East Asian pollen database: modern pollen distribution and its quantitative relationship with vegetation and climate

AimOur aims were to provide new pollen data for establishing a sub-continental surface pollen database (East Asian Pollen Database, EAPD) and to study relationships between vegetation and climate. LocationThe sample sites covered most regions of East Asia, including China, Mongolia, the Russian Far East, Vietnam, Cambodia and Thailand. MethodsData quality control procedures were applied, including taxonomic standardization, removal of duplicates, and adjustment of geographical coordinates. Vegetation types and climate parameters were assigned to each sample. Modern pollen distribution maps were drawn using circle scattergrams. The plots of pollen percentages versus climate variables allowed quantitative estimates of climate values. The modern analogue technique (MAT) was used to predict modern biomes and climate parameters. ResultsPollen assemblages extracted from 2858 sites were used to model the geographical distribution of selected taxa and their relationships with climate. For most taxa, the reconstructed range fitted the observed geographical distribution rather well. Arboreal pollen (AP) and Pinus dominated the transition zone between forest and steppe. Use of the MAT revealed that the predicted and observed biomes matched in 71% of the cases. The warm temperate evergreen broadleaf forest had the best agreement between predictions and observations. Climate values reconstructed using MAT were highly correlated with observed values in January temperature. The correlation coefficient of the temperature variables ranged from 0.799 to 0.930 and was as high as 0.939 for precipitation. Main conclusionsThis paper documents a new modern pollen database for East Asia and makes the data readily available. The reconstructed biomes and climate variables are significantly correlated with the observed values, thus demonstrating the utility of the pollen database for future multiscale palaeoenvironmental studies.