A diatom-inference model for nutrients screened to reduce the influence of background variables: Application to varved sediments of Greifensee and evaluation with measured data

摘要

Palaeoenvironmental reconstructions of lake sediment records have been greatly facilitated by statistical comparisons with microfossil assemblages from the surface sediments of modern lakes. These modern sub-fossil assemblages from different lakes, which are often referred to as "training-sets", attempt to incorporate gradients of environmental parameters, such as e.g., temperature and phosphorus, are of interest for paleoenvironmental reconstruction. One major assumption of quantitative palaeoenvironmental reconstructions requires that the environmental variable to be reconstructed is, or is linearly related to, an ecologically important determinant in the ecological system of interest, and that the joint distribution with other variables in the fossil set is the same as in the training-set. The motivation for this paper is that present-day diatom species abundances in surface-sediment samples are often influenced by several environmental gradients. Partial least squares (PLS) or weighted-averaging partial least squares (WA-PLS) regression methods can be used to adjust species optima, if the additional PLS components, which are orthogonal to previous PLS components, are included.