Relationship between surface pollen and modern vegetation in northern Xinjiang, China: Implications for paleovegetation and paleoclimate reconstruction

Autor: Yun Zhang, Lixin Chen, Yuanyuan Li, Zhaochen Kong, Qi-Yao Yan, Zhen-Jing Yang, Yanhong Zhou, Xianguo Qiao
Rok vydání: 2021
Předmět:
Zdroj: Quaternary International. 589:124-134
ISSN: 1040-6182
DOI: 10.1016/j.quaint.2021.03.038
Popis: The relationship between surface pollen and modern vegetation is an important criterion for interpreting fossil pollen spectra in reconstructing of paleovegetation and the paleoenvironment. A total of 438 surface pollen samples were collected from the Junggar Basin, Tianshan and Altai Mountains, and other areas in northern Xinjiang, China. Surface pollen assemblages were divided into five horizontal pollen zones: alpine and subalpine meadows, mountain forests, steppes, deserts, and wetlands. A high abundance of tree pollen was detected in the Altai and Tianshan Mountains, primarily originating from conifers such as Picea and Larix. Desert steppe vegetation was predominant on both sides of the central Junggar Basin, with significant amounts of pollen from Ephedra, Chenopodiaceae, and Artemisia. The geographic distribution of the pollen source sites was analyzed using ArcGIS. R-value, Pearson correlation analysis, and principal component analysis were used to determine the distribution and central region for each of the main pollen taxa identified. Picea pollen at abundance levels above 30% indicated the existence of local spruce forests, as observed in the Tianshan and Altai mountains’ conifer forests. A considerable amount of Betula pollen was observed in the intrazonal vegetation dominated by Betula, such as wetlands and river valleys, indicating the distribution of birch forests in the local vegetation. The pollen taxa of Ephedra, Chenopodiaceae, and Artemisia was over-represented across all investigated areas. These data may improve the accuracy and reliability of vegetation and climate reconstructions based on pollen data; furthermore, our information provides a framework for predicting future environmental trends.
Databáze: OpenAIRE