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Title

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

Publication Year

2011

Author(s)
  • Peng, Changhui
  • Guiot, Joel
  • Wu, Haibin
  • Jiang, Hong
  • Luo, Yiqi
Source
ECOLOGY LETTERS Volume: 14 Issue: 5 Pages: 522-536 Published: 2011
ISSN
1461-023X
Abstract

P>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.

Author Keyword(s)
  • Carbon cycle
  • data assimilation
  • earth system modelling
  • ecological forecasting
  • global climate change
  • inverse modelling
  • palaeoclimatic reconstruction
  • sequential data assimilation
  • variational data assimilation
KeyWord(s) Plus
  • ENSEMBLE KALMAN FILTER
  • TERRESTRIAL ECOSYSTEM MODEL
  • LAST GLACIAL MAXIMUM
  • DATA ASSIMILATION
  • CARBON STORAGE
  • POLLEN DATA
  • SPECIES DISTRIBUTIONS
  • IMAGING SPECTROSCOPY
  • PARAMETER-ESTIMATION
  • CLIMATE-CHANGE
ESI Discipline(s)
  • Environment/Ecology
Web of Science Category(ies)
  • Ecology
Adress(es)

[Peng, Changhui] NW A&F Univ, Coll Forestry, Lab Ecol Forecasting & Global Change, Yangling 712100, Shaanxi, Peoples R China; [Peng, Changhui; Guiot, Joel] Aix Marseille Univ, CNRS, ECCOREV FR 3098, F-13545 Aix En Provence 4, France; [Peng, Changhui] Univ Quebec, Inst Environm Sci, Dept Biol Sci, Montreal, PQ H3C 3P8, Canada; [Wu, Haibin] Chinese Acad Sci, Key Lab Cenozo Geol & Environm, Inst Geol & Geophys, Beijing 100029, Peoples R China; [Jiang, Hong] Zhejiang Agr & Forestry Univ, State Key Lab Subtrop Forest Sci, Hangzhou 311300, Zhejiang, Peoples R China; [Jiang, Hong] Zhejiang Agr & Forestry Univ, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Hangzhou 311300, Zhejiang, Peoples R China; [Jiang, Hong] Nanjing Univ, Int Inst Earth Syst Sci, Nanjing 210093, Jiangsu, Peoples R China; [Luo, Yiqi] Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73019 USA

Reprint Adress

Peng, CH (reprint author), NW A&F Univ, Coll Forestry, Lab Ecol Forecasting & Global Change, Yangling 712100, Shaanxi, Peoples R China.

Country(ies)
  • Canada
  • France
  • People's Republic of China
  • United States
CNRS - Adress(es)
  • Ecosystèmes continentaux et risques environnementaux (ECCOREV), FR3098
Accession Number
WOS:000289474700011
uid:/FJD87DH6
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