Temperature-Dominated Driving Mechanisms of the Plant Diversity in Temperate Forests, Northeast China

Tree diversity, growth status, and spatial distribution affected soil N availability and N2O efflux: Interaction with soil physiochemical properties

Soil nitrogen (N) is an essential nutrient for tree growth, and excessive N is a source of pollution. This paper aims to define the effects of plant diversity and forest structure on various aspects of soil N cycling. Herein, we collected soils from 720 plots to measure total N content (TN), alkali-hydrolyzed N (AN), nitrate N (NO3--N), ammonium N (NH4+-N) in a 7.2 ha experimental forest in northeast China. Four plant diversity indices, seven structural metrics, four soil properties, and in situ N2O efflux were also measured. We found that: 1) high tree diversity had 1.3-1.4-fold NO3--N, 1.1-fold NH4+-N, and 1.5-1.8-fold N2O efflux (p < 0.05). 2) Tree growth decreased soil TN, AN, and NO3--N by more than 13%, and tree mixing and un-uniform distribution increased TN, AN, and NH4+-N by 11-22%. 3) Soil organic carbon (SOC) explained 34.3% of the N variations, followed by soil water content (1.5%), tree diameter (1.5%) and pH (1%), and soil bulk density (0.5%). SOC had the most robust linear relations to TN (R2 = 0.59) and AN (R2 = 0.5). 4) The partial least squares path model revealed that the tree diversity directly increased NO3--N, NH4+-N, and N2O efflux, and they were strengthened indirectly from soil properties by 1%-4%. The effects of tree size-density (-0.24) and spatial structure (0.16) were mainly achieved via their soil interaction and thus indirectly decreased NH4+-N, AN, and TN. Overall, high tree diversity forests improved soil N availability and N2O efflux, and un-uniform spatial tree assemblages could partially balance the soil N consumed by tree growth. Our data support soil N management in high northern hemisphere temperate forests from tree diversity and forest structural regulations.

Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China

Non-spatial structure of forest is an important aspect for harvesting regimes, silvicultural treatments, and ecosystem service provisions. In this pursuit, the present research envisaged the measurement of the crown and diameter structure of Pinus massoniana Lamb. Specifically, the forests were assessed with a range of nine cities in Hunan Province, China. The gradient boosting model was used to quantify the contribution of seven drivers of the diameter at breast height (DBH) diversity. Moreover, the relationship between the crown structure and DBH/tree height was explored using TSTRAT and path analysis. The Anderson-Darling test results indicated that DBH distributions of nine cities did not occur from the same population, the maturing diameter distribution was the main type among the cities. Slope direction was identified as the most impacted factor affecting the DBH diversity, followed by landform and stand density. The vertical stratification indicated a simple vertical structure, and the relationship between the DBH/tree height and crown structure changed in different stages, which reflected the competition mechanism and adaption strategy in the forest. Our study summarized the diameter and crown structure of pure P. massoniana forest in Hunan province, which can provide valuable information in the forest management, planning, and valuation of ecosystem services.

Correlations and dominant climatic factors among diversity patterns of plant families, genera, and species

At present, the relationship between the diversity distribution patterns of different taxonomic levels of plants and climatic factors is still unclear. This paper explored the diversity pattern of vascular plant families, genera, and species in China at the municipal scale. It also studied the effects of accumulated temperature ≥ 10°C, annual precipitation, and hydrothermal base which reflect the effect of hydrothermal resources on the plant diversity pattern. The results showed that: There were extremely significant correlations among the diversities of plant families, genera, and species, and the interpretation degree of diversity between adjacent the taxonomic levels was more than 90%. The diversity pattern of plant families was mainly affected by dominant climatic state indicators such as the maximum value of accumulated temperature, annual precipitation, and hydrothermal base, and the gradient range of the hydrothermal base, which showed a clear latitudinal gradient law. The diversity pattern of plant species was found to be mainly dependent on the climatic heterogeneity indicators, being closely related to the heterogeneity indicators and sum indicators of the hydrothermal base. It was also affected by the range of precipitation gradient range. Plant genus and its diversity pattern are not only significantly affected by heterogeneity and sum indicators but also closely related to climate state indicators. In comparison with the humidity index in vegetation ecological studies, the related indicators of the hydrothermal base proposed in this paper excelled at revealing the relationship between climate and diversity patterns of plant families, genera, and species, and could effectively solve the species-area relationship issue in arid and low-temperature areas. The results of this paper have presented important theoretical and practical values for comprehensively understanding the correlation between climate and diversity of plant families, genera, and species, clarifying the impact of climate difference and climate change on plant diversity.

