Cornell Lab of Ornithology, Ithaca, NY 14850, USA.
Trends Ecol Evol. 2012 Feb;27(2):130-7. doi: 10.1016/j.tree.2011.11.006. Epub 2011 Dec 20.
Identifying ecological patterns across broad spatial and temporal extents requires novel approaches and methods for acquiring, integrating and modeling massive quantities of diverse data. For example, a growing number of research projects engage continent-wide networks of volunteers ('citizen-scientists') to collect species occurrence data. Although these data are information rich, they present numerous challenges in project design, implementation and analysis, which include: developing data collection tools that maximize data quantity while maintaining high standards of data quality, and applying new analytical and visualization techniques that can accurately reveal patterns in these data. Here, we describe how advances in data-intensive science provide accurate estimates in species distributions at continental scales by identifying complex environmental associations.
在广泛的空间和时间范围内识别生态模式需要新颖的方法来获取、整合和建模大量不同的数据。例如，越来越多的研究项目利用遍布大陆的志愿者网络（“公民科学家”）来收集物种出现数据。尽管这些数据信息丰富，但在项目设计、实施和分析中也提出了许多挑战，包括：开发最大限度地增加数据量同时保持高标准数据质量的数据收集工具，以及应用能够准确揭示这些数据模式的新分析和可视化技术。在这里，我们描述了如何通过识别复杂的环境关联，在大陆尺度上通过数据密集型科学提供物种分布的精确估计。
