Predicting Fish Movement In Rivers Near Infrastructure: Elam Model Apps From A Growing User Community

Predicting an individual with volitional control of their own movement is exceptionally challenging. Federal agencies frequently confront scenarios where the successful outcome of a management action or engineering design depends largely on the decisions that animals make. In rivers, fish often must be managed with care where one cannot simply exclude, or repulse, them from undesirable locations; volitional guidance is commonly needed. Guiding an individual under its own will to a specific location is challenging and knowing in advance the likely outcome is even more so. The emerging field of cognitive movement ecology pursues techniques and tools for understanding past and predicting future animal movement decisions. While terrestrial, avian, and aquatic applications share a lot in common, rivers are a unique environment in which to interpret animal movement. Animals in rivers such as fish move within a media that itself is moving. Research into fish movement behavior dates back more than a century. For the past 25 years, the U.S. Army Corps of Engineers, Research and Development Center (ERDC), has been working on a management tool that can hindcast and predict future fish response to infrastructure designs and management actions. Lessons accrued along the way show that merely calibrating a model to past data does not guarantee successful prediction. However, the model (the Eulerian-Lagrangian-agent Method – ELAM) has experienced unique success in the field of animal prediction. The ELAM model has accurately predicted future, out-of-sample fish movement and passage/entrainment at infrastructure. There have also been failures. Much of the past 15 years has involved studying why the model performs well in some applications and not so well in others. I will present recent developments from the last few years that give rise to new capability in fish movement prediction, informed by lessons the past 25 years (successes and failures). Part of the learning stems from a growing community of ELAM model users worldwide, including the German Government and researchers in the United Kingdom, Canada, Greece, and China. Recent developments suggest the potential exists to develop a single calibration, or nearly so, for describing the movement of downstream-migrating fish in both dam forebay and tidal river environments. Further, emerging theoretical developments suggest the potential exists for inverting downstream-moving behavior rules to describe upstream-moving fishes. Fish movement depends on the species, but work unifying past data into a common framework – and advanced by a growing community of users – facilitates value-added benefits to existing data, the ability to understand fish behavior more quickly, and the ability to better incorporate animal behavior into the fast-paced nature of engineering design projects.