Surface Pollen And Its Quantitative Relationships With Vegetation And Climate In Central Qinling Mountains

As an important biological indicator,fossil pollen is widely used to reconstruct past ve.getation and climate changes.However,the prerequisite is that we establish the relationships between surface pollen and modern vegetation and climate.For this purpose,many works have been undertaken by previous researchers.However,the previous studies cover limited regions and the spatial distribution is uneven.Therefore,more researches are clearly needed to solve these problems.The Qinling Mountains is located in central China and it is an important geographic boundary between northern and southern China.It lies in a transition zone from the subtropical evergreen forest to the warm temperate deciduous forest of China,as well as from humid monsoon climate to semi-humid monsoon climate in China,where the natural vegetation is sensitive to climate changes,making it an ideal place for investigating the quantitative relationships between surface pollen assemblages and modern vegetation and climate.In this study,we collected 46 surface pollen samples(moss)from natural vegetation landscapes,without significant human disturbance,along a north-to-south section in the central part of Qinling Mountains,covering different altitudes,vegetation and climate.Then we performed pollen analysis on the collected moss samples and established quantitative relationships between surface pollen and modern vegetation and climate in the central Qinling Mountains.Our results reveal that:1)Surface pollens in central Qinling Mountains are mainly composed of arboreal pollens,which account for more than 60%.The coniferous trees include Pinus,Abies,Picea,Larix,etc.Broad-leaved trees include deciduous Quercus,evergreen Quercus,Betula,Carpinus,Castonea,Acer,Corylus,Liquidambar,Pterocarya,Tilia,Ulmus,etc.Shrubs include Pyracantha,Cotinus,Dalbergia,etc.Herbs include Gramineae,Carex,Saussurea,Aster,Rubia,etc.Overall,surface pollen assemblages are consistent with vegetation compositions.Besides,both correspondence analysis and cluster analysis generally can distinguish surface pollen assemblages from different vegetation types,for example,alpine coniferous and broad-leaved mixed forest,Pinus massoniana forest,Quercus aiiena var.acuteserrata forest,cork oak forest,and shrub.Therefore,surface pollen assemblages in the central Qinling Mountains can represent modern vegetation in terms of major taxa and dominant types.2)In order to solve the problem that the accuracy of climate data is low in some previous studies,we used thin plate smoothing spline for spatial interpolation,using latitude,longitude,and elevation as independent variables,and obtained a very high resolution(?29 m2)climate data set for the Qinling-Daba Mountains.Our interpolation can well capture the spatial distributions of air temperature and precipitation in the Qinling-Daba Mountains,which is generally consistent with the WorldClim 2.0 dataset.However,our interpolation is based on a larger number of weather stations as well as higher-resolution elevation data,therefore is more accurate and can show more details.Further analysis indicates that the southern foot of the Qinling Mountain is the 0?boundary of the coldest month.The Qinling-Daba Mountains have obvious vertical temperature zones,which is most significant in summer but weakest in winter.The maximum temperature lapse rate occurs in June and is 0.608? per 100 m.The lowest temperature lapse rate occurs in December and is 0.376? per 100 m.The mean annual temperature lapse rate is 0.508? per 100 m.Precipitation decreases from south to north.Daba Mountain is the contour of 1000 mm annual precipitation and Qinling Mountains is the contour of 800 mm annual precipitation.The southwestern slope of the Daba Mountains is a center for heavy precipitation.Our comparison with large-scale atmospheric circulation indicates that the spatial distributions of air temperature and precipitation in the Qinling-Daba Mountains are mainly controlled by the East Asian winter/summer monsoon and topography.3)The quantitative relationships between surface pollen and climate was studied using canonical correspondence analysis(CCA).The annual mean temperature(Tann)can explain 9.4%variance in the surface pollen assemblages,while January temperature(Tjan)and July temperature(Tjul)can explain 8.9%and 9.6%,respectively.The annual precipitation(Pann)can explain 3.6%of the variance.However,the variance inflation factors(VIF)suggest that Tann and Tjan are highly correlated with Tjul.Therefore,July temperature and annual precipitation are the two most important climate variables that affect the spatial distribution of surface pollen and vegetation in the central Qinling Mountains.Pollen percentages of Abies,Larix,and other conifer trees are negatively correlated with air temperature,while pollen percentages of subtropical trees such as Cyclobalanopsis and Liquidambar are positively correlated with annual precipitation.Variation partitioning(pCCA)was used to separate the contributions of temperature and precipitation,which shows that Tjul contributes 71.6%and Pann contributes 28.3%.Therefore,it becomes clear that air temperature is more important than precipitation in influencing vegetation dynamics in the Qinling Mountains.