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Models on the Move: Memory and Temporal Discretization in Animal Movement Open Access


Other title
random walk
statistical inference
animal movement
Canis lupus
sampling rate
movement ecology
wolf behaviour
Type of item
Degree grantor
University of Alberta
Author or creator
Schlaegel, Ulrike E
Supervisor and department
Lewis, Mark (Mathematical and Statistical Sciences)
Examining committee member and department
Moorcroft, Paul (Organismic and Evolutionary Biology)
Wiens, Douglas (Mathematical and Statistical Sciences)
Lele, Subhash (Mathematical and Statistical Sciences)
Merrill, Evelyn (Biological Sciences)
Kouritzin, Mike (Mathematical and Statistical Sciences)
Department of Mathematical and Statistical Sciences
Applied Mathematics
Date accepted
Graduation date
Doctor of Philosophy
Degree level
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 modelling approaches, discretize the temporal dimension of movement processes. This discretization is a challenge for data analysis, because results may be affected by data sampling rate. In my thesis, I develop the formal concept of movement models' robustness against varying temporal resolution. I provide a series of definitions for movement model robustness. These definitions vary in their strength of conditions but all rest on the same requirement that a model can validly be applied to data with varying resolutions, while parameters change in a systematic way that can be predicted. In an analysis of random walks and spatially-explicit extensions thereof, I found that while true robustness is rare, approximate robustness is more widely present in models. I further demonstrate how robustness can be used to mitigate the influence of temporal resolution on statistical inference.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Schlägel, U. E., & Lewis, M. A. (2014). Detecting effects of spatial memory and dynamic information on animal movement decisions. Methods in Ecology and Evolution, 5(11), 1236–1246. doi:10.1111/2041-210X.12284

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