Managers of large regulated rivers must make decisions considering the health of the resident fish population as well as many competing interests. Spatially explicit individual based models (IBMs) can serve as decision support tools (DSTs) by providing information on fish population dynamics while accounting for numerous ecological drivers. Here, I demonstrate how a suite of free, graphical user interfaced 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 if 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.
Results/Conclusions
The proposed DTS results match the historical data well when using a validation/calibration approach. This method also compare favorably with the predictive power of a more conventional general additive model (GAM). The DST provides a rich data set including information on the relations of spawners to superimposition risk, the number of fry in the system versus predation risk, and the relations between size class distribution and the total number of fry in the system. This type of information rich data set is helpful to managers as they consider many competing interests when managing large regulated rivers.