A salmonid individual‐based model as a proposed decision support tool for management of a large regulated river

• Main Author: Peter N. Dudley
• Format: Article
• Language: English
• Published: Wiley 2018-01-01
• Series: Ecosphere
• Subjects: Chinook salmon; dams; decision support tools; endangered species; individual ecology; individual‐based models
• Online Access: https://doi.org/10.1002/ecs2.2074

Abstract Large regulated rivers often require fisheries and water managers to make management decisions involving resident fish population dynamics that have many ecological drivers. Because of the large scale of the system and often competing interests and demands for water, there is a critical need for decision support tools (DSTs) that allow examination of alternative management scenarios while considering key ecological interactions. Spatially explicit individual‐based models (IBMs) can serve as effective DSTs by providing information on fish population dynamics while accounting for, and providing extensive, spatially explicit information on, the numerous ecological drivers. Spatially explicit IBMs are often difficult to implement owing to the numerous and often complex inputs the models require. Here, I demonstrate how a suite of free, graphical user interface equipped programs, along with three custom‐built and publicly available plugins, can streamline the modeling process and serve as a IBM‐based DST for fisheries management on large regulated rivers. The main program is a spatially explicit IBM of juvenile salmonid dynamics, inSALMO, with two other programs that generate the key input data in the required spatially explicit formats. I then use this proposed DST to simulate a Chinook salmon population on a portion of California's Sacramento River to determine whether an IBM‐based DST is appropriate to evaluate management impacts on a large regulated river. The Sacramento is a large river of major concern in California and is representative of many rivers in the United States and worldwide in that it is dammed, has a resident fish population, and is heavily used for water supply. The proposed DTS results compare favorably with the predictive power of a general additive model, while providing a much fuller and richer data set that could significantly aid and inform management decisions.