Uncovering the Spatial Pattern of Feedback Effects at Alpine Treeline

这项研究将评估正反馈在空间模式中结合在一起的方式，以改变冰川国家公园高山林线上现有树木和Krumholz植物附近的环境。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与在多光谱遥感图像中观察到的模式相似的模式的好坏而改变。模拟和遥感图像都将达到一米的分辨率。不同的非生物(反馈前)立地质量模式、植被的静态格局、非生物立地质量的变化率以及不同的尺度关系将通过所创建的规则来检验。这项在更精细尺度上检验空间细节的研究将允许探索响应中的非线性以及将交错带区域组织为复杂适应系统的可能性。将交错带的生物地理现象与复杂性理论的工作联系起来，可以提高我们对生物群边界组织的理解，并对这一理论做出地理贡献。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。这项研究还将通过将空间适应度测量纳入遗传算法来推进地理建模领域。将空间模式作为遗传规划的关键参数，分析了遗传规划产生的规则的空间依赖关系。最后，这项研究将促进对一个重要的生物地理学概念--交错带--背后的机制的理解。由于正反馈引起的潜在非线性，因此将生态交错带作为气候变化影响的指标的想法将是切合实际的。

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.