Uncovering the Spatial Pattern of Feedback Effects at Alpine Treeline

Texas State University - San Marcos, San Marcos TX

Investigators

Abstract

The research will assess the way in which positive feedbacks combine in spatial patterns to alter the environment in the neighborhood of existing tree and krummholz plants at alpine treeline in Glacier National Park. The project will develop a cellular automaton, and a stochastic version, that uses genetic programming to uncover the spatial patterns of feedback processes. Genetic programming will be used so that the rules change in response to how well the simulations produce patterns similar to patterns observed in multispectral remote sensing imagery. Both the simulations and the remotely-sensed patterns will be at one-meter resolution. Different patterns of abiotic (pre-feedback) site quality, static patterns of vegetation, changing rates of abiotic site quality, and different scale relations will be examined through the rules created. This research to examine spatial detail at a finer scale will allow exploration of nonlinearities in responses and the potential for the organization of ecotone areas as complex adaptive systems. Linking the biogeographical phenomena of ecotones to the body of work on complexity theory can improve our understanding of the organization of biome boundaries and make a geographical contribution to the theory. In particular, the research will increase our understanding of how a geographic phenomenon may share traits with other spatial systems. This research will also advance the area of geographical modeling by incorporating spatial measures of fitness in genetic algorithms. Spatial pattern will be used as the key parameter in genetic programming, and the spatial dependencies of the rules produced by this programming will be analyzed. Lastly, this research will advance understanding of the mechanisms underlying an important biogeographical concept, the ecotone. It will be germane to the idea of ecotones as indicators of the impacts of climatic change because of the potential nonlinearities induced by positive feedbacks.