Modern pollen rain, vegetation and climate along elevation gradients in the upper-middle Yellow River: numerical approaches for quantitative environmental reconstruction

Abstract

A total of 245 modern pollen samples available in the East Asian Pollen Database and our unpublished pollen data (n = 204 and 41, respectively) were compiled to determine the correlation between modern pollen rain and vegetation in the watershed regions along the upper and middle Yellow River (UMYR) using principle component analysis (PCA). Despite considerable geographical differences and an uneven spatial vegetation distribution, pollen assemblages could differentiate major vegetation and climate zones. The PCA result reveals a high correlation between pollen taxa and key environmental factors, suggesting the high potential of this database for quantitative climate reconstruction. After screening, 199 samples were finally used to quantitatively reconstruct climate parameters with three methods namely, the weighted averaging partial least squares (WA-PLS) method, locally weighed weighted averaging (LWWA) method, and modern analogue technique (MAT).The comparison of multiple reconstructions indicates that WA-PLS provides quite accurate climate reconstructions from limited pollen data, whereas LWWA and MAT appear more suitable for large pollen datasets associated with a broad geographic range. We also discovered that the selection of sub-divided watershed areas yielded significant reduction in prediction errors and, in turn, improved accuracy of the quantitative climate reconstruction. The statistical results indicate that the transfer functions are reliable for both climate variables and elevation that is controlled mainly by the lapse rate of the altitudinal temperature. The current study provides evidence that modern pollen data in the UMYR can be used as a reliable reference dataset for pollen-based quantitative climate reconstructions.