Applying Hidden Markov Modelling to Fine-Scale Telemetry

Recent developments in fine-scale acoustic telemetry have resulted in large datasets containing highly detailed information on fish movement. A common tool in movement ecology is the application of hidden Markov models (HMMs) to uncover hidden behavioural states from telemetry data. Currently, the data collection can take place at a finer temporal scale than is typically used for HMMs. Although HMMs can still provide valuable insights into fish behaviour, the current fine-scale data can introduce some conceptual and practical challenges in model development. In this paper, we look at the potential of straightness index. This index retains fine-scale movement data while smoothing movement data, allowing for the development of HMMs. Using such an approach can be essential in finding behavioural responses of fish to the ecohydraulic environment, and might, in turn, inform fishway design.