A wildlife journey in space and time: methodological advancements in the assessment and analysis of spatio-temporal patterns of animal movement across European landscapes

Movement is one of the most fundamental processes for living entities on earth at the core of scientific disciplines such as ecology and geography. In animal ecology, ongoing progress in tracking and remote sensing technologies has spurred an explosion of movement and environmental data collected at high spatial and temporal resolution, at a large scale, so that the interaction between animal movement and habitat features can now be investigated in much more detail. As a result, in recent years the field of animal ecology has produced a growing body of studies on movement-based patterns leading to habitat use and selection. In this regard, GIScience has contributed with several visual analytical approaches to study animals in relation to their environment and habitat. However, the pat - terns behind the sequential use of different habitat classes have remained largely unexplored. Sequential habitat use is defined as the consecutive use of habitat features along the trajectory of an animal, extracted from the context of its spatial movement. By account - ing for the sequence of use, it is possible to distinguish fundamentally different behavioural habitat use strategies that are important for the survival and fitness of an animal, such as habitat alternation versus random sequential use. Such distinctions would remain undetected by only considering the proportion of use. Sequential habitat use patterns occur in a spatial context, meaning sequential patterns are affected by what is actually available to the animal. In this dissertation we merge knowledge from different fields to present an innovative method to study the relation between animals and their environment by accounting for the sequential use of habitats, and animal movement rules. We developed a visually effective method to analyse and visualise sequential habitat use patterns of animals at multiple spatio- temporal scales by combining real and simulated sequences of habitat use. To study sequential habitat use patterns we use Sequence Analysis Methods (SAM), an approach widely applied in molecular biology, as well as many applications in different fields, to measure dissimilarity between sequences of characters. In brief, we use dissimilarity algorithms to measure the distance between all pairs of sequences, and then apply a cluster - ing algorithm to investigate how these sequences group together, which are visualised as dissimilarity trees. We propose a procedure consisting of three steps, including explo- ration, simulation and classification. In the exploration phase, we build exploratory trees, which visualise real sequential habitat use patterns. Second, by applying animal movement models we simulate expected sequential habitat use patterns, and assess how spatial context, and especially habitat availability, affects the clustering of sequential patterns. Third, we combine real and simulated sequences to identify which simulated pattern is most parsimonious with the real sequences. The research progress has been presented in three main chapters. In Chapter 3 we present seminal methodological development where SAM was applied to animal movement data. In Chapter 4 we introduce further methodological advancements to extend the applicability of SAM to animal ecology. In Chapter 5 we present a large-scale multi-population ecological application. All research was performed using GPS movement data of roe deer and environmental data provided by the Euroungulates database project. Chapter 3 presents the first application of SAM to identify ecologically relevant sequential patterns in animal habitat use. We exemplify the method using ecological data consisting of simulated and real trajectories from a roe deer population (Capreolus capreolus) in the Italian Alps, expressed as ordered sequences of four habitat use classes, i.e. high/open, high/closed, low/open, low/closed. In essence, the SAM framework identifies relevant sequential patterns in real trajectories by measuring their similarity to spatially-explicit simulated trajectories with known sequential patterns. Simulation trajectories were generated in arenas resembling the landscape structure of the roe deer population. Chapter 4 extends SAM to an individual-based approach (i.e. IM-SAM, Individual Movement – Sequence Analysis Methods), that is applicable over multiple populations. Specifically, instead of performing simulations in landscape-like arenas, we use real individual home ranges, thus accounting for individual spatial context, and landscape composition and structure. To assess usability of our advanced framework we investigate the sequential use of open and forest habitats for nine roe deer populations ranging in landscapes with different geographic contexts and anthropogenic disturbance. We also discuss implications for conservation and management. Chapter 5 addresses the functional role of landscapes throughout seasons by identifying both population level and individual level variability in the sequential habitat use patterns of roe deer, identified in the former nine roe deer populations. We show how identified sequential habitat use patterns can be treated as variables, and analysed with standard and well-accepted statistical methods. While the (IM-)SAM framework was developed for studying sequential habitat use in specific, we highlight that its methodological steps and study design can easily be gener- alised. Indeed, its dissimilarity and clustering algorithms, temporal resolution, sampling units, and number of classes for which sequential patterns are investigated can all be customised for the specific research questions in mind. (IM-)SAM is easily applicable to different types of sequential data that describe aspects of an animal's internal (e.g. heart rate) or external state (e.g. temperature). Through improvements in technology, including the growing number of information that can be collected through sensors (GPS trackers, biologgers and satellites), improving database infrastructures and the instant availability of advanced R packages dedicated to animal movement, (IM-)SAM could be easily integrated in a wide range of both local and broad-scaled behavioural spatio-temporal studies.