| Summary: | Specialization: Applied Mathematics Degree: Doctor of Philosophy Abstract: Movement ecology thrives from a successful synergy of data and models. In a field where experiments are difficult or impossible, linking field data with mathematical and statistical models allows us to test hypotheses and increase our quantitative understanding of movement processes. Owing to technological progress, data availability and quality are growing rapidly, inspiring new questions and challenging methodology. In my thesis, I address two modelling challenges, one at the forefront of current research on memory-based movement and the other long-standing, yet prevailing, in movement data analysis. Movement serves needs, such as foraging, but also requires time and energy. Therefore, we expect animals to have evolved strategies for efficient movement, likely drawing on cognitive abilities. Indeed, one of the current challenges in movement ecology is to understand the role of cognition, including memory, for movement. To date, very few models that include memory mechanisms have been confronted with data. In my thesis, I present a new cognitive-based model, in which an individual's travel history feeds back to future movement decisions. I focused on the pure spatio-temporal aspect of the travel history, assuming that an individual keeps track of elapsed times since last visits to locations and uses this information during the movement process. I showed that, despite the dynamic interplay of information gain and use, statistical inference can successfully identify this mechanism. I further applied the new modelling framework to wolf (Canis lupus) movement data to test whether wolves adopt a prey management strategy, based on memory, that is directed at reducing impacts of behavioural depression of prey through optimal timing of returns to hunting sites. I found support for the hypothesis but also point out the need to analyze a larger number of individuals to reach stronger conclusions. Data collection methods, as well as standard ...
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