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

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

18 Citeringar (SciVal)
235 Nedladdningar (Pure)

Sammanfattning

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.
Originalspråkengelska
Sidor (från-till)127-141
TidskriftEcological Complexity: An International Journal on Biocomplexity in the Environment and Theoretical Ecology
Volym20
UtgåvaDecember 2014
DOI
StatusPublished - 2014

Ämnesklassifikation (UKÄ)

  • Geovetenskap och miljövetenskap
  • Sannolikhetsteori och statistik

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