Department of Botany and Microbiology, University of Oklahoma, Norman, Oklahoma 73019, USA.
Ecol Appl. 2011 Jul;21(5):1429-42. doi: 10.1890/09-1275.1.
Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible. This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.
几种力量正在汇聚，以改变生态研究并增强其对定量预测的重视。这些力量包括：（1）观测和实验网络中数据量的大幅增加，（2）计算能力的提高，（3）生态模型及相关统计和优化方法的进步，最重要的是，（4）在全球持续变化的背景下，社会需要制定更好的自然资源管理策略。传统上，生态预测基于过程导向模型，并以数据为依据，主要是凭经验的方式。尽管大多数生态模型都包含对机制过程的某种表示，但当今的模型通常不足以量化现实世界的动态，并提供可靠的预测及其不确定性估计。改进生态预测和不确定性估计的关键工具是数据同化 (DA)，它使用数据来告知初始条件和模型参数，从而在模拟过程中约束模型，使其结果尽可能接近现实。本文讨论了 DA 在生态研究中的含义和历史，并强调了其在改进推理和生成预测方面的作用。DA 可以通过以下方式推进生态预测：（1）改善模型参数和状态变量的估计，（2）促进替代模型结构的选择，以及（3）量化来自观测、模型及其相互作用的不确定性。然而，当生态过程不被很好地理解或从未被观测到时，DA 可能不会改善预测。总体而言，我们认为 DA 是将原始数据转化为具有生态意义的产品的关键技术，在观测和实验网络中数据可用性大幅增加的时代尤其重要。
