Integrating models with data in ecology and palaeoecology: Advances towards a model-data fusion approach

Changhui Peng, Joel Guiot, Haibin Wu, Hong Jiang, Yiqi Luo

Research output: Contribution to journalReview articlepeer-review

80 Scopus citations


It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process-based ecological models and data in cohesive, systematic ways. Model-data fusion (MDF) is an emerging area of research in ecology and palaeoecology. It provides a new quantitative approach that offers a high level of empirical constraint over model predictions based on observations using inverse modelling and data assimilation (DA) techniques. Increasing demands to integrate model and data methods in the past decade has led to MDF utilization in palaeoecology, ecology and earth system sciences. This paper reviews key features and principles of MDF and highlights different approaches with regards to DA. After providing a critical evaluation of the numerous benefits of MDF and its current applications in palaeoecology (i.e. palaeoclimatic reconstruction, palaeovegetation and palaeocarbon storage) and ecology (i.e. parameter and uncertainty estimation, model error identification, remote sensing and ecological forecasting), the paper discusses method limitations, current challenges and future research direction. In the ongoing data-rich era of today's world, MDF could become an important diagnostic and prognostic tool in which to improve our understanding of ecological processes while testing ecological theory and hypotheses and forecasting changes in ecosystem structure, function and services.

Original languageEnglish (US)
Pages (from-to)522-536
Number of pages15
JournalEcology Letters
Issue number5
StatePublished - May 2011
Externally publishedYes


  • Carbon cycle
  • Data assimilation
  • Earth system modelling
  • Ecological forecasting
  • Global climate change
  • Inverse modelling
  • Palaeoclimatic reconstruction
  • Sequential data assimilation
  • Variational data assimilation

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics


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