Creating spatially continuous maps of past land cover from point estimates: A new statistical approach applied to pollen data |
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| Institution: | 1. Bjerknes Centre for Climate Research, Uni Research Climate, Allégaten 55, N-5007 Bergen, Norway;2. Department of Biology, University of Bergen, PO Box 7803, N-5020 Bergen, Norway;3. Bjerknes Centre for Climate Research, Allégaten 55, N-5007 Bergen, Norway;4. Environmental Change Research Centre, University College London, Gower Street, London WC1E 6BT, UK;5. School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK;1. Department of Geography, Environment and Earth Sciences, University of Hull, Cottingham Road, Hull, HU6 7RX, UK;2. Coastal and Offshore Archaeological Research Services (COARS), Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, European Way, Southampton, SO14 3ZH, UK;3. Department of Geography and Geology, Kingston University, Penrhyn Road, Kingston upon Thames, Surrey, KT1 2EE, UK;1. Department of Physical Geography and Ecosystem Science, Sölvegatan 12, Lund University, Lund, Sweden;2. Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, Kreuzeckbahnstraße 19, 82467 Garmisch-Partenkirchen, Germany;3. School of Geography, Earth & Environmental Science and Birmingham Institute of Forest Research, University of Birmingham, B15 2TT, United Kingdom;1. Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Research Unit Potsdam, Telegrafenberg A43, 14473 Potsdam, Germany;2. Plant Ecology and Natural Conservation, Institute of Biochemistry and Biology, University of Potsdam, Maulbeerallee 2, 14469 Potsdam, Germany;3. State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guanshui Road 46, 550002 Guiyang, China;4. Institute of Earth and Environmental Sciences, University of Potsdam, Karl-Liebknecht-Str. 24, 14476 Potsdam, Germany;5. ZALF, Leibniz-Centre for Agricultural Landscape Research, Eberswalder Str. 84, 15374 Müncheberg, Germany;6. Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany;1. Department of Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, Maulbeerallee 2, 14469 Potsdam, Germany;2. DFG Graduate School Shaping the Earth''s Surface in a Variable Environment, University of Potsdam, Karl-Liebknecht-Str. 24, 14476 Potsdam, Germany;3. Biodiversity and Ecological Modelling, Institute of Biology, Freie Universität Berlin, Altensteinstr. 6, 14195 Berlin, Germany;4. Berlin-Brandenburg, Institute of Advanced Biodiversity Research (BBIB), D-14195 Berlin, Germany;5. ZALF, Leibniz-Centre for Agricultural Landscape Research, Eberswalder Str. 84, D-15374 Müncheberg, Germany;1. The Natural History Collections, University Museum of Bergen, University of Bergen, Bergen, Norway;2. Department of Biology, University of Bergen, Bergen, Norway |
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| Abstract: | 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. |
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| Keywords: | Land cover Spatial modeling Paleoecology Pollen Compositional data Gaussian Markov random fields |
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