. This study suggests that memory-driven foraging behaviours are likely to be important in landscapes with high-value, spatially aggregated resources, and information regarding both biological attributes and environmental structures need to be considered when modelling animal movement behaviour. Finally, I discuss the wider application of statistical and simulation models for analysing data describing animal movements. I advocate consideration of influences from both internal and external stimuli, as well as the costs of movement, cognition, and metabolism in scale-dependent movement models in future research.

The study of animal movement has always been a key element in ecological science, because it is inherently linked to critical processes that scale from individuals to populations and communities to ecosystems. Rapid improvements in biotelemetry data collection and processing technology have given rise to a variety of statistical methods for characterizing animal movement. The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement. Animal Movement is an essential reference for wildlife biologists, quantitative ecologists, and statisticians who seek a deeper understanding of modern animal movement models. A wide variety of modeling approaches are reconciled in the book using a consistent notation. Models are organized into groups based on how they treat the underlying spatio-temporal process of movement. Connections among approaches are highlighted to allow the reader to form a broader view of animal movement analysis and its associations with traditional spatial and temporal statistical modeling. After an initial overview examining the role that animal movement plays in ecology, a primer on spatial and temporal statistics provides a solid foundation for the remainder of the book. Each subsequent chapter outlines a fundamental type of statistical model utilized in the contemporary analysis of telemetry data for animal movement inference. Descriptions begin with basic traditional forms and sequentially build up to general classes of models in each category. Important background and technical details for each class of model are provided, including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models. The book also covers the essential elements for how to accommodate multiple sources of uncertainty, such as location error and latent behavior states. In addition to thorough descriptions of animal movement models, differences and connections are also emphasized to provide a broader perspective of approaches.

A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used. Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.

The study of animal movement has always been a key element in ecological science, because it is inherently linked to critical processes that scale from individuals to populations and communities to ecosystems. Rapid improvements in biotelemetry data collection and processing technology have given rise to a variety of statistical methods for characterizing animal movement. The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement. Animal Movement is an essential reference for wildlife biologists, quantitative ecologists, and statisticians who seek a deeper understanding of modern animal movement models. A wide variety of modeling approaches are reconciled in the book using a consistent notation. Models are organized into groups based on how they treat the underlying spatio-temporal process of movement. Connections among approaches are highlighted to allow the reader to form a broader view of animal movement analysis and its associations with traditional spatial and temporal statistical modeling. After an initial overview examining the role that animal movement plays in ecology, a primer on spatial and temporal statistics provides a solid foundation for the remainder of the book. Each subsequent chapter outlines a fundamental type of statistical model utilized in the contemporary analysis of telemetry data for animal movement inference. Descriptions begin with basic traditional forms and sequentially build up to general classes of models in each category. Important background and technical details for each class of model are provided, including spatial point process models, discrete-time dynamic models, and continuous-time stochastic process models. The book also covers the essential elements for how to accommodate multiple sources of uncertainty, such as location error and latent behavior states. In addition to thorough descriptions of animal movement models, differences and connections are also emphasized to provide a broader perspective of approaches.

In the last two decades it has become increasingly clear that the spatial dimension is a critically important aspect of ecological dynamics. Ecologists are currently investing an enormous amount of effort in quantifying movement patterns of organisms. Connecting these data to general issues in metapopulation biology and landscape ecology, as well as to applied questions in conservation and natural resource management, however, has proved to be a non-trivial task. This book presents a systematic exposition of quantitative methods for analyzing and modeling movements of organisms in the field. Quantitative Analysis of Movement is intended for graduate students and researchers interested in spatial ecology, including applications to conservation, pest control, and fisheries. Models are a key ingredient in the analytical approaches developed in the book; however, the primary focus is not on mathematical methods, but on connections between models and data. The methodological approaches discussed in the book will be useful to ecologists working with all taxonomic groups. Case studies have been selected from a wide variety of organisms, including plants (seed dispersal, spatial spread of clonal plants), insects, and vertebrates (primarily, fish, birds, and mammals).