TY - JOUR
T1 - Creating spatially continuous maps of past land cover from point estimates: A new statistical approach applied to pollen data
AU - Pirzamanbein, Behnaz
AU - Lindström, Johan
AU - Poska, Anneli
AU - Sugita, Shinya
AU - Trondman, Anna-Kari
AU - Fyfe, Ralph
AU - Mazier, Florence
AU - Nielsen, Anne Birgitte
AU - Kaplan, Jed O.
AU - Bjune, Anne E.
AU - B. Birks, H. John
AU - Giesecke, Thomas
AU - Kangur, Mikhel
AU - Latałowa, Małgorzata
AU - Marquer, Laurent
AU - Smith, Benjamin
AU - Gaillard, Marie-José
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Land cover
KW - Spatial modeling
KW - Paleoecology
KW - Pollen
KW - Compositional data
KW - Gaussian Markov random fields
U2 - 10.1016/j.ecocom.2014.09.005
DO - 10.1016/j.ecocom.2014.09.005
M3 - Article
SN - 1476-945X
VL - 20
SP - 127
EP - 141
JO - Ecological Complexity: An International Journal on Biocomplexity in the Environment and Theoretical Ecology
JF - Ecological Complexity: An International Journal on Biocomplexity in the Environment and Theoretical Ecology
IS - December 2014
ER -