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Creating spatially continuous maps of past land cover from point estimates: A new statistical approach applied to pollen data

  • Behnaz Pirzamanbein
  • Johan Lindström
  • Anneli Poska
  • Shinya Sugita
  • Anna-Kari Trondman
  • Ralph Fyfe
  • Florence Mazier
  • Anne Birgitte Nielsen
  • Jed O. Kaplan
  • Anne E. Bjune
  • H. John B. Birks
  • Thomas Giesecke
  • Mikhel Kangur
  • Małgorzata Latałowa
  • Laurent Marquer
  • Benjamin Smith
  • Marie-José Gaillard
Publishing year: 2014
Language: English
Pages: 127-141
Publication/Series: Ecological Complexity: An International Journal on Biocomplexity in the Environment and Theoretical Ecology
Volume: 20
Issue: December 2014
Document type: Journal article
Publisher: Elsevier

Abstract english

Reliable estimates of past land cover are critical for assessing potential effects of anthropogenic land-cover changes on past earth surface-climate feedbacks and landscape complexity. Fossil pollen records from lakes and bogs have provided important information on past natural and human-induced vegetation cover. However, those records provide only point estimates of past land cover, and not the spatially continuous maps at regional and sub-continental scales needed for climate modelling.

We propose a set of statistical models that create spatially continuous maps of past land cover by combining two data sets: 1) pollen-based point estimates of past land cover (from the REVEALS model) and 2) spatially continuous estimates of past land cover, obtained by combining simulated potential vegetation (from LPJ-GUESS) with an anthropogenic land-cover change scenario (KK10). The proposed models rely on statistical methodology for compositional data and use Gaussian Markov Random Fields to model spatial dependencies in the data.

Land-cover reconstructions are presented for three time windows in Europe: 0.05, 0.2, and 6 ka years before present (BP). The models are evaluated through cross-validation, deviance information criteria and by comparing the reconstruction of the 0.05 ka time window to the present-day land-cover data compiled by the European Forest Institute (EFI). For 0.05 ka, the proposed models provide reconstructions that are closer to the EFI data than either the REVEALS- or LPJ-GUESS/KK10-based estimates; thus the statistical combination of the two estimates improves the reconstruction. The reconstruction by the proposed models for 0.2 ka is also good. For 6 ka, however, the large differences between the REVEALS- and LPJ-GUESS/KK10-based estimates reduce the reliability of the proposed models. Possible reasons for the increased differences between REVEALS and LPJ-GUESS/KK10 for older time periods and further improvement of the proposed models are discussed.


  • Earth and Related Environmental Sciences
  • Probability Theory and Statistics
  • Land cover
  • Spatial modeling
  • Paleoecology
  • Pollen
  • Compositional data
  • Gaussian Markov random fields


  • ISSN: 1476-945X
E-mail: anneli [dot] poska [at] nateko [dot] lu [dot] se

Department of Physical Geography and Ecosystem Science
Lund University
Sölvegatan 12
S-223 62 Lund

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