Impacts of self-organizing mechanism and topography on wetland ecosystem dynamics
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Understanding the first order controls over resource cycling and limitation in ecosystems is critical for predicting ecosystem response to disturbances. Topography and vegetation self-organizing mechanisms are first order controls over resource fluxes across the landscape. Topography controls downslope flow of resources (i.e water and nutrients). Through spatial feedbacks, vegetation is able to actively modify its environment and maximize resource flows towards it. To date, the impacts of these controls on ecosystem dynamics have mostly been investigated separately. As such, there is a knowledge gap in the understanding of how these first order controls together dictate the dynamics of the ecosystem. This dissertation aims to gain a better understanding of how self-organizing mechanisms and topography operate together to affect wetland ecosystem dynamics. A spatially explicit, wetland vegetation patterning model that includes for both vegetation self-organizing control and topographic control is developed (Nutrient Depletion Model, NDM). The model describes a scale dependent feedback between vegetation, transpiration and nutrient accumulation that drives the formation of vegetation patterns. The model is applied to investigate the effects of topography and self-organizing mechanisms on form and orientation of vegetation patterns and vegetation growth dynamics of wetland ecosystems. Results show that the two first order controls synergistically impact the formation of the various patterns as observed in wetland ecosystems. Results also show the following: (1) Self-organizing mechanisms result in a more efficient retention of resources, which result in higher biomass in the model that include for both self-organizing mechanism and topographic control (SO+TC) than in the model that that includes only for topographic control (TC). (2) However, when resources or topographic gradients increase or annual rainfall decrease, the vegetation growth dynamics of the TC+SO and TC models converge. The NDM is applied to arctic Alaska to investigate how do the two first order controls impact present and future C-N dynamics of an arctic ecosystem. Simulation results show no significant difference in the dynamics between the SO+TC model and the TC model. The climate change simulation results suggest that changes in daily variability of temperature and precipitation can impact ecosystem dynamics as much as the changes in mean temperature and precipitation. Results from this dissertation provide a more complete picture on the relative roles of the two first order controls over ecosystem nutrient cycling and vegetative growth dynamics. Finally, in this thesis, in order to simulate small-scale feedbacks over large spatial domains, the NDM is implemented in a GPU computing language, which accelerates computational simulation by at least two orders of magnitude. These tools for grid-based simulations can provide a platform for using GPUs in other areas of scientific investigation.