The ability to quantify spatial patterns and detect change in terrestrial vegetation across large landscapes depends on linking ground‐based measurements of vegetation to remotely sensed data. Unlike non‐overlapping categorical vegetation types (i.e., typical vegetation and land cover maps), species‐level gradients of foliar cover are consistent with the ecological theories of individualistic response of species and niche space. We collected foliar cover data for vascular plant, bryophyte, and lichen species and 17 environmental variables in the Arctic Coastal Plain and Brooks Foothills of Alaska from 2012 to 2017. We integrated these data into a standardized database with 13 additional vegetation survey and monitoring data sets in northern Alaska collected from 1998 to 2017. To map the patterns of foliar cover for six dominant and widespread vascular plant species in arctic Alaska, we statistically associated ground‐based measurements of species distribution and abundance to environmental and multi‐season spectral covariates using a Bayesian statistical learning approach. For five of the six modeled species, our models predicted 36% to 65% of the observed species‐level variation in foliar cover. Overall, our continuous foliar cover maps predicted more of the observed spatial heterogeneity in species distribution and abundance than an existing categorical vegetation map. Mapping continuous foliar cover at the species level also revealed ecological patterns obscured by aggregation in existing plant functional type approaches. Species‐level analysis of vegetation patterns enables quantifying and monitoring landscape‐level changes in species, vegetation communities, and wildlife habitat independently of subjective categorical vegetation types and facilitates integrating spatial patterns across multiple ecological scales. The novel species‐level foliar cover mapping approach described here provides spatial information about the functional role of plant species in vegetation communities and wildlife habitat that are not available in categorical vegetation maps or quantitative maps of broadly defined vegetation aggregates.

中文翻译：

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物种水平连续叶面覆盖的区域制图：不包括分类植被制图。

量化空间格局并检测大景观中陆地植被变化的能力取决于将地面对植被的测量与遥感数据联系起来。与非重叠的分类植被类型（即典型的植被和土地覆盖图）不同，叶面覆盖的物种层次梯度与物种和生态位空间的个体反应的生态学理论相一致。我们收集了2012年至2017年阿拉斯加的北极沿海平原和布鲁克斯山麓丘陵的维管束植物，苔藓植物和地衣物种的叶面覆盖数据以及17个环境变量。我们将这些数据整合到一个标准化数据库中，其中包含13个其他植被调查和监测数据集从1998年至2017年在阿拉斯加北部收集。为了绘制北极阿拉斯加六种主要和广泛的维管植物物种的叶面覆盖图，我们使用贝叶斯统计学习方法对地面分布的物种分布和丰度与环境和多季节光谱协变量进行了统计关联。对于六个建模物种中的五个，我们的模型预测了所观察到的物种水平的叶面变化的36％至65％。总体而言，与现有的分类植被图相比，我们的连续叶面覆盖图预测的物种分布和丰度更多的空间异质性。在物种水平上绘制连续的叶面覆盖图也揭示了现有植物功能类型方法中聚集所掩盖的生态模式。通过对植被格局进行物种级分析，可以量化和监测物种，植被群落和野生动植物栖息地的景观格局变化，而与主观分类植被类型无关，并有助于整合多个生态规模的空间格局。此处介绍的新颖的物种级叶面覆盖图绘制方法提供了有关植物物种在植被群落和野生动植物栖息地中的功能作用的空间信息，而这些信息在分类植被图或广义植被集合的定量图中是不可用的。