Data from: Spectral diversity area relationships for assessing biodiversity in a wildland-agriculture matrix

Species-area relationships have long been used to assess patterns of species diversity across scales. Here this concept is extended to spectral diversity using hyperspectral data collected by NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over western Michigan. This mixture of mesic forest and agricultural lands offers two end-points on the local-scale diversity continuum – one set of well mixed forest patches and one set of highly homogeneous agricultural patches. Using the sum of the first three principal component values and the principal components’ convex hull volume, spectral diversity was compared within and among these plots and to null expectations for perfectly random and perfectly patchy landscapes. Overall the spectral diversity area relationship confirms the patterns that would be expected for this landscape, but this application suggests that this approach could be extended to less well understood landscapes and could reveal key insights about the relative importance of different drivers of community assembly, even in the absence of additional data about plant functional traits or species’ identities.

物种-面积关系长期以来被用于评估不同尺度下的物种多样性格局。本研究将这一概念拓展至光谱多样性（spectral diversity）研究，所用数据为美国国家航空航天局（NASA）机载可见光/红外成像光谱仪（Airborne Visible/Infrared Imaging Spectrometer，AVIRIS）在密歇根州西部采集的高光谱数据（hyperspectral data）。该区域兼具中生性森林与农用地两种景观类型，构成了局域尺度多样性连续谱上的两个端点：一组为充分混合的森林斑块，另一组为高度均质的农业斑块。本研究选取前三个主成分（principal component）分值之和以及主成分凸包体积（convex hull volume）作为指标，对这些样地内部、样地之间的光谱多样性进行了对比，并与完全随机景观和完全斑块状景观的零假设预期（null expectations）进行了比对。总体而言，光谱多样性-面积关系验证了该景观的预期格局，而本研究的应用表明，该方法可推广至认知程度较低的景观类型，且即便缺乏植物功能性状（plant functional traits）或物种身份（species’ identities）的额外数据，也能够揭示群落构建（community assembly）不同驱动因子相对重要性的关键认知。