| Summary: | Bayesian methods have been popular in modelling complex ecological data collected using modern animal tracking technologies such as acoustic telemetry for multiple reasons, including their extreme flexibility, ability to incorporate prior knowledge and better precision. Acoustic telemetry systems technology has been increasingly used to study fish movement patterns and habitat use and estimate demographic parameters, including survival probabilities and population size. However, the data generated using omnidirectional acoustic telemetry studies are complex, with multiple sources of variability. In this thesis, I develop methods to effectively analyze data generated with omnidirectional acoustic telemetry systems. The thesis consists of four manuscripts with three different Bayesian models in the first three manuscripts: (1) Bayesian state-space modelling approach to estimate the hidden fish movement paths of walleye in lake Winnipeg, (2) Bayesian multi-state mark-recapture models to estimate survival of Arctic char living in multiple habitats of the Cambridge bay region of Nunavut, and (3) Bayesian hierarchical modelling to understand the biological and environmental drivers behind the survival of Cambridge bay Arctic char. In the fourth manuscript, we develop a novel set of fishery metrics to further understand the vulnerability of walleye to fishing activities in lake Winnipeg. Furthermore, the thesis provides practical tools for many challenges that will arise from the planning stage of the study to the data analysis stage of acoustic telemetry studies. In addition, the finding of each study provides valuable and influential information for fishery managers to make effective fish management and conservation decisions that will affect the future of the aquatic species in those regions. May 2023
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