Insights from an individual based model of a fish population on a large regulated river

Abstract

On regulated rivers, managers must understand how drivers they can influence interact with the system to affect the health of resident fish populations. One potential way to gain insight into how these drivers interact or act in insolation is with individual based models (IBM), which can take numerous environmental drivers as inputs and mechanistically simulate their effects on the individuals in the system. This paper uses inSALMO, a spatially explicit IBM for salmon freshwater life stages, to examine how eight different drivers affect nine response variables for winter run Chinook salmon on the Sacramento River, CA. This paper examines the effects spawner numbers, spawner timing, water temperature, flow rate, turbidity, habitat cover, gravel area, and food concentration on superimposition, temperature induced egg mortality, predation, stranding, poor condition mortality, age at out-migration, length at out-migration, number of out-migrants, and juvenile size distribution. Notable results included: flow’s lack of effect on juvenile stranding and small effect on final out-migrant count, the degree to which flow affects superimposition risk, temperature having the largest effect on final juvenile out-migrant count, the interaction between predation and temperature induced egg mortality which produces a constantly decreasing out-migration count with temperature, and the level at which gravel additions would not have added benefits for superimposition mortality. While this method uses simulations, and thus will not have perfectly fidelity to the system, it is a cost-effective and quick method for gaining a mechanistically derived understanding of the complex relationships between numerous drivers and response variables.