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Multiscale Relationships Between Alpine Treeline Elevation and Hypothesized Environmental Controls in the Western United States

机译:美国西部高山林线高程与假想环境控制之间的多尺度关系

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Multiple environmental factors contribute to the spatial and compositional character and elevational patterns of alpine treeline ecotones (ATEs), and the relative influence of these factors is scale dependent and spatially variable. Frameworks detailing the hierarchical structure of the ATE have been developed to characterize scale dependencies of the pattern and controls of treeline, but this topic has not been studied across a broad range of scales (e.g., from the hillslope to the region). This research directly examines scaling by comparing relationships among treeline elevations and a set of possible controls as geographic extent is varied. The data set used for this research consists of elevational data at the ATE and a set of hypothesized controls for 1,006 sites in twenty-six mountain ranges across the Western United States. The response and predictor variables are quantified from digital data sets using geographic information systems and remote sensing methodologies and then analyzed using a Mantel test framework. Results generally support, and add empirically derived detail to, existing theoretical frameworks, with climatic controls (i.e., variables characterizing temperature and precipitation) having higher correlations with ATE elevation at coarser scales and topographic variables having higher correlations at finer scales. These scale relations support the conceptual hierarchical frameworks that have been proposed, and they are useful guides of covariate selection for future ATE modeling endeavors.
机译:多种环境因素影响着高山林线过渡带(ATEs)的空间和组成特征以及海拔格局,这些因素的相对影响取决于规模和空间变化。已经开发了详细描述ATE的层次结构的框架来表征模式和树线控件的比例依赖关系,但是尚未在广泛的比例尺(例如,从山坡到该地区)研究此主题。这项研究通过比较树线高程与一组可能的控制之间的关系(随着地理范围的变化)来直接检查缩放比例。这项研究使用的数据集包括ATE的高程数据和美国西部26个山脉中1,006个地点的一组假设控制。使用地理信息系统和遥感方法从数字数据集中量化响应和预测变量,然后使用Mantel测试框架进行分析。结果通常支持现有的理论框架,并在经验基础上增加现有的理论框架,其中气候控制(即表征温度和降水的变量)与ATE海拔在较高的尺度上具有较高的相关性,而地形变量在较高的尺度上具有较高的相关性。这些比例关系支持已提出的概念层次框架,并且它们是将来ATE建模工作的协变量选择的有用指南。

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