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Realistic depictions of past land cover are needed to investigate prehistoricenvironmental changes, effects of anthropogenic deforestation, and long termland cover-climate feedbacks. Observation based reconstructions of past landcover are rare and commonly used model based reconstructions exhibitconsiderable differences. Recently \citet[Spatial Statistics,24:14--31,][]{PirzaLPG2018_24} developed a statistical interpolation methodthat produces spatially complete reconstructions of past land cover from pollenassemblage. These reconstructions incorporate a number of auxiliary datasetsraising questions regarding the method's sensitivity to different auxiliarydatasets.  Here the sensitivity of the method is examined by performing spatialreconstructions for northern Europe during three time periods (1900 CE, 1725 CEand 4000 BCE). The auxiliary datasets considered include the most commonlyutilized sources of past land-cover data --- e.g.\ estimates produced by adynamic vegetation (DVM) and anthropogenic land-cover change (ALCC) models.Five different auxiliary datasets were considered, including different climatedata driving the DVM and different ALCC models. The resulting reconstructionswere also evaluated using cross-validation for all the time periods. For therecent time period, 1900 CE, the different land-cover reconstructions werecompared against a present day forest map.  The validation confirms that the statistical model provides a robust spatialinterpolation tool with low sensitivity to differences in auxiliary data andhigh capacity to capture information in the pollen based proxy data. Furtherauxiliary data with high spatial detail improves model performance for areaswith complex topography or few observations.

Bayesian reconstruction of past land-cover from pollen data: model robustness and sensitivity to auxiliary variables