Zeng W.,Central South University of forestry and Technology |
Zeng W.,National Engineering Laboratory of Applied Technology for Forestry and Ecology in Southern China |
Zhou B.,Central South University of forestry and Technology |
Lei P.,Central South University of forestry and Technology |
And 9 more authors.
Frontiers in Plant Science | Year: 2015
Understanding of belowground interactions among tree species and the fine root (≤2 mm in diameter) contribution of a species to forest ecosystem production are mostly restricted by experimental difficulties in the quantification of the species composition. The available approaches have various defects. By contrast, DNA-based methods can avoid these drawbacks. Quantitative real-time polymerase chain reaction (PCR) is an advanced molecular technology, but it is difficult to develop specific primer sets. The method of next-generation sequencing has several limitations, such as inaccurate sequencing of homopolymer regions, as well as being time-consuming, and requiring special knowledge for data analysis. This study evaluated the potential of the DNA-sequence-based method to identify tree species and to quantify the relative proportion of each species in mixed fine root samples. We discriminated the species by isolating DNA from individual fine roots and amplifying the plastid trnL(UAA; i.e., tRNA-Leu-UAA) intron using the PCR. To estimate relative proportions, we extracted DNA from fine root mixtures. After the plastid trnL(UAA) intron amplification and TA-cloning, we sequenced the positive clones from each mixture. Our results indicated that the plastid trnL(UAA) intron spacer successfully distinguished tree species of fine roots in subtropical forests. In addition, the DNA-sequence-based approach could reliably estimate the relative proportion of each species in mixed fine root samples. To our knowledge, this is the first time that the DNA-sequence-based method has been used to quantify tree species proportions in mixed fine root samples in Chinese subtropical forests. As the cost of DNA-sequencing declines and DNA-sequence-based methods improve, the molecular method will be more widely used to determine fine root species and abundance. © 2015 Zeng, Zhou, Lei, Zeng, Liu, Liu and Xiang.
Zhu W.,Central South University of forestry and Technology |
Xiang W.,Central South University of forestry and Technology |
Xiang W.,National Engineering Laboratory of Applied Technology for Forestry and Ecology in Southern China |
Pan Q.,Central South University of forestry and Technology |
And 12 more authors.
Biogeosciences | Year: 2016
Leaf area index (LAI) is an important parameter related to carbon, water, and energy exchange between canopy and atmosphere and is widely applied in process models that simulate production and hydrological cycles in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have yet to be fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (Pinus massoniana-Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber-Cyclobalanopsis glauca evergreen broadleaved forests) from April 2014 to January 2015. Spatial heterogeneity of LAI and its controlling factors were analysed using geostatistical methods and the generalised additive models (GAMs) respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for the three forests measured in January and for the L. glaber-C. glauca forest in April, July, and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stem number, crown coverage, proportion of evergreen conifer species on basal area basis, proportion of deciduous species on basal area basis, and forest types affected the spatial variations in LAI values in January, while stem number and proportion of deciduous species on basal area basis affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity, and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests. © Author(s) 2016.
Yang D.,Central South University of forestry and Technology |
Yang D.,National Engineering Laboratory of Applied Technology for Forestry and Ecology in Southern China |
Xiang W.,Central South University of forestry and Technology |
Xiang W.,Research of Chinese Fir Plantation Ecosystem in Hunan Province |
And 11 more authors.
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2014
Subtropical evergreen broadleaved forests play an important role in regional carbon balance and sustainable development owing to their highest productivity, diverse ecosystem functions and complex habitat for abundant biological diversity in southern China. Spatial heterogeneity of soil nutrients in subtropical forests can provide useful information for understanding the spatial pattern of plants and for explaining to some extent, coexistence mechanism of diverse tree species. To investigate spatial variations in soil nutrients and the causes of the variations, soil samples at 0-10 cm, 10-20 cm and 20-30 cm depth were collected at the center of each 10 m × rat within 1 hectare permanent plot of Lithocarpus glaber-Cyclobalanopsis glauca subtropical evergreen broadleaved forest. Soil organic C and total nitrogen (N) concentrations were determined for all samples. Based on regional variable theory and spatial analysis functions of GS+ Version 9, spatial heterogeneity of soil organic C and total N concentrations was examined by using semivariogram of geostatistics. The results showed that averaged soil organic C concentration was 18.61 g/ kg, ranging from 9.53 to 39.40 g/ kg, and the average value of total N concentration was 1.63 g/ kg with a range between 0.73 and 3.32 g/ kg. Theoretical semivariogram model of soil organic C approached spherical model while the best semivariogram model of total N was close to Gaussian model. The spatial variability of soil nutrient primarily resulted from the structural factors and the spatial heterogeneity degree of those indices was moderate. Fractal dimensions from log-log semivariograms quantitatively described spatial pattern differences and scale dependence of the soil organic C and total N. Fractal dimension was high for soil organic C, so soil organic C spatial structure had strong scale dependence with a complex spatial pattern. Kriging was used to analyze the spatial distribution of soil nutrients. Spatial distribution patterns of soil organic C and total N concentrations similarly revealed an apparent belt- shaped and spot massive gradient change. Within the plot, soil organic C concentration was negatively correlated with topographic factors (i.e. elevation and convexity), but the relationship was not significant. Soil organic C showed very significantly a positive relationship with litter biomass. Total soil N concentration exhibited a significant negative relationship with topographic factors, however, positive relationship was found between total soil N and litter, indicating leaching characteristics of soil N. Spatial variations in soil organic C and total N highlight the importance of vegetation and litter protection in the hilly area of subtropical China.