Multiscale Context for Change in Alpine Tundra

高山冻原受到气候变化的威胁，因为它所存在的低温气候可能会在全球范围内减少。 本研究将通过在多个空间尺度上调查高山冻原的群落结构，为北美西部高山冻原对气候变化的响应提供一个背景。 重点是高山冻原多样性相关的过程的空间范围。本研究探讨的猜想，植物物种的混合生长在一起（或次要物种多样性）不同沿着细空间尺度梯度的土壤特性。 土壤梯度嵌套在由地形（如斜坡形状和位置）确定的更广泛的地理尺度梯度内，这些环境进一步嵌套在气候变量（辐射，温度和降水）的区域梯度内。 例如，一个地点的降水量会受到地形如何改变雪的再分布的影响，而植物可用的融雪量会受到土壤质地的影响。 将收集关于高山冻原地点物种丰富程度的新数据，并将其用于推导生物多样性（因）变量。 土壤和地表条件的现场条件（自变量）将在细尺度上进行检查;地形测量，由于其范围大于植物，因此是更一般的条件，将在中等尺度上进行检查，并将根据为山区地形开发的外推法，在粗尺度上检查已建立的气象站的气候变量。 为了探讨可能的关系，将采用三种分析方法的物种组成：曼特尔回归与成对的植被和环境差异的差异;多尺度排序使用典型对应分析;和多层次回归与社区组成的因变量统计得出的额外的排序。 对于群落多样性，将使用以物种丰富度为因变量的多级回归。这项研究将通过测试跨空间尺度的层次关系来促进我们对生物多样性和群落地理学的理解。将开发多层次模型，以解决在更大范围的尺度比以前的生态研究中影响植物多样性和群落结构的层次控制。这项研究将为气候变化的潜在生态响应提供新的见解，这是一个大规模的现象，因为它将考虑到存在于最小尺度上的空间异质性。研究结果将告知国家公园服务资源管理人员关于高山冻原生物多样性未来可能发生的变化，这是一种重要的视觉和美学资源。更广泛地说，这项研究将改善解释，并有助于全球高山监测网络的结果。

Alpine tundra is threatened by climate change because the low-temperature climates where it exists may be reduced globally. This study will provide a context for the response of alpine tundra in western North America to climate change by investigating the community structure of alpine tundra at multiple spatial scales. The focus is on the spatial extent of processes relevant to alpine tundra diversity. This study examines the conjecture that the mix of plant species growing together (or secondarily species diversity) differs along fine spatial scale gradients in soil characteristics. Soil gradients are nested within broader geographic-scale gradients determined by topography such as slope shape and position and these environments are further nested within regional gradients in climate variables (radiation, temperature, and precipitation). For example, the amount of water from precipitation at a site is modified by how the terrain alters the redistribution of snow, and the snowmelt available to plants is affected by soil texture. New data on the abundance of species on alpine tundra sites will be collected and used to derive biodiversity (dependent) variables. Site conditions (independent variables) of soil and surficial conditions will be examined at fine scale; topographic measures, which are more general conditions given that they are at larger extents than the plants, will be examined at a middle scale, and climate variables from established weather stations will be examined at coarse scale based on extrapolations developed for mountain topography. In order to explore possible relationships, three analytical methods will be applied for species composition: Mantel regression with pairwise dissimilarities of vegetation and environmental difference; multiscale ordination using canonical correspondence analysis; and multilevel regression with community composition dependent variables statistically derived by additional ordinations. For community diversity, multilevel regression will be used with species richness as the dependent variable. This research will advance our understanding of biodiversity and community biogeography by testing hierarchical relations across spatial scales. Multilevel models will be developed to address the kind of hierarchical controls that affect plant diversity and community structure across greater range of scales than done in prior ecological research. This research will provide new insight into potential ecological responses to climate change, which is a large-scale phenomenon, because it will take into account the spatial heterogeneity that exists at the finest scale. The results will inform National Park Service resource managers about possible future change in alpine tundra biodiversity, an important visual and aesthetic resource. More broadly, this study will improve interpretation and contribute to the findings of a global alpine monitoring network.