Functional diversity is considered a key link between ecosystem functions and biodiversity, and forms the basis for making community diversity conservation strategies. Here, we chose a subtropical forest community in China as the research object, which is unique in that other regions of the world at the same latitude have almost no vegetation cover. We measured 17 functional traits of 100 plant species and calculated seven different functional diversity indices, based on functional richness, evenness, and divergence. We found that most functional diversity and species diversity indices significantly differed with plant habit. There was a significant positive correlation among functional richness indices. However, functional divergence indices, multidimensional functional divergence (FDiv), and Rao’s quadratic entropy index (RaoQ) were significantly negatively correlated, and RaoQ and functional divergence indices (FDis) were uncorrelated. The correlations between three types (richness, evenness, and divergence) of functional diversity indices and three species diversity indices were different. Lineage regression results generally showed that three functional richness indices (Average distance of functional traits (MFAD), Functional volume (FRic) and Posteriori functional group richness (FGR)) were increased with three species diversity indices (species richness (S), Shannon-Wiener index (H) and Pielou index (E)). The functional evenness index (FEve) decreased with species richness (S), Shannon-Wiener index (H) and increased with species evenness (Pielou index (E)), but the change trends were small. All three types of functional diversity indices declined with altitude, although altitude had a weak influence on them. Other environmental factors affected the functional diversity of the community. Here, soil total phosphorus (TP) was the most critical environmental factor and the convex had the least effect on functional diversity in our subtropical forest community. These results will contribute to our understanding of functional diversity in subtropical forests, and provide a basis for biodiversity conservation in this region.

Background: Forests play a crucial role in absorbing CO₂ from the atmosphere. 55% of the carbon in terrestrial ecosystems is stored in forests, with the majority of forest carbon stored in soil. To better understand soil organic carbon (SOC) of forests and to access interventions that affect their SOC concentration, we conducted a comparative analysis between natural and planted forests. Forest interventions refer to the actions taken by humans to manage, protect, or transform forests, and can be divided into two main categories: environmental intervention and anthropogenic intervention. This study focused on the effects of different interventions on SOC in natural and planted forests by reviewing a total of 75 randomized controlled trials in the global literature and extracting a total of 15 different interventions. Results: Through network meta-analysis, we found that natural forests have 22.3% higher SOC than planted forests, indicating their stronger carbon storage function. In natural forests, environmental interventions have a stronger impact. SOC is significantly influenced by forest age, fertilization, and elevation. In planted forests, however, anthropogenic interventions have a stronger impact. Pruning branches and fertilization are effective interventions for planted forests. Furthermore, forest degradation has a significantly negative impact on SOC in planted forests. Conclusion: Overall, interventions to enhance soil carbon storage function differ between natural and planted forests. To address global climate change, protect biodiversity, and achieve sustainable development, it is imperative to globally protect forests and employ scientifically sound forest management practices. Regarding natural forests, the emphasis should be on comprehending the effects of environmental interventions on SOC. Conversely, concerning planted forests, the emphasis should be on comprehending the effects of anthropogenic interventions.

Here, we characterize the temporal and spatial dynamics of forest community structure and species diversity in a subtropical evergreen broad-leaved forest in China. We found that community structure in this forest changed over a 15-year period. Specifically, renewal and death of common species was large, with the renewal of individuals mainly concentrated within a few populations, especially those of Aidia canthioides and Cryptocarya concinna. The numbers of individual deaths for common species were concentrated in the small and mid-diameter level. The spatial distribution of community species diversity fluctuated in each monitoring period, showing a more dispersed diversity after the 15-year study period, and the coefficient of variation on quadrats increased. In 2010, the death and renewal of the community and the spatial variation of species diversity were different compared to other survey years. Extreme weather may have affected species regeneration and community stability in our subtropical monsoon evergreen broad-leaved forests. Our findings suggest that strengthening the monitoring and management of the forest community will help better understand the long- and short-term causes of dynamic fluctuations of community structure and species diversity, and reveal the factors that drive changes in community structure.

Understanding the role of dominant species in structuring the distribution of neighbor species is an important part of understanding community assembly, a central goal of ecology. Phylogenetic information helps resolve the multitude of processes driving community assembly and the importance of evolution in the assembly process. In this study, we classified species in a 20-ha subtropical forest in southern China into groups with different degrees of phylogenetic relatedness to the dominant species Castanopsis chinensis. Species surrounding individuals of C. chinensis were sampled in an equal area annulus at six spatial scales, counting the percent of relatives and comparing this to permutation tests of a null model and variance among species groups. The results demonstrated that dominant species affected their relatives depending on community successional stage. Theory would predict that competitive exclusion and density-dependence mechanisms should lead to neighbors that are more distant in phylogeny from C. chinensis. However, in mature forests distant relatives were subjected to competitive repulsion by C. chinensis, while environment filtering led to fewer distant species, regardless of scale. A variety of biological and non-biological factors appear to result in a U-shaped quantitative distribution determined by the dominant species C. chinensis. Scale effects also influenced the dominant species. As a dominant species, C. chinensis played an important role in structuring the species distributions and coexistence of neighbor species in a subtropical forest.

The species-area relationship is one of the most important topic in the study of species diversity, conservation biology and landscape ecology. The species-area relationship curves describe the increase of species number with increasing area, and have been modeled by various equations. In this paper, we used detailed data from six 1-ha subtropical forest communities to fit three species-area relationship models. The coefficient of determination and F ratio of ANOVA showed all the three models fitted well to the species-area relationship data in the subtropical communities, with the logarithm model performing better than the other two models. We also used the three species-abundance distributions, namely the lognormal, logcauchy and logseries model, to fit them to the species-abundance data of six communities. In this case, the logcauchy model had the better fit based on the coefficient of determination. Our research reveals that the rare species always exist in the six communities, corroborating the neutral theory of Hubbell. Furthermore, we explained why all species-abundance figures appeared to be left-side truncated. This was due to subtropical forests have high diversity, and their large species number includes many rare species.

Motivated by the need to enhance the accuracy of forest aboveground carbon storage (ACS) assessments, this study aimed to explore the effectiveness of different machine learning models in predicting ACS across various subtropical forest types in southern China. The study was conducted in southern China, focusing on different types of subtropical forests. This region harbors several types of subtropical forests, which are rarely found at similar latitudes in the world. Variance inflation factor was employed to screen independent variables, resulting in the selection of 13 significant predictors. Four machine learning models—support vector machine (SVM), random forest (RF), multi‐layer perceptron (MLP), and extreme gradient boosting (XGB)—were constructed to estimate carbon storage. Model performance was evaluated using root mean square error, coefficient of determination (R²), and mean absolute error. The model with the best generalization ability was selected to calculate SHAP values for each predictor. The XGB model demonstrated superior performance across all forest types, with R² values ranging from 0.898 to 0.974. In mountainous evergreen broad‐leaved forests, the prediction accuracy followed the order of XGB>MLP>SVM>RF. In valley rainforests, MLP showed the highest R² value, but with higher MAE and RMSE, making it the second‐best choice. The RF model performed moderately, while the SVM model showed the poorest performance. The SHAP values indicated that maximum diameter at breast height, slope, mean DBH, species evenness, altitude, and maximum tree height had significant effects on ACS. XGB model exhibits the best prediction performance and strongest adaptability for estimating ACS in subtropical southern China forests. Additionally, the MLP model can serve as an effective model for assessing carbon storage in valley rainforests within this region. Machine learning methods provide valuable references for predicting and assessing ACS in different types of zonal forests.