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

Daniel J. Weiss, G. Malanson, S. Walsh
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引用次数: 32

Abstract

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.
美国西部高山树线高程与假设环境控制的多尺度关系
多种环境因子共同影响着高寒乔木线交错带的空间组成特征和海拔格局,其相对影响具有尺度依赖性和空间变异性。已经开发了详细描述ATE层次结构的框架,以表征模式和树线控制的尺度依赖性,但该主题尚未在广泛的尺度范围内进行研究(例如,从山坡到区域)。这项研究通过比较树木线高度和一系列可能的控制因素之间的关系,直接考察了地理范围的变化。本研究使用的数据集包括美国西部26个山脉1006个地点的海拔数据和一组假设的对照数据。利用地理信息系统和遥感方法从数字数据集对响应和预测变量进行量化,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,并为其增加了经验推导的细节,即气候控制(即表征温度和降水的变量)在较粗的尺度上与ATE海拔具有较高的相关性,而地形变量在较细的尺度上具有较高的相关性。这些尺度关系支持已经提出的概念层次框架,它们是未来ATE建模工作的协变量选择的有用指南。
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