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.

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.

Summary: Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life‐form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal‐to‐ambient CO₂ ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (N_area)), and photosynthetic capacities (V_cmax, J_max at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life‐form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R²) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.