Lake surface sediment pollen dataset for the alpine meadow vegetation type from the eastern Tibetan Plateau and its potential in past climate reconstructions

A modern pollen dataset with an even distribution of sites is essential for pollen-based past vegetation and climate estimations. As there were geographical gaps in previous datasets covering the central and eastern Tibetan Plateau, lake surface sediment samples (n = 117) were collected from the alpine meadow region on the Tibetan Plateau between elevations of 3720 and 5170 m.a.s.l Pollen identification and counting were based on standard approaches, and modern climate data were interpolated from a robust modern meteorological dataset. A series of numerical analyses revealed that precipitation is the main climatic determinant of pollen spatial distribution: Cyperaceae, Ranunculaceae, Rosaceae, and Salix indicate wet climatic conditions, while Poaceae, Artemisia, and Chenopodiaceae represent drought. Model performance of both weighted-averaging partial least squares (WA-PLS) and the random forest (RF) algorithm suggest that this modern pollen dataset has good predictive power in estimating the past precipitation from pollen spectra from the eastern Tibetan Plateau. In addition, a comprehensive modern pollen dataset can be established by combining our modern pollen dataset with previous datasets, which will be essential for the reconstruction of vegetation and climatic signals for fossil pollen spectra on the Tibetan Plateau. Pollen datasets including both pollen counts and percentages for each sample, together with their site location and climatic data, are available at the National Tibetan Plateau Data Center (TPDC; Cao et al., 2021; https://doi.org/10.11888/Paleoenv.tpdc.271191).

Automated summary

A modern pollen dataset with an even distribution of sites is essential for estimating past vegetation and climate. Precipitation is the main climatic determinant of pollen spatial distribution in the eastern Tibetan Plateau. The modern pollen dataset has good predictive power in estimating past precipitation from pollen spectra.