The obvious periodic variations of 2-7, 8-15, 18-28, 75-96, and 100-125 years were found in the reconstruction sequence, in which the quasi-113, 88 and 22 years were the first, second and third main periods, respectively. These variations might potentially be the fingerprints of some climate change forces such as solar activity, monsoon and EI Niño-Southern Oscillation (ENSO) activity.The whole root excavation method was used to examine root configuration of Juglans mandshurica, with the age of 5-6 years in three habitats (forest edge, gap, and canopy) in a secondary forest on the western part of Zhangguangcailing Mountains. Root structure and fine root function were measured. The root topological index, average joint length, cross-sectional area ratio before and after root branching were calculated and fine root chemical compositions were analyzed. Roots of J. mandshurica at forest edge tended to be dichotomous branch (Topological indexTI=0.68), that under the canopy were herringbone-like branch (TI=0.79), and the gap was between the two (TI=0.72). The average connection length of roots among the three habitats was not significant. The cross-sectional area ratio of roots before and after root branching in three habitats was 1.06, 1.04 and 1.07, respectively, which was not affected by root diameter, in accordance with the Leonardo da Vinci rule. For the same order fine root in different habitats, its length and specific surface area gradually increased from the edge of the forest to the canopy. The N content decreased first and then increased, while the C content and C/N increased first and then decreased. From the forest edge to the gap and to the under canopy, roots tended to move from the dichotomous branch to the herringbone-like branch by reducing the overlap between the secondary branches and roots, increasing specific root length, specific surface area and changing the contents of C and N to cope with environmental change and improve nutrient absorption efficiency.Ecological stoichiometry provides a new method for understanding the characteristics, driving forces and mechanisms of C, N and P coupled cycles. However, there are few reports on the variation in ecological stoichiometry of plants during their growth. In this study, we fitted the total elemental mass of different module based on the size of Nitraria tangutorum, and derived the ecological stoichiometry models of different module and whole ramet by measuring the biomass and nutrient concentrations of the current-year stems in 2017, 2-year-old stems, more than 2-year-old stems, leaves, roots and layerings of N. tangutorum ramet. Our results showed that the derivation model could well reflect the changes in ecological stoichiometry during plant growth. The old stems and the layering had higher NP and CP, while leaves,current-year stems, and roots had lower NP and CP. The whole plant nutrient elements cumulative rate was PNC during the growth process. These results were consistent with the growth rate hypothesis and allometric theory, and provide evidence for nutrient reabsorption. This model could be used as an effective way to analyze the dynamic characteristics of elements in plant growth.We investigated Betula luminifera populations in three regions (Mulinzi, Qizimei Mountains, and Jinzi Mountains) in the southwest Hubei Province, China. Population structure was divided by age classes and height classes. Population structure figures were drawn. The static life tables of B. luminifera populations in different regions were analyzed using the method of substitution of space for time. The survival curve, mortality rate curve and disappearance rate curve were created. Four functions of survival analysis were used to analyze the dynamics of B. luminifera population in different regions. The results showed that the B. luminifera populations in three regions were the increasing type. The height class structures were relatively complete. Some age classes were absent from the age structures of B. luminifera populations in Qizimei Mountains and Jinzi Mountains. Although the dynamic index of trees number Vpi＞0, but it was sensitive to external disturbance. The survival of B. luminifera of different age classes varied greatly in static life table, which gradually decreased with increasing age class, with Deevey-type 2 survival curve. The trend of mortality rate changed similarly to the disappearance rate, but fluctuated differently. All B. luminifera populations in different regions appeared to decrease in the early stage and keep dynamically stable in the medium-late stage.We examined biomass characteristics and the potential driving factors of different forest types of Quercus spp. secondary forest in Hunan. A total of fifty plots were divided into five forest types Castanopsis eyri - Rhododendron latoucheae mixed forest (CR), Fagus lucida - Fargesia spathacea mixed forest (FF), Lithocarpus glaber - Damnacanthus indicus + Camellia japonica mixed forest (LDC), C. eyri + Quercus serrata - R. latoucheae mixed forest (CQR), Cyclobalanopsis glauca - Camellia oleifera + R. latoucheae mixed forest (CCR). The biomass of understory vegetation was low in the five forest types, being smaller than 2.3 t·hm-2. There was no significant difference in the biomass of understory shrubs among the five forest types. The biomass of herbage layer in CR was significantly lower than that of the other four forest types. The factors affecting the biomass of understory vegetation varied in different forests types. In CR, biomass of herbaceous layer was negatively correlated with canopy and uniform angle index, whereas total understory biomass was positively correlated with opening degree index. In FF, biomass of shrub layer was negatively correlated with stand canopy density and uniform angle index, while herbaceous biomass and total understory biomass were positively correlated with stand closure.  https://www.selleckchem.com/products/nvp-bgt226.html  In LDC, herbaceous biomass was positively correlated with the stand aggregation index. In CQR, shrub biomass was negatively correlated with stand mingling index, while herbaceous biomass was positively related with stand density. In CCR, there was no significant correlation between stand structure and understory biomass. To adjust the understory biomass, we should first adjust the horizontal distribution pattern of stand and then adjust the degree of forest cover and tree species structure.