The extended downscaling approach: A new R-tool for pollen-based reconstruction of vegetation patterns

Abstract

The extended downscaling approach (EDA) is a quantitative method in palynology that aims to detect past vegetation patterns and communities in the landscape. The EDA uses iterative forward modelling to fit vegetation composition to robust landscape patterns by comparing simulated with actually observed pollen deposition. The approach employs a set of pollen records, preferably from medium sized to large lakes or peatlands, as well as maps of robust landscape patterns, such as soils and relief. So far, the EDA has been applied in simple settings with only few taxa. To be able to apply the model also in more complex situations, we have implemented the EDA in the R environment for statistical computing. We here test the performance of the EDAinR function in five synthetic scenarios of increasing complexity. In all cases, the EDA is well able to reconstruct vegetation composition, also on rare landscape units. If uncertainty is added both to the pollen data and pollen productivity estimates, the EDA still correctly reconstructs species composition on more than 90% of the total landscape in all scenarios, underlining that the EDA performs well also in complex settings. The EDAinR function will be available within the R package DISQOVER.