Department of Mathematics, Augsburg College, Minneapolis, MN 55454, USA.
Oecologia. 2011 Nov;167(3):599-611. doi: 10.1007/s00442-011-2107-9. Epub 2011 Aug 27.
Data assimilation, or the fusion of a mathematical model with ecological data, is rapidly expanding knowledge of ecological systems across multiple spatial and temporal scales. As the amount of ecological data available to a broader audience increases, quantitative proficiency with data assimilation tools and techniques will be an essential skill for ecological analysis in this data-rich era. We provide a data assimilation primer for the novice user by (1) reviewing data assimilation terminology and methodology, (2) showcasing a variety of data assimilation studies across the ecological, environmental, and atmospheric sciences with the aim of gaining an understanding of potential applications of data assimilation, and (3) applying data assimilation in specific ecological examples to determine the components of net ecosystem carbon uptake in a forest and also the population dynamics of the mayfly (Hexagenia limbata, Serville). The review and examples are then used to provide guiding principles to newly proficient data assimilation practitioners.
数据同化，或数学模型与生态数据的融合，正在迅速扩展对生态系统的多时空尺度的认识。随着更广泛受众可获得的生态数据量的增加，在这个数据丰富的时代，具备数据同化工具和技术的定量能力将成为生态分析的一项基本技能。我们为新手用户提供了一份数据同化入门指南，内容包括：(1) 回顾数据同化术语和方法；(2) 展示生态、环境和大气科学领域的各种数据同化研究，旨在了解数据同化的潜在应用；(3) 将数据同化应用于特定的生态实例，以确定森林的净生态系统碳吸收成分，以及蜉蝣（Hexagenia limbata，Serville）的种群动态。然后，我们将使用这些综述和示例为新的熟练数据同化从业者提供指导原则。
