A North European pollen–climate calibration set: analysing the climatic responses of a biological proxy using novel regression tree methods

Abstract: We present a new pollen–climate calibration data-set from northern Europe consisting of 583 modern pollen samples and high-resolution, GIS-based modern climate data. The pollen data are characterised by high taxonomic resolution (167 taxa) and homogenous taphonomy (all samples are from small-to-medium-sized lakes). To assess the potential of this calibration set for the reconstruction of different climatic parameters, we use novel regression tree methods to analyse the effect on pollen composition and variability of four parameters: summer temperature, winter temperature, water balance, and continentality index. We use multivariate regression trees to analyse the variation in pollen assemblages in modern climate space, while boosted regression trees are used to analyse the relative influence of different climatic parameters on each taxon. We find taxon responses to be relatively individualistic. While most taxa (65%) are most responsive to summer temperature, other parameters are either primary determinants or significant secondary determinants for many taxa. At the assemblage level, significant variation is found in assemblages from similar summer temperature regimes, with distinct clusters of assemblages also identified along the continentality gradient. As a multivariate method, we consider boosted regression trees highly effective in describing and modelling modern climate–taxon relationships. [Copyright &y& Elsevier]