Fossil pollen data can provide important information of past vegetation diversity,on the basis of established relationship between modern palynological and floristic diversity.However,current studies on modern pollen ...Fossil pollen data can provide important information of past vegetation diversity,on the basis of established relationship between modern palynological and floristic diversity.However,current studies on modern pollen assemblages in China have not examined this relationship yet.Herein,we report a case study from Northeast China,aiming to investigate the representation of modern palynological diversity to regional floristic diversity.A total of 87 sets of modern pollen and vegetation data from various vegetation types were applied to assess modern palynological diversity and floristic diversity in Northeast China,and the relationship between palynological and floristic diversity was studied using spatial pattern comparison and correlation analysis.Moreover,to reduce representation bias related to pollen production and dispersal,we calibrated pollen data using the Regional Estimates of Vegetation Abundance from Large Sites(REVEALS)model with Pollen Productivity Estimates(PPEs)and Fall Speeds of Pollen(FSP).The results show that the spatial variations of palynological and floristic richness among vegetation types are similar,and have a good positive correlation(r=0.41,p<0.01).However,palynological evenness presents a different spatial pattern from floristic evenness,with a weaker positive correlation(r=0.21,p>0.05).The calibration on pollen data using REVEALS model minimized the differences in spatial patterns between palynological and floristic diversity,and improved the correlations between them(richness,r=0.50,p<0.01;evenness,r=0.33,p<0.01).Our study indicates that palynological richness in Northeast China could reflect regional floristic richness in general,and the calibration with REVEALS model is recommended for reconstructing past floristic diversity from pollen data.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2022YFF0801501)the National Natural Science Foundation of China(Grant Nos.41888101,42071114,42277454&41977395)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA20070101).
文摘Fossil pollen data can provide important information of past vegetation diversity,on the basis of established relationship between modern palynological and floristic diversity.However,current studies on modern pollen assemblages in China have not examined this relationship yet.Herein,we report a case study from Northeast China,aiming to investigate the representation of modern palynological diversity to regional floristic diversity.A total of 87 sets of modern pollen and vegetation data from various vegetation types were applied to assess modern palynological diversity and floristic diversity in Northeast China,and the relationship between palynological and floristic diversity was studied using spatial pattern comparison and correlation analysis.Moreover,to reduce representation bias related to pollen production and dispersal,we calibrated pollen data using the Regional Estimates of Vegetation Abundance from Large Sites(REVEALS)model with Pollen Productivity Estimates(PPEs)and Fall Speeds of Pollen(FSP).The results show that the spatial variations of palynological and floristic richness among vegetation types are similar,and have a good positive correlation(r=0.41,p<0.01).However,palynological evenness presents a different spatial pattern from floristic evenness,with a weaker positive correlation(r=0.21,p>0.05).The calibration on pollen data using REVEALS model minimized the differences in spatial patterns between palynological and floristic diversity,and improved the correlations between them(richness,r=0.50,p<0.01;evenness,r=0.33,p<0.01).Our study indicates that palynological richness in Northeast China could reflect regional floristic richness in general,and the calibration with REVEALS model is recommended for reconstructing past floristic diversity from pollen data.