Patterns and Driving Factors of Diversity in the Shrub Community in Central and Southern China

Climate, topography, and human activities are known to influence plant diversity. In the present study, species-abundance distribution (SAD) patterns of the shrub community were fitted, and the mechanism of contribution of 22 driving factors was assessed. The results showed that the α-diversity index exhibited no significant differences between artificial disturbance and the natural community. The Zipf and Zipf–Mandelbrot models were found to exhibit a good SAD fitting of the communities, thereby exhibiting a different diversity structure. It was observed that the SAD followed more than one rule, and the Zipf–Mandelbrot model was better than other models. The gradient boosting model indicated that precipitation in the wettest month, annual precipitation, and slope direction showed the strongest impact on plant richness. The indicator species of the artificial disturbance and natural community were identified from a multiple regression tree. Furthermore, an increase in species diversity was observed with a rise in latitude, exhibiting a single-peaked curve with increased altitude. β-diversity analysis indicated that both habitat filtering and the neutral effect influenced the establishment of the natural community, while the establishment of the artificial disturbance community was only affected by habitat filtering. Our study provides a better understanding of the ecological process of the maintenance of shrub-community diversity.

Nonlinear Mixed Effect Model Used in a Simulation of the Impact of Climate Change on Height Growth of Cyclobalanopsis glauca

Localized climate is sensitive to terrain, underlying surface material, building distribution, green coverage and CO2 emissions. The Regional Climate Model (RegCM) was used to make a statistical detailed analysis of the climate change data in a specific study area to obtain fine-scale distribution of climatic elements data over time. The effects of climate change factors on height growth trends of a climate-sensitive tree species (Cyclobalanopsis glauca) were simulated based on historical climate base line data (1961–2010) and future climate change (2010–2100) predictions. Cyclobalanopsis glauca growth trends were simulated and analyzed by using a nonlinear mixed effect model (NLME). The results showed that under the RCP8.5 emissions scenario, the growth promotion effect on the height growth of Cyclobalanopsis glauca will be obvious. Under RCP4.5 and RCP2.6 emissions scenarios, although the inhibition intensity is not exactly the same, height growth will still be inhibited to a certain extent, which may lead to the gradual extinction of this species, affecting the composition of dominant tree species in the study area. The results indirectly reflect the impact of climate change on tree species diversity in the future.

A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

Plant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the difficulties of data acquisition and unsatisfactory performance of predicting algorithms over large areas. A surge in the number of remote sensing imagery, along with the great success of machine learning, opens new opportunities for the mapping of PSD. Therefore, different machine learning algorithms combined with high-accuracy surface modeling (HASM) were firstly proposed to predict the PSD in the Xinghai, northeastern Qinghai-Tibetan Plateau, China. Spectral reflectance and vegetation indices, generated from Landsat 8 images, and environmental variables were taken as the potential explanatory factors of machine learning models including least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction generated from these machine learning methods and in situ observation data were integrated by using HASM for the high-accuracy mapping of PSD including three species diversity indices. The results showed that PSD was closely associated with vegetation indices, followed by spectral reflectance and environmental factors. XGBoost combined with HASM (HASM-XGBoost) showed the best performance with the lowest MAE and RMSE. Our results suggested that the fusion of heterogeneous data and the ensemble of heterogeneous models may revolutionize our ability to predict the PSD over large areas, especially in some places limited by sparse field samples.