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