SYMP 23-2 - A complex stream fish IBM synergizes with empirical research to address 21st century river management problems

Predicting the effects of resource management actions on fish populations typifies the many ecological problems too complex to address with field studies or simple models alone. Managers of aquatic resources commonly must forecast the combined effects of streamflow, turbidity, and temperature regimes on highly valued stream fish populations. Real-world problems in this management area are often further complicated by additional factors such as the potential influence of invasive species, barriers to fish movement, and plans to restore physical habitat. The process-based, spatially explicit, individual-based model InSTREAM offers unique capability to address complex forecasting problems concerning stream fish, in part because its complexity allows explicit and simultaneous incorporation of the many potentially important aspects of relevant future management scenarios.
However, the mechanistic nature and temporal extent and resolution of InSTREAM create the need for novel and in some cases hard-to-acquire inputs. In addition, the model’s complexity and novelty require making assumptions about poorly understood processes while also creating a need to address model credibility. This situation sets the stage for a variety of beneficial connections to empirical research, which have included: 1) more meaningful field measurements of habitat characteristics in comparison to traditional approaches, as a result of meeting the need to simulate habitat in a process-based model; 2) use of model sensitivity analyses to identify important empirical research questions, resulting in novel field experiments such as one that quantified predation risk for stream fish along gradients of depth and cover; 3) resolution of a conflict between model results and empirical observations via research on an under-recognized feeding capability of a stream fish; 4) empirical assessment of model calibration parameters to address model credibility; and 5) linked empirical and modeling studies to evaluate and understand site-specific resource management issues, such as the effects of stream diversion and habitat restoration on fish populations of special concern.