Fish migration modeling and habitat assessment in a complex fluvial system

Fish migration patterns are driven by hydrodynamic factors, which are essential in aquatic ecology. This study investigated the hydrodynamic drivers of Gymnocypris przewalskii fish migration in two distinct river reaches—a straight reach (SR) and a confluence reach (CR)— in the area of Qinghai Lake, China, using a 3D numerical model, fish density field data, and four predictive models. Thirteen hydrodynamic factors, with a focus on water depth and velocity, were analyzed to identify their influence on fish migration. It was found that in the SR, linear factors of flow velocity and turbulent kinetic energy were most influential, while in the CR, nonlinear factors of water temperature and vortex intensity dominated. For CR, fish migration patterns are also important nonlinear factors. Methods that accurately reveal fish migration patterns, such as Random Forest, offer higher precision for habitat assessment. Our research also shows that fish swimming ability can, to some extent, reflect migration direction. Combining fish swimming ability with traditional linear habitat assessment methods can improve the adaptability of these methods in complex fluvial system. Based on our research findings, we propose a new workflow for fish habitat assessment that integrates both linear and nonlinear predictive methods. This framework provides valuable insights for enhancing fish conservation strategies in various fluvial systems.