Field Scale Fish Movement Analysis Using Methods Based in Perceptual Decision-Making

Movement is a fundamental process in ecology, yet still poorly understood even as it is increasingly important for sustaining populations within increasingly fragmented landscapes. Animals may use a variety of sensory and cognitive means to obtain information about their environment and process it towards a decision. As these are becoming more clear to science we are integrating the underlying neurocognitive processes into engineering (hydrodynamic and water quality) decision-support models using an Eulerian-Lagrangian-agent Method (ELAM). The ELAM model works with any 2-D or 3-D mesh (e.g., any hydrodynamic and/or water quality model) and can use alternative algorithms for sensory perception and cognitive decision-making to drive individual movement behavior. We describe an ELAM currently used to quantitatively analyze past, observed fish movement behavior near hydropower dams and the ability to forecast plausible fish movement response to engineered hydraulic structures in virtual reality during design. We discuss ELAM applications, strengths, and weaknesses and how the method is being expanded to include feeding and bioenergetics towards describing habitat selection and growth potential impacted by water resource management over the spatiotemporal scales afforded by the latest 2-D and 3-D hydrodynamic and water quality models of river basins.