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This thesis explores animal foraging behaviour, focusing on the spatial-dynamics of movement patterns, and its implications for resource management. Despite recent advances in technology and scientific methods, understanding movement patterns remains a challenge due to the lack of accurate data and robust quantification models. The study investigates the existence of Lévy flights in foraging animals, a phenomenon previously thought to be common but later disputed by Edwards (2011). Using two Bayesian statistical approaches, the thesis re-analyses three data sets and finds that humans, reindeer, and other species exhibit a bounded Lévy flight foraging pattern, disproving previous findings. The study also concludes that the likelihood-free Bayesian framework is better at capturing underlying movement patterns, especially when data is incomplete or imprecise. The thesis explores the robustness and relevance of Bayesian approaches through simulations and applications, including wildlife corridor development and invasive species containment, highlighting the importance of improved understanding of species movement for effective resource management.