Mangrove forests, one of the highest carbon density ecosystems, are very different from other forests as they occupy saline and tidal habitats. Although previous studies in forests, shrublands and grasslands have shown a positive effect of biodiversity on plant biomass and carbon storage, it remains unclear whether this relation to biodiversity also exists in mangrove forests. Here, we evaluate the possible effects of mangrove species diversity, structural characteristics and environmental factors on mangrove biomass production and carbon storage, using survey data from 234 field plots of 30 transects in the mangrove forests along the coastlines of Hainan Island, China, during 2017 and 2018. We found that mangrove species diversity had a positive effect, not only on mangrove biomass production but also on soil carbon storage. This positive effect was more strongly evident in the forest communities than in either the shrub communities or forest‐shrub mixed communities, with the forest type having the biggest mangrove biodiversity and carbon storage. Besides, the diversity effect was affected by structural characteristics, namely, mangrove biomass increased exponentially with tree stem diameter and decreased with tree density. Furthermore, we observed a resource‐dependent mediation of the mangrove ecosystem when linking diversity to biomass. The areas with high soil Nitrogen content and Mean annual precipitation (MAP) showed higher mangrove biomass and carbon storage. This suggests that the spatial pattern of mangrove carbon storage and diversity was driven by both climate factors (MAP) and soil fertility (soil N). To our knowledge, this is the first study based on an intensive field survey that has verified the positive effect of biodiversity on mangrove biomass and carbon storage. Our findings suggest that mangrove forests with greater diversity also have higher carbon storage capacities and conservation potential. Thus, biodiversity conservation is crucial for mangroves to mitigate the greenhouse effect. Our findings strengthen the understanding of the diversity effects on mangrove ecosystem services and have important implications for mangrove restoration and conservation. A free Plain Language Summary can be found within the Supporting Information of this article.

The assessment of ecological environmental quality (EEQ) has provided an important knowledge base for protecting human health and realizing sustainable development. Previous studies have often used only principal component analysis (PCA) to perform the EEQ evaluation by determining the remote sensing based ecological index (RSEI) in a single year, and the assessment results are not comparable between years. Thus, a comparable and accurate method needs to be found and applied. In this paper, we applied the PCA combined with a random forest algorithm (a machine learning algorithm) to quantify the EEQ of Beijing, China, in 2014 and 2020 and analysed the relationship between the RSEI and four ecological indicators (greenness, wetness, dryness and heat). The results suggested that the RSEI and the ecological indicators of Beijing all changed substantially from 2014 to 2020, and the method of combining PCA and random forest was suitable for calculating the time-series data of RSEI in the study period. Specifically, the RSEI in Beijing increased slightly from 0.31 to 0.33 overall, the greenness of Beijing increased drastically (26.09%), the wetness decreased by 10.00%, and the dryness and heat increased by 8.62% and 2.00%, respectively. The Pearson correlation coefficient test showed that both the greenness and wetness had positive effects on the RSEI, while the dryness and heat had negative effects. Of the four ecological indicators in Beijing, the greenness contributed greatly as the main positive factor, and dryness was the most negative factor during the six years. This paper developed an improved framework for continuous EEQ monitoring, and these results provide a scientific basis for the sustainable development and ecological environmental monitoring of Beijing and other megacities.

Shrub layer diversity is an essential component of the forest ecosystem diversity, that contributes significantly to structuring the community and maintaining diversity, especially in plantation forests. In previous studies, researchers have reported the strong relationship among various factors (i.e., soil composition, mean annual temperature, etc.) and shrub diversity. However, how these factors jointly influence shrub diversity and which factors could be considered the key factors is still unknown. In this study, we attempted to quantify the effect among environmental factors, soil factors and forest stand factors on shrub diversity. Twenty-seven variables were selected from 57 Chinese pine plantation plots in Huanglong Mountain, Yanan City, Shaanxi Province, China. The path models showed that latent variable of soil properties is the main effective factor of latent variable of shrub diversity (directly effect, path coefficient = 0.344) and the latent variable of site conditions is another effective factor of latent variable of shrub diversity (indirectly effect, path coefficient = 0.177); Besides, the latent variable of site conditions and forest properties directly affect the latent variable of soil properties (path coefficient = 0.514 and 0.326, respectively). Among the latent variable of soil properties, soil water content (SWC) has the biggest weight of 0.666, which indicated the most significant contribution of SWC to latent variables of shrub diversity. Total nitrogen, weighted 0.375, and total phosphorus, weighted 0.308, are also important factors and make a similar contribution to latent variable of shrub diversity. Soil organic matter (SOM) has a minimal impact (lowest weight, 0.059); among the objective variables of site condition, altitude contributes the most and is followed by litter thickness, weighted at 0.722 and 0.448, respectively. Furthermore, among all the variables affecting the latent variable of forest properties, forest age is recognized as the maximum impactor of soil property change, which weighted −0.941; and is followed by forest stock volume and diameter at breast height (DBH), weighted 0.795 and 0.788, respectively. The crowding index (C) has the lowest weight (−0.235) and demonstrated that spatial distribution and crowding of trees have minimal impact on the latent variable of Soil properties. diversity Overall, our study provides new insights into quantifying the relationships among different driving factors that potentially play a significant role in determining shrub layer diversity within the plantation forest.