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Abstract:
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We construct a hierarchical model for spatial compositional data which is used to reconstruct past land-cover compositions (coniferous forest, broadleaved forest, and unforested/open land) for 5 time periods during the past 6000 years over Europe. The model consists of a Gaussian Markov Random Field (GMRF) with Dirichlet observations. A block updated Markov chain Monte Carlo (MCMC), including an adaptive Metropolis adjusted Langevin step, is used to estimate model parameters. The sparse precision matrix in the GMRF provides computational advantages leading to a fast MCMC algorithm. Reconstructions are obtained by combining sparse pollen-based estimates of vegetation cover with output from a dynamic vegetation model and scenarios of past deforestation. To evaluate uncertainties in the predictions a novel way of constructing joint confidence regions for the composition at each prediction location is proposed. The model is evaluated through cross validation for 5 time periods, and by comparing reconstructions for the recent past to a present day European forest map. The evaluation results are promising and the model is able to capture known structures in past land-cover compositions.
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