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.

Significant efforts have been invested to restore mangrove forests worldwide through reforestation and afforestation. However, blue carbon benefit has not been compared between these two silvicultural pathways at the global scale. Here, we integrated results from direct field measurements of over 370 restoration sites around the world to show that mangrove reforestation (reestablishing mangroves where they previously colonized) had a greater carbon storage potential per hectare than afforestation (establishing mangroves where not previously mangrove). Greater carbon accumulation was mainly attributed to favorable intertidal positioning, higher nitrogen availability, and lower salinity at most reforestation sites. Reforestation of all physically feasible areas in the deforested mangrove regions of the world could promote the uptake of 671.5–688.8 Tg CO2-eq globally over a 40-year period, 60% more than afforesting the same global area on tidal flats (more marginal sites). Along with avoiding conflicts of habitat conversion, mangrove reforestation should be given priority when designing nature-based solutions for mitigating global climate change.

Background Although great efforts have been made to quantify mangrove carbon stocks, accurate estimations of below-ground carbon stocks remain unreliable. In this study, we examined the distribution patterns of mangrove carbon stocks in China and other countries using our own field survey data and datasets from published literature. Based on these data, we investigated the possible relationships between above-ground carbon stock (AGC) and below-ground carbon stock (BGC) for mangrove forests, aiming to provide a scientific basis for estimation of total mangrove carbon stocks. Results The average above-ground carbon stock in each region was sizeable (ranging from 12.0 to 150.2 Mg/ha), but average below-ground carbon stock was dominant (ranging from 46.6 to 388.6 Mg/ha), accounting for 69–91% of total carbon stock at the sites studied in China. Significant positive relationships were found between above-ground and below-ground mangrove carbon stocks, with the best fitting equation as BGC = 1.58 * AGC + 81.06 (Mg/ha, R2 = 0.62, p < 0.01, n = 122) for China. Such linear relationships vary for mangrove forests of different types and locations, from different geographical regions in China to other countries worldwide. Conclusion The positive relationship we found between above- and below-ground carbon stocks of mangrove forests in China and worldwide can facilitate more accurate assessments of mangrove blue carbon stocks at regional or global scales using modern techniques including remote sensing.

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. INDEX TERMSRemote sensing based ecological index (RSEI); ecological environmental quality (EEQ); principal component analysis (PCA); random forest; dynamic monitoring