The role of data assimilation in predictive ecology
The role of data assimilation in predictive ecology
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DOI：
10.1890/es13-00273.1
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发表时间：
2014-05-01
期刊：
影响因子：
2.7
通讯作者：
Chapin, F. Stuart, III
中科院分区：
文献类型：
--
作者：
Niu, Shuli;Luo, Yiqi;Chapin, F. Stuart, III
在这个快速变化的世界中，提高预测生态系统及其服务未来动态的能力对于更好地管理地球系统至关重要。预测依赖于模型，这些模型描述了我们对系统动力学基础的主要过程的理解以及有关这些过程和生态系统现状的数据。当模型充分了解数据时，预测就会变得更加有效。与许多标准统计测试最初开发时相比，现在收集数据能力的技术革命为检验假设和预测未来动态提供了截然不同的机会。数据同化是一种新兴的统计方法，将模型与数据严格结合起来，以约束模型参数和系统状态，识别模型误差，改进生态预测。在本文中，我们通过回顾数据同化在四个不同研究领域的应用，说明数据同化如何改进生态预测以支持决策：（1）新出现的传染病，（2）渔业，（3）火灾和（4）陆地碳循环。在这些领域中，数据同化大大提高了预测准确性，凸显了其在实现预测生态学方面的重要作用。区域和全球模型的数据同化面临着重大挑战，例如需要估计的参数数量大、计算需求高、需要整合多个异构数据集以及复杂的社会生态相互作用。尽管如此，数据同化提供了一种重要的统计方法，在增强气候变化中生态模型的预测能力方面具有巨大潜力。
In this rapidly changing world, improving the capacity to predict future dynamics of ecological systems and their services is essential for better stewardship of the earth system. Prediction relies on models that describe our understanding of the major processes that underlie system dynamics and data about these processes and the present state of ecosystems. Prediction becomes more effective when models are well informed by data. A technological revolution in the capacity to collect data now provides very different opportunities to test hypotheses and project future dynamics than when many standard statistical tests were first developed. Data assimilation is an emerging statistical approach to combine models with data in a rigorous way to constrain model parameters and system states, identify model error, and improve ecological prediction. In this paper, we illustrate how data assimilation can improve ecological prediction to support decision-making by reviewing applications of data assimilation across four different research fields: (1) emerging infectious disease, (2) fisheries, (3) fire, and (4) the terrestrial carbon cycle. Across these fields, data assimilation substantially improves prediction accuracy, highlighting its important role in enabling predictive ecology. Data assimilation with regional and global models faces major challenges, such as the large number of parameters to be estimated, high computational demands, the need to integrate multiple and heterogeneous data sets, and complex social-ecological interactions. Nevertheless, data assimilation provides an important statistical approach that has great potential to enhance the predictive capacity of ecological models in a changing climate.