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

## TL;DR

This study assesses the robustness of a Bayesian land-cover reconstruction method from pollen data, finding it to be resilient to auxiliary dataset variations and effective in capturing past land cover changes.

## Contribution

It demonstrates the robustness and sensitivity of a Bayesian reconstruction method to different auxiliary datasets across multiple historical periods.

## Key findings

- Reconstruction method shows low sensitivity to auxiliary data variations.
- High spatial detail auxiliary data improves accuracy in complex terrains.
- Model effectively captures past land cover from pollen data.

## Abstract

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.