Abstract. Human impact is a well-known confounder in pollen-based quantitative climate reconstructions as most terrestrial ecosystems have been artificially affected to varying degrees. In this paper, we use a human-induced pollen dataset (H-set) and a corresponding natural pollen dataset (N-set) to establish pollen-climate calibration sets for temperate eastern China (TEC). The two calibration sets, taking a Weighted Averaging Partial Least Squares (WA-PLS) approach, are used to reconstruct past climate variables from a fossil record, which is located at the margin of the East Asian Summer Monsoon in north-central China and covers the late glacial–Holocene from 14.7 ka BP (thousand years before AD 1950). Ordination results suggest that mean annual precipitation (Pann) is the main explanatory variable of both pollen composition and percentage distributions in both datasets. The Pann reconstructions, based on the two calibration sets, demonstrate consistently similar patterns and general trends, suggesting a relatively strong climate impact on the regional vegetation and pollen spectra. However, our results also indicate that human impact may obscure climate signals derived from fossil pollen assemblages. In a test with modern climate and pollen data, the Pann influence on pollen distribution decreases in the H-set while the human influence index (HII) rises. Moreover, the relatively strong human impact reduces woody pollen taxa abundances, particularly in the sub-humid forested areas. Consequently, this shifts their model-inferred Pann optima to the arid-end of the gradient compared to Pann tolerances in the natural dataset, and further produces distinct deviations when the total tree pollen percentages are high in the fossil sequence (i.e. about 40 % for the Gonghai area). In summary, the calibration set with human impact used in our experiment can produce a reliable general pattern of past climate, but the human impact on vegetation affects the pollen-climate relationship and biases the pollen-based climate reconstruction.
Examining bias in pollen-based quantitative climate reconstructions
induced by human impact on vegetation
