Uncovering the Spatial Pattern of Feedback Effects at Alpine Treeline

该研究将评估正反馈联合收割机在空间模式中改变冰川国家公园高山树线现有树木和krummholz植物附近环境的方式。 该项目将开发一个细胞自动机和一个随机版本，使用遗传编程来揭示反馈过程的空间模式。 将使用遗传编程，以便规则根据模拟产生的模式与多光谱遥感图像中观察到的模式相似的程度而变化。 模拟和遥感模式都将达到一米的分辨率。 不同的模式的非生物（反馈前）的网站质量，植被的静态模式，非生物网站质量的变化率，和不同的尺度关系将通过创建的rules created.This研究，以审查在一个更精细的尺度空间细节将允许探索的非线性响应和潜在的组织生态交错区作为复杂的适应系统。 将生态交错带的地理现象与复杂性理论的研究联系起来，可以提高我们对生物群落边界组织的理解，并对该理论做出地理贡献。 特别是，这项研究将增加我们对地理现象如何与其他空间系统共享特征的理解。 这项研究还将推进该地区的地理建模，将空间措施的适应遗传算法。 将空间模式作为遗传规划中的关键参数，并对遗传规划产生的规则进行空间依赖性分析。 最后，这项研究将促进对生态地理学的一个重要概念--生态交错带的机制的理解。 由于正反馈引起的潜在非线性，这与生态交错区作为气候变化影响指标的想法密切相关。

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.