Controls on conifer coexistence in a vegetation demographic model

Abstract:

Land surface models that incorporate competition and resolve forest structure are critical for assessing future forest responses to changing climate and disturbance regimes. How such models are parameterized, in terms of the plant strategies they represent, affects the simulated carbon cycle, disturbance, and vegetation composition. Here we developed a framework that used observations of physiological traits, a parameter sensitivity analysis, and observations of forest structure and composition, to understand model sensitivity to parameters, initial conditions, and disturbance, and to define plant functional types that allow for realistic coexistence between a shade-intolerant fire resistant conifer, and a shade-tolerant more fire-sensitive conifer. First, we ran the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at a flux tower site in the Sierra Nevada Mountains of California, USA. The probability of coexistence was most influenced by parameters controlling carbon allocation, specific leaf area, the maximum rate of carboxylation, and seed supply from outside the grid cell. Coexistence was more likely when plant types were more similar. We found that fire had an important effect on coexistence that could not be compensated for by physiological parameterization. We filtered the tower site simulations based on ecologically expected outcomes and retained four parameterizations. We then ran FATES at four-kilometer resolution over the mixed conifer zone of the Sierra Nevada. Differences between FATES and benchmarking data showed the shade-intolerant type was underrepresented in the driest areas, indicating further improvements in soil characteristics or plant hydraulics could improve simulations. Coexistence was less well represented in areas with the least amount of area burned, reinforcing the importance of fire. Final parameterizations will require iteration with refinements of the fire module within FATES. Our framework was an efficient means of assessing parameter sensitivity and defining ecologically refined plant strategies based on observations. This ecological resolution will allow us to use the model to address important questions related to future climate and disturbance effects on forest structure, composition and carbon storage.