The role of data assimilation in predictive ecology

Authors

  • Shuli Niu,

    Corresponding author
    1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101 China
    2. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73072 USA
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  • Yiqi Luo,

    1. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73072 USA
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  • Michael C. Dietze,

    1. Department of Earth and Environment, Boston University, Boston, Massachusetts 02215 USA
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  • Trevor F. Keenan,

    1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138 USA
    2. Department of Biological Sciences, Macquarie University, North Ryde, New South Wales 2109 Australia
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  • Zheng Shi,

    1. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73072 USA
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  • Jianwei Li,

    1. Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73072 USA
    2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, No. 12 ZhongGuanCun Southern Street, Beijing 100081 China
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  • F. Stuart Chapin III

    1. Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska 99775 USA
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  • Corresponding Editor: S. LaDeau.

Abstract

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.

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