Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America.
Center for Biodiversity and Global Change, Yale University, New Haven, Connecticut, United States of America.
PLoS Biol. 2021 Nov 15;19(11):e3001460. doi: 10.1371/journal.pbio.3001460. eCollection 2021 Nov.
A vast range of research applications in biodiversity sciences requires integrating primary species, genetic, or ecosystem data with other environmental data. This integration requires a consideration of the spatial and temporal scale appropriate for the data and processes in question. But a versatile and scale flexible environmental annotation of biodiversity data remains constrained by technical hurdles. Existing tools have streamlined the intersection of occurrence records with gridded environmental data but have remained limited in their ability to address a range of spatial and temporal grains, especially for large datasets. We present the Spatiotemporal Observation Annotation Tool (STOAT), a cloud-based toolbox for flexible biodiversity-environment annotations. STOAT is optimized for large biodiversity datasets and allows user-specified spatial and temporal resolution and buffering in support of environmental characterizations that account for the uncertainty and scale of data and of relevant processes. The tool offers these services for a growing set of near global, remotely sensed, or modeled environmental data, including Landsat, MODIS, EarthEnv, and CHELSA. STOAT includes a user-friendly, web-based dashboard that provides tools for annotation task management and result visualization, linked to Map of Life, and a dedicated R package (rstoat) for programmatic access. We demonstrate STOAT functionality with several examples that illustrate phenological variation and spatial and temporal scale dependence of environmental characteristics of birds at a continental scale. We expect STOAT to facilitate broader exploration and assessment of the scale dependence of observations and processes in ecology.
生物多样性科学中的广泛研究应用需要将主要的物种、遗传或生态系统数据与其他环境数据相结合。这种整合需要考虑到与所研究的数据和过程相适应的空间和时间尺度。但是,生物多样性数据的灵活和可扩展的环境标注仍然受到技术障碍的限制。现有的工具已经简化了出现记录与网格化环境数据的交叉,但在处理一系列空间和时间粒度方面仍然存在局限性,特别是对于大型数据集。我们提出了 Spatiotemporal Observation Annotation Tool(STOAT),这是一个基于云的灵活生物多样性-环境标注工具箱。STOAT 针对大型生物多样性数据集进行了优化,允许用户指定空间和时间分辨率以及缓冲区,以支持考虑数据和相关过程不确定性和尺度的环境特征描述。该工具为越来越多的近全球、遥感或建模环境数据提供了这些服务,包括 Landsat、MODIS、EarthEnv 和 CHELSA。STOAT 包括一个用户友好的基于网络的仪表板,提供用于注释任务管理和结果可视化的工具,链接到生命地图,并提供一个专门的 R 包(rstoat)用于编程访问。我们通过几个示例展示了 STOAT 的功能,这些示例说明了鸟类在大陆尺度上的物候变化和环境特征的空间和时间尺度依赖性。我们期望 STOAT 能够促进更广泛地探索和评估生态学中观察和过程的尺度依赖性。